From a41878826d54c8a53d145e06cdb70b4b448a40db Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 3 Apr 2024 17:29:09 +0000 Subject: [PATCH 1/4] Setting up GitHub Classroom Feedback From ea7c8d18abda2172894b3ddd23b5f5be5b3eaa5e Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Wed, 3 Apr 2024 17:29:11 +0000 Subject: [PATCH 2/4] add deadline --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 295a252..dc8fbad 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ +[![Review Assignment Due Date](https://classroom.github.com/assets/deadline-readme-button-24ddc0f5d75046c5622901739e7c5dd533143b0c8e959d652212380cedb1ea36.svg)](https://classroom.github.com/a/yYhm1UCY) # Introduction to Data Processing ## Introduction From 9d141e25530a47bccad62cd48630900f89a10cdd Mon Sep 17 00:00:00 2001 From: Faheem <“faheemuddinsayyed789@gmail.com”> Date: Thu, 4 Apr 2024 19:44:08 +0530 Subject: [PATCH 3/4] Completed Assignment --- titanic.csv | 419 ++++++++++++++++++++++++++++++++++++++++++++++++++ titanic.ipynb | 2 +- 2 files changed, 420 insertions(+), 1 deletion(-) create mode 100644 titanic.csv diff --git a/titanic.csv b/titanic.csv new file mode 100644 index 0000000..fe412f2 --- /dev/null +++ b/titanic.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0.0 +893,0.0 +894,0.0 +895,0.0 +896,0.0 +897,0.0 +898,0.0 +899,0.0 +900,0.0 +901,0.0 +902,0.0 +903,0.0 +904,1.0 +905,0.0 +906,1.0 +907,0.0 +908,0.0 +909,0.0 +910,0.0 +911,0.0 +912,1.0 +913,0.0 +914,0.0 +915,1.0 +916,1.0 +917,0.0 +918,1.0 +919,0.0 +920,0.0 +921,0.0 +922,0.0 +923,0.0 +924,0.0 +925,0.0 +926,1.0 +927,0.0 +928,0.0 +929,0.0 +930,0.0 +931,1.0 +932,0.0 +933,0.0 +934,0.0 +935,0.0 +936,1.0 +937,0.0 +938,0.0 +939,0.0 +940,1.0 +941,0.0 +942,1.0 +943,0.0 +944,0.0 +945,1.0 +946,0.0 +947,0.0 +948,0.0 +949,0.0 +950,0.0 +951,1.0 +952,0.0 +953,0.0 +954,0.0 +955,0.0 +956,1.0 +957,0.0 +958,0.0 +959,0.0 +960,0.0 +961,1.0 +962,0.0 +963,0.0 +964,0.0 +965,0.0 +966,1.0 +967,1.0 +968,0.0 +969,0.0 +970,0.0 +971,0.0 +972,1.0 +973,1.0 +974,0.0 +975,0.0 +976,0.0 +977,0.0 +978,0.0 +979,0.0 +980,0.0 +981,1.0 +982,0.0 +983,0.0 +984,0.0 +985,0.0 +986,0.0 +987,0.0 +988,1.0 +989,0.0 +990,0.0 +991,0.0 +992,1.0 +993,0.0 +994,0.0 +995,0.0 +996,0.0 +997,0.0 +998,0.0 +999,0.0 +1000,0.0 +1001,0.0 +1002,0.0 +1003,0.0 +1004,0.0 +1005,0.0 +1006,1.0 +1007,0.0 +1008,0.0 +1009,1.0 +1010,1.0 +1011,0.0 +1012,0.0 +1013,0.0 +1014,1.0 +1015,0.0 +1016,0.0 +1017,0.0 +1018,0.0 +1019,0.0 +1020,0.0 +1021,0.0 +1022,0.0 +1023,0.0 +1024,0.0 +1025,0.0 +1026,0.0 +1027,0.0 +1028,0.0 +1029,0.0 +1030,0.0 +1031,0.0 +1032,0.0 +1033,1.0 +1034,1.0 +1035,0.0 +1036,0.0 +1037,0.0 +1038,0.0 +1039,0.0 +1040,0.0 +1041,0.0 +1042,1.0 +1043,0.0 +1044,0.0 +1045,0.0 +1046,0.0 +1047,1.0 +1048,0.0 +1049,0.0 +1050,0.0 +1051,0.0 +1052,0.0 +1053,0.0 +1054,0.0 +1055,0.0 +1056,0.0 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+1157,0.0 +1158,0.0 +1159,0.0 +1160,0.0 +1161,1.0 +1162,0.0 +1163,1.0 +1164,0.0 +1165,0.0 +1166,0.0 +1167,0.0 +1168,0.0 +1169,0.0 +1170,0.0 +1171,0.0 +1172,1.0 +1173,0.0 +1174,0.0 +1175,1.0 +1176,0.0 +1177,0.0 +1178,1.0 +1179,0.0 +1180,0.0 +1181,0.0 +1182,0.0 +1183,0.0 +1184,1.0 +1185,0.0 +1186,0.0 +1187,1.0 +1188,0.0 +1189,0.0 +1190,0.0 +1191,0.0 +1192,0.0 +1193,0.0 +1194,0.0 +1195,0.0 +1196,0.0 +1197,1.0 +1198,0.0 +1199,1.0 +1200,0.0 +1201,0.0 +1202,0.0 +1203,0.0 +1204,0.0 +1205,1.0 +1206,0.0 +1207,1.0 +1208,0.0 +1209,0.0 +1210,0.0 +1211,0.0 +1212,0.0 +1213,0.0 +1214,0.0 +1215,1.0 +1216,0.0 +1217,0.0 +1218,1.0 +1219,0.0 +1220,0.0 +1221,0.0 +1222,0.0 +1223,0.0 +1224,0.0 +1225,0.0 +1226,0.0 +1227,0.0 +1228,0.0 +1229,0.0 +1230,0.0 +1231,0.0 +1232,0.0 +1233,1.0 +1234,1.0 +1235,0.0 +1236,0.0 +1237,0.0 +1238,0.0 +1239,0.0 +1240,0.0 +1241,1.0 +1242,0.0 +1243,1.0 +1244,1.0 +1245,1.0 +1246,0.0 +1247,0.0 +1248,0.0 +1249,0.0 +1250,0.0 +1251,0.0 +1252,0.0 +1253,0.0 +1254,0.0 +1255,1.0 +1256,1.0 +1257,0.0 +1258,0.0 +1259,1.0 +1260,0.0 +1261,0.0 +1262,1.0 +1263,0.0 +1264,0.0 +1265,1.0 +1266,1.0 +1267,0.0 +1268,0.0 +1269,0.0 +1270,0.0 +1271,0.0 +1272,0.0 +1273,0.0 +1274,0.0 +1275,0.0 +1276,1.0 +1277,0.0 +1278,0.0 +1279,0.0 +1280,0.0 +1281,1.0 +1282,0.0 +1283,0.0 +1284,0.0 +1285,0.0 +1286,1.0 +1287,0.0 +1288,1.0 +1289,0.0 +1290,0.0 +1291,1.0 +1292,0.0 +1293,1.0 +1294,0.0 +1295,0.0 +1296,0.0 +1297,0.0 +1298,1.0 +1299,0.0 +1300,1.0 +1301,0.0 +1302,1.0 +1303,0.0 +1304,0.0 +1305,1.0 +1306,0.0 +1307,0.0 +1308,0.0 +1309, diff --git a/titanic.ipynb b/titanic.ipynb index cde5079..7b03595 100644 --- a/titanic.ipynb +++ b/titanic.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n","# Ignore this"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Load and Check Data"]},{"cell_type":"markdown","metadata":{},"source":["DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n","CSV data is read into 'df' using Pandas' read_csv() function."]},{"cell_type":"code","execution_count":null,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-04-01T06:45:27.416192Z","iopub.status.busy":"2024-04-01T06:45:27.415763Z","iopub.status.idle":"2024-04-01T06:45:27.433162Z","shell.execute_reply":"2024-04-01T06:45:27.431944Z","shell.execute_reply.started":"2024-04-01T06:45:27.416105Z"},"trusted":true},"outputs":[],"source":["train_df = pd.read_csv(\"./data/train.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Try to read the test .csv file into test_df"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.436611Z","iopub.status.busy":"2024-04-01T06:45:27.435916Z","iopub.status.idle":"2024-04-01T06:45:27.449974Z","shell.execute_reply":"2024-04-01T06:45:27.448230Z","shell.execute_reply.started":"2024-04-01T06:45:27.436517Z"},"trusted":true},"outputs":[],"source":["test_df = pd.read_csv(\"./data/test.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.execute_input":"2024-04-01T06:45:27.452397Z","iopub.status.busy":"2024-04-01T06:45:27.451949Z","iopub.status.idle":"2024-04-01T06:45:27.462622Z","shell.execute_reply":"2024-04-01T06:45:27.461859Z","shell.execute_reply.started":"2024-04-01T06:45:27.452348Z"},"trusted":true},"outputs":[],"source":["print(\"The Columns of train_df are: \")\n","train_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["### We can use head() to see the first few rows in the dataframe"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.464289Z","iopub.status.busy":"2024-04-01T06:45:27.463866Z","iopub.status.idle":"2024-04-01T06:45:27.491984Z","shell.execute_reply":"2024-04-01T06:45:27.491110Z","shell.execute_reply.started":"2024-04-01T06:45:27.464242Z"},"trusted":true},"outputs":[],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.494326Z","iopub.status.busy":"2024-04-01T06:45:27.493637Z","iopub.status.idle":"2024-04-01T06:45:27.541999Z","shell.execute_reply":"2024-04-01T06:45:27.541210Z","shell.execute_reply.started":"2024-04-01T06:45:27.494251Z"},"jupyter":{"source_hidden":true},"trusted":true},"outputs":[],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Without looking above, try checking the first few rows of test_df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### 3. Now try checking for a description of test_df's data"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["# Variable Description\n","
    \n","
  1. PassengerId: unique id number to each passenger
    2. \n","
  2. Survived: passenger survive(1) or died(0)
    3. \n","
  3. Pclass: passenger class
    4. \n","
  4. Name: name
    5. \n","
  5. Sex: gender of passenger
    6. \n","
  6. Age: age of passenger
    7. \n","
  7. SibSp: number of siblings/spouses
    8. \n","
  8. Parch: number of parents/children
    9. \n","
  9. Ticket: ticket number
    10. \n","
  10. Fare: amount of money spent on ticket
    11. \n","
  11. Cabin: cabin category
    12. \n","
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n","
\n"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.544377Z","iopub.status.busy":"2024-04-01T06:45:27.543901Z","iopub.status.idle":"2024-04-01T06:45:27.557229Z","shell.execute_reply":"2024-04-01T06:45:27.555972Z","shell.execute_reply.started":"2024-04-01T06:45:27.544320Z"},"trusted":true},"outputs":[],"source":["train_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### Slice Rows and Columsn of DF (Assigmennt)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:53:12.214069Z","iopub.status.busy":"2024-04-01T06:53:12.213708Z","iopub.status.idle":"2024-04-01T06:53:12.223150Z","shell.execute_reply":"2024-04-01T06:53:12.222195Z","shell.execute_reply.started":"2024-04-01T06:53:12.214014Z"},"trusted":true},"outputs":[],"source":["# Printing the Second Row\n","train_df.iloc[2]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Print the 5th Row"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:14.398373Z","iopub.status.busy":"2024-04-01T06:54:14.398006Z","iopub.status.idle":"2024-04-01T06:54:14.407886Z","shell.execute_reply":"2024-04-01T06:54:14.406590Z","shell.execute_reply.started":"2024-04-01T06:54:14.398326Z"},"trusted":true},"outputs":[],"source":["# Print the Sex Column\n","train_df['Sex']"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:24.550687Z","iopub.status.busy":"2024-04-01T06:54:24.550286Z","iopub.status.idle":"2024-04-01T06:54:24.555255Z","shell.execute_reply":"2024-04-01T06:54:24.553923Z","shell.execute_reply.started":"2024-04-01T06:54:24.550616Z"},"trusted":true},"outputs":[],"source":["# Print the Name Column"]},{"cell_type":"markdown","metadata":{},"source":["## Visualization (Assignment)"]},{"cell_type":"markdown","metadata":{},"source":["### Age -- Survived"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:13:34.450088Z","iopub.status.busy":"2024-04-01T07:13:34.449302Z","iopub.status.idle":"2024-04-01T07:13:34.932717Z","shell.execute_reply":"2024-04-01T07:13:34.930449Z","shell.execute_reply.started":"2024-04-01T07:13:34.450021Z"},"trusted":true},"outputs":[],"source":["plt.figure(figsize=(8, 6))\n","# Plot 1: Survivors vs Non Survivors\n","\n","# Creating a plot for the Survived Column\n","sns.countplot(x='Survived', data=train_df)\n","\n","plt.title('Survivors vs Non Survivors')\n","plt.xlabel('Survived')\n","plt.ylabel('Count')\n","plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Plotting Passenger Class"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:14:31.768779Z","iopub.status.busy":"2024-04-01T07:14:31.768341Z","iopub.status.idle":"2024-04-01T07:14:32.062495Z","shell.execute_reply":"2024-04-01T07:14:32.060660Z","shell.execute_reply.started":"2024-04-01T07:14:31.768690Z"},"trusted":true},"outputs":[],"source":["plt.figure(figsize=(8, 6))\n","\n","# Make the plot for Pclass here:\n","\n","\n","plt.title('Count of Passengers In each Passenger Class')\n","plt.xlabel('Passenger Class')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try it for \"Embarked\""]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Try Making a histogram for \"Fare\""]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{},"source":["### Here is the distplot for \"Fare\", refer to it after you tried it yourself:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:18:24.402882Z","iopub.status.busy":"2024-04-01T07:18:24.402274Z","iopub.status.idle":"2024-04-01T07:18:24.798062Z","shell.execute_reply":"2024-04-01T07:18:24.796669Z","shell.execute_reply.started":"2024-04-01T07:18:24.402828Z"},"trusted":true},"outputs":[],"source":["sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Make a histogram for \"Age\" (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:19:53.874413Z","iopub.status.busy":"2024-04-01T07:19:53.873686Z","iopub.status.idle":"2024-04-01T07:19:54.244996Z","shell.execute_reply":"2024-04-01T07:19:54.243521Z","shell.execute_reply.started":"2024-04-01T07:19:53.874351Z"},"trusted":true},"outputs":[],"source":["# Create the plot below"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Fill Missing: Age Feature"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:50.370496Z","iopub.status.busy":"2024-04-01T06:27:50.369419Z","iopub.status.idle":"2024-04-01T06:27:50.427731Z","shell.execute_reply":"2024-04-01T06:27:50.426655Z","shell.execute_reply.started":"2024-04-01T06:27:50.370387Z"},"trusted":true},"outputs":[],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### Try Checking for Null Values in Test Df"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Run this to fix the Null Values"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:21:48.194895Z","iopub.status.busy":"2024-04-01T07:21:48.194020Z","iopub.status.idle":"2024-04-01T07:21:49.449282Z","shell.execute_reply":"2024-04-01T07:21:49.447918Z","shell.execute_reply.started":"2024-04-01T07:21:48.194825Z"},"trusted":true},"outputs":[],"source":["index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = train_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," train_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," train_df[\"Age\"].iloc[i] = age_med\n","\n","index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = test_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," test_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," test_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"markdown","metadata":{},"source":["## Analysing the correlation between the different columns"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:24:33.644174Z","iopub.status.busy":"2024-04-01T07:24:33.643621Z","iopub.status.idle":"2024-04-01T07:24:34.404306Z","shell.execute_reply":"2024-04-01T07:24:34.402938Z","shell.execute_reply.started":"2024-04-01T07:24:33.643935Z"},"trusted":true},"outputs":[],"source":["numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n","sns.heatmap(train_df[numerical_columns].corr(), annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)."]},{"cell_type":"markdown","metadata":{},"source":["## Embarked"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.378151Z","iopub.status.busy":"2024-04-01T06:27:55.377756Z","iopub.status.idle":"2024-04-01T06:27:55.384785Z","shell.execute_reply":"2024-04-01T06:27:55.384101Z","shell.execute_reply.started":"2024-04-01T06:27:55.378107Z"},"trusted":true},"outputs":[],"source":["train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.386265Z","iopub.status.busy":"2024-04-01T06:27:55.385875Z","iopub.status.idle":"2024-04-01T06:27:55.635178Z","shell.execute_reply":"2024-04-01T06:27:55.633609Z","shell.execute_reply.started":"2024-04-01T06:27:55.386223Z"},"trusted":true},"outputs":[],"source":["sns.countplot(x = \"Embarked\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.638242Z","iopub.status.busy":"2024-04-01T06:27:55.637447Z","iopub.status.idle":"2024-04-01T06:27:55.699106Z","shell.execute_reply":"2024-04-01T06:27:55.698208Z","shell.execute_reply.started":"2024-04-01T06:27:55.638150Z"},"trusted":true},"outputs":[],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Ticket (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.700760Z","iopub.status.busy":"2024-04-01T06:27:55.700330Z","iopub.status.idle":"2024-04-01T06:27:55.708542Z","shell.execute_reply":"2024-04-01T06:27:55.707466Z","shell.execute_reply.started":"2024-04-01T06:27:55.700715Z"},"trusted":true},"outputs":[],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.710291Z","iopub.status.busy":"2024-04-01T06:27:55.709980Z","iopub.status.idle":"2024-04-01T06:27:55.722810Z","shell.execute_reply":"2024-04-01T06:27:55.721839Z","shell.execute_reply.started":"2024-04-01T06:27:55.710231Z"},"trusted":true},"outputs":[],"source":["example_ticket = \"A/5. 2151\"\n","example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.726116Z","iopub.status.busy":"2024-04-01T06:27:55.725689Z","iopub.status.idle":"2024-04-01T06:27:55.738095Z","shell.execute_reply":"2024-04-01T06:27:55.737043Z","shell.execute_reply.started":"2024-04-01T06:27:55.726039Z"},"trusted":true},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets\n","\n","# Do the same for the test set"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.740389Z","iopub.status.busy":"2024-04-01T06:27:55.739797Z","iopub.status.idle":"2024-04-01T06:27:55.755416Z","shell.execute_reply":"2024-04-01T06:27:55.754317Z","shell.execute_reply.started":"2024-04-01T06:27:55.740333Z"},"trusted":true},"outputs":[],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.790832Z","iopub.status.busy":"2024-04-01T06:27:55.790500Z","iopub.status.idle":"2024-04-01T06:27:55.841011Z","shell.execute_reply":"2024-04-01T06:27:55.839862Z","shell.execute_reply.started":"2024-04-01T06:27:55.790770Z"},"trusted":true},"outputs":[],"source":["train_df = pd.get_dummies(train_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","test_df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Pclass"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.843395Z","iopub.status.busy":"2024-04-01T06:27:55.842833Z","iopub.status.idle":"2024-04-01T06:27:56.089225Z","shell.execute_reply":"2024-04-01T06:27:56.087578Z","shell.execute_reply.started":"2024-04-01T06:27:55.843168Z"},"trusted":true},"outputs":[],"source":["sns.countplot(x = \"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.092270Z","iopub.status.busy":"2024-04-01T06:27:56.091722Z","iopub.status.idle":"2024-04-01T06:27:56.162888Z","shell.execute_reply":"2024-04-01T06:27:56.161841Z","shell.execute_reply.started":"2024-04-01T06:27:56.092186Z"},"trusted":true},"outputs":[],"source":["train_df[\"Pclass\"] = train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns= [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Sex"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.164709Z","iopub.status.busy":"2024-04-01T06:27:56.164391Z","iopub.status.idle":"2024-04-01T06:27:56.205775Z","shell.execute_reply":"2024-04-01T06:27:56.204761Z","shell.execute_reply.started":"2024-04-01T06:27:56.164639Z"},"trusted":true},"outputs":[],"source":["train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Drop Passenger ID and Cabin (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.207602Z","iopub.status.busy":"2024-04-01T06:27:56.207299Z","iopub.status.idle":"2024-04-01T06:27:56.215886Z","shell.execute_reply":"2024-04-01T06:27:56.214401Z","shell.execute_reply.started":"2024-04-01T06:27:56.207550Z"},"trusted":true},"outputs":[],"source":["train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.217917Z","iopub.status.busy":"2024-04-01T06:27:56.217536Z","iopub.status.idle":"2024-04-01T06:27:56.228150Z","shell.execute_reply":"2024-04-01T06:27:56.227230Z","shell.execute_reply.started":"2024-04-01T06:27:56.217854Z"},"trusted":true},"outputs":[],"source":["train_df.columns"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Drop the PassengerId and Cabin columns from the test set"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Print the columns of the test set"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Modeling"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.230086Z","iopub.status.busy":"2024-04-01T06:27:56.229809Z","iopub.status.idle":"2024-04-01T06:27:56.238557Z","shell.execute_reply":"2024-04-01T06:27:56.237679Z","shell.execute_reply.started":"2024-04-01T06:27:56.230040Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{},"source":["## Train - Test Split (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.240103Z","iopub.status.busy":"2024-04-01T06:27:56.239830Z","iopub.status.idle":"2024-04-01T06:27:56.256809Z","shell.execute_reply":"2024-04-01T06:27:56.255463Z","shell.execute_reply.started":"2024-04-01T06:27:56.240056Z"},"trusted":true},"outputs":[],"source":["train_df_len = len(train_df)\n","train_df_len"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.314730Z","iopub.status.busy":"2024-04-01T06:27:56.313986Z","iopub.status.idle":"2024-04-01T06:27:56.333564Z","shell.execute_reply":"2024-04-01T06:27:56.332507Z","shell.execute_reply.started":"2024-04-01T06:27:56.314635Z"},"trusted":true},"outputs":[],"source":["\n","train = train_df[:train_df_len]\n","test = test_df\n","\n","# Select all numerical values from train and test\n","numeric_train = train.select_dtypes(include=[np.number])\n","numeric_test = test.select_dtypes(include=[np.number]) \n","\n","\n","X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n","y_train = numeric_train[\"Survived\"]\n","\n","# Split the train data into train and test sets with a 1/3 ratio\n","X_train, X_test, y_train, y_test = # Use the train_test_split function here\n","\n","\n","print(\"X_train\", len(X_train))\n","print(\"X_test\", len(X_test))\n","print(\"y_train\", len(y_train))\n","print(\"y_test\", len(y_test))\n","print(\"test\", len(numeric_test))"]},{"cell_type":"markdown","metadata":{},"source":["## Simple Logistic Regression (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.335970Z","iopub.status.busy":"2024-04-01T06:27:56.335281Z","iopub.status.idle":"2024-04-01T06:27:56.368083Z","shell.execute_reply":"2024-04-01T06:27:56.366489Z","shell.execute_reply.started":"2024-04-01T06:27:56.335561Z"},"trusted":true},"outputs":[],"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, y_train)\n","acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n","acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n","# Print the accuracy on the training and test set"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Hyperparameter Tuning -- Grid Search -- Cross Validation\n","We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n","\n","* Decision Tree\n","* SVM\n","* Random Forest\n","* KNN\n","* Logistic Regression"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.371066Z","iopub.status.busy":"2024-04-01T06:27:56.370400Z","iopub.status.idle":"2024-04-01T06:27:56.401742Z","shell.execute_reply":"2024-04-01T06:27:56.396867Z","shell.execute_reply.started":"2024-04-01T06:27:56.370802Z"},"trusted":true},"outputs":[],"source":["random_state = 42\n","classifier = [DecisionTreeClassifier(random_state = random_state),\n"," SVC(random_state = random_state),\n"," RandomForestClassifier(random_state = random_state),\n"," LogisticRegression(random_state = random_state),\n"," KNeighborsClassifier()]\n","\n","dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n"," \"max_depth\": range(1,20,2)}\n","\n","svc_param_grid = {\"kernel\" : [\"rbf\"],\n"," \"gamma\": [0.001, 0.01, 0.1, 1],\n"," \"C\": [1,10,50,100,200,300,1000]}\n","\n","rf_param_grid = {\"max_features\": [1,3,10],\n"," \"min_samples_split\":[2,3,10],\n"," \"min_samples_leaf\":[1,3,10],\n"," \"bootstrap\":[False],\n"," \"n_estimators\":[100,300],\n"," \"criterion\":[\"gini\"]}\n","\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n"," \"penalty\": [\"l1\",\"l2\"]}\n","\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n"," \"weights\": [\"uniform\",\"distance\"],\n"," \"metric\":[\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n"," svc_param_grid,\n"," rf_param_grid,\n"," logreg_param_grid,\n"," knn_param_grid]"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.413811Z","iopub.status.busy":"2024-04-01T06:27:56.404322Z","iopub.status.idle":"2024-04-01T06:29:38.718970Z","shell.execute_reply":"2024-04-01T06:29:38.717807Z","shell.execute_reply.started":"2024-04-01T06:27:56.413658Z"},"trusted":true},"outputs":[],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n"," clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n"," clf.fit(X_train,y_train)\n"," cv_result.append(clf.best_score_)\n"," best_estimators.append(clf.best_estimator_)\n"," print(cv_result[i])"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:38.722928Z","iopub.status.busy":"2024-04-01T06:29:38.722207Z","iopub.status.idle":"2024-04-01T06:29:39.075423Z","shell.execute_reply":"2024-04-01T06:29:39.073987Z","shell.execute_reply.started":"2024-04-01T06:29:38.722582Z"},"trusted":true},"outputs":[],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n"," \"LogisticRegression\",\n"," \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Scores\")"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Modeling (Assignment)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.078654Z","iopub.status.busy":"2024-04-01T06:29:39.077840Z","iopub.status.idle":"2024-04-01T06:29:39.862871Z","shell.execute_reply":"2024-04-01T06:29:39.860937Z","shell.execute_reply.started":"2024-04-01T06:29:39.078554Z"},"trusted":true},"outputs":[],"source":["votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n"," (\"rfc\",best_estimators[2]),\n"," (\"lr\",best_estimators[3])],\n"," voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(X_train, y_train)\n","\n","# Print the accuracy score of the voting classifier"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Drop the null values which are going to cause you an error in the next cell"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Prediction and Submission"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.865981Z","iopub.status.busy":"2024-04-01T06:29:39.865330Z","iopub.status.idle":"2024-04-01T06:29:39.977357Z","shell.execute_reply":"2024-04-01T06:29:39.973301Z","shell.execute_reply.started":"2024-04-01T06:29:39.865906Z"},"trusted":true},"outputs":[],"source":["test_survived = pd.Series(votingC.predict(numeric_test), name=\"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis=1)\n","results.to_csv(\"titanic.csv\", index=False)\n","print(results)"]},{"cell_type":"markdown","metadata":{},"source":["# Congratulations on finishing the assignment!!\n","\n","### The submission is the titanic.csv which was just created, and this file which you have modified."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":26502,"sourceId":3136,"sourceType":"competition"}],"dockerImageVersionId":29852,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"}},"nbformat":4,"nbformat_minor":4} +{"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n","# Ignore this"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]}],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Load and Check Data"]},{"cell_type":"markdown","metadata":{},"source":["DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n","CSV data is read into 'df' using Pandas' read_csv() function."]},{"cell_type":"code","execution_count":3,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-04-01T06:45:27.416192Z","iopub.status.busy":"2024-04-01T06:45:27.415763Z","iopub.status.idle":"2024-04-01T06:45:27.433162Z","shell.execute_reply":"2024-04-01T06:45:27.431944Z","shell.execute_reply.started":"2024-04-01T06:45:27.416105Z"},"trusted":true},"outputs":[],"source":["train_df = pd.read_csv(\"./data/train.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Try to read the test .csv file into test_df"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.436611Z","iopub.status.busy":"2024-04-01T06:45:27.435916Z","iopub.status.idle":"2024-04-01T06:45:27.449974Z","shell.execute_reply":"2024-04-01T06:45:27.448230Z","shell.execute_reply.started":"2024-04-01T06:45:27.436517Z"},"trusted":true},"outputs":[],"source":["test_df = pd.read_csv(\"./data/test.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":5,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.execute_input":"2024-04-01T06:45:27.452397Z","iopub.status.busy":"2024-04-01T06:45:27.451949Z","iopub.status.idle":"2024-04-01T06:45:27.462622Z","shell.execute_reply":"2024-04-01T06:45:27.461859Z","shell.execute_reply.started":"2024-04-01T06:45:27.452348Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The Columns of train_df are: \n"]},{"data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n"," 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n"," dtype='object')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["print(\"The Columns of train_df are: \")\n","train_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["### We can use head() to see the first few rows in the dataframe"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.464289Z","iopub.status.busy":"2024-04-01T06:45:27.463866Z","iopub.status.idle":"2024-04-01T06:45:27.491984Z","shell.execute_reply":"2024-04-01T06:45:27.491110Z","shell.execute_reply.started":"2024-04-01T06:45:27.464242Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked \n","0 0 A/5 21171 7.2500 NaN S \n","1 0 PC 17599 71.2833 C85 C \n","2 0 STON/O2. 3101282 7.9250 NaN S \n","3 0 113803 53.1000 C123 S \n","4 0 373450 8.0500 NaN S "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.494326Z","iopub.status.busy":"2024-04-01T06:45:27.493637Z","iopub.status.idle":"2024-04-01T06:45:27.541999Z","shell.execute_reply":"2024-04-01T06:45:27.541210Z","shell.execute_reply.started":"2024-04-01T06:45:27.494251Z"},"jupyter":{"source_hidden":true},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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"],"text/plain":[" PassengerId Survived Pclass Age SibSp \\\n","count 891.000000 891.000000 891.000000 714.000000 891.000000 \n","mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n","std 257.353842 0.486592 0.836071 14.526497 1.102743 \n","min 1.000000 0.000000 1.000000 0.420000 0.000000 \n","25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n","50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n","75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n","max 891.000000 1.000000 3.000000 80.000000 8.000000 \n","\n"," Parch Fare \n","count 891.000000 891.000000 \n","mean 0.381594 32.204208 \n","std 0.806057 49.693429 \n","min 0.000000 0.000000 \n","25% 0.000000 7.910400 \n","50% 0.000000 14.454200 \n","75% 0.000000 31.000000 \n","max 6.000000 512.329200 "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Without looking above, try checking the first few rows of test_df"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked \n","0 34.5 0 0 330911 7.8292 NaN Q \n","1 47.0 1 0 363272 7.0000 NaN S \n","2 62.0 0 0 240276 9.6875 NaN Q \n","3 27.0 0 0 315154 8.6625 NaN S \n","4 22.0 1 1 3101298 12.2875 NaN S "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Now try checking for a description of test_df's data"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
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"],"text/plain":[" PassengerId Pclass Age SibSp Parch Fare\n","count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\n","mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\n","std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\n","min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n","25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n","50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n","75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\n","max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["test_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["# Variable Description\n","
    \n","
  1. PassengerId: unique id number to each passenger
    2. \n","
  2. Survived: passenger survive(1) or died(0)
    3. \n","
  3. Pclass: passenger class
    4. \n","
  4. Name: name
    5. \n","
  5. Sex: gender of passenger
    6. \n","
  6. Age: age of passenger
    7. \n","
  7. SibSp: number of siblings/spouses
    8. \n","
  8. Parch: number of parents/children
    9. \n","
  9. Ticket: ticket number
    10. \n","
  10. Fare: amount of money spent on ticket
    11. \n","
  11. Cabin: cabin category
    12. \n","
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n","
\n"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.544377Z","iopub.status.busy":"2024-04-01T06:45:27.543901Z","iopub.status.idle":"2024-04-01T06:45:27.557229Z","shell.execute_reply":"2024-04-01T06:45:27.555972Z","shell.execute_reply.started":"2024-04-01T06:45:27.544320Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 PassengerId 891 non-null int64 \n"," 1 Survived 891 non-null int64 \n"," 2 Pclass 891 non-null int64 \n"," 3 Name 891 non-null object \n"," 4 Sex 891 non-null object \n"," 5 Age 714 non-null float64\n"," 6 SibSp 891 non-null int64 \n"," 7 Parch 891 non-null int64 \n"," 8 Ticket 891 non-null object \n"," 9 Fare 891 non-null float64\n"," 10 Cabin 204 non-null object \n"," 11 Embarked 889 non-null object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["train_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### Slice Rows and Columsn of DF (Assigmennt)"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:53:12.214069Z","iopub.status.busy":"2024-04-01T06:53:12.213708Z","iopub.status.idle":"2024-04-01T06:53:12.223150Z","shell.execute_reply":"2024-04-01T06:53:12.222195Z","shell.execute_reply.started":"2024-04-01T06:53:12.214014Z"},"trusted":true},"outputs":[{"data":{"text/plain":["PassengerId 3\n","Survived 1\n","Pclass 3\n","Name Heikkinen, Miss. Laina\n","Sex female\n","Age 26.0\n","SibSp 0\n","Parch 0\n","Ticket STON/O2. 3101282\n","Fare 7.925\n","Cabin NaN\n","Embarked S\n","Name: 2, dtype: object"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Printing the Second Row\n","train_df.iloc[2]"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 6\n","Survived 0\n","Pclass 3\n","Name Moran, Mr. James\n","Sex male\n","Age NaN\n","SibSp 0\n","Parch 0\n","Ticket 330877\n","Fare 8.4583\n","Cabin NaN\n","Embarked Q\n","Name: 5, dtype: object"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Print the 5th Row\n","train_df.iloc[5]"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:14.398373Z","iopub.status.busy":"2024-04-01T06:54:14.398006Z","iopub.status.idle":"2024-04-01T06:54:14.407886Z","shell.execute_reply":"2024-04-01T06:54:14.406590Z","shell.execute_reply.started":"2024-04-01T06:54:14.398326Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 male\n","1 female\n","2 female\n","3 female\n","4 male\n"," ... \n","886 male\n","887 female\n","888 female\n","889 male\n","890 male\n","Name: Sex, Length: 891, dtype: object"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Sex Column\n","train_df['Sex']"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:24.550687Z","iopub.status.busy":"2024-04-01T06:54:24.550286Z","iopub.status.idle":"2024-04-01T06:54:24.555255Z","shell.execute_reply":"2024-04-01T06:54:24.553923Z","shell.execute_reply.started":"2024-04-01T06:54:24.550616Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 Braund, Mr. Owen Harris\n","1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n","2 Heikkinen, Miss. Laina\n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n","4 Allen, Mr. William Henry\n"," ... \n","886 Montvila, Rev. Juozas\n","887 Graham, Miss. Margaret Edith\n","888 Johnston, Miss. Catherine Helen \"Carrie\"\n","889 Behr, Mr. Karl Howell\n","890 Dooley, Mr. Patrick\n","Name: Name, Length: 891, dtype: object"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Name Column\n","train_df['Name']"]},{"cell_type":"markdown","metadata":{},"source":["## Visualization (Assignment)"]},{"cell_type":"markdown","metadata":{},"source":["### Age -- Survived"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:13:34.450088Z","iopub.status.busy":"2024-04-01T07:13:34.449302Z","iopub.status.idle":"2024-04-01T07:13:34.932717Z","shell.execute_reply":"2024-04-01T07:13:34.930449Z","shell.execute_reply.started":"2024-04-01T07:13:34.450021Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","# Plot 1: Survivors vs Non Survivors\n","\n","# Creating a plot for the Survived Column\n","sns.countplot(x='Survived', data=train_df)\n","\n","plt.title('Survivors vs Non Survivors')\n","plt.xlabel('Survived')\n","plt.ylabel('Count')\n","plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Plotting Passenger Class"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:14:31.768779Z","iopub.status.busy":"2024-04-01T07:14:31.768341Z","iopub.status.idle":"2024-04-01T07:14:32.062495Z","shell.execute_reply":"2024-04-01T07:14:32.060660Z","shell.execute_reply.started":"2024-04-01T07:14:31.768690Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","\n","# Make the plot for Pclass here:\n","sns.countplot(x='Pclass', data=train_df)\n","\n","plt.title('Count of Passengers In each Passenger Class')\n","plt.xlabel('Passenger Class')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try it for \"Embarked\""]},{"cell_type":"code","execution_count":17,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.countplot(x='Embarked', data=train_df)\n","plt.title('Count of Passengers by Embarkation Point')\n","plt.xlabel('Embarkation Point')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['C', 'Q', 'S'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Making a histogram for \"Fare\""]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Here is the distplot for \"Fare\", refer to it after you tried it yourself:"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:18:24.402882Z","iopub.status.busy":"2024-04-01T07:18:24.402274Z","iopub.status.idle":"2024-04-01T07:18:24.798062Z","shell.execute_reply":"2024-04-01T07:18:24.796669Z","shell.execute_reply.started":"2024-04-01T07:18:24.402828Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Make a histogram for \"Age\" (Assignment)"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:19:53.874413Z","iopub.status.busy":"2024-04-01T07:19:53.873686Z","iopub.status.idle":"2024-04-01T07:19:54.244996Z","shell.execute_reply":"2024-04-01T07:19:54.243521Z","shell.execute_reply.started":"2024-04-01T07:19:53.874351Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Create the plot below\n","plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Age'], bins=20, color='green')\n","plt.title('Distribution of Age')\n","plt.xlabel('Age')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Fill Missing: Age Feature"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:50.370496Z","iopub.status.busy":"2024-04-01T06:27:50.369419Z","iopub.status.idle":"2024-04-01T06:27:50.427731Z","shell.execute_reply":"2024-04-01T06:27:50.426655Z","shell.execute_reply.started":"2024-04-01T06:27:50.370387Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
.......................................
85986003Razi, Mr. RaihedmaleNaN0026297.2292NaNC
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
\n","

177 rows × 12 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass Name \\\n","5 6 0 3 Moran, Mr. James \n","17 18 1 2 Williams, Mr. Charles Eugene \n","19 20 1 3 Masselmani, Mrs. Fatima \n","26 27 0 3 Emir, Mr. Farred Chehab \n","28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" \n",".. ... ... ... ... \n","859 860 0 3 Razi, Mr. Raihed \n","863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" \n","868 869 0 3 van Melkebeke, Mr. Philemon \n","878 879 0 3 Laleff, Mr. Kristo \n","888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n","\n"," Sex Age SibSp Parch Ticket Fare Cabin Embarked \n","5 male NaN 0 0 330877 8.4583 NaN Q \n","17 male NaN 0 0 244373 13.0000 NaN S \n","19 female NaN 0 0 2649 7.2250 NaN C \n","26 male NaN 0 0 2631 7.2250 NaN C \n","28 female NaN 0 0 330959 7.8792 NaN Q \n",".. ... ... ... ... ... ... ... ... \n","859 male NaN 0 0 2629 7.2292 NaN C \n","863 female NaN 8 2 CA. 2343 69.5500 NaN S \n","868 male NaN 0 0 345777 9.5000 NaN S \n","878 male NaN 0 0 349217 7.8958 NaN S \n","888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n","\n","[177 rows x 12 columns]"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### Try Checking for Null Values in Test Df"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 0\n","Pclass 0\n","Name 0\n","Sex 0\n","Age 86\n","SibSp 0\n","Parch 0\n","Ticket 0\n","Fare 1\n","Cabin 327\n","Embarked 0\n","dtype: int64"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["test_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Run this to fix the Null Values"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:21:48.194895Z","iopub.status.busy":"2024-04-01T07:21:48.194020Z","iopub.status.idle":"2024-04-01T07:21:49.449282Z","shell.execute_reply":"2024-04-01T07:21:49.447918Z","shell.execute_reply.started":"2024-04-01T07:21:48.194825Z"},"trusted":true},"outputs":[],"source":["index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = train_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," train_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," train_df[\"Age\"].iloc[i] = age_med\n","\n","index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = test_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," test_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," test_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"markdown","metadata":{},"source":["## Analysing the correlation between the different columns"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:24:33.644174Z","iopub.status.busy":"2024-04-01T07:24:33.643621Z","iopub.status.idle":"2024-04-01T07:24:34.404306Z","shell.execute_reply":"2024-04-01T07:24:34.402938Z","shell.execute_reply.started":"2024-04-01T07:24:33.643935Z"},"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":24,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n","sns.heatmap(train_df[numerical_columns].corr(), annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)."]},{"cell_type":"markdown","metadata":{},"source":["## Embarked"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.378151Z","iopub.status.busy":"2024-04-01T06:27:55.377756Z","iopub.status.idle":"2024-04-01T06:27:55.384785Z","shell.execute_reply":"2024-04-01T06:27:55.384101Z","shell.execute_reply.started":"2024-04-01T06:27:55.378107Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 S\n","1 C\n","2 S\n","3 S\n","4 S\n","Name: Embarked, dtype: object"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.386265Z","iopub.status.busy":"2024-04-01T06:27:55.385875Z","iopub.status.idle":"2024-04-01T06:27:55.635178Z","shell.execute_reply":"2024-04-01T06:27:55.633609Z","shell.execute_reply.started":"2024-04-01T06:27:55.386223Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Embarked\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.638242Z","iopub.status.busy":"2024-04-01T06:27:55.637447Z","iopub.status.idle":"2024-04-01T06:27:55.699106Z","shell.execute_reply":"2024-04-01T06:27:55.698208Z","shell.execute_reply.started":"2024-04-01T06:27:55.638150Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNFalseFalseTrue
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85TrueFalseFalse
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNFalseFalseTrue
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123FalseFalseTrue
4503Allen, Mr. William Henrymale35.0003734508.0500NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked_C Embarked_Q Embarked_S \n","0 0 A/5 21171 7.2500 NaN False False True \n","1 0 PC 17599 71.2833 C85 True False False \n","2 0 STON/O2. 3101282 7.9250 NaN False False True \n","3 0 113803 53.1000 C123 False False True \n","4 0 373450 8.0500 NaN False False True "]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNFalseTrueFalse
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNFalseTrueFalse
38953Wirz, Mr. Albertmale27.0003151548.6625NaNFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked_C Embarked_Q \\\n","0 34.5 0 0 330911 7.8292 NaN False True \n","1 47.0 1 0 363272 7.0000 NaN False False \n","2 62.0 0 0 240276 9.6875 NaN False True \n","3 27.0 0 0 315154 8.6625 NaN False False \n","4 22.0 1 1 3101298 12.2875 NaN False False \n","\n"," Embarked_S \n","0 False \n","1 True \n","2 False \n","3 True \n","4 True "]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Ticket (Assignment)"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.700760Z","iopub.status.busy":"2024-04-01T06:27:55.700330Z","iopub.status.idle":"2024-04-01T06:27:55.708542Z","shell.execute_reply":"2024-04-01T06:27:55.707466Z","shell.execute_reply.started":"2024-04-01T06:27:55.700715Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A/5 21171\n","1 PC 17599\n","2 STON/O2. 3101282\n","3 113803\n","4 373450\n","5 330877\n","6 17463\n","7 349909\n","8 347742\n","9 237736\n","10 PP 9549\n","11 113783\n","12 A/5. 2151\n","13 347082\n","14 350406\n","15 248706\n","16 382652\n","17 244373\n","18 345763\n","19 2649\n","Name: Ticket, dtype: object"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.710291Z","iopub.status.busy":"2024-04-01T06:27:55.709980Z","iopub.status.idle":"2024-04-01T06:27:55.722810Z","shell.execute_reply":"2024-04-01T06:27:55.721839Z","shell.execute_reply.started":"2024-04-01T06:27:55.710231Z"},"trusted":true},"outputs":[{"data":{"text/plain":["'A5'"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["example_ticket = \"A/5. 2151\"\n","example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.726116Z","iopub.status.busy":"2024-04-01T06:27:55.725689Z","iopub.status.idle":"2024-04-01T06:27:55.738095Z","shell.execute_reply":"2024-04-01T06:27:55.737043Z","shell.execute_reply.started":"2024-04-01T06:27:55.726039Z"},"trusted":true},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets\n","\n","# Do the same for the test set\n","tickets = []\n","for i in list(test_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","test_df[\"Ticket\"] = tickets"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.740389Z","iopub.status.busy":"2024-04-01T06:27:55.739797Z","iopub.status.idle":"2024-04-01T06:27:55.755416Z","shell.execute_reply":"2024-04-01T06:27:55.754317Z","shell.execute_reply.started":"2024-04-01T06:27:55.740333Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A5\n","1 PC\n","2 STONO2\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 x\n","10 PP\n","11 x\n","12 A5\n","13 x\n","14 x\n","15 x\n","16 x\n","17 x\n","18 x\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/plain":["0 x\n","1 x\n","2 x\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 A4\n","10 x\n","11 x\n","12 x\n","13 x\n","14 WEP\n","15 SCPARIS\n","16 x\n","17 x\n","18 STONO2\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.790832Z","iopub.status.busy":"2024-04-01T06:27:55.790500Z","iopub.status.idle":"2024-04-01T06:27:55.841011Z","shell.execute_reply":"2024-04-01T06:27:55.839862Z","shell.execute_reply.started":"2024-04-01T06:27:55.790770Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchFareCabin...TcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_x
0103Braund, Mr. Owen Harrismale22.0107.2500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2313Heikkinen, Miss. Lainafemale26.0007.9250NaN...FalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
4503Allen, Mr. William Henrymale35.0008.0500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
5603Moran, Mr. Jamesmale26.0008.4583NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
6701McCarthy, Mr. Timothy Jmale54.00051.8625E46...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
7803Palsson, Master. Gosta Leonardmale2.03121.0750NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00211.1333NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01030.0708NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
\n","

10 rows × 44 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","5 6 0 3 \n","6 7 0 1 \n","7 8 0 3 \n","8 9 1 3 \n","9 10 1 2 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","5 Moran, Mr. James male 26.0 0 \n","6 McCarthy, Mr. Timothy J male 54.0 0 \n","7 Palsson, Master. Gosta Leonard male 2.0 3 \n","8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n","9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n","\n"," Parch Fare Cabin ... TcktName_SOPP TcktName_SOTONO2 \\\n","0 0 7.2500 NaN ... False False \n","1 0 71.2833 C85 ... False False \n","2 0 7.9250 NaN ... False False \n","3 0 53.1000 C123 ... False False \n","4 0 8.0500 NaN ... False False \n","5 0 8.4583 NaN ... False False \n","6 0 51.8625 E46 ... False False \n","7 1 21.0750 NaN ... False False \n","8 2 11.1333 NaN ... False False \n","9 0 30.0708 NaN ... False False \n","\n"," TcktName_SOTONOQ TcktName_SP TcktName_STONO TcktName_STONO2 \\\n","0 False False False False \n","1 False False False False \n","2 False False False True \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False True \n","4 False False False True \n","5 False False False True \n","6 False False False True \n","7 False False False True \n","8 False False False True \n","9 False False False True \n","\n","[10 rows x 44 columns]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SOCTcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_x
08923Kelly, Mr. Jamesmale34.5007.8292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
18933Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0009.6875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
38953Wirz, Mr. Albertmale27.0008.6625NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
58973Svensson, Mr. Johan Cervinmale14.0009.2250NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
68983Connolly, Miss. Katefemale30.0007.6292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
78992Caldwell, Mr. Albert Francismale26.01129.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.0007.2292NaNTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
99013Davies, Mr. John Samuelmale21.02024.1500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
\n","

10 rows × 40 columns

\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","5 897 3 Svensson, Mr. Johan Cervin male \n","6 898 3 Connolly, Miss. Kate female \n","7 899 2 Caldwell, Mr. Albert Francis male \n","8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female \n","9 901 3 Davies, Mr. John Samuel male \n","\n"," Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SOC \\\n","0 34.5 0 0 7.8292 NaN False ... False \n","1 47.0 1 0 7.0000 NaN False ... False \n","2 62.0 0 0 9.6875 NaN False ... False \n","3 27.0 0 0 8.6625 NaN False ... False \n","4 22.0 1 1 12.2875 NaN False ... False \n","5 14.0 0 0 9.2250 NaN False ... False \n","6 30.0 0 0 7.6292 NaN False ... False \n","7 26.0 1 1 29.0000 NaN False ... False \n","8 18.0 0 0 7.2292 NaN True ... False \n","9 21.0 2 0 24.1500 NaN False ... False \n","\n"," TcktName_SOPP TcktName_SOTONO2 TcktName_SOTONOQ TcktName_STONO \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","5 False False False False True \n","6 False False False False True \n","7 False False False False True \n","8 False False False False True \n","9 False False False False False \n","\n","[10 rows x 40 columns]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","test_df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Pclass"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.843395Z","iopub.status.busy":"2024-04-01T06:27:55.842833Z","iopub.status.idle":"2024-04-01T06:27:56.089225Z","shell.execute_reply":"2024-04-01T06:27:56.087578Z","shell.execute_reply.started":"2024-04-01T06:27:55.843168Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.092270Z","iopub.status.busy":"2024-04-01T06:27:56.091722Z","iopub.status.idle":"2024-04-01T06:27:56.162888Z","shell.execute_reply":"2024-04-01T06:27:56.161841Z","shell.execute_reply.started":"2024-04-01T06:27:56.092186Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
010Braund, Mr. Owen Harrismale22.0107.2500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85True...FalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
231Heikkinen, Miss. Lainafemale26.0007.9250NaNFalse...FalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123False...FalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
450Allen, Mr. William Henrymale35.0008.0500NaNFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 46 columns

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"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Sex Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SP \\\n","0 male 22.0 1 0 7.2500 NaN False ... False \n","1 female 38.0 1 0 71.2833 C85 True ... False \n","2 female 26.0 0 0 7.9250 NaN False ... False \n","3 female 35.0 1 0 53.1000 C123 False ... False \n","4 male 35.0 0 0 8.0500 NaN False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP \\\n","0 False False False False False \n","1 False False False False False \n","2 False True False False False \n","3 False False False False False \n","4 False False False False False \n","\n"," TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False False False True \n","1 False True False False \n","2 False False False True \n","3 True True False False \n","4 True False False True \n","\n","[5 rows x 46 columns]"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Pclass\"] = train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns= [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameSexAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
0892Kelly, Mr. Jamesmale34.5007.8292NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
2894Myles, Mr. Thomas Francismale62.0009.6875NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
3895Wirz, Mr. Albertmale27.0008.6625NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 42 columns

\n","
"],"text/plain":[" PassengerId Name Sex Age \\\n","0 892 Kelly, Mr. James male 34.5 \n","1 893 Wilkes, Mrs. James (Ellen Needs) female 47.0 \n","2 894 Myles, Mr. Thomas Francis male 62.0 \n","3 895 Wirz, Mr. Albert male 27.0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 \n","\n"," SibSp Parch Fare Cabin Embarked_C Embarked_Q ... TcktName_SOTONOQ \\\n","0 0 0 7.8292 NaN False True ... False \n","1 1 0 7.0000 NaN False False ... False \n","2 0 0 9.6875 NaN False True ... False \n","3 0 0 8.6625 NaN False False ... False \n","4 1 1 12.2875 NaN False False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_STONOQ TcktName_WC \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","\n"," TcktName_WEP TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False True False False True \n","1 False True False False True \n","2 False True False True False \n","3 False True False False True \n","4 False True False False True \n","\n","[5 rows x 42 columns]"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Sex"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.164709Z","iopub.status.busy":"2024-04-01T06:27:56.164391Z","iopub.status.idle":"2024-04-01T06:27:56.205775Z","shell.execute_reply":"2024-04-01T06:27:56.204761Z","shell.execute_reply.started":"2024-04-01T06:27:56.164639Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
010Braund, Mr. Owen Harris22.0107.2500NaNFalseFalse...FalseFalseFalseFalseFalseFalseFalseTrueFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833C85TrueFalse...FalseFalseFalseFalseFalseTrueFalseFalseTrueFalse
231Heikkinen, Miss. Laina26.0007.9250NaNFalseFalse...TrueFalseFalseFalseFalseFalseFalseTrueTrueFalse
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123FalseFalse...FalseFalseFalseFalseTrueTrueFalseFalseTrueFalse
450Allen, Mr. William Henry35.0008.0500NaNFalseFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
\n","

5 rows × 47 columns

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"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Age SibSp Parch Fare Cabin Embarked_C Embarked_Q ... \\\n","0 22.0 1 0 7.2500 NaN False False ... \n","1 38.0 1 0 71.2833 C85 True False ... \n","2 26.0 0 0 7.9250 NaN False False ... \n","3 35.0 1 0 53.1000 C123 False False ... \n","4 35.0 0 0 8.0500 NaN False False ... \n","\n"," TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False False \n","1 False False False False False \n","2 True False False False False \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 True False False True False \n","2 False False True True False \n","3 True False False True False \n","4 False False True False True \n","\n","[5 rows x 47 columns]"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameAgeSibSpParchFareCabinEmbarked_CEmbarked_QEmbarked_S...TcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
0892Kelly, Mr. James34.5007.8292NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)47.0107.0000NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
2894Myles, Mr. Thomas Francis62.0009.6875NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseTrueFalseFalseTrue
3895Wirz, Mr. Albert27.0008.6625NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)22.01112.2875NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
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5 rows × 43 columns

