predicting_spaceship_survivors.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"-vXZLHL5KkEO"},"source":["# SPACESHIP TITANIC WITH 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project uses data from the **Spaceship Titanic** dataset from [Kaggle](https://www.kaggle.com/)"]},{"cell_type":"markdown","metadata":{"id":"oPfasfwtIiAi"},"source":["# IMPORTS"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_y9iR_q6IhIe"},"outputs":[],"source":["# !pip install keras_tuner"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"muhe4j4XCxhW"},"outputs":[],"source":["# Keras & Tensorflow libraries\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Activation, Dense\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.metrics import categorical_crossentropy\n","import keras_tuner as kt\n","\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","\n","# sklearn libraries\n","from sklearn.impute import SimpleImputer\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n","from sklearn.preprocessing import LabelEncoder, Normalizer, StandardScaler"]},{"cell_type":"markdown","metadata":{"id":"2NXLi1flInDY"},"source":["## OPERATING THE KAGGLE API IN GOOGLE COLAB"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FQlhwb8aEiS7"},"outputs":[],"source":["from google.colab import files\n","files.upload() #upload kaggle.json\n","\n","!pip install -q kaggle\n","!mkdir -p ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!ls ~/.kaggle\n","!chmod 600 /root/.kaggle/kaggle.json\n","\n","!kaggle kernels list — user YOUR_USER — sort-by dateRun\n","\n","!kaggle competitions download -c spaceship-titanic"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yr_oSok-E26x"},"outputs":[],"source":["!unzip spaceship-titanic.zip "]},{"cell_type":"markdown","metadata":{"id":"vZJQz37WItlX"},"source":["# DATA"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HhrRDRdHE9-9"},"outputs":[],"source":["train = pd.read_csv('/content/train.csv')\n","test = pd.read_csv('/content/test.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1673615530567,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"25MA8Ta9OoTK","outputId":"a28ed8ee-850f-4301-ce10-e3cbc2516f57"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-0e4d518a-4bfd-44f5-8d31-b0272c9a813a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" 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<td>1283.0</td>\n","      <td>371.0</td>\n","      <td>3329.0</td>\n","      <td>193.0</td>\n","      <td>Solam Susent</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0004_01</td>\n","      <td>Earth</td>\n","      <td>False</td>\n","      <td>F/1/S</td>\n","      <td>TRAPPIST-1e</td>\n","      <td>16.0</td>\n","      <td>False</td>\n","      <td>303.0</td>\n","      <td>70.0</td>\n","      <td>151.0</td>\n","      <td>565.0</td>\n","      <td>2.0</td>\n","      <td>Willy Santantines</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e4d518a-4bfd-44f5-8d31-b0272c9a813a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["  PassengerId HomePlanet CryoSleep  Cabin  Destination   Age    VIP  \\\n","0     0001_01     Europa     False  B/0/P  TRAPPIST-1e  39.0  False   \n","1     0002_01      Earth     False  F/0/S  TRAPPIST-1e  24.0  False   \n","2     0003_01     Europa     False  A/0/S  TRAPPIST-1e  58.0   True   \n","3     0003_02     Europa     False  A/0/S  TRAPPIST-1e  33.0  False   \n","4     0004_01      Earth     False  F/1/S  TRAPPIST-1e  16.0  False   \n","\n","   RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n","0          0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n","1        109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n","2         43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n","3          0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n","4        303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n","\n","   Transported  \n","0        False  \n","1         True  \n","2        False  \n","3        False  \n","4         True  "]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["train.