diff --git a/.ipynb_checkpoints/lab_feature_engeneering-checkpoint.ipynb b/.ipynb_checkpoints/lab_feature_engeneering-checkpoint.ipynb new file mode 100644 index 0000000..44a6d6c --- /dev/null +++ b/.ipynb_checkpoints/lab_feature_engeneering-checkpoint.ipynb @@ -0,0 +1,2706 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "60b86b8b", + "metadata": {}, + "source": [ + "\n", + "Here we will work on cleaning some of the other columns in the dataset using the techniques that we used before in the lessons.\n", + "\n", + "- Check for null values in the numerical columns.\n", + "- Use appropriate methods to clean the columns `GEOCODE2`, `WEALTH1`, `ADI`, `DMA`,and `MSA`.\n", + "- Use appropriate EDA technique where ever necessary.\n", + " ```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1384030c", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8b83680e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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481 rows × 2 columns

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" + ], + "text/plain": [ + " column_name nulls_percentage\n", + "414 RDATE_5 0.999906\n", + "436 RAMNT_5 0.999906\n", + "412 RDATE_3 0.997464\n", + "434 RAMNT_3 0.997464\n", + "413 RDATE_4 0.997055\n", + ".. ... ...\n", + "168 ETHC3 0.000000\n", + "167 ETHC2 0.000000\n", + "166 ETHC1 0.000000\n", + "165 HHD12 0.000000\n", + "240 TPE11 0.000000\n", + "\n", + "[481 rows x 2 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Lets deal with sparcity part \n", + "# Lets check null values in percentage \n", + "\n", + "nulls_percent_df= data.isna().sum()/len(data)\n", + "nulls_percent_df\n", + "\n", + "# put it in a dataframe \n", + "nulls_percent_df= pd.DataFrame(data.isna().sum()/len(data))\n", + "nulls_percent_df\n", + "\n", + "# Take out of the index \n", + "nulls_percent_df= pd.DataFrame(data.isna().sum()/len(data)).reset_index()\n", + "nulls_percent_df\n", + "\n", + "# Lets change columns name\n", + "nulls_percent_df.columns = ['column_name', 'nulls_percentage']\n", + "nulls_percent_df\n", + "\n", + "# Lets sort \n", + "nulls_percent_df.sort_values(by = ['nulls_percentage'], ascending = False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4040a63b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['NUMCHLD',\n", + " 'WEALTH1',\n", + " 'MBCRAFT',\n", + " 'MBGARDEN',\n", + " 'MBBOOKS',\n", + " 'MBCOLECT',\n", + " 'MAGFAML',\n", + " 'MAGFEM',\n", + " 'MAGMALE',\n", + " 'PUBGARDN',\n", + " 'PUBCULIN',\n", + " 'PUBHLTH',\n", + " 'PUBDOITY',\n", + " 'PUBNEWFN',\n", + " 'PUBPHOTO',\n", + " 'PUBOPP',\n", + " 'WEALTH2',\n", + " 'ADATE_5',\n", + " 'ADATE_10',\n", + " 'ADATE_13',\n", + " 'ADATE_15',\n", + " 'ADATE_17',\n", + " 'ADATE_19',\n", + " 'ADATE_20',\n", + " 'ADATE_21',\n", + " 'ADATE_22',\n", + " 'ADATE_23',\n", + " 'ADATE_24',\n", + " 'RDATE_3',\n", + " 'RDATE_4',\n", + " 'RDATE_5',\n", + " 'RDATE_6',\n", + " 'RDATE_7',\n", + " 'RDATE_8',\n", + " 'RDATE_9',\n", + " 'RDATE_10',\n", + " 'RDATE_11',\n", + " 'RDATE_12',\n", + " 'RDATE_13',\n", + " 'RDATE_14',\n", + " 'RDATE_15',\n", + " 'RDATE_16',\n", + " 'RDATE_17',\n", + " 'RDATE_18',\n", + " 'RDATE_19',\n", + " 'RDATE_20',\n", + " 'RDATE_21',\n", + " 'RDATE_22',\n", + " 'RDATE_23',\n", + " 'RDATE_24',\n", + " 'RAMNT_3',\n", + " 'RAMNT_4',\n", + " 'RAMNT_5',\n", + " 'RAMNT_6',\n", + " 'RAMNT_7',\n", + " 'RAMNT_8',\n", + " 'RAMNT_9',\n", + " 'RAMNT_10',\n", + " 'RAMNT_11',\n", + " 'RAMNT_12',\n", + " 'RAMNT_13',\n", + " 'RAMNT_14',\n", + " 'RAMNT_15',\n", + " 'RAMNT_16',\n", + " 'RAMNT_17',\n", + " 'RAMNT_18',\n", + " 'RAMNT_19',\n", + " 'RAMNT_20',\n", + " 'RAMNT_21',\n", + " 'RAMNT_22',\n", + " 'RAMNT_23',\n", + " 'RAMNT_24']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First create the variable with the threshold \n", + "threshold =0.25 \n", + "\n", + "# define a condition \n", + "condition = nulls_percent_df['nulls_percentage']>threshold\n", + "columns_above_threshold = nulls_percent_df[condition]\n", + "columns_above_threshold\n", + "\n", + "# Create a list with column names\n", + "drop_columns_list = list(columns_above_threshold['column_name'])\n", + "drop_columns_list" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bca2c434", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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08901GRI0IL6108137120...0.00L4EXXX39.0C
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95412 rows × 409 columns

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" + ], + "text/plain": [ + " ODATEDW OSOURCE TCODE STATE ZIP MAILCODE PVASTATE DOB NOEXCH \\\n", + "0 8901 GRI 0 IL 61081 3712 0 \n", + "1 9401 BOA 1 CA 91326 5202 0 \n", + "2 9001 AMH 1 NC 27017 0 0 \n", + "3 8701 BRY 0 CA 95953 2801 0 \n", + "4 8601 0 FL 33176 2001 0 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 9601 ASE 1 AK 99504 0 0 \n", + "95408 9601 DCD 1 TX 77379 5001 0 \n", + "95409 9501 MBC 1 MI 48910 3801 0 \n", + "95410 8601 PRV 0 CA 91320 4005 0 \n", + "95411 8801 MCC 2 NC 28409 1801 0 \n", + "\n", + " RECINHSE ... TARGET_D HPHONE_D RFA_2R RFA_2F RFA_2A MDMAUD_R MDMAUD_F \\\n", + "0 ... 0.0 0 L 4 E X X \n", + "1 ... 0.0 0 L 2 G X X \n", + "2 ... 0.0 1 L 4 E X X \n", + "3 ... 0.0 1 L 4 E X X \n", + "4 X ... 0.0 1 L 2 F X X \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 ... 0.0 0 L 1 G X X \n", + "95408 ... 0.0 1 L 1 F X X \n", + "95409 ... 0.0 1 L 3 E X X \n", + "95410 X ... 18.0 1 L 4 F X X \n", + "95411 X ... 0.0 1 L 1 G C 1 \n", + "\n", + " MDMAUD_A CLUSTER2 GEOCODE2 \n", + "0 X 39.0 C \n", + "1 X 1.0 A \n", + "2 X 60.0 C \n", + "3 X 41.0 C \n", + "4 X 26.0 A \n", + "... ... ... ... \n", + "95407 X 12.0 C \n", + "95408 X 2.0 A \n", + "95409 X 34.0 B \n", + "95410 X 11.0 A \n", + "95411 C 12.0 C \n", + "\n", + "[95412 rows x 409 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Removing the null values with a threshold above 25% using the list created above that shows all the columns within this th\n", + "data_drop1 = data.drop(columns=drop_columns_list)\n", + "data_drop1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "afc4d49c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "F 51277\n", + "M 39094\n", + " 2957\n", + "U 1715\n", + "J 365\n", + "C 2\n", + "A 2\n", + "Name: GENDER, dtype: int64\n" + ] + } + ], + "source": [ + "# Check and fill the null values with F in the GENDER column\n", + "print(data['GENDER'].value_counts())\n", + "data['GENDER'] = data['GENDER'].fillna('F')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "432c1919", + "metadata": {}, + "outputs": [], + "source": [ + "# Lets settle the values of GENDER to only M F or other \n", + "def frequent_values(df, column, n=2, replace_value='other'):\n", + " value_counts = df[column].value_counts()\n", + " top_n_values = value_counts.index[:n]\n", + " df[column] = df[column].apply(lambda x: x if x in top_n_values else replace_value)\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c3336b39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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95412 rows × 481 columns

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column_namenulls_percentage
346RDATE_50.999906
368RAMNT_50.999906
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366RAMNT_30.997464
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.........
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407 rows × 2 columns

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" + ], + "text/plain": [ + " column_name nulls_percentage\n", + "346 RDATE_5 0.999906\n", + "368 RAMNT_5 0.999906\n", + "344 RDATE_3 0.997464\n", + "366 RAMNT_3 0.997464\n", + "345 RDATE_4 0.997055\n", + ".. ... ...\n", + "145 HUPA7 0.000000\n", + "144 HUPA6 0.000000\n", + "143 HUPA5 0.000000\n", + "142 HUPA4 0.000000\n", + "203 LFC6 0.000000\n", + "\n", + "[407 rows x 2 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking for null values in numerical_data DF\n", + "nulls_percent_numerical= pd.DataFrame(numerical_data.isna().sum()/len(numerical_data)).reset_index()\n", + "nulls_percent_numerical.columns = ['column_name', 'nulls_percentage']\n", + "\n", + "# Sorting the values to see the highest first\n", + "nulls_percent_numerical.sort_values(by = ['nulls_percentage'], ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e9e8845a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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ODATEDWOSOURCETCODESTATEZIPMAILCODEPVASTATEDOBNOEXCHRECINHSE...TARGET_DHPHONE_DRFA_2RRFA_2FRFA_2AMDMAUD_RMDMAUD_FMDMAUD_ACLUSTER2GEOCODE2
08901GRI0IL6108137120...0.00L4EXXX39.0C
19401BOA1CA9132652020...0.00L2GXXX1.0A
29001AMH1NC2701700...0.01L4EXXX60.0C
38701BRY0CA9595328010...0.01L4EXXX41.0C
486010FL3317620010X...0.01L2FXXX26.0A
..................................................................