\n","
"],"text/plain":[" PassengerId Name Age SibSp \\\n","0 892 Kelly, Mr. James 34.5 0 \n","1 893 Wilkes, Mrs. James (Ellen Needs) 47.0 1 \n","2 894 Myles, Mr. Thomas Francis 62.0 0 \n","3 895 Wirz, Mr. Albert 27.0 0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 22.0 1 \n","\n"," Parch Fare Cabin Embarked_C Embarked_Q Embarked_S ... \\\n","0 0 7.8292 NaN False True False ... \n","1 0 7.0000 NaN False False True ... \n","2 0 9.6875 NaN False True False ... \n","3 0 8.6625 NaN False False True ... \n","4 1 12.2875 NaN False False True ... \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 False False True True False \n","2 False True False False True \n","3 False False True False True \n","4 False False True True False \n","\n","[5 rows x 43 columns]"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Drop Passenger ID and Cabin (Assignment)"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.207602Z","iopub.status.busy":"2024-04-01T06:27:56.207299Z","iopub.status.idle":"2024-04-01T06:27:56.215886Z","shell.execute_reply":"2024-04-01T06:27:56.214401Z","shell.execute_reply.started":"2024-04-01T06:27:56.207550Z"},"trusted":true},"outputs":[],"source":["train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.217917Z","iopub.status.busy":"2024-04-01T06:27:56.217536Z","iopub.status.idle":"2024-04-01T06:27:56.228150Z","shell.execute_reply":"2024-04-01T06:27:56.227230Z","shell.execute_reply.started":"2024-04-01T06:27:56.217854Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Index(['Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C',\n"," 'Embarked_Q', 'Embarked_S', 'TcktName_A4', 'TcktName_A5', 'TcktName_AS',\n"," 'TcktName_C', 'TcktName_CA', 'TcktName_CASOTON', 'TcktName_FC',\n"," 'TcktName_FCC', 'TcktName_Fa', 'TcktName_LINE', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_PPP', 'TcktName_SC', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCOW', 'TcktName_SCPARIS',\n"," 'TcktName_SCParis', 'TcktName_SOC', 'TcktName_SOP', 'TcktName_SOPP',\n"," 'TcktName_SOTONO2', 'TcktName_SOTONOQ', 'TcktName_SP', 'TcktName_STONO',\n"," 'TcktName_STONO2', 'TcktName_SWPP', 'TcktName_WC', 'TcktName_WEP',\n"," 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',\n"," 'Sex_male'],\n"," dtype='object')"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["train_df.columns"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[],"source":["# Drop the PassengerId and Cabin columns from the test set\n","test_df.drop(labels=[\"PassengerId\", \"Cabin\"], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q',\n"," 'Embarked_S', 'TcktName_A', 'TcktName_A4', 'TcktName_A5',\n"," 'TcktName_AQ3', 'TcktName_AQ4', 'TcktName_C', 'TcktName_CA',\n"," 'TcktName_FC', 'TcktName_FCC', 'TcktName_LP', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_SC', 'TcktName_SCA3', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCPARIS', 'TcktName_SCParis', 'TcktName_SOC',\n"," 'TcktName_SOPP', 'TcktName_SOTONO2', 'TcktName_SOTONOQ',\n"," 'TcktName_STONO', 'TcktName_STONO2', 'TcktName_STONOQ', 'TcktName_WC',\n"," 'TcktName_WEP', 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n"," 'Sex_female', 'Sex_male'],\n"," dtype='object')"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["# Print the columns of the test set\n","test_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Modeling"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.230086Z","iopub.status.busy":"2024-04-01T06:27:56.229809Z","iopub.status.idle":"2024-04-01T06:27:56.238557Z","shell.execute_reply":"2024-04-01T06:27:56.237679Z","shell.execute_reply.started":"2024-04-01T06:27:56.230040Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{},"source":["## Train - Test Split (Assignment)"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.240103Z","iopub.status.busy":"2024-04-01T06:27:56.239830Z","iopub.status.idle":"2024-04-01T06:27:56.256809Z","shell.execute_reply":"2024-04-01T06:27:56.255463Z","shell.execute_reply.started":"2024-04-01T06:27:56.240056Z"},"trusted":true},"outputs":[{"data":{"text/plain":["891"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["train_df_len = len(train_df)\n","train_df_len"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.314730Z","iopub.status.busy":"2024-04-01T06:27:56.313986Z","iopub.status.idle":"2024-04-01T06:27:56.333564Z","shell.execute_reply":"2024-04-01T06:27:56.332507Z","shell.execute_reply.started":"2024-04-01T06:27:56.314635Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train 596\n","X_test 295\n","y_train 596\n","y_test 295\n","test 418\n"]}],"source":["\n","train = train_df[:train_df_len]\n","test = test_df\n","\n","# Select all numerical values from train and test\n","numeric_train = train.select_dtypes(include=[np.number])\n","numeric_test = test.select_dtypes(include=[np.number]) \n","\n","\n","X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n","y_train = numeric_train[\"Survived\"]\n","\n","# Split the train data into train and test sets with a 1/3 ratio\n","X_train, X_test, y_train, y_test = train_test_split(numeric_train.drop(labels=[\"Survived\"], axis=1), numeric_train[\"Survived\"], test_size=0.33, random_state=42)\n","\n","\n","print(\"X_train\", len(X_train))\n","print(\"X_test\", len(X_test))\n","print(\"y_train\", len(y_train))\n","print(\"y_test\", len(y_test))\n","print(\"test\", len(numeric_test))\n"]},{"cell_type":"markdown","metadata":{},"source":["## Simple Logistic Regression (Assignment)"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.335970Z","iopub.status.busy":"2024-04-01T06:27:56.335281Z","iopub.status.idle":"2024-04-01T06:27:56.368083Z","shell.execute_reply":"2024-04-01T06:27:56.366489Z","shell.execute_reply.started":"2024-04-01T06:27:56.335561Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy on the training set: 68.62%\n","Accuracy on the test set: 68.81%\n"]}],"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, y_train)\n","acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n","acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n","# Print the accuracy on the training and test set\n","print(f\"Accuracy on the training set: {acc_log_train}%\")\n","print(f\"Accuracy on the test set: {acc_log_test}%\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Hyperparameter Tuning -- Grid Search -- Cross Validation\n","We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n","\n","* Decision Tree\n","* SVM\n","* Random Forest\n","* KNN\n","* Logistic Regression"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.371066Z","iopub.status.busy":"2024-04-01T06:27:56.370400Z","iopub.status.idle":"2024-04-01T06:27:56.401742Z","shell.execute_reply":"2024-04-01T06:27:56.396867Z","shell.execute_reply.started":"2024-04-01T06:27:56.370802Z"},"trusted":true},"outputs":[],"source":["random_state = 42\n","classifier = [DecisionTreeClassifier(random_state = random_state),\n"," SVC(random_state = random_state),\n"," RandomForestClassifier(random_state = random_state),\n"," LogisticRegression(random_state = random_state),\n"," KNeighborsClassifier()]\n","\n","dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n"," \"max_depth\": range(1,20,2)}\n","\n","svc_param_grid = {\"kernel\" : [\"rbf\"],\n"," \"gamma\": [0.001, 0.01, 0.1, 1],\n"," \"C\": [1,10,50,100,200,300,1000]}\n","\n","rf_param_grid = {\"max_features\": [1,3,10],\n"," \"min_samples_split\":[2,3,10],\n"," \"min_samples_leaf\":[1,3,10],\n"," \"bootstrap\":[False],\n"," \"n_estimators\":[100,300],\n"," \"criterion\":[\"gini\"]}\n","\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n"," \"penalty\": [\"l1\",\"l2\"]}\n","\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n"," \"weights\": [\"uniform\",\"distance\"],\n"," \"metric\":[\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n"," svc_param_grid,\n"," rf_param_grid,\n"," logreg_param_grid,\n"," knn_param_grid]"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.413811Z","iopub.status.busy":"2024-04-01T06:27:56.404322Z","iopub.status.idle":"2024-04-01T06:29:38.718970Z","shell.execute_reply":"2024-04-01T06:29:38.717807Z","shell.execute_reply.started":"2024-04-01T06:27:56.413658Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n"]},{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]},{"name":"stdout","output_type":"stream","text":["0.6996045197740112\n","Fitting 10 folds for each of 28 candidates, totalling 280 fits\n","0.7130508474576271\n","Fitting 10 folds for each of 54 candidates, totalling 540 fits\n","0.7081073446327684\n","Fitting 10 folds for each of 14 candidates, totalling 140 fits\n","0.6777683615819209\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","0.6979943502824858\n"]}],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n"," clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n"," clf.fit(X_train,y_train)\n"," cv_result.append(clf.best_score_)\n"," best_estimators.append(clf.best_estimator_)\n"," print(cv_result[i])"]},{"cell_type":"code","execution_count":52,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:38.722928Z","iopub.status.busy":"2024-04-01T06:29:38.722207Z","iopub.status.idle":"2024-04-01T06:29:39.075423Z","shell.execute_reply":"2024-04-01T06:29:39.073987Z","shell.execute_reply.started":"2024-04-01T06:29:38.722582Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Cross Validation Scores')"]},"execution_count":52,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n"," \"LogisticRegression\",\n"," \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Scores\")"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Modeling (Assignment)"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.078654Z","iopub.status.busy":"2024-04-01T06:29:39.077840Z","iopub.status.idle":"2024-04-01T06:29:39.862871Z","shell.execute_reply":"2024-04-01T06:29:39.860937Z","shell.execute_reply.started":"2024-04-01T06:29:39.078554Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy of the voting classifier on the test set: 70.85%\n"]}],"source":["votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n"," (\"rfc\",best_estimators[2]),\n"," (\"lr\",best_estimators[3])],\n"," voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(X_train, y_train)\n","\n","# Print the accuracy score of the voting classifier\n","acc_votingC = round(votingC.score(X_test, y_test) * 100, 2)\n","print(f\"Accuracy of the voting classifier on the test set: {acc_votingC}%\")"]},{"cell_type":"code","execution_count":56,"metadata":{},"outputs":[],"source":["# Drop the null values which are going to cause you an error in the next cell\n","# Drop rows with missing values in numeric_test\n","numeric_test_dropna = numeric_test.dropna()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Prediction and Submission"]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.865981Z","iopub.status.busy":"2024-04-01T06:29:39.865330Z","iopub.status.idle":"2024-04-01T06:29:39.977357Z","shell.execute_reply":"2024-04-01T06:29:39.973301Z","shell.execute_reply.started":"2024-04-01T06:29:39.865906Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":[" PassengerId Survived\n","0 892 0.0\n","1 893 0.0\n","2 894 0.0\n","3 895 0.0\n","4 896 0.0\n",".. ... ...\n","413 1305 1.0\n","414 1306 0.0\n","415 1307 0.0\n","416 1308 0.0\n","417 1309 NaN\n","\n","[418 rows x 2 columns]\n"]}],"source":["test_survived = pd.Series(votingC.predict(numeric_test_dropna), name=\"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis=1)\n","results.to_csv(\"titanic.csv\", index=False)\n","print(results)"]},{"cell_type":"markdown","metadata":{},"source":["# Congratulations on finishing the assignment!!\n","\n","### The submission is the titanic.csv which was just created, and this file which you have modified."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":26502,"sourceId":3136,"sourceType":"competition"}],"dockerImageVersionId":29852,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.4"}},"nbformat":4,"nbformat_minor":4} From 7775bd4b8431fb74037deec9800ff8eefbc763c6 Mon Sep 17 00:00:00 2001 From: Faheem <“faheemuddinsayyed789@gmail.com”> Date: Thu, 4 Apr 2024 20:06:53 +0530 Subject: [PATCH 4/4] Update --- titanic.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/titanic.ipynb b/titanic.ipynb index 7b03595..8e3f37f 100644 --- a/titanic.ipynb +++ b/titanic.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n","# Ignore this"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]}],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Load and Check Data"]},{"cell_type":"markdown","metadata":{},"source":["DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n","CSV data is read into 'df' using Pandas' read_csv() function."]},{"cell_type":"code","execution_count":3,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-04-01T06:45:27.416192Z","iopub.status.busy":"2024-04-01T06:45:27.415763Z","iopub.status.idle":"2024-04-01T06:45:27.433162Z","shell.execute_reply":"2024-04-01T06:45:27.431944Z","shell.execute_reply.started":"2024-04-01T06:45:27.416105Z"},"trusted":true},"outputs":[],"source":["train_df = pd.read_csv(\"./data/train.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Try to read the test .csv file into test_df"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.436611Z","iopub.status.busy":"2024-04-01T06:45:27.435916Z","iopub.status.idle":"2024-04-01T06:45:27.449974Z","shell.execute_reply":"2024-04-01T06:45:27.448230Z","shell.execute_reply.started":"2024-04-01T06:45:27.436517Z"},"trusted":true},"outputs":[],"source":["test_df = pd.read_csv(\"./data/test.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":5,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.execute_input":"2024-04-01T06:45:27.452397Z","iopub.status.busy":"2024-04-01T06:45:27.451949Z","iopub.status.idle":"2024-04-01T06:45:27.462622Z","shell.execute_reply":"2024-04-01T06:45:27.461859Z","shell.execute_reply.started":"2024-04-01T06:45:27.452348Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The Columns of train_df are: \n"]},{"data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n"," 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n"," dtype='object')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["print(\"The Columns of train_df are: \")\n","train_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["### We can use head() to see the first few rows in the dataframe"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.464289Z","iopub.status.busy":"2024-04-01T06:45:27.463866Z","iopub.status.idle":"2024-04-01T06:45:27.491984Z","shell.execute_reply":"2024-04-01T06:45:27.491110Z","shell.execute_reply.started":"2024-04-01T06:45:27.464242Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked \n","0 0 A/5 21171 7.2500 NaN S \n","1 0 PC 17599 71.2833 C85 C \n","2 0 STON/O2. 3101282 7.9250 NaN S \n","3 0 113803 53.1000 C123 S \n","4 0 373450 8.0500 NaN S "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.494326Z","iopub.status.busy":"2024-04-01T06:45:27.493637Z","iopub.status.idle":"2024-04-01T06:45:27.541999Z","shell.execute_reply":"2024-04-01T06:45:27.541210Z","shell.execute_reply.started":"2024-04-01T06:45:27.494251Z"},"jupyter":{"source_hidden":true},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
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"],"text/plain":[" PassengerId Survived Pclass Age SibSp \\\n","count 891.000000 891.000000 891.000000 714.000000 891.000000 \n","mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n","std 257.353842 0.486592 0.836071 14.526497 1.102743 \n","min 1.000000 0.000000 1.000000 0.420000 0.000000 \n","25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n","50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n","75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n","max 891.000000 1.000000 3.000000 80.000000 8.000000 \n","\n"," Parch Fare \n","count 891.000000 891.000000 \n","mean 0.381594 32.204208 \n","std 0.806057 49.693429 \n","min 0.000000 0.000000 \n","25% 0.000000 7.910400 \n","50% 0.000000 14.454200 \n","75% 0.000000 31.000000 \n","max 6.000000 512.329200 "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Without looking above, try checking the first few rows of test_df"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked \n","0 34.5 0 0 330911 7.8292 NaN Q \n","1 47.0 1 0 363272 7.0000 NaN S \n","2 62.0 0 0 240276 9.6875 NaN Q \n","3 27.0 0 0 315154 8.6625 NaN S \n","4 22.0 1 1 3101298 12.2875 NaN S "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Now try checking for a description of test_df's data"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
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"],"text/plain":[" PassengerId Pclass Age SibSp Parch Fare\n","count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\n","mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\n","std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\n","min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n","25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n","50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n","75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\n","max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["test_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["# Variable Description\n","
    \n","
  1. PassengerId: unique id number to each passenger
    2. \n","
  2. Survived: passenger survive(1) or died(0)
    3. \n","
  3. Pclass: passenger class
    4. \n","
  4. Name: name
    5. \n","
  5. Sex: gender of passenger
    6. \n","
  6. Age: age of passenger
    7. \n","
  7. SibSp: number of siblings/spouses
    8. \n","
  8. Parch: number of parents/children
    9. \n","
  9. Ticket: ticket number
    10. \n","
  10. Fare: amount of money spent on ticket
    11. \n","
  11. Cabin: cabin category
    12. \n","
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n","
\n"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.544377Z","iopub.status.busy":"2024-04-01T06:45:27.543901Z","iopub.status.idle":"2024-04-01T06:45:27.557229Z","shell.execute_reply":"2024-04-01T06:45:27.555972Z","shell.execute_reply.started":"2024-04-01T06:45:27.544320Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 PassengerId 891 non-null int64 \n"," 1 Survived 891 non-null int64 \n"," 2 Pclass 891 non-null int64 \n"," 3 Name 891 non-null object \n"," 4 Sex 891 non-null object \n"," 5 Age 714 non-null float64\n"," 6 SibSp 891 non-null int64 \n"," 7 Parch 891 non-null int64 \n"," 8 Ticket 891 non-null object \n"," 9 Fare 891 non-null float64\n"," 10 Cabin 204 non-null object \n"," 11 Embarked 889 non-null object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["train_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### Slice Rows and Columsn of DF (Assigmennt)"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:53:12.214069Z","iopub.status.busy":"2024-04-01T06:53:12.213708Z","iopub.status.idle":"2024-04-01T06:53:12.223150Z","shell.execute_reply":"2024-04-01T06:53:12.222195Z","shell.execute_reply.started":"2024-04-01T06:53:12.214014Z"},"trusted":true},"outputs":[{"data":{"text/plain":["PassengerId 3\n","Survived 1\n","Pclass 3\n","Name Heikkinen, Miss. Laina\n","Sex female\n","Age 26.0\n","SibSp 0\n","Parch 0\n","Ticket STON/O2. 3101282\n","Fare 7.925\n","Cabin NaN\n","Embarked S\n","Name: 2, dtype: object"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Printing the Second Row\n","train_df.iloc[2]"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 6\n","Survived 0\n","Pclass 3\n","Name Moran, Mr. James\n","Sex male\n","Age NaN\n","SibSp 0\n","Parch 0\n","Ticket 330877\n","Fare 8.4583\n","Cabin NaN\n","Embarked Q\n","Name: 5, dtype: object"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Print the 5th Row\n","train_df.iloc[5]"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:14.398373Z","iopub.status.busy":"2024-04-01T06:54:14.398006Z","iopub.status.idle":"2024-04-01T06:54:14.407886Z","shell.execute_reply":"2024-04-01T06:54:14.406590Z","shell.execute_reply.started":"2024-04-01T06:54:14.398326Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 male\n","1 female\n","2 female\n","3 female\n","4 male\n"," ... \n","886 male\n","887 female\n","888 female\n","889 male\n","890 male\n","Name: Sex, Length: 891, dtype: object"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Sex Column\n","train_df['Sex']"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:24.550687Z","iopub.status.busy":"2024-04-01T06:54:24.550286Z","iopub.status.idle":"2024-04-01T06:54:24.555255Z","shell.execute_reply":"2024-04-01T06:54:24.553923Z","shell.execute_reply.started":"2024-04-01T06:54:24.550616Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 Braund, Mr. Owen Harris\n","1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n","2 Heikkinen, Miss. Laina\n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n","4 Allen, Mr. William Henry\n"," ... \n","886 Montvila, Rev. Juozas\n","887 Graham, Miss. Margaret Edith\n","888 Johnston, Miss. Catherine Helen \"Carrie\"\n","889 Behr, Mr. Karl Howell\n","890 Dooley, Mr. Patrick\n","Name: Name, Length: 891, dtype: object"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Name Column\n","train_df['Name']"]},{"cell_type":"markdown","metadata":{},"source":["## Visualization (Assignment)"]},{"cell_type":"markdown","metadata":{},"source":["### Age -- Survived"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:13:34.450088Z","iopub.status.busy":"2024-04-01T07:13:34.449302Z","iopub.status.idle":"2024-04-01T07:13:34.932717Z","shell.execute_reply":"2024-04-01T07:13:34.930449Z","shell.execute_reply.started":"2024-04-01T07:13:34.450021Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAq8AAAIcCAYAAADYP0dGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5E0lEQVR4nO3de1RVZeL/8Q+CIIiogJdA07wRKgJiqIkiZgmK0qBdHbUpM0fUssmMLDUVr006KaamNhmWN6xJ0Uwac8xSCwccc0qzNJBQGG8pCHL5/dHP8+2EIBJyfPT9WstV7GefvZ99Fmv7dp99zrErKSkpEQAAAGCAGraeAAAAAFBRxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrgGt26NAhjRs3Tt26dVP79u0VEhKiZ555Rt988021zWHBggXy8fGptv3dCPbs2SMfHx+NGjXqiuMbNmyQj4+PMjIyqm1OGzZs0MMPP6yOHTvK399f/fr109/+9jedP3++2ubQq1cvvfDCC9W2PwC25WDrCQAwy+HDh/XQQw8pICBAL730kjw8PJSVlaWEhAQ9+OCDWrlypQICAq77PB544AF17979uu/nRvTJJ5/oww8/1IABA2w6j4ULF2rx4sV6/PHH9ec//1k1a9bUgQMHtGzZMu3cuVPvvfeeatasWS3zcHV1ve77AXBjIF4BXJO33npL9evX15tvvikHh/87hfTu3Vvh4eFatGiRli5det3n0bhxYzVu3Pi67+dG5Obmpri4ON19993y9PS0yRwKCgr05ptv6oknntC4ceMsy++++261aNFCMTExSk5OVkRExHWfS9u2ba/7PgDcOLhtAMA1ycnJUUlJiYqLi62Wu7i46MUXX7SKlSu9nPvbl7YXLFige++9VwsXLlRwcLBCQkL00ksvqVu3bioqKrJ6bFxcnDp37qxLly5Z3TawePFitW/fXmfPnrVa/+9//7vatWun//3vf5Kko0ePauzYserWrZsCAgI0ZMgQpaSkWNbPyMiQj4+P3nrrLYWHh8vf31+JiYm6ePGipkyZoh49eqh9+/YKDw/X8uXLy3yONm7cKB8fHx06dMhqeXJysnx8fHTw4EFJ0ttvv63w8HD5+fmpe/fumjJlSoVebh83bpxyc3M1ZcqUq65b0WPesmWLxo4dq8DAQAUHB+ull15Sbm5umds9f/68Ll68WOr3QJJCQ0M1btw4NW3aVNL/3e6wZ88eq/WGDBmiIUOGWH7u1auXZsyYoWHDhqlDhw6aOHGigoKCNHv2bKvHFRYWqkuXLpo+fbrlcZd/z/r06aOxY8eWmlNUVJT+/Oc/W37evHmzoqOjFRgYqG7dumnSpElWvz9X+r08e/asDhw4oGHDhikoKEiBgYF67LHHlJqaWubzBKDqEa8ArknPnj2VmZmphx9+WKtWrdKRI0dUUlIiSQoPD9cf/vCHa95mZmamduzYoXnz5ik2Nlb333+/cnJyrGKnuLhYW7ZsUb9+/Uq9FN2/f38VFhbq448/tlqelJSkkJAQeXh46LvvvlN0dLQyMjL00ksv6dVXX5WdnZ2GDRumvXv3Wj1uwYIFevLJJzVnzhx169ZNM2bM0L/+9S9NmDBBy5cv1z333KM5c+YoMTHxisfTu3dvubi4KCkpyWr5pk2b1Lp1a7Vt21abNm3S3LlzNXjwYC1fvlwxMTH6xz/+oWnTpl31+WrZsqXGjBmjbdu2adOmTWWudy3HPHnyZHl7e2vRokV64okntH79er3xxhtlbtvd3V3+/v5avny5JkyYoOTkZJ06dUqSVLNmTY0cOVLt27e/6rH81qpVq+Tn56dFixZp0KBB6tOnj7Zs2WL5HZOkXbt26fTp04qKiir1+AEDBmjHjh1W/wg4cuSIvvnmG8v6ixYt0rPPPquAgAC9/vrriomJ0datWzVkyBBdvHjR8rjf/l7a29tr+PDhql+/vhYsWKB58+YpLy9PTzzxhH7++edrPlYAlcNtAwCuyaOPPqrs7GwtX75cU6dOlSTVr19fISEhGjp0qDp06HDN2ywsLNSECRPUqVMnSVJJSYm8vb21adMm3X333ZJ+uXqXnZ19xWDx9vbWXXfdpU2bNumBBx6QJP3444/av3+/5s2bJ+mX+yIdHR21cuVKy/2RPXv2VGRkpObMmaP169dbthcREaGBAwdaft67d6+6deumfv36SZI6d+4sFxcXeXh4XPF4nJ2d1adPH23evNnykvqFCxe0fft2xcTEWLbZpEkTDR48WDVq1FBwcLBcXFxKXT0uyxNPPKFt27Zp2rRp6tKlyxVvH7iWYw4NDdWECRMkSV27dtWuXbv06aef6i9/+UuZc3j99df1/PPP64MPPtAHH3wgOzs7tW7dWvfee6+GDRumunXrVuhYfs3Ly0vPPfec5eeCggIlJiYqJSXF8vuRlJSkFi1ayM/Pr9TjBwwYoAULFig5OVn333+/pF/+0eDm5qZevXrp7NmzeuONN/Tggw9q0qRJlse1adNGgwcPVmJiogYPHiyp9O9lamqqTp8+raFDh6pjx46SpBYtWmjNmjW6cOGC6tSpc83HC+DaceUVwDV7+umntXPnTv31r3/VoEGD5Orqqo0bN1resFUZvr6+lv+3s7PTgAEDlJycrIKCAkm/BEvz5s3l7+9/xccPGDBAX375pbKzsy3ru7q6qlevXpJ+icWwsDCrN/Y4ODioX79+OnDggC5cuHDFuUi/xOratWv15JNPKiEhQenp6YqJiVHPnj3LPJ6oqChLQEu/vMmqoKDA8iarLl266IcfflB0dLQWLlyo//znP+rfv7/Vy+jlsbe318yZM5Wbm6tXXnnliutcyzH/9k12jRs3Lve2gcvrrFy5UklJSZowYYJCQ0N1/PhxxcfHq1+/fjp69GiFjuXXfvvcBwcHy8vLy3IVOz8/X8nJyVf8R4wkNW3aVB07dtTmzZsty5KSkhQeHi5HR0elpqaqoKBAkZGRVo/r1KmTvL29S12R/vV8WrduLXd3d40cOVKTJk3Stm3b5OnpqfHjx9+y918DtkC8AqiUunXrKjIyUnFxcUpOTtb777+vli1bau7cuTp9+vQ1b6927dpWP0dFRens2bPauXOnCgoK9PHHH5f77vrw8HA5ODhoy5Ytkn4Jlj59+qhWrVqSpLNnz17x6qSnp6dKSkqsXmZ2cXGxWmfixIl65plnlJGRoWnTpql37956+OGHy/1osM6dO6tRo0aW6EpKSlJwcLAlcvr27au//vWvcnFxsbxEfs8991hF19W0atVKo0eP1scff1zqFoVrPWZnZ2erdWrUqGH1Uv3V5vH4449ryZIl2rNnj6ZOnarTp0/rtddeq/CxXPbb597Ozk79+/fXRx99pKKiIm3fvl25ubnq379/mduIiorS559/rtOnT+s///mPjh07Zondy1e2y3pefvvy/69/L2vXrq1Vq1YpNDRUW7Zs0ejRo9W1a1dNmjTJ8o8sANcf8Qqgwk6cOKGQkBCtW7eu1Fjbtm01btw4FRQUKD093bL8t2+6utrVvMvuuOMOdejQQVu2bNGuXbt07ty5cuO1Tp066tWrl7Zs2aJDhw7p8OHDVlfn6tatq5ycnFKPu3yltn79+mVu29HRUX/+85+1ZcsWbd++XZMmTVJ6enq5L6nXqFHDEl2nT5/Wrl27Sl0tjIyM1Lvvvqs9e/Zo/vz5qlevnsaPH68TJ06Uud3fGj58uNq3b69p06ZZ3phWFcd8NW+//ba6dOmivLw8q+U1a9bUQw89pNDQUH333XeSfglQSaXe3PXrK7/liYqK0qlTp7Rnzx5t3rxZd911l7y9vctcPyIiQnZ2dkpOTtbmzZvl7e2toKAgSbLcylDW83K156RFixaaO3eudu/erdWrV+sPf/iD1qxZU+lXHABcO+IVQIV5enrKwcFB7777rvLz80uNf//993JyclKzZs0kSa6ursrKyrJa59fvdL+aqKgo7dy5U0lJSerYsaPl3evlrZ+amqr33ntPXl5eCg4Otozddddd2r59u9XVxqKiIiUlJcnPz0+Ojo5X3ObFixfVp08frVixQtIv92QOHjxY/fr1U2Zm5lXnk5WVpfj4eNnb2+u+++6zjD3zzDOW+1/r1KmjiIgIjRo1SoWFhTp58mT5T8yv2Nvba9asWTp//ryWLFliNVbZY66IVq1a6fTp03rnnXdKjRUVFSk9PV1t2rSRJMttC7/+XTh79qyOHDlSoX21bNlS7dq1U1JSknbs2HHVz7d1c3NTWFiYPvnkE23dulUDBgywBLS/v78cHR1LvdHtq6++UmZmpuVe1iv56KOP1KVLF2VnZ8ve3l6BgYGaMmWK3Nzcrvq7AKDq8IYtABVmb2+vKVOmKCYmRgMHDtTgwYPVsmVL5eXladeuXVq1apWefvppy9WtsLAwLVmyREuWLJG/v7/++c9/avfu3RXeX9++fTVr1ixt3rxZkydPvur63bt3V7169bRmzRoNHz7cEiySNHr0aP3rX//S0KFDNWLECNWsWdNy/+qyZcvK3GatWrXUrl07LVy4UDVr1pSPj49++OEHvf/+++rTp0+582nTpo18fX317rvvKiIiwure0y5dumjy5MmaPXu2evTooXPnzmnhwoVq3ry57rzzzgo8O/+ndevWiomJ0fz5862WV/aYK6Jbt26KjIzUa6+9pm+//VZ9+vSRu7u7srKytHr1amVlZVnm4+Pjo9tuu03x8fFydXWVnZ2dlixZUupWhfJERUVp9uzZcnBwUHh4+FXXHzBggMaOHauioiKrK9716tXTiBEjFB8fr5o1ayosLEwZGRn629/+platWpX7aRkdO3ZUcXGxYmJiNGLECNWuXVtbtmzRzz//bPUPEwDXF/EK4Jr07NlTa9eu1fLly7V48WKdOnVKjo6Oatu2rebNm2f1l/hTTz2lU6dOafny5bp06ZJ69uypuLg4q8/bLI+7u7tCQkK0a9euCgXL5TcjvfPOO6WuzrVu3VrvvvuuXnvtNcXGxsrOzk4dOnTQypUrLe8mL8vUqVM1f/58rVixQtnZ2fLw8NCgQYP09NNPX3VOUVFRmjVrVqn5PPzww7p06ZJWr16td999V7Vq1VLXrl01fvz4Sn0r1ZNPPqlt27bp66+/rpJjroi5c+cqODhYH374oeVzYd3d3dWtWzfNnDnTcqXc3t5er7/+umbMmKFnn31Wnp6eGjZsmL7//nv98MMPFdrX5U9ICAsLq9C7+kNDQ1WnTh01bdpUd9xxh9XYmDFj5OnpqYSEBK1Zs0b16tVTeHi4nnnmmVL33P5aw4YNtWzZMv3tb3/TxIkTlZeXp9atW2vBggXq0qVLhY4DwO9nV1LRO/IBAAAAG+OeVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDFuic95LSws1NmzZ+Xk5KQaNeh1AACAG01xcbHy8/NVt25dOTiUnai3RLyePXtWR48etfU0AAAAcBXNmzeXh4dHmeO3RLw6OTlJ+uXJuJavIwQAAED1yMvL09GjRy3dVpZbIl4v3yrg7Oxc7lf/AQAAwLaudosnN4ACAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGA62nsCtIGj8SltPAcB1kjJ3qK2nAAC3FK68AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYNo3Xbdu2ycfHx+rP2LFjJUkHDx7UAw88IH9/fw0cOFAHDhyweuymTZvUu3dv+fv7KyYmRqdOnbLFIQAAAKAa2TRev/vuO4WFhemzzz6z/Jk+fbpyc3M1YsQIderUSRs2bFBgYKCeeuop5ebmSpL279+viRMnavTo0VqzZo3OnTun2NhYWx4KAAAAqoFN4/XIkSNq06aNGjRoYPnj5uamzZs3y8nJSc8//7xatmypiRMnqnbt2vroo48kSQkJCYqIiND999+vO++8U3PmzNGOHTuUnp5uy8MBAADAdWbzeG3evHmp5WlpaQoKCpKdnZ0kyc7OTh07dlRqaqplvFOnTpb1b7vtNnl5eSktLa06pg0AAAAbcbDVjktKSvTDDz/os88+05IlS1RUVKTw8HCNHTtW2dnZatWqldX6Hh4eOnz4sCTp5MmTatiwYanxrKyscvdZVFSkoqKiqj0QALc0zikAUDUqej61WbxmZmYqLy9Pjo6Omj9/vjIyMjR9+nRdvHjRsvzXHB0dVVBQIEm6ePFiueNlOXToUNUeBIBb3uVXhAAA1cNm8ert7a09e/aobt26srOzk6+vr4qLizV+/HgFBweXCtGCggLVqlVLkuTk5HTFcWdn53L32aZNG7m4uFTtgVTE6q+rf58AqkVAQICtpwAAN4Xc3NwKXWi0WbxKUr169ax+btmypfLz89WgQQPl5ORYjeXk5FhuFWjUqNEVxxs0aFDu/uzt7WVvb//7Jw4A/x/nFACoGhU9n9rsDVs7d+5U586dlZeXZ1n23//+V/Xq1VNQUJD+/e9/q6SkRNIv98fu27dP/v7+kiR/f3+lpKRYHvfTTz/pp59+sowDAADg5mSzeA0MDJSTk5Neeuklff/999qxY4fmzJmj4cOHKzw8XOfOnVNcXJy+++47xcXFKS8vTxEREZKkRx55RP/4xz+0bt06ffPNN3r++efVs2dPNW3a1FaHAwAAgGpgs3h1dXXV8uXLderUKQ0cOFATJ07UQw89pOHDh8vV1VVLlixRSkqKoqOjlZaWpqVLl1ruVw0MDNTUqVMVHx+vRx55RHXr1tXMmTNtdSgAAACoJnYll1+bv4nl5ubqv//9r3x9fW3yhq2g8SurfZ8AqkfK3KG2ngIA3BQq2ms2/ZICAAAA4FoQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADDGDROvI0aM0AsvvGD5+eDBg3rggQfk7++vgQMH6sCBA1brb9q0Sb1795a/v79iYmJ06tSp6p4yAAAAqtkNEa9JSUnasWOH5efc3FyNGDFCnTp10oYNGxQYGKinnnpKubm5kqT9+/dr4sSJGj16tNasWaNz584pNjbWVtMHAABANbF5vJ45c0Zz5syRn5+fZdnmzZvl5OSk559/Xi1bttTEiRNVu3ZtffTRR5KkhIQERURE6P7779edd96pOXPmaMeOHUpPT7fVYQAAAKAa2DxeZ8+eraioKLVq1cqyLC0tTUFBQbKzs5Mk2dnZqWPHjkpNTbWMd+rUybL+bbfdJi8vL6WlpVXr3AEAAFC9HGy58y+++EJfffWVNm7cqClTpliWZ2dnW8WsJHl4eOjw4cOSpJMnT6phw4alxrOyssrdX1FRkYqKiqpm8gAgcU4BgCpS0fOpzeI1Pz9fkydP1qRJk1SrVi2rsby8PDk6Olotc3R0VEFBgSTp4sWL5Y6X5dChQ1UwcwD4P5dfEQIAVA+bxevChQvVvn17de/evdSYk5NTqRAtKCiwRG5Z487OzuXus02bNnJxcfmdM6+E1V9X/z4BVIuAgABbTwEAbgq5ubkVutBos3hNSkpSTk6OAgMDJckSo1u3blVkZKRycnKs1s/JybHcKtCoUaMrjjdo0KDcfdrb28ve3r6qDgEAOKcAQBWp6PnUZvH6zjvvqLCw0PLzq6++Kkl67rnn9OWXX+rNN99USUmJ7OzsVFJSon379mnkyJGSJH9/f6WkpCg6OlqS9NNPP+mnn36Sv79/9R8IAAAAqo3N4tXb29vq59q1a0uSmjVrJg8PD/31r39VXFycHn74Ya1evVp5eXmKiIiQJD3yyCMaMmSIAgIC5Ofnp7i4OPXs2VNNmzat9uMAAABA9bH5R2Vdiaurq5YsWWK5upqWlqalS5da7lcNDAzU1KlTFR8fr0ceeUR169bVzJkzbTxrAAAAXG92JSUlJbaexPWWm5ur//73v/L19bXJG7aCxq+s9n0CqB4pc4faegoAcFOoaK/dkFdeAQAAgCshXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGcLD1BAAA5gkav9LWUwBwnaTMHWrrKZSLK68AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAY1R5vJ46daqqNwkAAABIqmS8+vr6XjFSjx8/rnvuued3TwoAAAC4EoeKrvjBBx9ow4YNkqSSkhLFxMSoZs2aVuucPHlSDRo0qPDOjx07pqlTp2rfvn2qW7eu/vjHP2r48OGSpPT0dL388stKTU2Vl5eXXnzxRYWEhFge+/nnn2vGjBlKT0+Xv7+/4uLi1LRp0wrvGwAAAOapcLzee++9ysjIkCTt3btXAQEBql27ttU6Li4uuvfeeyu0veLiYo0YMUJ+fn56//33dezYMT377LNq1KiRIiMjFRMTozZt2igxMVHJyckaPXq0Nm/eLC8vL2VmZiomJkZjxoxR9+7dFR8fr1GjRunDDz+UnZ3dNRw+AAAATFLheK1du7ZGjx4tSfL29lbfvn3l5ORU6R3n5OTI19dXU6ZMkaurq5o3b66uXbsqJSVFnp6eSk9P1+rVq+Xi4qKWLVvqiy++UGJiosaMGaN169apffv2evzxxyVJM2fOVLdu3bR371517ty50nMCAADAja3C8fprf/jDH3Ts2DEdOHBAly5dKjV+//33X3UbDRs21Pz58yX9chvCvn379OWXX2ry5MlKS0tT27Zt5eLiYlk/KChIqampkqS0tDR16tTJMubs7Kx27dopNTWVeAUAALiJVSpely1bpldffVV169YtdeuAnZ1dheL113r16qXMzEyFhYWpT58+mjFjhho2bGi1joeHh7KysiRJ2dnZ5Y6XpaioSEVFRdc0NwAoD+cUADcbW53XKrrfSsXrihUrNH78eD3xxBOVeXgpr7/+unJycjRlyhTNnDlTeXl5cnR0tFrH0dFRBQUFknTV8bIcOnSoSuYLAJddfkUIAG4WN/p5rVLxmp+fr/vuu6/KJuHn52fZ7nPPPaeBAwcqLy/Pap2CggLVqlVLkuTk5FQqVAsKCuTm5lbuftq0aWN1K0K1Wf119e8TQLUICAiw9RRsg/MacNOy1XktNze3QhcaKxWv/fv317vvvqvnn3++0u/uz8nJUWpqqnr37m1Z1qpVK126dEkNGjTQ999/X2r9y7cKNGrUSDk5OaXGfX19y92nvb297O3tKzVfALgSzikAbja2Oq9VdL+Vitfz589r/fr12rRpk5o0aVLq815Xrlx51W1kZGRo9OjR2rFjhxo1aiRJOnDggNzd3RUUFKQVK1bo4sWLlqutKSkpCgoKkiT5+/srJSXFsq28vDwdPHjQ8mkIAAAAuDlVKl6bN2+ukSNH/q4d+/n5qV27dnrxxRcVGxur48ePa+7cuRo5cqSCg4N12223KTY2VqNGjdL27du1f/9+zZw5U5I0cOBALV++XEuXLlVYWJji4+PVpEkTPmkAAADgJlepeK2KK5z29vZatGiRpk2bpoceekjOzs4aMmSIhg4dKjs7Oy1atEgTJ05UdHS0mjVrpvj4eHl5eUmSmjRpogULFmjGjBmKj49XYGCg4uPj+YICAACAm1yl4jU2Nrbc8ctXSK+mUaNGWrhw4RXHmjVrpoSEhDIfGxoaqtDQ0ArtBwAAADeHGlWxkcLCQv3www/avHmz3N3dq2KTAAAAQCmVuvJa1pXVZcuW8VmqAAAAuG6q5MrrZeHh4dq2bVtVbhIAAACwqLJ4zc3N1dq1a1W/fv2q2iQAAABgpVK3Ddx5551XfGe/k5OTpk+f/rsnBQAAAFxJpeL1t19CYGdnp5o1a6pVq1ZydXWtkokBAAAAv1WpeA0ODpYkHT16VEeOHFFxcbHuuOMOwhUAAADXVaXi9dy5c4qNjdUnn3yiunXrqqioSBcuXNBdd92l+Ph41alTp6rnCQAAAFTuDVvTp09XVlaWNm/erD179uirr77Sxo0blZubW+EvKAAAAACuVaXi9Z///KemTJmiFi1aWJa1atVKkyZN0ieffFJlkwMAAAB+rVLx6uTkpBo1Sj/Uzs5ORUVFv3tSAAAAwJVUKl579eqlV155RT/++KNl2dGjRzV9+nSFhoZW2eQAAACAX6vUG7bGjx+vmJgY9enTR25ubpKks2fPqkePHnr55ZerdIIAAADAZdccr8eOHZOXl5feeecdffvttzpy5IicnJzUvHlztWzZ8nrMEQAAAJB0DbcNlJSUaPr06YqIiNC///1vSZKPj4/69u2rxMRERUZGatasWSopKblukwUAAMCtrcLxunLlSm3evFnx8fGWLym4bNGiRYqPj9f777+v9957r8onCQAAAEjXEK9r167Vyy+/rLCwsCuO9+rVS8899xzxCgAAgOumwvF6/PhxdejQodx1unTpovT09N89KQAAAOBKKhyvHh4eOn78eLnrZGVlqV69er93TgAAAMAVVThe7733Xi1YsECXLl264nhhYaEWLlyokJCQKpscAAAA8GsV/qisUaNGadCgQYqOjtaQIUPUvn171alTR2fPntXXX3+thIQEXbhwQXPmzLme8wUAAMAtrMLx6ubmprVr1+rVV1/VrFmzlJeXJ+mXj9CqU6eO+vbtqzFjxsjT0/O6TRYAAAC3tmv6koJ69epp+vTpmjRpktLT03Xu3DnVq1dPt99+u+zt7a/XHAEAAABJlfx6WEdHR75NCwAAANWuwm/YAgAAAGyNeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYw6bxeuLECY0dO1bBwcHq3r27Zs6cqfz8fElSenq6HnvsMQUEBKhv37767LPPrB77+eefKzIyUv7+/ho6dKjS09NtcQgAAACoRjaL15KSEo0dO1Z5eXlatWqV5s2bp+3bt2v+/PkqKSlRTEyMPD09lZiYqKioKI0ePVqZmZmSpMzMTMXExCg6Olrr16+Xu7u7Ro0apZKSElsdDgAAAKqBg612/P333ys1NVW7du2Sp6enJGns2LGaPXu2evToofT0dK1evVouLi5q2bKlvvjiCyUmJmrMmDFat26d2rdvr8cff1ySNHPmTHXr1k179+5V586dbXVIAAAAuM5sFq8NGjTQsmXLLOF62fnz55WWlqa2bdvKxcXFsjwoKEipqamSpLS0NHXq1Mky5uzsrHbt2ik1NbXceC0qKlJRUVHVHgiAWxrnFAA3G1ud1yq6X5vFq5ubm7p37275ubi4WAkJCerSpYuys7PVsGFDq/U9PDyUlZUlSVcdL8uhQ4eqaPYA8IvL/6gGgJvFjX5es1m8/tbcuXN18OBBrV+/Xn//+9/l6OhoNe7o6KiCggJJUl5eXrnjZWnTpo3V1dxqs/rr6t8ngGoREBBg6ynYBuc14KZlq/Nabm5uhS403hDxOnfuXL399tuaN2+e2rRpIycnJ505c8ZqnYKCAtWqVUuS5OTkVCpUCwoK5ObmVu5+7O3tZW9vX6VzB3Br45wC4GZjq/NaRfdr8895nTZtmt566y3NnTtXffr0kSQ1atRIOTk5Vuvl5ORYbhUoa7xBgwbVM2kAAADYhE3jdeHChVq9erVee+019evXz7Lc399fX3/9tS5evGhZlpKSIn9/f8t4SkqKZSwvL08HDx60jAMAAODmZLN4PXLkiBYtWqQnn3xSQUFBys7OtvwJDg7WbbfdptjYWB0+fFhLly7V/v37NWjQIEnSwIEDtW/fPi1dulSHDx9WbGysmjRpwsdkAQAA3ORsFq+ffPKJioqK9MYbbygkJMTqj729vRYtWqTs7GxFR0frww8/VHx8vLy8vCRJTZo00YIFC5SYmKhBgwbpzJkzio+Pl52dna0OBwAAANXAZm/YGjFihEaMGFHmeLNmzZSQkFDmeGhoqEJDQ6/H1AAAAHCDsvkbtgAAAICKIl4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgjBsiXgsKChQZGak9e/ZYlqWnp+uxxx5TQECA+vbtq88++8zqMZ9//rkiIyPl7++voUOHKj09vbqnDQAAgGpm83jNz8/Xs88+q8OHD1uWlZSUKCYmRp6enkpMTFRUVJRGjx6tzMxMSVJmZqZiYmIUHR2t9evXy93dXaNGjVJJSYmtDgMAAADVwKbx+t133+nBBx/Ujz/+aLV89+7dSk9P19SpU9WyZUs99dRTCggIUGJioiRp3bp1at++vR5//HG1bt1aM2fO1PHjx7V3715bHAYAAACqiYMtd75371517txZ48aNU0BAgGV5Wlqa2rZtKxcXF8uyoKAgpaamWsY7depkGXN2dla7du2Umpqqzp07l7m/oqIiFRUVVflxALh1cU4BcLOx1Xmtovu1abw++uijV1yenZ2thg0bWi3z8PBQVlZWhcbLcujQod8xWwAo7fI/qgHgZnGjn9dsGq9lycvLk6Ojo9UyR0dHFRQUVGi8LG3atLG6mlttVn9d/fsEUC1+/arRLYXzGnDTstV5LTc3t0IXGm/IeHVyctKZM2eslhUUFKhWrVqW8d+GakFBgdzc3Mrdrr29vezt7at0rgBubZxTANxsbHVeq+h+bf5pA1fSqFEj5eTkWC3Lycmx3CpQ1niDBg2qbY4AAACofjdkvPr7++vrr7/WxYsXLctSUlLk7+9vGU9JSbGM5eXl6eDBg5ZxAAAA3JxuyHgNDg7WbbfdptjYWB0+fFhLly7V/v37NWjQIEnSwIEDtW/fPi1dulSHDx9WbGysmjRpUu4nDQAAAMB8N2S82tvba9GiRcrOzlZ0dLQ+/PBDxcfHy8vLS5LUpEkTLViwQImJiRo0aJDOnDmj+Ph42dnZ2XjmAAAAuJ5umDdsffvtt1Y/N2vWTAkJCWWuHxoaqtDQ0Os9LQAAANxAbsgrrwAAAMCVEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwhtHxmp+frxdffFGdOnVSSEiIVqxYYespAQAA4DpysPUEfo85c+bowIEDevvtt5WZmakJEybIy8tL4eHhtp4aAAAArgNj4zU3N1fr1q3Tm2++qXbt2qldu3Y6fPiwVq1aRbwCAADcpIy9beCbb75RYWGhAgMDLcuCgoKUlpam4uJiG84MAAAA14uxV16zs7NVv359OTo6WpZ5enoqPz9fZ86ckbu7u2X55Zi9cOGCioqKqn2ut9evVe37BFA9fv75Z1tPwSY4rwE3L1ud1y5evChJV70IaWy85uXlWYWrJMvPBQUFVsvz8/MlST/++GP1TO43XurT0ib7BXD9HTp0yNZTsAnOa8DNy9bntfz8fLm6upY5bmy8Ojk5lYrUyz/XqmV9RaBu3bpq3ry5nJycVKOGsXdKAAAA3LSKi4uVn5+vunXrlruesfHaqFEjnT59WoWFhXJw+OUwsrOzVatWLbm5uVmt6+DgIA8PD1tMEwAAABVU3hXXy4y9DOnr6ysHBwelpqZalqWkpMjPz4+rqwAAADcpYyvP2dlZ999/v6ZMmaL9+/crOTlZK1as0NChQ209NQAAAFwnxsarJMXGxqpdu3YaNmyYXnnlFY0ZM0b33XefraeFm5yPj4/+8pe/lFq+YcMG9erVywYzAoBr8+yzzyo0NFR5eXmlxv70pz/p4YcfVklJiQ1mBlyd0fHq7Oys2bNn69///rd27typxx57zNZTwi1i06ZN+uKLL2w9DQColAkTJujnn3/W4sWLrZZ//PHH+vLLLzV16lTZ2dnZaHZA+YyOV8BWvL29NXXq1FKfeAEAJmjUqJHGjBmjt956S+np6ZJ++YzNWbNm6U9/+pPatGlj4xkCZSNegUp45plndOLECS1fvrzMdbKysvT0008rODhYnTt31vTp04ldADeMIUOGqFmzZpo7d64kadmyZapRo4ZiYmL0008/aeTIkfL391evXr20cOFCy5f8XLp0SS+99JI6d+6swMBAjRw5UidOnLDloeAWQ7wCldCoUSONHTtWixcvtly1+LWCggINGzZMeXl5eueddzR//nx9+umnmjNnjg1mCwClOTg4aNKkSfr444+VnJys5cuXa/LkyXJyctLo0aPl4eGh999/XzNnztTGjRsttxisWrVKX375pVasWKH169frwoULmjFjho2PBrcS4hWopMtXLeLi4kqN7dy5UydOnNDcuXPl4+Ojrl27atKkSXrvvfd04cIFG8wWAEq766671L9/fz399NMKCwtT9+7dtXv3bmVmZmratGlq0aKFOnfurAkTJmjlypWSpIyMDDk5Ocnb21stW7bUrFmzNGLECBsfCW4lxn5JAWBr9vb2mjJlih599FElJydbjR05ckTNmze3+paQjh07qrCwUD/++KN8fX2re7oAcEUjR47Uhx9+qJiYGEm/nL/OnDmjoKAgyzrFxcW6ePGiTp8+rYceekhJSUkKCQlRcHCwevfurejoaFtNH7cg4hX4HTp27KiBAwcqLi5Ow4cPtyx3cnIqte7l+8Uu/xcAbgSXz1eX/1tYWKgWLVpo0aJFpdatU6eO6tevr3/+85/69NNP9emnn+q1117Tpk2btGrVKj6hANWC2waA3+m5555Tbm6u1Zu37rjjDh09elRnzpyxLEtNTZWDg4Nuv/12G8wSACrmjjvuUGZmptzd3dWsWTM1a9ZMGRkZev3112VnZ6cPPvhA27dvV0REhGbPnq1ly5YpJSVF//vf/2w9ddwiiFfgd6pfv76ee+45HT9+3LKsW7duatq0qZ5//nl9++232r17t6ZNm6bIyEi5ubnZcLYAUL6QkBB5e3tr/Pjx+vbbb/XVV1/p5ZdflrOzs+zt7fXzzz8rLi5OX3zxhdLT07Vx40Y1btxY9evXt/XUcYvgtgGgCgwaNEiJiYk6efKkpF/uh120aJGmTZumBx98ULVr11b//v317LPP2nimAFA+e3t7vfHGG5bzl4uLi8LDwzVhwgRJ0uDBg5WVlaXx48fr7Nmzat++vd544w3Z29vbeOa4VdiV8P1vAAAAMAS3DQAAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCQBW7dOmSFixYoHvuuUft27dXz549NXPmTJ0/f77K97VgwQINGTKkyrcrST4+PtqzZ8912TYAVBZfDwsAVezVV1/V559/runTp6tp06ZKT09XXFycjh07psWLF1fpvh5//PHrFq8AcCMiXgGgir3//vuaMWOGunbtKklq0qSJpkyZosGDB+vkyZNq2LBhle2rdu3aVbYtADABtw0AQBWzs7PT7t27VVxcbFkWGBiopKQk1a9fX7169dKGDRssY3v27JGPj48kKSMjQz4+PoqPj9ddd92l2NhY+fn5affu3Zb1z58/Lz8/P3311VeW2waKi4vVvXt3JSYmWtYrKSlRjx499I9//EOS9NVXXyk6OlodOnRQ//79tXXrVqt5L1y4UF27dlXnzp21bt266/LcAMDvxZVXAKhiQ4cO1euvv67k5GSFhobq7rvvVkhIiFq1alXhbezbt0+JiYkqLi7W2bNntW3bNnXp0kWS9Omnn8rd3V1BQUH64osvJEk1atRQeHi4tm3bpoEDB0qSUlNTdebMGd1zzz3Kzs7WU089pXHjxql79+5KTU3VCy+8IA8PD3Xq1Elr1qzRypUrNXv2bDVu3FivvPJK1T8xAFAFuPIKAFUsJiZGc+fOVePGjbV27VqNHTu21FXRqxk2bJhuv/12NW/eXP369dO2bdtUUlIiSdq6dasiIiJkZ2dn9Zh+/fpp165dljeGbd26VaGhoXJ1ddWqVat09913649//KOaNWumqKgoPfTQQ3r77bclSWvXrtWwYcMUFhYmX19fTZ8+vYqeDQCoWsQrAFwHAwYM0OrVq/X555/r1VdfVevWrTVx4kQdOHCgQo/39va2/H9YWJjOnTuntLQ05eXlaefOnerbt2+pxwQEBKhBgwbasWOHJOnjjz+2rPf9999r+/btCgwMtPxJSEjQ0aNHJUlHjhyRr6+vZVutWrWSi4tLZQ8fAK4bbhsAgCr0zTff6IMPPtALL7wgSapfv7769++vPn366L777rO6d/WyoqKiUsucnJws/+/i4qKwsDBt3bpVJ06ckKenpzp06HDF/fft21dbt25Vs2bNdPr0afXs2VOSVFhYqP79+2vkyJFW6zs4/N9fA5ev7F5pDABuFFx5BYAqVFRUpLfeeksHDx60Wu7o6KhatWrJ3d1dNWvW1IULFyxj6enpV91uv379tGPHDiUnJ1/xquuv19u1a5e2bt2qXr16ydnZWZJ0xx136NixY2rWrJnlzyeffKKNGzdKklq3bq3//Oc/lu1kZGTo3Llz13TsAFAdiFcAqELt2rVTz549NWrUKG3cuFEZGRlKTU3V5MmTVVBQoPvuu09+fn5av369Dh06pD179mjFihVX3W6PHj108uTJq8arr6+vGjZsqISEBEVERFiWP/roozpw4IDmzZuno0ePauPGjXrttdfk5eUlSfrjH/+olStXauvWrTp06JAmTpyoGjX4KwLAjYczEwBUsfnz5ysqKkoLFy5URESEnnrqKZ0/f14JCQlydXXVM888Izc3N0VHRysuLk5PP/30Vbfp6Oio3r17q3HjxrrzzjvLXbdv376yt7dXjx49LMu8vb21ePFi7dy5U5GRkZo/f75eeOEFDRgwQJIUFRWlsWPHatq0aXr00UfVrVs3ubm5/b4nAgCuA7uS397kBAAAANyguPIKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABj/D8DOAcAMdIsWgAAAABJRU5ErkJggg==","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","# Plot 1: Survivors vs Non Survivors\n","\n","# Creating a plot for the Survived Column\n","sns.countplot(x='Survived', data=train_df)\n","\n","plt.title('Survivors vs Non Survivors')\n","plt.xlabel('Survived')\n","plt.ylabel('Count')\n","plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Plotting Passenger