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":476},"executionInfo":{"elapsed":582,"status":"ok","timestamp":1673615531132,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"yCxl_-VdOrQB","outputId":"f394c704-b036-4674-dfe2-22ad66c4ab3e"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-23d570d0-36de-4777-af2a-77e805ae649f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>HomePlanet</th>\n","      <th>CryoSleep</th>\n","      <th>Cabin</th>\n","      <th>Destination</th>\n","      <th>Age</th>\n","      <th>VIP</th>\n","      <th>RoomService</th>\n","      <th>FoodCourt</th>\n","      <th>ShoppingMall</th>\n","      <th>Spa</th>\n","      <th>VRDeck</th>\n","      <th>Name</th>\n","      <th>Transported</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>8693</td>\n","      <td>8492</td>\n","      <td>8476</td>\n","      <td>8494</td>\n","      <td>8511</td>\n","      <td>8514.000000</td>\n","      <td>8490</td>\n","      <td>8512.000000</td>\n","      <td>8510.000000</td>\n","      <td>8485.000000</td>\n","      <td>8510.000000</td>\n","      <td>8505.000000</td>\n","      <td>8493</td>\n","      <td>8693</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>8693</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>6560</td>\n","      <td>3</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>8473</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>0001_01</td>\n","      <td>Earth</td>\n","      <td>False</td>\n","      <td>G/734/S</td>\n","      <td>TRAPPIST-1e</td>\n","      <td>NaN</td>\n","      <td>False</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Gollux Reedall</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>1</td>\n","      <td>4602</td>\n","      <td>5439</td>\n","      <td>8</td>\n","      <td>5915</td>\n","      <td>NaN</td>\n","      <td>8291</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>4378</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>28.827930</td>\n","      <td>NaN</td>\n","      <td>224.687617</td>\n","      <td>458.077203</td>\n","      <td>173.729169</td>\n","      <td>311.138778</td>\n","      <td>304.854791</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>14.489021</td>\n","      <td>NaN</td>\n","      <td>666.717663</td>\n","      <td>1611.489240</td>\n","      <td>604.696458</td>\n","      <td>1136.705535</td>\n","      <td>1145.717189</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>19.000000</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>27.000000</td>\n","      <td>NaN</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>38.000000</td>\n","      <td>NaN</td>\n","      <td>47.000000</td>\n","      <td>76.000000</td>\n","      <td>27.000000</td>\n","      <td>59.000000</td>\n","      <td>46.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>79.000000</td>\n","      <td>NaN</td>\n","      <td>14327.000000</td>\n","      <td>29813.000000</td>\n","      <td>23492.000000</td>\n","      <td>22408.000000</td>\n","      <td>24133.000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["       PassengerId HomePlanet CryoSleep    Cabin  Destination          Age  \\\n","count         8693       8492      8476     8494         8511  8514.000000   \n","unique        8693          3         2     6560            3          NaN   \n","top        0001_01      Earth     False  G/734/S  TRAPPIST-1e          NaN   \n","freq             1       4602      5439        8         5915          NaN   \n","mean           NaN        NaN       NaN      NaN          NaN    28.827930   \n","std            NaN        NaN       NaN      NaN          NaN    14.489021   \n","min            NaN        NaN       NaN      NaN          NaN     0.000000   \n","25%            NaN        NaN       NaN      NaN          NaN    19.000000   \n","50%            NaN        NaN       NaN      NaN          NaN    27.000000   \n","75%            NaN        NaN       NaN      NaN          NaN    38.000000   \n","max            NaN        NaN       NaN      NaN          NaN    79.000000   \n","\n","          VIP   RoomService     FoodCourt  ShoppingMall           Spa  \\\n","count    8490   8512.000000   8510.000000   8485.000000   8510.000000   \n","unique      2           NaN           NaN           NaN           NaN   \n","top     False           NaN           NaN           NaN           NaN   \n","freq     8291           NaN           NaN           NaN           NaN   \n","mean      NaN    224.687617    458.077203    173.729169    311.138778   \n","std       NaN    666.717663   1611.489240    604.696458   1136.705535   \n","min       NaN      0.000000      0.000000      0.000000      0.000000   \n","25%       NaN      0.000000      0.000000      0.000000      0.000000   \n","50%       NaN      0.000000      0.000000      0.000000      0.000000   \n","75%       NaN     47.000000     76.000000     27.000000     59.000000   \n","max       NaN  14327.000000  29813.000000  23492.000000  22408.000000   \n","\n","              VRDeck            Name Transported  \n","count    8505.000000            8493        8693  \n","unique           NaN            8473           2  \n","top              NaN  Gollux Reedall        True  \n","freq             NaN               2        4378  \n","mean      304.854791             NaN         NaN  \n","std      1145.717189             NaN         NaN  \n","min         0.000000             NaN         NaN  \n","25%         0.000000             NaN         NaN  \n","50%         0.000000             NaN         NaN  \n","75%        46.000000             NaN         NaN  \n","max     24133.000000             NaN         NaN  "]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["train.describe(include='all')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T9YA1DT4nrnO"},"outputs":[],"source":["train.isna().