954079601ASE1AK9950400...0.00L1GXXX12.0C
954089601DCD1TX7737950010...0.01L1FXXX2.0A
954099501MBC1MI4891038010...0.01L3EXXX34.0B
954108601PRV0CA9132040050X...18.01L4FXXX11.0A
954118801MCC2NC2840918010X...0.01L1GC1C12.0C
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95412 rows × 480 columns

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" + ], + "text/plain": [ + " ODATEDW OSOURCE TCODE STATE ZIP MAILCODE PVASTATE DOB NOEXCH \\\n", + "0 8901 GRI 0 IL 61081 3712 0 \n", + "1 9401 BOA 1 CA 91326 5202 0 \n", + "2 9001 AMH 1 NC 27017 0 0 \n", + "3 8701 BRY 0 CA 95953 2801 0 \n", + "4 8601 0 FL 33176 2001 0 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 9601 ASE 1 AK 99504 0 0 \n", + "95408 9601 DCD 1 TX 77379 5001 0 \n", + "95409 9501 MBC 1 MI 48910 3801 0 \n", + "95410 8601 PRV 0 CA 91320 4005 0 \n", + "95411 8801 MCC 2 NC 28409 1801 0 \n", + "\n", + " RECINHSE ... TARGET_D HPHONE_D RFA_2R RFA_2F RFA_2A MDMAUD_R MDMAUD_F \\\n", + "0 ... 0.0 0 L 4 E X X \n", + "1 ... 0.0 0 L 2 G X X \n", + "2 ... 0.0 1 L 4 E X X \n", + "3 ... 0.0 1 L 4 E X X \n", + "4 X ... 0.0 1 L 2 F X X \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 ... 0.0 0 L 1 G X X \n", + "95408 ... 0.0 1 L 1 F X X \n", + "95409 ... 0.0 1 L 3 E X X \n", + "95410 X ... 18.0 1 L 4 F X X \n", + "95411 X ... 0.0 1 L 1 G C 1 \n", + "\n", + " MDMAUD_A CLUSTER2 GEOCODE2 \n", + "0 X 39.0 C \n", + "1 X 1.0 A \n", + "2 X 60.0 C \n", + "3 X 41.0 C \n", + "4 X 26.0 A \n", + "... ... ... ... \n", + "95407 X 12.0 C \n", + "95408 X 2.0 A \n", + "95409 X 34.0 B \n", + "95410 X 11.0 A \n", + "95411 C 12.0 C \n", + "\n", + "[95412 rows x 480 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For the Wealth1 variable \n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['WEALTH1'].value_counts())\n", + "print(data['WEALTH1'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['WEALTH1'].isna().sum())\n", + "\n", + "# There is almost 50% of null values in this column so I will just drop it \n", + "\n", + "data = data.drop(columns = 'WEALTH1', axis=1)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f4a07c91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.0 7296\n", + "51.0 4622\n", + "65.0 3765\n", + "57.0 2836\n", + "105.0 2617\n", + " ... \n", + "651.0 1\n", + "103.0 1\n", + "601.0 1\n", + "161.0 1\n", + "147.0 1\n", + "Name: ADI, Length: 204, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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EHrDGMJYADcaYchGpclfXGHMAl4RlJY98Y0y1l9cxppQ3OFe69amrKtUaIK9uZU5WvC1xKXhm6/nzNm5fMikAkSjlf94mjvdE5PtAlIhcC3wdeHWwCsaYbhF5ANgMOIB1xpgDInKvdX4tsAnnwolFQCtwz2B1fb66Ma6iYSgtDmfiOF7doolDKTUk3iaOh4AvA/uAr+H8hf+kp0rGmE1WWddja12eG5zdYF7VdVMm11MMY1m5lTjiI30ZHNcpuUqp4fEqcRhjekXkFeAVY8zYnaI0ylQ0tBMV5iA81Psd/WIiQpkQH6E3ASqlhmzQ3zjWnd0/FJFq4DBQKCJVIuJxVpOyX3lDu0/dVH1yU2K0xaGUGjJPf6p+G+dsqouNMSnGmGScg9jLReQ7dgenBlfR2DakxDElLUb35VBKDZmnxHEXcJsx5njfAetu7jutcyqAKhrafZpR1Sc3JYaals6zCyQqpZQvPI1xhLmb6mqMqRIR339jKb/p6O6hurmTBTm+bKni9PcpuS1cmJPo58gCz91UWNDpsEr5i6cWR+cQzymbnWl0rjU5pK4qlym5SinlK09/rl4oIo1ujgsQaUM8yktnp+IOIXHkJEcjoolDKTU0gyYOY4xuExek+u4aT/DhHo4+kWEOJiZGDSlxNLV3ISLERvjeRaaUGhv0p3+UGspd467yUn2bktvZ3cvmAxV8WFRNmCOEz+VnMytD7zxXajzy/s4xFVTKG9qJiwglImxojcK81BiOV7Xg7W7Aa987xntHqpibFU9idBjrPz5B0Rldnl2p8UgTxyhV0dBORsLQh5lmTIijqaObsro2j2XLG9p47N1jzMuK5/MXT+LeK6aSGBXGmwcrvE48SqmxQxPHKFXeOLzEMW9iAgAHTjd4LPuL1wvpMYaV8zIBCHOEcNWsdErr2ni3UFegUWq80cQxSlU0tJE5jMQxKyMOR4iw/5S7SXN/V9PcwcY9p7lzyWSSYsLPHl84KYmk6DB+/deiIceglBqdNHGMQl09vZxp6iAjIWrI7xEZ5mB6eiz7PbQ4Xt51iu5ew62Lc8457ggRFucms+NEHaW1uqOgUuOJJo5RqKqpA2MYVosDYG5WAvtPNQw4TmGM4fmCUhbkJDJjQtx55/u6u/6yv3xYcSilRhdNHAHU1tnDu4VnqG/17Sb8vpv/hjPGATBvYjzVzZ2caepwe35vWQNHKpu5JT/H7fmU2AgumJjAn/dVDCsOpdToYmviEJEVIlIoIkUi8pCb8yIij1jn94rIQk91ReTHVtndIvKGiGTZeQ12aevs4Svrt/PGwUpe3Fnm0+ykvpv/htvi6Gsx7D/lvrvq+YJSIsNCuOHCzAHf45PzM9lTWq/dVUqNI7YlDhFxAI8CK4E5wG0iMqdfsZXAdOuxBnjMi7q/MMbMN8YsAF4DRuXeIP930yE+OlbD3Kx4jlW1sKfM8+ymPqesKbRZiUMf4wCYkxmPCOwurT/vXFtnDxt3n2bVvMxBdxhcOS8DgLcOVQ4rFqXU6GFni2MxUGSMKTbGdAIbgNX9yqwG1hunLUCiiGQOVtcY4zoNKAYYdTcSGGN442AFq+ZlctviSWQnRbH5gPf3RJTVOffh8GXLWHdiIkJZNCmJtw+dOe/c5gMVNHV087kBuqn6TE6JYVJyNB8W1QwrFqXU6GFn4pgIlLq8LrOOeVNm0Loi8u8iUgrcwQAtDhFZIyIFIlJQVRVc9xoUVjZR2djB5TNSCRFhSV4KDW1dVDS2e1W/rK6VnOThtTb6XDd3AgfLGymrO7er6bntpUxKjmZJXrLH91g+LZWtxTV09/T6JSalVHCzM3GIm2P9/6QeqMygdY0xPzDG5ABPAw+4+3BjzOPGmHxjTH5aWpqXIY+M9484tzi5fIYzrmnpsQAcrfRuCY+yujayE6P9Esu1c5xdTW8e/HtX057Sej4uruH2JZMICXH3X3GuS6el0tTRzd4BxkqUUmOLnYmjDHDt58gGTntZxpu6AM8Anx12pCPsb0ermJ4eS6Z1H0ZCVBjpcRFerf1kjHEmjiT/tDjyUmOYnh7LGwf+njge/WsR8ZGh3OHlxkfLpqYgAh8ePW/Pr4DRpVCUso+diWM7MF1E8kQkHLgV2NivzEbgLmt21VKgwRhTPlhdEZnuUv9G4LCN1+B37V09bD1ee7a10WfGhDhKalro7B68u6empZO2rh6/JQ6A6+dmsPV4DR8VVbPrZB1vHKzki8vziPNyDCU5Jpy5WfG8XxT4xNHU3sUfC0r50WsHefKDYoqrdCFGpfzNtsRhjOnG2Y20GTgEPG+MOSAi94rIvVaxTUAxUAQ8AXx9sLpWnYdFZL+I7AWuA75l1zXYYf+pBjq7e1k2JeWc49PSY+nuNR6XOu9blDA7yT9dVQBfvXwK09JjWfOHHdz6+BYmxEdwzyW5Pr3H0rwUdpfW09Hd47e4fNXa2c3qRz9kd2k9szLiqG3p5KmPS84bv1FKDY+t+3EYYzbhTA6ux9a6PDfA/d7WtY6Puq4pV4crmgCYnXXuXha5KTGECBRXeUoczl+C2X4aHAdnV9nv71nMLb/5mGnpsfzi5gvPWZfKG/m5STz5wXH2n2pk0eQkv8Xmi/9+p4jiqha+eEmuc/Xf9i7WvneMpz4+wbc+Md3zGyilvKIbOY2wwoom4iJCyep38154aAhZiVGc9HAjXV+LY+Iw7+HoLysxir89eJVXg+HuLJrsnH2140RtQBJH0Zlmnny/mJsXZZ9dHiUuMowvLMvl1+8c5a2Dlay5fIrX7/fM1pPnHbvdyzEfpcY6XXJkhBVWNDEjIw6R839BT0qO5lR9K12DTGstq2slMTrM6/EHXww1aQCkxUUwOSWaHSfq/BiR9379zlEiQh08tHLWOccz4iNZNiWF7SW1Xi0hr5TyTBPHCDLGcLiikZkZ5y8YCM7E0dVjOFQ+8FLn/pxR5W+LJiWx40TdiM9oqmnuYNO+Cm5elE1qbMR556+eNYGocAf//udDIxqXUmOVdlWNoMrGDhrbu5k1QOKYnBIDwI4TdczPTnRbprS29ex9H76yu/tlUW4SL+06xYmaVnJTY/z2vp48X1BGZ08vdy51fy1R4Q6umpnOn/eV81FRNZdMSx2x2JQai7TFMYIOVzhbEu6WKAfnIHVCVBg7T9a7Pd/Z3cuJmlampA0tcdgt/+w4x8h1V/X2Gp7eeoKlU5KZlu7+3xVgcV4ymQmR/OKNQr3HQ6lh0sQxggqtGVUDtTjA2V21c4BfvMerW+juNYPWH2nPbD159rG9pJbw0BD2lNWP2OcXnKijrK6N2xYP3nIKc4TwrU9MZ9fJerdrcymlvKeJYwQVVjQxIT6CxOiBp7o6B8jbqGg4f92qwkpn4hmoxRJoISJMTIxij5vVdu3y2t7TRIaFcM3sCR7LfnZRNnmpMfzyjUJ6e7XVodRQ6RjHCDpW3eJxfGJSsvPGvp0n61h1wbn7YBRWNBIaIkwN0q4qgJykKLYU19LR3UNEqMPWz+rpNWzaV8HVs9KJifD8rRzmCOE7187gm8/u4tW9p1m9oP+am8FNpwirYKEtjhFUUt1Cbsrgg8aZiZFEhIa47a4qrGgmLzWG8NDg/W+bmBRNZ08vh8ubbP+srcdrqG7u4JMXeLeX1zNbT9LY1kVmQiT/+sp+1n1w3OYIlRqbgvc30BhT19JJQ1uXx8QRGhLC/OwEdpx0kzgqG5kRROMb7uRYU4VHYpxj075yosIcXD0r3es6ISJ85qKJNLV3s2mf7pWu1FBoV9UI6VuDyptpqgsnJ/G7D0po7+ohMszZ3dPS0U1pbRu3LBp8YyV/cNcl4q2EqDBSYyPYU9oAy/wYVD/GGN4+dIbLZ6QSFe5bl1h2UjSXTU/jb0ereGFHGTcvyrYpSqXGJm1xjJC+xJGX6nlxwoWTkujs6T3nTucjfQPjQd7iEBEuzE6wvcVx4HQj5Q3tfMKLQXF3rpmdzrS0WP75hT28usfdiv1KqYFo4hghJdWtiHi3qu3CSc61nrYd/3t31f7TzntAgmkq7kAuzEnkWFUzTe1dtn3GW4cqEcGnbipXoY4Q7lg6iYWTkvjGs7v43kv7aLQxXqXGEu2qGiElNS1kJUSd7XoaTFpcBPOzE3h1z2nuu3IqAH/ZV05uSvTZWVfBrLalE2Pgv946es4MsOHOAHLtQvtjQRk5SdHnbEDlq4hQB09/dQn/+cYRHn+/mDcPVvDg9TO5eZjdgR3dPRSdaSY2MpS02Agcw1gDTKlgpC2OEVJS3UKuF91UfW5elM3B8kYOnG7gTGM7HxfXcOOFWW4XRww2fWtp9a3k62+NbV2cqm/zS+srItTB91bN5k/3L2dySgzffXEfqx/9gDNN3u3/3t/JmlY++9hHrPvwOI+8fZQn3y/2uDmXUqONtjhGSElNKzfMz/Rc0PKp+Vn85LVDvLjjFDnJURgDNy7wbtqpL4YzED6Q6PBQkmPCbdtAqW9Pk1mZ8R5KeuZ6/TddNJFpabG8tq+cte8d4wtLc8nzYc2t6uYOPrv2Izq6erh5YTatXT38ZV85G7af5I4lk4cdq1LBwtYWh4isEJFCESkSkYfcnBcRecQ6v1dEFnqqKyK/EJHDVvmXRSTRzmvwh76puL78EkqKCeeaOels2H6SR/9axOzM+EHXYgo22UlRtrU4DpU3khQdxoS481fCHQ4R4cKcRO67YipxEWH8/qPjnPLyGnp7Df/4/B4a27p47mvLWDg5iUunpXLDhVkcrmgacBkZpUYj2xKHiDiAR4GVwBzgNhGZ06/YSmC69VgDPOZF3TeBecaY+cAR4Ht2XYO/9M2omuzhHo7+HrhqOsunpZKZEHV2rGO0yEmKpqGty+8Dzp3dvRyramZWZrxt3XbJMeF85bI8YiJCWf9xCfWtnR7r/GHLCd47UsW/3DCH2S4toaV5yeQkRfFO4RnauwK3ra5S/mRni2MxUGSMKTbGdAIbgNX9yqwG1hunLUCiiGQOVtcY84a1JznAFiDoJ+GfqHF22XgzFdfVnKx4nrgrn1e/cSk3Xuj/bio79Y1zePsXu7eOVTXT3WuYnTH8bqrBxEWGcfeyXDp7enl660m6B9lc61R9Gz97/TBXzEjjzn4TAESE6+Zm0NDWxdM2dAsqFQh2Jo6JQKnL6zLrmDdlvKkL8CXgL8OO1GYlNS1eT8UdKzIToggRKPXzOMeh8kYiQkN8mmgwVBPiI/ncomxO1bfx2l73d5k/veUE9/xuG909hsV5yTy7rfS8caOpabHkpcbwxN+KB01ASo0WdiYOd/0I/ZckHaiMx7oi8gOgG3ja7YeLrBGRAhEpqKqq8iJc+5RUez8Vd6wIDw1hQnykX1scvcZwuKKJGRPiCA0ZmQmBc7ISuHx6GttKavndh+evbfVBUTVHKpu5fu4EkgZZ9Xj51FQqGtt5y09Lurd19tDY1kWPrvKrAsDOn74ywHVCfDbQ/xbdgcoMWldE7gZuAO4wA+zKY4x53BiTb4zJT0tLG/JF+ENJTSuTU8ZPa6NPdlI0pXWtfts46VRdG80dA++gaJfr5k5gTmY8P37tIBu2nTx7Pa/vr2DzgQrmZcWzdErKoO8xMyOOrIRI/nfLiWHF0tbZw6t7TvPTvxzi4dcPM/+Hm/n9h8d1mXg1ouycjrsdmC4iecAp4Fbg9n5lNgIPiMgGYAnQYIwpF5GqgeqKyArgu8AVxhh75nv62YmaFlbM834q7liRnRTF9pJaalo63e4F7qvDFY0IMHOE9yMJEeGW/BzePFTBQy/t45Xdp4gOD+Wdw2fISozkpoXZHgfqHSHC7Usm8cs3jnCsqnlIS+O3dfaw7sPjlDe0sXBSElmJUdS3dfHDVw/y4bEa/ueOhYQ59NYsZT/bvsusAewHgM3AIeB5Y8wBEblXRO61im0CioEi4Ang64PVter8GogD3hSR3SKy1q5r8IeG1i7qWrvIHZctDv/eCHi4oonJKdFEe7H3hr+Fh4aw/ktL+O6KWVQ3d3Kksol7r5jKvZdP9boL8paLcwhzCE9v8X2QvLunl/Ufl1DR0M6dSydz08Jslk5J4al7LuZfPjmbNw9W8t0X92rLQ40IW38CjTGbcCYH12NrXZ4b4H5v61rHp/k5TFudqPV+VdyxJj0ukjCHUFbXyoKcxGG9V1ldK+UN7aycl+Gf4IbAESLcd+XUc6ZG+3IDZXpcJCvmZfLHHaX80/UziA73/sfvsXePcaK2lVvyc5jlMqPs2W2lRIeHcs3sdF7aeYrWjh4un5GmGzwpW2m71mYl1lRcT/twjEWOEOdWsv5ocfStSTXbD3eLB9JdyybT1N7t04q8B0438Ku3jzI/O2HABHzVzHTmZcXzxsEKTlj3DSllF00cNjtR7fwhHg2LE9ohOyma0/Vtw5798/qBCjLiI/0yVhJI+ZOTmJURx+8+LPFq0kBndy//+PwekmLCuXH+wPfyiAg3LcwmISqMDdtLqWvxfNOiUkOlicNmJTWtZMRH+rzZ0FiRnRRFd6+hsnFoiwYCVDV1sL2kljlZo7u1Ac5f8F++NI/DFU28e8TzNPFH3j7K4YomfvqZCzyO7USGObht8SSa27t58IU9fpvNplR/mjhsdqKmZVxOxe3Td9PjcG4EfOtQJcbA3DGQOABWL5hIZkIkj717bNBye0rreey9Y3x2YTbXzPFuw6rspGhWXpDBW4fO8FvdU13ZRBOHzUpqWsfl+EafpOgwosMdwxrneH1/BZNTosmIj/RjZIETHhrCVy+bwrbjtXx8rMZtmfauHv7xj3tIi43g3z7Vf4m3wS2bksL1cyfw8F8Os8vN3vVKDZcmDhs1d3RT3dzB5BFYHiNYiQjZSVFDvoO8oa2Lj45Vs2JuxqjYi8Rbty7OYWJiFD94ed95ix8aY/jRqwcpOtPMz26eT0JUmE/vLSL8/LMXkpEQydef3jmsbkKl3NHEYaO+2S1547jFAc7uk8rGdlo6uj0X7uevh8/Q1WO4PoDTcO0QHR7Kw5+9gOLqFn7+euE54xFPvF/Ms9tOct+VU7lixtBWPUiIDuM3X1hEY1sX9/xuO81D+LdXaiCaOGxUUu3s1/d1OfWxJicpCgPsP9Xgc93X91cwIT6CBdmJfo8r0C6bnsadSyex7sPjfPu53bx5sJJvb9jF/910mFUXZPDgdTOH9f5zsxL49R0LKaxs4gu/3UpDm+6prvxDE4eN/r4Px/jtqgKYaA2Q7y6t96leW2cP7x45w4q5GYSM0X27Z2XEc83sCWzcfZqvri/g1T3lXD0rnf/6/EV+uearZqbz6O0L2X+qgdse30JNc4cfolbjnSYOG52oaSEtLoKYACyREUxiI0JJjY3g42L3A8EDeetQJe1dvWOum8pViAhXz0rnwetncv9V0/in62dyzewJhIf670dzxbwMnrgrn2NVzXz+8S065qGGTROHjZwzqsZ3a6PPtPRYthbX0tHt/S54L+86RWZCJEvzBl95dixIjA5nYmKUzwPh3rpyZjpPfWkx5fVt3PQ/H3GsqtmWz1Hjw/j+U9hmJ2pauGx6YJd0DxbT02PZUlzDzhP1LJvqORFUNXXw3pEq1lw+Zcx2U9nJ3Rpaty+ZxIY1y7jn99u4+bGPWPfFizlU3uS2fqDXuhoofhUctMVhk9bObiobO7TFYclLjcERInxQ5N2mWq/uOU1Pr+Gmi9xt/KiG6oLsBF687xLiIsO4/YmtFFY0BjokNQppi8MmJ2utxQ3H4aq47kSGObgoJ5EPjlbz4PWDlzXG8HxBKRdMTGD6CO+9ESx8WXXXV5NTYnjxvku45/fb+MOWE9x0UTYLJyfZ9nlq7NEWh02Kq6zl1Mf5VFxXl01PY++pBs54GJzddryWwxVN3KFdE7ZJi4tgw5plTEmN5YWdZRSU1AY6JDWKaOKwyZHKJkQY0k5vY9Un52diDGz0sKT4Ux+XkBAVxuoF2k1lp9iIUL6wbDLT02N5edcpdpfq8iTKO9pVZZMjlU1MTo4et6viujMtPZYLsxN4aecpvnLZFLdlTte3sflAJV+5NE//7UZAmCOEO5dO5qmPSvhjQRmOkBAumJgw7Pf1patNB71HH1tbHCKyQkQKRaRIRB5yc15E5BHr/F4RWeiproh8TkQOiEiviOTbGf9wHKlsZsY47Z8fzGcumsjB8kYOW4Oyz2w9ec7jm8/uIkTgC8smBzjS8SPMEcJdy3KZlBLNc9tPcqjcvwPmvcZQ3tDG7tJ6dpyo5XBFI7Utnbrs+yhmW4tDRBzAo8C1QBmwXUQ2GmMOuhRbCUy3HkuAx4AlHuruB24CfmNX7MPV0d3D8eoWVswduzeuDdWnLsziJ38+xNNbTvLjT88751xlYzs7TtRxz/K8s8uxq5ERHhrC3ctyWffhcZ7ZdpLr5k4Y9lTyrp5eth2v5f2jVTS2n79WVlJ0GNPSY0mICmP5tBQSo8OH9Xlq5NjZVbUYKDLGFAOIyAZgNeCaOFYD6629x7eISKKIZAK5A9U1xhyyjtkY+vAUV7XQ02uYkaEtjv5SYiP4/MU5PLPtJHcs/XsXRa8x/HlvORFhIXzj6lG1rfyYERnm4IuX5PLbD47zpd9v54uX5JHnMitwoC4ld91Sp+vbeG57KVXNHeSlxnDd3AwmJkYR5gihqb2L0w3tFJ1pZm9ZA/c/sxMRWJCTyHVzMviMTsEOenYmjolAqcvrMpytCk9lJnpZd1AisgZYAzBp0sj2oR6pdN5UNWOCDoy78+D1M9m0r5x/fWU/N8zPIswRwqZ95RRVNfPpBRP5y/6KQIc4bkWHh3LP8jye+FsxT31cwpeX55Hj47bHu07W8dKuU0SHOxNR/y7b5JhwJqfEsGxKCj29hjlZcfztSDXvHD7Dz14/zC82H2bexASum5NBcoy2QoKRnYnDXZOgf6fmQGW8qTsoY8zjwOMA+fn5I9qZeqSyidAQYUqqJg53EqPD+f6q2Tz4wl6OVbUQFebgVH0by6emsDgvOdDhjXuxEaF86dI8nni/mN99dJyvXDqFrMQoj/V6jeHNg5W8d6SKKWkx3HbxJI/rtDlChEWTk1k0OZnvXDuDEzUtPLPtJOs+OM6B0418an6Wfk8EITsTRxmQ4/I6G+g/D3OgMuFe1A1ahRXN5KbG+HWhurHmc/nOjYz+6YU9AHzygkyvliJRQ+fLTKeEqDC+vDyPx98v5on3iz3OfOro6uGFnWUcON3IxbnJ3HhhFo4hLBUzOSWG762cTWJUOC/tLOOV3aeobGznhvmZPr+Xso+diWM7MF1E8oBTwK3A7f3KbAQesMYwlgANxphyEanyom7QOlTeyIJJieccs/NO4NHqkmmpPHDV9ECHoQaQFBPO1y6fwvqPT/DURyXERYTywNXTz/uD6GRtK38sKKW2pZNVF2SyfGrKsMcgE6LCuPuSXP6yr5wPj9UQHhrCHUt1pl2wsC1xGGO6ReQBYDPgANYZYw6IyL3W+bXAJmAVUAS0AvcMVhdARD4D/DeQBvxZRHYbYzwsYjFyzjS1c6q+jXuW5wY6FKWGLTE6nDWXT+HVPad55J0iXtx5is/lZzNjQhx1rZ28fegM7xw+Q0JUGF+5bMo5g+nDFSLCqgsy6eoxvHekiue3l3LLxTmeKyrb2XoDoDFmE87k4HpsrctzA9zvbV3r+MvAy/6N1H92n6wHnDNElBoLIsMcfC4/h29dM50n3i/mv946evZcamw418yewPKpKUSE+f+GTRHhxgVZ1LR08G8b97NgUqLeHxUE9M5xP9tdWk9oiDDPD3ffKhVMrpyZzpUz06lv7eR0fTsxEQ4mJUfz7LZSz5WHIUSEW/JzeOL943zz2V28+o1LCXPo+GEgaeLws10n65mVGUekDX99qcAa7jjVWBnnSowO9+vNet78u8RFhvHTmy7gq+sLePL949x35VS/ff5IGiv7jGja9qOeXsPesnouytElqpXyt2vnTOC6ORP41dtHKLW2LVCBoYnDj4rONNPS2aPjG0rZ5Ic3zsUhwr/+ab+udRVAmjj8aJu1p8FF/abiKqX8Iysxin+4bibvFlaxaZ+uMBAomjj86K+Hz5CTHOXXKYlKqXPdvWwy8ybG86NXD9DY3hXocMYlTRx+0t7Vw0fHqrl6ZnpQL8Co1GjVt/T+8wVlXD49jaqmDtasLwh0WOOSJg4/+bi4hvauXq6alR7oUJQa87KTolkyJYWtxbXsKa0PdDjjjiYOP/nr4TNEhTlYOkXXW1JqJFw3ZwJxkaF8/+V9dPf0BjqccUXv4/CDnl7nqqDLp6Xo/RtqzAq2+1Aiwxx8cn4Wz247yVMfn+DLl+YFOqRxQ1scfvDWoUrKG9q5eVF2oENRalyZlxXPVTPT+OXmQo5VNQc6nHFDE4cfPPVRCVkJkVwze0KgQ1FqXBERfnrTfCLDQvjms7vo6O4JdEheqWvt5HB5I3vL6tlSXENb5+iIu492VQ3TkcomPjpWw4PXzyRU189RasRlJETys8/OZ80fdvD//ekAP73pgqCc2djba9h1so6Pi2soq2s7e3zD9lKiwhxcN3cC9105lVkZ8QGM0juaOIbBGMOPXztITLiDW3W5Z6UC5rq5Gdx/1VQe/esxcpKjuf+q4Nq3ftfJOn746kH2lNaTHhfBynkZTEqOJiLMwYKcBN4+dIZXdp3iT7tP85mLJvLQyllMiI8MdNgD0sQxDH/cUcb7R6v58eq5pMRGBDocpca1f7x2JqW1bfxicyG9vYYHrp4W8JbHmcZ2fvZ6IS/uLCMtLoKbF2WzICeREJe4rp41gatnTeDB62fym78V89v3j7P5QAX3XzWNL1+aF5QTbjRxDNGW4hp+tPEAF+cmcccS3ZlMqUALCRH+45YLCXUI//HmEY5VNfOj1fNIiAob8Vg6u3v53YfHeeTto3T1GO69YioPXD2NjbsH3gE7MTqc766YxW0XT+Infz7ILzYX8tz2Ur6/ahbXzckgZAhb8drF1sQhIiuAX+Hcxe9JY8zD/c6LdX4Vzh0Av2iM2TlYXRFJBp4DcoES4BZjTJ2d1+Gqu6eX5wpK+T+vHiQnOZpf375wRP5Dg20qpFLBov/PxsJJSUxOjuGRd47y4bEa7rtiKp+/OIeYCPv/Tq5r6eTlXadY9+Fxyura+MSsdP7lhjk+LUM0KSWax+/K54Oj1fyf1w5w7//uZMaEWL6wLJcbLsgkKcZ/S9oPldi1wqSIOIAjwLVAGc49yG8zxhx0KbMK+AbOxLEE+JUxZslgdUXk50CtMeZhEXkISDLGfHewWPLz801Bge9LE/T0GupbO6lp6eR4dQs7T9bx+v4KTtS0siQvmcfuXESyl/+Jvvzid7c+/1hIHGP1ulTwuX3JJPaW1fOTPx9i2/FaIsNCuHRaGosmJzElLYb0uAgmxEcSFxlKmCOEcEfIoH8AGmMwBnqNoddAc0c3DW1d1Ld2UlbXxpHKJgpK6thWUktPr+Hi3CTuv2oaV848dyUJb7/f+35Wunt6eW1vOWvfO8bhiiZCBC7MSeTC7ESmpMWQlxpz9jpiI0KJDHMQGiJ+66ITkR3GmPz+x+1MwYuBImNMsRXABmA1cNClzGpgvbWF7BYRSRSRTJytiYHqrgautOo/BbwLDJo4hupfXtl3zu5moSHCkinJfG/lbK6fO8G2/tOx+st0rF6XCk7zsxN5/mvL2HGilo27T/PXwireOlQ5YPkQ4ezMSGMliL6vnoQIzM6MZ83lU7hhfiZzs/yzA2ioI4RPXzSR1QuyOHC6kc0HKviwqJo/FpTSMsgUXhHn76sQEZ64K5/LZ6T5JZ6zcfn13c41EXDdU7IMZ6vCU5mJHupOMMaUAxhjykXE7eJQIrIGWGO9bBaRwqFcRH/HgGeGVjUVqPZHDCNsNMatMY+coIz7jsFP2xLzcWAT8JAf3stN/EOO+Yp/H1Yobgdw7Uwc7v4c75+7ByrjTd1BGWMeBx73pY6dRKTAXZMv2I3GuDXmkTMa49aYh8/OO9bKANebG7KB/lMKBiozWN1KqzsL6+sZP8aslFLKAzsTx3ZguojkiUg4cCuwsV+ZjcBd4rQUaLC6oQaruxG423p+N/AnG69BKaVUP7Z1VRljukXkAWAzzim164wxB0TkXuv8WpxdgquAIpzTce8ZrK711g8Dz4vIl4GTwOfsugY/C5puMx+Nxrg15pEzGuPWmIfJtum4SimlxiZdlU8ppZRPNHEopZTyiSaOESAiK0SkUESKrLvdg4KIrBORMyKy3+VYsoi8KSJHra9JLue+Z11DoYhcH6CYc0TkryJySEQOiMi3RknckSKyTUT2WHH/aDTEbcXhEJFdIvLaaIhZREpEZJ+I7BaRgtEQsxVHooi8ICKHre/vZUEbt/NWen3Y9cA5uH8MmAKEA3uAOYGOy4rtcmAhsN/l2M+Bh6znDwE/s57PsWKPAPKsa3IEIOZMYKH1PA7n0jRzRkHcAsRaz8OArcDSYI/biuUfcN73+too+R4pAVL7HQvqmK1YngK+Yj0PBxKDNW5tcdjv7NIrxphOoG/5lIAzxvwNqO13eDXOb2Csr592Ob7BGNNhjDmOcybc4pGI05UxptxYC2EaY5qAQzhXGgj2uI0xpm9v0zDrYQjyuEUkG/gk8KTL4aCOeQBBHbOIxOP8Q+63AMaYTmNMPUEatyYO+w20rEqwOmdJF6BvSZeguw4RyQUuwvnXe9DHbXX57MZ50+qbxpjREPd/Af8M9LocC/aYDfCGiOywlh6C4I95ClAF/M7qFnxSRGII0rg1cdhv2MunBImgug4RiQVeBL5tjGkcrKibYwGJ2xjTY4xZgHMlhMUiMm+Q4gGPW0RuAM4YY3Z4W8XNsUD8Wy83xiwEVgL3i8jlg5QNlphDcXYbP2aMuQhoYfBlrwIatyYO+3mz9EowGWhJl6C5DhEJw5k0njbGvGQdDvq4+1hdEO8CKwjuuJcDN4pICc4u1qtF5H8J7pgxxpy2vp4BXsbZhRPUMVtxlFmtUIAXcCaSoIxbE4f9vFl6JZgMtKTLRuBWEYkQkTxgOrBtpIMTEcHZD3zIGPOfLqeCPe40EUm0nkcB1wCHCeK4jTHfM8ZkG2NycX7fvmOMuTOYYxaRGBGJ63sOXAfsD+aYAYwxFUCpiMy0Dn0C5zYSwRl3IGYPjLcHzmVVjuCc+fCDQMfjEtezQDnQhfMvmC8DKcDbwFHra7JL+R9Y11AIrAxQzJfibJLvBXZbj1WjIO75wC4r7v3Av1nHgzpul1iu5O+zqoI2ZpxjBXusx4G+n7dgjtkljgVAgfU98gqQFKxx65IjSimlfKJdVUoppXyiiUMppZRPNHEopZTyiSYOpZRSPtHEoZRSyieaOJQaASLyGRExIjLLep0rIm3W8hKHrJVz73Yp/0UR+XXgIlZqYJo4lBoZtwEf4LyRrs8xY8xFxpjZ1vHviMg9AYlOKR9o4lDKZta6Wstx3mB5q7syxphinMuXf3MEQ1NqSDRxKGW/TwOvG2OOALUisnCAcjuBWSMWlVJDpIlDKfvdhnORQKyvtw1Qzt2Kp0oFndBAB6DUWCYiKcDVwDwRMTh3hDTA/7gpfhHOjamUCmra4lDKXjcD640xk40xucaYHOA4zmWwz7I2pfol8N8jH6JSvtEWh1L2ug14uN+xF4HvA1NFZBcQCTQB/22M+d0Ix6eUz3R1XKWUUj7RriqllFI+0cShlFLKJ5o4lFJK+UQTh1JKKZ9o4lBKKeUTTRxKKaV8oolDKaWUT/5/3c8xl1U1vf0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# ADI variable\n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['ADI'].value_counts())\n", + "print(data['ADI'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['ADI'].isna().sum())\n", + "\n", + "sns.distplot(data['ADI'])\n", + "plt.show()\n", + "\n", + "# Since this is a continuous variable and we only have 132 null values I will fill them with the mean\n", + "data['ADI'] = data['ADI'].fillna(np.mean(data['ADI']))\n", + "\n", + "# Check if the distribution changed\n", + "sns.distplot(data['ADI'])\n", + "plt.show()\n", + "\n", + "# Check if all the null values were actually filled\n", + "print(data['ADI'].isna().sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "44164ac6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "803.0 7296\n", + "602.0 4632\n", + "807.0 3765\n", + "505.0 2839\n", + "819.0 2588\n", + " ... \n", + "502.0 2\n", + "569.0 1\n", + "554.0 1\n", + "552.0 1\n", + "516.0 1\n", + "Name: DMA, Length: 201, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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3X3Gn5HpdVdUzjHE0VIdQYDxWXt++vTGObMp1M6fZuqo+pqp/C2wQkd+d/ryqfiZLMWPMEjE84XxgeXtieDa31fkQOGZvcUD5DRTHkinqKmcIlmV6z7MNjnsdoHVAfZYfY8wSNhSNE5DzH2CezSvrONI3XtSkg17gyLaeAc7Xodym5MZydFXVhIMEA7K8Whyq+n/c//7XhamOMaaYhiMJGqorCE5bY7B5ZR1jsSR9YzHaG6qKcq1oPEVVReCia3m8bpuRMptZFUukqcyyahzcPTmqyi/tSL4LAD8tIg0iUiEiz4rIgIh8zO/KGWPmZygSv6ibCjIGyIvYXeWsGp/5u2htZYiAwFiZddvEc4xxAGW5mVO+6zjer6qjwB1AD7AF+I++1coYUxTDkQSN1Renk/MjcEQTqRnHNwACItSXWe6mWDJFSnXG6bhQnqnV8w0c3l/e7cDfqeq5fAqJyK0iclhEukXkwSzPi4g85D7/qohcX0DZ/yAiKiKted6DMctKMp1mNJqgqfbiFsfK+kpqwkGODU4U7XpOZtyZAwe4iwDLqNtmIuaM68w0xgHluZlTvoHjn0TkELANeFZE2oCcK3lEJAg8DNwGbAXuEpGt0067Dehyf3bipG+ftayIdAK3ACfyrL8xy85oNIlC1haHiLC+pZbjg8VbPR6Jp7LuSZGp3LLFTrhTi3MGjuXa4lDVB4GbgG2qmgAmgB2zFLsB6FbVo6oaB57MUmYH8Lg69gKNIrI6j7L/G/g9oLxWEhlTREPuHt/ZWhwA65tritziyD3GAeXX3z/hrl0JZxnjeOL5Ezzx/An6x2KcHZ3kiefL53vubCvHM12Os54js8zjOc7vAE5mPO4BtudxTkeusiLyQeCUqr6SLamYR0R24rRiWLduXY5qGlOehr3AkWVwHGB9aw3PHeojldYZZ0LlK5lKM5lIZ82Mm6mhKsRkIp1XkCkF+bQ4qiqCZbdlbl7/ciLyN8AmYB/gvQNK7sCR7S9xegthpnOyHheRGuAPgffnqi+Aqj4KPAqwbds2a5mYZWcokkA4Pw12ug0ttcRTaXpHoqxtml/qOa8Pf7YxDi9309nRGBtbSz9wjOcxxlFVESSRUpLp8tmTI99/uW3AVi1stVAP0JnxeC1wOs9zwjMc3wRsBLzWxlrgZyJyg6qeKaBuxpQ9bw1HKJD9Q219ixMsjg9G5h04vG6xWQNHlRc4JtlYxASLi+V8i2Pm+65213hMJsoncOQ7OL4fWFXga78AdInIRhEJA3cCu6edsxu4x51ddSMw4u73kbWsqr6mqitVdYOqbsAJPNdb0DDmYiPR+FRa72w2tDgf3MUY5/CS+M0+OO58Vz1bJllyvbxbuabjeivpJ8uouyrfFkcr8LqI/BSIeQdV9YMzFVDVpIg8ADwNBIHHVPWAiNznPr8L2IMzxbcbiAD35ipb6M0Zs5yNTSZpq6+86Lg3SJtWJRQQ/vm1M3x0+/p5XWs038DhBrIzI+UROPIZ46hexoHjj+fy4qq6Byc4ZB7blfG7AvfnWzbLORvmUi9jloOxySSXtM3cHRQQoak2zOBEfN7X8locVbN0VVWGAoSDAc6OxnKeVyryChzue1JOA+R5BQ5V/b6IrAe6VPUZd5A691+IMWbRTCZSRBMp6qtm7qoCaKkNc64IgcMbHJ+txSEi1FeFyqirKkVQhFAwn66qZTbGISL/BvgK8H/cQx3A132qkzFmnvrHnG/09TOk+/a01IYZnIiRnufmSl5X1UyZcTM1VFeUTeCIxJM5xzcgI3DEy6fFke/g+P3AO4FRAFV9E1jpV6WMMfPT5wWOWVocrfWVJFJK7zw/yEeiCUIBmdrXPJcVZbSF7HgsOWNmXI/XCiunrqp8A0fMXcENgLsI0NZGGLNE9Y85H8z1VblbHN7g+ZF5JjscjSZmXfznqa8K0TcaK+peIItlIpYkPEuwrAgKAVmegeP7IvIHQLWI3AL8A/BP/lXLGDMf51scswSOOidwzDdL7kg0Mev4hmdFdQXxVJqB8fmPrSy2iVgq58A4OOM6VRXBsppVlW/geBDoB14D/i3ObKf/7FeljDHz0zcaIyDOHhi51FWGqK4IcqR/ni2OyURe4xsAzW4KlJNDxUuwuFicrqrZ77u6zNKO5DurKi0iXwe+rqr9/lbJGDNffWOT7sZJuXNQiQht9ZUL2uLwki6ePBfh+nVN87ruYpuIJWdtcQDLq8Xhruj+YxEZAA4Bh0WkX0T+aGGqZ4yZi76x2KzdVJ62+kqO9M9v9fhIAWMcXtLFk+dKv8WRb+Corgguq+m4v4Mzm+rtqtqiqs04WWrfKSKf9Ltyxpi56RuNUV+Ze0aVp62ukoHxGCORuac7H40m8+6qCocCtNZVcvJcdM7XWyrGY8msKdWnq6oIlFVX1WyB4x7gLlV9yzugqkeBj7nPGWOWoEJbHADdcxznSKeV0cnEVDK/fKxrri75MQ5VJRKffXAclllXFVChqgPTD7rjHPl9nTHGLKhUWjk3EZt1DYdnpTcld46BYzyeRHX2VeOZOptrOFHiXVWxZJpkWqnKu6tq+QSOXPPlSn8unTFlaHA8Rlpnn4rraawJUxkKcPjM2Jyu53Vx5TvGAbCuuYbekUkSqdLt9x9z06zkM6uqKuzsyRFLlkfwmO0v6xoRGc1yXIAqH+pjjJknbw1HQ56BIxgQrljTwKs9w3O6nrcVbL5jHACdTTWk0krv8CTrWua3F8hiGc8jwaHHe2/GJpNU1pV+mr+cd6yqQVVtyPJTr6rWVWXMEtQ/7gSOujy7qgCu6WzktVMjJOfQAsh3L45Mnc1OsCjlcY7xydk3cfJ44z9eTq9Sl/9oljGmJAy6K7LrZln8l+nazkYmE2neOFv4OEchCQ49nc3VQGlPyZ1qceQxKcB7b7wswqXOAocxZWbQbXHUVub/QX7N2kYAXplDd9Vo1E2pXsAYx+oV1YQCwvEyCBxVebU43MBhLQ5jzFI0MB6jqiIwa/K9TOtbamisqeCVk8MFX28uXVXBgLC+pWbeyRUX03jMue/CWhwWOIwxS9DgeJyW2kpklnQjmUSEa9Y2sm8OgWN0MkFAcu+7nc1lqxo4fHZuM7mWgvNjHPlNx4XzrbNS52vgEJFbReSwiHSLyINZnhcRech9/lURuX62siLy39xz94nIt0VkjZ/3YEypGZiI05plr/Fcnnj+BMGAcPjMGF/44Vs88fyJqb3JZzMSTdBQXTFrXqzpLl1Vz4lzESLx0vwwHfO6qvKZjmstjvyISBB4GLgN2ArcJSJbp512G9Dl/uwEHsmj7J+p6tWqei3wTcDyZhmTYXA8RqubSLAQG1pqUeCtwcLyVo1EEzQUMIPLc+mqelSZ04D8UjA+mSQUEEKB2QNmRVAIitgYRx5uALpV9ai7CdSTwI5p5+wAHlfHXqBRRFbnKquqmetKarENpYy5wMB4jJa6wgPH+pYaQgHhaIEJD0eiCVZUFx44LltVD8DhM9mWii1947EkdVWhvLoEnT05AlPjQaUu//l6hesATmY87sFJkDjbOR2zlRWR/46TK2sEeG+2i4vITpxWDOvWrZvTDRhTalTVGeOoK6yrCqAiGGBdc03BqUeGIgkaawoPHJ1NNVRXBDk0xxXrxTBTd9zd22f/zBifTBY05bmqImjTcfOQLQxPbx3MdE7Osqr6h6raCXwJeCDbxVX1UVXdpqrb2tra8qyyMaVtNJokmVZa5tBVBbBpZR29I5NTU03zMRKJT6VKL0QgIGxpr5tzqpPFNhYrLHBUh4PWVZWHHqAz4/Fa4HSe5+RTFuAJ4JfnXVNjysTAhLOGo63AwXHPprY6AN4ayL+7ai4tDm/wPRQM8ErPSEGD8UvF+GQy73xg4LU4LHDM5gWgS0Q2ikgYuBPYPe2c3cA97uyqG4ERVe3NVVZEujLKfxBngyljDDDg5qlqqZ1b4OhorKYyFMh7fUXKTaneOIcWB8CqhiomYknGSvADdbzAFkdVRfm0OHwb41DVpIg8ADwNBIHHVPWAiNznPr8LZ+/y24FuIALcm6us+9KfEpFLgTRwHLjPr3swptQMTjjpRlrqwnNKWx4MCBtba/Me5xiJJlCFpjmMcQB0NjmpR44PRriyY8WcXmOxTMSSbGitzfv86ooAZ0fLY4zDz8FxVHUPTnDIPLYr43cF7s+3rHvcuqaMmYGXbmQus6o8m9rqOHRmjOHI7DsnDLnnNNWEicQLTxm+ptFJPXLiXOkFjkLHOMqpxWErx40pIwPjcUSgeY5dR3B+nCOffci94DKXWVUAoWCAtU3VHC9w7chSUOgYR3VFkFgyXRYbOlngMKaMDE7EaKoJEyogT9V07Q2V1IaDeXVXDU0436DnMqvKs76lllPDUeLJ0tnUKZlKE02kqA0X1uKA8xtAlTILHMaUkYGx+Jyn4npEhEva6jjaP47Tmzyz4WgRAkdzDWmFnuHSyZQ7EXNaDXUFzqqC8kg7YoHDmDIyOBGjeZ6BA+CStlpGJ5OzDrBPdVXVzn1fN28HwOODpRM4xtzMuPWFrOMoo82cLHAYU0YGxwtPcJjN2kbnw/zA6dzpQIYicYIBKegDdLqacIj2hkqOFrhifTF5CyTn1uKwripjzBIyMMcEh9O1N1QSENh/aiTneUORBI3VFQWlcM9my8p6jg1GmChgxfpi8lKqFzqrCqzFYYxZQmLJFKOTSVrnkKdqulAwQHtDFftnaXEMR+JznlGVacuqelJp5cdHBuf9WgthbA4tjmob4zDGLDXnphb/zT9wgLO964FTIzkHyIcmEvMaGPesb6khHArwvcN9836theC1OAoa4wiXz2ZOFjiMKROD4+dXjRfDmsYqBifinB2NzXjOcHTu6UYyhQIBNrXV8b3D/bPO5FoK5jLGEQoI4WDAWhzGmKVjwF013lqswLHCSQeSa5xjOBKfc7qR6ba013FqOFoSGzt5YzGFjHGICA3VIRvjMMYsHVMtjjkmOJxu9YoqRHLPrBqKxGkqwmA8wNbVDYjAU6/1FuX1/OTNjCpkASBAQ1WFzaoyxiwdUy2OIkzHBaisCLKhpZbXe7O3OCYTKSYT6Tnt/pdNfVUF2zc289Srp5d8d9VIJM6K6goCeWwbm6m+uqIsdgH0NcmhMcZ/3j4WP3xzgFBA+MbLp+Y9PdbTtbKO7hlSrGcmOCyWO65ew3/++n4OnRnj8tUNRXvdYhuKJObURbeiuiKv5JFLnbU4jCkT3v4QxQoaAF3tdRwbjGTNI3U+T1VxWhwAt165ioDAN1/Ntm/b0jHXLrqW2vBUl2Ips8BhTJmYiCepnccK7my6VjrrK45lyV47MJXCvThdYwCtdZW8u6uNr750ikRq6SY9PDcxt+1ym2vDU9OmS5l1VRlTJpw038X79g+weaWTYv3Ns+Nsaa+/4LnekSjgDKIX0z03refXv/gi3z5wll+8ejVA1m1l796+rqjXLcRwJMFlqwrvSmuuDRNNpIjGU1PrOkqRtTiMKROFbmWaj01tdYjAm31jFz13engSEVhV5MDxnktX0tlczRd/cqyor1tM5ybiNM8hsaOXuXhwYua1MaXA18AhIreKyGER6RaRB7M8LyLykPv8qyJy/WxlReTPROSQe/4/ikijn/dgTClQVSZiqaJ3VVWHg3Q21fBmlgHy3pEobXWVVMxj749sggHh125cz0/fOserPcNFfe3Z5LPJ0mQiRTSRmtPCRy9zcal3V/kWOEQkCDwM3AZsBe4Ska3TTrsN6HJ/dgKP5FH2O8CVqno18Abw+37dgzGlYjKRJqVKXWXxuz+6VtZxJGvgmGR1Y3XRrwdw1w3raKkN8ydPHVywqbmvnx7hT556ndfzyAgMzCl9vbeqf9ACx4xuALpV9aiqxoEngR3TztkBPK6OvUCjiKzOVVZVv62q3gqavcBaH+/BmJIwMYcUGPna3F7H0f4JktMGq08PR1lT5G4qT31VBZ+8ZQs/fescTx8468s1MkXjKb7xymnSCs8dOpszWHmthbkNjjsTCc6V+MwqPwNHB3Ay43GPeyyfc/IpC/CvgG9lu7iI7BSRF0Xkxf7+/gKrbkxp8bK1FrurCpyZVfFUmmMZGy2pqtPiWOFPiwPgzrd3sqW9jv/2zdd936f7mYNnGZ9McuMlzZwemeT7b8z8mTGfacjWVTW7bJPJp4fxmc6ZtayI/CGQBL6U7eKq+qiqblPVbW1tbXlU15jSNZfcSfm6fLUzm+rQmfNdOKPRJJF4ijWN/rQ4wEnt/qe/fDW9I1GeetXfNCSv945yxZoGbr9qNSuqK/jcD96a8dz5dFU1VIWoCIp1VeXQA3RmPF4LTF/VM9M5OcuKyMeBO4CP6lLPTWDMAhj3ucVREZQL+v5PT03F9a/FAXDduibuf+9mXjoxxOEzucce5mp0MsFINMG6llpCgQBXrmnghWPnZlxH4gWOuQyOiwhNNWHO2ayqGb0AdInIRhEJA3cCu6edsxu4x51ddSMwoqq9ucqKyK3AfwI+qKqls0mxMT7yWhyFJt3LRzgUYPPKel7vPf/B7a3hKPZU3Gx+8xe6WFlfye5XTmddwT5fPeece+lscoLg2uYaYsk0h89cPAUZznczzXUDq3JYBOhb4HAHsB8AngYOAl9W1QMicp+I3Oeetgc4CnQDnwV+I1dZt8xfAfXAd0Rkn4js8usejCkV47EkNeEgwQKT7uVr6+qGC1scw5MAvnZVecKhADuu7WAokuC7Pmz0dHIoQkBgjTtDrLPJ2W/95ZPDWc8fjiTcLqe5fXy21IVLvqvK15XjqroHJzhkHtuV8bsC9+db1j2+ucjVNKbkjceKn24k0+Wr6/nqz3roH4vRVl/JmZFJggFhZb3/gQNgY2st13Y28uMjA7xzc2tRx3J6hiKsWlE1FQiaaipoqQ2z78Qwv3bj+ovOPzcxv1TyLbWV9AwNz7n8UmApR4wpAxM+rBrPtHWNk17jYO8obfVtnB6J0l5fWfQWTrbUIp73bGlj38lh9h4d5ObL24tyvbQqPUNRru1snDomIlzb2ci+k0NZywxF5panytNcG7bpuMaYxTfuw6rxTFvdFOfeOMexgYmprp2FsrKhistXN/CTI4NFG+sYGIsRS6ZZ63ZPea7tbORI/0TWbV6HIvE5zajytNSGGYsliSX9nWLsJwscxpQBv1scjTVhOhqr2XdimL6xSV4+Ocw7Nrf6dr2Z/FxXK9FEin0zjD8U6vSIM1bTMS0IXruuEYBXT168idXQRGLOA+MAze7qcW89SCmywGFMiUum00QTKV/SjWR6/xXtPHPwLF/40TFU4Q43c+1CWtdcQ1t9JS+fyN6NVKi+0UkCAq31F7Ygru5oBOC1LPutn5uI0zyPrqpySHRoYxzGlLiJmNPl4UdXVeaYQ0ttJam08sj3jrClve6iNOsLQUS4vrORp18/y/HBCda31M7r9c6OxWipqyQUuPA79IqaCjqbq9l/+sLAEY07CQ7nMzjupR0p5Q2drMVhTInzc9V4pubaMFd0rADgF69a4+u1crmmsxEB/vHlU/N+rb7RSdpn2KP9yjUrODCtxXH8nLOhVWdzTbYiefH2L/HWwpQiCxzGlLjxBQocAO