Class"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:14:31.768779Z","iopub.status.busy":"2024-04-01T07:14:31.768341Z","iopub.status.idle":"2024-04-01T07:14:32.062495Z","shell.execute_reply":"2024-04-01T07:14:32.060660Z","shell.execute_reply.started":"2024-04-01T07:14:31.768690Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAq8AAAIcCAYAAADYP0dGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABIrUlEQVR4nO3deXwN9/7H8fcRTawhYinS0iKEREQioXbaWqrlWooqtbRBgqtqC62d2KlI7FrKrS2oqm66uFVqSSuu7VpLUMROE1nn94drfo6gQTimXs/HI49HznznzHxmTubkfb7nOzM2wzAMAQAAABaQzdEFAAAAAJlFeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFbAY7isCAHiSZXd0AcDj6D//+Y8WLlyobdu26fz58ypcuLCqVaum4OBgPfPMMw6rKyoqSs7Oznr77bcfaDmpqal6//339fXXX8tmsykqKkpVq1a1m2fLli3q0KGD3TSbzaZcuXLJ09NTwcHBqlev3gPVAXsDBw7U1q1b9f333zu6lEyrV6+eAgMDNXbs2Ht+3okTJ+ymOTs76+mnn1bDhg3Vo0cPubi4ZGWpT4TMvneVLVtWPXr0UM+ePR1YLXB/CK/ALRYvXqwxY8YoKChI7733ngoXLqyjR49q3rx5+uabb7RgwQKVK1fOIbV9+OGH6tGjxwMv56efftKqVasUEhKiF154QeXLl7/jvEOGDFGFChUkXe/1vXTpkubPn6+QkBDNmjVLtWvXfuB68GSqXbu2QkJCzMdJSUnasmWLoqKidOLECU2ePNmB1VnP4/zeBWQlwitwk5iYGI0ePVrt2rXT4MGDzelBQUF68cUX1axZMw0aNEgrV650YJUP7uLFi5Kk5s2b/2VPcunSpVWpUiW7aQEBAapTp44WLlxIeMV9K1CgQIa/raCgIJ06dUorV67UwIEDVbhwYccUZzFPynsXIDHmFbAzb9485c2bV3369MnQVqBAAQ0cOFD169dXQkKCJCktLU2LFy/Wq6++qooVK6pOnTqaOHGikpKSzOe1b99e7du3t1vWli1bVLZsWW3ZskWStHLlSpUvX16xsbFq3bq1fHx8VLduXc2bN898TtmyZSVJ06dPN3+/nb+qaeDAgRo4cKAk6cUXX8xQW2bkyZNHzz33nE6ePGlO27dvn3r06KGqVauqQoUKqlmzpkaNGqVr166Z8/z88896/fXX5efnpypVqqh79+46dOiQ2X7s2DF169ZNQUFB8vX1VevWrbVhwwa7de/fv19du3ZV5cqVVblyZYWGhiouLi7Dvt28ebM6d+4sX19fVa9eXRMmTFBaWpo539WrVzVkyBBVq1ZNfn5+evfdd/Xxxx9n2Lfr169X8+bN5ePjo+rVq2vUqFHm6y9JEREReumllzR9+nQFBgaqRo0aunTpknbt2qW33npL/v7+8vPzU8eOHbVjx4572s83lv3jjz/q1Vdflbe3txo0aKDVq1f/5XO3b9+uN998U76+vgoMDNSAAQN0/vx5u3m2bdumLl26qEqVKvL29la9evUUERGh9PR0u/00cuRI1axZU5UqVVKLFi30448/2i0nJSVF48ePV/Xq1VWpUiV17txZR48evadtvZm3t7cMw9Aff/whSbp27ZomTZqkl19+Wd7e3qpcubI6deqkvXv3ms85f/683nvvPVWvXl0+Pj5q2rSp3X5KT0/XlClTVK9ePXNbJ02apJSUFHOepKQkjR8/XrVr15a3t7deffVVrVu3zq62evXqadq0aRo3bpxeeOEFVaxYUV26dNHvv/9uN9+qVavUuHFj+fj46LXXXtPmzZtVvnx5u/B48uRJ9enTR4GBgfL19dVbb72lPXv2mO3Hjx9X2bJl9dFHH6lhw4by9fVVdHT0bffZvb533epRHb9AViC8Av9jGIY2btyoatWqKWfOnLedp3HjxgoNDVWuXLkkXf9KPTw8XC+++KJmzJihdu3aadGiRQoJCbnnE6vS09PVu3dvNW7cWLNnz1blypU1fvx4/fTTT5KkpUuXSpJatmxp/n47f1VTSEiIunfvLul6EB46dOg91SlJycnJOn78uJ599llJ0pkzZ9SuXTslJiZq7NixmjNnjl555RV98sknWrhwoSQpLi5OISEh8vb21owZMzR69GgdOXJEwcHBSk9PV3p6urp27arExESNHz9eUVFRyp8/v7p3724GoSNHjqhNmzY6d+6cxo0bp9GjRysuLk5t27bVuXPn7Grs27ev/P39NXPmTDVp0kRz587V8uXLzfaQkBB9+eWX6tmzp6ZMmaI///xTkyZNslvG559/rtDQUD3//POKjIxUjx49tGbNmgyv78mTJ7VhwwZNmTJFYWFhcnJy0ttvvy03NzdFRERoypQpSkxMVJcuXXTlypV72tfx8fEaMWKEOnTooNmzZ8vDw0MDBgywCw232rZtmzp27KgcOXJo6tSpGjRokLZu3aoOHTqYYWTfvn3q2LGj8ufPrylTpmjGjBkKCAjQ9OnT9eWXX0q6/kGoc+fO+vzzz9W1a1dFRUXp+eefV2hoqLZv326ub926dTpw4IDGjh2roUOHateuXXr33XfvaTtvduTIEUkyvxXo37+/oqOjFRwcrPnz5yssLEwHDhzQe++9Z74O/fr106FDhzR8+HDNmTNH5cuX14ABA/TLL79IkubMmaNPP/1UoaGhmj9/vtq2bat58+ZpxowZkq4f/6GhoVqyZIk6deqkGTNmmB9qbv2wsHDhQh0+fFjh4eEaNWqUdu3apQEDBpjtq1ev1sCBA1W5cmVFRUWpQYMGCgkJsfvwdP78ebVp00a7d+/WBx98oEmTJik9PV3t2rXL8NpGRETonXfeMT8g3Op+3rtu9qiOXyDLGAAMwzCMc+fOGZ6ensaECRMyNf+BAwcMT09PY9asWXbTV69ebXh6eho//vijYRiG8eabbxpvvvmm3Ty//PKL4enpafzyyy+GYRhGdHS04enpaSxbtsycJykpyfDx8TFGjBhhTvP09DSmTZv2wDXdWF9cXNwdl3Wjxk2bNhkpKSlGSkqKkZiYaBw8eNDo06eP4enpaWzYsMEwDMP46aefjHbt2hlXrlyxW0aTJk2Mzp07G4ZhGGvXrjU8PT2NU6dOme2xsbHG5MmTjStXrhhnzpwxPD09jTVr1pjtly9fNsaMGWPs37/fMAzD6NOnj/HCCy/YrefChQuGv7+/MXbsWLu6p0yZYldLvXr1jK5duxqGYRibNm0yPD09ja+//tpsT0tLMxo1amR4enoahmEY6enpRq1atYwuXbrYLefGc3/44QfDMAxj2rRphqenp7Ft2zZznt9++83w9PQ0YmJizGlHjx41xo8fb/zxxx933OcDBgww6tataz6+sexNmzaZ006cOGF4enoa8+bNu+NyWrdubTRp0sRITU01px0+fNjw8vIyFi1aZBiGYaxatcp4++23jbS0NLt94O/vb3zwwQeGYRjG999/b3h6ehrffvut3TytW7c2IiIiDMMwjLp16xq1a9c2kpOTzXmmTJlieHp6Zvh7uFndunWN/v37m39bKSkpxqlTp4xly5YZ3t7eRu/evQ3DuH4cdO7c2fjiiy/snj9//nzD09PTOHPmjGEYhuHt7W3MmDHDrs6xY8ear0Hnzp2NTp062S3jk08+MVavXm0YhmFs3LjR8PT0zLCevn37GtWrVzdSUlLMuuvWrWu3byMiIgxPT0/j/PnzhmEYRp06dcy/tRtmzZpleHp6GtHR0YZhGMbkyZMNHx8f4/jx4+Y8SUlJRv369Y2ePXsahmEYcXFxhqenpzFo0KA77kfDuPf3LsOwfy95VMcvkFUY8wr8j5OTkyTZ9Y7czdatWyVJr7zyit30V155RWFhYdqyZcs9jwf18/Mzf3d2dlaBAgXu+DXfo6qpY8eOGaa5u7vr/fffV61atSRJNWrUUI0aNZSSkqKDBw/q6NGj2r9/v86fP6/8+fNLknx9feXi4qKWLVuqYcOGqlWrloKCglSxYkVJUu7cuVW6dGl98MEH2rhxo2rUqKFatWopLCzMXO8vv/yiwMBA5ciRQ6mpqZKuD2EICAjQpk2b7Gq8eV9K0tNPP23uy19++UVPPfWUXnzxRbM9W7Zsaty4sSIiIiRJhw8f1qlTp9S1a1dzXZJUpUoV5cmTRz///LPq1KljTvfy8jJ/L1OmjAoUKKBu3bqpYcOGqlmzpqpXr65+/fplap/f6uZxoU8//bQk3fHvIjExUbGxserSpYsMwzBrf+aZZ1SqVCn9/PPPateunZo1a6ZmzZopKSlJR44c0dGjR7V3716lpaWZX6XHxMToqaeesruqRLZs2bRkyRK7dVasWFFPPfWU+djDw0OSdPnyZeXJk+eO27V69eoMvZrZs2fXSy+9ZH4j4OzsbA6fOX36tI4cOaLff/9dP/zwg6Tr3wJI18d2RkREaM+ePapZs6Zq165t1xsaFBSkSZMm6Y033lC9evVUp04dvfnmm2b75s2bZbPZVLt2bbvXu169elqzZo0OHDhgvsY+Pj7m+4X0/69JYmKiLl++rJMnT+qf//yn3Xa98sordj37mzdvlpeXl4oUKWKuL1u2bKpVq5bWrFlj99yb/7Zu517fu271qI5fIKsQXoH/yZcvn3Lnzm03jvNWCQkJSklJUb58+XTp0iVJUqFChezmyZ49u9zc3O7562FJypEjh93jbNmy3dPwg4dR0/Dhw82rDTg5OSlfvnwqVqyYbDabOU96eromT56sxYsXKyEhQUWLFlXFihXtLnXk4eGhRYsWafbs2VqxYoUWLlwoV1dXvfHGG+rdu7dsNpvmz5+vGTNm6Ntvv9Xq1avNgDl8+HDly5dPFy9e1Lp16zKMQ5Suj+u72d325YULF5Q/f35ly2Y/csrd3d38/cZJbcOHD9fw4cMzrO/MmTN2j3Pnzm33++LFizVjxgx9+eWXWrp0qXLkyKGmTZvq/fffl7Ozc8YdfRc3fxV8o+Y7/V1cvnxZ6enpmjNnjubMmZOh/cZrcu3aNY0cOVKfffaZUlNT5eHhIT8/P2XPnt1c9sWLF2+7n25161fRN+a/eezs7dStW1ehoaGSrl+GLWfOnCpevHiG1+6nn37SmDFjdPjwYeXOnVvlypUz13mj1ilTpmjmzJn68ssv9fXXXytbtmx64YUXNGLECBUvXlxvv/22cufOrejoaE2cOFETJkxQmTJl9P7776tq1aq6ePGiDMNQ5cqVb1vrmTNnzBB561fzN2/vjXHFN/8tSVLBggXtHl+8eFFHjx41j61bJSYmmr/f7qv+m93re9etHtXxC2QVwitwkxo1amjLli1KSkq67TUmly1bpnHjxmnFihXmm3F8fLyKFy9uzpOSkqILFy7Izc3NnHZrj8i99Kbei3upKbOee+45+fj43HWe2bNn6+OPP9bw4cP18ssvK2/evJKuj8+9WcWKFTV9+nQlJycrJiZGS5cu1cyZM1WuXDk1atRIRYoU0bBhwzR06FDt27dPX331lebMmSM3NzcNHTpUefPm1QsvvKBOnTplqCF79sy/nRUpUkQXLlxQenq6XTC7edysq6urpOvjLQMDAzMs46/+GT///PPmSWI7d+7UZ599pk8//VTPPvvsA1+n925y584tm82mjh07ZuiBl/4/eI0ePVpff/21pk6dqhdeeMEMSNWqVTPnzZs3rxnqbv6wsmfPHhmGccfglVn58+f/y7+tY8eOKTQ0VC+++KJmzZqlZ555RjabTYsXLzbHg9+otV+/furXr58OHz6s7777TlFRURo+fLhmz56tbNmyqV27dmrXrp3OnTunDRs2aObMmerZs6d+/vln5c2bV7ly5TLHeN6qRIkSmdqmG72wt47BvvVx3rx5FRgYqP79+992Off6Aede3rtufd0e1fELZBVO2AJu0rlzZ128eFFTp07N0BYfH6/58+erdOnSqlChghlovvjiC7v5vvjiC6Wlpcnf31/S9a+1T506ZTdPTEzMfdX3Vz1gma0pq8XExKh06dJq0aKF+Y/v9OnT2r9/v9n79vHHH6tu3bpKTk6Ws7OzqlWrppEjR0q6fsLTb7/9phdeeEE7d+6UzWaTl5eX3n33XXl6epo9SoGBgTp48KC8vLzk4+MjHx8feXt76+OPP9a3336b6XoDAwOVmppqdzMAwzC0fv168/Hzzz8vd3d3HT9+3FyXj4+PihQpokmTJtmdFX6rr776SlWrVlV8fLycnJzk5+enYcOGydXV9a69Y1khT548Kl++vA4fPmxXd5kyZRQREWFe4SImJsa8jNKN4Lpr1y6dP3/efM0CAgKUkpKif//73+byDcNQWFiYZs2a9VC344Zdu3YpKSlJwcHBevbZZ80QfSO4GoahEydOqHbt2vrqq68kXX/t3nnnHb3wwgvm/m7Tpo1GjRol6XqvaPPmzdWuXTtdvnxZV69eVWBgoBISEmQYht1+279/vyIjI+2GEtzN008/rWeffTbD3+M333xj9zgwMFBHjhwxPxze+Pnss8+0YsUKu2EJmXEv7123elTHL5BV6HkFblKpUiX985//1NSpU3Xo0CE1a9ZMbm5uOnDggObNm6ekpCTzn0Pp0qX1j3/8Q9OmTVNiYqKqVKmivXv3avr06QoKClLNmjUlXf9q9Pvvv1d4eLjq1aun7du3Z+pSR7fj6uqqX3/9Vdu2bVNAQIBdb9i91JTVKlasqKioKM2ePVuVKlXS0aNHNWvWLCUnJ5tff1atWlUTJ05UaGio3nzzTTk5OWnJkiVydnZW3bp1za+L+/fvr549e6pgwYLatGmT9u7da97pKyQkRG3atFHXrl3Vtm1bubi4aOnSpVq/fr2mTZuW6XqrVKmi6tWra/DgwTp79qyKFSumFStW6L///a+5T52cnPTuu+9qyJAhcnJyUt26dXX58mVFRUXp9OnTd+11rFy5stLT0xUaGqrg4GDlzp1bX375pa5cuaKXX375AfZ05vTp00fBwcF677339NprryktLU3z589XbGyseVOAihUr6ssvv9Snn36qUqVKad++fZoxY4ZsNpv5mtWpU0d+fn4aOHCgevfurWeeeUafffaZDh06ZAaXh61ChQrKnj27JkyYoM6dOys5OVkrV640L9eVkJCgsmXL6umnn9aoUaN09epVPfvss9q1a5c2bNigrl27Srr+ms+fP18FCxaUn5+fTp8+rY8++kiBgYEqUKCAateurSpVqigkJEQhISEqVaqUdu7cqWnTpqlmzZoZhqXcic1mU69evdS3b18NHTpUL730kvbt26fIyEhJ//8BtGPHjvrss8/UsWNHde7cWW5ublq3bp2WLVt2X+NE7+W961aP6vgFsgrhFbhF9+7dVb58efNuNZcuXVLRokVVp04ddevWTUWLFjXnHT16tEqUKKHo6GjNmTNHhQsXVocOHRQSEmL+k2rRooWOHTumVatWacmSJapSpYqmTZumtm3b3nNt3bp1U1RUlN555x2tW7dOxYoVyzBPZmrKal27dtWFCxe0cOFCRUZGqmjRomratKlsNptmzZqly5cvq1y5cpo5c6YiIyPVp08fpaWlydvbW/Pnz9fzzz8vSZo/f74mTZqk0aNH6/LlyypZsqRGjBih5s2bS5LKlSunxYsXa8qUKerfv78Mw5Cnp6ciIyNVv379e6p5ypQpGjt2rCZNmqTU1FTVr19fbdu2tftg0apVK+XOnVtz587V0qVLlStXLlWuXFkTJ068680dChcurLlz5+rDDz/U4MGDlZiYaPZ83nob3oehRo0amjdvnqZPn65evXrpqaeeUoUKFfTRRx+ZJ38NHDhQKSkpmjp1qpKTk+Xh4aHu3bvr4MGD+v7775WWliYnJyfNmTNHEydO1IcffqjExESVLVtW8+fPN0/UedhKlCihSZMmafr06erevbvy5cunSpUq6ZNPPlH79u21fft2lS1bVtOnT9fkyZP14Ycf6sKFCypatKh69Oih4OBgSdI///lPOTs7Kzo6WpGRkcqbN6/q1aun9957T9L1UDl79mx9+OGHmjVrls6dO6ciRYqoU6dO5rjczHr11VeVkJCgefPmKTo6WmXKlNHgwYM1ePBgs5e7SJEiWrJkiSZNmqRhw4YpKSlJJUuW1OjRozN8XZ9Z9/LedbNHdfwCWcVm3MvZIADwN3DixAnt2LFD9evXtzs5qFevXoqLi9OqVascWB2sbu3atSpfvrwZ6iTpxx9/VNeuXfXZZ59xi1bgAdHzCuCJky1bNvOOQy1btpSTk5N++uknffPNNwoPD3d0ebC4NWvWaMqUKerdu7eKFi2qo0ePatq0aQoMDCS4AlmAnlcAT6RffvlFkZGR2rt3r1JTU1WqVCl16tRJTZo0cXRpsLgLFy5o0qRJ+ve//63z58+rYMGCatCggXr16mV3STUA94fwCgAAAMvgUlkAAACwDMIrAAAALIPwCgAAAMt4Iq42kJqaqkuXLsnFxeWhXecSAAAA9y89PV1JSUnKly/fXW/5/USE10uXLun33393dBkAAAD4CyVLlpS7u/sd25+I8Ori4iLp+s7ImTOng6sBAADArRITE/X777+bue1OnojwemOoQM6cOc1b8wEAAODx81dDPBkACgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyHBpev/32W5UtW9bup1evXpKkPXv2qFWrVvL19VWLFi20a9cuu+euXbtWL774onx9fRUaGqrz5887YhMAAADwCDk0vB48eFB169bVxo0bzZ9Ro0YpISFBwcHBCggI0MqVK+Xn56euXbsqISFBkrRz504NHjxYPXr00NKlS3X58mWFhYU5clMAAADwCDg0vB46dEienp4qVKiQ+ePq6qp169bJxcVF/fv3V6lSpTR48GDlzp1bX331lSRp0aJFatSokZo1a6Zy5cpp/Pjx2rBhg+Li4hy5OQAAAHjIHB5eS5YsmWF6bGys/P39ZbPZJEk2m02VK1fWjh07zPaAgABz/qJFi6pYsWKKjY19FGUDAADAQRx2e1jDMHTkyBFt3LhRs2bNUlpamho2bKhevXopPj5epUuXtpvf3d1dBw4ckCSdOXNGhQsXztB+6tSpu64zLS1NaWlpWbshAAAAeGCZzWgOC68nT55UYmKinJ2dNXXqVB0/flyjRo3StWvXzOk3c3Z2VnJysiTp2rVrd22/k/3792ftRgAAAOCRclh4LV68uLZs2aJ8+fLJZrPJy8tL6enp6tevnwIDAzME0eTkZOXIkUOS5OLictv2nDlz3nWdnp6eypUrV9ZuCAAAAB5YQkJCpjoaHRZeJSl//vx2j0uVKqWkpCQVKlRIZ8+etWs7e/asOVSgSJEit20vVKjQXdfn5OQkJyenBy8cAAAAWSqzGc1hJ2z99NNPCgoKUmJiojlt7969yp8/v/z9/fXbb7/JMAxJ18fH/vrrr/L19ZUk+fr6KiYmxnzeH3/8oT/++MNsBwAAwN+Tw8Krn5+fXFxc9P777+vw4cPasGGDxo8fr7ffflsNGzbU5cuXNXr0aB08eFCjR49WYmKiGjVqJElq27atPvvsMy1fvlz79u1T//79VadOHT3zzDOO2hwAAAA8Ag4Lr3ny5NG8efN0/vx5tWjRQoMHD1br1q319ttvK0+ePJo1a5ZiYmLUvHlzxcbGavbs2eZ4VT8/P40YMUKRkZFq27at8uXLp/DwcEdtCgAAAB4Rm3Hju/m/sYSEBO3du1deXl6csAUAAPAYymxec+hNCgAAAIB74dCrDQAAgMzx77fQ0SUAdmImdHDIeul5BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYxmMTXoODgzVw4EDz8Z49e9SqVSv5+vqqRYsW2rVrl938a9eu1YsvvihfX1+Fhobq/Pnzj7pkAAAAPGKPRXj94osvtGHDBvNxQkKCgoODFRAQoJUrV8rPz09du3ZVQkKCJGnnzp0aPHiwevTooaVLl+ry5csKCwtzVPkAAAB4RBweXi9evKjx48fLx8fHnLZu3Tq5uLiof//+KlWqlAYPHqzcuXPrq6++kiQtWrRIjRo1UrNmzVSuXDmNHz9eGzZsUFxcnKM2AwAAAI+Aw8PruHHj1LRpU5UuXdqcFhsbK39/f9lsNkmSzWZT5cqVtWPHDrM9ICDAnL9o0aIqVqyYYmNjH2ntAAAAeLSyO3Llmzdv1vbt2/X5559r2LBh5vT4+Hi7MCtJ7u7uOnDggCTpzJkzKly4cIb2U6dO3XV9aWlpSktLy5riAQAAnmBZnakyuzyHhdekpCQNHTpUQ4YMUY4cOezaEhMT5ezsbDfN2dlZycnJkqRr167dtf1O9u/fnwWVAwAA4MY34o+aw8Lr9OnT5e3trZo1a2Zoc3FxyRBEk5OTzZB7p/acOXPedZ2enp7KlSvXA1YOAIADLNnt6AoAO5UqVcrS5SUkJGSqo9Fh4fWLL77Q2bNn5efnJ0lmGP3666/VpEkTnT171m7+s2fPmkMFihQpctv2QoUK3XWdTk5OcnJyyqpNAAAAeGJldabK7PIcFl4/+eQTpaammo8nTpwoSerbt6+2bdumOXPmyDAM2Ww2GYahX3/9Vd26dZMk+fr6KiYmRs2bN5ck/fHHH/rjjz/k6+v76DcEAAAAj4zDwmvx4sXtHufOnVuSVKJECbm7u2vSpEkaPXq02rRpoyVLligxMVGNGjWSJLVt21bt27dXpUqV5OPjo9GjR6tOnTp65plnHvl2AAAA4NFx+KWybidPnjyaNWuW2bsaGxur2bNnm+NV/fz8NGLECEVGRqpt27bKly+fwsPDHVw1AAAAHjabYRiGo4t42BISErR37155eXlxwhYAwJL8+y10dAmAnZgJHbJ0eZnNa49lzysAAABwO4RXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWAbhFQAAAJZBeAUAAIBlEF4BAABgGYRXAAAAWIZDw+vRo0fVpUsX+fn5qU6dOpo7d67ZFhcXp44dO6pSpUpq3LixNm7caPfcTZs2qUmTJvL19VWHDh0UFxf3qMsHAADAI+aw8Jqenq7g4GC5ublp1apVGj58uGbMmKHPP/9chmEoNDRUBQsWVHR0tJo2baoePXro5MmTkqSTJ08qNDRUzZs314oVK1SgQAGFhITIMAxHbQ4AAAAegeyOWvHZs2fl5eWlYcOGKU+ePCpZsqSqVaummJgYFSxYUHFxcVqyZIly5cqlUqVKafPmzYqOjlbPnj21fPlyeXt7q3PnzpKk8PBwVa9eXVu3blVQUJCjNgkAAAAPmcN6XgsXLqypU6cqT548MgxDMTEx2rZtmwIDAxUbG6vy5csrV65c5vz+/v7asWOHJCk2NlYBAQFmW86cOVWhQgWzHQAAAH9PDut5vVm9evV08uRJ1a1bVw0aNNCYMWNUuHBhu3nc3d116tQpSVJ8fPxd2+8kLS1NaWlpWVs8AADAEyirM1Vml/dYhNdp06bp7NmzGjZsmMLDw5WYmChnZ2e7eZydnZWcnCxJf9l+J/v378/awgEAAJ5QjvrG+7EIrz4+PpKkpKQk9e3bVy1atFBiYqLdPMnJycqRI4ckycXFJUNQTU5Olqur613X4+npaTcUAQAAy1iy29EVAHYqVaqUpctLSEjIVEejQ0/Y2rFjh1588UVzWunSpZWSkqJChQrp8OHDGea/MVSgSJEiOnv2bIZ2Ly+vu67TyclJTk5OWbQFAAAAT66szlSZXZ7DTtg6fvy4evToodOnT5vTdu3apQIFCsjf31+7d+/WtWvXzLaYmBj5+vpKknx9fRUTE2O2JSYmas+ePWY7AAAA/p4cFl59fHxUoUIFDRo0SAcPHtSGDRs0YcIEdevWTYGBgSpatKjCwsJ04MABzZ49Wzt37lTLli0lSS1atNCvv/6q2bNn68CBAwoLC5OHhweXyQIAAPibc1h4dXJyUlRUlHLmzKnWrVtr8ODBat++vTp06GC2xcfHq3nz5lqzZo0iIyNVrFgxSZKHh4ciIiIUHR2tli1b6uLFi4qMjJTNZnPU5gAAAOARsBlPwG2pEhIStHfvXnl5eXHCFgDAkvz7LXR0CYCdmAkdsnR5mc1rDut5BQAAAO4V4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWQXgFAACAZRBeAQAAYBmEVwAAAFgG4RUAAACWkeXh9fz581m9SAAAAEDSfYZXLy+v24bUEydOqH79+g9cFAAAAHA72TM74+rVq7Vy5UpJkmEYCg0N1VNPPWU3z5kzZ1SoUKGsrRAAAAD4n0yH15deeknHjx+XJG3dulWVKlVS7ty57ebJlSuXXnrppaytEAAAAPifTIfX3Llzq0ePHpKk4sWLq3HjxnJxcXlohQEAAAC3ynR4vdk//vEPHT16VLt27VJKSkqG9mbNmj1oXQAAAEAG9xVe586dq4kTJypfvnwZhg7YbDbCKwAAAB6K+wqv8+fPV79+/dSlS5esrgcAAAC4o/u6VFZSUpJefvnlrK4FAAAAuKv7Cq+vvvqq/vWvf8kwjKyuBwAAALij+xo2cPXqVa1YsUJr166Vh4dHhuu9Lly4MEuKAwAAAG52X+G1ZMmS6tatW1bXAgAAANzVfYXXG9d7BQAAAB6l+wqvYWFhd20PDw+/r2IAAACAu7mvE7ZulZqaqiNHjmjdunUqUKBAViwSAAAAyOC+el7v1LM6d+5c7d+//4EKAgAAAO4kS3peb2jYsKG+/fbbrFwkAAAAYMqy8JqQkKBly5bJzc0tqxYJAAAA2LmvYQPlypWTzWbLMN3FxUWjRo164KIAAACA27mv8HrrTQhsNpueeuoplS5dWnny5MmSwgAAAIBb3Vd4DQwMlCT9/vvvOnTokNLT0/Xcc88RXAEAAPBQ3Vd4vXz5ssLCwvTdd98pX758SktL059//qkqVaooMjJSefPmzeo6AQAAgPs7YWvUqFE6deqU1q1bpy1btmj79u36/PPPlZCQwA0KAAAA8NDcV3j9/vvvNWzYMD3//PPmtNKlS2vIkCH67rvvsqw4AAAA4Gb3FV5dXFyULVvGp9psNqWlpT1wUQAAAMDt3Fd4rVevnoYPH65jx46Z037//XeNGjVKtWvXzrLiAAAAgJvd1wlb/fr1U2hoqBo0aCBXV1dJ0qVLl1SrVi198MEHWVogAAAAcMM9h9ejR4+qWLFi+uSTT/Tf//5Xhw4dkouLi0qWLKlSpUo9jBoBAAAASfcwbMAwDI0aNUqNGjXSb7/9JkkqW7asGjdurOjoaDVp0kRjx46VYRgPrVgAAAA82TIdXhcuXKh169YpMjLSvEnBDVFRUYqMjNSqVav06aefZnmRAAAAgHQP4XXZsmX64IMPVLdu3du216tXT3379iW8AgAA4KHJdHg9ceKEKlaseNd5qlatqri4uAcuCgAAALidTIdXd3d3nThx4q7znDp1Svnz53/QmgAAAIDbynR4femllxQREaGUlJTbtqempmr69OmqUaNGlhUHAAAA3CzTl8oKCQlRy5Yt1bx5c7Vv317e3t7KmzevLl26pN27d2vRokX6888/NX78+IdZLwAAAJ5gmQ6vrq6uWrZsmSZOnKixY8cqMTFR0vVLaOXNm1eNGzdWz549VbBgwYdWLAAAAJ5s93STgvz582vUqFEaMmSI4uLidPnyZeXPn1/PPvusnJycHlaNAAAAgKT7vD2ss7Mzd9MCAADAI5fpE7YAAAAARyO8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsI7ujC7Aq/34LHV0CkEHMhA6OLgEAgIeKnlcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYhkPD6+nTp9WrVy8FBgaqZs2aCg8PV1JSkiQpLi5OHTt2VKVKldS4cWNt3LjR7rmbNm1SkyZN5Ovrqw4dOiguLs4RmwAAAIBHyGHh1TAM9erVS4mJiVq8eLGmTJmiH374QVOnTpVhGAoNDVXBggUVHR2tpk2bqkePHjp58qQk6eTJkwoNDVXz5s21YsUKFShQQCEhITIMw1GbAwAAgEcgu6NWfPjwYe3YsUM///yzChYsKEnq1auXxo0bp1q1aikuLk5LlixRrly5VKpUKW3evFnR0dHq2bOnli9fLm9vb3Xu3FmSFB4erurVq2vr1q0KCgpy1CYBAADgIXNYeC1UqJDmzp1rBtcbrl69qtjYWJUvX165cuUyp/v7+2vHjh2SpNjYWAUEBJhtOXPmVIUKFbRjx467hte0tDSlpaVl7YYAjxH+vgEAj0pW/8/J7PIcFl5dXV1Vs2ZN83F6eroWLVqkqlWrKj4+XoULF7ab393dXadOnZKkv2y/k/3792dR9cDj6cYHPAAAHjZH/c9xWHi91YQJE7Rnzx6tWLFCH3/8sZydne3anZ2dlZycLElKTEy8a/udeHp62vXmPpAlu7NmOUAWqlSpkqNLAPCw8H8Hj5ms/p+TkJCQqY7GxyK8TpgwQQsWLNCUKVPk6ekpFxcXXbx40W6e5ORk5ciRQ5Lk4uKSIagmJyfL1dX1rutxcnKSk5NTltYOPE74+wYAPCpZ/T8ns8tz+HVeR44cqY8++kgTJkxQgwYNJElFihTR2bNn7eY7e/asOVTgTu2FChV6NEUDAADAIRwaXqdPn64lS5Zo8uTJeuWVV8zpvr6+2r17t65du2ZOi4mJka+vr9keExNjtiUmJmrPnj1mOwAAAP6eHBZeDx06pKioKL3zzjvy9/dXfHy8+RMYGKiiRYsqLCxMBw4c0OzZs7Vz5061bNlSktSiRQv9+uuvmj17tg4cOKCwsDB5eHhwmSwAAIC/OYeF1++++05paWmaMWOGatSoYffj5OSkqKgoxcfHq3nz5lqzZo0iIyNVrFgxSZKHh4ciIiIUHR2tli1b6uLFi4qMjJTNZnPU5gAAAOARcNgJW8HBwQoODr5je4kSJbRo0aI7tteuXVu1a9d+GKUBAADgMeXwE7YAAACAzCK8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMh13nFcCTyb/fQkeXANiJmdDB0SUAuAf0vAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyCK8AAACwDMIrAAAALIPwCgAAAMsgvAIAAMAyHovwmpycrCZNmmjLli3mtLi4OHXs2FGVKlVS48aNtXHjRrvnbNq0SU2aNJGvr686dOiguLi4R102AAAAHjGHh9ekpCT16dNHBw4cMKcZhqHQ0FAVLFhQ0dHRatq0qXr06KGTJ09Kkk6ePKnQ0FA1b95cK1asUIECBRQSEiLDMBy1GQAAAHgEHBpeDx48qNdff13Hjh2zm/7LL78oLi5OI0aMUKlSpdS1a1dVqlRJ0dHRkqTly5fL29tbnTt3VpkyZRQeHq4TJ05o69atjtgMAAAAPCIODa9bt25VUFCQli5dajc9NjZW5cuXV65cucxp/v7+2rFjh9keEBBgtuXMmVMVKlQw2wEAAPD3lN2RK3/jjTduOz0+Pl6FCxe2m+bu7q5Tp05lqv1O0tLSlJaW9gAVA483/r6Be8dxA9yfrD52Mrs8h4bXO0lMTJSzs7PdNGdnZyUnJ2eq/U7279+ftYUCjxm+fQDuHccNcH8cdew8luHVxcVFFy9etJuWnJysHDlymO23BtXk5GS5urredbmenp52QxEeyJLdWbMcIAtVqlTJ0SX8NY4dPGYscdxIHDt47GT1sZOQkJCpjsbHMrwWKVJEBw8etJt29uxZc6hAkSJFdPbs2QztXl5ed12uk5OTnJycsrZY4DHC3zdw7zhugPuT1cdOZpfn8Etl3Y6vr692796ta9eumdNiYmLk6+trtsfExJhtiYmJ2rNnj9kOAACAv6fHMrwGBgaqaNGiCgsL04EDBzR79mzt3LlTLVu2lCS1aNFCv/76q2bPnq0DBw4oLCxMHh4eCgoKcnDlAAAAeJgey/Dq5OSkqKgoxcfHq3nz5lqzZo0iIyNVrFgxSZKHh4ciIiIUHR2tli1b6uLFi4qMjJTNZnNw5QAAAHiYHpsxr//973/tHpcoUUKLFi264/y1a9dW7dq1H3ZZAAAAeIw8lj2vAAAAwO0QXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZlg6vSUlJGjRokAICAlSjRg3Nnz/f0SUBAADgIcru6AIexPjx47Vr1y4tWLBAJ0+e1IABA1SsWDE1bNjQ0aUBAADgIbBseE1ISNDy5cs1Z84cVahQQRUqVNCBAwe0ePFiwisAAMDflGWHDezbt0+pqany8/Mzp/n7+ys2Nlbp6ekOrAwAAAAPi2V7XuPj4+Xm5iZnZ2dzWsGCBZWUlKSLFy+qQIEC5vQbYfbPP/9UWlpalqz/WbccWbIcICtduXLF0SX8JY4dPG6scNxIHDt4/GT1sXPt2jVJ+stOSMuG18TERLvgKsl8nJycbDc9KSlJknTs2LEsW//7DUpl2bKArLJ//35Hl/CXOHbwuLHCcSNx7ODx87COnaSkJOXJk+eO7ZYNry4uLhlC6o3HOXLYfzrNly+fSpYsKRcXF2XLZtmREgAAAH9b6enpSkpKUr58+e46n2XDa5EiRXThwgWlpqYqe/brmxEfH68cOXLI1dXVbt7s2bPL3d3dEWUCAAAgk+7W43qDZbshvby8lD17du3YscOcFhMTIx8fH3pXAQAA/qYsm/Jy5sypZs2aadiwYdq5c6fWr1+v+fPnq0OHDo4uDQAAAA+JZcOrJIWFhalChQp66623NHz4cPXs2VMvv/yyo8vCHSQnJ6tJkybasmXLX85rGIYWL178CKoCHi+nT59Wr169FBgYqJo1ayo8PNw86fRelS1bNlPHG/B3cvToUXXp0kV+fn6qU6eO5s6dm+nnHj9+XGXLltXx48cfYoV4UJYd8ypd730dN26cxo0b5+hS8BeSkpL03nvv6cCBA5maf9u2bRoxYoTatWv3kCsDHh+GYahXr15ydXXV4sWLdenSJQ0aNEjZsmXTgAEDHF0e8NhLT09XcHCwfHx8tGrVKh09elR9+vRRkSJF9Oqrrzq6PGQRS/e8whoOHjyo119//Z4uVWYYxkOsCHg8HT58WDt27FB4eLjKlCmjgIAA9erVS2vXrnV0aYAlnD17Vl5eXho2bJhKliyp2rVrq1q1aoqJiXF0achChFc8dFu3blVQUJCWLl1qNz0lJUXvv/++goKC5Ofnp27duun06dM6fvy4OXaZrz3xJClUqJDmzp2rggUL2k2/evWqVq5cqfbt22vatGkKCgpSQECAwsPD7T7oTZ8+XdWqVVNQUJCWL1/+qMsHHK5w4cKaOnWq8uTJI8MwFBMTo23btikwMFArV65UmzZtFBoaKn9/f61Zs0YpKSkaOXKkAgICVKtWLW3YsMHRm4BMILzioXvjjTc0aNAg5cyZ02764sWLtW3bNs2fP18rVqzQn3/+qTFjxqho0aKKiIiQJG3cuNHuFsDA35mrq6tq1qxpPk5PT9eiRYtUtWpVSdJvv/2mI0eO6NNPP9UHH3yghQsXatOmTZKkpUuXauHChRozZow+/vhjRUdHO2QbgMdFvXr19MYbb8jPz08NGjSQdP0YKl26tJYtW6YaNWooIiJCP/zwg2bMmKEPP/xQCxcudHDVyAzCKxzm+PHjcnFxUfHixVWqVCmNHTtWwcHBcnJyMi9QXKhQoQx3UgOeFBMmTNCePXv07rvvSpLS0tI0cuRIPf/882ratKnKlSun//znP5KkZcuW6a233lLdunXl5eWlUaNGObJ0wOGmTZummTNnau/evQoPD5ck2Ww2de/eXaVKlZKbm5uWL1+uXr16qUqVKvLz89OgQYMcXDUyg/AKh2ndurXi4+NVo0YNde7cWRs2bFCpUtz+EJCuB9cFCxZowoQJ8vT0lCS5u7vbXcA7T548Sk1NlSQdOnRIXl5eZlvp0qWVK1euR1s08Bjx8fFR3bp1FRYWpiVLliglJUXu7u7mXTgvXLig8+fP2x03Pj4+jioX94DwCocpU6aMvv/+e02YMEGFChXS5MmT1blzZ07WwhNv5MiR+uijjzRhwgTz605Jt/0W4ubj5dZj58bdB4EnxdmzZ7V+/Xq7aaVLl1ZKSoquXr0qFxeXDM+5+bh56qmnHnqNeHCEVzjM6tWr9cMPP6hRo0YaN26c5s6dq5iYGJ07d042m83R5QEOMX36dC1ZskSTJ0/WK6+8kunnlSlTxhxCIF0flnP58uWHUSLw2Dp+/Lh69Oih06dPm9N27dqlAgUKyM3NzW5eNzc3FSxY0O642bNnzyOrFfePj+VwmCtXrmjmzJlyc3OTh4eHPv/8cz399NNyc3MzT+7atWuXypQpc9tPy8DfzaFDhxQVFaXg4GD5+/srPj4+08998803NXz4cHl5eem5557T6NGjuVU2njg+Pj6qUKGCBg0apLCwMJ04cUITJkxQt27dMsxrs9nUrl07TZs2TcWLF1fevHnNsbF4vBFe4TDt2rXTqVOn1K9fP126dEne3t6aMWOGnJycVLZsWVWvXl1t2rTR5MmTuXMangjfffed0tLSNGPGDM2YMcOu7a/+qTZt2lQXLlzQyJEjde3aNQUHB2vfvn0Ps1zgsePk5KSoqCiNHDlSrVu3Vs6cOdW+fXt16NBBq1atyjB/t27dlJiYqHfffVdOTk4KDQ3ViBEjHFA57oXNYIAhAAAALILvlAAAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGVwhy0AT4x69erpxIkT5uPs2bPrmWeeUZs2bdSxY0fHFfaYWL16tRYvXqyDBw8qd+7cql69unr37q2iRYtKkgYOHChJGjt2rCPLBPCEI7wCeKIMGjRIjRs3liSlpqbql19+0eDBg5U/f341a9bMscU5UHh4uFavXq2+ffsqMDBQFy9e1Icffqg333xTy5cvV4ECBRxdIgBIYtgAgCdM3rx5VahQIRUqVEhFixbVP/7xD1WrVk3ffPONo0tzmO3bt2vBggWKjIxUq1atVKJECfn6+ioyMlKpqalasGCBo0sEABPhFcATL3v27HrqqackSVevXlVYWJiqVasmb29vNWzYUOvXrzfnXbdunRo0aCAfHx81btzYrm3hwoWqW7eufHx81Lx5c23fvt1s279/v9q3b6+KFSuqQYMGWrx4sdkWERGh9957T0OHDlXlypVVrVo1zZkzx2xPT0/XxIkTFRQUpKCgIEVFRemll17Sli1bJEmXL19Wv379VLlyZdWoUUMjR47UtWvXJElbtmxRvXr1NHToUPn7+2v27NkZtn/16tWqWLGiAgIC7KbnzJlTM2bMULt27TI8xzAMzZw5U/Xq1ZO3t7dq1Kih6dOnm+379u1TmzZt5Ovrq5o1a9q1bd68WU2bNpWPj4/q16+vJUuW/MUrBAD/j/AK4ImVkpKib775Rj///LPq168vSRo9erSOHDmi+fPna+3atQoICNDgwYOVnJysc+fOqX///uratau++uortWjRQn369NHFixe1Z88ejR8/XkOHDtWXX36pgIAA9e7dW+np6bp27Zreeecd+fv7a82aNRowYICioqK0evVqs5avv/5aLi4uWrVqlbp06aKJEyfqyJEjkqRZs2Zp9erVmjRpkj766CP9+OOPiouLM587ePBgXblyRZ9++qmioqL0n//8RyNGjDDbT5w4oeTkZK1cuVJNmjTJsB/27dsnHx+f2+6j8uXLq3Dhwhmmr169WgsWLNDo0aP11VdfKTQ0VBEREdq9e7ckqX///vLy8tLatWs1evRozZ07Vxs2bFBaWpp69+6thg0b6ssvv9Q///lPDR8+XAcPHrz3FxDAE4kxrwCeKEOHDtXIkSMlSdeuXVOOHDn01ltv6bXXXpMkValSRZ06dZKnp6ckqXPnzlq+fLnOnTunCxcuKCUlRU8//bSKFy+uzp07q2zZsnJxcdGJEydks9lUrFgxeXh4qHfv3qpbt67S09P1+eefy93dXb1795YklSxZUidOnNDChQvNcbb58+fXgAED5OTkpLfffltz5szRrl279Nxzz+lf//qXevfurRo1aki6fsJUo0aNJEnHjh3T+vXrtXXrVuXNm1eSNHLkSDVr1kxhYWHmdr/99tsqUaLEbffJlStXlCdPnnvaj0WLFlV4eLiqVasmSWrbtq0iIyN14MABVahQQSdOnFD9+vVVvHhxPfPMM/roo4/k4eGhK1eu6OLFiypYsKA8PDzk4eGhwoULq1ChQve0fgBPLsIrgCdKr1699PLLL0uSXFxcVKhQITk5OZntzZo10/r167Vs2TIdPnzY7ElMS0uTl5eX6tSpo06dOum5555T/fr11apVK+XMmVM1atSQp6enXn31VZUvX95sy549uw4fPqx9+/bJz8/PXE9aWprdej08POwe586dW6mpqTp//rzOnDlj1zP6/PPPK1++fJKkQ4cOKT09XbVq1bLbzvT0dB09etRu+XeSP39+Xb58+Z72Y9WqVRUbG6tJkybp0KFD2rt3r+Lj45Weni5J6tq1qyZPnqylS5eqTp06atq0qRlQ27Ztq/fff19RUVGqW7euWrRoYW4PAPwVwiuAJ4q7u/sdeyCl6193//bbb2ratKnatm2rQoUKqXXr1pIkm82mWbNmaefOnfruu+/07bff6l//+pf+9a9/ycvLS8uXL9fWrVv1ww8/aOXKlfr000+1cuVKpaamqlq1ahoyZMgd13tjzO3NDMNQ9uzZzd9vbZOuh+C8efMqOjo6w/OLFCmi2NhYSdeD+p1UqFBBu3btum3bggULdPbsWb333nt205cvX64xY8aoVatWevnllzVgwAB16NDBbA8ODlajRo20fv16ff/993rrrbc0cuRItWrVSsOGDVO7du20fv16rV+/XkuXLlVUVJRq1659xxoB4AbGvALA/1y9elVr167VlClT1KtXL7300ku6dOmSpOth8dChQxo3bpwqVqyod999V1988YWKFi2qn376Sb/99ptmzZqlqlWrKiwsTF999ZWSkpIUExOj5557TkeOHJGHh4dKlCihEiVKaMeOHfrkk0/+siZXV1cVLlzY7AGWpLi4OLOn9LnnntOVK1dks9nMZV+7dk3jx49XcnJyprb71Vdf1c6dOxUTE2M3/c8//9SCBQuUlpaW4TmffvqpQkNDNWjQIDVr1kxubm46d+6cDMNQUlKSRo0aJWdnZ3Xq1EmffPKJXn/9dX399deKj4/X8OHDVaJECXXv3l3R0dGqWrWqvv/++0zVCgCEVwD4H2dnZ+XMmVPffPONjh8/rp9++sk88Sk5OVmurq7mSVFxcXH68ccfdeLECZUvX145cuRQZGSkli9fruPHj+uLL75QQkKCypYtq9dee03Xrl3TkCFDdOjQIW3YsEGjR4+Wu7t7pupq3769pk2bps2bN2vfvn3mWFabzaZSpUqpZs2a6tu3r3bu3Kndu3crLCxMCQkJcnV1zdTy/fz81KpVK4WEhGjFihU6duyYtm7dqnfeeUfZsmXTO++8k+E5bm5u2rx5s44cOaJdu3bp3XffVUpKipKTk+Xi4qJff/1VI0eO1OHDh/Wf//xH27dvV/ny5ZUvXz59++23GjNmjI4dO6Zt27Zp3759Kl++fCZfJQBPOoYNAMD/ODs7a8KECRo3bpw++eQTeXh4qHv37po6dar27t2rJk2aKCIiQhMnTtTMmTPl7u6uPn36mCdSjR49WlFRURoxYoSKFSumCRMmqFSpUpKkOXPmaMyYMWrWrJny58+vdu3aqWvXrpmqq3Pnzjpz5ox69uwpJycnBQcHa/v27eZQg/Hjx2vUqFHq2LGjsmfPrpo1a+r999+/p20fPny4SpUqpQULFmjUqFFydXVV9erVNWXKFLm5uWWYf9CgQRo0aJCaNm0qd3d3NWrUSDlz5tTevXslSVOmTNGIESPUsmVLZc+eXQ0bNlRISIicnZ0VFRWlMWPG6LXXXlPu3LnVsmVLtWrV6p7qBfDkshm3DqQCADxW/v3vf8vb29u8y9X58+dVrVo1fffdd3c9EQsA/o4IrwDwmAsNDVVaWpr69u0rm82mDz/8UCdPntSKFSscXRoAPHKMeQWAx9yQIUOULVs2tWnTRq+//rrS09MVGRnp6LIAwCHoeQUAAIBl0PMKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAsg/AKAAAAyyC8AgAAwDIIrwAAALAMwisAAAAs4/8Ans4Xp6g2MpYAAAAASUVORK5CYII=","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","\n","# Make the plot for Pclass here:\n","sns.countplot(x='Pclass', data=train_df)\n","\n","plt.title('Count of Passengers In each Passenger Class')\n","plt.xlabel('Passenger Class')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try it for \"Embarked\""]},{"cell_type":"code","execution_count":17,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.countplot(x='Embarked', data=train_df)\n","plt.title('Count of Passengers by Embarkation Point')\n","plt.xlabel('Embarkation Point')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['C', 'Q', 'S'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Making a histogram for \"Fare\""]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Here is the distplot for \"Fare\", refer to it after you tried it yourself:"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:18:24.402882Z","iopub.status.busy":"2024-04-01T07:18:24.402274Z","iopub.status.idle":"2024-04-01T07:18:24.798062Z","shell.execute_reply":"2024-04-01T07:18:24.796669Z","shell.execute_reply.started":"2024-04-01T07:18:24.402828Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Make a histogram for \"Age\" (Assignment)"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:19:53.874413Z","iopub.status.busy":"2024-04-01T07:19:53.873686Z","iopub.status.idle":"2024-04-01T07:19:54.244996Z","shell.execute_reply":"2024-04-01T07:19:54.243521Z","shell.execute_reply.started":"2024-04-01T07:19:53.874351Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAq8AAAIcCAYAAADYP0dGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABDWUlEQVR4nO3deVxUhR738e8IAoO4gmhabhmuiIiipqbyWFlqi0vdzGx5CkttuVkuZbaYmVm2aRYterv2ZNe1zLLbnrkmpmKigktQlDHmEs4AMpznD2OuCCgOzHLg8369eCVnzpzf7/wc6OuZc+ZYDMMwBAAAAJhADV83AAAAAJQX4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwA4gz/cu8UfegAAf0R4BWAqt9xyi9q0aeP6atu2rWJjYzV06FC9++67KigoKLZ+QkKCJk+eXO7tf/nll5o0adI515s8ebISEhLcrlOW48ePa+LEidqyZYtr2S233KJbbrmlwtuuLAUFBZo8ebJiY2PVpUsXbdy4scx1c3JyFBMTow4dOig7O9uLXQKoqgJ93QAAnK/27dvr8ccflyQ5nU4dO3ZM3333nWbOnKktW7bopZdeUo0ap/5tPnfuXIWFhZV72wsXLizXemPHjtXo0aPPu/dzSU1N1Ycffqhhw4a5lhXtq79Yu3atVqxYobFjx+rSSy9V+/bty1z3448/Vu3ateV0OrV06VLdc889XuwUQFVEeAVgOmFhYercuXOxZQkJCWrVqpVmzJihjz/+WNdcc40knTVYVUSzZs08st3StG7d2mu1yuPo0aOSpKFDh+qiiy4667rLly9Xnz59VLNmTS1ZskRjxoxx/cMCANzBbxAAVcaoUaPUqFEjLV682LXszLfzi4Jtp06d1KNHDz300EM6dOiQpFNvz2/evFmbN29WmzZttGnTJm3atElt2rTR4sWL1b9/f3Xp0kXr1q0rcdqAJJ08eVJPP/20unXrpq5du2rSpEn6888/XY+X9vZ/0faLahUdzR09erRr3TOfl5eXp3nz5mngwIGKjo7WFVdcoaSkJBUWFhar9eijjyopKUn9+vVTdHS0/vGPf2jHjh1nnaHT6dR7772nIUOGqFOnTurXr5+ef/555eXlSTp1ukTRPAcMGHDW0xnS09O1fft29evXT9dcc41+/fVXrV27tsR6f/zxh/75z38qPj5e3bp107Rp0/Tiiy+WmO+SJUs0aNAgdezYUf369dOrr74qp9N51v0BUPVw5BVAlVGjRg317NlTq1evVkFBgQIDi/+KS05O1sSJEzV27Fh169ZNv//+u2bPnq0JEyZo0aJFevzxx/Xwww9LOvVWfevWrfXTTz9JOnX6wdSpU5Wbm6vY2FitWrWqRP1PP/1UMTExevbZZ/Xnn3/q+eefV3p6uv7zn/8oICDgnP136NBB06ZN01NPPaVp06ape/fuJdYxDEN33323tm3bpvHjx6tt27batGmTXnrpJWVmZmr69OmudT/77DNdfPHFmjp1qgzD0KxZs3Tvvffqq6++KrOfadOm6cMPP9Rdd92lrl27ateuXZo3b55SU1P11ltvaezYsWrcuLHmz5+vuXPnqmXLlmXuz7Jly1SvXj31799fQUFBat68ud5//3317dvXtU5+fr5uvfVW2e12PfLIIwoLC1NSUpJSU1PVsGFD13pvvPGGXnzxRY0aNUpTpkxRamqqXn31Vf3222965plnzjlbAFUH4RVAlRIREaGTJ0/q6NGjioiIKPZYcnKyQkJClJiYqKCgIElSvXr1lJKSIsMw1Lp1a9f5sWeeljBy5EgNHDjwrLXr16+vt99+W6Ghoa7vx40bp++++079+/c/Z+9hYWGuUwRat25d6ukC3333ndavX685c+Zo0KBBkqRevXopJCREL7/8skaPHq1LLrlE0qkLq95++23XPp04cUKTJk1SamqqOnbsWGLb6enpWrp0qSZMmKDExETXtiMjIzVx4kR999136tu3r+uUiXbt2unCCy8sdV8KCgr00UcfafDgwa5ZX3/99a7AecEFF0iSPvroI+3fv1/Lli1z9dSjRw8NGDDAta2//vpLr732mm688UZNnTpVktS7d2/Vq1dPU6dO1e233+7aZwBVH6cNAKhSij5iymKxlHisW7ducjgcGjx4sF544QVt2bJFvXv31vjx40td/3Tt2rU7Z+2+ffu6gqt06pSFwMBA/fDDD+e5F2XbvHmzAgMDSwTponN8N2/e7Fp2ehiXpEaNGkmSHA5HmduW5ArFRQYNGqSAgABt2rSp3H1+8803stlsGjBggI4fP67jx48rISFBhYWFWrJkiWu9jRs36qKLLioWpsPCwoqF/R9//FG5ublKSEhQQUGB66votIJ169aVuy8A5seRVwBVyqFDhxQSEqJ69eqVeCw2NlZJSUlauHChFixYoKSkJEVEROjuu+8+50dRnR5Ky3L629zSqdMY6tevr+PHj5/XPpzNsWPHVL9+/RJv+xfV/uuvv1zLrFZriX4kFTs39sxtn76tIoGBgapfv36xbZ/LsmXLJEm33XZbiceWLl2qsWPHKjAwUEeOHFF4eHiJdU5fVnSBWNHR4DP98ccf5e4LgPkRXgFUGQUFBdq0aZO6dOlS5jmdffr0UZ8+feRwOLRx40a9++67evrppxUTE6NOnTpVqH5RyCridDpLhLMzLzCy2+3nVaNu3bo6cuSInE5nsX0sCnD169c/z66Lb1uSsrOz1bRpU9fykydP6siRI+Xets1m03fffVfqqRbbtm3TnDlz9PXXX+vyyy9Xo0aNdPDgwRLbOHz4sOvPderUkSQ9//zzatGiRYl1zzw9BEDVxmkDAKqMDz74QNnZ2brppptKfXzWrFkaNmyYDMOQ1WpV//79XTckyMrKkqQKfYzTunXrit0k4bPPPlNBQYHrwquwsDD9/vvvxZ6TnJxc7PtzXdgVHx+vgoICrVmzptjyjz76SJIUFxfndv/x8fGSpNWrVxdbvnr1ajmdznJv+8MPP1RBQYFuvfVWde/evdjXrbfeqrCwMNcnQsTHx+uXX35Ramqq6/m5ubnFPpUgJiZGNWvW1KFDhxQdHe36CgwM1Jw5c/TLL7+4vc8AzIcjrwBMJycnR9u2bZN06i3wI0eO6Pvvv9cHH3yga665RldccUWpz+vRo4cWLFigyZMn65prrtHJkyf11ltvqV69eurRo4ekU0f5fvzxR23YsOG8PyM2Oztb9957r2655RYdPHhQc+bMUa9evdSzZ09JUv/+/fXVV19p5syZSkhI0JYtW7Ry5cpi26hdu7akU+eM1q1bV23bti32+GWXXabu3btr6tSpOnTokNq2bavNmzfrzTff1PXXX1+hz4Rt3bq1rr/+er3yyityOBzq1q2bUlNTNXfuXHXv3l19+vQp13aWL1+uDh06lHqUNCQkRFdeeaWWL1+uzMxMDR48WElJSRo3bpzuv/9+1alTRwsWLNDhw4fVpEkTSaeOJt955516+eWXlZOTo+7du+vQoUN6+eWXZbFYSswIQNVGeAVgOrt27dKNN94o6dSFWbVq1VJUVJSeeOIJjRgxoszn9e3bV88//7zeeecd10VacXFxevfdd13nyN58883auXOn7rrrLs2cOVORkZHl7mvkyJH666+/NG7cOAUFBWnIkCF6+OGHXReDDRs2TBkZGVqxYoUWL16sbt266ZVXXil2pPiSSy7R4MGD9d5772nt2rX6+OOPi9WwWCx644039Morr2jhwoX6888/deGFF+rBBx/U7bffXu5eyzJjxgw1b95cy5Yt05tvvqnIyEiNHj1aY8eOLddR6e3btys9PV0TJ04sc53rrrtOy5Yt0wcffKCHHnpIb7/9tmbMmKEnnnhCgYGBuuaaa1SvXj0dOHDA9ZwHHnhADRs21P/7f/9Pb731lurWrauePXvqwQcfdAV+ANWDxSi6NBcAAC9LS0vT/v37dcUVVxT7xIfhw4ercePGmjt3rg+7A+CPOPIKAPAZu92u+++/XyNHjtTll18up9OpTz75RDt37tRDDz3k6/YA+CGOvAIAfGrNmjV6++23tW/fPhmGofbt2+uee+5R7969fd0aAD9EeAUAAIBp8FFZAAAAMA3CKwAAAEyD8AoAAADTqBafNlBQUKBjx44pODi4QnfPAQAAgGcUFhYqLy9PdevWVWBg2RG1WoTXY8eOlXrvbAAAAPiXFi1aKDw8vMzHq0V4DQ4OlnRqGFar1SM1nE6n9u7dq6ioqHPem7y6YTZlYzalYy5lYzZlYzZlYzZlYzal88VcHA6HDh486MptZakW4bXoVAGr1arQ0FCP1HA6nZKk0NBQXvxnYDZlYzalYy5lYzZlYzZlYzZlYzal8+VcznWKJyeAAgAAwDQIrwAAADANwisAAABMg/AKAAAA0yC8AgAAwDT8Irzm5+dr8ODB2rRpk2tZZmambrvtNnXu3FlXX321vv/++2LPWb9+vQYPHqyYmBiNHj1amZmZ3m4bAAAAXubz8JqXl6cHH3xQaWlprmWGYWjcuHGKiIjQsmXLdO2112r8+PHKysqSJGVlZWncuHEaOnSoli5dqgYNGmjs2LEyDMNXuwEAAAAv8Gl4TU9P1w033KCMjIxiyzdu3KjMzEw99dRTuvjiizVmzBh17txZy5YtkyQtWbJEHTt21B133KFLLrlEM2fO1K+//qrNmzf7YjcAAADgJT4Nr5s3b1b37t31wQcfFFu+fft2tW/fvtgNBeLi4rRt2zbX4127dnU9ZrVa1aFDB9fjAAAAqJp8eoetkSNHlro8OztbkZGRxZaFh4fr999/L9fjZXE6na47RlS2ou16avtmxmzKxmxKx1zKxmzKxmzKxmzKxmxK54u5lLeWX94e1uFwKCgoqNiyoKAg5efnl+vxsuzdu7dyGy1FSkqKx2uYFbMpG7MpHXMpG7MpG7MpG7MpG7MpnT/OxS/Da3BwsI4ePVpsWX5+vkJCQlyPnxlU8/PzVadOnbNuNyoqqtipCJXJ6XQqJSVF0dHR3Bv5DMymbMymdMylbMymbMymbMymbMymdL6Yi91uL9eBRr8Mr40aNVJ6enqxZTabzXWqQKNGjWSz2Uo83q5du7NuNyAgwON/Ad6oYVbMpmzMpnTMpWzMpmzMpmzMpmzMpnTenEt56/j8o7JKExMTo59++km5ubmuZcnJyYqJiXE9npyc7HrM4XBo165drscBAABQNflleI2Pj9cFF1ygKVOmKC0tTUlJSdqxY4eGDx8uSRo2bJi2bt2qpKQkpaWlacqUKbrwwgvVvXt3H3cOAAAAT/LL8BoQEKDXXntN2dnZGjp0qD766CPNmzdPTZo0kSRdeOGFevXVV7Vs2TINHz5cR48e1bx582SxWHzcOQAAADzJb8553bNnT7HvmzdvrkWLFpW5ft++fdW3b19PtwUAAAA/4pdHXgEAAIDS+M2RV6C6y8jIKPEpGp4UERGhZs2aea0eAACVgfAK+IGMjAy179BeuY7cc69cSUKsIdqzew8BFgBgKoRXwA/YbDblOnLVLrGdQpt45kYap7Nn2ZWalCqbzUZ4BQCYCuEV8COhTUJVu0VtX7cBAIDf4oItAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKYR6OsGAPhOamqq12pFRESoWbNmXqsHAKiaCK9ANZR/NF+ySKNGjfJazRBriPbs3kOABQBUCOEVqIYK7AWSIbUY3ULhrcI9Xs+eZVdqUqpsNhvhFQBQIYRXoBqzNraqdovavm4DAIByI7wC8JrynmPrdDq1d+9eFRYWKiAgwK1anGMLAFUT4RWAx3GOLQCgshBeAXjc+Z5jaxiGHA6HrFarLBbLedfjHFsAqLoIrwC8przn2BqGIUuORWFhYW6FVwBA1cVNCgAAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApuHX4fW3337TmDFj1KVLFyUkJGjhwoWux3bt2qURI0YoJiZGw4YN086dO33XKAAAALzCr8PrAw88oNDQUC1fvlyPPPKIXnrpJX3++eey2+1KTExU165dtXz5csXGxmrMmDGy2+2+bhkAAAAe5Lfh9dixY9q2bZvuuecetWjRQgMGDFCfPn20YcMGffLJJwoODtbEiRN18cUX69FHH1WtWrW0Zs0aX7cNAAAADwr0dQNlCQkJkdVq1fLlyzVhwgRlZmZq69ateuCBB7R9+3bFxcXJYrFIkiwWi7p06aJt27Zp6NChZW7T6XTK6XR6pN+i7Xpq+2bGbMpWNJPCwkJJkmEYMgzD43UNGUV/8M96xv/+63ru+dT7u4Ynf+Z9hZ+nsjGbsjGbsjGb0vliLuWt5bfhNTg4WNOmTdP06dP17rvvyul0aujQoRoxYoS+/PJLtW7dutj64eHhSktLO+s29+7d68mWJUkpKSker2FWzKZs6enpkiSHwyFLjsXj9XJzc0/Vy3MoJyfHb+vlnHCvN4fDIenUz3yNGn77BlOF8PNUNmZTNmZTNmZTOn+ci9+GV0nat2+f+vfvr9tvv11paWmaPn26evbsKYfDoaCgoGLrBgUFKT8//6zbi4qKUmhoqEd6dTqdSklJUXR0tAICAjxSw6yYTdmKZlP0jzGr1aqwsDCP1z0RcuJUvWA/rWecCq5htcIkN7K8YT115DUqKkqdO3c+/w34MX6eysZsysZsysZsSueLudjt9nIdaPTb8LphwwYtXbpU3377rUJCQhQdHa1Dhw5p/vz5uuiii0oE1fz8fIWEhJx1mwEBAR7/C/BGDbNiNmUrOjposVhcp8N4kqUoEVrkl/Vcpwq42V/Rc6rya64q71tFMZuyMZuyMZvSeXMu5a3jt++n7dy5U82bNy8WSNu3b6+srCw1atRINput2Po2m02RkZHebhMAAABe5LdHXiMjI/Xzzz8rPz/fdYrA/v37deGFFyomJkZvvvmmDMOQxWKRYRjaunWr7r77bh93jaokIyOjxD+SKpvT6dTevXtdF2wBAICz89vwmpCQoNmzZ2vq1Km65557dODAAb3++uv65z//qYEDB+qFF17QjBkz9I9//EOLFy+Ww+HQVVdd5eu2UUVkZGSoTds2ynXkerXuuc7bBgCguvPb8Fq7dm0tXLhQM2bM0PDhw9WgQQPdc889uvHGG2WxWPTGG2/o8ccf13/+8x+1adNGSUlJHrsYC9WPzWZTriNX7RLbKbSJ515XhmHI4XDIke7QweUHVVBQ4LFaAABUBX4bXiWpdevWWrBgQamPderUSStWrPByR6huQpuEqnaL2h7bvmEYsuRYZBz1/GetAgBQFfjtBVsAAADAmQivAAAAMA3CKwAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEwj0NcNAICnpKameq1WRESEmjVr5rV6AFBdEV4BVDn5R/MlizRq1Civ1QyxhmjP7j0EWADwMMIrgCqnwF4gGVKL0S0U3irc4/XsWXalJqXKZrMRXgHAwwivAKosa2Orareo7es2AACViAu2AAAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACmQXgFAACAaRBeAQAAYBqEVwAAAJgG4RUAAACm4dfhNT8/X08++aS6deumSy+9VHPmzJFhGJKkXbt2acSIEYqJidGwYcO0c+dOH3cLAAAAT/Pr8Pr0009r/fr1evvtt/XCCy/oP//5jz744APZ7XYlJiaqa9euWr58uWJjYzVmzBjZ7XZftwwAAAAPCvR1A2U5evSoli1bpgULFqhTp06SpDvuuEPbt29XYGCggoODNXHiRFksFj366KP67rvvtGbNGg0dOtTHnQMAAMBT/Da8JicnKywsTPHx8a5liYmJkqTHHntMcXFxslgskiSLxaIuXbpo27ZtZw2vTqdTTqfTI/0WbddT2zczM86mqFfDMFynqnjEmZs25Nl6rjKGf9cz/vdfo8SQPFCvgopqePJ3TBEz/jx5C7MpG7MpG7MpnS/mUt5afhteMzMz1bRpU61cuVKvv/66Tp48qaFDh+qee+5Rdna2WrduXWz98PBwpaWlnXWbe/fu9WTLkqSUlBSP1zArM82m6LXicDhkybF4vF5ubu6penkO5eTkUO9vOSfc683b++dwOCSdet3UqOGds7HM9PPkbcymbMymbMymdP44F78Nr3a7XT///LMWL16smTNnKjs7W9OmTZPVapXD4VBQUFCx9YOCgpSfn3/WbUZFRSk0NNQj/TqdTqWkpCg6OloBAQEeqWFWZpxNYWGhJMlqtSosLMxzhYxTAS0kJORUvWAP1/vbiZAT/l3v77mE1QqT3Pi3g7f3z7CeOvIaFRWlzp07e7SWGX+evIXZlI3ZlI3ZlM4Xc7Hb7eU60Oi34TUwMFA5OTl64YUX1LRpU0lSVlaW3n//fTVv3rxEUM3Pz3cFgLIEBAR4/C/AGzXMykyzKerTYrG4Tk/xhBJviVvk0Xr/K2Px63quubjZn9f37+8a3nyNm+nnyduYTdmYTdmYTem8/XutPPz20wYaNmyo4OBgV3CVpJYtW+q3335To0aNZLPZiq1vs9kUGRnp7TYBAADgRX4bXmNiYpSXl6cDBw64lu3fv19NmzZVTEyMfvzxR9dFEoZhaOvWrYqJifFVuwAAAPACvw2vrVq1Ur9+/TRlyhTt3r1ba9euVVJSkm666SYNHDhQx48f14wZM5Senq4ZM2bI4XDoqquu8nXbAAAA8CC/Da+S9Pzzz6tZs2a66aabNGnSJN1888265ZZbFBYWpjfeeEPJyckaOnSotm/frqSkJI9djAUAAAD/4LcXbElS7dq19dxzz5X6WKdOnbRixQovdwQAAABf8usjrwAAAMDp3AqvGzdu9MpdawAAAIDTuXXawP3336+aNWtq4MCBGjx4sMc/lBsAAACQ3Ayv69at07p167RmzRolJiYqLCxMV111lQYNGqT27dtXdo8AAACAJDfDa2BgoPr27au+ffuqoKBA69ev11dffaWRI0eqUaNGGjJkiIYOHaomTZpUdr8AAACoxip0wVZ+fr6+/fZbrV69Wp9++qnq16+vhIQEHTx4UIMGDdKiRYsqq08AAADAvSOvX3zxhdasWaNvvvlGNWvW1JVXXql58+apa9eurnXee+89zZkzR6NGjaq0ZgEAAFC9uRVeJ02apAEDBmjOnDnq1auXAgICSqzTsWNH3X777RVuEAAAACjiVnhdv369cnJydPz4cVdw/eSTT9StWzc1bNhQkhQTE6OYmJjK6xQAAADVnlvnvG7dulWXX365Vq1a5Vr27rvv6uqrr1ZycnKlNQcAAACczq3wOmvWLN1999267777XMsWL16sO++8U88880ylNQcAAACczq3wevDgQQ0cOLDE8quuukrp6ekVbgoAAAAojVvhtVWrVvr0009LLP/qq6/UrFmzCjcFAAAAlMatC7YeeOABjR07VuvWrVOHDh0kSXv27NGWLVv06quvVmqDAAAAQBG3jrxedtllWrFihdq3b6/9+/crIyNDbdu21erVq9W3b9/K7hEAAACQ5OaRV0m65JJLNHny5MrsBQAAADgrt8Lr8ePH9c477yglJUUFBQUyDKPY4++++26lNAcAAACczq3wOnHiRKWkpGjIkCEKCwur7J4AAACAUrl9h61FixapU6dOld0PAAAAUCa3Lthq1KiRatRw66kAAACA29w+beCJJ57Qfffdp+bNm6tmzZrFHm/SpEmlNAcAAACczq3weu+990qSEhMTJUkWi0WSZBiGLBaLUlNTK6k9AAAA4H/cCq9ffvllZfcBAAAAnJNbJ642bdpUTZs2ld1u165du1S/fn0VFhaqSZMmatq0aWX3CAAAAEhy88jrsWPHdP/992vz5s2SpM8++0wzZsxQZmamkpKSCLAAAADwCLeOvD799NOyWq3auHGjgoODJUnPPPOMGjdurKeffrpSGwQAAACKuBVe165dqwcffFB16tRxLWvQoIGmTJmiH374odKaAwAAAE7n9oe15uXllVj2559/KjDQrTMRAAAAgHNyK7wOHjxYM2bMUFpamiwWi+x2uzZu3KjHHntMV199dWX3CAAAAEiqwE0K5syZo6FDh+rkyZO69tprFRAQoBEjRmjixImV3SMAAAAgyc3wGhQUpMmTJ+uBBx5QZmamnE6nLrroItWqVauy+wMAAABc3AqvpV2UtWvXLtefu3Xr5n5HAAAAQBncCq+33HJLqcuDgoLUsGFD7sAFAAAAj3ArvO7evbvY906nUxkZGZo+fbqGDBlSKY0BAAAAZ3L7o7JOFxAQoJYtW2ry5Ml6+eWXK2OTAAAAQAmVEl6LHD58WMePH6/MTQIAAAAubp02MGXKlBLLTpw4ofXr12vgwIEVbgoAAAAoTaXdDqtevXqaNGmSrr322sraJAAAAFCMW+F15syZld0HAAAAcE5uhde5c+eWe93x48e7UwIAAAAowa3w+vPPP2vNmjWqV6+eOnbsqKCgIO3evVsZGRnq3LmzAgNPbdZisVRqswAAAKje3L497JAhQ/Tkk0+qZs2aruWzZs3SsWPH9Mwzz1RagwAAAEARtz4q65NPPtGdd95ZLLhK0g033KBPPvmkUhoDAAAAzuRWeG3UqJHWrl1bYvlnn32miy66qMJNAQAAAKVx67SBCRMm6IEHHtA333yjtm3bSpJSUlK0a9cuvf7665XaIAAAAFDErSOvl19+uZYvX66oqCjt27dPv/76q+Lj4/XZZ58pPj6+snsEAAAAJFXgJgVt2rTRlClTdOzYMYWFhalGjRp8ugAAAAA8yq0jr4ZhaP78+erevbt69uyprKwsPfzww5o2bZry8/Mru0cAAABAkpvhdd68efroo4/07LPPKigoSJJ0/fXXa926dXruuecqtUEAAACgiFvhdcWKFXrqqafUv39/16kCvXr10qxZs/Tpp59WaoMAAABAEbfC6+HDhxUZGVlieZ06dWS32yvcFAAAAFAat8Jrjx499PbbbxdblpOTozlz5qh79+6V0hgAAABwJrfC6xNPPKFdu3apV69eysvL09ixY9W3b1/9+uuvmjp1amX3CAAAAEhy86Oy6tSpo6VLl2rDhg3av3+/CgoK1LJlS/Xu3Vs1ariVhwEAAIBzciu8Dh48WHPnzlXPnj3Vs2fPyu4JAAAAKJVbh0lr1KihkydPVnYvAAAAwFm5deS1X79+uv3229W/f381bdrU9VmvRcaPH18pzQEAAACncyu87tmzRx06dNAff/yhP/74o9hj3CIWAAAAnlLu8HrzzTdr/vz5qlOnjv79739LknJzcxUSEuKx5gAAAIDTlTu8JicnlzjP9dJLL9WHH36oiy66qNIbAwCzSU1N9XgNp9OpvXv3qrCwUI0aNVKzZs08XhMA/Ilbpw0UMQyjsvoAANPKP5ovWaRRo0Z5tW6INUR7du8hwAKoVioUXgEAUoG9QDKkFqNbKLxVuEdrGYYhh8Mh46ih3Um7ZbPZCK8AqhXCKwBUEmtjq2q3qO3RGoZhyJJjkWHlnS8A1dN5hddPP/1UYWFhru8LCwv1+eefq0GDBsXWu+666yqlOQAAAOB05Q6vTZo00TvvvFNsWXh4uBYtWlRsmcViIbwCAADAI8odXr/66itP9gEAAACck1u3hwUAAAB8gfAKAAAA0yC8AgAAwDQIrwAAADANwisAAABMg/AKAAAA0yC8AgAAwDQIrwAAADANwisAAABMg/AKAAAA0yC8AgAAwDRME14TExM1efJk1/e7du3SiBEjFBMTo2HDhmnnzp0+7A4AAADeYIrwunr1an377beu7+12uxITE9W1a1ctX75csbGxGjNmjOx2uw+7BAAAgKf5fXg9evSonnvuOUVHR7uWffLJJwoODtbEiRN18cUX69FHH1WtWrW0Zs0aH3YKAAAAT/P78Dpr1ixde+21at26tWvZ9u3bFRcXJ4vFIkmyWCzq0qWLtm3b5qMuAQAA4A2Bvm7gbDZs2KAtW7Zo1apVeuKJJ1zLs7Ozi4VZSQoPD1daWtpZt+d0OuV0Oj3Rqmu7Rf/NyMiQzWbzSK3SREREqFmzZl6rdz7OnI0ZFPVqGIYMw/BcoTM3bciz9VxlDP+uZ/zvv0aJIXmgXgV5td4Zm/fk7zWzMePvGm9hNmVjNqXzxVzKW8tvw2teXp4ef/xxTZs2TSEhIcUeczgcCgoKKrYsKChI+fn5Z93m3r17K73PM6WkpOj333/XsOHDlJeb5/F6RYJDgrVs6TI1btzYazXPV0pKiq9bKLei14rD4ZAlx+Lxerm5uafq5TmUk5NDvb/lnHCvN7PsX0U4HA5Jp16rNWr4/ZtoXmWm3zXexmzKxmxK549z8dvwOnfuXHXs2FF9+vQp8VhwcHCJoJqfn18i5J4pKipKoaGhldpnEafTqZSUFEVHR6uwsFB5uXlqm9hWoRd4pt7p7L/ZtTtptyIjI9W5c2eP1ztfp88mICDA1+2US2FhoSTJarUqLCzMc4WMUwGt6LVrDfZwvb+dCDnh3/X+nktYrTDJjX87+P3+VcTfs7FarZJO/V7zx597XzDj7xpvYTZlYzal88Vc7HZ7uQ40+m14Xb16tWw2m2JjYyXJFVY/++wzDR48uMRb8jabTZGRkWfdZkBAgMf/Ak6vUatJLdVuUduj9SS5zv31xv5VhL/3d7qiPi0Wi2u+nlDiLXGLPFrvf2Usfl3PNRc3+/P3/auIM18zZvq58hZmUjZmUzZmUzpvzqW8dfw2vP773/9WQUGB6/vnn39ekvTQQw/phx9+0JtvvinDMGSxWGQYhrZu3aq7777bV+0CAADAC/w2vDZt2rTY97Vq1ZIkNW/eXOHh4XrhhRc0Y8YM/eMf/9DixYvlcDh01VVX+aJVAAAAeIkpz/IPCwvTG2+8oeTkZA0dOlTbt29XUlKSx85nBQAAgH/w2yOvZ3r22WeLfd+pUyetWLHCR90AAADAF0x55BUAAADVE+EVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAAphHo6wYAAO5LTU31Wq28vDwFBwd7rV5ERISaNWvmtXoAzIHwCgAmlH8sX7JIo0aN8l5RiyTDe+VCrCHas3sPARZAMYRXADChAnuBZEgtRrdQeKtwj9c7vOOwDi4/6LV69iy7UpNSZbPZCK8AiiG8AoCJWRtbVbtFbY/XsWfZvVoPAMrCBVsAAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATCPQ1w0A5ZGRkSGbzea1eqmpqV6rBQAAyo/wCr+XkZGhNm3bKNeR6/Xa+fn5Xq8JAADKRniF37PZbMp15KpdYjuFNgn1Ss3DOw7r4PKDKigo8Eo9AABQPoRXmEZok1DVblHbK7XsWXav1AEAAOeHC7YAAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGnzaAADAb53PDUOcTqf27t2rwsJCBQQEnHetiIgINWvW7LyfB8C7CK8AAL+TfzRfskijRo3yWs0Qa4j27N5DgAX8HOEVAOB3CuwFkiG1GN1C4a3Cy/UcwzDkcDhktVplsVjOq549y67UpFTZbDbCK+DnCK8AAL9lbWwt981JDMOQJceisLCw8w6vAMyDC7YAAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGoRXAAAAmAbhFQAAAKZBeAUAAIBpEF4BAABgGn4dXg8dOqT77rtP8fHx6tOnj2bOnKm8vDxJUmZmpm677TZ17txZV199tb7//nsfdwsAAABP89vwahiG7rvvPjkcDr333nt68cUX9fXXX+ull16SYRgaN26cIiIitGzZMl177bUaP368srKyfN02AAAAPCjQ1w2UZf/+/dq2bZvWrVuniIgISdJ9992nWbNm6bLLLlNmZqYWL16s0NBQXXzxxdqwYYOWLVume++918edAwAAwFP89shrw4YN9dZbb7mCa5GcnBxt375d7du3V2hoqGt5XFyctm3b5uUuAQAA4E1+e+S1Tp066tOnj+v7wsJCLVq0SD169FB2drYiIyOLrR8eHq7ff//9rNt0Op1yOp0e6bdou6fXMAxDhmF4pN7pimp4cv8q4vTZVOT53pqnJBkyiv7g2ZpnbtrT9VxlvLR/7tYz/vdfo8SQPFCvgrxaj9fM2Z70v+ec5+vG33+PVlRFfw9XZcymdL6YS3lr+W14PdPs2bO1a9cuLV26VAsXLlRQUFCxx4OCgpSfn3/Wbezdu9eTLUqSUlJSXHUcDocsORaP13Q4HJJO7V+NGn57MF0pKSluPc/b85Sk3NzcUzXzHMrJyaGej+rlnHCvN7Psn5lqmqmeO68bs/werSh3fw9XB8ymdP44F1OE19mzZ+tf//qXXnzxRUVFRSk4OFhHjx4ttk5+fr5CQkLOup2oqKhipxpUJqfTqZSUFEVHR6uwsFCSZLVaFRYW5pF6pzOsp44YREVFqXPnzh6vd75On01AQMB5P9/b85SkEyEnTtUM9nBN49T/aIteux6v9zev7Z+79f6eS1itMMmNf6/4/f5VBK+ZslXgdePvv0crqqK/h6syZlM6X8zFbreX60Cj34fX6dOn6/3339fs2bN15ZVXSpIaNWqk9PT0YuvZbLYSpxKcKSAgwON/AafXsFgsslg8f6SwqIY39q8i3O3P2/OUJEvR//ks8mjNEm9terje/8p4Z//creeai5v9+fv+VQSvmbJV5HVjlt+jFVXV968imE3pvDmX8tbx6/dG5s6dq8WLF2vOnDkaNGiQa3lMTIx++ukn19tKkpScnKyYmBhftAkAAAAv8dvwum/fPr322mu66667FBcXp+zsbNdXfHy8LrjgAk2ZMkVpaWlKSkrSjh07NHz4cF+3DQAAAA/y29MGvvzySzmdTs2fP1/z588v9tiePXv02muv6dFHH9XQoUPVvHlzzZs3T02aNPFRtwAAAPAGvw2viYmJSkxMLPPx5s2ba9GiRV7sCAAAAL7mt6cNAAAAAGcivAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0/Pb2sAAAeFtqaqrXakVERKhZs2ZeqwdUFYRXAEC1l380X7JIo0aN8lrNEGuI9uzeQ4AFzhPhFQBQ7RXYCyRDajG6hcJbhXu8nj3LrtSkVNlsNsIrcJ4IrwAA/M3a2KraLWr7ug0AZ0F4hVsyMjJks9nKta7T6dTevXtVWFiogICA867lzXPQAACAfyO84rxlZGSoTds2ynXkerVufn6+V+sBAAD/Q3jFebPZbMp15KpdYjuFNgk95/qGYcjhcMhqtcpisZx3vcM7Duvg8oMqKChwp10AAFCFEF7httAmoeU6N8wwDFlyLAoLC3MrvNqz7O60BwAAqiBuUgAAAADT4MhrFeKtC5u4gAoAAPgK4bUK8MWHa0tcQAUAALyP8FoFePvDtbmACgAA+ArhtQrx1odrcwEVAADwFcIrAAA+4q1rCIpuFtOgQQO1bNnSKzUBTyG8AgDgZb66ViHEGqI9u/eoWbNmXq0LVCbCKwAAXubtaxUMw9CfB/7UwXcPymazEV5haoRXAAB8xFvXKhTd6RCoCrhJAQAAAEyD8AoAAADTILwCAADANAivAAAAMA3CKwAAAEyDTxsAAKAa8daNESQpIiKCj+VCpSO8AgBQDZw8ftLrN0bgpgjwBMIrAADVgLdvjGDPsis1KZWbIqDSEV4BAKhGvHVjBMBTuGALAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAagb5uAAAAoDJkZGTIZrOd13OcTqf27t2rwsJCBQQEnNdzIyIi1KxZs/N6DiqO8AoAAEwvIyNDbdq2Ua4j12s1Q6wh2rN7DwHWywivAADA9Gw2m3IduWqX2E6hTULL/TzDMORwOGS1WmWxWMr9PHuWXalJqbLZbIRXLyO8AgCAKiO0Sahqt6hd7vUNw5Alx6KwsLDzCq/wHS7YAgAAgGlw5BUAAHhMampqlaoD3yO8AgCASpd/NF+ySKNGjfJu3fx8r9aD9xFeAQBApSuwF0iG1GJ0C4W3Cvd4vcM7Duvg8oMqKCjweC34FuEVAAB4jLWx9bwuoHKXPcvu8RrwD1ywBQAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANPgDlsAAABuSk1N9VqtvLw8BQcHe6WW0+nUH3/84ZVa58vU4TUvL09PPvmk/vvf/yokJER33HGH7rjjDl+3BQAAqrj8o/mSRRo1apT3ilokGd4rFxwSrNRdqWrZsqX3ipaDqcPrc889p507d+pf//qXsrKyNGnSJDVp0kQDBw70dWsAAKAKK7AXSIbUYnQLhbcK93i9wzsO6+Dyg16rdyLrhHYn7ZbNZiO8Vha73a4lS5bozTffVIcOHdShQwelpaXpvffeI7wCAACvsDa2qnaL2h6vY8+ye7WeYXjxEO95Mu0FW7t371ZBQYFiY2Ndy+Li4rR9+3YVFhb6sDMAAAB4immPvGZnZ6t+/foKCgpyLYuIiFBeXp6OHj2qBg0auJYXhdkTJ07I6XR6pJ+iGjk5OXI6nWrTpo0a5jVUyJ8hHql3OkuARYVtCtW4oLHq/lnXL+tZc60KOeneLLy9f96uac21qk5AHRltDL/+O/R2PTO9Zrxdj9dM2dx93Zhl/yrCGeRUcJvgKruPFannzuvGTPvnjpD8ELVp00aFhYX666+/PF5PknJzcyXpnAchLYY/Hxc+i5UrV+rll1/W119/7VqWmZmpAQMG6Ntvv1Xjxo1dyw8fPqyDBw/6oEsAAACcjxYtWig8vOzzek175DU4OFj5+fnFlhV9HxJS/F9PdevWVYsWLRQcHKwaNUx7pgQAAECVVVhYqLy8PNWte/Yjy6YNr40aNdKRI0dUUFCgwMBTu5Gdna2QkBDVqVOn2LqBgYFnTfAAAADwvbCwsHOuY9rDkO3atVNgYKC2bdvmWpacnKzo6GiOrgIAAFRRpk15VqtV1113nZ544gnt2LFDX3zxhd555x2NHj3a160BAADAQ0wbXiVpypQp6tChg2699VY9+eSTuvfee3XFFVd4vY+8vDw98sgj6tq1q3r37q133nnH6z34m/z8fA0ePFibNm1yLcvMzNRtt92mzp076+qrr9b333/vww6969ChQ7rvvvsUHx+vPn36aObMmcrLy5NUveciST///LP+7//9v4qNjVW/fv301ltvuR6r7rM5XWJioiZPnuz6fteuXRoxYoRiYmI0bNgw7dy504fded/nn3+uNm3aFPu67777JDGb/Px8Pfnkk+rWrZsuvfRSzZkzx/WZndV5NsuXLy/xmmnTpo3atm0rqXrP5rffftOYMWPUpUsXJSQkaOHCha7H/HIuBirsqaeeMoYMGWLs3LnT+O9//2vExsYan376qa/b8pnc3Fxj3LhxRlRUlLFx40bDMAyjsLDQGDJkiDFhwgQjPT3deP31142YmBjj119/9XG3nldYWGjccMMNxp133mns3bvX+OGHH4zLL7/cePbZZ6v1XAzDMJxOp3HFFVcYEyZMMA4cOGB88803RpcuXYyPPvqo2s/mdB9//LERFRVlTJo0yTAMwzhx4oTRq1cv49lnnzXS09ON6dOnG5deeqlx4sQJH3fqPa+99poxZswY448//nB9HTt2jNkYhvHYY48ZV1xxhbF9+3Zj/fr1Rvfu3Y3333+/2s/G4XAUe71kZWUZl19+uTFjxoxqP5sbbrjBeOCBB4wDBw4Yn3/+uRETE2P897//9du5EF4r6MSJE0Z0dLQrpBmGYcybN88YNWqUD7vynbS0NOOaa64xhgwZUiy8rl+/3ujcuXOxF/ytt95qvPLKK75q1WvS09ONqKgoIzs727Vs1apVRu/evav1XAzDMA4dOmTcf//9xl9//eVaNm7cOOPxxx+v9rMpcuTIEeOyyy4zhg0b5gqvS5YsMRISEozCwkLDME79A+nyyy83li1b5stWvWrChAnGCy+8UGJ5dZ/NkSNHjPbt2xubNm1yLXvjjTeMyZMnV/vZnOn11183BgwYYOTl5VXr2Rw9etSIiooy9uzZ41o2fvx448knn/TbuZj6tAF/wJ2+itu8ebO6d++uDz74oNjy7du3q3379goNDXUti4uLK3bBXVXVsGFDvfXWW4qIiCi2PCcnp1rPRZIiIyP10ksvKSwsTIZhKDk5WT/88IPi4+Or/WyKzJo1S9dee61at27tWrZ9+3bFxcXJYrFIkiwWi7p06VKtZrNv3z61aNGixPLqPpvk5GSFhYUpPj7etSwxMVEzZ86s9rM53dGjR/Xmm29qwoQJCgoKqtazCQkJkdVq1fLly3Xy5Ent379fW7duVbt27fx2LoTXCjrXnb6qm5EjR+qRRx6R1Wottjw7O1uRkZHFloWHh+v333/3Zns+UadOHfXp08f1fWFhoRYtWqQePXpU67mcKSEhQSNHjlRsbKyuvPJKZiNpw4YN2rJli8aOHVtseXWfjWEYOnDggL7//ntdeeWVGjBggJ5//nnl5+dX+9lkZmaqadOmWrlypQYOHKj/83/+j+bNm6fCwsJqP5vTvf/++4qMjNTAgQMlVe+fqeDgYE2bNk0ffPCBYmJidNVVV+myyy7TiBEj/HYupv2cV3/hcDiKBVdJru/PvIlCdVbWnKrjjGbPnq1du3Zp6dKlWrhwIXP52yuvvCKbzaYnnnhCM2fOrPavmby8PD3++OOaNm1aiRuvVPfZZGVluWbw0ksv6ZdfftHTTz+t3Nzcaj8bu92un3/+WYsXL9bMmTOVnZ2tadOmyWq1VvvZFDEMQ0uWLNGdd97pWlbdZ7Nv3z71799ft99+u9LS0jR9+nT17NnTb+dCeK2g87nTV3UWHBxc4kh0fn5+tZvR7Nmz9a9//UsvvviioqKimMtpoqOjJZ0KbQ899JCGDRsmh8NRbJ3qNJu5c+eqY8eOxY7aFynr9051mU3Tpk21adMm1a1bVxaLRe3atVNhYaEefvhhxcfHV+vZBAYGKicnRy+88IKaNm0q6VTYf//999W8efNqPZsiKSkpOnTokAYNGuRaVp1/pjZs2KClS5fq22+/VUhIiKKjo3Xo0CHNnz9fF110kV/OhdMGKuj0O30VKetOX9VZo0aNZLPZii2z2Wwl3o6oyqZPn64FCxZo9uzZuvLKKyUxF5vNpi+++KLYstatW+vkyZNq2LBhtZ7N6tWr9cUXXyg2NlaxsbFatWqVVq1apdjY2Gr/upGkevXquc7Dk6SLL75YeXl51f5107BhQwUHB7uCqyS1bNlSv/32G6+bv61du1Zdu3YtdgvS6jybnTt3qnnz5sUCafv27ZWVleW3cyG8VhB3+iqfmJgY/fTTT8rNzXUtS05OVkxMjA+78p65c+dq8eLFmjNnTrF/7Vf3ufzyyy8aP368Dh065Fq2c+dONWjQQHFxcdV6Nv/+97+1atUqrVy5UitXrlRCQoISEhK0cuVKxcTE6Mcff3R9dqdhGNq6dWu1mc3atWvVvXv3YkfmU1NTVa9ePcXFxVXr2cTExCgvL08HDhxwLdu/f7+aNm1a7V83RXbs2KEuXboUW1adZxMZGamff/652BHW/fv368ILL/TbuZCuKog7fZVPfHy8LrjgAk2ZMkVpaWlKSkrSjh07NHz4cF+35nH79u3Ta6+9prvuuktxcXHKzs52fVXnuUinThXo0KGDHnnkEaWnp+vbb7/V7Nmzdffdd1f72TRt2lTNmzd3fdWqVUu1atVS8+bNNXDgQB0/flwzZsxQenq6ZsyYIYfDoauuusrXbXtFbGysgoODNXXqVO3fv1/ffvutnnvuOd15553VfjatWrVSv379NGXKFO3evVtr165VUlKSbrrppmo/myJpaWnFPr1DUrWeTUJCgmrWrKmpU6fqwIED+uqrr/T666/rlltu8d+5+OQDuqoYu91uTJw40ejcubPRu3dvY8GCBb5uyS+c/jmvhmEYBw8eNG6++WajY8eOxqBBg4x169b5sDvveeONN4yoqKhSvwyj+s6lyO+//26MGzfO6NKli9GrVy9j/vz5rs8UrO6zOd2kSZNcn/NqGIaxfft247rrrjOio6ON4cOHGz/99JMPu/O+vXv3GrfddpvRuXNno1evXsarr77qet1U99kcP37cePjhh43OnTsbPXv2ZDZniI6ONr777rsSy6vzbNLS0ozbbrvN6NKlizFgwABjwYIFfv2asRjG38eCAQAAAD/HaQMAAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8AAAAwDcIrAAAATIPwCgAAANMgvAIAAMA0CK8A4IeWL1+uNm3aaMmSJb5uBQD8CuEVAPzQ6tWr1axZM3344Ye+bgUA/ArhFQD8zOHDh7VhwwaNGzdOW7ZsUWZmpq9bAgC/QXgFAD+zZs0a1a5dW9dcc40iIyOLHX3Nzc3Vo48+qri4OPXp00dLlixR+/bt9csvv0iSfvvtN919992KiYlRQkKC5s6dK6fT6atdAYBKF+jrBgAAxa1evVr9+vVTjRo1lJCQoJUrV2rcuHGyWCx6+umn9eOPP+rtt99WQUGBHn30UVc4NQxD48ePV9u2bbVixQplZ2dr2rRpslgsGjdunI/3CgAqB0deAcCP/Pbbb9q6dasGDBggSbriiiuUmZmp5ORknThxQitXrtRjjz2mzp07q2vXrpo6darruRs3blRWVpamT5+uVq1aqXv37po0aZLeffddX+0OAFQ6jrwCgB9ZvXq1goOD1bt3b0lSfHy86tatqxUrVig4OFgnT55UdHS0a/3Y2FjXn/ft26ejR48qLi7OtaywsFC5ubk6cuSI6tev770dAQAPIbwCgB9ZvXq1cnNziwVQp9OpNWvWaPjw4SXWNwzD9eeCggK1atVKr732Won1ateu7ZmGAcDLCK8A4CcOHDigXbt2aerUqerevbtreXp6uv75z3/q559/Vs2aNbVz50716NFDkrRz507Xei1btlRWVpYaNGjgCqvr1q3T8uXL9dxzz3l3ZwDAQzjnFQD8xOrVq1WvXj3deOONioqKcn1dffXVat26tVatWqWhQ4dqxowZ2r59u7Zt26YZM2ZIkiwWi3r37q2mTZvq4Ycf1p49e7RlyxY99thjslqtCggI8PHeAUDlILwCgJ9YvXq1hgwZoqCgoBKP3XTTTVq/fr3GjBmjNm3a6LbbbtO9996rwYMHS5Jq1qypgIAAzZ8/X4WFhbrhhht07733qm/fvsUu6gIAs7MYp58wBQDwa1988YV69uypWrVqSZJ27NihkSNH6scff1TNmjV93B0AeB7nvAKAicydO1dff/21EhMTdeLECc2ePVsJCQkEVwDVBkdeAcBE0tPTNX36dO3YsUNBQUFKSEjQI488wqcJAKg2CK8AAAAwDS7YAgAAgGkQXgEAAGAahFcAAACYBuEVAAAApkF4BQAAgGkQXgEAAGAahFcAAACYBuEVAAAApvH/AYw8l4m0zwU5AAAAAElFTkSuQmCC","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Create the plot below\n","plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Age'], bins=20, color='green')\n","plt.title('Distribution of Age')\n","plt.xlabel('Age')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Fill Missing: Age Feature"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:50.370496Z","iopub.status.busy":"2024-04-01T06:27:50.369419Z","iopub.status.idle":"2024-04-01T06:27:50.427731Z","shell.execute_reply":"2024-04-01T06:27:50.426655Z","shell.execute_reply.started":"2024-04-01T06:27:50.370387Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
.......................................
85986003Razi, Mr. RaihedmaleNaN0026297.2292NaNC
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
\n","