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ec35VLWpnyjX"},"outputs":[],"source":["train.info()"]},{"cell_type":"markdown","metadata":{"id":"VvNq4SqqIz7E"},"source":["## DATA PREPROCESSING"]},{"cell_type":"markdown","metadata":{"id":"RFGMvCr6I5Tl"},"source":["Here, we'll do some pre-processing which involves filling the missing values."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OIiZwgi9FH3J"},"outputs":[],"source":["imputer_cols = [\"Age\", \"FoodCourt\", \"ShoppingMall\", \"Spa\", \"VRDeck\" ,\"RoomService\"]\n","imputer = SimpleImputer()\n","imputer.fit(train[imputer_cols])\n","train[imputer_cols] = imputer.transform(train[imputer_cols])\n","test[imputer_cols] = imputer.transform(test[imputer_cols])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZpFpB5hBFPj8"},"outputs":[],"source":["train['HomePlanet'].fillna('Earth', inplace=True)\n","train['CryoSleep'].fillna('False', inplace=True)\n","train['Cabin'].fillna('None', inplace=True)\n","train['Destination'].fillna('TRAPPIST-1e', inplace=True)\n","train['VIP'].fillna('False', inplace=True)\n","\n","test['HomePlanet'].fillna('Earth', inplace=True)\n","test['CryoSleep'].fillna('False', inplace=True)\n","test['Cabin'].fillna('G/109/P', inplace=True)\n","test['Destination'].fillna('TRAPPIST-1e', inplace=True)\n","test['VIP'].fillna('False', inplace=True)\n","\n","train['Name'].fillna('no name', inplace=True)\n","test['Name'].fillna('no name', inplace=True)"]},{"cell_type":"markdown","metadata":{"id":"us5-yOkkI8Bj"},"source":["## FEATURE ENGINEERING"]},{"cell_type":"markdown","metadata":{"id":"TA87UOnFJAVT"},"source":["We'll be doing some feature engineering by creating new features out of the others."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YcCZQ1FWFWj7"},"outputs":[],"source":["train['Deck'] = train['Cabin'].apply(lambda x:str(x)[:1])\n","train['Side'] = train['Cabin'].apply(lambda x:str(x)[-1:])\n","test['Deck'] = train['Cabin'].apply(lambda x:str(x)[:1])\n","test['Side'] = train['Cabin'].apply(lambda x:str(x)[-1:])\n","\n","train['PGroup'] = train['PassengerId'].apply(lambda x: x.split('_')[0])\n","train['PNumber'] = train['PassengerId'].apply(lambda x: x.split('_')[1])\n","test['PGroup'] = test['PassengerId'].apply(lambda x: x.split('_')[0])\n","test['PNumber'] = test['PassengerId'].apply(lambda x: x.split('_')[1])\n","\n","train['LastName'] = train['Name'].apply(lambda x:x.split(\" \")[1])\n","test['LastName'] = train['Name'].apply(lambda x:x.split(\" \")[1])\n","\n","train['AllServices'] = train['RoomService'] + train['FoodCourt'] + train['ShoppingMall'] + train['Spa'] + train['VRDeck']\n","test['AllServices'] = test['RoomService'] + test['FoodCourt'] + test['ShoppingMall'] + test['Spa'] + test['VRDeck']"]},{"cell_type":"markdown","metadata":{"id":"JmiG8sl-JWi4"},"source":["As machine learning models only love numbers, we'll encode the categorical columns using **Label Encoder**."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"w2r7xVFeFcA0"},"outputs":[],"source":["label_cols = [\"HomePlanet\", \"CryoSleep\", \"Destination\" ,\"VIP\", \"Deck\", \"Side\", \"LastName\", \"PGroup\", \"PNumber\"]\n","\n","for i in label_cols:\n","    train[i] = train[i].astype(str)\n","    test[i] = test[i].astype(str)\n","    train[i] = LabelEncoder().fit_transform(train[i])\n","    test[i] = LabelEncoder().fit_transform(test[i])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":661},"executionInfo":{"elapsed":90,"status":"ok","timestamp":1673615532159,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"klFV-hvrFekr","outputId":"86bcae6f-2309-4ede-a12f-959d1653f563"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-fd4161df-e8ee-4478-9f55-56ae56d1415f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>HomePlanet</th>\n","      <th>CryoSleep</th>\n","      <th>Cabin</th>\n","      <th>Destination</th>\n","      <th>Age</th>\n","      <th>VIP</th>\n","      <th>RoomService</th>\n","      <th>FoodCourt</th>\n","      <th>ShoppingMall</th>\n","      <th>Spa</th>\n","      <th>VRDeck</th>\n","      <th>Name</th>\n","      <th>Transported</th>\n","      <th>Deck</th>\n","      <th>Side</th>\n","      <th>PGroup</th>\n","      <th>PNumber</th>\n","      <th>LastName</th>\n","      <th>AllServices</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0001_01</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>B/0/P</td>\n","      <td>2</td>\n","      <td>39.