+9tI0t7XV8+PpsqeMWRmNNmI1ttXz95VPzypw7mUhxbiLOyobsU4qv7FjBscHIBQPkR/qcwHFJ69xbOqtXVBEMCD1DFjiMMYtkoVoc4KQY+fYnf35e37iL4Zq1jRwbjLD/1NzTkHT3jaNA+wyB4wp3CnLmwsej/U56+Uva5h44QsEAqxqqLHAYYxaPn3mqlqor1jQQCgjffG16+rv8ebsarpyhq+qKNU633P6M7qoj/eOsWVFFzTxTu3Q0VXPKAocxZrGMx5KEAkJlaPn871wTDvGurlaeerV3zt1Vb5wdd2ZU1WUPHG31lbQ3VHIgs8UxMMEmdx/2+VjbVE3PUOmm2ls+f2nGlKkJN92IiD95qpaqO65eQ89QdM5rOt48O0ZrXe7V71euWTG1fa2qcrR/Yl7jG561TTWcGZ2cMQPvUmeBw5gSN+7z4r+l6pat7YSDAb45x706Dp0Zm3F8w3PTphaO9E9w8lyEvrEY47Fk0VocaYUz7gLEUrP8/tqMKTPLNXCsqK7g57Y43VV/ePvlBArImzUcidMzFOVKdxxjJrdsbedPnjrId14/y2XuhlaXtBYhcLgr1U8ORehsrpkxR9fd29fN+1p+sBaHMSVuLJqkoWruKTBK2R1Xr+HM6CQvFbiS3Bu3mC3f1vqWWra01/HMwbMc6Xem4m5aWZyuKqBkZ1ZZ4DCmhMWTacZjSVZUL8/AcfPWdipDAb75SmGzq7yZUmvy2Ijq5svbef6tc3xp73HqK0OsmqV7Kx+rVlQREAscxphF0Dc2icKyDRx1lSHee+lKnnqtt6CMuftPj9LRWE1NHl18N29tJ5VWTg1H+fOPXFOUSQjhkLOWY6YpuWdGJvmbnxzj+2/0z/tafrDAYUwJ8wZXG5Zp4AD41Rs6GRiP8639+Q+SHzg1wpUdDXmde11nI3/xq9ey57fezfuvWDXXal6ko6mak1mm5L7aM8zD3+3m4JkxfufJl+kbXXoD6BY4jClhvW7gWK4tDoCf72pjY2stX/jRsbzOH5tMcHRgYtaBcY+I8EvXdRR9tfylq+o5cGrkgim5yXSab+0/w6oVVex89yVEEyl+76uvFvW6xeBr4BCRW0XksIh0i8iDWZ4XEXnIff5VEbl+trIi8isickBE0iKyzc/6G7PUTbU4lungOEAgIHz8pvXsOzmcV7p1L4XIlR35BQ6/vGtzKxPxFK9krEPZd2KYkWiCW7a2s6G1lt96XxffO9zPETfVyVLhW+AQkSDwMHAbsBW4S0S2TjvtNqDL/dkJPJJH2f3Ah4H/51fdjSkVvSOThIMBqiqWd+fBv9zWSWNNBf9jz0H+du9xnnj+xEU/nh+8OUBAnCy7i+nGS1oQgR92DwDONrbff6OfNY1VdLlrRX7pWieZ5NMHzixaPbPx86/tBqBbVY+qahx4Etgx7ZwdwOPq2As0isjqXGVV9aCqHvax3saUjDOjURqqK5bdqvHp6ipD/MHtl/PCsSFeOp671fGt/b1s39iSc/vXXIGnWBprwlzVsYIfuYFj34lhBifivGfLyql/zzWN1VzT2cg/718+gaMDOJnxuMc9ls85+ZTNSUR2isiLIvJif//SnJlgzHydGZlkRfXyW/yXza+8bS03XtLMntd6Z8wD9ebZMY70T3DbVcUb5J6Pd25u5eUTw0RiSZ45eJaOxuqprLyeW69Yxas9I5waXjpTd/0MHNm+Ak3PRjbTOfmUzUlVH1XVbaq6ra2trZCixpQMJ3As3/GNTCLCZz5yLTXhII/96C2OD05cdM639p9BBD5QxNlR8/Guza0k08pDz73JcDTB+69ov6j1+IEr2gF4egm1Ovz8qtIDdGY8XgtMX6Uz0znhPMoas6yl0srZsRhbVi38Fq5LwUzdR//63Zfw+R++xWd/cJT3XrqSn9/SRigYYCSS4Csv9fC2dU2z5qhaKNs3NvO7t2zhn145zWWrw2xuuzCdiXePrXVhnnzhBFUVwSWRhsTPwPEC0CUiG4FTwJ3A3dPO2Q08ICJPAtuBEVXtFZH+PMoas6wNjMdIpdVaHNM01YR54L2b2f3KaZ491Me+k8Ncv76Jf3jpJGdGJvnUL1+12FWcEgoG+K33dc2Y2t2zeWUdLx0fIpleGtl0feuqUtUk8ADwNHAQ+LKqHhCR+0TkPve0PcBRoBv4LPAbucoCiMiHRKQHuAl4SkSe9usejFnKem0q7oyqKoJ8ZFsnn3jHBiqCAb7z+lle6xnhobuu4x2bWhe7egXb3FZHIqWcPLc0xjl8HVVT1T04wSHz2K6M3xW4P9+y7vF/BP6xuDU1pvT0uoOl1uKY2Zb2era01xNLpvjQdR001sw8k2o2fsysytfG1joEZ7vbpWB5T/42poS95Q7+5ppWahyVoeC8gsZiqw4HWdtUvWQWAlrgMKZEHe2foK2+kqqK4GJXxSyAzSvr6BmKMDqZWOyqWOAwplQd7R9nU9v894YwpWHTyjrSCnuPDC52VSxwGFOqjg5McEnb/HejM6VhXVMNFUGZWmm+mCxwGFOCzk3EGY4kuKTVWhzLRSgYYGNr7VRuq8VkgcOYEuQNkm6yFseysrmtjiP9E/SOLO60XAscxpSgoxY4lqVNbtbcH3Uv7jiHBQ5jStDR/gnCoQAdTdWLXRWzgNobqmirr+S7h/oWtR4WOIwpQUf6J9jQUkMwsLzTqS83ARE+cEU7zx3qIxpPLVo9LB+zMSWou2+My1blt2e2cSzmyu9iuv3K1fzt3hN8/40+br1y9aLUwQKHMSWmb2ySY4MR7rph8bOkmsIUI3jdsLGZltowT712ZtECh3VVGVNifuIuALtpU8si18QshlAwwPuvWMVzB88uWneVBQ5jSszeo4PUV4W4Ys2Kxa6KWSQfuq6DiXiKr+87tSjXt64qY0rMT44Msn1j86INjJfLWEEpe/uGJq5Y08BjP3yLO9/eueB7zluLw5gS0jsS5dhghBsvsW6q5UxE+Ffv3MibfeOLspLcAocxJeR7h/sBG98wcMc1q2mrr+Qz33mDdFoX9NoWOIwpEclUmkf/31G2rm5g62qbirvcVYaCPHjrZbx8YpgvPX98Qa9tgcOYErH7ldO8NTDBb9/cteB92mZp+vD1Hby7q5U//efDHDozumDX9XVwXERuBf4SCAKfU9VPTXte3OdvByLAJ1T1Z7nKikgz8PfABuAY8BFVHfLzPmYSjaf46bFzvHTsHEf6J+juGyccCtDeUMW65hrWt9RQEQxw93abb2/m5+S5CH/+7TfYurqB929tX+zqmCVCRPgfH7qKf7nrx9z16F6+cO8NXNvZ6Pt1fQscIhIEHgZuAXqAF0Rkt6q+nnHabUCX+7MdeATYPkvZB4FnVfVTIvKg+/g/+XUfqkosmWYilmQoEufYQISfnRji+bfO8WrPMImUEhBY11xDPJVmcjzNa6dGAKgIChtaahmKxNm6poFVDVW01IVprgkTClpjz+Q2mUhxZmSSH7zZz1888ybJtPJXd19nrQ1zgc7mGv5+503c/dm9fOivf8S/uHoNt1+1iivWrPBth0g/Wxw3AN2qehRARJ4EdgCZgWMH8LiqKrBXRBpFZDVOa2KmsjuA97jlvwh8D58Cxx/vPsDjPznG9HGnUEC4au0Kfv1dl3DTphbevqGJmnBoapriZCLFscEJ3uwbp/vsOH/29OGLXlvEyTsTEOdbQ8B9nC8tYCxMyf/kwl63ACVWXy3ghQt73QJOznDZqnoe/uj1lg3XZLWhtZZv/fbP8cj3j/D4T46x+5XTU899/uPbeN/lxW2l+hk4OoCTGY97cFoVs53TMUvZdlXtBVDVXhFZme3iIrIT2Ok+HBeRiz+95+EI8PWLD7cCi7/LyuKx+/fp/o8Dmz/pxysXlf37L8D9f7TA82/+03ldbn22g34Gjmxfn6d/35rpnHzK5qSqjwKPFlJmvkTkRVXdtpDXXErs/u3+7f6Xx/372dHeA3RmPF4LnM7znFxlz7rdWbj/XdzE9MYYs8z4GTheALpEZKOIhIE7gd3TztkN3COOG4ERtxsqV9ndwMfd3z8OfMPHezDGGDONb11VqpoUkQeAp3Gm1D6mqgdE5D73+V3AHpypuN0403HvzVXWfelPAV8WkV8HTgC/4tc9zMGCdo0tQXb/y5vd/zIhhcweMcYYY2wxgTHGmIJY4DDGGFMQCxxFIiK3ishhEel2V7SXHRHpFJHvishBETkgIr/tHm8Wke+IyJvuf5syyvy++54cFpEPLF7ti0NEgiLysoh80328bO4dwF2k+xUROeT+Hdy0XN4DEfmk+3e/X0T+TkSqlsu9T2eBowgyUqTcBmwF7hKRrYtbK18kgX+vqpcDNwL3u/fppYHpAp51H+M+dydwBXAr8Nfue1XKfhs4mPF4Od07OPnj/llVLwOuwXkvyv49EJEO4LeAbap6Jc6knTtZBveejQWO4phKr6KqccBLkVJWVLXXS0KpqmM4HxodOPf6Rfe0LwK/5P6+A3hSVWOq+hbO7LkbFrTSRSQia4FfBD6XcXhZ3DuAiDQAPwd8HkBV46o6zPJ5D0JAtYiEgBqctWXL5d4vYIGjOGZKnVK2RGQDcB3wPNPSwABeGphye1/+Avg9IJ1xbLncO8AlQD/wBbe77nMiUssyeA9U9RTwv3CWAPTirDn7Nsvg3rOxwFEc806RUkpEpA74KvA7qpprE4CyeV9E5A6gT1VfyrdIlmMlee8ZQsD1wCOqeh0wgds1M4OyeQ/csYsdwEZgDVArIh/LVSTLsZK892wscBRHPulVyoKIVOAEjS+p6tfcwzOlgSmn9+WdwAdF5BhOV+QviMjfsjzu3dMD9Kjq8+7jr+AEkuXwHtwMvKWq/aqaAL4GvIPlce8XscBRHPmkVyl54mwE8XngoKp+JuOpmdLA7AbuFJFKEdmIs+/KTxeqvsWkqr+vqmtVdQPOv+9zqvoxlsG9e1T1DHBSRC51D70PZ6uD5fAenABuFJEa9/+D9+GM8S2He7+IrzsALhezpEgpJ+8Efg14TUT2ucf+gBnSwLgpZr6M8+GSBO5X1dSC19pfy+3efxP4kvsF6ShOmqAAZf4eqOrzIvIV4Gc49/IyToqROsr83rOxlCPGGGMKYl1VxhhjCmKBwxhjTEEscBhjjCmIBQ5jjDEFscBhjDGmIBY4jPGBiKREZJ+bTfUVEfldEQm4z71HRNSdwumdf5177D9kHAuJyICI/M/FuAdjZmKBwxh/RFX1WlW9ArgFZ4vk/5Lx/GvAr2Y8vhN4ZdprvB84DHzEXXRmzJJggcMYn6lqH7ATeCAjAJwAqkSk3T12K/CtaUXvwkljfgInjb0xS4IFDmMWgKoexfn/bWXG4a/grDR+B86K5Jj3hIhU46S1+CbwdzhBxJglwQKHMQtnenfTl3ECx104wSHTHcB3VTWCk1TyQ+W0EZApbRY4jFkAInIJkOJ89lQvaWACZwzk2WlF7gJudrPxvgS0AO9dkMoaMwtLcmiMz0SkDdgF/JWq6rRx7j8CVqpqyjvu7rT3LqBTVWPusXtxgskzC1l3Y7KxwGGMP6rdDMIVONlR/wb4zPSTVPXHWcp+GCdteyzj2DeAT4tI5bTjxiw4y45rjDGmIDbGYYwxpiAWOIwxxhTEAocxxpiCWOAwxhhTEAscxhhjCmKBwxhjTEEscBhjjCnI/wejJNspOLa+0gAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# DMA variable\n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['DMA'].value_counts())\n", + "print(data['DMA'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['DMA'].isna().sum())\n", + "\n", + "sns.distplot(data['DMA'])\n", + "plt.show()\n", + "\n", + "# Since this is a continuous variable and we only have 132 null values I will fill them with the mean\n", + "data['DMA'] = data['DMA'].fillna(np.mean(data['DMA']))\n", + "\n", + "# Check if the distribution