177 rows × 12 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass Name \\\n","5 6 0 3 Moran, Mr. James \n","17 18 1 2 Williams, Mr. Charles Eugene \n","19 20 1 3 Masselmani, Mrs. Fatima \n","26 27 0 3 Emir, Mr. Farred Chehab \n","28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" \n",".. ... ... ... ... \n","859 860 0 3 Razi, Mr. Raihed \n","863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" \n","868 869 0 3 van Melkebeke, Mr. Philemon \n","878 879 0 3 Laleff, Mr. Kristo \n","888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n","\n"," Sex Age SibSp Parch Ticket Fare Cabin Embarked \n","5 male NaN 0 0 330877 8.4583 NaN Q \n","17 male NaN 0 0 244373 13.0000 NaN S \n","19 female NaN 0 0 2649 7.2250 NaN C \n","26 male NaN 0 0 2631 7.2250 NaN C \n","28 female NaN 0 0 330959 7.8792 NaN Q \n",".. ... ... ... ... ... ... ... ... \n","859 male NaN 0 0 2629 7.2292 NaN C \n","863 female NaN 8 2 CA. 2343 69.5500 NaN S \n","868 male NaN 0 0 345777 9.5000 NaN S \n","878 male NaN 0 0 349217 7.8958 NaN S \n","888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n","\n","[177 rows x 12 columns]"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### Try Checking for Null Values in Test Df"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 0\n","Pclass 0\n","Name 0\n","Sex 0\n","Age 86\n","SibSp 0\n","Parch 0\n","Ticket 0\n","Fare 1\n","Cabin 327\n","Embarked 0\n","dtype: int64"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["test_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Run this to fix the Null Values"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:21:48.194895Z","iopub.status.busy":"2024-04-01T07:21:48.194020Z","iopub.status.idle":"2024-04-01T07:21:49.449282Z","shell.execute_reply":"2024-04-01T07:21:49.447918Z","shell.execute_reply.started":"2024-04-01T07:21:48.194825Z"},"trusted":true},"outputs":[],"source":["index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = train_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," train_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," train_df[\"Age\"].iloc[i] = age_med\n","\n","index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = test_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," test_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," test_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"markdown","metadata":{},"source":["## Analysing the correlation between the different columns"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:24:33.644174Z","iopub.status.busy":"2024-04-01T07:24:33.643621Z","iopub.status.idle":"2024-04-01T07:24:34.404306Z","shell.execute_reply":"2024-04-01T07:24:34.402938Z","shell.execute_reply.started":"2024-04-01T07:24:33.643935Z"},"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":24,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAj8AAAHdCAYAAADyyBgjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAADVTUlEQVR4nOzdd3xN5x/A8U/GXRHZi4QUiYgQMqyKTez9q4pSjSqtVVXaihI1a7SlVKlWbW3N2rPaoiUSghgZRiKCJDLIvFm/PxJXbm6C1r2i7vN+vc7r5Z7znJPne5bvfZ7nnGtQVFRUhCAIgiAIgp4wrOwKCIIgCIIgPE8i+REEQRAEQa+I5EcQBEEQBL0ikh9BEARBEPSKSH4EQRAEQdArIvkRBEEQBEGviORHEARBEAS9IpIfQRAEQRD0ikh+BEEQBEHQKyL5EQRBEAThuVAqlfTo0YNTp05VWObSpUu89tprNGrUiP79+xMREaH1eojkRxAEQRAEncvNzWXChAlER0dXWCYrK4sRI0bg6+vLtm3b8PLyYuTIkWRlZWm1LiL5EQRBEARBp2JiYhgwYABxcXGPLbd3715kMhkfffQRderUYcqUKVSpUoX9+/drtT4i+REEQRAEQadCQkJo1qwZP//882PLnTt3Dh8fHwwMDAAwMDDA29ub8PBwrdbHWKtbEwRBEARBKGPQoEFPVS4pKQkXFxe1edbW1o/tKvs3RPJTCfKSr1V2FXRusfe0yq6CTmUbFFV2FXTOttCgsqugcwlGL/dxNNeDY6gP/4m9H7dep9vX5v9JEpvaz7R+dnY2UqlUbZ5UKkWpVD7TdsvSh/NGEARBEISKFBZUdg1UZDKZRqKjVCqRy+Va/Tsi+REEQRAEfVZUWNk1ULG3tyc5OVltXnJyMnZ2dlr9O2LAsyAIgiAIL4RGjRpx9uxZioqKu6SLioo4c+YMjRo10urfEcmPIAiCIOizwkLtTf9CUlISOTk5AHTp0oX79+8ze/ZsYmJimD17NtnZ2XTt2lWbEYvkRxAEQRD0WVFRodamf8PPz4+9e/cCYGpqyooVKwgLC6Nfv36cO3eO7777DhMTE22GLMb8CIIgCILw/ERGRj72s6enJ9u3b9dpHUTyIwiCIAj67F92V/2XieRHEARBEPTZC/S01/MixvwIgiAIgqBXRMuPIAiCIOizF+glh8+LSH4EQRAEQZ+Jbi9BEARBEISXm2j5EQRBEAR9Jp72EgRBEARBn/zblxP+l4nkRxAEQRD0mR62/IgxP4IgCIIg6BXR8iMIgiAI+kx0ewmCIAiCoFf08D0/ottLEARBEAS98o9aftq3b8+tW7cerWxsTI0aNRg4cCBvvfWWtuv2Qjp16hRvvvmmxq/QPu3yyqBUKhkwbBxBE0bR1NuzsqvzWHYeznSaE4hNvRrci7rFoaBV3L1wo8Ly3m93psnI7shMFUTuPsWRaWvJz1ECYCST0HHmUFy7NiE/N4/QFXsIXblPtW676UPwGdZZbXtHpq7h7JpDOokNwMHDmW6zh2FXrwZJUfHsCVrFnYgbFZZvOqwLLUriu7TnFPunrVHFZ+lsT9eZb1HDty7ZaRmcXnOQv1fsUa3rHzyEZsO6qG1v37TVhOowPiOZBL9ZQ6ndtQn5OXmc+24P57/bV25Zaw9nWs8NxKpeDVKjbvHn5FUklxxrA0MDmn40ALfXWmFsIuPm0fMcn7aG7OT7qvV9J/Sj/uAOGEqMuLYnhBPB6yjIzdNZbKU5eDjTa/Yw7NyKj+POKau4/Zjj2GJYF1qOKD6OEXtOsTd4DXklx9G9sy8BKz5QK39xbwg/j1qsWt5x0gDMqllz51Ise6ev5fbFiv/Wv2Xn4UzHMtde4hOuPd9S195vZa69DmWuvbCVmueBhbM9bx6ay9d1h6nmDT/xFeY1bDXKnvhiCycX73jmOB/Wr93MobiU1C9sxR7OllM/AFsPZ9rPCcS6Xg1Som7xWwX7pcmYXljUcuDQh9+p5imszWg3ayg1WzUkP0fJ5a3H+Gv+ZooKXrBuJj3s9vrHLT9BQUEcP36c48ePc/jwYUaOHMn8+fPZsWOHDqonPKvcXCWTgucRcz22sqvyRBKFjH5rJhEfEsm67lO5FRZNvx8nIlHIyi3v2rUJr47vx6HJq/hl4FyqebvQJmiganmboADsPWvxS8BcDk9ZTYvx/ajbrYlqubVrdf78/GeW+YxWTRd+/kOn8Q1cPYm405F83+NTboZFE/DjpArjq9e1CW3G92Pv5FWsC5iDk5cLHYMCihcaGBDw40SyUu6zslsQe6eswm9MHxr0flW1vq2rI0c+/4kvfUeppnAdxgfQfEoAtp612DVwLsc+XY3v+H7ULrXPHzJWyOi2ZhK3QyLZ2m0qd0Oj6bZ6IsYl+8JrdE9cejXn0KglbO8VjMyiCu0Xv6dav/Gonni82ZHDY75h75D5OLb0wGd8X53G9pBEIWPIj5OIDYlkeY9PiTsTzeDHHMf6XZrQdnw/dgat4sdBc6jh5YL/5ADVclsXR64cCmN+k1Gq6ddPVhYvc3Xkf4tH8+eynSzrOpk7l2IZ/ONEJHKpVmMyVsjou2YSt0IiWd99Kgkl157xY669FuP7cbjUtde6nGtvc8BcjpRce65lzoOq1azo++OHGrFs6DmNb31Gq6YjU9eQk57JxS3HtBavX1AAdp612Bowl6NTVtNsfD9cKjhPe5fsl00l+6VXOfulbq8WNJ/QX2P9Ll+/h7SqCb/0nc7eUUtw69UC33d7aC0OrSks1N70H/GPk5+qVatia2uLra0t1apVo2/fvrRo0YKDBw/qon7CM7h6PZZBIz7gZsLtyq7KU3Hr2Yz8HCV/zN5ESkwCR6evQ5mZQ93uTcst7z2sM2dWHeDakXDunL/GocmraPB6G4zlUiQKGQ0D2nJ0+noSI24QcyCU08t34zXUX7W+tYsjdy9cJyspXTU9/OaqC/V7Nic/J4/DszeSHJPAwc/WkZuZg3v3ZuWWbxrYhVOr9hP921lun7/Gnsk/0HhAcXymtubcuRTH3ik/knLjLjFHz3Hjr4vUaFJXtb6NiyN3Im6QmZSumnQZn7FChntAW/4KXk9yxA1u7A8lfPluGrzlr1G2Tq9mFOQoOTlrE2kxCZyYvo68zBzq9Cg+1gZGRpyYsYHbpyJJjU7gwo8HcSiJzcDQgEbvdOHvWZtI+OsSieHXCP1iK7aetXQWW2kNejQnLyePA3M2knw1gX2frUOZkYNHBcexeWAXTq7aT9RvZ0k4f42dQT/gNaCN6j99W5fqJEbFk5GUrppy7mcB4NK6IYlR8ZzbdpzUuEQOzf+ZqnaW2Lo6ajWmehVce25Pce3dPX+Nw6WuPWOFjAZPuPZc/H0YvGcm+UrNlrrslAdq12OL9/vwx6yNPLh1TyuxPqzfH9PXkxRxg6sHQglbvptGQzXP07ol++X47E2kxiTwZ8l56tr94XlqSLvZb9Fp4TukxyaqrWskNSYr6T5Hp6wmJTqBhJBIoveepnqpa1SoPFoZ82NsbIxEIiEjI4PJkyfTokULGjRoQJcuXTh8+LCq3N69e+ncuTMNGzakW7duasvWrl1Lu3btaNiwIf369SM0NFS1LCoqiiFDhuDp6Unnzp3ZsGGDatmSJUv48MMPCQ4OxtvbmxYtWrBy5UrV8sLCQhYuXEizZs1o1qwZy5Yto1OnTpw6dQqA+/fvM2nSJLy9vfHz82PmzJnk5OQAxV1Y7du3Jzg4GB8fH7777lFz5kMZGRlMmDABLy8vOnfuzIULF7SxS7XidPgFmnp7smHFl5VdladSzduFW6fVuwtvhUZR3cdVo6yBoQEOjWpz89QV1byEMzEYSYyxrV8T2/o1MTI24lZY1KNtnY7CwasOGBggNVVQtZoVqdfv6C6gMpy8XLhZJr740CicvF00yhoYGlC9UW3iQh7FF3+2OD77+jXJSExj25glKDOLz1Un37rUbFqP2L8vAyA1VWBWzYp7159f4mtdvyaGxkbcKbXP74REYVeyz0uz93Lhdpl9cSc0Cnvv4mMdtmg7N/YX3wPk1ma4B7TldklslnWdkFtV5caBR/eI6B1/seeNeTqJq6waXi7Elal7XFgUNSo4jo6NanPjlOZxdKhfEyhu3Umu4DzMSs3Arq4TNX3qYmBggNdrbci5n0VK7F0tRlTxtVetgmvPvlFt4iu49uxKrr2ECq49gFodGnPii60cnb7+sfXyHdGdjMQ0In7581nCU2Nbcp7eLlW/hDL1e8jB24WEMvslodR+kVSRY+Nek597BXP7TLRauQJlPgfGf0t6ybGyqutI7U7exJ+8rLVYtKaoUHvTf8QzPe2Vl5fH0aNHOXHiBHPmzGH27Nlcv36dVatWoVAo+P7775kyZQqtW7fmwYMHfPTRR8yYMYNmzZqxf/9+JkyYwJ9//klCQgLz589n6dKluLi4sHbtWsaPH8+ff/6JUqnknXfeoW/fvsycOZNr164xdepUqlSpQp8+fQA4cOAAgwYNYvv27Rw6dIgFCxbQsWNHatWqxYoVK9ixYwdffPEFVlZWTJ8+nZs3b6pimDJlCnl5eWzatInc3FxmzZrFjBkzmDNnDgC3bt1CqVSybds2JBKJ2roAwcHBXLt2jfXr15OSksInn3zyLLtUqwb2fQGbVx/D1M6C5KhbavOyktOxqVtDo6zMrAoSuZTMu6mqeUUFhWSnZlC1mhVFhUVkpzygMO/RUwyZyelI5FIUlqaY17SjqLCQ5mN7U6ttI7JTHxD2/X6tNq2XF19SVLzavMzkdGzdnDTKykvie1AmvqzUDMwcrCi9l8aeWIyFkw1Rh89weV8IADYu1SkqLMRvTB9cSuI7+f0+zm/VXXwmdhbklNnnWcnpGMulyC1NyUl5oFY2teyxTkrHyk39WPtO6IfvB/3ISctgR98ZAJg525GTlom9jyvNPh6A3Koq1/ae5uTcnyhU5ussvoeq2lmQGK1+HDOS07Gv+5jjmPjoOBaWnKdmDlYA2NSuhkvrhrQe1QtDI0Mu7j3Fb19uoSCvgIjdJ6nX0YfhW4MpyC+gqLCIDcMWqFqGtKWKnQX3dHjtZZW69rJTHnDo4x8AcGruXmGdjOVSvN7qxOGgVVBU9KwhqlSxsyi3fsal6le6bEo5+8W6ZL8o72exud+MJ/7N/r9Mwam5O3fPX+PcmsNPLP/c/Ye6q7TlHyc/wcHBzJw5E4CcnBzkcjlDhw6lV69e5OfnExgYSN26xc16w4YNY/Pmzdy7d4/U1FTy8vJwcHDA0dGRYcOG4ebmhkwm49atWxgYGFC9enWcnJwYP3487dq1o7CwkF27dmFtbc348eMBeOWVV7h16xZr165VJT8WFhZ8/PHHGBkZMXz4cFauXElERAS1atVi48aNjB8/Hj8/PwA+//xzunbtCkBcXByHDx8mJCSEqlWrAjBz5kz69OnD5MmTVTEPHz4cZ2dnALXk58GDB+zbt4+1a9fi4eEBwKhRo5gx48kXg6DJWCGjoEwzeEFuPkYyzdNUoijuMigo859dgTIPI6kEA4PylwEYSSVYu1SnqAhSYhI48+NBajSvR6e5w8h9kE1MqRYFbZIopBp1yi+pb3lli+usGYOxTL38lncXYWprQdfZgfhPG8KB6WuxqVMc372rCZxefQDn5u50n/s2uRnZROooPuNy4iu9z9XLlnOslfkYSdWPddS2E8QePkvjd3vQY8PH/NzhEyQmcowVUppNfp2/PtuAoZEhreYGYmBkyIlpa3UQmTqJQkp+2ThzH38c83PLP+7mjjZITeQUKPP5ZcwSLGvY0i34TYzlUvZ9tg4TC1NMbc3ZPXU1N89G03RwR/osGMnyHlPIvHdf4+/9W//m2tPYBw/P5Sdce0/LrWdz8rJyidp7+qnXeRr/5DyV/IP98jh/BK9DZl6FtjPepOvS0ex6+7/RGv8y+8fJz7hx4/D3L+4blclk2NraYmRkBECfPn04fPgwv/zyC9euXePixYsAFBQU4O7uTtu2bQkMDKRWrVp06NCB1157DYVCgZ+fH3Xr1qVnz57Ur19ftczY2Jhr165x5coVvLy8VHUoKChQ/U0AJycntc9VqlQhPz+flJQUEhMTadiwoWpZ7dq1MTc3B+Dq1asUFhbSunVrtRgLCwuJjX00QNjJSfMbHcD169cpKCigXr16qnml/5bweM1G96LZmF6qz7fDr2rcfIxkxuRla45Tyc99eLNSP4WNpBLys3MxMDIsdxlAfnYuF7cc4+qhM+SkZwKQfOUmlrWq0XhIB60lPy1H98JvdG/V51vhMRp1Mi6pb1mPiy+vTPnbF64Xb2umhD6LRnFo9gbObz1G1OFH8SVeuYlVLQd8BnfUWfJTnABUsM9zcsspW+ZYS401xiTdv1HcZfDbB8sZHPI1tbv6kp+bh0Qh40TwOm6fLO56+XvmRjouHc2J4HVabSUAaD2qF63KHEfjsnHKNI8LPDqOxjLN456XnUv6rWTmNhpBdslxunMpFgMDA/ovGsX+mevp9EkAdyNvErKu+Am9nZN/YOzh+Xi91prjy3f/65iaPuW1l/+Ya09jH5TEZPiEa+9p1e3WlMhdJ7X+ZFT+Y87TsscwP6ec87SC/fI4yZfjADg08TsCds+kqpMND+KT/2nVdaaoSP/e8/OPkx9ra2tVK0hZH330EWfPnqV3794EBARga2vL66+/DoCBgQErVqzg/PnzHDlyhEOHDrFx40Y2btyIu7s7mzdvJiQkhKNHj7Jt2zY2bdrEtm3byM/Pp0WLFkybNq3COkkkmt8mioqKMDY2Vv277DIoTqKqVq3K1q1bNda3t7fn3LlzQHGS97SkUu0+hfEyO7f+CJG7T6k+Nx3Vgyq25mplqthakJmYprFudmoGeTlKqthakHK1eFyLgZEhCktTMhLTMDAwQGFVFQMjQ9XNs4qtOXnZuaoug4eJwUMpMQnUbFlfa/GFrT/CpVLxvfpeT0xtLcrEZ86DcuLLKonP1NaCe6XiM7E05UFiGlVszHDydiXyYJhqnaToWxjLJMhMFWSnZmjElxyTQK1XPbQWX1mZd1KRl9nnJiX7PDc9q0zZFEzs1I+1ia0FWXfTAKjZoTH3LsaSeae4a6UgN48HcYnIraqSeO4aAGkxCap1067eLu62sK6q9ji8NpzecISIPY+Oo9+7msfR1NacB0lpGutmlzqOySXH0fDheVpSPrvMcUqKSSjuIrIwpXrDWpxcfUC1rKioiDuX47BwtHmmmM6vP0JUqXOzyagemJRz7WU85tozKefay3zKa+9JjKTG1GhRj5Bvd/3LCCuWeSe1wvrllqlfxt0Ujf1iUsE9qSypqQLndp5E7w5RJeQPu9AUVlVfqOTnvzRWR1u09pLDjIwMdu/ezVdffcW4cePo1KkT6enpQPEFe/XqVebNm4enpycffPABe/bsoVq1ahw7doyzZ8+yYsUKmjdvzuTJk9m/fz+5ubmEhYVRq1Ytrl+/jpOTE87Ozjg7OxMeHs66deueWCczMzPs7OxULVBQ3G11/37xzbFWrVo8ePAAAwMD1bZzcnKYP38+SuWTM/vatWsjkUjUBjlfunTpn+46vZWTnkla7F3VlBAWrTG4ubpvXW6fidFcuaiIO+eu4VjqyYnqPq4U5BeQdCmOxIuxFOQXUL3UIFTHJm7cOXcNiopoOaE/r21UH59l61GTlFL/oWojvtTYu6op/kw0TmXiq+Fbl1tny48v4dw1ajRxU81y8i6O7+6lOCxq2PHaivFUtbdULa/WoBaZyelkp2bQZkJ/3tgwWW2TDvWdSb6qvfjKuncxlsL8AuxL7XOHpm4klezz0u6ejcG+zL5waFKXuyX7osWng6jb30+1TFJFjnktB1KjE0iOiKUgNw/rkgHDAJYu1VE+yCYnNUPrcWWnZ5ISe1c13TwTTQ1v9brX9KlLfDnHsaioiFvnruHs++g41vB2pTC/gDuX4nBp3ZBPzi5Xe9zbob4zmSkPyEp5wIO7qdi5qD/ZZVOnGqk3k54pprLX3u1/eO3dfYZr72nY1KuBobExd8Kv/rsAHyOp5DytVqp+1Zu4cbec+t05E6Mx6Lu6b13ulLdfyjBWSOn2zdjigdQl7DxrUZhfQNq15/egxVMRj7r/e1KpFIVCwcGDB4mPj+fYsWOqsS9KpRIzMzM2bdrEsmXLuHnzJr///ju3bt2ifv36yOVyvvnmGzZv3kx8fDx79uwhKysLNzc3evXqRU5ODtOmTePq1av88ccfzJ49G2tr66eq15AhQ/j666/5+++/uXLlimosj4GBAXXq1KFVq1ZMnDiR8+fPc/HiRSZPnkxWVhZmZmZP3LapqSm9e/dm5syZnDt3jlOnTrF06dJ/vxP1XNTe08jNTGg3fQjWrtVpN30IEhOZqnXIWCZR+xYWvvYwTUZ2x8XfBwfP2nSa/RYXNh0lP0dJfo6Si1uO0WlOIA6etXHx96HJiG6cWVX8Lfrq4bM4NauH74humDvb0WhwBzz6+XH6u706i+/y3hDkZib4Bw/BxtUR/+Di+C6Viq90y1fYukO0GNEdN38fqnnWptvsQM6WxJdw7iq3L1yn54IR2Lg64tKuER2DAji+9NfifXn4LM7N6tF8RDcsa9rhM7gDnv38+Pu7PeXWTRvyc5REbj5G67mB2DaqzSudfWg0ohsXfije5wpbc4zkxa201/acRmZmQsvpQ7B0rU7L6UMwVsi4uqt4X1xcc4hG73anZrtGWNZ1pP3X75Eee5e4o+fIy8jm8qaj+M0Yip1XHey9XWgeNJDLP/3+XF4ed2lf8XHsGjwEWxdHugYPQWoiI6LUcTQtdRxD1h+i5Yju1PP3obpnbXrMCiRs01HycpTEhUWTl5NH73nvYF27Gq5tG9E5KIATK4q7tMJ+OopPQDsa9fXDytmeTh+/jrmjDeFaHrhe+tqz+ofXnr1nbTqWufYubTlGxzmB2Jdce76lrr2nYVPXifS4RI2xOdqQn6Pk8pZjtCupX21/H7xHdCO8pH4mtuYYlYyri9lbfJ62LtkvrUv2S+lWs4pkJaUTszeEtjOHYuvhTPWmbnScN5xzqw+izMjWelzCP6O13/aSSqUsWLCAefPmsW7dOpycnHjvvfdYtGgRly9fpkePHixZsoSFCxeyfPlyrK2tmTBhgmog8uzZs1m2bBkzZsygevXqLFiwgDp1ijPmlStXMmfOHPr06YOFhQVvvPEGI0eOfKp6DRs2jMTERMaOHYuRkREjRowgNDRU1VU2f/58Zs2axVtvvYWxsTGtWrXi008/feq4p06dysyZMwkMDMTc3JwhQ4Ywb97zeeT2ZaPMyGZb4Bd0mjMMz0HtSL4cx7ahC1T98G49m9P1y5EsrDkYgMhdJzGvYUunucMwkhoTve80f8z5SbW932dsoNOcQAb8HETugyxOfLWV6JLHp++cv8bOd7+m5Yf9aTnxf9y/mcSeccvK/6arxfh+GraQbnOG4T2oPYmX4/jprUfx1e/Zgt5fjGSm8xsAXNx1EnMnW7rNeRtjqTGX95/m8NxNABQVFvHz8C/pOvMtArdNJy87l5DVBwj5sfgGfvv8Nba89zVtJ/Sn7YevkR6fxPb3v+GWDuMD+HvGBlrNCaTXz0EoH2QR+uVWrpfs86FnvuHohBVEbj5GXkY2+wK/oNWcYbi/0Y57l+PYO3SBakxIxJrDGJvIaTUnELl1VeL/vMD+YV+qvpn/NWMDzYMC6LZ2EgYYELX9BKc+/1mnsT2Um5HNhrcX0nP2MHwD2nP3ShzrAh8dxwY9W9Bv4UimvVJ8HCN2ncTSyZZes9/GSGbMpX2nOfh58XFUZuaw9s3P6TZtCO/unEluZg6hG3/jeEnyE7H7JFITGa1H98LMwYo7l2JZHTBHq4Odofjc3B74BR3nDKNhybW3vdTxcOvZnC5fjuSLMtdex1LX3p9lrr2Opa69v77aSsz+px9rZmJrrtFtq01/zthAuzmB9Cs5T09+tZWrJfV7J+wbDk5YweUtx1BmZLMz8Aval9ovv5baL09yaNJKWk8bTN8Nxa3Ml7ce58TnPz1hrUqgh91eBkVlB8S8ZP78808aNGiAlVXxY6UpKSm0aNGCI0eOVDiQWdfykq9Vyt99nhZ7VzxG62WQbfBSXzYA2BYaPLnQf1yC0ct9HM314Bjqw69zvx/3+PchPauc05rjXv8teRPNN12/iF768+bnn39m48aNTJw4EQMDAxYvXkzDhg0rLfERBEEQBKFyvfS/6j5t2jQMDQ0ZOHAgAwYMoLCwkG+++aayqyUIgiAILwbxhueXj729PcuWLavsagiCIAjCi+k/9JSWtrz0LT+CIAiCIAilvfQtP4IgCIIgPMZ/qLtKW0TyIwiCIAj6THR7CYIgCIIgvNxEy48gCIIg6DM9bPkRyY8gCIIg6DHxq+6CIAiCIOgXPWz5EWN+BEEQBEHQKyL5EQRBEAR99hze8Jybm0tQUBC+vr74+fmxatWqCsseOnSIrl274uXlRUBAABcvXtR6yCL5EQRBEAR9VliovakC8+fPJyIigjVr1hAcHMzSpUvZv3+/Rrno6Gg+/PBDRo4cya+//oq7uzsjR44kOztbqyGL5EcQBEEQBJ3Jyspi8+bNTJkyBQ8PDzp16sTw4cPZsGGDRtkTJ07g4uJCnz59qFmzJhMmTCApKYmYmBit1kkkP4IgCIKgz3Tc7XXlyhXy8/Px8vJSzfPx8eHcuXMUlmktsrCwICYmhrCwMAoLC9m2bRumpqbUrFlTqyGLp70EQRAEQZ/p+GmvpKQkLC0tkUqlqnk2Njbk5uaSlpaGlZWVan63bt347bffGDRoEEZGRhgaGrJixQrMzc21WifR8iMIgiAIgs5kZ2erJT6A6rNSqVSbn5qaSlJSEtOmTeOXX36hd+/eTJ48mXv37mm1TiL5EQRBEAR9puNuL5lMppHkPPwsl8vV5i9cuJC6devyxhtv0KBBA2bOnIlCoWDr1q1aDVkkP4IgCIKgz3T8tJe9vT2pqank5+er5iUlJSGXyzEzM1Mre/HiRerVq6f6bGhoSL169UhISNBqyCL5EQRBEARBZ9zd3TE2NiY8PFw1LywsjIYNG2JoqJ6G2NnZcfXqVbV5169fx8nJSat1EgOeK8Fi72mVXQWde//MjMqugk61aDi0squgcz2k2n264kUkKarsGuiWQ/6Ty/zXecrSK7sK/306HvCsUCjo06cP06dPZ86cOSQmJrJq1Srmzp0LFLcCVa1aFblczoABA/jkk09o0KABXl5ebN68mYSEBPr27avVOonkRxAEQRD02WPezKwtkydPZvr06QwdOhRTU1PGjh2Lv78/AH5+fsydO5d+/frRrVs3MjMzWbFiBXfu3MHd3Z01a9ZgbW2t1fqI5EcQBEEQ9Nlz+GFThULBvHnzmDdvnsayyMhItc+vvfYar732mk7rI8b8CIIgCIKgV0TLjyAIgiDos+fQ7fWiEcmPIAiCIOiz59Dt9aIR3V6CIAiCIOgV0fIjCIIgCPpMdHsJgiAIgqBXRLeXIAiCIAjCy020/AiCIAiCPtPDlh+R/AiCIAiCPit6yX/npRyi20sQBEEQBL0iWn4EQRAEQZ+Jbi9BEARBEPSKSH4EQRAEQdArevieHzHmRxAEQRAEvSJafgRBEARBn4luL0EQBEEQ9Ip41F0QBEEQBOHlJlp+BEEQBEGfiW6vypWXl8fy5cvZsWMHd+/excbGhs6dOzN27FhMTU21+reWLFlCSEgI69at0+p2Adzc3Fi7di3NmjXT6nbtPJzpNCcQm3o1uBd1i0NBq7h74UaF5b3f7kyTkd2RmSqI3H2KI9PWkp+jBMBIJqHjzKG4dm1Cfm4eoSv2ELpyn2rddtOH4DOss9r2jkxdw9k1h7Qak7YolUoGDBtH0IRRNPX2rOzqPDW3Bq5MnjcRF/faXI28ztyPF3LlfFS5ZeUKOR/OHEe7rq0xNDTk8O6jfBW8lOysbI2yi9bNJ/VeGp+Nn6PrEJ7IwcOZHrOHYV+vBolR8ewOWsXtiBsVlm8+rAuvlpy3F/ecYt+0NeSVnLf1Ovsy8LsP1Mpf2hvCL+8t1mUIGhw8nOk2exh29WqQFBXPnqBV3HlMTE2HdaFFSUyX9pxi/7Q1qmvR0tmerjPfooZvXbLTMji95iB/r9ijWtc/eAjNhnVR296+aasJ1dG1aCiT0GTOUGp2a0JBTh6Xlu/hyop95Za1bOBM088DsXCvQXrkLUI+XkVKqXtSww/74RLQFiMTGXf+vMDpoDXkpjwAQOFgie+MIdi3rE9BjpLYnacI//wXCnPzdBJXRQykEqrPeBfzrq9SmKMkeeV2kr/fUW7Zqu18sZ84BKlzNZQ373D3i/U8OByiWm7W9VUcJr2JxN6azLDL3Jq8hLxbSc8pkmegh8nPC9XttXDhQg4ePMisWbPYv38/c+fO5cSJE0ycOFHrf2vYsGEsWbJE69vVFYlCRr81k4gPiWRd96ncCoum348TkShk5ZZ37dqEV8f349DkVfwycC7VvF1oEzRQtbxNUAD2nrX4JWAuh6espsX4ftTt1kS13Nq1On9+/jPLfEarpgs//6HzOP+N3Fwlk4LnEXM9trKr8o/IFXIWr1/A2VPnGNx5OOdDI1i0bj5yhbzc8h/OHEd9TzfGBEzgvQHj8WjszgefjdEo59+7A34dW+i6+k9FopDxxupJxJ2OZEWPT7kZFs0bP06q8Lx179qEtuP7sXvyKtYEzMHJy4VOQQGq5baujkQeCmOh7yjVtPPjlc8rHKA4poElMX1fElPAY2Kq17UJbcb3Y+/kVawrianjw5gMDAj4cSJZKfdZ2S2IvVNW4TemDw16v6pa39bVkSOf/8SXvqNUU7gOr0XvqQFYe9bi8IC5hExejeeEftTo3kSjnJFCRrt1k0gMiWRf56kkhUbTdt1EjEr2g8vg9tQJaMOJMcs41HcmCntLmn0xXLV+q+/GYaSQcqjvTI6/9w2Onbxo9NH/dBZXRRyCAlF4unJt0KckTP0Wu3EBmHV9VaOcvN4r1Pw2iNTNh4np/j4pG/dT85tPkLu/AoCJdz1qLp5E8sodRPccT5Eyj5pff/ScoxGe1guV/Gzfvp3333+fFi1a4OTkRIsWLZg+fTpHjx4lMTFRq3+rSpUqWFhYaHWbuuTWsxn5OUr+mL2JlJgEjk5fhzIzh7rdm5Zb3ntYZ86sOsC1I+HcOX+NQ5NX0eD1NhjLpUgUMhoGtOXo9PUkRtwg5kAop5fvxmuov2p9axdH7l64TlZSump6+E31RXL1eiyDRnzAzYTblV2Vf8y/d3tycnJZPGMZN6Jj+WLq12RlZNGxZ7tyy+cp85g/ZRFXzkcReSGKnT/toXFT9VYuM4uqjJv6HhfPXnoeITxRg57Nyc/J4+DsjSTHJLD/s3XkZubg0b38VtHmgV04uWo/Ub+dJeH8NXZP/gGvAW2QyKUA2LpUJzEynoykdNWUcz/reYZE/ZKYDpfEdLAkJvcKYmoa2IVTq/YT/dtZbp+/xp7JP9B4QPG1aGprzp1Lceyd8iMpN+4Sc/QcN/66SI0mdVXr27g4cifiBplJ6apJV9eikUJGnYC2hE5bT+qFG8TvD+XSst24BfprlHXuXXxPOjtjE/djEgibto68jBycexbfk6p3aETszpMknrxCemQ8l5btxsHPAwAzl2rY+rpy8oPvSI+6RVJIJOcXbuWVPs83aTdQyLB63Z/bn31HzsWr3D94kqTvtmL9Zg+Nsua92pD593nurd6FMvY2Kev2knnyAubdWwFg805fUnf8Tsqm/Siv3SJh+ncY21liZGn2XGP6V4oKtTf9R7xQyY+BgQEnT56ksFQTnJeXF3v27MHS0pL27duzbds21bJTp07h5uYGQHx8PG5ubnzzzTc0adKEyZMn07BhQ06ePKkqn5GRQcOGDQkNDWXJkiUMGTKEwsJCWrVqxdatW1XlioqKaN26Nb/++isAoaGh9OvXD09PT3r27MmBAwfU6r106VJatGhBs2bN2Lx5s072TTVvF26djlSbdys0iuo+rhplDQwNcGhUm5unrqjmJZyJwUhijG39mtjWr4mRsRG3wh51r9w6HYWDVx0wMEBqqqBqNStSr9/RSSzadDr8Ak29Pdmw4svKrso/1sDHg3Mh59XmnTsdgaevR7nl5wd9xbnTFwCo5uRAl76dCPvrrFqZ8dNGs3frQa5FvRitYE5eLsSVOW9vhkbh5O2iUdbA0IDqjWoTG/LovI0/W3ze2tevCRS3gtyr5PPSycuFm2Viin9CTHEVxJSRmMa2MUtQZuYUb9u3LjWb1iP278sASE0VmFWz4t7155PcW3rUxFBiRHLoo3tDYkgU1iX3htJsvF1IClHfD0mno7ApuScpUzNw7NAYhYMlRnIJr/RpQWpE8XmZnZjObwHzyEm+r7a+xMxEF2FVSOFeCwNjY7LOPDo+WacvYdK4rka8aVuPcGf+ao1tGFUtrnOV5g25f+Av1fy8+LtEthpOQep9jXVeNEWFRVqb/iteqDE/b775Jl9//TWHDx+mTZs2vPrqq/j5+eHionlTqciZM2fYunUrhYWFpKenc+jQIZo3bw7A77//jpWVFT4+Pvz9998AGBoa0qVLFw4dOkT//v0BCA8PJy0tjQ4dOpCUlMTIkSP54IMPaNWqFeHh4XzyySdYW1vj6+vLzz//zNq1a5k3bx4ODg589tln2t8xgKmdBclRt9TmZSWnY1O3hkZZmVkVJHIpmXdTVfOKCgrJTs2gajUrigqLyE55QGFegWp5ZnI6ErkUhaUp5jXtKCospPnY3tRq24js1AeEfb+fi1uO6SS2ZzGwr+Y3tP8KGztrrkVeV5uXkpRCnXq1H7ve9MVB9BjQlVtxCXz/5WrVfN+W3ng1b8TA9kP55HPtdxX/G6Z2FiRFxavNy0hOx87NSaOsvOS8fVDqvC0sKCQrNQMzBysArGtXo07rhrQa3QsDI0Mu7TnF0S+3UFDqXNa18mLKTE7H9iljKioVU+kreuyJxVg42RB1+AyX9xWPI7FxqU5RYSF+Y/rgUnItnvx+H+e36uZaVNhZkFvm3pCTlI6xQorM0lQ1XgdAYW9BeqT6PSknOR0Lt+J70oUvt9N2zYf0O7OEwvwCshPTONCz+P6Ydz+L239ceLSigQFugZ24c/yiTuKqiLGdFfmp9ynKy1fNy09Ow1Auw8iyKgUpjxKX3Kvqx1zmWhPTVxsRt2EfhlWrYGxRFQMjI15Z8xly91pkh0dya+q35N9NeW7xCE/vhWr5GT16NAsWLMDBwYFffvmFcePGabTKPMnQoUOpWbMmr7zyCt27d+fQoUMUlbzD4MCBA3Tt2hWDMhl99+7dOXHiBBkZGapybdq0wdTUlA0bNvDqq68yePBgnJ2d6d27N6+//jpr1qwB4JdffmHo0KG0a9cOd3d3Zs2apaW9oc5YIaNAqT4QsCA3HyOZZv4qURR3ERQo89XLK/MwkkqQKKTlLgMwkkqwdqlOURGkxCSwdegCLvz0O53mDsOls682Q9J7coUcZZljqlTmIZFKHrvemm828lb3kdyJv8vXGxZgYGCAVCYlaP5E5gV9Re4L1D0pUUjJL+dcMy4nxofnbbnlZRLMHW2QmsgpUOazefQSDs7eSMM+LekUNEh3AZSjvOsnv+TaKq8slH8tGsvUy295dxE/BS7Evr4z/tOGAGBTp/havHc1gU1vzefsz7/Tfe7buOnoWjRWSCksU9fCknPUsEx9jeWa96RCZT6GJfekKjVsyc/O5fc3F3K4/yyyElJo8eU75f5dr6kDsWzwCuc+103LeUUMFTKKNGIo/mzwmOvQyNKMmt9OJjPsMvcPncKwSvE4vWrBI0jb8Tuxw2diIJXwyg/TNFqQXkiFhdqb/iNeqJYfgF69etGrVy9SU1M5fvw469evZ8qUKarurSdxdHRU/btdu3ZMmTKFc+fO4ebmxrFjx1i7dq3GOo0bN8bW1pY//viD7t27c/DgQSZNmgTAtWvXOHr0KF5eXqryeXl51KpVC4CrV68yevRo1TIXFxdMTJ696bbZ6F40G9NL9fl2+FWNm6uRzJi8bM3/6PJzHyYy6ofXSCohPzsXAyPDcpcB5GfncnHLMa4eOkNOeiYAyVduYlmrGo2HdCDmQOgzx6avAscNIXDcYNXniDOXkZY5plKphNzsnMdu53rUDQAmjwxmX/h2vJs3onm7Zlw+F8nJ30Meu66utRrdi1aje6s+x4fHYFzOuZaXnaux7sPztqLy6beSmec5guyS8/LOpVgMDA3ot2gUB2au11mTe8vRvfArFdOt8BiN68e45Noq63HXYtl9cPtCcSug8UwJfRaN4tDsDZzfeoyow4+uxcQrN7Gq5YDP4I5E6uBaLMjNw7BMXQ1LztGCMvUtyNVM+AylxhSU3JNe/XokZ2du4tbhcACOv7uEPiGLsPaqw72zV1XrNJ7yOvWGd+H4u0tJj1RvXdG1wlylRpLzMN6ico4ngLGNBa+sm4GBoQFxo+YWvyAwv7ilLPXng6RtPwrAzfFf4H56HSZebmrdai+k/9BYHW15YZKfK1eusGPHDj755BMALC0t6dmzJ507d8bf319t7M5DBQWaTd0y2aMnLkxMTGjXrh0HDhxQPTrv6Vn+Y9DdunXjwIEDODs7k5qaStu2bQHIz8+nZ8+evPvuu2rljY0f7bqiMm/HLL3s3zq3/giRu0+pPjcd1YMqtuZqZarYWpCZmKaxbnZqBnk5SqrYWpBytXisgIGRIQpLUzIS0zAwMEBhVRUDI0OKCgpLtmVOXnauavDow5vtQykxCdRsWf+Z49JnW9fu4NDO31Sfh455A2s7a7Uy1nZWJCfe01jXWGJMa/+WnPrjNJkZxccoJTmV9NT7WFhZ4N+7Pda21vwZUzweTSotbnHo0KMNrV06a2xPV0LXH+FiqfO25Xs9MbW1UCtjamvOg8ect6a2FiSXnLeGRoaYWJqqymeXOS+TYxKKu2stTMkq1SWjTWHrj3CpVEyvlhNTlQpiyioV071S1+LDmKrYmOHk7UrkwTDVOknRtzCWSZCZKshOzdC4FpNjEqj1avnjwp5V1p1UZGXuDQo7c/Kzc1GmZ5Upm4K8zD1JYWtB9t00ZNZmVHG0IfVS3KPyCSnkpjygipONKvnxnfUmrm924K+x33Jz72mdxPQ4+XfuYWxpBkaGUBKvsa0lhdm5FNzP1ChvbG9F7Y2zAbg2MEjVLZafep9CZZ5a11hB2gPy0+4jqWbzHCJ5Rv+hsTra8sJ0exUUFPDjjz9y6ZL6UypSqRS5XI6VlRUSiYTMzEcn5M2bN5+43e7du/PHH39w+PBhunXr9thyJ06c4MCBA7Rv3x6FQgFArVq1iI2NxdnZWTUdOXKEXbt2AeDq6sqFC4/6ruPj47l//9kHuOWkZ5IWe1c1JYRFawxuru5bl9tnYjRXLirizrlrOJZ6YqS6jysF+QUkXYoj8WIsBfkFVC81QNOxiRt3zl2DoiJaTujPaxs/UdukrUdNUmISnjkufXY/7QHxN26ppguhEXj6NlAr06hJQy6EaT6pVVRYxPTFQWqPsNs72mFhZc716BuM7D+Oge2HMqjjMAZ1HMafB4/z58HjDOo4TOdxlZadnklK7F3VFH8mmhplztuavnWJP6t53hYVFZFw7ho1mzxq5XXyLj5v716Ko07rhnwUvlz15BeAQ31nslIe6CzxgeJrMTX2rmqKPxONU5mYavjW5VY5MVESU40KYrKoYcdrK8ZT1d5Stbxag1pkJqeTnZpBmwn9eWPDZLVNOtR3Jvmqbq7F1IuxFOYVYOPz6N5g29SNe+HXNH4CIflMDLa+6vvBtkldks/EoEzLoCBHibnro5Z4mZUpUsuqZMQVv/em4YS+uA5pz/H3lhL7q+aX2+ch+9J1ivLzMfGqp5pXpUl9ss5Ha8RroJBRa/VnFBUWce31yeQnlhrLU1BITsRV5O61VLOMLM0wtjRDeUu7TyoL2vHCJD8eHh60bduWUaNGsWvXLuLj4wkPDyc4OBilUom/vz8NGzZky5YtREVFcerUKVatWvXE7bZu3ZrExMQnJj/u7u7Y2dmxfv16unbtqpo/aNAgIiIi+Oqrr7hx4wa7du3iyy+/pHr16gAMHjyYtWvXcuDAAaKiopgyZQqGhtrfrVF7TyM3M6Hd9CFYu1an3fQhSExkqtYhY5kEk1LfwsLXHqbJyO64+Pvg4FmbTrPf4sKmo+TnKMnPUXJxyzE6zQnEwbM2Lv4+NBnRjTOrilsNrh4+i1OzeviO6Ia5sx2NBnfAo58fp7/bq/W49NmR3b9T1dyUD2eOo1bdV/hw5jgUJgpV65BMLsXatnigb0FBAdvW7WTU5BE0atqQep51mbv8M/44cJxrUTe4E39XLbHKzMgmMyOb+Bu3HlcFnbu0NwS5mQldgodg6+pIl+Di8/ZiqfPWtNR5e3rdIV4d0Z16/j5U96xNj9mBnNl0lLwcJTfDosnPyaPX/Hewrl0Nl7aN6BQUwInlu59rTJdLYvIPHoKNqyP+JTFdKhVT6VbasHWHaDGiO27+PlTzrE232YGcLbkWE85d5faF6/RcMAIbV0dc2jWiY1AAx5cWP2kadfgszs3q0XxENyxr2uEzuAOe/fz4+7s95dbtWRVkK7m++RhNPw/EqlFtnLr44P5uNyK/L743yG3NMZIXdwvF7T6N1NwEnxlDMHOtjs+MIRibyIjdeYqigkKu/vwnXtMGYdfMDXM3J15dMop7Z2JIOXcNM5fqNBjfh4tLd5MUEoXc1lw1PU9FObmkbv0Nx1mjUHi6YtapOTbD+3Lvx51AcReXgaw42bYbPQCpczXiJ36lWmZsY4FhydNeSd9vx/qtnph1a4msjhNOC94n59J1ssPLf2npC0UPx/wYFJXts6lE2dnZLF++nP3795OQkICJiQl+fn58+OGHVK9enfj4eCZPnszZs2epXbs27777Lh988AGRkZHEx8fToUMHjhw5gpOT+lMXn3zyCefOnWPfvkdvKS3vDc9LlixhzZo1/PXXX6puA4C//vqLhQsXEhUVhb29PYGBgQwe/GjsxurVq/n+++/JyclhxIgR/PDDD3z99dcVvuF5Yc3B5c5/EodGtek0ZxhWrtVJvhzHoaAfSbxY/Oiox/9a0fXLkWrbbjqqJz5vd8FIakz0vtMcnrqGgofjKuRSOs0JxLVrE3IfZHF6xR7O/PDoEf46nbxp+WF/LGtX4/7NJI4v2Ez0/qcfY/D+mRn/KsZn0aBlV1Ytmfdc3vDcouFQrWzHo7E7k+dP5BUXZ2IuX2XuxwuJjIgGoMeArkxfHIRvteL3iEikEkZ98g5d+/kjN5FzdO+fLPx0kaobrLTgRUEAz/SG5x7Smv963dIcG9Wmx5xh2Lg4cvdyHLunrOJOyXnb+H+t6fPFSKY7v6Eq7/deT5q/3RVjqTGX9p9m79TVqrEzDxMoJy8XlJk5hG74jT8Wbyv37z4No3+5XvVGtelWElPi5Tj2lorJ83+t6f3FSGaWiunV93rSrCSmy/tPs2/qatW1aGpnQdeZb/HKqx7kZedyes1BTnyzU7Vu3U4+tJ3QH6va1UiPT+Logl+48pTXYu28fz7Y1kghpencQGp0b0Le/SwufbtHlfy8kbCev8ev4NovxU+bWTeuTdN5wzBzqU7a5ThCPvlR9Ti7oUxCo4//xyu9WmAkl3D7WAShU9aSm/KA+mN64hX0erl/f0P1f3Z/9JSl/+MYSzOQy3Cc9R5mXV6l8EEWSd9tUyU/Da/v4ubERaRtPYLr4W+R19F8oi91yxHiJy0CwHKgP3ajB2BsbUHmyQvEBy0l/45mN/Y/1fD6rmfexuNkLX73yYWeksn7y7W2LV16oZIfffFvk5//kspIfp4nbSU/LzJtJT8vsn+b/PxX/Jvk57/mWZOf/wKR/GjfCzPgWRAEQRCESqCHbSAi+REEQRAEffYfGqujLS/MgGdBEARBEITnQbT8CIIgCII+08P3/IjkRxAEQRD0mR6+4Vl0ewmCIAiCPiss0t5UgdzcXIKCgvD19cXPz++x7+mLjIwkICAAT09PevbsWe4vPDwrkfwIgiAIgqBT8+fPJyIigjVr1hAcHMzSpUvZv3+/RrkHDx4wbNgwXFxc2LVrF506dWLMmDHcu/fs70sqTXR7CYIgCIIeK9Lx015ZWVls3ryZlStX4uHhgYeHB9HR0WzYsIEuXbqold2+fTsmJiZMnz4dIyMjxo0bxx9//EFERARt2rTRWp1E8iMIgiAI+kzHA56vXLlCfn4+Xl5eqnk+Pj4sX76cwsJCtZ+ECgkJoUOHDhgZPXoF6datW7VeJ9HtJQiCIAiCziQlJWFpaan2s1E2Njbk5uaSlpamVvbmzZtYWVkxdepUWrZsyYABAwgLC9N6nUTyIwiCIAj6rKhQe1M5srOz1RIfQPVZqVSqzc/KyuK7777D1taWlStX0qRJE95++21u376t1ZBFt5cgCIIg6DMdd3vJZDKNJOfhZ7lcrjbfyMgId3d3xo0bB0D9+vU5ceIEv/76K+++q73fIBMtP4IgCIIg6Iy9vT2pqank5+er5iUlJSGXyzEzM1Mra2trS+3atdXmvfLKK1pv+RHJjyAIgiDos8JC7U3lcHd3x9jYmPDwcNW8sLAwGjZsqDbYGaBx48ZERkaqzbt27RqOjo5aDVkkP4IgCIKgz3T8kkOFQkGfPn2YPn0658+f5/Dhw6xatYo333wTKG4FysnJAWDgwIFERkayZMkSYmNjWbx4MTdv3qR3795aDVkkP4IgCIIg6NTkyZPx8PBg6NChfPbZZ4wdOxZ/f38A/Pz82Lt3LwCOjo58//33HD16lB49enD06FG+++477O3ttVofMeBZEARBEPTZc/htL4VCwbx585g3b57GsrLdXD4+Pmzbtk2n9RHJjyAIgiDoM/Gr7oIgCIIg6BNd/7zFi0iM+REEQRAEQa+Ilp9KkG3w8jcxtmg4tLKroFN/X1hT2VXQufrur1V2FXRuusS9squgUylGTy7zX+d160xlV0Hn8p9c5NmIbi9BEARBEPSKHiY/ottLEARBEAS9Ilp+BEEQBEGfPYdH3V80IvkRBEEQBH0mur0EQRAEQRBebqLlRxAEQRD0WJEetvyI5EcQBEEQ9JkeJj+i20sQBEEQBL0iWn4EQRAEQZ/p4c9biORHEARBEPSZHnZ7ieRHEARBEPSZHiY/YsyPIAiCIAh6RbT8CIIgCIIeKyrSv5YfkfwIgiAIgj4T3V6CIAiCIAgvN9HyIwiCIAj6TA9bfkTyIwiCIAh6TB9/3kJ0ewmCIAiCoFdEy48gCIIg6DPR8vPf0759e9zc3FSTh4cHXbp0YfXq1U+17rZt23RfSS1x8HBm2I7P+OTKKt7eOQOHBq88tnzTYV14/9QSPrr4PT3mv4OxXKpaZulsz6C1H/PxpR8Y99diWozsrrauf/AQpsZuUJt8h3bSRViP5dbAldV7VnD82iHW7PuOep51KywrV8iZsvAjDl/czW+X9xK0YBIKE0W5ZRetm0/woiBdVVvrlEolfQa/S8iZ85VdlWdWv6EbW/av4XzsCbYeXIuHZ72nWm/46CEcDdul49o9nqFMQrMvhvPa5RX0O7uUeiO7VljWsoEznXdP5/WrP9Bl7wysGr6itrzhh/3oG/o1/7u0Ar/lY5BZVVUtM33FnnYbP2JA9Pf0Ob0I9/e687wYySS0XzCcERErGBa6FK8RFcdo4+HMazun827UDwzYPQPbMjE+5Du2Fx2/HKE2T2ZuQqfF7/HOheUEnv6aFh8PAAMDLUbyzzRu7MFfx3dxPy2Gv//ag7dXwwrLWliYk6+8pTbdSbigWv7+uHc0ls//fOrzCOPfKdTi9B/xn09+AIKCgjh+/DjHjx/n8OHDjBw5kvnz57Njx47KrprWSBQyBq6eRNzpSL7v8Sk3w6IJ+HESEoWs3PL1ujahzfh+7J28inUBc3DycqFjUEDxQgMDAn6cSFbKfVZ2C2LvlFX4jelDg96vqta3dXXkyOc/8aXvKNUU/vMfzyNUFblCzuL1Czh76hyDOw/nfGgEi9bNR66Ql1v+w5njqO/pxpiACbw3YDwejd354LMxGuX8e3fAr2MLXVdfa3JzlUwKnkfM9djKrsozU5jIWbnpa06fPEvfjm9w9vR5Vm5ajMKk/GP6UA1nR8ZOGvmcalkx76kBWHvW4vCAuYRMXo3nhH7U6N5Eo5yRQka7dZNIDIlkX+epJIVG03bdRIxKrleXwe2pE9CGE2OWcajvTBT2ljT7YnjxygYGtFs3kdx7D9jnP4WQj3+kwfu9eaXv8zlnW04JwN6zFtsHzuX3T1fTdHw/6nTTjNFYIaPXmkkkhETyc7ep3A6NpufqiRiXuSe59m5Bswn9NdZvOzuQKvYWbO0/k4PjvsX9tdY0fruzzuJ6HBMTBbt+Xcfx4yE0bd6Fv/8OZeevazGp4MtTfXdXkpNTcKzRWDU1bNRWtdzd3ZVl365WWz5j1pfPKRrhabwUyU/VqlWxtbXF1taWatWq0bdvX1q0aMHBgwcru2paU79nc/Jz8jg8eyPJMQkc/GwduZk5uHdvVm75poFdOLVqP9G/neX2+WvsmfwDjQe0wVguxdTWnDuX4tg75UdSbtwl5ug5bvx1kRpNHrWq2Lg4cifiBplJ6aopP0f5vMIFwL93e3Jyclk8Yxk3omP5YurXZGVk0bFnu3LL5ynzmD9lEVfORxF5IYqdP+2hcVNPtTJmFlUZN/U9Lp699DxCeGZXr8cyaMQH3Ey4XdlV0YruffzJzc5h3vRFXI2+wawpC8nMyKJrr8e3Ks5YEMSliMjnVMvyGSlk1AloS+i09aReuEH8/lAuLduNW6C/Rlnn3s3Iz1FydsYm7sckEDZtHXkZOTj3bApA9Q6NiN15ksSTV0iPjOfSst04+HkAILc1J/ViLCGf/MiD63dJ+O0cd45fxLaJm85jNFbI8Ahoy5/B60mKuMG1/aGELd+N51uaMbr2Ko7xxKxNpMYkcGz6OvIyc3DpURyjgZEhbee8RceF75Aem6ixvnO7RoSv3EdK1C1u/X2ZqF//wqmlh85jLM+A13qRnZ3DR5/M5MqVGCZ8GMyDB5n8r3/PcsvXq+dKdPQ17t5NUk1JSfdUy93ruXL+/CW15RkZmc8rnH+sqLBIa9N/xUuR/JTH2NgYiURCfn4+X375JX5+fvj4+DBu3DhSU1M1ymdkZDB58mRatGhBgwYN6NKlC4cPH1Yt37t3L507d6Zhw4Z069ZNbdnatWtp164dDRs2pF+/foSGhmo9HicvF26eVr/5x4dG4eTtolHWwNCA6o1qExdy5VHZszEYSYyxr1+TjMQ0to1ZgjIzp3jbvnWp2bQesX9fBkBqqsCsmhX3rlfuf7gNfDw4F6LezXPudASevuXfIOcHfcW508VNz9WcHOjStxNhf51VKzN+2mj2bj3Itaj/RivK6fALNPX2ZMOKl+NbYyOfhoSeClebdybkHF6+FXcx9BnQHbmJnC0bdui2ck9g6VETQ4kRyaFRqnmJIVFYe9XR6K6x8XYhKUT9ek06HYWNjysAytQMHDs0RuFgiZFcwit9WpAaUXxO5iSmcfzdpeSXXJ+2TVyxa16PuyXXpy7Z1K+JobERt8MexXg7JAqHcmJ08HLhdpl70u3QKKp5F8coqSLHul5NfukZzJ0z0Rp/Kyc1A7d+LTGWS6lib0HNNp4kXayc67JZM29O/HVabd5ff5+meXOfcsvXd69LVPS1Crf3MDn6zygs0t70H/HSJT95eXkcPHiQEydO0KFDBxYvXsz27duZM2cOP//8M/fu3SM4OFhjvdmzZ3P9+nVWrVrF7t278fX1ZcqUKSiVSu7du8dHH33EyJEj2b9/P/3792fChAmkpaVx6dIl5s+fT3BwMPv27cPX15fx48dTWKjdzk9TOwse3FVP2jKT0zGrZqVRVm5WBYlcqla+qKCQrNQMzBzUy489sZjArcHEn4nm8r4QAGxcqlNUWIjfmD68f3IJI/bNwbN/K63G8zRs7KxJupOsNi8lKQW7anaPXW/64iB2nd6Mla0l33+5WjXft6U3Xs0b8cNXqytc90UzsG8PPn5/JAr547uF/ivs7G1IvKt+TJMT7+FQ3b7c8lbWFkyaNo6pH86mst/Ar7CzIDflAYV5Bap5OUnpGCukyCxN1cvaW5B9N01tXk5yOiYl1+uFL7dTlF9IvzNLGBD1PbbN3Dg+6huNv9knZBH+vwaTHBbNzT0h2g+qjCp2FmSXiTErOR1juRR5mRir2FmQWSbGrKR0TEtiVN7PYmu/Gdy7crPcv/X7p6up0dKDkVe+Z1joUjLvphLyVeWMwaxWzZ7bt++ozUtMTMLJsVq55evVc8HJsRp/n9hN7PVQNqxfhoND8X3Jzs4Ga2tL3nxzADFRJ7lw/ncmfFD5XbaPJcb8/DcFBwfj5eWFl5cXnp6efPzxxwwdOpSePXvyyy+/8MEHH9C6dWtcXFz47LPPcHV11dhGkyZNmDFjBu7u7rzyyisMGzaMtLQ07t27x927d8nLy8PBwQFHR0eGDRvGsmXLkMlk3Lp1CwMDA6pXr46TkxPjx49nwYIFWk9+JAopBcp8tXn5yjyMpJJyywIa5QuUeRjL1MtveXcRPwUuxL6+M/7ThgBgU6c6RUVw72oCm96az9mff6f73Ldx6+yrzZCeSK6Qo1Tmqc1TKvOQlBNzaWu+2chb3UdyJ/4uX29YgIGBAVKZlKD5E5kX9BW5z7n7TnhErpCjzFXf/487pkEzP2TbT7uIiaz8b9HGCimFZa6pwpLz07DMdWUsl1FQ5twtVOZjKCt+wLZKDVvys3P5/c2FHO4/i6yEFFp8+Y7G3/xz+GJ+f3Mhlh7OeH82WJvhlMu4nPvMwzjK3muMFZoxFijzMZI+3UPElrWrkXj+Olv6fsaedxZh7VYDn1E9nqH2/56JQkFumfMyN1eJTCYtt7ybmwtmZqZ8OHE6g954j+rV7Pl1xxoMDQ2p51bcGp+YmESffm8xb/5Sgia/z/vjNI+vUHleikfdx40bh79/cZ+0TCbD1tYWIyMjUlJSSEtLw8PjUTeJi4sLY8eO1dhGnz59OHz4ML/88gvXrl3j4sWLABQUFODu7k7btm0JDAykVq1adOjQgddeew2FQoGfnx9169alZ8+e1K9fX7XM2PjZdm3L0b3wG91b9flWeIzGTcVYKiE/O1dj3fzchzcr9fJGUgl5ZcrfvnC9eFszJfRZNIpDszdwfusxog6fISe9uI868cpNrGo54DO4I5EHtN+l91DguCEEjnt0g484cxlpmRuuVCohNzvnsdu5HnUDgMkjg9kXvh3v5o1o3q4Zl89FcvJ33X97Fh55d3wg744fpvp8LiwCaZn/UKRSCTnlHFO/di3w8vVkSusBOq/n0yjIzcOwzDVlWHJ+FpS5rgpyNb+YGEqNKcgu/g/21a9HcnbmJm4dDgfg+LtL6BOyCGuvOtw7e1W1Tsr54uvTMHg9LZeO4uyMjWqtMtpWXG/N+wZAfs6TYzSSGpP3FF8uzF+xx2/qIH5s9j5ZiWkAGMultJsTSNiy3RQV6LYJ4ZOPx/LJx4/+HwgJOauR6MhkUrKys8td37NxO4qKICen+LwdMHAE8XFnadbUmz+PncTOoQEpKcUt7xERV7C1sWbkyDdZ/PVKHUX0bP5LY3W05aVIfqytrXF2dtaY/08SkI8++oizZ8/Su3dvAgICsLW15fXXXwfAwMCAFStWcP78eY4cOcKhQ4fYuHEjGzduxN3dnc2bNxMSEsLRo0fZtm0bmzZtYtu2bdjbl9+U/zTC1h/h0u5Tqs+vvtcTU1sLtTJVbM15UHLjKC0rNYO8HCWmthbcu1o8bsfAyBATS1MeJKZRxcYMJ29XIg+GqdZJir6FsUyCzFRBdmqGKvF5KDkmgVqv6nYw4ta1Ozi08zfV56Fj3sDazlqtjLWdFcmJ98quirHEmNb+LTn1x2kyM7IASElOJT31PhZWFvj3bo+1rTV/xhwAQCotvtF16NGG1i6V84SJPti0eit7fz2k+jxi7FvYljmmNvY2JJXpCgPo0dcfB0d7Tl0pHl9nbGSERCoh/MYxhg8cS+jJcJ3WvaysO6nIrKpiYGSo+s9ZYWdOfnYuyvSsMmVTkNuaq81T2BZ3hcmszajiaEPqpbhH5RNSyE15QBUnGzJvJmHj60r8/kfXZ3rULYxkEiRVFeSmZOgsxow7qSjKxGhia05edi65ZWLMuJOCiZ16jCa2FmSV6Qorj22DV8hOeaBKfACSLt5AWlWB3MKU7Hv3nzmWx1nx3To2b3n02oRJE0dhb6/enW5vb8ft25oDtQGyyyTrSUn3uHcvleqODgCqxOehK1eicazuoI2q68Z/qLtKW16Kbq+KmJmZYWlpyZUrjwb+Xr58mdatW6sydige7Lx7926++uorxo0bR6dOnUhPTwegqKiIq1evMm/ePDw9Pfnggw/Ys2cP1apV49ixY5w9e5YVK1bQvHlzJk+ezP79+8nNzSUsLEyjPv9ETnomqbF3VVP8mWicfNS762r41uXW2RjNlYuKSDh3jRqlng5x8nalIL+Au5fisKhhx2srxlPV3lK1vFqDWmQmp5OdmkGbCf15Y8NktU061Hcm+WrCM8X0JPfTHhB/45ZquhAagadvA7UyjZo05EKY5pNaRYVFTF8cpPYIu72jHRZW5lyPvsHI/uMY2H4ogzoOY1DHYfx58Dh/HjzOoI7DNLYlaE962n3irserpvDQ83g1UX8Cz6dpI8LDLmisO3/G13T1+x+92gXQq10Ai+ctJ/FOEr3aBXAhXPeDf8tKvRhLYV4BNj6PHjKwberGvfBrlB2QlHwmBltf9evVtkldks/EoEzLoCBHibmro2qZzMoUqWVVMuKSqFLTltbfv4/C4dH1ae1Zi5zkdJ0mPgDJF2MpzC/AodSDFNWbupF4TjPGO2djcChzT6rWpC53yrsnlZF5tzjJUlibqeZZ1amOMiNb54kPQGpqGlev3lBNJ0+G0aKFerf+qy18OXVK8z5etaopSXcv0rbNo1eDVK/ugI2NFZGRMQwLDOBixJ9q6zRq5MGVyCfvF+H5eamTH4AhQ4awePFiTp48SXR0NLNnz6Zx48bISw0glUqlKBQKDh48SHx8PMeOHWPGjBlA8cvlzMzM2LRpE8uWLePmzZv8/vvv3Lp1i/r16yOXy/nmm2/YvHkz8fHx7Nmzh6ysLNzctPtY6uW9IcjNTPAPHoKNqyP+wUOQmMhUrUPGMglVSn3TDFt3iBYjuuPm70M1z9p0mx3I2U1Hyc9RknDuKrcvXKfnghHYuDri0q4RHYMCOL70VwCiDp/FuVk9mo/ohmVNO3wGd8Cznx9/f7dHqzE9yZHdv1PV3JQPZ46jVt1X+HDmOBQmClXrkEwuxdq2eHBlQUEB29btZNTkETRq2pB6nnWZu/wz/jhwnGtRN7gTf1ctscrMyCYzI5v4G7eea0z6bv/OI5iZVeXT2RNxqVuLT2dPRGGiULUOyeQybEpahlKSU9USp3vJqeTnFxB3PZ7cHM3uXl0ryFZyffMxmn4eiFWj2jh18cH93W5Efl/cmii3NcdIXtwNFLf7NFJzE3xmDMHMtTo+M4ZgbCIjducpigoKufrzn3hNG4RdMzfM3Zx4dcko7p2JIeXcNVLCr5Fy/gbNv3wHM9fqVG/fCK+pAUQs3qnzGPNzlFzefIx2cwOxa1Sb2p198BrRjXM/FMdoUirGmD2nkZmZ0Gr6ECxdq9Nq+hAkChnRu0497k8AcOdMDCnRt+i06F2s6jpSvXk9Wn4awPnVh564ri5s3bYHC3MzvvziM9zdXfnyi8+oUsVE1Tokl8uxt7cF4MGDDI6fCGHhwun4+jTCq3EDNq5fxoEDR4mIuMLhI39SzcGOBfOmUafOKwwY0ItJE0exYOGySontaYhH3V9CI0aMwN/fn/HjxxMQEICDgwMzZ85UKyOVSlmwYAEHDhyge/fufP7557z33nvY2tpy+fJlbG1tWbJkiWr5jBkzmDBhAn5+fri7uzN79my+//57unbtyvLly1mwYAF16tTRahzKjGx+GraQmk3dGL57Fk5eLvz01gLVGJ76PVswIfTRxXVx10lOLNtJtzlvM3j9J9wKv8rhuZuA4hP95+FfkpedS+C26fSY9w4hqw8Q8mPxDe72+Wtsee9rPPv6MfLQPJoGdmb7+99w68zz/eaSmZHFB0M+xqtZI9bt/56G3h68P3iSanxIp14dOHD+V1X5b+Z+x297/mDedzNZvuVrYq/eZPq42c+1zsLjZWRkMuKN8fg292L74fU09mnIOwHjyM4qPqbd+/jz98UX9/1cYZ9tIOX8DTpuCaLJ7KGcX7iVm/uKx8H1P/cNzr2aA5Cfkc3vb36BXTM3uu6fhY13HY4OWaAaGxQ2fQM3952m5Tej6bR1Csr7mfwR+BVQfH3+Efgl+Vm5dN41nWYLhxP5w0EiSxIQXTs+YwOJ52/Q9+cg2swayqkvt3J1f3GMb5/5BteexTHmZWSzO/ALqjd1Y+DeWTh412Hn0AXljkMsq6igkJ1vLiQvK4f+W6fiv+hdonae5NQXW3UaW0UePMigd5+h+Pk1I+TkPpo386Zn7yFkZRWP+RnwWi9u3QxXlQ8cNp6zZy+wa+c6jhzewo3YeIYMLR5DFBd3i569htCihS9nww4ze+ZkJgfNYcuWyn07+WPp4dNeBkVFlf0Aqf6Z6fxGZVdB535Vxj250H/Y3xfWVHYVdK6++2uVXQWdmy5xr+wq6FSKUWXXQPc+uHu0squgc/lK3bZQp/Ruo7VtWf36fH8J4N96KQY8C4IgCILw7xT9h1pstOWl7/YSBEEQBOExnkO3V25uLkFBQfj6+uLn58eqVaueWK34+Hi8vLw4derJ48j+KdHyIwiCIAiCTs2fP5+IiAjWrFlDQkICH3/8MdWrV6dLly4VrjN9+nSysrIqXP4sRPIjCIIgCHpM191eWVlZbN68mZUrV+Lh4YGHhwfR0dFs2LChwuRn586dZGbq7sdgRbeXIAiCIOgzHXd7Xblyhfz8fLy8vFTzfHx8OHfuXLk/BZWamsqCBQtUr5zRBZH8CIIgCIIeKyrU3lSepKQkLC0tVW/WB7CxsSE3N5e0tDSN8p9//jl9+/Yt93c4tUV0ewmCIAiCoDPZ2dlqiQ88+okhpVL9t+D++usvwsLC2L17t07rJJIfQRAEQdBjuh7zI5PJNJKch59L/9pCTk4O06ZNIzg4WG2+LojkRxAEQRD0mK6TH3t7e1JTU8nPz1f94HhSUhJyuRwzs0e/73b+/Hlu3rzJuHHj1NZ/55136NOnj1bHAInkRxAEQRAEnXF3d8fY2Jjw8HB8fYt/QDYsLIyGDRtiaPho6LGnpycHD6r/vI2/vz+zZs2iZcuWWq2TSH4EQRAEQZ8VGeh08wqFgj59+jB9+nTmzJlDYmIiq1atYu7cuUBxK1DVqlWRy+U4OztrrG9vb4+1tbVW6ySe9hIEQRAEPabrp70AJk+ejIeHB0OHDuWzzz5j7Nix+Pv7A+Dn58fevXufU7TFRMuPIAiCIAg6pVAomDdvHvPmzdNYFhkZWeF6j1v2LETyIwiCIAh6rKhQt91eLyKR/AiCIAiCHhO/6i4IgiAIgvCSEy0/giAIgqDHinT8tNeLSCQ/giAIgqDH9LHbSyQ/giAIgqDH9HHAsxjzIwiCIAiCXhEtP4IgCIKgx4qKKrsGz59IfiqBrR40MfaQ1qzsKuhUfffXKrsKOnfp8ubKroLO9fYeU9lV0CkrA93+MvaLYItVm8quwn+e6PYSBEEQBEF4yYmWH0EQBEHQY/rY8iOSH0EQBEHQY/o45kd0ewmCIAiCoFdEy48gCIIg6DHR7SUIgiAIgl7Rx5+3EN1egiAIgiDoFdHyIwiCIAh6TPy2lyAIgiAIeqVQD7u9RPIjCIIgCHpMjPkRBEEQBEF4yYmWH0EQBEHQY+JRd0EQBEEQ9Ip4w7MgCIIgCMJLTrT8CIIgCIIeE91egiAIgiDoFX181F10ewmCIAiCoFdEy48gCIIg6DF9fM+PSH4EQRAEQY+Jp7303LZt23Bzc2Pz5s2VXRU1RjIJbRYMJzBiBUNCl+I5omuFZa09nOm7czpvR/1Av90zsGn4imqZgaEBzT55nTfDljLs8ko6LRuLwsZMbX3fCf1488w3vHVhOa0/H4aRTKKjqJ6Og4czw3d8xpQrq3hn5wyqNXjlseWbD+vChFNLmHzxe3rNfweJXKpaVq+zL9NjN6hNA759X8cR/DP1G7qxZf8azseeYOvBtXh41nuq9YaPHsLRsF06rp1uKZVK+gx+l5Az5yu7Kv9YbY/afPXrV2yL3MaiXYtwaehSYVljqTHDgoax9tRafr7wM59+9ynWDtaq5S06t2Bv3F61KWh50PMIo0LOHrWYvuNzfriyiRk75/NKg9pPtd7bn79Hv/Gvq80zszZn3LeTWHF+HV/88Q2t/tdOF1V+aoYyCV5fvkP3yJV0OfcNLu92e+I6Vk3d6HRqkcZ81zE96RSyiO7R39NycxBV6zrqoMaCNojkp5Q9e/ZQs2ZNfv3118quiprmUwKw9azFroFzOfbpanzH96N2tyYa5YwVMrqtmcTtkEi2dpvK3dBouq2eiLFCBoDX6J649GrOoVFL2N4rGJlFFdovfk+1fuNRPfF4syOHx3zD3iHzcWzpgc/4vs8tzrIkChlvrJ5E3OlIVvT4lJth0bzx4yQkJfGU5d61CW3H92P35FWsCZiDk5cLnYICVMttXR2JPBTGQt9RqmnnxyufVzhPpDCRs3LT15w+eZa+Hd/g7OnzrNy0GIWJ/LHr1XB2ZOykkc+plrqRm6tkUvA8Yq7HVnZV/jGZQsaMNTOICIng/e7vcznsMtN/nI6sgvN08ITBvNrlVeaPm8/EfhMxlhjz6XefqpbXdK3JyUMnecPnDdW0+KPFzyscDTKFjEmrPyXy9GWm9phIdNgVJv44pcL4Huo+sg/tAjppzB//3cdYOVgzJ2Aa6z5bxRtT38K3SzNdVf+JGkwbhEWj2hz/32zOffIjbh/2o3qPphWWN6tXg6bfv4+BoXpX0StvdsDlve6cn7KG3zt/SmZcEi02foSRQlrBll4chUUGWpv+K0TyU+LevXv8/fffjB49mtDQUG7evFnZVQKKExr3gLb8Fbye5Igb3NgfSvjy3TR4y1+jbJ1ezSjIUXJy1ibSYhI4MX0deZk51Cm5kA2MjDgxYwO3T0WSGp3AhR8P4tCkbvEyQwMavdOFv2dtIuGvSySGXyP0i63YetZ6rvGW1qBnc/Jz8jg4eyPJMQns/2wduZk5eHQv/0bZPLALJ1ftJ+q3syScv8buyT/gNaCNqvXH1qU6iZHxZCSlq6ac+1nPM6TH6t7Hn9zsHOZNX8TV6BvMmrKQzIwsuvbS/A+ktBkLgrgUEfmcaql9V6/HMmjEB9xMuF3ZVflXWvdsTW5OLj/M/oGbMTdZMX0F2ZnZtOreqtzyHf/XkbUL1hJxKoKb0TdZ/PFi3Bq7Uf2V6gDUcKlBbGQsqUmpqinzfubzDElNs54tUebksmn2GhJibrHus1XkZGbTtPur5ZZXmCoY9+0keo7qS/KtJLVltRrWoa5vPb4Z9xWxF68T/lsYu7/dQfeRfZ5DJJqMTGQ4D2rHhalrSb9wg9v7Qon+Zje1h2neXwFeGdKeVrunk5uUrrGs5uutifl2D3cPnSXz2h3OfbwKqWVVrErusS+yoiIDrU3/FSL5KbF//36qVq1Kr169sLOzU2v9ycnJYcqUKfj4+NCqVSs2b95M/fr1iY+PB+D27du8++67NGrUiPbt27N06VIKCgq0Ui/r+jUxNDbiTliUat6dkCjsvOqAgfqJZu/lwu3T6v8J3gmNwt7bFYCwRdu5sT8UALm1Ge4Bbbn992UALOs6Ibeqyo0Doap1o3f8xZ435mkljn/DycuFuDLx3AyNwslbs0vBwNCA6o1qExtyRTUv/mwMRhJj7OvXBIpbfu5dv6PbSj+DRj4NCT0VrjbvTMg5vHwbVrhOnwHdkZvI2bJhh24rp0Onwy/Q1NuTDSu+rOyq/Cv1vOtx6fQltXmXQi9Rz0ezy9LAwICF4xdy5s8zGsuqmFUBilt+bl27pZvK/gsuXnWJPH1FbV5U6BVcvd3KLW9bwx6JTMKn3SeSFHdXbZldTXvSk9NJuvloftyVG9RqWAcjYyPtV/4JzOvXxEBixL3Tj+6vKSGRWHq5aNxfAezbN+bMuOVc/W6fxrKIzzZyc+uJRzOKisAAJGYmOqm7NhUVaW/6rxADnkvs2bOHtm3bYmhoSPv27dmxYwejR4/GwMCAWbNmcfbsWX744Qfy8/OZMmWKKrkpKipizJgx1KtXj+3bt5OUlMS0adMwMDBg9OjRz1wvEzsLclIeUJj3KJnKSk7HWC5FbmlKTsoDtbKpUeo3zaykdKzcaqjN853QD98P+pGTlsGOvjMAMHO2IyctE3sfV5p9PAC5VVWu7T3Nybk/UajMf+Y4/g1TOwuSouLV5mUkp2Pn5qRRVm5WBYlcyoO7qap5hQWFZKVmYOZgBYB17WrUad2QVqN7YWBkyKU9pzj65RYK8rSTqD4rO3sboiOvqc1LTrxHXffyx49YWVswado4hvZ/D08vj+dRRZ0Y2LdHZVfhmVjZWREbpd5dl5achnNdZ42yRUVFhB8PV5vXe1hv0u+lc/3ydQCc6jjh3cab18e8jqGRIcf2HGP9F+vJz6uc69DCzpJbUeot4enJ6dRwq1lu+bjLN/hi2Jxyl6Unp1HFzASpXIoyRwmAdTUbjCXGKKqakJH6oNz1dEVub4Ey5QFFpe4BOUnpGCmkSK1MUd5Tr8+pwOIEvebrrTW2lRKi/kXN+Y12GBgZce/Uf7dV9mUmWn4obrk5c+YMHTt2BMDf35+bN28SFhZGZmYmO3bsYOrUqTRu3BhfX18+/fRR//zJkydJSEhg5syZ1K5dm2bNmvHxxx+zdu1ardTNWCGloEzyUaDMA8BIKilTVqZa9qhsPkZS9Rw3atsJtnafyq1jF+mx4WMkpgokJnKMFVKaTX6dv2Zu5PeJK3Hu5EWLTwdpJY5/Q6KQkl9O7MZSzUHYkpJ+9XLLyySYO9ogNZFToMxn8+glHJy9kYZ9WtIpqPLiK0uukKPMVarNUyrzkJQTL0DQzA/Z9tMuYsokTMLzJVPIyCtz3eXl5iF5iocFmndqTv8R/Vk9bzX5efnYOdohN5GTp8xj7qi5fD/re9r1acfbU97WVfWfqLz48pV5GEv/+Xfnq+HRpN5N5c0Zw5EpZNg7O9B1eE8AjCXP/7u4kUJGYZnYCnOL7yGGFVx3T8PSqw4Npr9BzLLd5XaRvWj0ccyPaPmhuNVHJpPh5+cHQNOmTTE3N2f79u3IZDLy8vJo2PBR14OXl5fq31evXiUtLQ0fHx/VvMLCQnJyckhNTcXS0vKZ6laQm6eRvDxMevJzcsspKylT1pj8HPX/UO/fKG5y/u2D5QwO+ZraXX3Jz81DopBxIngdt08WN3H/PXMjHZeO5kTwuufSntlqdC9aje6t+hwfHqNxgzWSSsjLzi27Kvm5xTewisqn30pmnucIstOLx07cuRSLgaEB/RaN4sDM9RQVPv/22nfHB/Lu+GGqz+fCIpDK1AdHSqUScrJzNNb1a9cCL19PprQeoPN6CuoGjB7A62MePcEUGR6pkaBKZBJyyzlPS2vh34JPvvmEnat3cuCnAwAk3kpkQMMBZKRnAHDt0jUMDQ2ZuHgiK2espLCwUMvRaOo1uj+9RvdTfb4aHq0Rn7FUgjJbWXbVJ8rLzWPJqIWMWfYhKy+u5/69++xevp3B04aRnZH9zHX/pwpy8zSSHENZ8T2k4AnHryKWPq68uvEj7v52jsvztzxzHZ+H/9JYHW0RyQ/FyU9OTo5aAlNQUMD+/fv53//+p1G+qFQikJ+fT+3atVm2bJlGuapVqz5z3TLvpCK3qoqBkSFFBcU3PhNbc/Kyc8lNzypTNgUTO3O1eSa2FmTdTQOgZofG3LsYS+ad4q6hgtw8HsQlIreqSuK54taDtJgE1bppV29jLJeisK5KdvL9Z47lSULXH+Hi7lOqzy3f64mprYVaGVNbcx4kpmmsm52aQV6OElNbC5KvFg+cNTQyxMTSVFX+YeLzUHJMAhK5FIWFKVkpz7e5HWDT6q3s/fWQ6vOIsW9ha2etVsbG3oaku8ka6/bo64+Doz2nrhwGwNjICIlUQviNYwwfOJbQk+E6rbs+27t+L8d2H1N9fm3Ua1jaqn/JsbS1JCUxpcJttO7ZmomLJrJ3/V5WzlB/4vBh4vNQXEwcMrkMUwtT7qfo/jo8sv4Ap3Y/GrvS472+mJe5Di1sLUhLTOXfuHY+hgl+72Fua8GDlPs0bN2Y+/fSyc3STPJ1Led2CtIy91e5nQX5Wbnkpf/zhyFsXnWn+bpJJP5+ntB3l/63BsHoGb3v9rp+/TqXLl3i008/ZceOHarpq6++IiMjg9jYWCQSCREREap1Sv+7Vq1aJCQkYGVlhbOzM87OzsTHx/P1119jUM6AuX/q3sVYCvMLsC81yNehqRtJ565pXFh3z8Zg7+OqNs+hSV3uno0BoMWng6jb30+1TFJFjnktB1KjE0iOiKUgNw/r+o/68S1dqqN8kE1OqvrNWFey0zNJib2rmuLPRFOjTDw1fesSXxJPaUVFRSScu0bNJo8GYTp5u1KQX8DdS3HUad2Qj8KXq733x6G+M1kpDyol8QFIT7tP3PV41RQeeh6vJp5qZXyaNiI87ILGuvNnfE1Xv//Rq10AvdoFsHjechLvJNGrXQAXwi8/rxD0UkZ6Brdjb6umy2GXqe9TX61Mfd/6RJ4pf6xHo5aNmLhoIrvW7GJ58HK1Zd6tvfnp3E/I5I8eI69Tvw7pKenPJfEByEzP4G7sHdUUfSYS1zKDt+v61iPmbFQFW6hYFXNTpm6ZjamFKelJaRQWFNK4vQ+XT17UVvX/kfSLsRTlFWBZ6j5j3dSNtHLur09StZ4TzdZ8yN3fznF6xNcU5b8YYwmfxvPo9srNzSUoKAhfX1/8/PxYtWpVhWV///13evfujZeXFz179uTIkSNaj1nvk589e/ZgYWHB66+/Tt26dVVTt27dcHFxYdeuXfTr14/Zs2dz7tw5wsPDmT17NlD85Iafnx+Ojo5MmjSJyMhIQkNDmTp1KgqFAiOjZ396IT9HSeTmY7SeG4hto9q80tmHRiO6ceGH4mZyha05RvLiZttre04jMzOh5fQhWLpWp+X0IRgrZFzdVdyacnHNIRq9252a7RphWdeR9l+/R3rsXeKOniMvI5vLm47iN2Modl51sPd2oXnQQC7/9LvqG9HzdmlvCHIzE7oED8HW1ZEuwUOQmMhUrUPGMgmmto9auk6vO8SrI7pTz9+H6p616TE7kDObjpKXo+RmWDT5OXn0mv8O1rWr4dK2EZ2CAjixfHelxFae/TuPYGZWlU9nT8Slbi0+nT0RhYlC1Tokk8uwKWkZSklOVUuc7iWnkp9fQNz1eHJz/l1zvfDvHN97nCpmVRg5fSQ1XGswcvpI5CZy/tz9JwBSmVTVMmRoZMgHCz7gwqkLbPl2C5a2lqrJWGLM5bDLKHOUvD//fRxrO+Lb1pdhU4axdfnWSovv9N6/MTGrwpDgYVR3dWJI8DBkJnJV65BEJtVoGapIZnoGchM5A4PexLaGPW0HdqTNgPbsWb5DdwE8RkG2krhf/qTx/GFYNK5NtS6+uLzXnasr9wMgszXHUP50Y38az3+b7IQUIoLXIbWqiszW/B+tX5mKtDhVZP78+URERLBmzRqCg4NZunQp+/fv1yh35coVxowZQ//+/dmxYwcDBw7k/fff58qVK+Vs9d8Tyc+ePfTs2ROpVPNFVAEBAfz111+MHDkSNzc33nrrLcaOHUuPHsVPp0gkEoyMjPj2228pLCxkwIABjB07ljZt2qgNin5Wf8/YQNL5G/T6OYhWs4YS+uVWrpc8sj70zDe49GwOQF5GNvsCv8ChqRv9987CzrsOe4cuIL+k7zpizWHCv91DqzmB9Ns9A4qK2D/sS9U3nL9mbCDu6Dm6rZ1EtzWTiPv9PKc+/1lrcfxTuRnZbBy2EOembozYPQsnLxc2vLVANeanQc8WTAx91N0Yseskx5ftpMect3lz/SfEh1/l0NxNACgzc1g35HNMrKoyYtdMes9/h7CNRzmx4sVJfjIyMhnxxnh8m3ux/fB6Gvs05J2AcWSXdAd07+PP3xcPVnIthbKyM7KZHjgdjyYefL3na+p51WPa0GmqMT+te7ZmQ9gGAFw9XbFzssPLz4sNYRvUJncfd7Izs/l0yKeYW5uzePdi3l/wPvs37mfL8sobO5Kdkc0Xw2bj1rQ+s3YvoI5XXRa8NUsVX/OeLfkmtOJv8WUtHfMF9jUdmHvwKzoP68HX7y3k2nnN1tznJWL6etLOX8dv66d4zn2LKwu2cHvvaQC6XvgWp94tnrgNma051k3dMHNzovOZpXS98K1qepr1X3ZZWVls3ryZKVOm4OHhQadOnRg+fDgbNmzQKLt7926aN2/Om2++ibOzM2+88QbNmjVj3z7N1ws8C4OiItEp+SSHDx+mRYsWVKlS/B6O8+fPM2jQIM6ePYtE8s+z+uU1Bmu7ii+cO4Yv92m1IUu730JeRJcuv1g/86ILvb3HVHYVdMrK4PFvB38Z/C/35Y+xz52NOt3+X9X6a21br97WbKU8c+YMgwcPJjw8XNXQcOrUKd555x3Cw8MxNHzUDnP16lXy8vKoV+9RV+vbb7+Ns7Mz06ZN01o9xYDnp7B06VKOHj3KiBEjyMzMZMGCBbRv3/5fJT6CIAiC8CLR9dNeSUlJWFpaqvWw2NjYkJubS1paGlZWVqr5derUUVs3Ojqav//+m4EDB2q1Tnrf7fU0Fi5cSHx8PH369CEwMBAnJyfVuB9BEARBECqWnZ2tMbTk4WelsuJXJqSkpDB27Fi8vb3p0KGDVuskWn6egouLC2vWrKnsagiCIAiC1un6kRaZTKaR5Dz8LJeX322ZnJxMYGAgRUVFfP3112pdY9ogkh9BEARB0GNF6Lbby97entTUVPLz8zE2Lk47kpKSkMvlmJmZaZS/e/cub775JgBr165V6xbTFtHtJQiCIAh6rLBIe1N53N3dMTY2Jjw8XDUvLCyMhg0barToZGVlMXz4cAwNDVm/fj329vY6iVkkP4IgCIIg6IxCoaBPnz5Mnz6d8+fPc/jwYVatWqVq3UlKSiInp/iVHitWrCAuLo558+apliUlJfHggXZfRiu6vQRBEARBjxXquNsLYPLkyUyfPp2hQ4diamrK2LFj8ff3B8DPz4+5c+fSr18/Dhw4QE5ODq+99pra+n379uXzzz/XWn1E8iMIgiAIekzXY36guPVn3rx5qhad0iIjH/0UTHlvfdYF0e0lCIIgCIJeES0/giAIgqDHKufXGyuXSH4EQRAEQY89j26vF43o9hIEQRAEQa+Ilh9BEARB0GOi20sQBEEQBL2ij8mP6PYSBEEQBEGviJYfQRAEQdBj+jjgWSQ/giAIgqDHCvUv9xHJjyAIgiDos+fx8xYvGjHmRxAEQRAEvSJafgRBEARBjxVVdgUqgUh+BEEQBEGP6eOj7iL5qQQJRi9/ni15yUOcLnGv7CroXG/vMZVdBZ379czSyq6CTt0fGljZVdC5M6EOlV0F4T9IJD+CIAiCoMcKDfRvwLNIfgRBEARBj73kDfXlEk97CYIgCIKgV0TLjyAIgiDoMTHgWRAEQRAEvaKPb3gW3V6CIAiCIOgV0fIjCIIgCHpMH3/eQiQ/giAIgqDH9PFpL5H8CIIgCIIeE2N+BEEQBEEQXnKi5UcQBEEQ9Jh41F0QBEEQBL2ij2N+RLeXIAiCIAh6RbT8CIIgCIIe08cBzyL5EQRBEAQ9po9jfkS3lyAIgiAIekW0/AiCIAiCHtPHlh+R/AiCIAiCHivSwzE/ottLEARBEAS98lK2/OTl5bF8+XJ27NjB3bt3sbGxoXPnzowdOxZTU1Pat2