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Maham Ofracculy</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1431</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0002_01</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>F/0/S</td>\n","      <td>2</td>\n","      <td>24.0</td>\n","      <td>0</td>\n","      <td>109.0</td>\n","      <td>9.0</td>\n","      <td>25.0</td>\n","      <td>549.0</td>\n","      <td>44.0</td>\n","      <td>Juanna Vines</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2109</td>\n","      <td>736.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0003_01</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/0/S</td>\n","      <td>2</td>\n","      <td>58.0</td>\n","      <td>1</td>\n","      <td>43.0</td>\n","      <td>3576.0</td>\n","      <td>0.0</td>\n","      <td>6715.0</td>\n","      <td>49.0</td>\n","      <td>Altark Susent</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1990</td>\n","      <td>10383.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0003_02</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/0/S</td>\n","      <td>2</td>\n","      <td>33.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1283.0</td>\n","      <td>371.0</td>\n","      <td>3329.0</td>\n","      <td>193.0</td>\n","      <td>Solam Susent</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1990</td>\n","      <td>5176.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0004_01</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>F/1/S</td>\n","      <td>2</td>\n","      <td>16.0</td>\n","      <td>0</td>\n","      <td>303.0</td>\n","      <td>70.0</td>\n","      <td>151.0</td>\n","      <td>565.0</td>\n","      <td>2.0</td>\n","      <td>Willy Santantines</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1778</td>\n","      <td>1091.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>8688</th>\n","      <td>9276_01</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/98/P</td>\n","      <td>0</td>\n","      <td>41.0</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>6819.0</td>\n","      <td>0.0</td>\n","      <td>1643.0</td>\n","      <td>74.0</td>\n","      <td>Gravior Noxnuther</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>6213</td>\n","      <td>0</td>\n","      <td>1416</td>\n","      <td>8536.0</td>\n","    </tr>\n","    <tr>\n","      <th>8689</th>\n","      <td>9278_01</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>G/1499/S</td>\n","      <td>1</td>\n","      <td>18.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>Kurta Mondalley</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>6214</td>\n","      <td>0</td>\n","      <td>1341</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>8690</th>\n","      <td>9279_01</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>G/1500/S</td>\n","      <td>2</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1872.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>Fayey Connon</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>6215</td>\n","      <td>0</td>\n","      <td>470</td>\n","      <td>1873.0</td>\n","    </tr>\n","    <tr>\n","      <th>8691</th>\n","      <td>9280_01</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>E/608/S</td>\n","      <td>0</td>\n","      <td>32.0</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1049.0</td>\n","      <td>0.0</td>\n","      <td>353.0</td>\n","      <td>3235.0</td>\n","      <td>Celeon Hontichre</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>6216</td>\n","      <td>0</td>\n","      <td>996</td>\n","      <td>4637.0</td>\n","    </tr>\n","    <tr>\n","      <th>8692</th>\n","      <td>9280_02</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>E/608/S</td>\n","      <td>2</td>\n","      <td>44.0</td>\n","      <td>0</td>\n","      <td>126.0</td>\n","      <td>4688.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>12.0</td>\n","      <td>Propsh Hontichre</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>6216</td>\n","      <td>1</td>\n","      <td>996</td>\n","      <td>4826.