changed\n", + "sns.distplot(data['DMA'])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3119646d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 21187\n", + "4480.0 4606\n", + "1600.0 4059\n", + "2160.0 2586\n", + "520.0 1685\n", + " ... \n", + "9140.0 1\n", + "3200.0 1\n", + "9280.0 1\n", + "743.0 1\n", + "8480.0 1\n", + "Name: MSA, Length: 294, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ], + "text/plain": [ + " ODATEDW OSOURCE TCODE STATE ZIP MAILCODE PVASTATE DOB NOEXCH \\\n", + "0 8901 GRI 0 IL 61081 3712 0 \n", + "1 9401 BOA 1 CA 91326 5202 0 \n", + "2 9001 AMH 1 NC 27017 0 0 \n", + "3 8701 BRY 0 CA 95953 2801 0 \n", + "4 8601 0 FL 33176 2001 0 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 9601 ASE 1 AK 99504 0 0 \n", + "95408 9601 DCD 1 TX 77379 5001 0 \n", + "95409 9501 MBC 1 MI 48910 3801 0 \n", + "95410 8601 PRV 0 CA 91320 4005 0 \n", + "95411 8801 MCC 2 NC 28409 1801 0 \n", + "\n", + " RECINHSE ... TARGET_D HPHONE_D RFA_2R RFA_2F RFA_2A MDMAUD_R MDMAUD_F \\\n", + "0 ... 0.0 0 L 4 E X X \n", + "1 ... 0.0 0 L 2 G X X \n", + "2 ... 0.0 1 L 4 E X X \n", + "3 ... 0.0 1 L 4 E X X \n", + "4 X ... 0.0 1 L 2 F X X \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 ... 0.0 0 L 1 G X X \n", + "95408 ... 0.0 1 L 1 F X X \n", + "95409 ... 0.0 1 L 3 E X X \n", + "95410 X ... 18.0 1 L 4 F X X \n", + "95411 X ... 0.0 1 L 1 G C 1 \n", + "\n", + " MDMAUD_A CLUSTER2 GEOCODE2 \n", + "0 X 39.0 C \n", + "1 X 1.0 A \n", + "2 X 60.0 C \n", + "3 X 41.0 C \n", + "4 X 26.0 A \n", + "... ... ... ... \n", + "95407 X 12.0 C \n", + "95408 X 2.0 A \n", + "95409 X 34.0 B \n", + "95410 X 11.0 A \n", + "95411 C 12.0 C \n", + "\n", + "[95412 rows x 481 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"learningSet.txt\")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3fcd94ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['OSOURCE', 'ZIP']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Lets create a drop_list we will use later as well\n", + "drop_list = list(data[['OSOURCE', 'ZIP']])\n", + "drop_list" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47815d17", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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481 rows × 2 columns

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" + ], + "text/plain": [ + " column_name nulls_percentage\n", + "414 RDATE_5 0.999906\n", + "436 RAMNT_5 0.999906\n", + "412 RDATE_3 0.997464\n", + "434 RAMNT_3 0.997464\n", + "413 RDATE_4 0.997055\n", + ".. ... ...\n", + "168 ETHC3 0.000000\n", + "167 ETHC2 0.000000\n", + "166 ETHC1 0.000000\n", + "165 HHD12 0.000000\n", + "240 TPE11 0.000000\n", + "\n", + "[481 rows x 2 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Lets deal with sparcity part \n", + "# Lets check null values in percentage \n", + "\n", + "nulls_percent_df= data.isna().sum()/len(data)\n", + "nulls_percent_df\n", + "\n", + "# put it in a dataframe \n", + "nulls_percent_df= pd.DataFrame(data.isna().sum()/len(data))\n", + "nulls_percent_df\n", + "\n", + "# Take out of the index \n", + "nulls_percent_df= pd.DataFrame(data.isna().sum()/len(data)).reset_index()\n", + "nulls_percent_df\n", + "\n", + "# Lets change columns name\n", + "nulls_percent_df.columns = ['column_name', 'nulls_percentage']\n", + "nulls_percent_df\n", + "\n", + "# Lets sort \n", + "nulls_percent_df.sort_values(by = ['nulls_percentage'], ascending = False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4040a63b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['NUMCHLD',\n", + " 'WEALTH1',\n", + " 'MBCRAFT',\n", + " 'MBGARDEN',\n", + " 'MBBOOKS',\n", + " 'MBCOLECT',\n", + " 'MAGFAML',\n", + " 'MAGFEM',\n", + " 'MAGMALE',\n", + " 'PUBGARDN',\n", + " 'PUBCULIN',\n", + " 'PUBHLTH',\n", + " 'PUBDOITY',\n", + " 'PUBNEWFN',\n", + " 'PUBPHOTO',\n", + " 'PUBOPP',\n", + " 'WEALTH2',\n", + " 'ADATE_5',\n", + " 'ADATE_10',\n", + " 'ADATE_13',\n", + " 'ADATE_15',\n", + " 'ADATE_17',\n", + " 'ADATE_19',\n", + " 'ADATE_20',\n", + " 'ADATE_21',\n", + " 'ADATE_22',\n", + " 'ADATE_23',\n", + " 'ADATE_24',\n", + " 'RDATE_3',\n", + " 'RDATE_4',\n", + " 'RDATE_5',\n", + " 'RDATE_6',\n", + " 'RDATE_7',\n", + " 'RDATE_8',\n", + " 'RDATE_9',\n", + " 'RDATE_10',\n", + " 'RDATE_11',\n", + " 'RDATE_12',\n", + " 'RDATE_13',\n", + " 'RDATE_14',\n", + " 'RDATE_15',\n", + " 'RDATE_16',\n", + " 'RDATE_17',\n", + " 'RDATE_18',\n", + " 'RDATE_19',\n", + " 'RDATE_20',\n", + " 'RDATE_21',\n", + " 'RDATE_22',\n", + " 'RDATE_23',\n", + " 'RDATE_24',\n", + " 'RAMNT_3',\n", + " 'RAMNT_4',\n", + " 'RAMNT_5',\n", + " 'RAMNT_6',\n", + " 'RAMNT_7',\n", + " 'RAMNT_8',\n", + " 'RAMNT_9',\n", + " 'RAMNT_10',\n", + " 'RAMNT_11',\n", + " 'RAMNT_12',\n", + " 'RAMNT_13',\n", + " 'RAMNT_14',\n", + " 'RAMNT_15',\n", + " 'RAMNT_16',\n", + " 'RAMNT_17',\n", + " 'RAMNT_18',\n", + " 'RAMNT_19',\n", + " 'RAMNT_20',\n", + " 'RAMNT_21',\n", + " 'RAMNT_22',\n", + " 'RAMNT_23',\n", + " 'RAMNT_24']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First create the variable with the threshold \n", + "threshold =0.25 \n", + "\n", + "# define a condition \n", + "condition = nulls_percent_df['nulls_percentage']>threshold\n", + "columns_above_threshold = nulls_percent_df[condition]\n", + "columns_above_threshold\n", + "\n", + "# Create a list with column names\n", + "drop_columns_list = list(columns_above_threshold['column_name'])\n", + "drop_columns_list" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bca2c434", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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95412 rows × 409 columns

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95412 rows × 481 columns

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95412 rows × 407 columns

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column_namenulls_percentage
346RDATE_50.999906
368RAMNT_50.999906
344RDATE_30.997464
366RAMNT_30.997464
345RDATE_40.997055
.........
145HUPA70.000000
144HUPA60.000000
143HUPA50.000000
142HUPA40.000000
203LFC60.000000
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407 rows × 2 columns