/PmDFj6NevH0OGDKFp06aMHTu2wu2tXbuWn376ibi4OMzNzWnTpg0ffPABtra2zzEqcPBwptfsYdi51SApKp6dU1ZxO+JGheVbDOtCyxHdkZkqiNhzir3Ba8jLUQLg3tmXgBUfqJW/uDeEn0ctVi3vOGkAZtWsuXMplr3T13L7YsV/S1scPJzpNnsYdvWKY9wTtIo7j4mx6bAutBhZHOOlPafYP20N+SUxWjrb03XmW9TwrUt2Wgan1xzk7xV7VOv6Bw+h2bAuatvbN201oWsO6SQ2Q5mEJnOGUrNbEwpy8ri0fA9XVuwrt6xlA2eafh6IhXsN0iNvEfLxKlIu3FAtb/hhP1wC2mJkIuPOnxc4HbSG3JQHAJi+Yk+TOUOxbVIXZVoGkasOcfnbPeX+neehtkdtxs4Zi3M9Z+Ki4lgatJSYCzHlljWWGvPmxDdp27stMhMZF/6+wLfTvuXenXsAtOjcgqkrp6qtc3zvcea8O0fncWiDUqlkwLBxBE0YRVNvz8quztORSKkyajzSV1uDUkn2tp/I2f7LY1cxtHPAYtmP3P9sMvkXwjWWVxk7icJ7SWRvXK2bOv8DhjIJ9T4fhl33ZhTmKIldtpvY5bsfu45FUzc8lo7mRNNxavPtejTDJWgg8mpWpIVEcunD78iJT9Zl9bVCH7u9XsqWn4ULF3Lw4EFmzZrF/v37mTt3LidOnGDixIkAbNmyhW7duj3VttauXcsPP/zAxIkT2b9/P0uWLOHGjRsMHz6cwsLnd8pIFDKG/DiJ2JBIlvf4lLgz0Qz+cRIShazc8vW7NKHt+H7sDFrFj4PmUMPLBf/JAarlti6OXDkUxvwmo1TTr5+sLF7m6sj/Fo/mz2U7WdZ1MncuxTL4x4lI5FKdxzhw9STiTkfyfY9PuRkWTcBjYqzXtQltxvdj7+RVrAuYg5OXCx2DSmI0MCDgx4lkpdxnZbcg9k5Zhd+YPjTo/eqjfeDqyJHPf+JL31GqKfznP3QWn/fUAKw9a3F4wFxCJq/Gc0I/anRvolHOSCGj3bpJJIZEsq/zVJJCo2m7biJGJfvBZXB76gS04cSYZRzqOxOFvSXNvhiuirvduonk3nvAPv8phHz8Iw3e780rfVvoLK7HkSlkzFgzg4iQCN7v/j6Xwy4z/cfpyCo4poMnDObVLq8yf9x8JvabiLHEmE+/+1S1vKZrTU4eOskbPm+opsUfLX5e4TyT3Fwlk4LnEXM9trKr8o+YvP0exi5u3A/6gMxlX6EY9BbSlm0eu06V0RMwUJiUu0zePwB5lx66qOq/4ho8GLNGdQjrP5PLH/9A7Yn9sevRrMLypu418PxhAgYG6n1F5r51abh8HLHf7uFkx08oVObTcMX7uq6+VhRqcfqveCmTn+3bt/P+++/TokULnJycaNGiBdOnT+fo0aMkJiZiZWWFXC5/6m0FBgbSvn17nJyc8Pb25ssvv+TKlSucP39ex5E80qBHc/Jy8jgwZyPJVxPY99k6lBk5eHQv/yJtHtiFk6v2E/XbWRLOX2Nn0A94DWijSmBsXaqTGBVPRlK6asq5nwWAS+uGJEbFc27bcVLjEjk0/2eq2lli6+qo0xjr92xOfk4eh2dvJDkmgYOfrSM3Mwf3CmJsGtiFU6v2E/3bWW6fv8aeyT/QeEAbjOVSTG3NuXMpjr1TfiTlxl1ijp7jxl8XqdGkrmp9GxdH7kTcIDMpXTU9bDXSNiOFjDoBbQmdtp7UCzeI3x/KpWW7cQv01yjr3LsZ+TlKzs7YxP2YBMKmrSMvIwfnnk0BqN6hEbE7T5J48grpkfFcWrYbBz8PAOS25qRejCXkkx95cP0uCb+d487xi9g2cdNJXE/SumdrcnNy+WH2D9yMucmK6SvIzsymVfdW5Zbv+L+OrF2wlohTEdyMvsnijxfj1tiN6q9UB6CGSw1iI2NJTUpVTZn3M59nSP/K1euxDBrxATcTbld2Vf4ZmRy5f3cyv1tCwdVolH8fI2fLJuQ9+la4irRtRwwUCo35BgoTTCd/huK1QRQk3tVlrZ+aoYkMx0Htifx0NQ8uXCdp32lufLOLGsM6l1vecUhHmuyeiTIpXWOZ86ie3N5ynFvrDpN19TZXpvyIzM4CiVVVXYfxzIq0OP1XvJTJj4GBASdPnlRrmfHy8mLPnj1YWlrSvn17tm3bplp2584dBg8eTMOGDRkwYABXrlxR21ZoaChK5aP/FB0cHNi7dy/16tUDYMiQISxdupSAgAAaNWrEoEGDuHr1qlZjquHlQtzpSLV5cWFR1PB20ShrYGiAY6Pa3Dj1KI74szEYSYxxqF8TKG71SL5+p9y/lZWagV1dJ2r61MXAwACv19qQcz+LlFjd3rCcvFy4WSbG+NAonCqIsXqj2sSFaMZoX78mGYlpbBuzBGVmTvG2fetSs2k9Yv++DIDUVIFZNSvuXX8+/xlZetTEUGJEcmiUal5iSBTWXnWgzDdIG28XkkLU90PS6ShsfFwBUKZm4NihMQoHS4zkEl7p04LUiOLWhJzENI6/u5T8krhtm7hi17wed0vift7qedfj0ulLavMuhV6ink89jbIGBgYsHL+QM3+e0VhWxawKUNzyc+vaLd1UVodOh1+gqbcnG1Z8WdlV+UeMa9cBYyPyL0eo5uVduoCxW32N8xbAoKoZVQLfJXPpFxrLDB2qYSCVkj7uHQrvJOi03k+ran1nDCRGpJW676SduoK5t2u58dl0aMzFscuIXaHZjWz1an0S955Sfc6JS+J4k7HklXRH67vc3FyCgoLw9fXFz8+PVatWVVj20qVLvPbaazRq1Ij+/fsTERFRYdl/66VMft58803WrVtH+/btCQ4O5sCBA+Tk5ODi4oJEItEov337drp06cKOHTuoUaMGY8aMoaCgQLWtQ4cO0aZNGyZPnsyvv/5KWloaderUUWs9WrFiBZ07d2bbtm3Y29szYsQItYTpWVW1s+BBYqravIzkdMwdrDTKys2qIJFL1coXFhSSnZqBWUl5m9rVcGndkHG/LWT8H1/S6ePXMZIYARCx+yRRv4UzfGsw06LX0DloED+PWqxqGdIVUzsLHtxVjzEzOR2zao+JsVT5ooJCskrF+NDYE4sJ3BpM/JloLu8LAcDGpTpFhYX4jenD+yeXMGLfHDz7l98aoQ0KOwtyUx5QmFegmpeTlI6xQorM0lS9rL0F2XfT1OblJKdjUrIfLny5naL8QvqdWcKAqO+xbebG8VHfaPzNPiGL8P81mOSwaG7uCdF+UE/Bys6Ke3fvqc1LS07DxsFGo2xRURHhx8PJSM9Qzes9rDfp99K5fvk6AE51nPBu483K31fyw7EfeOuTtzCWvPhDFwf27cHH749E8ZQtzi8KQ0triu6nQ36+al5RWioGMhkGVc00ypsMH03OkQMUxN3QWFZw/SoPPptMYWL5X7oqg8zegryUBxSVui6VSekYKaRIrEw1yp97ayGJezWvJWMzEySWphgYGeH1UxCtL6yg0ZqJyBwsdVp/bSk00N5Ukfnz5xMREcGaNWsIDg5m6dKl7N+/X6NcVlYWI0aMwNfXl23btuHl5cXIkSPJytLu/z8vZfIzevRoFixYgIODA7/88gvjxo2jVatWbN26tdzyHTt2ZPDgwdSpU4fPPvuMe/fuceLECQD69OnDd999h7u7O7t27eKjjz6iVatWfPvtt2rbaN26NW+99RZ16tRh5syZpKSkqLahDRKFlHxlvtq8gtw8jKSayZxEUdy1lZ+rXj5fWVze3NEGqYmcAmU+v4xZwoE5G/Hs3RL/oEEAmFiYYmprzu6pq/muzzTObTtGnwUjqWKtebPTJolCSoGy/DqXVxbQKF+gzMNYpl5+y7uL+ClwIfb1nfGfNgQAmzrVKSqCe1cT2PTWfM7+/Dvd576NW2dfbYakYqyQUlimroXKPKB4wKVaWbmMgpJlj8rmYygr/k++Sg1b8rNz+f3NhRzuP4ushBRafPmOxt/8c/hifn9zIZYeznh/Nlib4Tw1mUJGXplY8nLzkMg0j2lZzTs1p/+I/qyet5r8vHzsHO2Qm8jJU+Yxd9Rcvp/1Pe36tOPtKW/rqvqCTE5RnvrxK8or/lJnIFEfAyhp7IPEoyHZP615btV7VkYKGYW5Za+1kuuynPtOhdupUpzU1pv9Fne2HCN8yHwMpRIar/+43BakF42ux/xkZWWxefNmpkyZgoeHB506dWL48OFs2LBBo+zevXuRyWR89NFH1KlThylTplClSpVyE6Vn8eJ/ZfqXevXqRa9evUhNTeX48eOsX7+eKVOm4OamOfbB0/PRUxempqbUqlWLa9eu0bp1awDatGlDmzZtyMjI4OTJk/z0008sWrQIFxcXOnXqBIC3t7fGNq5evUq7du3+Vf1bj+pFq9G9VZ9vhcdgLFU/XEYyCXnZuRrr5pdczMYy9fLG0uLy6beSmdtoBNnpxWMl7lyKxcDAgP6LRrF/5no6fRLA3cibhKwrfupp5+QfGHt4Pl6vteb4E56C+Cdaju6FX5kYjaSadc5/TIxlyxtJNffJ7QvFrQbGMyX0WTSKQ7M3cH7rMaIOnyGnZB8kXrmJVS0HfAZ3JPJA6LMHV0ZBbh6GZer68OZaUKa+5SW1hlJjCrKL/9N59euRnJ25iVuHwwE4/u4S+oQswtqrDvfOPupuTTlfHLdh8HpaLh3F2Rkb1VqedGHA6AG8PuZ11efI8EgkZWKRyCTklnNMS2vh34JPvvmEnat3cuCnAwAk3kpkQMMBqpaha5euYWhoyMTFE1k5Y+VzfQBBb+QpMSjTWv4w6SnKzXk0UyqlypgPyVz2FWixxVvXCnLzNL58VHRdPk5RfvF1dWvDUW5vOQZAxKgltI74DnMfV9JLdXfroytXrpCfn4+Xl5dqno+PD8uXL6ewsBBDw0ftMOfOncPHx0c1oNzAwABvb2/Cw8Pp16+f1ur00iU/V65cYceOHXzyyScAWFpa0rNnTzp37oy/vz8nT57UWMfIyEjtc2FhIRKJhNu3b7N8+XKmTJmCVCrF1NSUjh070qFDBwYOHMhff/2lSn6MjdV3ZUFBgdoB/adObzhCxJ5H/cd+7/bE1NZCrYyprTkPktI01s1OzSAvR4mprQXJV4vHtBgaGaKwNCWjpPzDxOehpJgEJHIpCgtTqjesxcnVB1TLioqKuHM5DgtHza6KZxG2/giXdj+K8dX3NGOsYmvOg8Q0jXWzSsV4ryRGAyNDTCxNeZCYRhUbM5y8XYk8GPYoxuhbGMskyEwVZKdmqBKfh5JjEqj1qof2Aixd3zupyKyqYmBkSFFB8X/SCjtz8rNzUaZnlSmbgtzWXG2ewra4K0xmbUYVRxtSL8U9Kp+QQm7KA6o42ZB5MwkbX1fi9z+KOz3qFkYyCZKqCnJTMtClvev3cmz3MdXn10a9hqWtetO/pa0lKYkpFW6jdc/WTFw0kb3r97Jyxkq1ZaW7xADiYuKQyWWYWphyP+W+FiIQSiu8l4SBmTkYGkFh8X/wBpZWFOXkUJT56FgY13XHqJojVYNmqK1v9tl8co/sJ/ObF3OsU+7tFCRlrkupnQUFWbnkpz99N0teygMKlflkxjwaj5aXmkFe6gPkjtaka//7lFbp+mtDUlISlpaWSKWPWgttbGzIzc0lLS0NKysrtbIuLurjPK2trYmOjtZqnV66bq+CggJ+/PFHLl1SH2QplUqRy+VqO/mhqKhHWfn9+/e5ceMGtWvXRiqVsnnzZv7880+18gYGBpiamqptq/Qg6QcPHhAXF1duK9PTyk7PJCX2rmq6eSaaGt6uamVq+tQl/qzm+1KKioq4de4azr6P/n4Nb1cK8wu4cykOl9YN+eTscrVH1x3qO5OZ8oCslAc8uJuKnYv6k102daqRejPpX8dTnpz0TFJj76qm+DPROPmox1jDty63yomRoiISzl2jRqmnmJy8XSnIL+DupTgsatjx2orxVLV/9B9vtQa1yExOJzs1gzYT+vPGhslqm3So70zyVd0MxEy9GEthXgE2Po8uatumbtwLvwZF6s9IJJ+JwdZXfT/YNqlL8pkYlGkZFOQoMS/15J3MyhSpZVUy4pKoUtOW1t+/j6LUWANrz1rkJKfrPPGB4uTkduxt1XQ57DL1feqrlanvW5/IM5Hlrt+oZSMmLprIrjW7WB68XG2Zd2tvfjr3EzL5o8fk69SvQ3pKukh8dCT/WgzkF2Bc79ExlNRvSH70FbXzNj/qMqnDB5E2drhqAsj4ej5Z6yse2FrZHly8QVFeAeal7jsWTd24H35V47p8nKKCQu6fv0ZVD2fVPIlVVaRWZmRr+b6pC7p+2is7O1st8QFUn8uOja2orDbH0MJLmPx4eHjQtm1bRo0axa5du4iPjyc8PJzg4GCUSiX+/pqPFu/atYtffvmFmJgYgoKCcHZ2pnnz5lhbWzNw4ECCgoLYtGkTcXFxXLx4kcWLF3PhwgX69++vto0dO3Zw9epVpkyZQvXq1WnWrOJ3RfxTl/aFIDczoWvwEGxdHOkaPASpiYyIkpYTY5kE01KtBSHrD9FyRHfq+ftQ3bM2PWYFErbpKHk5SuLCosnLyaP3vHewrl0N17aN6BwUwIkVxV1aYT8dxSegHY36+mHlbE+nj1/H3NGG8K3Hyq2btlzeWxyjf/AQbFwd8Q8egsREpmodMpZJqFIqxrB1h2gxojtu/j5U86xNt9mBnN10lPwcJQnnrnL7wnV6LhiBjasjLu0a0TEogONLfwUg6vBZnJvVo/mIbljWtMNncAc8+/nx93e6eRlgQbaS65uP0fTzQKwa1capiw/u73Yj8vviFja5rTlG8uLm9rjdp5Gam+AzYwhmrtXxmTEEYxMZsTtPUVRQyNWf/8Rr2iDsmrlh7ubEq0tGce9MDCnnrpESfo2U8zdo/uU7mLlWp3r7RnhNDSBi8U6dxPUkx/cep4pZFUZOH0kN1xqMnD4SuYmcP3cXf6GQyqSqliFDI0M+WPABF05dYMu3W7C0tVRNxhJjLoddRpmj5P357+NY2xHftr4MmzKMrcvLH8snaEFuLrlH9lNl9ASMXOshae6HvN/r5OzcAhS3AiGVglJJ4e1bahNA4b1kitLTKjGAxyvMVnL7lz9wX/AOZo3rYNvVF+dRPYlbWfzyUamtOYbypxv7E/vtbmoM74pdz+ZUcXXEY/F7PIi4wf0z5b/QU5/IZDKN5OXh57Kvnamo7NO+nuZpvXTdXgCLFi1i+fLlLF26lISEBExMTPDz82P9+vWYmmqO4B8yZAhbtmxh5syZeHl5/b+9+w5r6mwDOPwLJISgIMpS3ANx4gRH3VXrwkH71VV362odtWqLe+9RR+uodWuHWhdqpY5a98CNouLALSgIsle+P9BoDG4wkjy3V67LnPOe5Hk5ITznXYe5c+fq+huHDBlC3rx5Wb16NZMmTUKpVOLp6cnKlStxdXXVvYa3tze///47I0eOpHLlyvzyyy8GXWHvIiE6jlXdpuE9viuV29bjXtB1VnSZqhvfUsa7Gj7TejCiUHsAzm4+RM58TjQf3w1LtZJz247iP+k3ABJj4lnecRJNRnSg56axJMTEc2z1LvY9Tn7O+h3CykZNra+bY5c7F3fPhbC07QRiHmTu1XVidBy/d51GkwldqdiuHqHnr/N756d1LOVdjRbTezC2YFodAzcfIkc+J5pM6IbSSsn5v4+yY2JaHbWpWv74cgaNx3amy1+jSIpL4MjS7RxZkpZs3Dl9hbW9ZlNnwKfU+e5/RN4MY32/n7iViV9UAaNX4TWxC/XXDiEpKpbT09ZxY1tae/inp37iYP8FXPlzL8nRcfzbcTpek7tSrH1dHp6/zu4OU3VjEAJGraLc9wl89NPXWFqruLP3LAf6zNPVe0+XGVQe34lPNo8iOTaBC7/6c+HX7S+MKzPFRccxqssovpnwDY3aNeLa+WuM6DRCN+anlnctBswYQJMCTXDzcMM5nzPO+ZxZFaA/EPL7z7/nzKEzDOswjB4jezDLbxZxMXFsW7WNtfPXGqNqZiNm0U9k6z2AHBNnoo2JIW7VEhIPpF0I5Vq5nuiZE0nYkbGDUd+nCyOXU3Lyl1T6awTJUbFcmbpGN6Or9tmFnO37M3deY/HTUL/DqOyzUXxEe6wccxBx4BwnO03N7PAzxMtmaWUEFxcXIiIiSE5O1v1dDAsLw9raGjs7O4Oy9+/rr4p9//59nJ2dMzQmhVb7Bm17Il2vc4uMZz1JUEyZysQ/VUWSPvwZHO9qlfLF43JMxcbjc40dQqaK6tTF2CFkuuPHchs7hEzX4N4fmfr6kwpm3GzQH0JWGmyLi4ujSpUqLF68mMqV02bU/vTTTxw8eJCVK/XLr127ll9++YW///4bhUKBVqulYcOG9OzZU6+35V2ZXLeXEEIIIT4cGo2Gli1bMmrUKE6fPs2OHTtYvHgxHTt2BNJageLj02YPNmrUiKioKMaPH09wcDDjx48nLi6Oxo0bZ2hMkvwIIYQQZux93N7C19eX0qVL06lTJ0aPHk2fPn10Y3Br1KjB1q1bgbSlYhYsWEBAQAA+Pj6cOnWKhQsXYmOT/r3i3pZJjvl531asWGHsEIQQQoi3kvoe7sql0WiYPHkykydPNth34YL+7E8PDw/Wr1+fqfFI8iOEEEKYMXNcHlS6vYQQQghhVqTlRwghhDBjJj45N12S/AghhBBmTLq9hBBCCCFMnLT8CCGEEGYss1d4/hBJ8iOEEEKYsfcx1f1DI91eQgghhDAr0vIjhBBCmDHza/eR5EcIIYQwazLbSwghhBDCxEnLjxBCCGHGzHHAsyQ/QgghhBkzv9RHkh8hhBDCrMmYHyGEEEIIEyctP0IIIYQZkzE/QgghhDAr5pf6SLeXEEIIIcyMtPwYQQ4zuItc7mRjR5C5wi2NHUHmy6WwNnYImS6qUxdjh5Cp7JYtMXYImc7OY6CxQ8jyzHHAsyQ/QgghhBnTmmHHl3R7CSGEEMKsSMuPEEIIYcak20sIIYQQZsUcp7pLt5cQQgghzIq0/AghhBBmzPzafST5EUIIIcyaOXZ7SfIjhBBCmDFzHPAsY36EEEIIYVak5UcIIYQwY+a4yKEkP0IIIYQZk24vIYQQQggTJy0/QgghhBmTbi8hhBBCmBXp9hJCCCGEMHHS8iOEEEKYsVSt+XV7ScuPEEIIYca0Gfh46xi0WqZNm0bVqlXx8vJiypQppKa+uEPu5MmTtGnThgoVKvDJJ5+wZs2aN3o/afkRQgghhFEtWbIEPz8/5s6dS3JyMoMGDcLBwYFu3boZlA0LC+Orr76ibdu2TJo0icDAQHx9fXFycqJOnTqv9X7S8iOEEEKYsVS0GfZ4W8uXL6dv375UrlyZqlWrMnDgQFatWpVu2R07duDo6MiAAQMoVKgQTZs2pWXLlmzevPm1388kW37q1avHrVu3dM+VSiX58+enTZs2dO7cOUPfa86cORw5coQVK1Zk6OsCOJcuSP0JXXAskZ8HF2/xz5DFhJ659sLyFbt9QuUeTVFn13DB7zC7RiwnOT4RAEu1io/HdsKtsSfJCUkcW7CFgF+2GbyGfUEXOv4zkdnFu+q2fbl/JjnyOxmU3T99LYdmbXjnegJYqFV4TuhEgSaepMQncW7+FoIWGMYHkLNMQbwmdcG+ZH4iL9ziyPeLCX/m51L2Ox+Kta2DpY2au/+d4eiQZSSEPwJAkzsnlcd0wOWjUqTEJxKy6TAnJ/1JakJShtTjZSzVKmqP60Sxxp4kxydxYuEWTixMv46OpQtSd2IXHErkJ/ziLXb7LiYsnXNfuU9z7AvnZseAhbpt6hw21BrTiUL1ypEcn0jQ2n0cnLIGjNSvX7B0YbqM70H+EgW5dfEGi4fM59rZK688rtukXkTcDeevH//QbbNzyEHncd0p/ZEH0RFRbJizlr1rd2dm+C+nsiJb7/5YVa8FiYnE/fU78ev/fOkhFs65sf95CVGjfUk+c9Jgf7Y+g0h9EEbc6qWZE3MmSkxM5POufRkyoDdeFT2MHc5rUahVFJrwFbmaVCM1PpE78zdyd8GmdMvaf1yJfD+0w7pQbhJC7nFjym889D+q25/n61Y4d2iIKqct0SeDCRm2iLhLN99XVd6asae637t3jzt37uDp6anbVqlSJW7dukVoaCjOzs565WvWrEnJkiUNXic6Ovq139NkW36GDBnCvn372LdvHzt27KBHjx5MmTKFDRs2GDu016LUqGm1bBC3jlxgZdPh3A64hM+SgSg16nTLuzX2pFp/H3b4LubPNhPJU7EYtYa00e2vPaQtLh6FWdN2IjuHLqVafx/cmnjqvYZtnly0WvIdKmsrve2rvEcwr9LXusfO4cuIj4whcO3eDKtvxeFtcfAozI7PJ3LEdykeA3zI39TToJylRk3dFYMIPXKBbZ8MJ+zYJeqsGIjl459LsS/qUbRtbfZ/8zP/tBqLxiUnVaZ/qTu+5sK+WGqs+KfVWPb1+om8DSpQbvBnGVaPl/loaNo5WN9mIv8OW4pXfx+KNjGso1KjpvmyQdw+coE/mgznzrFLeC81PPduLapRZcCnBsfXGd+FbC72rPt0LP5951Hyf7Uo3+2TTKvXy6g1agYtHcaFo+cZ3mwglwKCGLhkKOoXfI6faNqjJXXbNjDY3n/h9+TK7cCEtiNYMXox7Yd3pnKjKpkV/ivZdOuFspg7UUO+JebnmWjadcbqo9ovPSbb1wNQaGzS3Wf9aVusGzXLjFAzXUJCIoNGTib4aoixQ3kjBYZ3IptHMc5/PpJrvgvJN+BzcjWtZlBOU7IgbosGE/b7Ts40+I7Qlf64LRyITalCADh3aEiens0JGbaIs40HkXAjFPdVw7HQWBm81ocmNQMfbyMsLAxAL8lxdHQE4O7duwbl8+XLR/ny5XXPHzx4wJYtW6hWzfC8vYjJJj+2trY4OTnh5OREnjx5aNWqFdWqVcPf39/Yob2WEt5VSI5PZM/43wgPvs3uUStIjInHvalXuuUrdv2E44u3c2XnSe6dvsIO38WUaV0bpbUVSo2aMm3rsHvUSkLPXiN4+zGOzvejQqeGuuOLNazEF1vGkpxo2AISF/6I2LBIYsMiSY5PpFq/luwZt5pHtx5kSF0tNWqKtq3DsREriThzjZt/H+Pcz364d2loULZgi7Sfy4kxvxEVfJuAEStIio6noHfaz8X143KEbDpE6KEgIi/c5NzPfuSuURoAu2J5cKrsxqFvFxJ58RZhRy5weto6CrV8/V+Yt6XUqCndtg7/jVxJ2NlrXPn7GAHz/fDobFhHt+Zpddw/7jcigm+zd9QKkmLiKdYsrY4KSwvqTOhM/WlfERkSanB8wbrlOPnLNsIv3uLWwfNc3HiAfB+VzvQ6pqeK90ckxifw2/hl3A6+xYrRi4mPicOrafV0y2uya+g7bxDevVtx/1aY3r7CZYtSvHIJfuo7k5DAq5zcFYDfvA007dHyPdQkHWprrBs2JWbhHFIuXyLx4F7i1/6GdbNWLzzEqk59FBqNwXaFxobsvqPR/K8dKaH3MjPqTHH5agjtun/Ljdt3jB3KG7HQqHFu+zEhI34l9swVIv4+zO2fN+DSpbFBWcdWNYnaf4Z7v24l4dpd7i39m6gDZ8nlnfZZdvq8Hnfmb+LhjgDir9zh2g8LUObMTnZPwxYKcxQfH09ISEi6j9jYWACsrJ4mik/+n5iY+MrX7dOnD46OjrRu3fq14zHZ5Cc9SqUSlUpFdHQ0vr6+VKtWjTJlytCoUSN27NihK+fu7s6sWbOoUqUKPXv2BOC///6jVatWlCtXjubNm3Pw4EFd+aSkJEaPHk3FihWpXr06S5YseedY81Qsxq2jF/S23Tp2kTyV3AzKKiwUuJQrws3DQbptt48HY6lS4lSqAM6lCmCptOR2wMWnr3X0IrkrFAWFAoDCH5dn//R17B618qVxVe7elOjQh5z98793qZ6enKULYKGy5P6xp/GFHrmIwzPxPeFYsRhhR/R/LmFHL+L4+OeSGBFN3o/Lo8mdE0trFYVaViPibNqVaFxoJLvaTib+fpTe8Sq79K/CM5JjqQJYKC2588w5uHNE/xw8kbtCMe48d+7vHLtInoppdVRls8ahRAH+9B7J3eOXDN4rPiIad5+PUFpbkc3FngK1PQgLNM7VeLEKxblwNEhv28VjQbhVdE+3vFN+F1RqFcOaDiTsun4S4FzAhcj7kYTdeLr9etA1CpctiqXSMuODfwVlkaKgtCT5/FndtqRzZ1C6lzI4pwAKWzuydelJzNzpBvsscudBYWVFZN+vSL17O1PjzgxHT57Bq6IHqxbMMHYob8SmdCEUKiXRx57+vj06cp7sFdwMzmHYn7u5McHw+9Hy8fdHyNilPPjr6feiVqsFhQKlbeZ/v7yr9zHm59SpUzRs2DDdx+nTpwH9ROfJ/zXpXCw8ERMTQ48ePbh27RoLFix4adnnmeSYn+clJSWxe/du9u/fz4QJExg/fjxXr15l8eLFaDQaFi1axNChQ6lVq5Yu29y9eze//fYbqampXLp0iV69evH111/TpEkT/P396d27t64V6cSJE3h4eLBhwwZ27drFxIkTqVWrFkWLFn3rmLM52/Pg4i29bbH3I3Esnt+grNouGyprK2LuRei2aVNSiYuIxjZPLrSpWuLCH5GalKL3WiprKzQ5sxMX/oh/vv8VgHxVX3yVorS2okLnBuwYsjhDx49onO1JeC6++LBIlBor1Dmz68brAGhc7Im8oP9zib8fib172s/lzIz11Fn2HT7H55CanEJc6EO2e48GICkqljt7zjw9UKHAvUsD7u4LzLC6vEg2Z/t0z4HS2grrnNmJf6aO2ZztCX/+3IdF4vC4jolRsazzGfPC9/p32FIa/tiTHkGLsLC04Pp/Zzgy868MrtHrsXfOya2LN/S2Rd6PJL97gXTLXz9/jeldJ6S7L/L+Q7LZ2WBlbUXi47FsDnkcUaqUaGxtiI54lO5xmcUipwPaqEhITtZt0z6MQKFWo7C1S9v3DJsvvyZ+53ZSrl8zeK2Uq5d5NNo3s0PONG1aZc2uOpVzTpLCo9AmPT2HSWEPsdCoUea0JTn86YVSfLD+76SmeH5y1PAgdHna34HoI/pJvnO7+igsLXl05Hwm1iBjvI8xP1WqVOHChQvp7rt37x5Tp04lLCyMfPnyAU+7wpycDMebQtr4ni+//JLr16+zbNkyChUq9EbxmGzLz8iRI6lQoQIVKlTAw8OD77//nk6dOtG8eXM8PT0ZM2YMJUuWpFChQnTt2pWHDx/y4MHTbpzWrVtTpEgRihUrxtq1a6lYsSK9e/emUKFCdO/enU6dOhEVlfaL4eLigq+vLwUKFKBz587Y2dm98CS/LqVGTcpzXVApCclYqg3zVdXjPuXkxGT98olJWFqpUGqsSElnH4Clleq1Y3L3rkpSbAIXtx59deE3oNRYkfpcfKmP47NQ68entDb8uaQmJmPx+OeSLb8TyXEJ/NtxGjs+HUfs7XCqzfgq3fetMLwNOcsU4tSkN1sf4m28yTlI99wnJmNp9XrXKjmL5CH09FXWthrNlq9+xME9P5V6G+ePk1qjJum5uiQnJqF8zbo86/LJS0Tci6DjmC9Ra9S4FMxN4y+9AVCqjHAdp7ZGm6RfN21SWlKmUOmP81CVr4SqdFnifl/23sITr2apUaN97vfyyfPnv3uepcxli9uiQTw6GkTE9iMG+7NVcKPAyM7cmbeBpLCHGRqzKXJxccHV1ZWAgADdtoCAAFxdXQ0GOwOkpqbyzTffcPPmTVasWIGbm2GPyKuYbMtP3759adgwbTyFWq3GyckJS8u0pvGWLVuyY8cO/vzzT65cuUJgYNqVf0rK06vyvHnz6v5/9epVSpfWHzPRv39/3f/z5cuH4pkmUltbWxISEt4oXq+vm1Plm+a653dOXjb4o2ipVpIcZ9j/mfx4ptLzf1AsrVQkxSVgYWlh8IfzyWsnx71+nMWbeHFh8yG0KRl7J5iUhCQsnovP4nF8Kc/Fl5KQZPBzsbBSkvL451J9dg9OjP2NWztOArCv5xxaHvkRhwpFeXDisu6Y8kNbU+LLRuzrOZfIC5k/GyMt7hecg/hX19HSSklS/Mv7vgFyFHKhxvB2LKnSj9jQh0Bai13dCV0I+Nkvw8/d85p//SnNv/bRPb988hKq55M7KxWJ6XyOXyUpIYk5vafxzc/f8UvgSqIeROE3fz1fjOhKXHTcO8f+5gElolDp1+1J0qNNiH+60cqKbN98R8zPM+EV4xfE+5WakIjiud/LJ8+f/+55QumYg5K/jwSFBZe+mmrQCp69UnHcVw4nctcJbk79PXMCz2Afwr292rZty7Rp08idOzcA06dPp2vXp7OOw8PDUavVZMuWjbVr13L48GHmzZuHnZ2drpVIpVJhb2//Wu9nssmPg4MDBQsWTHff4MGDOXHiBC1atKBt27Y4OTkZDJRSq5/ORlEqX/5jepJUPUv7ht1Cp1fu5KLfYd1zz97NsHHKoVcmm5M90Y//oD0rLiKapPhEbJzsCb+cNuBQYWmBJmd2YkIfolAo0OSyRWFpofvjl80pB0lxCcRHxb5WfJZWSvJXK8GRea+/jsLrir0bgfq5+DTOOUiOSyAxMva5suFYP/dz0TjZE3fvIWoHO7LldSTi3PWn5W+HkxD+iGz5HHXJT+VxHXHr+DEH+szjRga3Yr1I9N0Ig3Ng8/gcJDxXx+i74dg469fRxsme2HsPX/k+TmUKpQ1Qf+ZzEhZ4DStbDdb22Yl7EPXigzPAzpXbOey3X/e8Wa9W5HCy1ytj72TPw9AI3saV08EMqNGLHE72PAqPomyt8kQ9iCQhNv7VB2ew1AdhKOxygIUlpKZdOCly5kIbH4825umUW2XxkljmyYvtEP2uSrvRU0jY+TcxP2WtcTKmJPFuOKpcdmBpAY9/L62cc5ISl0BKZIxBeVXuXJRck9aNfv6z4XrdYgC21UrjvnwIkXtOEdx7htGWl3hTb/r3KjN069aNBw8e8M0332Bpaclnn32mtzTNZ599RqtWrejTpw/bt28nNTWVHj166L2Gl5fXay87Y7LJz4tER0fj5+fHn3/+iYdH2joUe/bsAV78AShYsCDnz+v327Zp04YOHTpkWFzxkTHEP/PLdifgEp69vfXKuFYuzuE5Gw0P1mq5d+oKeT2Lc/NQWpyuldxISU4h7HEikJKcgmvFYtw6mjbgNq+nO3dPXXntX07HEvmxUCq5e/Lyqwu/oYjAEFKTUnCsVIywI2nxOXm58+CkYXz3jwdT+mv9n4uTZ3HOzt5I4sNoUuITyeGWl6jgtCRQnSs7Vjltib6edmVQdkAr3DrUY1+vudzY8n4SH4D7gSGkJqeQu2Ix7jw+B65e7oSmcw7ungim0nPnPo9ncY6ld+6fE3MvLcnSONjpEp1cRV1JjI7L9MQHICYympjIp3/4Lx2/gHcvH70yxSuXYOPcdW/82tlyZGfAr77M/HIikY+7EsrXq8T5Q5k/Zis9yVeCITkFZYlSJJ9LG0umKlWW5EtBeuc0+eJ5Ir5sp3dszkWriZ49haQTx95rzEJfbOBVtEnJZK9UXDdmJ7tXSWJOBhv8Xlpo1JRYNRxStZz/bIRBd5bGvQDuS315uOtEWuKTya2spsbS0hJfX198fdMf+7Zr1y7d/3/99dd3fj+THfPzIlZWVmg0Gvz9/bl58yZ79+5lzJi0K7IXTalr27Ytx44dY8mSJYSEhLBgwQIuXbpE5cqVMy3Oi1uPYm1nQ91RHcjl5krdUR1Q2ai58Lh1SKlW6bUMnVy+A88eTSnWsBIuHkWoP74zZ37bTXJ8IsnxiZxbu5f6E7rg4lGEYg0rUbl7E44v3v7a8TgWz0fk9VCDcSsZISUukatr9uI1qQu5yhUhX6NKlOzZhAuL0uKzdsqBpXVa98J1v6NY5bCh0pgO2Lm5UmlMB5Q2akI2HUabksrlP/6jwoh2OFdxJ4d7PqrP6c2D48GEn7qCXTFXyvRvSeBcP8KOXMTaKYfukdmS4xM5v2YvdSd2wblcEYp8UokK3Ztw6te0Oto8U8fgLUdR29lQc1QHcrq5UnNUB1QaNZc2H37ZWwBw93gw4Zdu0eDHnuQqnhfXqiX4aFhbTi/9J1Pr9yJHtx7Exi4bHUZ2xdUtHx1GdkVtY61rHVKprQxahl4kJjIaaxtr2gzpiFN+F+q0qU/tz+uxZf6GzKvAyyQkkLDzb7J9PQBLtxKoqtbA2qc18ZvWAmmtQFhZQWIiqXdu6T0AUh/cRxv50DixCwBS4xIJW/MvhSf1JFu5YuRs5EWens25u8gPAJWTPYrH65659v0UdaHcXO43W7dP5WSP5ePZXIWn9CTh9n2uj16CKpedbr/COius82P8FZ7fN7NMfqZOncr27dtp2rQpkyZNolevXjg5ORm07jxRoEAB5syZw7p162jWrBnbt29n/vz5uLi4ZFqcidFxrO8ynbye7nyxZRx5KhRlfaepujE67t5V6RXwk678hc2HOPLzZupP7Mpnq77nzonL/DfhaX/zv2NWce/MNT7/Ywj1xnXiwMx1BP/9+ledNk459FqmMlrA6FWEn75G/bVD8BzfidPT1nFjW1p8n576iYLNqwKQHB3Hvx2n41zFncZ/j8OxYlF2d5iq658PGLWKG9uO8tFPX9Ng3VASo2LY02UmAPkaVcJCaUnZb1vy6amf9B7vw74xqwg9fY1Wfwyh9rhOHJ6xjsuPz0G34z/h5p1Wx6ToOPy6TMfVy502W8eRu2JRNj1z7l9Gm5LKpo7TSIqN59N1w2n4Y08ubjrE4elv3tKSEeKi45jedTzuXqUY5zeVohWKM7XzOBIe16Wq90f8dGzxa7/e3G+m41IgNxP9Z/JJ12bM7jWNK6eDMyv8V4pZ9BPJwRfJMXEm2Xv1J27VEhIPpC3+mWvletS16hktNvF6ro9eQszpy5RcO5pC47/i1rQ/iNiWdqFR8dRiHJp/BECuplWx1Kgps3UKFU8t1j0KjumKyskeW88S2LgXoMKxX/T2Pzn+Q2bsRQ6NQaH9EDr7zMz0Al8YO4RMlzvjG4g+KOHvf1mZ9+6wxeuNB8vKZpXJmIU6P1R2y959zbEP3XGPgcYOIdNVuZ25S1V4F8i42aCbr/tl2GtlJrMb8yOEEEKIp4x9by9jkORHCCGEMGNZaaxORpHkRwghhDBj5jj6xewGPAshhBDCvEnLjxBCCGHGstIsrYwiyY8QQghhxsxxwLN0ewkhhBDCrEjLjxBCCGHGZLaXEEIIIcyKzPYSQgghhDBx0vIjhBBCmDHp9hJCCCGEWZHZXkIIIYQQJk5afoQQQggzlmqGA54l+RFCCCHMmPmlPpL8CCGEEGbNHAc8y5gfIYQQQpgVafkRQgghzJg5tvxI8iOEEEKYMVnhWQghhBDCxEnLjxGYww/dQx1p7BAyVYVbx40dQqZbm6u2sUPIdMeP5TZ2CJnKzmOgsUPIdBVPTzN2CFmedHsJIYQQwqzICs9CCCGEECZOWn6EEEIIM2aOA54l+RFCCCHMmIz5EUIIIYRZMceWHxnzI4QQQgizIi0/QgghhBmTbi8hhBBCmBWZ6i6EEEIIYeKk5UcIIYQwY6lmOOBZkh8hhBDCjEm3lxBCCCGEiZPkRwghhDBjqVpthj3ellarZdq0aVStWhUvLy+mTJlCamrqK4979OgRNWvW5K+//nqj95NuLyGEEMKMfQjdXkuWLMHPz4+5c+eSnJzMoEGDcHBwoFu3bi89burUqYSGhr7x+0nLjxBCCCGMavny5fTt25fKlStTtWpVBg4cyKpVq156zLFjxzh06BBOTk5v/H6S/AghhBBmzNjdXvfu3ePOnTt4enrqtlWqVIlbt269sFUnMTGR4cOHM2LECKysrN74PSX5EUIIIcyYNgP/vY2wsDAAnJ2dddscHR0BuHv3brrHzJ8/n1KlSlGjRo23ek8Z8yOEEEKYsfexzk98fDz37t1Ld19sbCyAXgvOk/8nJiYalA8ODub3339n06ZNbx2PJD9CCCGEyFSnTp2iY8eO6e4bNGgQkJboqNVq3f8BNBqNXlmtVsuwYcPo27evrnXobUjyI4QQQpix9zHbq0qVKly4cCHdfffu3WPq1KmEhYWRL18+4GlX2PODmW/fvs2JEye4cOECkydPBiAuLo6RI0eydetWFi1a9FrxmOWYn3r16uHu7m7waNu2rbFDS5elWkX9KV/S88wCvjw2lwpfNX5hWafSBWm9cRS9L/xKm81jcC5bKN1ynt80p8H07nrbNA52NJnXh55nF/Llsbl85NsahaVxPiIKKxV5J/Wh1KnfKHF4GY5ftnxhWdu6lSm2ZRalzv5JsW2zsa3vpbffrnF1iu+aT+nANRRaPgZV3jefGZBZypcvzYF9m4l6GMzBA1uoWKHsC8va2+cgOfGW3uPu7TO6/f36fmWwf8qk4e+jGumyUKuoMOMrml74hUanfqJYzyavPCaXlzsNDv9osN3tG28aHPmRppcW8dGaIdgWz5sJEb85C7WKUjN7UOfiYmqdnk/Bns1eeYy9lzsfHZltsN25WRWqH5hJvavLqPjHEKzzvf1VbUZSqFUUnt6bSudXUOHEr+Tu0fyFZe0/rkSZf6ZT+dIqyu6YgX1DT739eb5uRblD86h8YSUl/hiFxi1fZoef4RITE2n5RU+OHD9t7FAyjFabmmGPt+Hi4oKrqysBAQG6bQEBAbi6uuqNA3pS1t/fnw0bNugezs7O9O3bl/Hjx7/2e5pl8gMwZMgQ9u3bp/eYN2+escNKV40hbXH2KMy6thPZPXQpVfr7UKyJp0E5pUZNi2WDuHXkAr81Hc7tgEs0XzIQpUatV65482pUHfCpwfGNZvfCytaGP1uNYmvvObg3r0bl1/gyzwy5h3RB4+HGlXbDuD18Hs5922LXuLpBOesShSgwbwgRa3YQ3LQf4av/psBPP2BdshAANhVLUGDWIO7/soFL3v3RJiZRYPbg91yb9NnYaNi8cQX79h3Bq2ojDh48xqaNy7Gx0aRbvlRJN+7fDydv/vK6R9lydXT7S5Z04+d5S/X2jxk34z3VxlCZEe2wL1eEfZ+N59QPS3D/zgfXZl4vLG9XIj9ei/qhsFDobS/U8WOK9WrK6aHL+PeTYcRcD6Pa6sFYat58hkdGcxv5BXblihLw6VjOf/8rRQZ+inOzKi8sn71kfjx+HYBCoV/HHJWLU3Z+X0LmbeFQ/R9ITUym7IJ+mR3+aykwvBPZPIpx/vORXPNdSL4Bn5OraTWDcpqSBXFbNJiw33dypsF3hK70x23hQGxKFQLAuUND8vRsTsiwRZxtPIiEG6G4rxqOxQdwHl9XQkIig0ZOJvhqiLFDMTlt27Zl2rRpHD58mMOHDzN9+nS9brLw8HBiYmJQKpUULFhQ76FUKnFwcMDFxeW1389skx9bW1ucnJz0Hvb29sYOy4BSo6ZM2zrsGbWSsLPXuLz9GAHz/SjXqaFB2eLeVUiOT2Tf+N+ICL7Nf6NWkBQTj1vTtD84CksL6o7vTINpXxEZoj990NJKSWxYFLuHLiX80m1uH7nApa1HcfUs/l7q+SyFRk2u1g25M3oh8YGXifI/RNjCdTh0NEzEcjSvTczB0zxYupnEkDuEr9hKzKEz5GhaEwDHr1oRseFfwn/7m8Qrt7g9aiFK55xY5rR739Uy8Pn/mhMXF8/gH8YSFBTMgO9G8uhRDJ996p1u+RIl3Lh06Qr37oXpHmFhD3T7S5Zw4/Tpc3r7o6Nj3ld19FjaqCnYri5nhi8n8sw17mw7xqWf/CjS1fBzC1CoQz1q+o0iISzSYF+B1rUInreFe/+cIObKXU59vxirnLbkMsJn81kWNmrytqvHhWFLeXTmKmHbjnLtp83k7/pJuuXzdqiPp99YEtOpY8He3txZu49bK3YQe/kOQUOXoHa2R5XLNrOr8VIWGjXObT8mZMSvxJ65QsTfh7n98wZcuhi2Pju2qknU/jPc+3UrCdfucm/p30QdOEsu77SLFqfP63Fn/iYe7ggg/sodrv2wAGXO7GT3LPm+q/VWLl8NoV33b7lx+46xQ8lwqWgz7PG2unXrRpMmTfjmm2/o168fLVq0oHPnzrr9n332GYsXL86A2qYx2+TnRaKjo/H19aVatWqUKVOGRo0asWPHDt1+d3d3Zs2aRZUqVejZsyeQttCSj48PHh4eeHt7s3379gyLx6lUASyUltwJuKjbdvvoRXJXKArPXT3mrliM20f1+1RvH7tInkpuAKiyWeNYsgB/NB/JneOX9MqlJCazvf88IkPSRuPnKp6XIg0qcvPQ+Qyry+vSlCyMQqkk9niQblvs0XPYlC9uUOeH63Zyd8pSg9ewtLUBIFvVskRtP6DbnnTzHhdqfklKRFTmBP8GqlSpyP4DR/W2HTh4lKpVK6VbvlTJ4ly8dOWFr/ckOfoQ5ChVAIXKkgdHn35uw49cIGeFYgbnEMClXnmO953P5YXbDPadHb2aG+v2P92g1YICVHY2mRL767ItVRCFypKHz/zOPTwcRI6KbunW0fHj8gT2+ZmQBVsM9uWqXorQrYd1z+Ovh7HPsw9J4Y8yJ/jXZFO6EAqVkuhjT+v46Mh5slcwrGPYn7u5MWGlwWtYPj5PIWOX8uCv/3TbtVotKBQobY17Hl/X0ZNn8KrowaoFxmtNzSxarTbDHm/L0tISX19fjh49yqFDhxg4cKBeC+muXbvo06dPusfu2rULHx+fN3o/GfD8nPHjx3P16lUWL16MRqNh0aJFDB06lFq1aumm3u3evZvffvuN1NRUwsLC6NGjB99++y01a9bk5MmT/PDDDzg4OFC5cuV3jiebsz1x4Y9ITUrRbYu9H4nS2gpNzuzEPfPlmM3ZnvCLt/SOj70fiUPx/AAkRsWyxmfMK9/z0z+Hkq9qSe6dvsKpZTteWT6jKZ1zkRwRhTYpWbct+f5DLKzVWOa0JSX8aeKScPmm3rFqtwJkr16O66u2YWGbDaW9LQpLSwotG411ycLEnbzAreHzSL4X/t7q8yJ58rhw7px+shoaGkbpUiXSLV+iRDFUKhUH9/vh6pqbffuP8N3AUdy9G4qzsyMODjnp2PFzfl00k7j4eJYs+Y0ZMxe8j6oYsHaxJzH8EdpnPrfxYZFYaqywypWdxAf6f9QPd0n7g1KgdS2D1wo/ov8zKti+LgpLSx4cTn/w5PuidrEn6bk6Jj6uoypXdpKeq+OpztMAyNO6tt52pZ0NqpzZUVhaUuH3IdiWLkjk8UsEff8rCXcjMr8iL6FyzklSuP7vYlLYQyw0apQ5bUl+5ncxPlj/u0dTPD85angQutwfgOgjQXr7ndvVR2FpyaMj7/8C6220aWWcIQAic5hty8/IkSOpUKGC3iM2NhZPT0/GjBlDyZIlKVSoEF27duXhw4c8ePC0e6F169YUKVKEYsWKsWrVKqpXr84XX3xBwYIFadGiBa1bt2bZsmUZEqdSY0VKYrLetpTEJAAsrVR621UatW6frmxCMpbqN8tx94xcwdrPx2NppaLx3K/fIup3Y6FRo32uHqmPnyueq/OzLHPaUWCeLzEB54n65zAW2awByDOyOw83/EvIl2NRWKko9OuIdK/M3zcbjYaEBP01LBISElGr0x8D4e5eDDu77Hw3cBTt2vfCNY8LGzcsw8LCghLuxYC05KmlT2cmT5nLEN9+9Ov7VabXIz2WGrXunD2RmpD2ObZ4yTl8lZwVilJmVHuCf/ZLt4vsfbLUqElNSP9z+iZ1tHz8OS0xvjN31+7lZIcpWFipKL/ye6N/Ti01arTPff88eW6hfnEdlblscVs0iEdHg4jYfsRgf7YKbhQY2Zk78zaQFPYwQ2MWb+5D6PZ638y25adv3740bKg//kCj0dCyZUt27NjBn3/+yZUrVwgMDAQgJeXp1V3evE9nmly5coXdu3dToUIF3bakpCQKFy6cIXEmJyRhaaV/mp4kPUlxCfpl45MMEiJLtZLkOMNFol7m/vnrAPwzcCFt/cZim8+RRzfvv2noby01IdEgyXnyx0T7XJ2fUDraU2jFGBQWCq73npjWNZKcds4i/vDn4frdANzoP52SR1dgU8Fdr1vtffjh+z788P3TZtsjR04YJDpqtRWxcXHpHu9Rvi5abdpiYQCft+nOzesnqOJVkf/2HsI5dxnCw9NaCs6eDcLJ0YEePToya/YvmVSjF0tJSDJIACweJ+EpLziHr5KzkhvVVw/m3q5TnJ+y9p1jfFcpCUkGCcCTOr9JHbWPP6e3Vu3mztq9AJztPYdaZxeSo5IbkccuvuzwTJX2u6j//fPk+YvqqHTMQcnfR4LCgktfTU37XXxG9krFcV85nMhdJ7g59ffMCVy8kXfprsqqzDb5cXBwoGDBggbbBw0axIkTJ2jRogVt27bFycmJ1q1b65V5sggTQHJyMt7e3rrxP08olRnzo425G4Emly0KSwu0KWnTCLM55SApLoGEqFi9stH3wrFxyqG3zcbJnpjQh698H6vsGgrW9eCS3xHdl9WTLjRNLtv3mvwk332AMqcdWFrA4zornXKSGpdASpThAF6lSy6KrE6b4nilzRBdt1hyRBSpiUl6XWMpDx+R/DAKVZ73P414wcIVrFm7Wfd80MDeuLg8P43TmTt30r+XTVxcvN7zsLAHPHgQgWve3AC6xOeJoKBL5HXNnRGhv7H4O+FYPfe5tXa2Jzk2gaTI2FccbcixekmqrhhE6L+nOdZzrsEfVGNIuBOO6rk6WjnbkxKbQPIb1DEp/BGpicnEPNNtlBQRTVLEI6zzOhB5LMNDf22Jd8NR5dL/XbRyzklKXAIpkYa/i6rcuSi5ZjQA5z8brtctBmBbrTTuy4cQuecUwb1nfBDnUZgns+32Sk90dDR+fn7MnDmTvn370qBBAyIj05rWX5QZFy5cmJCQEL1pdzt37mTz5s3pln9TYYEhpCankKdiMd02V0937p26YvDFcfd4sG5ws65s5eLcPR78yvdRaqxo8lOftIHUjzl7FCY1OYWHV9K/t0pmiTt3FW1yMjYVno59yeZZitjTlwzqrNCoKbx0NNpULVda+5Ic+sxYnpRU4s9exrrk01Y4y5x2KHPakXgr/QQjM0VEPOTy5Wu6x6FDAVSrpj8urHq1yhw+HGBwrK1tdsLuBVKn9tPp/q6uuXF0zMWFC8F07dKWwLP/6R1Trlxpgi68+txnhsjAELRJKeR85vPo4OXOw3Q+t69iWyIfVZZ9x71dpzjafbaupcTYHgVeQ5uUQo5n6mjv5U7UyctvVEdtSipRp69gW/rpxZgqly1WueyIuxGWoTG/qdjAq2iTksle6enMuuxeJYk5GWxQRwuNmhKrhkOqlvM+w0m6p5+Ma9wL4L7Ul4e7TnCpx7QP5jwK49/Y1Bgk+XmGlZUVGo0Gf39/bt68yd69exkzJm2AcHr3FwFo164dZ8+eZebMmVy7do3NmzczY8YMXF1dMySm5PhEzq/dS90JXXDxKEKRhpWo2L0JJxenzSizccqB5eOm9+CtR1Hb2VBrVAdyublSa1QHVDZqLvodftlbABAbFknw1iPUGdsJp9IFcfVyp/7kLzm11J/E6PS7YTKLNj6BiHW7yDuuNxoPN+waVMXxy1Y8WJJ2Hxeloz2Kx91Fzl9/jlXBPNwcOFO3T+loj8XjGSRhi9bj0NkbuyYfoS6aj3xT+xF/7ipxJ43XlfDEur+2YJ/DjhnTR1OypBszpo8mWzYbXeuQtbU1Li5pCzI+ehTNvv1HmDZtFJUrlaNC+TKsXvkz27fv5uzZIHbs/I88uZ2ZOnkERYsW4vPPmzNoYG+mTvvZKHVLiUvk+p//UX5KV+zLFyFPo8oU69WUy7/8DYDaKQcW1q83Lqb8lG7E3Q7n7MgVWOWyRe2U442OzyypcYnc+XMPJad+hV35ojg1rkzB3t5c/yVtxprVG8QYMs+P/F82xtm7Ktnc8lJ6Vi8enb1G1GtcuGSm1LhEwtb8S+FJPclWrhg5G3mRp2dz7i7yA0DlZI/COu130bXvp6gL5eZyv9m6fSone93My8JTepJw+z7XRy9BlctOt//J8cJ4jH1jU2Mw226v9FhZWTF16lQmT57MihUryJcvH7169eLHH3/k/PnzFC1a1OCYvHnzMn/+fKZNm8avv/6Ki4sLP/zwA82bv3gV1Df135hV1J3QBZ8/hpD4KJZDM9dx+e+0tvCvAn7Cf8ACzq/dS2J0HJu6TKfehK6UbVeX++evs7HTVJJfc/zBP4N+odaIL2i16gcAzq/bx/5JxumTvzPuV/KO60Xh1eNJfRTLvR9XE7X9IAAlj67gxsAfebhuJ3aNqmOhUVNsg/7004i1O7k56Eeith3gTo7s5PHtgtLBnphDZ7jWfZwxqmTg0aNoWrTsxE8/TeKrL9tz5sx5vFt0IDY2Ldn8/H/NWfzrTJRWaWPMunTtz9QpI9i8aQVqtRWbNvvT/9u0FZyvX7+Fd/MOTJ40nB49OnLvXhi+Qyawdm3GtEC+jbOjVlJucldqrBtGUlQsQVPXcmdr2tT+xmfmcbzffK7/8d9LX0PtlAMHL3cAPjk+V2/f6xyf2S6MXE7JyV9S6a8RJEfFcmXqGkK3pg3wrX12IWf7/sydP/a88nVC/Q6jss9G8RHtsXLMQcSBc5zsNDWzw38t10cvodDEHpRcO5qUqFhuTfuDiG1pF1QVTy3mcv853P9zN7maVsVSo6bM1il6x4f9sYsbE1Zi65nWklvhmP4YtCfHC+MxxzE/Cq051trIZhX4wtghZLp6lsadiZPZKtw6buwQMt3aXLVfXSiLy6Y17a4XO8ukVxfK4iqenmbsEDKdyrFIpr6+S470l9d4G/ci3+9EkrclLT9CCCGEGctKU9QziiQ/QgghhBkzxw4gGfAshBBCCLMiLT9CCCGEGctKU9QziiQ/QgghhBmTbi8hhBBCCBMnLT9CCCGEGZPZXkIIIYQwK9LtJYQQQghh4qTlRwghhDBjMttLCCGEEGYlK92QNKNI8iOEEEKYMXNs+ZExP0IIIYQwK9LyI4QQQpgxc5ztJcmPEEIIYcbMccyPdHsJIYQQwqxIy48QQghhxqTbSwghhBBmxRyTH+n2EkIIIYRZkZYfIYQQwoyZX7sPKLTm2N4lhBBCCLMl3V5CCCGEMCuS/AghhBDCrEjyI4QQQgizIsmPEEIIIcyKJD9CCCGEMCuS/AghhBDCrEjyI4QwqsjISFJTU81ylVkhhHHIIociyzh69Ohrl/X09MzESMS70mq1zJ8/n6VLl/Lo0SO2b9/OrFmzsLGxYdiwYVhZWRk7xAyTlJTEgQMHuHz5MhYWFri7u1OlShUsLOTaUwhjkUUORZZRokQJvecKhQKtVotGo0GlUhEVFYWlpSV2dnYcPHjQSFFmvNTUVCwsLAgNDSUgIAB3d3eKFCli7LDeydy5c9myZQuDBw/m22+/ZfPmzVy/fp0RI0ZQt25dhg0bZuwQM8SVK1fo3r074eHhFCpUiNTUVEJCQsiXLx+//PILuXPnNnaIGeLKlStcuHCBhIQEg30tW7Z8/wFlsICAAJYtW0ZISAjz589n8+bN5M2bl6ZNmxo7NPGWpOXHRJQoUQKFQvFaZc+fP5/J0WSOoKAg3f/Xrl3L2rVrGT9+PEWLFgXg5s2bDBs2jBo1ahgrxAwVEBBA//79mTp1KkWKFMHHx4eEhATi4uKYOnUqjRs3NnaIb239+vVMmjQJT09P3ef2o48+YvLkyfTr189kkp8RI0bg4eHB2LFjyZYtGwCPHj1i6NChjBgxgoULFxo5wne3dOlSJk2ahJ2dHdmzZ9fbp1Aosnzy4+/vj6+vL59//jn//vsvycnJKJVKfvjhByIjI2nXrp2xQxRvQytMwuHDh3WPRYsWaT/66CPtqlWrtKdPn9aeP39eu27dOm3dunW1S5YsMXaoGaJq1ara8+fPG2y/cOGC1tPT0wgRZbxPP/1UO2HCBG1sbKx2zpw52nr16mkTEhK0f/31l7ZRo0bGDu+dlCtXTnvt2jWtVqvVli9fXnv9+nWtVpt2/sqXL2/M0DJUuXLltJcvXzbYHhwcbDL1rF69usl8r6TH29tbu2nTJq1Wq/9Z3bRpk7Z+/frGDE28A+l0NhFeXl66x5o1a5g8eTLt2rWjbNmylChRAh8fHyZOnMjy5cuNHWqGUCgU3Lt3z2D7tWvXUKvVRogo4128eJFOnTqh0WjYtWsXDRs2xMrKCi8vL27fvm3s8N5J1apV+fXXX/W2RUdHM2PGDKpUqWKkqDJeqVKl2L9/v8H2M2fOGHTjZlXx8fF8/PHHxg4j04SEhFC+fHmD7R4eHul+B4msQbq9TFBoaCgODg4G2zUaDVFRUUaIKOO1a9eOwYMH06VLF0qUKIFWq+XMmTMsX76cPn36GDu8DOHo6EhwcDCxsbGcO3eOH374AYADBw6QJ08eI0f3bkaNGsU333zDRx99REJCAr179+b27du4uroyb948Y4eXYapXr860adM4cuQIFStWRKlUcv78efz8/PD29mbu3Lm6st98840RI317LVq0YPXq1Xz//ffGDiVTFCtWjL179xp0b61fv55ixYoZKSrxrmTAswkaMGAA165dY9iwYXqJwbhx46hQoQJjx441dogZ4o8//mDNmjVcvnwZADc3N7744guaN29u5MgyxvLly5k6dSoWFhYUL16cNWvWMH/+fObOncvEiRPx9vY2dojv7ODBg1y5coXk5GQKFy5MjRo1TGoWVIcOHV6rnEKhyFKtsh06dNCN1UpKSuLEiRPkzp2bfPnyGZy/rFSv9Bw7doyePXtSvXp1du3aRYsWLQgJCeHs2bPMmzePatWqGTtE8RYk+TFB0dHRjBw5kr///pvU1FQAlEolLVq0YPjw4SbTLWQOzp07x+3bt6lRowbW1tacPHkSa2vrLN9l8qJuO4VCgUqlImfOnFhaWr7nqMTrerbF6lWyaovWs+7fv8+qVau4fPkyKSkpFC5cmHbt2uHq6mrs0MRbkuTHhD169IirV6+iUCgoXLiwwUyMrM4cpp9GRUWhVqtRq9UEBQWxb98+SpcuneWvNkuXLq1LzNOjVCqpX78+Y8eOzbKf29OnT1OiRAndmkU7duzg4MGD5MyZk//973+4uLgYOcKM8+DBA6KioihcuDAAW7duxdPTEycnJyNH9u569+7Nd999p5tVKkyD6bQvCwO2trZ4eHhQtmzZLPsH5EX8/f3p3r07efPm5erVq3rTT1evXm3s8DLEjh07qFWrFgEBAYSEhNC+fXvWr19P7969WblypbHDeyejR4+mYMGC/PLLLxw9epSjR4+yePFiihUrxrfffsuqVau4f/8+kyZNMnaob+z+/fu0bNmS1q1bc/PmTQDmz59Pnz59CAwM5OTJkzRv3pzg4GAjR5oxDh48SIMGDdi8ebNu2/Lly2nSpAkBAQFGjCxjHD9+HKVShseaGmn5MRHmsM7Ps5o3b85XX32Ft7c3FSpUYNOmTeTPn5/Nmzcze/Zs/vnnH2OH+M6aNWuGj48PXbt2Zdq0afz777/4+fmxe/duxo4dy65du4wd4lurXbs2M2fOpGLFinrbT548Sb9+/dizZw/nzp2ja9euHDp0yEhRvp2hQ4dy7do1ZsyYgYuLC5GRkdSsWZOqVavq1vWZNWsWQUFBJjG4u2XLljRp0oTu3bvrbV+wYAH+/v6sW7fOSJFljNmzZ/Pvv//Spk0bXF1dDYYNyGryWZOksyYiqw8qfFPmMP30+vXruoUMd+7cSaNGjYC0gd3h4eHGDO2dxcTEpHs1bWFhwaNHjwDInj07SUlJ7zu0d7Znzx7mzp2r69bas2cPSUlJtG7dWlemQYMGWb717olr167pPpvPaty4MT///LMRIspYT+owYsQIg30KhcIkLibNkSQ/JsLLy0v3/0WLFtGsWTOTWTo/PeYw/dTV1ZXDhw/j4uLC1atXqVevHgCbN2+mUKFCxg3uHX3yyScMGTKEESNGUKZMGbRaLYGBgYwbN4769esTFxfHwoUL8fDwMHaobywyMhJnZ2fd84MHD6JUKvXGadna2pKcnGyM8DJckSJF2LZtGz169NDbvmvXLgoUKGCkqDLOsyvLC9MhyY8Jmj9/Pp988omxw8hUvr6+9OzZk0OHDpGUlMT8+fP1pp+agr59+zJ48GBSUlKoU6cOZcuWZfLkyfz+++9vNNvmQzRixAjGjh1Lt27ddEmASqXCx8eHHj16sH//fgIDA5k2bZqRI31zBQoU4NKlS7i6upKUlMSePXvw8vLCxsZGV2b//v3kz5/fiFFmnP79+9O7d2/2799P6dKlAbhw4QLHjh1jzpw5Ro4uYyQnJ/PgwQNSUlKAtBvzJiYmcv78eZo0aWLk6MTbkDE/JmjUqFEkJibSvXt3XF1dTeoO2c8KCwtj9erVJj39NDw8nHv37lGyZEkg7QaSdnZ2ODo6GjmyjBEbG6tb5+fatWts3ryZQ4cOERgYaOzQ3try5ctZsmQJXbp04dixY/j7+/PLL79Qs2ZNkpKS+O+//xgyZAg9e/akS5cuxg43QwQHB7N27VquXr2KUqmkYMGCtG3b1iQSvB07djB8+HAePnxosM/JyYn//vvv/Qcl3pkkPyaoXr163L59+4UDoE2hj3rz5s3Ur18fjUZj7FAylalfcQYEBLBhwwb+/vtvoqOjKVq0KG3btqV9+/bGDu2dLF++nA0bNqBQKOjQoYPu5p6jRo1izZo1tGnThqFDh5rEgo6mPhW8cePGeHp60rlzZ9q2bcvChQt5+PAhY8eOpXfv3vj4+Bg7RPEWJPkxQUeOHHnp/mfHB2VVtWvXJjIyklq1atGsWTNq165tcos3muoV561bt9iwYQMbN27kxo0b2NnZERUVxfTp000ioXuZu3fvYmVlRa5cuYwdSoapWrUqf/zxBwULFjR2KJmiTJkybN26lQIFCtCtWzfatm1L/fr12bt3L1OmTNGb4i+yDhnzY4KeJDfR0dFcv36dYsWKkZiYaFJr/ezZs4cTJ07g7+/P5MmT+eGHH6hXrx5NmjShZs2aqFQqY4f4zqZPn06DBg1eeMWZ1axbt44NGzZw7NgxnJ2dqVevHg0bNsTT05Ny5cpRvHhxY4eYKbRaLfv37+fy5cuoVCqKFi1qUjdvbdeuHd9++63JTgW3s7MjLi4OgMKFCxMUFET9+vUpUqSIbh0nkfVIy48JSkxMZMyYMfz1118AbN++ncmTJxMXF8eMGTPIkSOHkSPMeIGBgWzfvp1Vq1ahVCo5fPiwsUN6Z6Z2xVmiRAkKFizI119/bXD/tdKlS7Nx40aTman3xIULF/jmm2948OABhQoVQqvVcu3aNQoXLszs2bPJly+fsUN8Zy+71YopTAX39fUlJCSEMWPGcPXqVaZMmcKPP/7I9u3bdQ+R9WT9DmdhYMqUKQQHB7N+/XrdVVifPn2IiIhg3LhxRo4uY8XGxrJ161Z++eUXVq9ejYuLy2vfTPJDl94VJ5BlrzgnTJhAvnz58PX1pVq1avj6+rJz504SEhKMHVqmGTlyJOXKlWPv3r389ddfrF+/nj179uDq6srw4cONHV6GCAoKeuEjqyc+kLZoZcGCBTl79iz169enXLlyfPbZZ6xatcpk72RvDqTlxwTVqlWLn376ibJly+qtfhwYGEjXrl1NolVk/fr1+Pv7c+DAARwdHWnSpAlNmzbN8jf8fJapXnGGh4ezbds2tm7dyvHjx7G2tiY+Pp5hw4bx+eefm0SX5RMeHh5s3LhRd8+rJy5fvoyPjw+nTp0yUmQZy9QG5rdv35558+ZhZ2en2xYfH4+1tTWQNqRArVab1GfV3MiYHxMUExOT7iyo1NRU3ZdTVjdz5kwaNWrE8uXLKVeunLHDyRRDhw5l/PjxnD17lhYtWrB9+3Y+++wzbGxsmDp1qrHDe2u5cuWiffv2tG/fnrt37+Ln58fWrVsZO3Ysc+bMoUWLFvj6+ho7zAxRrlw5Dh48aJD8HD9+XLd8QVb3qoH5WTH5CQgIMFhdvHr16mzcuJH8+fOb1PhJcyUtPyZo0KBBxMbGMnnyZGrWrMmmTZsAGDhwIPny5WP69OlGjvDdabXa176XmSkx5SvOa9eu6RKhrVu3Gjuct/bsApR3795lw4YN1K9fHw8PDywsLLh48SJ+fn588cUXDB482IiRZgxTnApeokQJ9u/fj4ODg27bs63oIuuT5McEPXr0iCFDhrBz505SU1Oxs7Pj0aNH1KhRg6lTp2Jvb2/sEN9Kx44dmTt3LnZ2dnTs2PGlZbPqvc42bNjw2mWfrB0jPiyvO+ZMoVBk2c/ps0xtYD5I8mMOpNvLBNna2jJnzhxu3LjB5cuXSU5OpnDhwll+ETIvLy9di4cprFWUntmzZ79WOYVCIcnPB2rFihXGDuG9kqngIiuS5McEHT16VPf/bNmyAWmDTCMiIlCpVDg5OWXJW0B88803uv9Xr16dihUrGjGazLFr166X7k9ISDC5xRxNzYYNG2jSpAlWVlavbMkzhQS2du3ajB49mjFjxlClShWmTJlC3bp12b59u94NXrOabdu26Y3tSU1N5Z9//jFYoNIUzqE5km4vE9SwYUNu3rxJamoqOXLkQKvVEhUVhUKhQKFQoNVq8fDwYM6cOVn2y8nDwwMHBwcaNWpEs2bNdDdUNCWRkZGMGDECNzc3XeJXu3ZtKlasyJgxY7C1tTVyhCI99erVY926deTMmZN69eq9sJxCoWDnzp3vMbLMER0dzfjx46lSpQotWrRg0KBBbNmyRTcw/2U/gw/V68ZsKufQHEnyY4LmzZvHv//+y6RJk3SzTG7cuMGQIUOoV68eLVq0YNSoUcDrd7N8aGJiYti9ezf+/v7s3bsXJycnGjduTJMmTXB3dzd2eBmiX79+PHjwgNGjR+u6LM+fP8+ECRNwdXVl8uTJRo5QvMz9+/ext7dHqUxrYA8MDOTQoUM4ODjQsGFDvbu8Z0UbN27kn3/+QaVS8fHHH9OsWTPdPlMemC9MgyQ/Jqhq1aosXbrUYM2boKAgOnfuzKFDh7h8+TJt2rTR6yLLquLj4/nvv//YsWMHO3fuJE+ePPj5+Rk7rHdWuXJl/vjjD4OxWpcuXaJ9+/avvIebMI7Y2FgGDBjAnj178PPzo2jRoqxfv56hQ4eSO3du1Go1iYmJukU5s6Jly5YxZcoUqlWrhlKpZP/+/XTp0oUBAwYYOzQhXous8GyiIiIi0t327Do/pjJV/OLFi5w6dYrAwEAsLCwoW7assUPKENbW1ty9e9dge3h4uK41QXx4Zs+eza1bt1i5ciVFihQhNjaWcePG4eHhwfbt29m2bZtu5mVW9fvvvzN+/HgWLVrE/PnzmT59OqtWrUKupUVWId+gJuizzz7j+++/59tvv6VMmTJotVoCAwOZNWsWrVq1IiIigqlTp2bpGVNHjhzB39+fHTt2EBkZSd26dfn222+pVasWVlZWxg4vQ/j4+DBkyBC+/fZb3ZimoKAgZs2aRYsWLYwcnXgRf39/JkyYQKVKlQDYt28fMTExdOjQQdcN5OPjQ48ePYwZ5ju5ceMG1apV0z2vV68ecXFxhIaGZtnWLGFeJPkxQd999x3ZsmVj5syZhIaGAuDs7MwXX3xBt27dOHDgAEqlkhEjRhg50rf35ZdfUqtWLQYPHkzdunXTXdE6q+vXrx9arZZJkybpVs/NmTMnHTp0oHv37sYNTrxQWFgYBQoU0D0/cOAAlpaW1KhRQ7fN0dFRNz08K0pOTtZrfVQqlbruPCGyAkl+TJBCoaBXr1706tWLiIgIlEql3sygmjVrUrNmTSNG+O6aNm1Kr1699P7ImIrnB5IeOnSI8PBwVCqVzPDKAlxcXLhx4waurq5otVr27NlDuXLlyJEjh67MiRMnyJMnjxGjFMK8SfJjokJCQjh79qzB/WnANNal2LlzJ19//bWxw8hwzw4kTU5OxtfXl4sXL8pA0iykRYsWjB8/nn79+nHo0CHu3LnDd999p9sfFBTEjBkzaN68uRGjfHeyDo7IymS2lwlatGgR06ZNI0eOHLpFDp8wlXUpfv75Z06cOEHnzp1xdXU1WPgvKy7iCGn3SerRo4fuD4a/vz++vr4cO3bMZAaom7rk5GSmTp3Khg0bUCgUdOzYkd69ewMwefJklixZQp06dZg1a1aWXbBS1sERWZ0kPyaoevXqdOvWjW7duhk7lEzz/DT+J4nBkxuenj9/3hhhvbMyZcqwc+dO3aDR5ORkPDw82L17twwkNQEXLlwgJSWFUqVKGTsUIcyadHuZoISEBBo2bGjsMDKVqV5NykBS02YqC3AKkdVJ8mOCvL29Wb16NYMHDzbZrpK8efMaOwQhhBBZlCQ/Jig6Opq1a9fi5+dHvnz5DJaYX758uZEiyzglSpR4aWKXVbu9QAaSCiFEZpMxPyZo7ty5L93/7N3Rs6rnb+2QkpLC9evXWbJkCf3796dRo0ZGiuzdyEBSIYTIfJL8CJNy+PBhJk6cyIYNG4wdihBCiA+U3NvLRG3atAkfHx8qV67MjRs3GD9+PAsXLjR2WJkuV65cXLlyxdhhCCGE+IDJmB8TtHr1an7++Wd69uypu3limTJlmDBhAomJiSbR7ZVey05MTAxr166lfPny7z0eIYQQWYd0e5mgxo0b8/3331OnTh0qVKjApk2byJ8/P3v27GHEiBHs2bPH2CG+s+fHxigUClQqFWXLlqV///4yG0wIIcQLScuPCbp9+zZFixY12J4/f37dDTKzsvv37+Pv769bDycwMJBDhw7h4OBAw4YNsbGxMXKEQgghPmQy5scElStXzqBbSKvVsnjxYjw8PIwTVAaIiYmhZ8+e1KxZk5CQEADWr1/P//73P1atWsWCBQvw9vbm7t27Ro5UCCHEh0y6vUzQxYsX6d69Ow4ODgQFBVGtWjWuXbtGXFwcixYtomTJksYO8a1MmjSJ/fv3M2rUKCpWrEhcXBw1a9bEzc2NFStWoFKpGDlyJDExMUybNs3Y4QohhPhASbeXCSpevDjbt29n8+bNXL58mZSUFD7++GOaN29ucKPTrMTf358JEyZQqVIlAPbt20dMTAwdOnTQLeTo4+NDjx49jBmmEEKID5wkPyZKrVbj4+ODhYUFoaGhBAQEEBoaSuHChY0d2lsLCwujQIECuucHDhzA0tKSGjVq6LY5OjoSFxdnjPCEEEJkETLmxwQFBARQs2ZNjhw5QmhoKD4+PowYMQJvb2+2bdtm7PDemouLCzdu3ADSxjDt2bOHcuXKkSNHDl2ZEydOkCdPHmOFKIQQIguQ5McETZw4kSZNmlCuXDn+/PNP1Go1+/fvZ+zYscyePdvY4b21Fi1aMH78eHbu3MmECRO4c+cO7dq10+0PCgpixowZWfbWFkIIId4PSX5M0MWLF+nUqRMajYZdu3bRsGFDrKys8PLy4vbt28YO76316tWLatWqMWTIEDZv3kzfvn1p1qwZAJMnT6Zly5YUL16cXr16GTlSIYQQHzIZ82OCHB0dCQ4OJjY2lnPnzvHDDz8AaWNksnKXkFKpxNfXF19fX4N9LVu2xNvbm1KlShkhMiGEEFmJJD8mqHPnznz99ddYWFhQtmxZvLy8mD9/PnPnzmXixInGDi9TuLu7GzsEIYQQWYSs82Oizp07x+3bt6lRowbW1tacPHkSa2trSpQoYezQhBBCCKOSlh8TlS9fPooWLYparSYoKIhjx45RunRpY4clhBBCGJ0MeDZBO3bsoFatWgQEBBASEkL79u1Zv349vXv3ZuXKlcYOTwghhDAqSX5M0I8//kjfvn2pXr06a9asIU+ePGzZsoUZM2awePFiY4cnhBBCGJUkPybo+vXrNG7cGICdO3fSoEEDANzc3AgPDzdmaEIIIYTRyZgfE+Tq6srhw4dxcXHh6tWr1KtXD4DNmzdTqFAh4wYnhBBCGJkkPyaob9++DB48mJSUFOrUqUPZsmWZPHkyv//+O3PnzjV2eEIIIYRRyVR3ExUeHs69e/coWbIkAFeuXMHOzg5HR0cjRyaEEEIYlyQ/Jio5OZkHDx6QkpICpN0INDExkfPnz9OkSRMjRyeEEEIYjyQ/JmjHjh0MHz6chw8fGuxzcnLiv//+e/9BCSGEEB8Ime1lgqZPn06DBg3YsmULdnZ2/P7778yfP5+8efPSv39/Y4cnhBBCGJUMeDZBN27cYMGCBRQoUIAyZcoQFhZG/fr1sbCwYMqUKfj4+Bg7RCGEEMJopOXHBNnZ2REXFwdA4cKFCQoKAqBIkSLcvHnTmKEJIYQQRifJjwmqXbs2o0ePJjg4mCpVqrBx40YCAwP5448/cHZ2NnZ4QgghhFFJ8mOChg4dSsGCBTl79iz169enXLlyfPbZZ6xatYrvv//e2OEJIYQQRiWzvUzIxo0b+eeff1CpVHz88cc0a9ZMty86Ohq1Wo1KpTJihEIIIYTxScuPiVi2bBlDhgwhPj6euLg4fH19mTFjhm5/9uzZJfERQgghkJYfk9G4cWN69OhBy5YtAfD398fX15djx46hUCiMG5wQQgjxAZGWHxNx48YNqlWrpnter1494uLiCA0NNWJUQgghxIdHkh8TkZycjFL5dNkmpVKJWq0mMTHRiFEJIYQQHx5JfoQQQghhVmSFZxOybds2smfPrnuemprKP//8Q65cufTKPRkXJIQQQpgjGfBsIurVq/da5RQKBTt37szkaIQQQogPlyQ/QgghhDArMuZHCCGEEGZFkh8hhBBCmBVJfoQQQghhViT5EUIIIYRZkeRHCCGEEGZFkh8hhBBCmBVJfoQQQghhVv4PY1kZpe4QrqQAAAAASUVORK5CYII=","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n","sns.heatmap(train_df[numerical_columns].corr(), annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)."]},{"cell_type":"markdown","metadata":{},"source":["## Embarked"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.378151Z","iopub.status.busy":"2024-04-01T06:27:55.377756Z","iopub.status.idle":"2024-04-01T06:27:55.384785Z","shell.execute_reply":"2024-04-01T06:27:55.384101Z","shell.execute_reply.started":"2024-04-01T06:27:55.378107Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 S\n","1 C\n","2 S\n","3 S\n","4 S\n","Name: Embarked, dtype: object"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.386265Z","iopub.status.busy":"2024-04-01T06:27:55.385875Z","iopub.status.idle":"2024-04-01T06:27:55.635178Z","shell.execute_reply":"2024-04-01T06:27:55.633609Z","shell.execute_reply.started":"2024-04-01T06:27:55.386223Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Embarked\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":27,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.638242Z","iopub.status.busy":"2024-04-01T06:27:55.637447Z","iopub.status.idle":"2024-04-01T06:27:55.699106Z","shell.execute_reply":"2024-04-01T06:27:55.698208Z","shell.execute_reply.started":"2024-04-01T06:27:55.638150Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNFalseFalseTrue
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85TrueFalseFalse
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNFalseFalseTrue
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123FalseFalseTrue
4503Allen, Mr. William Henrymale35.0003734508.0500NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked_C Embarked_Q Embarked_S \n","0 0 A/5 21171 7.2500 NaN False False True \n","1 0 PC 17599 71.2833 C85 True False False \n","2 0 STON/O2. 3101282 7.9250 NaN False False True \n","3 0 113803 53.1000 C123 False False True \n","4 0 373450 8.0500 NaN False False True "]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNFalseTrueFalse
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNFalseTrueFalse
38953Wirz, Mr. Albertmale27.0003151548.6625NaNFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked_C Embarked_Q \\\n","0 34.5 0 0 330911 7.8292 NaN False True \n","1 47.0 1 0 363272 7.0000 NaN False False \n","2 62.0 0 0 240276 9.6875 NaN False True \n","3 27.0 0 0 315154 8.6625 NaN False False \n","4 22.0 1 1 3101298 12.2875 NaN False False \n","\n"," Embarked_S \n","0 False \n","1 True \n","2 False \n","3 True \n","4 True "]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Ticket (Assignment)"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.700760Z","iopub.status.busy":"2024-04-01T06:27:55.700330Z","iopub.status.idle":"2024-04-01T06:27:55.708542Z","shell.execute_reply":"2024-04-01T06:27:55.707466Z","shell.execute_reply.started":"2024-04-01T06:27:55.700715Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A/5 21171\n","1 PC 17599\n","2 STON/O2. 3101282\n","3 113803\n","4 373450\n","5 330877\n","6 17463\n","7 349909\n","8 347742\n","9 237736\n","10 PP 9549\n","11 113783\n","12 A/5. 2151\n","13 347082\n","14 350406\n","15 248706\n","16 382652\n","17 244373\n","18 345763\n","19 2649\n","Name: Ticket, dtype: object"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.710291Z","iopub.status.busy":"2024-04-01T06:27:55.709980Z","iopub.status.idle":"2024-04-01T06:27:55.722810Z","shell.execute_reply":"2024-04-01T06:27:55.721839Z","shell.execute_reply.started":"2024-04-01T06:27:55.710231Z"},"trusted":true},"outputs":[{"data":{"text/plain":["'A5'"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["example_ticket = \"A/5. 2151\"\n","example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.726116Z","iopub.status.busy":"2024-04-01T06:27:55.725689Z","iopub.status.idle":"2024-04-01T06:27:55.738095Z","shell.execute_reply":"2024-04-01T06:27:55.737043Z","shell.execute_reply.started":"2024-04-01T06:27:55.726039Z"},"trusted":true},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets\n","\n","# Do the same for the test set\n","tickets = []\n","for i in list(test_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","test_df[\"Ticket\"] = tickets"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.740389Z","iopub.status.busy":"2024-04-01T06:27:55.739797Z","iopub.status.idle":"2024-04-01T06:27:55.755416Z","shell.execute_reply":"2024-04-01T06:27:55.754317Z","shell.execute_reply.started":"2024-04-01T06:27:55.740333Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A5\n","1 PC\n","2 STONO2\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 x\n","10 PP\n","11 x\n","12 A5\n","13 x\n","14 x\n","15 x\n","16 x\n","17 x\n","18 x\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"data":{"text/plain":["0 x\n","1 x\n","2 x\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 A4\n","10 x\n","11 x\n","12 x\n","13 x\n","14 WEP\n","15 SCPARIS\n","16 x\n","17 x\n","18 STONO2\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.790832Z","iopub.status.busy":"2024-04-01T06:27:55.790500Z","iopub.status.idle":"2024-04-01T06:27:55.841011Z","shell.execute_reply":"2024-04-01T06:27:55.839862Z","shell.execute_reply.started":"2024-04-01T06:27:55.790770Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchFareCabin...TcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_x
0103Braund, Mr. Owen Harrismale22.0107.2500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2313Heikkinen, Miss. Lainafemale26.0007.9250NaN...FalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
4503Allen, Mr. William Henrymale35.0008.0500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
5603Moran, Mr. Jamesmale26.0008.4583NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
6701McCarthy, Mr. Timothy Jmale54.00051.8625E46...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
7803Palsson, Master. Gosta Leonardmale2.03121.0750NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00211.1333NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01030.0708NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
\n","

10 rows × 44 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","5 6 0 3 \n","6 7 0 1 \n","7 8 0 3 \n","8 9 1 3 \n","9 10 1 2 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","5 Moran, Mr. James male 26.0 0 \n","6 McCarthy, Mr. Timothy J male 54.0 0 \n","7 Palsson, Master. Gosta Leonard male 2.0 3 \n","8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n","9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n","\n"," Parch Fare Cabin ... TcktName_SOPP TcktName_SOTONO2 \\\n","0 0 7.2500 NaN ... False False \n","1 0 71.2833 C85 ... False False \n","2 0 7.9250 NaN ... False False \n","3 0 53.1000 C123 ... False False \n","4 0 8.0500 NaN ... False False \n","5 0 8.4583 NaN ... False False \n","6 0 51.8625 E46 ... False False \n","7 1 21.0750 NaN ... False False \n","8 2 11.1333 NaN ... False False \n","9 0 30.0708 NaN ... False False \n","\n"," TcktName_SOTONOQ TcktName_SP TcktName_STONO TcktName_STONO2 \\\n","0 False False False False \n","1 False False False False \n","2 False False False True \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False True \n","4 False False False True \n","5 False False False True \n","6 False False False True \n","7 False False False True \n","8 False False False True \n","9 False False False True \n","\n","[10 rows x 44 columns]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SOCTcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_x
08923Kelly, Mr. Jamesmale34.5007.8292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
18933Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0009.6875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
38953Wirz, Mr. Albertmale27.0008.6625NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
58973Svensson, Mr. Johan Cervinmale14.0009.2250NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
68983Connolly, Miss. Katefemale30.0007.6292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
78992Caldwell, Mr. Albert Francismale26.01129.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.0007.2292NaNTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
99013Davies, Mr. John Samuelmale21.02024.1500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
\n","