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8693 rows × 20 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd4161df-e8ee-4478-9f55-56ae56d1415f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["     PassengerId  HomePlanet  CryoSleep     Cabin  Destination   Age  VIP  \\\n","0        0001_01           1          0     B/0/P            2  39.0    0   \n","1        0002_01           0          0     F/0/S            2  24.0    0   \n","2        0003_01           1          0     A/0/S            2  58.0    1   \n","3        0003_02           1          0     A/0/S            2  33.0    0   \n","4        0004_01           0          0     F/1/S            2  16.0    0   \n","...          ...         ...        ...       ...          ...   ...  ...   \n","8688     9276_01           1          0    A/98/P            0  41.0    1   \n","8689     9278_01           0          1  G/1499/S            1  18.0    0   \n","8690     9279_01           0          0  G/1500/S            2  26.0    0   \n","8691     9280_01           1          0   E/608/S            0  32.0    0   \n","8692     9280_02           1          0   E/608/S            2  44.0    0   \n","\n","      RoomService  FoodCourt  ShoppingMall     Spa  VRDeck               Name  \\\n","0             0.0        0.0           0.0     0.0     0.0    Maham Ofracculy   \n","1           109.0        9.0          25.0   549.0    44.0       Juanna Vines   \n","2            43.0     3576.0           0.0  6715.0    49.0      Altark Susent   \n","3             0.0     1283.0         371.0  3329.0   193.0       Solam Susent   \n","4           303.0       70.0         151.0   565.0     2.0  Willy Santantines   \n","...           ...        ...           ...     ...     ...                ...   \n","8688          0.0     6819.0           0.0  1643.0    74.0  Gravior Noxnuther   \n","8689          0.0        0.0           0.0     0.0     0.0    Kurta Mondalley   \n","8690          0.0        0.0        1872.0     1.0     0.0       Fayey Connon   \n","8691          0.0     1049.0           0.0   353.0  3235.0   Celeon Hontichre   \n","8692        126.0     4688.0           0.0     0.0    12.0   Propsh Hontichre   \n","\n","      Transported  Deck  Side  PGroup  PNumber  LastName  AllServices  \n","0               0     1     0       0        0      1431          0.0  \n","1               1     5     1       1        0      2109        736.0  \n","2               0     0     1       2        0      1990      10383.0  \n","3               0     0     1       2        1      1990       5176.0  \n","4               1     5     1       3        0      1778       1091.0  \n","...           ...   ...   ...     ...      ...       ...          ...  \n","8688            0     0     0    6213        0      1416       8536.0  \n","8689            0     6     1    6214        0      1341          0.0  \n","8690            1     6     1    6215        0       470       1873.0  \n","8691            0     4     1    6216        0       996       4637.0  \n","8692            1     4     1    6216        1       996       4826.0  \n","\n","[8693 rows x 20 columns]"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["train['Transported'] = LabelEncoder().fit_transform(train['Transported'])\n","train"]},{"cell_type":"markdown","metadata":{"id":"pNY2CFfiJfV6"},"source":["## PREPARING DATA FOR MODEL TRAINING"]},{"cell_type":"markdown","metadata":{"id":"6VK6a8A4Jnwy"},"source":["We'll drop following columns as they are no longer needed now as we have made features out of them."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zTo2c5-mFhXa"},"outputs":[],"source":["cols_to_drop = ['Name', 'PassengerId', 'Cabin']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HYVkOjlEFjNi"},"outputs":[],"source":["y = train['Transported']\n","train.drop(['Name', 'PassengerId', 'Cabin', 'Transported'], axis=1, inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FZqJ1cBnFmJL"},"outputs":[],"source":["transformer = Normalizer().fit(train) \n","transformer\n","train=transformer.transform(train)"]},{"cell_type":"markdown","metadata":{"id":"kpRW8HLMJtik"},"source":["Then, we can split the data into training and validation sets. This will help our model to train on the training dataset & evaluate the performance simultaneously on the validation dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p63lenH6FpEd"},"outputs":[],"source":["x_train, x_valid, y_train, y_valid = train_test_split(train, y, test_size=0.3, random_state=101)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3nLrjD1pFr6M"},"outputs":[],"source":["ids = test['PassengerId']\n","test.drop(cols_to_drop, axis=1, inplace=True)"]},{"cell_type":"markdown","metadata":{"id":"R0GcWhBnJ849"},"source":["We have to perform the normalization step for our final test dataset also."