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" + ], + "text/plain": [ + " column_name nulls_percentage\n", + "346 RDATE_5 0.999906\n", + "368 RAMNT_5 0.999906\n", + "344 RDATE_3 0.997464\n", + "366 RAMNT_3 0.997464\n", + "345 RDATE_4 0.997055\n", + ".. ... ...\n", + "145 HUPA7 0.000000\n", + "144 HUPA6 0.000000\n", + "143 HUPA5 0.000000\n", + "142 HUPA4 0.000000\n", + "203 LFC6 0.000000\n", + "\n", + "[407 rows x 2 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking for null values in numerical_data DF\n", + "nulls_percent_numerical= pd.DataFrame(numerical_data.isna().sum()/len(numerical_data)).reset_index()\n", + "nulls_percent_numerical.columns = ['column_name', 'nulls_percentage']\n", + "\n", + "# Sorting the values to see the highest first\n", + "nulls_percent_numerical.sort_values(by = ['nulls_percentage'], ascending = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e9e8845a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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ODATEDWOSOURCETCODESTATEZIPMAILCODEPVASTATEDOBNOEXCHRECINHSE...TARGET_DHPHONE_DRFA_2RRFA_2FRFA_2AMDMAUD_RMDMAUD_FMDMAUD_ACLUSTER2GEOCODE2
08901GRI0IL6108137120...0.00L4EXXX39.0C
19401BOA1CA9132652020...0.00L2GXXX1.0A
29001AMH1NC2701700...0.01L4EXXX60.0C
38701BRY0CA9595328010...0.01L4EXXX41.0C
486010FL3317620010X...0.01L2FXXX26.0A
..................................................................
954079601ASE1AK9950400...0.00L1GXXX12.0C
954089601DCD1TX7737950010...0.01L1FXXX2.0A
954099501MBC1MI4891038010...0.01L3EXXX34.0B
954108601PRV0CA9132040050X...18.01L4FXXX11.0A
954118801MCC2NC2840918010X...0.01L1GC1C12.0C
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95412 rows × 480 columns

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" + ], + "text/plain": [ + " ODATEDW OSOURCE TCODE STATE ZIP MAILCODE PVASTATE DOB NOEXCH \\\n", + "0 8901 GRI 0 IL 61081 3712 0 \n", + "1 9401 BOA 1 CA 91326 5202 0 \n", + "2 9001 AMH 1 NC 27017 0 0 \n", + "3 8701 BRY 0 CA 95953 2801 0 \n", + "4 8601 0 FL 33176 2001 0 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 9601 ASE 1 AK 99504 0 0 \n", + "95408 9601 DCD 1 TX 77379 5001 0 \n", + "95409 9501 MBC 1 MI 48910 3801 0 \n", + "95410 8601 PRV 0 CA 91320 4005 0 \n", + "95411 8801 MCC 2 NC 28409 1801 0 \n", + "\n", + " RECINHSE ... TARGET_D HPHONE_D RFA_2R RFA_2F RFA_2A MDMAUD_R MDMAUD_F \\\n", + "0 ... 0.0 0 L 4 E X X \n", + "1 ... 0.0 0 L 2 G X X \n", + "2 ... 0.0 1 L 4 E X X \n", + "3 ... 0.0 1 L 4 E X X \n", + "4 X ... 0.0 1 L 2 F X X \n", + "... ... ... ... ... ... ... ... ... ... \n", + "95407 ... 0.0 0 L 1 G X X \n", + "95408 ... 0.0 1 L 1 F X X \n", + "95409 ... 0.0 1 L 3 E X X \n", + "95410 X ... 18.0 1 L 4 F X X \n", + "95411 X ... 0.0 1 L 1 G C 1 \n", + "\n", + " MDMAUD_A CLUSTER2 GEOCODE2 \n", + "0 X 39.0 C \n", + "1 X 1.0 A \n", + "2 X 60.0 C \n", + "3 X 41.0 C \n", + "4 X 26.0 A \n", + "... ... ... ... \n", + "95407 X 12.0 C \n", + "95408 X 2.0 A \n", + "95409 X 34.0 B \n", + "95410 X 11.0 A \n", + "95411 C 12.0 C \n", + "\n", + "[95412 rows x 480 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For the Wealth1 variable \n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['WEALTH1'].value_counts())\n", + "print(data['WEALTH1'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['WEALTH1'].isna().sum())\n", + "\n", + "# There is almost 50% of null values in this column so I will just drop it \n", + "\n", + "data = data.drop(columns = 'WEALTH1', axis=1)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f4a07c91", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13.0 7296\n", + "51.0 4622\n", + "65.0 3765\n", + "57.0 2836\n", + "105.0 2617\n", + " ... \n", + "651.0 1\n", + "103.0 1\n", + "601.0 1\n", + "161.0 1\n", + "147.0 1\n", + "Name: ADI, Length: 204, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# ADI variable\n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['ADI'].value_counts())\n", + "print(data['ADI'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['ADI'].isna().sum())\n", + "\n", + "sns.distplot(data['ADI'])\n", + "plt.show()\n", + "\n", + "# Since this is a continuous variable and we only have 132 null values I will fill them with the mean\n", + "data['ADI'] = data['ADI'].fillna(np.mean(data['ADI']))\n", + "\n", + "# Check if the distribution changed\n", + "sns.distplot(data['ADI'])\n", + "plt.show()\n", + "\n", + "# Check if all the null values were actually filled\n", + "print(data['ADI'].isna().sum())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "44164ac6", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "803.0 7296\n", + "602.0 4632\n", + "807.0 3765\n", + "505.0 2839\n", + "819.0 2588\n", + " ... \n", + "502.0 2\n", + "569.0 1\n", + "554.0 1\n", + "552.0 1\n", + "516.0 1\n", + "Name: DMA, Length: 201, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# DMA variable\n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['DMA'].value_counts())\n", + "print(data['DMA'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['DMA'].isna().sum())\n", + "\n", + "sns.distplot(data['DMA'])\n", + "plt.show()\n", + "\n", + "# Since this is a continuous variable and we only have 132 null values I will fill them with the mean\n", + "data['DMA'] = data['DMA'].fillna(np.mean(data['DMA']))\n", + "\n", + "# Check if the distribution changed\n", + "sns.distplot(data['DMA'])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3119646d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 21187\n", + "4480.0 4606\n", + "1600.0 4059\n", + "2160.0 2586\n", + "520.0 1685\n", + " ... \n", + "9140.0 1\n", + "3200.0 1\n", + "9280.0 1\n", + "743.0 1\n", + "8480.0 1\n", + "Name: MSA, Length: 294, dtype: int64\n", + "float64\n", + "132\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# MSA variable\n", + "#First check frequency and if it is th write type and it is on float\n", + "print(data['MSA'].value_counts())\n", + "print(data['MSA'].dtypes)\n", + "\n", + "# Check null values \n", + "print(data['MSA'].isna().sum())\n", + "\n", + "sns.distplot(data['MSA'])\n", + "plt.show()\n", + "\n", + "# Since this is a continuous variable and we only have 132 null values I will fill them with the mean\n", + "data['MSA'] = data['MSA'].fillna(np.mean(data['MSA']))\n", + "\n", + "# Check if the distribution changed\n", + "sns.distplot(data['MSA'])\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}