10 rows × 40 columns

\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","5 897 3 Svensson, Mr. Johan Cervin male \n","6 898 3 Connolly, Miss. Kate female \n","7 899 2 Caldwell, Mr. Albert Francis male \n","8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female \n","9 901 3 Davies, Mr. John Samuel male \n","\n"," Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SOC \\\n","0 34.5 0 0 7.8292 NaN False ... False \n","1 47.0 1 0 7.0000 NaN False ... False \n","2 62.0 0 0 9.6875 NaN False ... False \n","3 27.0 0 0 8.6625 NaN False ... False \n","4 22.0 1 1 12.2875 NaN False ... False \n","5 14.0 0 0 9.2250 NaN False ... False \n","6 30.0 0 0 7.6292 NaN False ... False \n","7 26.0 1 1 29.0000 NaN False ... False \n","8 18.0 0 0 7.2292 NaN True ... False \n","9 21.0 2 0 24.1500 NaN False ... False \n","\n"," TcktName_SOPP TcktName_SOTONO2 TcktName_SOTONOQ TcktName_STONO \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","5 False False False False True \n","6 False False False False True \n","7 False False False False True \n","8 False False False False True \n","9 False False False False False \n","\n","[10 rows x 40 columns]"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","test_df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Pclass"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.843395Z","iopub.status.busy":"2024-04-01T06:27:55.842833Z","iopub.status.idle":"2024-04-01T06:27:56.089225Z","shell.execute_reply":"2024-04-01T06:27:56.087578Z","shell.execute_reply.started":"2024-04-01T06:27:55.843168Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":37,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.092270Z","iopub.status.busy":"2024-04-01T06:27:56.091722Z","iopub.status.idle":"2024-04-01T06:27:56.162888Z","shell.execute_reply":"2024-04-01T06:27:56.161841Z","shell.execute_reply.started":"2024-04-01T06:27:56.092186Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
010Braund, Mr. Owen Harrismale22.0107.2500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85True...FalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
231Heikkinen, Miss. Lainafemale26.0007.9250NaNFalse...FalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123False...FalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
450Allen, Mr. William Henrymale35.0008.0500NaNFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 46 columns

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"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Sex Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SP \\\n","0 male 22.0 1 0 7.2500 NaN False ... False \n","1 female 38.0 1 0 71.2833 C85 True ... False \n","2 female 26.0 0 0 7.9250 NaN False ... False \n","3 female 35.0 1 0 53.1000 C123 False ... False \n","4 male 35.0 0 0 8.0500 NaN False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP \\\n","0 False False False False False \n","1 False False False False False \n","2 False True False False False \n","3 False False False False False \n","4 False False False False False \n","\n"," TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False False False True \n","1 False True False False \n","2 False False False True \n","3 True True False False \n","4 True False False True \n","\n","[5 rows x 46 columns]"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Pclass\"] = train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns= [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameSexAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
0892Kelly, Mr. Jamesmale34.5007.8292NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
2894Myles, Mr. Thomas Francismale62.0009.6875NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
3895Wirz, Mr. Albertmale27.0008.6625NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 42 columns

\n","
"],"text/plain":[" PassengerId Name Sex Age \\\n","0 892 Kelly, Mr. James male 34.5 \n","1 893 Wilkes, Mrs. James (Ellen Needs) female 47.0 \n","2 894 Myles, Mr. Thomas Francis male 62.0 \n","3 895 Wirz, Mr. Albert male 27.0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 \n","\n"," SibSp Parch Fare Cabin Embarked_C Embarked_Q ... TcktName_SOTONOQ \\\n","0 0 0 7.8292 NaN False True ... False \n","1 1 0 7.0000 NaN False False ... False \n","2 0 0 9.6875 NaN False True ... False \n","3 0 0 8.6625 NaN False False ... False \n","4 1 1 12.2875 NaN False False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_STONOQ TcktName_WC \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","\n"," TcktName_WEP TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False True False False True \n","1 False True False False True \n","2 False True False True False \n","3 False True False False True \n","4 False True False False True \n","\n","[5 rows x 42 columns]"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Sex"]},{"cell_type":"code","execution_count":39,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.164709Z","iopub.status.busy":"2024-04-01T06:27:56.164391Z","iopub.status.idle":"2024-04-01T06:27:56.205775Z","shell.execute_reply":"2024-04-01T06:27:56.204761Z","shell.execute_reply.started":"2024-04-01T06:27:56.164639Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
010Braund, Mr. Owen Harris22.0107.2500NaNFalseFalse...FalseFalseFalseFalseFalseFalseFalseTrueFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833C85TrueFalse...FalseFalseFalseFalseFalseTrueFalseFalseTrueFalse
231Heikkinen, Miss. Laina26.0007.9250NaNFalseFalse...TrueFalseFalseFalseFalseFalseFalseTrueTrueFalse
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123FalseFalse...FalseFalseFalseFalseTrueTrueFalseFalseTrueFalse
450Allen, Mr. William Henry35.0008.0500NaNFalseFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
\n","

5 rows × 47 columns

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"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Age SibSp Parch Fare Cabin Embarked_C Embarked_Q ... \\\n","0 22.0 1 0 7.2500 NaN False False ... \n","1 38.0 1 0 71.2833 C85 True False ... \n","2 26.0 0 0 7.9250 NaN False False ... \n","3 35.0 1 0 53.1000 C123 False False ... \n","4 35.0 0 0 8.0500 NaN False False ... \n","\n"," TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False False \n","1 False False False False False \n","2 True False False False False \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 True False False True False \n","2 False False True True False \n","3 True False False True False \n","4 False False True False True \n","\n","[5 rows x 47 columns]"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameAgeSibSpParchFareCabinEmbarked_CEmbarked_QEmbarked_S...TcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
0892Kelly, Mr. James34.5007.8292NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)47.0107.0000NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
2894Myles, Mr. Thomas Francis62.0009.6875NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseTrueFalseFalseTrue
3895Wirz, Mr. Albert27.0008.6625NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)22.01112.2875NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
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5 rows × 43 columns

\n","
"],"text/plain":[" PassengerId Name Age SibSp \\\n","0 892 Kelly, Mr. James 34.5 0 \n","1 893 Wilkes, Mrs. James (Ellen Needs) 47.0 1 \n","2 894 Myles, Mr. Thomas Francis 62.0 0 \n","3 895 Wirz, Mr. Albert 27.0 0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 22.0 1 \n","\n"," Parch Fare Cabin Embarked_C Embarked_Q Embarked_S ... \\\n","0 0 7.8292 NaN False True False ... \n","1 0 7.0000 NaN False False True ... \n","2 0 9.6875 NaN False True False ... \n","3 0 8.6625 NaN False False True ... \n","4 1 12.2875 NaN False False True ... \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 False False True True False \n","2 False True False False True \n","3 False False True False True \n","4 False False True True False \n","\n","[5 rows x 43 columns]"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Drop Passenger ID and Cabin (Assignment)"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.207602Z","iopub.status.busy":"2024-04-01T06:27:56.207299Z","iopub.status.idle":"2024-04-01T06:27:56.215886Z","shell.execute_reply":"2024-04-01T06:27:56.214401Z","shell.execute_reply.started":"2024-04-01T06:27:56.207550Z"},"trusted":true},"outputs":[],"source":["train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.217917Z","iopub.status.busy":"2024-04-01T06:27:56.217536Z","iopub.status.idle":"2024-04-01T06:27:56.228150Z","shell.execute_reply":"2024-04-01T06:27:56.227230Z","shell.execute_reply.started":"2024-04-01T06:27:56.217854Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Index(['Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C',\n"," 'Embarked_Q', 'Embarked_S', 'TcktName_A4', 'TcktName_A5', 'TcktName_AS',\n"," 'TcktName_C', 'TcktName_CA', 'TcktName_CASOTON', 'TcktName_FC',\n"," 'TcktName_FCC', 'TcktName_Fa', 'TcktName_LINE', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_PPP', 'TcktName_SC', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCOW', 'TcktName_SCPARIS',\n"," 'TcktName_SCParis', 'TcktName_SOC', 'TcktName_SOP', 'TcktName_SOPP',\n"," 'TcktName_SOTONO2', 'TcktName_SOTONOQ', 'TcktName_SP', 'TcktName_STONO',\n"," 'TcktName_STONO2', 'TcktName_SWPP', 'TcktName_WC', 'TcktName_WEP',\n"," 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',\n"," 'Sex_male'],\n"," dtype='object')"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["train_df.columns"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[],"source":["# Drop the PassengerId and Cabin columns from the test set\n","test_df.drop(labels=[\"PassengerId\", \"Cabin\"], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q',\n"," 'Embarked_S', 'TcktName_A', 'TcktName_A4', 'TcktName_A5',\n"," 'TcktName_AQ3', 'TcktName_AQ4', 'TcktName_C', 'TcktName_CA',\n"," 'TcktName_FC', 'TcktName_FCC', 'TcktName_LP', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_SC', 'TcktName_SCA3', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCPARIS', 'TcktName_SCParis', 'TcktName_SOC',\n"," 'TcktName_SOPP', 'TcktName_SOTONO2', 'TcktName_SOTONOQ',\n"," 'TcktName_STONO', 'TcktName_STONO2', 'TcktName_STONOQ', 'TcktName_WC',\n"," 'TcktName_WEP', 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n"," 'Sex_female', 'Sex_male'],\n"," dtype='object')"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["# Print the columns of the test set\n","test_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Modeling"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.230086Z","iopub.status.busy":"2024-04-01T06:27:56.229809Z","iopub.status.idle":"2024-04-01T06:27:56.238557Z","shell.execute_reply":"2024-04-01T06:27:56.237679Z","shell.execute_reply.started":"2024-04-01T06:27:56.230040Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{},"source":["## Train - Test Split (Assignment)"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.240103Z","iopub.status.busy":"2024-04-01T06:27:56.239830Z","iopub.status.idle":"2024-04-01T06:27:56.256809Z","shell.execute_reply":"2024-04-01T06:27:56.255463Z","shell.execute_reply.started":"2024-04-01T06:27:56.240056Z"},"trusted":true},"outputs":[{"data":{"text/plain":["891"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["train_df_len = len(train_df)\n","train_df_len"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.314730Z","iopub.status.busy":"2024-04-01T06:27:56.313986Z","iopub.status.idle":"2024-04-01T06:27:56.333564Z","shell.execute_reply":"2024-04-01T06:27:56.332507Z","shell.execute_reply.started":"2024-04-01T06:27:56.314635Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train 596\n","X_test 295\n","y_train 596\n","y_test 295\n","test 418\n"]}],"source":["\n","train = train_df[:train_df_len]\n","test = test_df\n","\n","# Select all numerical values from train and test\n","numeric_train = train.select_dtypes(include=[np.number])\n","numeric_test = test.select_dtypes(include=[np.number]) \n","\n","\n","X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n","y_train = numeric_train[\"Survived\"]\n","\n","# Split the train data into train and test sets with a 1/3 ratio\n","X_train, X_test, y_train, y_test = train_test_split(numeric_train.drop(labels=[\"Survived\"], axis=1), numeric_train[\"Survived\"], test_size=0.33, random_state=42)\n","\n","\n","print(\"X_train\", len(X_train))\n","print(\"X_test\", len(X_test))\n","print(\"y_train\", len(y_train))\n","print(\"y_test\", len(y_test))\n","print(\"test\", len(numeric_test))\n"]},{"cell_type":"markdown","metadata":{},"source":["## Simple Logistic Regression (Assignment)"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.335970Z","iopub.status.busy":"2024-04-01T06:27:56.335281Z","iopub.status.idle":"2024-04-01T06:27:56.368083Z","shell.execute_reply":"2024-04-01T06:27:56.366489Z","shell.execute_reply.started":"2024-04-01T06:27:56.335561Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy on the training set: 68.62%\n","Accuracy on the test set: 68.81%\n"]}],"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, y_train)\n","acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n","acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n","# Print the accuracy on the training and test set\n","print(f\"Accuracy on the training set: {acc_log_train}%\")\n","print(f\"Accuracy on the test set: {acc_log_test}%\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Hyperparameter Tuning -- Grid Search -- Cross Validation\n","We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n","\n","* Decision Tree\n","* SVM\n","* Random Forest\n","* KNN\n","* Logistic Regression"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.371066Z","iopub.status.busy":"2024-04-01T06:27:56.370400Z","iopub.status.idle":"2024-04-01T06:27:56.401742Z","shell.execute_reply":"2024-04-01T06:27:56.396867Z","shell.execute_reply.started":"2024-04-01T06:27:56.370802Z"},"trusted":true},"outputs":[],"source":["random_state = 42\n","classifier = [DecisionTreeClassifier(random_state = random_state),\n"," SVC(random_state = random_state),\n"," RandomForestClassifier(random_state = random_state),\n"," LogisticRegression(random_state = random_state),\n"," KNeighborsClassifier()]\n","\n","dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n"," \"max_depth\": range(1,20,2)}\n","\n","svc_param_grid = {\"kernel\" : [\"rbf\"],\n"," \"gamma\": [0.001, 0.01, 0.1, 1],\n"," \"C\": [1,10,50,100,200,300,1000]}\n","\n","rf_param_grid = {\"max_features\": [1,3,10],\n"," \"min_samples_split\":[2,3,10],\n"," \"min_samples_leaf\":[1,3,10],\n"," \"bootstrap\":[False],\n"," \"n_estimators\":[100,300],\n"," \"criterion\":[\"gini\"]}\n","\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n"," \"penalty\": [\"l1\",\"l2\"]}\n","\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n"," \"weights\": [\"uniform\",\"distance\"],\n"," \"metric\":[\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n"," svc_param_grid,\n"," rf_param_grid,\n"," logreg_param_grid,\n"," knn_param_grid]"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.413811Z","iopub.status.busy":"2024-04-01T06:27:56.404322Z","iopub.status.idle":"2024-04-01T06:29:38.718970Z","shell.execute_reply":"2024-04-01T06:29:38.717807Z","shell.execute_reply.started":"2024-04-01T06:27:56.413658Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n"]},{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]},{"name":"stdout","output_type":"stream","text":["0.6996045197740112\n","Fitting 10 folds for each of 28 candidates, totalling 280 fits\n","0.7130508474576271\n","Fitting 10 folds for each of 54 candidates, totalling 540 fits\n","0.7081073446327684\n","Fitting 10 folds for each of 14 candidates, totalling 140 fits\n","0.6777683615819209\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","0.6979943502824858\n"]}],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n"," clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n"," clf.fit(X_train,y_train)\n"," cv_result.append(clf.best_score_)\n"," best_estimators.append(clf.best_estimator_)\n"," print(cv_result[i])"]},{"cell_type":"code","execution_count":52,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:38.722928Z","iopub.status.busy":"2024-04-01T06:29:38.722207Z","iopub.status.idle":"2024-04-01T06:29:39.075423Z","shell.execute_reply":"2024-04-01T06:29:39.073987Z","shell.execute_reply.started":"2024-04-01T06:29:38.722582Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Cross Validation Scores')"]},"execution_count":52,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n"," \"LogisticRegression\",\n"," \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Scores\")"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Modeling (Assignment)"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.078654Z","iopub.status.busy":"2024-04-01T06:29:39.077840Z","iopub.status.idle":"2024-04-01T06:29:39.862871Z","shell.execute_reply":"2024-04-01T06:29:39.860937Z","shell.execute_reply.started":"2024-04-01T06:29:39.078554Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy of the voting classifier on the test set: 70.85%\n"]}],"source":["votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n"," (\"rfc\",best_estimators[2]),\n"," (\"lr\",best_estimators[3])],\n"," voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(X_train, y_train)\n","\n","# Print the accuracy score of the voting classifier\n","acc_votingC = round(votingC.score(X_test, y_test) * 100, 2)\n","print(f\"Accuracy of the voting classifier on the test set: {acc_votingC}%\")"]},{"cell_type":"code","execution_count":56,"metadata":{},"outputs":[],"source":["# Drop the null values which are going to cause you an error in the next cell\n","# Drop rows with missing values in numeric_test\n","numeric_test_dropna = numeric_test.dropna()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Prediction and Submission"]},{"cell_type":"code","execution_count":57,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.865981Z","iopub.status.busy":"2024-04-01T06:29:39.865330Z","iopub.status.idle":"2024-04-01T06:29:39.977357Z","shell.execute_reply":"2024-04-01T06:29:39.973301Z","shell.execute_reply.started":"2024-04-01T06:29:39.865906Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":[" PassengerId Survived\n","0 892 0.0\n","1 893 0.0\n","2 894 0.0\n","3 895 0.0\n","4 896 0.0\n",".. ... ...\n","413 1305 1.0\n","414 1306 0.0\n","415 1307 0.0\n","416 1308 0.0\n","417 1309 NaN\n","\n","[418 rows x 2 columns]\n"]}],"source":["test_survived = pd.Series(votingC.predict(numeric_test_dropna), name=\"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis=1)\n","results.to_csv(\"titanic.csv\", index=False)\n","print(results)"]},{"cell_type":"markdown","metadata":{},"source":["# Congratulations on finishing the assignment!!\n","\n","### The submission is the titanic.csv which was just created, and this file which you have modified."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":26502,"sourceId":3136,"sourceType":"competition"}],"dockerImageVersionId":29852,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.4"}},"nbformat":4,"nbformat_minor":4} +{"cells":[{"cell_type":"markdown","metadata":{},"source":["
\n","# Ignore this"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]}],"source":["import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-v0_8-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Load and Check Data"]},{"cell_type":"markdown","metadata":{},"source":["DataFrames hold the dataset in a tabular format for easy manipulation and analysis.
\n","CSV data is read into 'df' using Pandas' read_csv() function."]},{"cell_type":"code","execution_count":2,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2024-04-01T06:45:27.416192Z","iopub.status.busy":"2024-04-01T06:45:27.415763Z","iopub.status.idle":"2024-04-01T06:45:27.433162Z","shell.execute_reply":"2024-04-01T06:45:27.431944Z","shell.execute_reply.started":"2024-04-01T06:45:27.416105Z"},"trusted":true},"outputs":[],"source":["train_df = pd.read_csv(\"./data/train.csv\")"]},{"cell_type":"markdown","metadata":{},"source":["### 1. Try to read the test .csv file into test_df"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.436611Z","iopub.status.busy":"2024-04-01T06:45:27.435916Z","iopub.status.idle":"2024-04-01T06:45:27.449974Z","shell.execute_reply":"2024-04-01T06:45:27.448230Z","shell.execute_reply.started":"2024-04-01T06:45:27.436517Z"},"trusted":true},"outputs":[],"source":["test_df = pd.read_csv(\"./data/test.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":4,"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","execution":{"iopub.execute_input":"2024-04-01T06:45:27.452397Z","iopub.status.busy":"2024-04-01T06:45:27.451949Z","iopub.status.idle":"2024-04-01T06:45:27.462622Z","shell.execute_reply":"2024-04-01T06:45:27.461859Z","shell.execute_reply.started":"2024-04-01T06:45:27.452348Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The Columns of train_df are: \n"]},{"data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n"," 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n"," dtype='object')"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["print(\"The Columns of train_df are: \")\n","train_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["### We can use head() to see the first few rows in the dataframe"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.464289Z","iopub.status.busy":"2024-04-01T06:45:27.463866Z","iopub.status.idle":"2024-04-01T06:45:27.491984Z","shell.execute_reply":"2024-04-01T06:45:27.491110Z","shell.execute_reply.started":"2024-04-01T06:45:27.464242Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked \n","0 0 A/5 21171 7.2500 NaN S \n","1 0 PC 17599 71.2833 C85 C \n","2 0 STON/O2. 3101282 7.9250 NaN S \n","3 0 113803 53.1000 C123 S \n","4 0 373450 8.0500 NaN S "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.494326Z","iopub.status.busy":"2024-04-01T06:45:27.493637Z","iopub.status.idle":"2024-04-01T06:45:27.541999Z","shell.execute_reply":"2024-04-01T06:45:27.541210Z","shell.execute_reply.started":"2024-04-01T06:45:27.494251Z"},"jupyter":{"source_hidden":true},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n","
"],"text/plain":[" PassengerId Survived Pclass Age SibSp \\\n","count 891.000000 891.000000 891.000000 714.000000 891.000000 \n","mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n","std 257.353842 0.486592 0.836071 14.526497 1.102743 \n","min 1.000000 0.000000 1.000000 0.420000 0.000000 \n","25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n","50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n","75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n","max 891.000000 1.000000 3.000000 80.000000 8.000000 \n","\n"," Parch Fare \n","count 891.000000 891.000000 \n","mean 0.381594 32.204208 \n","std 0.806057 49.693429 \n","min 0.000000 0.000000 \n","25% 0.000000 7.910400 \n","50% 0.000000 14.454200 \n","75% 0.000000 31.000000 \n","max 6.000000 512.329200 "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["### 2. Without looking above, try checking the first few rows of test_df"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked \n","0 34.5 0 0 330911 7.8292 NaN Q \n","1 47.0 1 0 363272 7.0000 NaN S \n","2 62.0 0 0 240276 9.6875 NaN Q \n","3 27.0 0 0 315154 8.6625 NaN S \n","4 22.0 1 1 3101298 12.2875 NaN S "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["### 3. Now try checking for a description of test_df's data"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassAgeSibSpParchFare
count418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000002.26555030.2725900.4473680.39234435.627188
std120.8104580.84183814.1812090.8967600.98142955.907576
min892.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500001.00000021.0000000.0000000.0000007.895800
50%1100.5000003.00000027.0000000.0000000.00000014.454200
75%1204.7500003.00000039.0000001.0000000.00000031.500000
max1309.0000003.00000076.0000008.0000009.000000512.329200
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"],"text/plain":[" PassengerId Pclass Age SibSp Parch Fare\n","count 418.000000 418.000000 332.000000 418.000000 418.000000 417.000000\n","mean 1100.500000 2.265550 30.272590 0.447368 0.392344 35.627188\n","std 120.810458 0.841838 14.181209 0.896760 0.981429 55.907576\n","min 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n","25% 996.250000 1.000000 21.000000 0.000000 0.000000 7.895800\n","50% 1100.500000 3.000000 27.000000 0.000000 0.000000 14.454200\n","75% 1204.750000 3.000000 39.000000 1.000000 0.000000 31.500000\n","max 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["test_df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["# Variable Description\n","
    \n","
  1. PassengerId: unique id number to each passenger
    2. \n","
  2. Survived: passenger survive(1) or died(0)
    3. \n","
  3. Pclass: passenger class
    4. \n","
  4. Name: name
    5. \n","
  5. Sex: gender of passenger
    6. \n","
  6. Age: age of passenger
    7. \n","
  7. SibSp: number of siblings/spouses
    8. \n","
  8. Parch: number of parents/children
    9. \n","
  9. Ticket: ticket number
    10. \n","
  10. Fare: amount of money spent on ticket
    11. \n","
  11. Cabin: cabin category
    12. \n","
  12. Embarked: port where passenger embarked ( C = Cherbourg, Q = Queenstown, S = Southampton )
    13. \n","
\n"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:45:27.544377Z","iopub.status.busy":"2024-04-01T06:45:27.543901Z","iopub.status.idle":"2024-04-01T06:45:27.557229Z","shell.execute_reply":"2024-04-01T06:45:27.555972Z","shell.execute_reply.started":"2024-04-01T06:45:27.544320Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 PassengerId 891 non-null int64 \n"," 1 Survived 891 non-null int64 \n"," 2 Pclass 891 non-null int64 \n"," 3 Name 891 non-null object \n"," 4 Sex 891 non-null object \n"," 5 Age 714 non-null float64\n"," 6 SibSp 891 non-null int64 \n"," 7 Parch 891 non-null int64 \n"," 8 Ticket 891 non-null object \n"," 9 Fare 891 non-null float64\n"," 10 Cabin 204 non-null object \n"," 11 Embarked 889 non-null object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["train_df.info()"]},{"cell_type":"markdown","metadata":{},"source":["### Slice Rows and Columsn of DF (Assigmennt)"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:53:12.214069Z","iopub.status.busy":"2024-04-01T06:53:12.213708Z","iopub.status.idle":"2024-04-01T06:53:12.223150Z","shell.execute_reply":"2024-04-01T06:53:12.222195Z","shell.execute_reply.started":"2024-04-01T06:53:12.214014Z"},"trusted":true},"outputs":[{"data":{"text/plain":["PassengerId 3\n","Survived 1\n","Pclass 3\n","Name Heikkinen, Miss. Laina\n","Sex female\n","Age 26.0\n","SibSp 0\n","Parch 0\n","Ticket STON/O2. 3101282\n","Fare 7.925\n","Cabin NaN\n","Embarked S\n","Name: 2, dtype: object"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["# Printing the Second Row\n","train_df.iloc[2]"]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 6\n","Survived 0\n","Pclass 3\n","Name Moran, Mr. James\n","Sex male\n","Age NaN\n","SibSp 0\n","Parch 0\n","Ticket 330877\n","Fare 8.4583\n","Cabin NaN\n","Embarked Q\n","Name: 5, dtype: object"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Print the 5th Row\n","train_df.iloc[5]"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:14.398373Z","iopub.status.busy":"2024-04-01T06:54:14.398006Z","iopub.status.idle":"2024-04-01T06:54:14.407886Z","shell.execute_reply":"2024-04-01T06:54:14.406590Z","shell.execute_reply.started":"2024-04-01T06:54:14.398326Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 male\n","1 female\n","2 female\n","3 female\n","4 male\n"," ... \n","886 male\n","887 female\n","888 female\n","889 male\n","890 male\n","Name: Sex, Length: 891, dtype: object"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Sex Column\n","train_df['Sex']"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:54:24.550687Z","iopub.status.busy":"2024-04-01T06:54:24.550286Z","iopub.status.idle":"2024-04-01T06:54:24.555255Z","shell.execute_reply":"2024-04-01T06:54:24.553923Z","shell.execute_reply.started":"2024-04-01T06:54:24.550616Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 Braund, Mr. Owen Harris\n","1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n","2 Heikkinen, Miss. Laina\n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n","4 Allen, Mr. William Henry\n"," ... \n","886 Montvila, Rev. Juozas\n","887 Graham, Miss. Margaret Edith\n","888 Johnston, Miss. Catherine Helen \"Carrie\"\n","889 Behr, Mr. Karl Howell\n","890 Dooley, Mr. Patrick\n","Name: Name, Length: 891, dtype: object"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Print the Name Column\n","train_df['Name']"]},{"cell_type":"markdown","metadata":{},"source":["## Visualization (Assignment)"]},{"cell_type":"markdown","metadata":{},"source":["### Age -- Survived"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:13:34.450088Z","iopub.status.busy":"2024-04-01T07:13:34.449302Z","iopub.status.idle":"2024-04-01T07:13:34.932717Z","shell.execute_reply":"2024-04-01T07:13:34.930449Z","shell.execute_reply.started":"2024-04-01T07:13:34.450021Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","# Plot 1: Survivors vs Non Survivors\n","\n","# Creating a plot for the Survived Column\n","sns.countplot(x='Survived', data=train_df)\n","\n","plt.title('Survivors vs Non Survivors')\n","plt.xlabel('Survived')\n","plt.ylabel('Count')\n","plt.xticks([0, 1], ['No', 'Yes']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Plotting Passenger Class"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:14:31.768779Z","iopub.status.busy":"2024-04-01T07:14:31.768341Z","iopub.status.idle":"2024-04-01T07:14:32.062495Z","shell.execute_reply":"2024-04-01T07:14:32.060660Z","shell.execute_reply.started":"2024-04-01T07:14:31.768690Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","\n","# Make the plot for Pclass here:\n","sns.countplot(x='Pclass', data=train_df)\n","\n","plt.title('Count of Passengers In each Passenger Class')\n","plt.xlabel('Passenger Class')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['1st', '2nd', '3rd']) # Setting custom tick labels\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try it for \"Embarked\""]},{"cell_type":"code","execution_count":16,"metadata":{"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.countplot(x='Embarked', data=train_df)\n","plt.title('Count of Passengers by Embarkation Point')\n","plt.xlabel('Embarkation Point')\n","plt.ylabel('Count')\n","plt.xticks([0, 1, 2], ['C', 'Q', 'S'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Try Making a histogram for \"Fare\""]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Here is the distplot for \"Fare\", refer to it after you tried it yourself:"]},{"cell_type":"code","execution_count":18,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:18:24.402882Z","iopub.status.busy":"2024-04-01T07:18:24.402274Z","iopub.status.idle":"2024-04-01T07:18:24.798062Z","shell.execute_reply":"2024-04-01T07:18:24.796669Z","shell.execute_reply.started":"2024-04-01T07:18:24.402828Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.histplot(train_df['Fare'], bins=20, color='orange')\n","plt.title('Distribution of Passenger Fares')\n","plt.xlabel('Fare')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Make a histogram for \"Age\" (Assignment)"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:19:53.874413Z","iopub.status.busy":"2024-04-01T07:19:53.873686Z","iopub.status.idle":"2024-04-01T07:19:54.244996Z","shell.execute_reply":"2024-04-01T07:19:54.243521Z","shell.execute_reply.started":"2024-04-01T07:19:53.874351Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Create the plot below\n","plt.figure(figsize=(8, 6))\n","sns.histplot(train_df['Age'], bins=20, color='green')\n","plt.title('Distribution of Age')\n","plt.xlabel('Age')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Fill Missing: Age Feature"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:50.370496Z","iopub.status.busy":"2024-04-01T06:27:50.369419Z","iopub.status.idle":"2024-04-01T06:27:50.427731Z","shell.execute_reply":"2024-04-01T06:27:50.426655Z","shell.execute_reply.started":"2024-04-01T06:27:50.370387Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
.......................................
85986003Razi, Mr. RaihedmaleNaN0026297.2292NaNC
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
\n","