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gNVJt_UyFvXf"},"outputs":[],"source":["transformer = Normalizer().fit(test) \n","transformer\n","test=transformer.transform(test)"]},{"cell_type":"markdown","metadata":{"id":"PkRpJ4M2KBog"},"source":["# MODEL ARCHITECTURE"]},{"cell_type":"markdown","metadata":{"id":"cIrKxQRgKMiU"},"source":["![Comp-1.gif](data:image/gif;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"KjNgyreRKKLe"},"source":["We'll use Keras, with Tensorflow as backend, to create the neural network model & perform the training."]},{"cell_type":"markdown","metadata":{"id":"BLW1TykoLx9Y"},"source":["<div class=\"alert alert-block alert-info\">\n","<p style='font-size: 17px'>Before the model, let's define some terms:</p>\n","\n","<p style='font-size: 17px'><strong>Sequential</strong> : Sequential class groups a linear stack of layers to form a model on which training & inference can be done.</p>\n","\n","<p style='font-size: 17px'><strong>Dense</strong>: Dense is a densely-connected (look at the figure) neural network layer.</p>\n","\n","<p style='font-size: 17px'><strong>activation</strong>: The type of function to perform mathematical operations on the layers. We'll be using <strong>sigmoid</strong> activation function to yield the value between 0 and 1.</p>\n","\n","<p style='font-size: 17px'><strong>input_dims</strong>: The shape of the values that are being passed.</p>\n","\n","<p style='font-size: 17px'><strong>kernel_initializer</strong>: Initializers define the way to set the initial random weights of Keras layers.</p>\n","\n","<p style='font-size: 17px'><strong>units</strong>: It is a positive integer & is used to define the dimensionality of the output space. </p>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":116,"status":"ok","timestamp":1673615532199,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"yYBN189uMErc","outputId":"6e12b903-f073-43a9-9019-47156b059c12"},"outputs":[{"name":"stdout","output_type":"stream","text":["The dimension of data is: 16\n"]}],"source":["print(f'The dimension of data is: {x_train.shape[1]}')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FZufbMs6F1iq"},"outputs":[],"source":["model = Sequential([\n","    Dense(units=32, input_dim=16, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=32, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=5, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=1, kernel_initializer = 'uniform', activation='sigmoid')\n","    ])"]},{"cell_type":"markdown","metadata":{"id":"THooHQPjMInz"},"source":["We can print out how our model looks or in short, it's summary, by running the following code:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":101,"status":"ok","timestamp":1673615532202,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"AaB7rSnHF2Sz","outputId":"8abbb89b-02cc-4e38-8ca6-026c8b4f828a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense_4 (Dense)             (None, 32)                544       \n","                                                                 \n"," dense_5 (Dense)             (None, 32)                1056      \n","                                                                 \n"," dense_6 (Dense)             (None, 5)                 165       \n","                                                                 \n"," dense_7 (Dense)             (None, 1)                 6         \n","                                                                 \n","=================================================================\n","Total params: 1,771\n","Trainable params: 1,771\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"markdown","metadata":{"id":"9dPwo2_wMP3C"},"source":["## MODEL TRAINING (W/ HYPERPARAMETER 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KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search."]},{"cell_type":"markdown","metadata":{"id":"MjP1wGoXMw4D"},"source":["For this, we define a function and use the hp argument to define the hyperparameters during model creation. Here, I have defined hyperparameter space for learning rate."