177 rows × 12 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass Name \\\n","5 6 0 3 Moran, Mr. James \n","17 18 1 2 Williams, Mr. Charles Eugene \n","19 20 1 3 Masselmani, Mrs. Fatima \n","26 27 0 3 Emir, Mr. Farred Chehab \n","28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" \n",".. ... ... ... ... \n","859 860 0 3 Razi, Mr. Raihed \n","863 864 0 3 Sage, Miss. Dorothy Edith \"Dolly\" \n","868 869 0 3 van Melkebeke, Mr. Philemon \n","878 879 0 3 Laleff, Mr. Kristo \n","888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n","\n"," Sex Age SibSp Parch Ticket Fare Cabin Embarked \n","5 male NaN 0 0 330877 8.4583 NaN Q \n","17 male NaN 0 0 244373 13.0000 NaN S \n","19 female NaN 0 0 2649 7.2250 NaN C \n","26 male NaN 0 0 2631 7.2250 NaN C \n","28 female NaN 0 0 330959 7.8792 NaN Q \n",".. ... ... ... ... ... ... ... ... \n","859 male NaN 0 0 2629 7.2292 NaN C \n","863 female NaN 8 2 CA. 2343 69.5500 NaN S \n","868 male NaN 0 0 345777 9.5000 NaN S \n","878 male NaN 0 0 349217 7.8958 NaN S \n","888 female NaN 1 2 W./C. 6607 23.4500 NaN S \n","\n","[177 rows x 12 columns]"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"markdown","metadata":{},"source":["### Try Checking for Null Values in Test Df"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"data":{"text/plain":["PassengerId 0\n","Pclass 0\n","Name 0\n","Sex 0\n","Age 86\n","SibSp 0\n","Parch 0\n","Ticket 0\n","Fare 1\n","Cabin 327\n","Embarked 0\n","dtype: int64"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["test_df.isnull().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Run this to fix the Null Values"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:21:48.194895Z","iopub.status.busy":"2024-04-01T07:21:48.194020Z","iopub.status.idle":"2024-04-01T07:21:49.449282Z","shell.execute_reply":"2024-04-01T07:21:49.447918Z","shell.execute_reply.started":"2024-04-01T07:21:48.194825Z"},"trusted":true},"outputs":[],"source":["index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) &(train_df[\"Parch\"] == train_df.iloc[i][\"Parch\"])& (train_df[\"Pclass\"] == train_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = train_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," train_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," train_df[\"Age\"].iloc[i] = age_med\n","\n","index_nan_age = list(test_df[\"Age\"][test_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n"," age_pred = test_df[\"Age\"][((test_df[\"SibSp\"] == test_df.iloc[i][\"SibSp\"]) &(test_df[\"Parch\"] == test_df.iloc[i][\"Parch\"])& (test_df[\"Pclass\"] == test_df.iloc[i][\"Pclass\"]))].median()\n"," age_med = test_df[\"Age\"].median()\n"," if not np.isnan(age_pred):\n"," test_df[\"Age\"].iloc[i] = age_pred\n"," else:\n"," test_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"markdown","metadata":{},"source":["## Analysing the correlation between the different columns"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T07:24:33.644174Z","iopub.status.busy":"2024-04-01T07:24:33.643621Z","iopub.status.idle":"2024-04-01T07:24:34.404306Z","shell.execute_reply":"2024-04-01T07:24:34.402938Z","shell.execute_reply.started":"2024-04-01T07:24:33.643935Z"},"trusted":true},"outputs":[{"data":{"text/plain":[""]},"execution_count":23,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["numerical_columns = train_df.select_dtypes(include=[np.number]).columns\n","sns.heatmap(train_df[numerical_columns].corr(), annot=True)"]},{"cell_type":"markdown","metadata":{},"source":["We see that Fare and Parch are positively correlated with Survived. Similarly, Fare and Class are negatively correlated, in the sense that the higher the higher the Fare, the lower the Class number (Remember that Class 1 < Class 2 < Class 3 in face value)."]},{"cell_type":"markdown","metadata":{},"source":["## Embarked"]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.378151Z","iopub.status.busy":"2024-04-01T06:27:55.377756Z","iopub.status.idle":"2024-04-01T06:27:55.384785Z","shell.execute_reply":"2024-04-01T06:27:55.384101Z","shell.execute_reply.started":"2024-04-01T06:27:55.378107Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 S\n","1 C\n","2 S\n","3 S\n","4 S\n","Name: Embarked, dtype: object"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.386265Z","iopub.status.busy":"2024-04-01T06:27:55.385875Z","iopub.status.idle":"2024-04-01T06:27:55.635178Z","shell.execute_reply":"2024-04-01T06:27:55.633609Z","shell.execute_reply.started":"2024-04-01T06:27:55.386223Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Embarked\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":26,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.638242Z","iopub.status.busy":"2024-04-01T06:27:55.637447Z","iopub.status.idle":"2024-04-01T06:27:55.699106Z","shell.execute_reply":"2024-04-01T06:27:55.698208Z","shell.execute_reply.started":"2024-04-01T06:27:55.638150Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNFalseFalseTrue
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85TrueFalseFalse
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNFalseFalseTrue
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123FalseFalseTrue
4503Allen, Mr. William Henrymale35.0003734508.0500NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","\n"," Parch Ticket Fare Cabin Embarked_C Embarked_Q Embarked_S \n","0 0 A/5 21171 7.2500 NaN False False True \n","1 0 PC 17599 71.2833 C85 True False False \n","2 0 STON/O2. 3101282 7.9250 NaN False False True \n","3 0 113803 53.1000 C123 False False True \n","4 0 373450 8.0500 NaN False False True "]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked_CEmbarked_QEmbarked_S
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNFalseTrueFalse
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNFalseTrueFalse
38953Wirz, Mr. Albertmale27.0003151548.6625NaNFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNFalseFalseTrue
\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","\n"," Age SibSp Parch Ticket Fare Cabin Embarked_C Embarked_Q \\\n","0 34.5 0 0 330911 7.8292 NaN False True \n","1 47.0 1 0 363272 7.0000 NaN False False \n","2 62.0 0 0 240276 9.6875 NaN False True \n","3 27.0 0 0 315154 8.6625 NaN False False \n","4 22.0 1 1 3101298 12.2875 NaN False False \n","\n"," Embarked_S \n","0 False \n","1 True \n","2 False \n","3 True \n","4 True "]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns=[\"Embarked\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Ticket (Assignment)"]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.700760Z","iopub.status.busy":"2024-04-01T06:27:55.700330Z","iopub.status.idle":"2024-04-01T06:27:55.708542Z","shell.execute_reply":"2024-04-01T06:27:55.707466Z","shell.execute_reply.started":"2024-04-01T06:27:55.700715Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A/5 21171\n","1 PC 17599\n","2 STON/O2. 3101282\n","3 113803\n","4 373450\n","5 330877\n","6 17463\n","7 349909\n","8 347742\n","9 237736\n","10 PP 9549\n","11 113783\n","12 A/5. 2151\n","13 347082\n","14 350406\n","15 248706\n","16 382652\n","17 244373\n","18 345763\n","19 2649\n","Name: Ticket, dtype: object"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.710291Z","iopub.status.busy":"2024-04-01T06:27:55.709980Z","iopub.status.idle":"2024-04-01T06:27:55.722810Z","shell.execute_reply":"2024-04-01T06:27:55.721839Z","shell.execute_reply.started":"2024-04-01T06:27:55.710231Z"},"trusted":true},"outputs":[{"data":{"text/plain":["'A5'"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["example_ticket = \"A/5. 2151\"\n","example_ticket.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.726116Z","iopub.status.busy":"2024-04-01T06:27:55.725689Z","iopub.status.idle":"2024-04-01T06:27:55.738095Z","shell.execute_reply":"2024-04-01T06:27:55.737043Z","shell.execute_reply.started":"2024-04-01T06:27:55.726039Z"},"trusted":true},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets\n","\n","# Do the same for the test set\n","tickets = []\n","for i in list(test_df.Ticket):\n"," if not i.isdigit():\n"," tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n"," else:\n"," tickets.append(\"x\")\n","test_df[\"Ticket\"] = tickets"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.740389Z","iopub.status.busy":"2024-04-01T06:27:55.739797Z","iopub.status.idle":"2024-04-01T06:27:55.755416Z","shell.execute_reply":"2024-04-01T06:27:55.754317Z","shell.execute_reply.started":"2024-04-01T06:27:55.740333Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0 A5\n","1 PC\n","2 STONO2\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 x\n","10 PP\n","11 x\n","12 A5\n","13 x\n","14 x\n","15 x\n","16 x\n","17 x\n","18 x\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"data":{"text/plain":["0 x\n","1 x\n","2 x\n","3 x\n","4 x\n","5 x\n","6 x\n","7 x\n","8 x\n","9 A4\n","10 x\n","11 x\n","12 x\n","13 x\n","14 WEP\n","15 SCPARIS\n","16 x\n","17 x\n","18 STONO2\n","19 x\n","Name: Ticket, dtype: object"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":33,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.790832Z","iopub.status.busy":"2024-04-01T06:27:55.790500Z","iopub.status.idle":"2024-04-01T06:27:55.841011Z","shell.execute_reply":"2024-04-01T06:27:55.839862Z","shell.execute_reply.started":"2024-04-01T06:27:55.790770Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedPclassNameSexAgeSibSpParchFareCabin...TcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_x
0103Braund, Mr. Owen Harrismale22.0107.2500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2313Heikkinen, Miss. Lainafemale26.0007.9250NaN...FalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
4503Allen, Mr. William Henrymale35.0008.0500NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
5603Moran, Mr. Jamesmale26.0008.4583NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
6701McCarthy, Mr. Timothy Jmale54.00051.8625E46...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
7803Palsson, Master. Gosta Leonardmale2.03121.0750NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00211.1333NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01030.0708NaN...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
\n","

10 rows × 44 columns

\n","
"],"text/plain":[" PassengerId Survived Pclass \\\n","0 1 0 3 \n","1 2 1 1 \n","2 3 1 3 \n","3 4 1 1 \n","4 5 0 3 \n","5 6 0 3 \n","6 7 0 1 \n","7 8 0 3 \n","8 9 1 3 \n","9 10 1 2 \n","\n"," Name Sex Age SibSp \\\n","0 Braund, Mr. Owen Harris male 22.0 1 \n","1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n","2 Heikkinen, Miss. Laina female 26.0 0 \n","3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n","4 Allen, Mr. William Henry male 35.0 0 \n","5 Moran, Mr. James male 26.0 0 \n","6 McCarthy, Mr. Timothy J male 54.0 0 \n","7 Palsson, Master. Gosta Leonard male 2.0 3 \n","8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n","9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n","\n"," Parch Fare Cabin ... TcktName_SOPP TcktName_SOTONO2 \\\n","0 0 7.2500 NaN ... False False \n","1 0 71.2833 C85 ... False False \n","2 0 7.9250 NaN ... False False \n","3 0 53.1000 C123 ... False False \n","4 0 8.0500 NaN ... False False \n","5 0 8.4583 NaN ... False False \n","6 0 51.8625 E46 ... False False \n","7 1 21.0750 NaN ... False False \n","8 2 11.1333 NaN ... False False \n","9 0 30.0708 NaN ... False False \n","\n"," TcktName_SOTONOQ TcktName_SP TcktName_STONO TcktName_STONO2 \\\n","0 False False False False \n","1 False False False False \n","2 False False False True \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False True \n","4 False False False True \n","5 False False False True \n","6 False False False True \n","7 False False False True \n","8 False False False True \n","9 False False False True \n","\n","[10 rows x 44 columns]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdPclassNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SOCTcktName_SOPPTcktName_SOTONO2TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_x
08923Kelly, Mr. Jamesmale34.5007.8292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
18933Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
28942Myles, Mr. Thomas Francismale62.0009.6875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
38953Wirz, Mr. Albertmale27.0008.6625NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
58973Svensson, Mr. Johan Cervinmale14.0009.2250NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
68983Connolly, Miss. Katefemale30.0007.6292NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
78992Caldwell, Mr. Albert Francismale26.01129.0000NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
89003Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.0007.2292NaNTrue...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
99013Davies, Mr. John Samuelmale21.02024.1500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
\n","

10 rows × 40 columns

\n","
"],"text/plain":[" PassengerId Pclass Name Sex \\\n","0 892 3 Kelly, Mr. James male \n","1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n","2 894 2 Myles, Mr. Thomas Francis male \n","3 895 3 Wirz, Mr. Albert male \n","4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n","5 897 3 Svensson, Mr. Johan Cervin male \n","6 898 3 Connolly, Miss. Kate female \n","7 899 2 Caldwell, Mr. Albert Francis male \n","8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female \n","9 901 3 Davies, Mr. John Samuel male \n","\n"," Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SOC \\\n","0 34.5 0 0 7.8292 NaN False ... False \n","1 47.0 1 0 7.0000 NaN False ... False \n","2 62.0 0 0 9.6875 NaN False ... False \n","3 27.0 0 0 8.6625 NaN False ... False \n","4 22.0 1 1 12.2875 NaN False ... False \n","5 14.0 0 0 9.2250 NaN False ... False \n","6 30.0 0 0 7.6292 NaN False ... False \n","7 26.0 1 1 29.0000 NaN False ... False \n","8 18.0 0 0 7.2292 NaN True ... False \n","9 21.0 2 0 24.1500 NaN False ... False \n","\n"," TcktName_SOPP TcktName_SOTONO2 TcktName_SOTONOQ TcktName_STONO \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","5 False False False False \n","6 False False False False \n","7 False False False False \n","8 False False False False \n","9 False False False False \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","5 False False False False True \n","6 False False False False True \n","7 False False False False True \n","8 False False False False True \n","9 False False False False False \n","\n","[10 rows x 40 columns]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.get_dummies(test_df, columns= [\"Ticket\"], prefix = \"TcktName\")\n","test_df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Pclass"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:55.843395Z","iopub.status.busy":"2024-04-01T06:27:55.842833Z","iopub.status.idle":"2024-04-01T06:27:56.089225Z","shell.execute_reply":"2024-04-01T06:27:56.087578Z","shell.execute_reply.started":"2024-04-01T06:27:55.843168Z"},"trusted":true},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjMAAAGsCAYAAAAoiibJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAjoklEQVR4nO3dfVCVdf7/8Rcd4kYNS0CSNHUwDBGBINRJ86Y2b9LR0GptRzMrbBXZck0X2U3TWEapNBVvyCzTJk2x3dbMbW1bN7PSjoGjYoKWkaiBimbcHD2c7x/9Or9YEgmQi488HzPMDtfnXJz3tXNWnntdF+d4uFwulwAAAAx1jdUDAAAA1AcxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBonlYP0BguXryos2fPytvbW9dcQ78BAGCCyspKVVRUqHXr1vL0vHSyNIuYOXv2rL7++murxwAAAHXQqVMn+fv7X3K9WcSMt7e3pB//y/D19bV4GgAAUBtlZWX6+uuv3b/HL6VZxMxPl5Z8fX3VokULi6cBAAC/xuVuEeEGEgAAYDRiBgAAGI2YAQAARrM0Zv71r3+pa9euVb6SkpIkSQcOHND999+vyMhIjRo1Svv27auy7+bNm3X33XcrMjJSkydP1unTp604BAAAYDFLYyY/P18DBgzQjh073F/PPfecSktLlZCQoNjYWG3atEnR0dGaOHGiSktLJUl79+5VSkqKEhMTtX79ep07d07JyclWHgoAALCIpTFz+PBhhYaGKjAw0P3l5+enLVu2yNvbW9OnT1dISIhSUlLUsmVLbd26VZK0du1aDRkyRCNHjtStt96q+fPna/v27SooKLDycAAAgAUsj5lOnTpV256Tk6OYmBh5eHhIkjw8PHTbbbcpOzvbvR4bG+t+fLt27RQcHKycnJzGGBsAADQhlr3PjMvl0ldffaUdO3ZoxYoVcjqdGjx4sJKSklRUVKQuXbpUeby/v7/y8vIkSd99953atm1bbf3EiRM1PqfT6ZTT6WzYAwEAAFdEbX9nWxYzhYWFKisrk5eXlxYuXKhvv/1Wzz33nMrLy93bf87Ly0sOh0OSVF5eXuP6pRw6dKhhDwIAAFjOspi56aab9Nlnn6l169by8PBQWFiYKisr9fTTTysuLq5amDgcDvn4+Ej68eMJfmn9ch9VEBoayjsAAwBgiNLS0lqdiLD04wyuv/76Kt+HhISooqJCgYGBKi4urrJWXFzsvrQUFBT0i+uBgYE1Pp/NZpPNZqv/4AAA4Iqr7e9sy24A/uijj9SzZ0+VlZW5t+Xm5ur6669XTEyMvvjiC7lcLkk/3l+zZ88eRUZGSpIiIyNlt9vd+x0/flzHjx93rwMAgObDspiJjo6Wt7e3/vznP+vIkSPavn275s+fr8cee0yDBw/WuXPnlJqaqvz8fKWmpqqsrExDhgyRJI0ZM0Z///vftWHDBh08eFDTp09X//791aFDB6sOBwAAWMSymGnVqpVeeeUVnT59WqNGjVJKSooefPBBPfbYY2rVqpVWrFghu92u+Ph45eTkKDMz032/S3R0tObMmaOMjAyNGTNGrVu3VlpamlWHAgAALOTh+ulazlWstLRUubm5CgsL4wZgAAAMUdvf35beAAwAuLrEPP261SOgCbGnj2uU5+FTswEAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGC0JhMzCQkJ+tOf/uT+/sCBA7r//vsVGRmpUaNGad++fVUev3nzZt19992KjIzU5MmTdfr06cYeGQAANAFNImbeffddbd++3f19aWmpEhISFBsbq02bNik6OloTJ05UaWmpJGnv3r1KSUlRYmKi1q9fr3Pnzik5Odmq8QEAgIUsj5mSkhLNnz9fERER7m1btmyRt7e3pk+frpCQEKWkpKhly5baunWrJGnt2rUaMmSIRo4cqVtvvVXz58/X9u3bVVBQYNVhAAAAi1geM/PmzdOIESPUpUsX97acnBzFxMTIw8NDkuTh4aHbbrtN2dnZ7vXY2Fj349u1a6fg4GDl5OQ06uwAAMB6lsbMJ598os8//1yTJk2qsr2oqEht27atss3f318nTpyQJH333Xc1rgMAgObD06onrqio0KxZs/TMM8/Ix8enylpZWZm8vLyqbPPy8pLD4ZAklZeX17h+KU6nU06nswGmBwAAl1Pf37m13d+ymFmyZIm6d++uvn37Vlvz9vauFiYOh8MdPZda9/X1rfE5Dx06VM+pAQBAbf10e8iVZlnMvPvuuyouLlZ0dLQkuePkn//8p4YNG6bi4uIqjy8uLnZfWgoKCvrF9cDAwBqfMzQ0VC1atGioQwAA/K91+62eAE1IVFRUvfYvLS2t1YkIy2JmzZo1unjxovv7559/XpI0bdo07d69Wy+//LJcLpc8PDzkcrm0Z88ePfHEE5KkyMhI2e12xcfHS5KOHz+u48ePKzIyssbntNlsstlsV+iIAADAz9X3d25t97csZm666aYq37ds2VKS1LFjR/n7++uFF15Qamqqfvvb32rdunUqKyvTkCFDJEljxozR2LFjFRUVpYiICKWmpqp///7q0KFDox8HAACwluV/mv1LWrVqpRUrVrjPvuTk5CgzM9N9iSg6Olpz5sxRRkaGxowZo9atWystLc3iqQEAgBU8XC6Xy+ohrrTS0lLl5uYqLCyMe2YA4AqKefp1q0dAE2JPH1ev/Wv7+7tJnpkBAACoLWIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0SyNmaNHj+rRRx9VdHS0+vfvr5UrV7rXCgoKNH78eEVFRWno0KHasWNHlX137typYcOGKTIyUuPGjVNBQUFjjw8AAJoAy2KmsrJSCQkJuuGGG/T222/r2Wef1bJly/SPf/xDLpdLkydPVkBAgLKysjRixAglJiaqsLBQklRYWKjJkycrPj5eGzduVJs2bTRp0iS5XC6rDgcAAFjE06onLi4uVlhYmGbPnq1WrVqpU6dO6t27t+x2uwICAlRQUKB169apRYsWCgkJ0SeffKKsrCxNmTJFGzZsUPfu3TVhwgRJUlpamu644w7t2rVLPXv2tOqQAACABSw7M9O2bVstXLhQrVq1ksvlkt1u1+7duxUXF6ecnBx169ZNLVq0cD8+JiZG2dnZkqScnBzFxsa613x9fRUeHu5eBwAAzUeTuAF44MCBeuihhxQdHa1BgwapqKhIbdu2rfIYf39/nThxQpIuuw4AAJoPyy4z/dyiRYtUXFys2bNnKy0tTWVlZfLy8qryGC8vLzkcDkm67PqlOJ1OOZ3Ohh0eAAD8ovr+zq3t/k0iZiIiIiRJFRUVmjZtmkaNGqWysrIqj3E4HPLx8ZEkeXt7VwsXh8MhPz+/Gp/n0KFDDTg1AACoSWPd/mHpDcDZ2dm6++673du6dOmiCxcuKDAwUEeOHKn2+J8uLQUFBam4uLjaelhYWI3PGRoaWuU+HABAA1u33+oJ0IRERUXVa//S0tJanYiwLGa+/fZbJSYmavv27QoKCpIk7du3T23atFFMTIxWrVql8vJy99kYu92umJgYSVJkZKTsdrv7Z5WVlenAgQNKTEys8TltNptsNtsVOiIAAPBz9f2dW9v9LbsBOCIiQuHh4Zo5c6by8/O1fft2paen64knnlBcXJzatWun5ORk5eXlKTMzU3v37tXo0aMlSaNGjdKePXuUmZmpvLw8JScnq3379vxZNgAAzZBlMWOz2bR06VL5+vrqwQcfVEpKisaOHatx48a514qKihQfH6933nlHGRkZCg4OliS1b99eixcvVlZWlkaPHq2SkhJlZGTIw8PDqsMBAAAW8XA1g7fNLS0tVW5ursLCwrhnBgCuoJinX7d6BDQh9vRx9dq/tr+/m8T7zAAAANQVMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwWp1iZty4cTp37ly17adPn1Z8fHy9hwIAAKitWn9q9n//+1/t3btXkrR7924tX7682lsLHz16VMeOHWvYCQEAAGpQ65jp3LmzVq5cKZfLJZfLpT179ujaa691r3t4eKhFixZKTU29IoMCAAD8klrHTIcOHfT66z9+gFhycrJSUlLUqlWrKzYYAABAbdQ6Zn4uLS1NklRUVKSLFy/qfz94Ozg4uP6TAQAA1EKdYubjjz/WX/7yFx0/flyS5HK55OHh4f7P3NzcBh0SAADgUuoUM3PmzFGPHj20bNkyLjUBAABL1SlmTpw4oZUrV6pDhw4NPQ8AAMCvUqf3mYmNjZXdbm/oWQAAAH61Op2Zuf322/Xss8/qP//5jzp27FjlT7QlKTExsUGGAwAAuJw63wDcvXt3nTp1SqdOnaqy5uHh0SCDAQAA1EadYmbNmjUNPQcAAECd1Clm/va3v9W4PnLkyLr8WAAAgF+tTjGzaNGiKt87nU6dOnVKnp6e6tGjBzEDAAAaTZ1i5t///ne1bT/88IOeeeYZde3atd5DAQAA1Fad/jT7l7Rs2VJTpkzRq6++2lA/EgAA4LIaLGYk6eDBg6qsrGzIHwkAAFCjOl1mGjt2bLU/wf7hhx/05Zdfavz48Q0xFwAAQK3UKWZ69uxZbZuXl5emTZum3r1713soAACA2qpTzPz8HX7Pnz8vp9Op1q1bN9hQAAAAtVWnmJGk1atXa+XKlSouLpYktWnTRmPGjOGjDAAAQKOqU8xkZGRo7dq1+sMf/qDo6GhVVlZqz549WrJkiby8vJSQkNDQcwIAAPyiOsXMW2+9pdTUVA0cONC9LSwsTEFBQUpNTSVmAABAo6nTn2afP39enTp1qra9c+fOOn36dH1nAgAAqLU6xUx0dLRWrVpV5T1lnE6nXnnlFfXo0aPBhgMAALicOl1mSk5O1u9+9zvt3LlT4eHhkqT9+/fL4XBo5cqVDTogAABATeoUMyEhIZo5c6ZKSkp05MgReXt768MPP9SiRYt06623NvSMAAAAl1Sny0xr1qzR7Nmzdd1112n27NlKTk7W2LFjNW3aNL311lsNPSMAAMAl1SlmXn31Vb3wwgu677773NtmzJih9PR0ZWZmNthwAAAAl1OnmDlz5oxuvvnmats7d+7sfhM9AACAxlCnmImJidHixYtVVlbm3lZRUaHly5crOjq6wYYDAAC4nDrdAPzMM89owoQJ6tOnj/v9Zr755hsFBARo6dKlDTkfAABAjeoUMzfffLO2bNmijz76SF9//bU8PT3VqVMn9enTRzabraFnBAAAuKQ6f9Ckl5eX7rrrroacBQAA4Fer0z0zAAAATQUxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKPV+bOZmpuYp1+3egQ0Ifb0cVaPAAD4fzgzAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMZmnMnDx5UklJSYqLi1Pfvn2VlpamiooKSVJBQYHGjx+vqKgoDR06VDt27Kiy786dOzVs2DBFRkZq3LhxKigosOIQAACAxSyLGZfLpaSkJJWVlemNN97QggUL9OGHH2rhwoVyuVyaPHmyAgIClJWVpREjRigxMVGFhYWSpMLCQk2ePFnx8fHauHGj2rRpo0mTJsnlcll1OAAAwCKWvc/MkSNHlJ2drY8//lgBAQGSpKSkJM2bN0933nmnCgoKtG7dOrVo0UIhISH65JNPlJWVpSlTpmjDhg3q3r27JkyYIElKS0vTHXfcoV27dqlnz55WHRIAALCAZWdmAgMDtXLlSnfI/OT8+fPKyclRt27d1KJFC/f2mJgYZWdnS5JycnIUGxvrXvP19VV4eLh7HQAANB+WnZnx8/NT37593d9XVlZq7dq16tWrl4qKitS2bdsqj/f399eJEyck6bLrl+J0OuV0OhvoCNCc8ToCgMur77+Vtd2/yXycQXp6ug4cOKCNGzfqtddek5eXV5V1Ly8vORwOSVJZWVmN65dy6NChhh0azRZnAQHg8hrr38omETPp6elavXq1FixYoNDQUHl7e6ukpKTKYxwOh3x8fCRJ3t7e1cLF4XDIz8+vxucJDQ2tcunqV1m3v2774aoUFRVl9QhA08S/lfiZ+v5bWVpaWqsTEZbHzNy5c/Xmm28qPT1dgwYNkiQFBQUpPz+/yuOKi4vdl5aCgoJUXFxcbT0sLKzG57LZbLLZbA04PZorXkcAcHn1/beytvtb+j4zS5Ys0bp16/Tiiy/q3nvvdW+PjIzU/v37VV5e7t5mt9sVGRnpXrfb7e61srIyHThwwL0OAACaD8ti5vDhw1q6dKkef/xxxcTEqKioyP0VFxendu3aKTk5WXl5ecrMzNTevXs1evRoSdKoUaO0Z88eZWZmKi8vT8nJyWrfvj1/lg0AQDNkWcx88MEHcjqdWrZsmfr06VPly2azaenSpSoqKlJ8fLzeeecdZWRkKDg4WJLUvn17LV68WFlZWRo9erRKSkqUkZEhDw8Pqw4HAABYxLJ7ZhISEpSQkHDJ9Y4dO2rt2rWXXO/Xr5/69et3JUYDAAAG4YMmAQCA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRLH8HYAB1F/P061aPgCbEnj7O6hEAS3BmBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYrUnEjMPh0LBhw/TZZ5+5txUUFGj8+PGKiorS0KFDtWPHjir77Ny5U8OGDVNkZKTGjRungoKCxh4bAAA0AZbHTEVFhaZOnaq8vDz3NpfLpcmTJysgIEBZWVkaMWKEEhMTVVhYKEkqLCzU5MmTFR8fr40bN6pNmzaaNGmSXC6XVYcBAAAsYmnM5Ofn64EHHtA333xTZfunn36qgoICzZkzRyEhIZo4caKioqKUlZUlSdqwYYO6d++uCRMm6JZbblFaWpqOHTumXbt2WXEYAADAQpbGzK5du9SzZ0+tX7++yvacnBx169ZNLVq0cG+LiYlRdna2ez02Nta95uvrq/DwcPc6AABoPjytfPKHHnroF7cXFRWpbdu2Vbb5+/vrxIkTtVoHAADNh6UxcyllZWXy8vKqss3Ly0sOh6NW65fidDrldDobdlg0S7yO0BTxukRTU9/XZG33b5Ix4+3trZKSkirbHA6HfHx83Ov/Gy4Oh0N+fn41/txDhw416JxovrikiaaI1yWamsZ6TTbJmAkKClJ+fn6VbcXFxe5LS0FBQSouLq62HhYWVuPPDQ0NrXIfzq+ybn/d9sNVKSoqyuoRfsTrEj/TJF6XvCbxM/V9TZaWltbqRESTjJnIyEhlZmaqvLzcfTbGbrcrJibGvW63292PLysr04EDB5SYmFjjz7XZbLLZbFducDQbvI7QFPG6RFNT39dkbfe3/H1mfklcXJzatWun5ORk5eXlKTMzU3v37tXo0aMlSaNGjdKePXuUmZmpvLw8JScnq3379urZs6fFkwMAgMbWJGPGZrNp6dKlKioqUnx8vN555x1lZGQoODhYktS+fXstXrxYWVlZGj16tEpKSpSRkSEPDw+LJwcAAI2tyVxm+vLLL6t837FjR61du/aSj+/Xr5/69et3pccCAABNXJM8MwMAAFBbxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjGR0zFRUVmjlzpmJjY9WnTx+tWrXK6pEAAEAj87R6gPqYP3++9u3bp9WrV6uwsFAzZsxQcHCwBg8ebPVoAACgkRgbM6WlpdqwYYNefvllhYeHKzw8XHl5eXrjjTeIGQAAmhFjLzMdPHhQFy9eVHR0tHtbTEyMcnJyVFlZaeFkAACgMRkbM0VFRbrhhhvk5eXl3hYQEKCKigqVlJRYNxgAAGhUxl5mKisrqxIyktzfOxyOKtt/OlPzww8/yOl01un5br7Bp0774er0/fffWz2CJF6XqKopvC55TeLn6vuaLC8vl6TLXnExNma8vb2rRctP3/v4VP0fU0VFhSTpm2++qfPz/XlQSJ33xdXn0KFDVo8gidclqmoKr0tek/i5hnpNVlRUqFWrVpdcNzZmgoKCdObMGV28eFGenj8eRlFRkXx8fOTn51flsa1bt1anTp3k7e2ta64x9soaAADNSmVlpSoqKtS6desaH2dszISFhcnT01PZ2dmKjY2VJNntdkVERFQLFk9PT/n7+1sxJgAAqIeazsj8xNjTFL6+vho5cqRmz56tvXv3atu2bVq1apXGjRtn9WgAAKARGRszkpScnKzw8HA9/PDDevbZZzVlyhTdc889Vo91VXM4HBo2bJg+++wzq0dBM3fy5EklJSUpLi5Offv2VVpamvv+OMAqR48e1aOPPqro6Gj1799fK1eutHqkZsHYy0zSj2dn5s2bp3nz5lk9SrNQUVGhP/7xj8rLy7N6FDRzLpdLSUlJ8vPz0xtvvKGzZ89q5syZuuaaazRjxgyrx0MzVVlZqYSEBEVEROjtt9/W0aNHNXXqVAUFBWn48OFWj3dVM/rMDBpPfn6+HnjggXr9RRjQUI4cOaLs7GylpaXplltuUWxsrJKSkrR582arR0MzVlxcrLCwMM2ePVudOnVSv3791Lt3b9ntdqtHu+oRM6iVXbt2qWfPnlq/fr3VowAKDAzUypUrFRAQUGX7+fPnLZoIkNq2bauFCxeqVatWcrlcstvt2r17t+Li4qwe7apn9GUmNJ6HHnrI6hEANz8/P/Xt29f9fWVlpdauXatevXpZOBXw/w0cOFCFhYUaMGCABg0aZPU4Vz3OzAAwXnp6ug4cOKCnnnrK6lEASdKiRYu0fPly5ebmKi0tzepxrnqcmQFgtPT0dK1evVoLFixQaGio1eMAkqSIiAhJP/7hxLRp0zR9+vRqH8GDhsOZGQDGmjt3rl599VWlp6dzKh+WKy4u1rZt26ps69Kliy5cuMD9XFcYMQPASEuWLNG6dev04osv6t5777V6HEDffvutEhMTdfLkSfe2ffv2qU2bNmrTpo2Fk139iBkAxjl8+LCWLl2qxx9/XDExMSoqKnJ/AVaJiIhQeHi4Zs6cqfz8fG3fvl3p6el64oknrB7tqsc9MwCM88EHH8jpdGrZsmVatmxZlbUvv/zSoqnQ3NlsNi1dulRz587Vgw8+KF9fX40dO5aP2WkEHi6Xy2X1EAAAAHXFZSYAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAI1u4MCB6tq1q/srPDxcgwcP1muvvVarfTdt2nTlhwRgDD7OAIAlZs6cqaFDh0qSLl68qE8//VQpKSm6/vrrNXLkSGuHA2AUzswAsMR1112nwMBABQYGql27drrvvvvUu3dvvf/++1aPBsAwxAyAJsPT01PXXnutLl68qBdffFF9+vRRTEyMkpKSdObMmWqPP3/+vJKTk9W7d291795dgwcP1rZt29zrW7Zs0aBBgxQREaGhQ4dWWXv99dc1YMAARUREKD4+Xp9//nmjHCOAhkfMALDchQsX9P777+vjjz/WXXfdpZdeeklvv/22/vrXv2r9+vU6deqUZs2aVW2/1NRUffXVV1q1apU2b96s2NhYpaSkyOFw6NSpU5o+fbomTpyorVu3atSoUZo6dapKSkp04MABzZ8/X7NmzdJ7772n2NhYPfnkk6qsrLTg6AHUF/fMALDErFmzNHfuXElSeXm5fHx89PDDD2v48OHq1auXZsyYoTvvvFOS9Oyzz+q9996r9jNuv/12PfLIIwoNDZUkTZgwQRs2bNCpU6d05swZXbhwQTfeeKNuuukmTZgwQV27dpW3t7eOHTsmDw8PBQcHq3379nryySc1YMAAVVZW6ppr+P94gGmIGQCWSEpK0j333CNJ8vb2VmBgoGw2m06fPq2SkhKFh4e7H9ulSxdNmTKl2s8YOXKktm3bprfeektHjhzR/v37JUlOp1NhYWHq37+/HnnkEXXu3Fl33XWX7r//fvn6+qpPnz4KDQ3V8OHD1a1bN/eapyf/JAIm4v+CALCEv7+/OnbsqI4dO+rGG2+UzWaTpF8VFNOnT9e8efPk5+enMWPGaMWKFe41Dw8PrVixQhs2bNCgQYP04Ycf6r777lNubq58fX21YcMGrV69WnFxcdq0aZPi4+N18uTJBj9OAFceMQOgSfHz89MNN9yggwcPurfl5ubqzjvvVHl5uXvb+fPntXnzZi1YsEBJSUn6zW9+o7Nnz0qSXC6XDh8+rHnz5qlHjx566qmn9O6776pdu3b66KOP9MUXX2jFihXq1auXkpOTtXXrVlVUVMhutzf68QKoP86pAmhyxo4dq5deeklBQUHy9/dXamqqoqKi5OPj436Ml5eXfH199f7776tNmzb66quvNGfOHEmSw+GQn5+f3nzzTV133XUaPny48vPzdezYMXXr1k0+Pj7KyMhQQECAevfurd27d6u0tFRdu3a16pAB1AMxA6DJSUhI0Pfff68nn3xSFy9eVP/+/fWXv/ylymO8vLyUnp6uefPmac2aNWrfvr1+//vfa+HChcrNzdWwYcO0ePFiPf/881q+fLn8/f01depU9enTR9KPfwm1dOlSzZkzR8HBwUpPT1dISIgVhwugnjxcLpfL6iEAAADqintmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARvs/geWNR2b7E1gAAAAASUVORK5CYII=","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.092270Z","iopub.status.busy":"2024-04-01T06:27:56.091722Z","iopub.status.idle":"2024-04-01T06:27:56.162888Z","shell.execute_reply":"2024-04-01T06:27:56.161841Z","shell.execute_reply.started":"2024-04-01T06:27:56.092186Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameSexAgeSibSpParchFareCabinEmbarked_C...TcktName_SPTcktName_STONOTcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
010Braund, Mr. Owen Harrismale22.0107.2500NaNFalse...FalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...female38.01071.2833C85True...FalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
231Heikkinen, Miss. Lainafemale26.0007.9250NaNFalse...FalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
341Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01053.1000C123False...FalseFalseFalseFalseFalseFalseTrueTrueFalseFalse
450Allen, Mr. William Henrymale35.0008.0500NaNFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 46 columns

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"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Sex Age SibSp Parch Fare Cabin Embarked_C ... TcktName_SP \\\n","0 male 22.0 1 0 7.2500 NaN False ... False \n","1 female 38.0 1 0 71.2833 C85 True ... False \n","2 female 26.0 0 0 7.9250 NaN False ... False \n","3 female 35.0 1 0 53.1000 C123 False ... False \n","4 male 35.0 0 0 8.0500 NaN False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP \\\n","0 False False False False False \n","1 False False False False False \n","2 False True False False False \n","3 False False False False False \n","4 False False False False False \n","\n"," TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False False False True \n","1 False True False False \n","2 False False False True \n","3 True True False False \n","4 True False False True \n","\n","[5 rows x 46 columns]"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Pclass\"] = train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns= [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameSexAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_SOTONOQTcktName_STONOTcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3
0892Kelly, Mr. Jamesmale34.5007.8292NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)female47.0107.0000NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
2894Myles, Mr. Thomas Francismale62.0009.6875NaNFalseTrue...FalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
3895Wirz, Mr. Albertmale27.0008.6625NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.01112.2875NaNFalseFalse...FalseFalseFalseFalseFalseFalseTrueFalseFalseTrue
\n","

5 rows × 42 columns

\n","
"],"text/plain":[" PassengerId Name Sex Age \\\n","0 892 Kelly, Mr. James male 34.5 \n","1 893 Wilkes, Mrs. James (Ellen Needs) female 47.0 \n","2 894 Myles, Mr. Thomas Francis male 62.0 \n","3 895 Wirz, Mr. Albert male 27.0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 \n","\n"," SibSp Parch Fare Cabin Embarked_C Embarked_Q ... TcktName_SOTONOQ \\\n","0 0 0 7.8292 NaN False True ... False \n","1 1 0 7.0000 NaN False False ... False \n","2 0 0 9.6875 NaN False True ... False \n","3 0 0 8.6625 NaN False False ... False \n","4 1 1 12.2875 NaN False False ... False \n","\n"," TcktName_STONO TcktName_STONO2 TcktName_STONOQ TcktName_WC \\\n","0 False False False False \n","1 False False False False \n","2 False False False False \n","3 False False False False \n","4 False False False False \n","\n"," TcktName_WEP TcktName_x Pclass_1 Pclass_2 Pclass_3 \n","0 False True False False True \n","1 False True False False True \n","2 False True False True False \n","3 False True False False True \n","4 False True False False True \n","\n","[5 rows x 42 columns]"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Pclass\"] = test_df[\"Pclass\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns= [\"Pclass\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Sex"]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.164709Z","iopub.status.busy":"2024-04-01T06:27:56.164391Z","iopub.status.idle":"2024-04-01T06:27:56.205775Z","shell.execute_reply":"2024-04-01T06:27:56.204761Z","shell.execute_reply.started":"2024-04-01T06:27:56.164639Z"},"trusted":true},"outputs":[{"data":{"text/html":["
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PassengerIdSurvivedNameAgeSibSpParchFareCabinEmbarked_CEmbarked_Q...TcktName_STONO2TcktName_SWPPTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
010Braund, Mr. Owen Harris22.0107.2500NaNFalseFalse...FalseFalseFalseFalseFalseFalseFalseTrueFalseTrue
121Cumings, Mrs. John Bradley (Florence Briggs Th...38.01071.2833C85TrueFalse...FalseFalseFalseFalseFalseTrueFalseFalseTrueFalse
231Heikkinen, Miss. Laina26.0007.9250NaNFalseFalse...TrueFalseFalseFalseFalseFalseFalseTrueTrueFalse
341Futrelle, Mrs. Jacques Heath (Lily May Peel)35.01053.1000C123FalseFalse...FalseFalseFalseFalseTrueTrueFalseFalseTrueFalse
450Allen, Mr. William Henry35.0008.0500NaNFalseFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
\n","

5 rows × 47 columns

\n","
"],"text/plain":[" PassengerId Survived Name \\\n","0 1 0 Braund, Mr. Owen Harris \n","1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n","2 3 1 Heikkinen, Miss. Laina \n","3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n","4 5 0 Allen, Mr. William Henry \n","\n"," Age SibSp Parch Fare Cabin Embarked_C Embarked_Q ... \\\n","0 22.0 1 0 7.2500 NaN False False ... \n","1 38.0 1 0 71.2833 C85 True False ... \n","2 26.0 0 0 7.9250 NaN False False ... \n","3 35.0 1 0 53.1000 C123 False False ... \n","4 35.0 0 0 8.0500 NaN False False ... \n","\n"," TcktName_STONO2 TcktName_SWPP TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False False \n","1 False False False False False \n","2 True False False False False \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 True False False True False \n","2 False False True True False \n","3 True False False True False \n","4 False False True False True \n","\n","[5 rows x 47 columns]"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":39,"metadata":{},"outputs":[{"data":{"text/html":["
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PassengerIdNameAgeSibSpParchFareCabinEmbarked_CEmbarked_QEmbarked_S...TcktName_STONO2TcktName_STONOQTcktName_WCTcktName_WEPTcktName_xPclass_1Pclass_2Pclass_3Sex_femaleSex_male
0892Kelly, Mr. James34.5007.8292NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
1893Wilkes, Mrs. James (Ellen Needs)47.0107.0000NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
2894Myles, Mr. Thomas Francis62.0009.6875NaNFalseTrueFalse...FalseFalseFalseFalseTrueFalseTrueFalseFalseTrue
3895Wirz, Mr. Albert27.0008.6625NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
4896Hirvonen, Mrs. Alexander (Helga E Lindqvist)22.01112.2875NaNFalseFalseTrue...FalseFalseFalseFalseTrueFalseFalseTrueTrueFalse
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5 rows × 43 columns

\n","
"],"text/plain":[" PassengerId Name Age SibSp \\\n","0 892 Kelly, Mr. James 34.5 0 \n","1 893 Wilkes, Mrs. James (Ellen Needs) 47.0 1 \n","2 894 Myles, Mr. Thomas Francis 62.0 0 \n","3 895 Wirz, Mr. Albert 27.0 0 \n","4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 22.0 1 \n","\n"," Parch Fare Cabin Embarked_C Embarked_Q Embarked_S ... \\\n","0 0 7.8292 NaN False True False ... \n","1 0 7.0000 NaN False False True ... \n","2 0 9.6875 NaN False True False ... \n","3 0 8.6625 NaN False False True ... \n","4 1 12.2875 NaN False False True ... \n","\n"," TcktName_STONO2 TcktName_STONOQ TcktName_WC TcktName_WEP TcktName_x \\\n","0 False False False False True \n","1 False False False False True \n","2 False False False False True \n","3 False False False False True \n","4 False False False False True \n","\n"," Pclass_1 Pclass_2 Pclass_3 Sex_female Sex_male \n","0 False False True False True \n","1 False False True True False \n","2 False True False False True \n","3 False False True False True \n","4 False False True True False \n","\n","[5 rows x 43 columns]"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["test_df[\"Sex\"] = test_df[\"Sex\"].astype(\"category\")\n","test_df = pd.get_dummies(test_df, columns=[\"Sex\"])\n","test_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["## Drop Passenger ID and Cabin (Assignment)"]},{"cell_type":"code","execution_count":40,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.207602Z","iopub.status.busy":"2024-04-01T06:27:56.207299Z","iopub.status.idle":"2024-04-01T06:27:56.215886Z","shell.execute_reply":"2024-04-01T06:27:56.214401Z","shell.execute_reply.started":"2024-04-01T06:27:56.207550Z"},"trusted":true},"outputs":[],"source":["train_df.drop(labels = [\"PassengerId\", \"Cabin\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.217917Z","iopub.status.busy":"2024-04-01T06:27:56.217536Z","iopub.status.idle":"2024-04-01T06:27:56.228150Z","shell.execute_reply":"2024-04-01T06:27:56.227230Z","shell.execute_reply.started":"2024-04-01T06:27:56.217854Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Index(['Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C',\n"," 'Embarked_Q', 'Embarked_S', 'TcktName_A4', 'TcktName_A5', 'TcktName_AS',\n"," 'TcktName_C', 'TcktName_CA', 'TcktName_CASOTON', 'TcktName_FC',\n"," 'TcktName_FCC', 'TcktName_Fa', 'TcktName_LINE', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_PPP', 'TcktName_SC', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCOW', 'TcktName_SCPARIS',\n"," 'TcktName_SCParis', 'TcktName_SOC', 'TcktName_SOP', 'TcktName_SOPP',\n"," 'TcktName_SOTONO2', 'TcktName_SOTONOQ', 'TcktName_SP', 'TcktName_STONO',\n"," 'TcktName_STONO2', 'TcktName_SWPP', 'TcktName_WC', 'TcktName_WEP',\n"," 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',\n"," 'Sex_male'],\n"," dtype='object')"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["train_df.columns"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[],"source":["# Drop the PassengerId and Cabin columns from the test set\n","test_df.drop(labels=[\"PassengerId\", \"Cabin\"], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"data":{"text/plain":["Index(['Name', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q',\n"," 'Embarked_S', 'TcktName_A', 'TcktName_A4', 'TcktName_A5',\n"," 'TcktName_AQ3', 'TcktName_AQ4', 'TcktName_C', 'TcktName_CA',\n"," 'TcktName_FC', 'TcktName_FCC', 'TcktName_LP', 'TcktName_PC',\n"," 'TcktName_PP', 'TcktName_SC', 'TcktName_SCA3', 'TcktName_SCA4',\n"," 'TcktName_SCAH', 'TcktName_SCPARIS', 'TcktName_SCParis', 'TcktName_SOC',\n"," 'TcktName_SOPP', 'TcktName_SOTONO2', 'TcktName_SOTONOQ',\n"," 'TcktName_STONO', 'TcktName_STONO2', 'TcktName_STONOQ', 'TcktName_WC',\n"," 'TcktName_WEP', 'TcktName_x', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n"," 'Sex_female', 'Sex_male'],\n"," dtype='object')"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["# Print the columns of the test set\n","test_df.columns"]},{"cell_type":"markdown","metadata":{},"source":["
\n","# Modeling"]},{"cell_type":"code","execution_count":44,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.230086Z","iopub.status.busy":"2024-04-01T06:27:56.229809Z","iopub.status.idle":"2024-04-01T06:27:56.238557Z","shell.execute_reply":"2024-04-01T06:27:56.237679Z","shell.execute_reply.started":"2024-04-01T06:27:56.230040Z"},"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{},"source":["## Train - Test Split (Assignment)"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.240103Z","iopub.status.busy":"2024-04-01T06:27:56.239830Z","iopub.status.idle":"2024-04-01T06:27:56.256809Z","shell.execute_reply":"2024-04-01T06:27:56.255463Z","shell.execute_reply.started":"2024-04-01T06:27:56.240056Z"},"trusted":true},"outputs":[{"data":{"text/plain":["891"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["train_df_len = len(train_df)\n","train_df_len"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.314730Z","iopub.status.busy":"2024-04-01T06:27:56.313986Z","iopub.status.idle":"2024-04-01T06:27:56.333564Z","shell.execute_reply":"2024-04-01T06:27:56.332507Z","shell.execute_reply.started":"2024-04-01T06:27:56.314635Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["X_train 596\n","X_test 295\n","y_train 596\n","y_test 295\n","test 418\n"]}],"source":["\n","train = train_df[:train_df_len]\n","test = test_df\n","\n","# Select all numerical values from train and test\n","numeric_train = train.select_dtypes(include=[np.number])\n","numeric_test = test.select_dtypes(include=[np.number]) \n","\n","\n","X_train = numeric_train.drop(labels=[\"Survived\",], axis=1)\n","y_train = numeric_train[\"Survived\"]\n","\n","# Split the train data into train and test sets with a 1/3 ratio\n","X_train, X_test, y_train, y_test = train_test_split(numeric_train.drop(labels=[\"Survived\"], axis=1), numeric_train[\"Survived\"], test_size=0.33, random_state=42)\n","\n","\n","print(\"X_train\", len(X_train))\n","print(\"X_test\", len(X_test))\n","print(\"y_train\", len(y_train))\n","print(\"y_test\", len(y_test))\n","print(\"test\", len(numeric_test))\n"]},{"cell_type":"markdown","metadata":{},"source":["## Simple Logistic Regression (Assignment)"]},{"cell_type":"code","execution_count":47,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.335970Z","iopub.status.busy":"2024-04-01T06:27:56.335281Z","iopub.status.idle":"2024-04-01T06:27:56.368083Z","shell.execute_reply":"2024-04-01T06:27:56.366489Z","shell.execute_reply.started":"2024-04-01T06:27:56.335561Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy on the training set: 68.62%\n","Accuracy on the test set: 68.81%\n"]}],"source":["logreg = LogisticRegression()\n","logreg.fit(X_train, y_train)\n","acc_log_train = round(logreg.score(X_train, y_train)*100,2) \n","acc_log_test = round(logreg.score(X_test,y_test)*100,2)\n","# Print the accuracy on the training and test set\n","print(f\"Accuracy on the training set: {acc_log_train}%\")\n","print(f\"Accuracy on the test set: {acc_log_test}%\")"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Hyperparameter Tuning -- Grid Search -- Cross Validation\n","We will compare 5 ml classifier and evaluate mean accuracy of each of them by stratified cross validation.\n","\n","* Decision Tree\n","* SVM\n","* Random Forest\n","* KNN\n","* Logistic Regression"]},{"cell_type":"code","execution_count":48,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.371066Z","iopub.status.busy":"2024-04-01T06:27:56.370400Z","iopub.status.idle":"2024-04-01T06:27:56.401742Z","shell.execute_reply":"2024-04-01T06:27:56.396867Z","shell.execute_reply.started":"2024-04-01T06:27:56.370802Z"},"trusted":true},"outputs":[],"source":["random_state = 42\n","classifier = [DecisionTreeClassifier(random_state = random_state),\n"," SVC(random_state = random_state),\n"," RandomForestClassifier(random_state = random_state),\n"," LogisticRegression(random_state = random_state),\n"," KNeighborsClassifier()]\n","\n","dt_param_grid = {\"min_samples_split\" : range(10,500,20),\n"," \"max_depth\": range(1,20,2)}\n","\n","svc_param_grid = {\"kernel\" : [\"rbf\"],\n"," \"gamma\": [0.001, 0.01, 0.1, 1],\n"," \"C\": [1,10,50,100,200,300,1000]}\n","\n","rf_param_grid = {\"max_features\": [1,3,10],\n"," \"min_samples_split\":[2,3,10],\n"," \"min_samples_leaf\":[1,3,10],\n"," \"bootstrap\":[False],\n"," \"n_estimators\":[100,300],\n"," \"criterion\":[\"gini\"]}\n","\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n"," \"penalty\": [\"l1\",\"l2\"]}\n","\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n"," \"weights\": [\"uniform\",\"distance\"],\n"," \"metric\":[\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n"," svc_param_grid,\n"," rf_param_grid,\n"," logreg_param_grid,\n"," knn_param_grid]"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:27:56.413811Z","iopub.status.busy":"2024-04-01T06:27:56.404322Z","iopub.status.idle":"2024-04-01T06:29:38.718970Z","shell.execute_reply":"2024-04-01T06:29:38.717807Z","shell.execute_reply.started":"2024-04-01T06:27:56.413658Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n"]},{"name":"stderr","output_type":"stream","text":["/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n","/Users/fahee/anaconda3/lib/python3.11/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n"," from pandas.core import (\n"]},{"name":"stdout","output_type":"stream","text":["0.6996045197740112\n","Fitting 10 folds for each of 28 candidates, totalling 280 fits\n","0.7130508474576271\n","Fitting 10 folds for each of 54 candidates, totalling 540 fits\n","0.7081073446327684\n","Fitting 10 folds for each of 14 candidates, totalling 140 fits\n","0.6777683615819209\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","0.6979943502824858\n"]}],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n"," clf = GridSearchCV(classifier[i], param_grid=classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1,verbose = 1)\n"," clf.fit(X_train,y_train)\n"," cv_result.append(clf.best_score_)\n"," best_estimators.append(clf.best_estimator_)\n"," print(cv_result[i])"]},{"cell_type":"code","execution_count":50,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:38.722928Z","iopub.status.busy":"2024-04-01T06:29:38.722207Z","iopub.status.idle":"2024-04-01T06:29:39.075423Z","shell.execute_reply":"2024-04-01T06:29:39.073987Z","shell.execute_reply.started":"2024-04-01T06:29:38.722582Z"},"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'Cross Validation Scores')"]},"execution_count":50,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\":cv_result, \"ML Models\":[\"DecisionTreeClassifier\", \"SVM\",\"RandomForestClassifier\",\n"," \"LogisticRegression\",\n"," \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(x=\"Cross Validation Means\",y= \"ML Models\", data=cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Scores\")"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Modeling (Assignment)"]},{"cell_type":"code","execution_count":51,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.078654Z","iopub.status.busy":"2024-04-01T06:29:39.077840Z","iopub.status.idle":"2024-04-01T06:29:39.862871Z","shell.execute_reply":"2024-04-01T06:29:39.860937Z","shell.execute_reply.started":"2024-04-01T06:29:39.078554Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy of the voting classifier on the test set: 70.85%\n"]}],"source":["votingC = VotingClassifier(estimators = [(\"dt\",best_estimators[0]),\n"," (\"rfc\",best_estimators[2]),\n"," (\"lr\",best_estimators[3])],\n"," voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(X_train, y_train)\n","\n","# Print the accuracy score of the voting classifier\n","acc_votingC = round(votingC.score(X_test, y_test) * 100, 2)\n","print(f\"Accuracy of the voting classifier on the test set: {acc_votingC}%\")"]},{"cell_type":"code","execution_count":52,"metadata":{},"outputs":[],"source":["# Drop the null values which are going to cause you an error in the next cell\n","numeric_test_dropna = numeric_test.dropna()"]},{"cell_type":"markdown","metadata":{},"source":["
\n","## Prediction and Submission"]},{"cell_type":"code","execution_count":53,"metadata":{"execution":{"iopub.execute_input":"2024-04-01T06:29:39.865981Z","iopub.status.busy":"2024-04-01T06:29:39.865330Z","iopub.status.idle":"2024-04-01T06:29:39.977357Z","shell.execute_reply":"2024-04-01T06:29:39.973301Z","shell.execute_reply.started":"2024-04-01T06:29:39.865906Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":[" PassengerId Survived\n","0 892 0.0\n","1 893 0.0\n","2 894 0.0\n","3 895 0.0\n","4 896 0.0\n",".. ... ...\n","413 1305 1.0\n","414 1306 0.0\n","415 1307 0.0\n","416 1308 0.0\n","417 1309 NaN\n","\n","[418 rows x 2 columns]\n"]}],"source":["test_survived = pd.Series(votingC.predict(numeric_test_dropna), name=\"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis=1)\n","results.to_csv(\"titanic.csv\", index=False)\n","print(results)"]},{"cell_type":"markdown","metadata":{},"source":["# Congratulations on finishing the assignment!!\n","\n","### The submission is the titanic.csv which was just created, and this file which you have modified."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":26502,"sourceId":3136,"sourceType":"competition"}],"dockerImageVersionId":29852,"isGpuEnabled":false,"isInternetEnabled":false,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.4"}},"nbformat":4,"nbformat_minor":4}