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"frfZ8OVJGAFt"},"outputs":[],"source":["def build_model(hp):\n","    lrate = hp.Float('lrate', 1e-4, 1e-1, sampling='log') # learning rate hyper-param space\n","    \n","    model = Sequential([\n","    Dense(units=32, input_dim=16, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=32, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=5, kernel_initializer = 'uniform', activation='relu'),\n","    Dense(units=1, kernel_initializer = 'uniform', activation='sigmoid')\n","    ])\n","    \n","    model.compile(optimizer=Adam(learning_rate=lrate), loss='binary_crossentropy', metrics=['accuracy'])\n","    return model"]},{"cell_type":"markdown","metadata":{"id":"7X8wiWpAM7Y5"},"source":["Then, we need to choose a tuner class. It can be any of these: **RandomSearch**, **BayesianOptimization** and **Hyperband**.\n","\n","We'll be going with the **BayesianOptimization**.\n","\n","Then, we declare the model function, objective function and maximum trials. Here, setting overwrite=True will start a new search and ignore any previous results."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aMFgx4mIGB9z"},"outputs":[],"source":["tuner = kt.BayesianOptimization(\n","    build_model,\n","    objective=kt.Objective('val_accuracy', 'max'),\n","    max_trials=10,\n","    num_initial_points=2,\n","    overwrite=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"HQ1B9-P2NI8o"},"source":["And then we search through that hyperparameters space. The syntax is quite similar to the tensorflow-keras predict function."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":77708,"status":"ok","timestamp":1673615609832,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"ZLwGrNahGD1S","outputId":"5359e5e0-98ac-4577-bc45-1dc3a3e8615b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Trial 10 Complete [00h 00m 04s]\n","val_accuracy: 0.7925613522529602\n","\n","Best val_accuracy So Far: 0.7956288456916809\n","Total elapsed time: 00h 01m 18s\n"]}],"source":["tuner.search(\n","    x_train, y_train, epochs=5, validation_data=(x_valid, y_valid)\n",")"]},{"cell_type":"markdown","metadata":{"id":"_fcLe3qVNND2"},"source":["Now, our tuner has searched through our own defined hyperparameter space. To get the best model, we just run the get_best_model[0] to get the model having the best objective function result."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gjAzBSqOGFUr"},"outputs":[],"source":["best_model = tuner.get_best_models()[0]"]},{"cell_type":"markdown","metadata":{"id":"VLbV17bPNTur"},"source":["And then, we perform the fit on that best model and validate on our validation set for our final training part."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":201978,"status":"ok","timestamp":1673615812748,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"oirhlJr0GW3m","outputId":"1fd68a80-8f5a-4655-8bc2-95f21dbc61d8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5524 - accuracy: 0.7504 - val_loss: 0.5177 - val_accuracy: 0.7803\n","Epoch 2/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5361 - accuracy: 0.7668 - val_loss: 0.5501 - val_accuracy: 0.7945\n","Epoch 3/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5438 - accuracy: 0.7638 - val_loss: 0.5360 - val_accuracy: 0.7949\n","Epoch 4/10\n","6085/6085 [==============================] - 15s 2ms/step - loss: 0.5315 - accuracy: 0.7734 - val_loss: 0.5088 - val_accuracy: 0.7929\n","Epoch 5/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5343 - accuracy: 0.7688 - val_loss: 0.4980 - val_accuracy: 0.7910\n","Epoch 6/10\n","6085/6085 [==============================] - 16s 3ms/step - loss: 0.5371 - accuracy: 0.7658 - val_loss: 0.4971 - val_accuracy: 0.7891\n","Epoch 7/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5301 - accuracy: 0.7699 - val_loss: 0.5090 - val_accuracy: 0.7814\n","Epoch 8/10\n","6085/6085 [==============================] - 19s 3ms/step - loss: 0.5314 - accuracy: 0.7696 - val_loss: 0.4980 - val_accuracy: 0.7880\n","Epoch 9/10\n","6085/6085 [==============================] - 17s 3ms/step - loss: 0.5395 - accuracy: 0.7630 - val_loss: 0.5167 - val_accuracy: 0.7814\n","Epoch 10/10\n","6085/6085 [==============================] - 18s 3ms/step - loss: 0.5338 - accuracy: 0.7719 - val_loss: 0.5159 - val_accuracy: 0.7872\n"]}],"source":["hist = best_model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=10, batch_size=1)"]},{"cell_type":"markdown","metadata":{"id":"p4HOqJE2NYx5"},"source":["## MODEL EVALUATION"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3245,"status":"ok","timestamp":1673615815955,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"mdYbee9rGHVj","outputId":"fcd6d0ba-81b1-4658-8d26-8650664bc5b1"},"outputs":[{"data":{"text/plain":["array([[0.707603  ],\n","       [0.16177617],\n","       [0.16177617],\n","       [0.56831807],\n","       [0.16177617]], dtype=float32)"]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["pred = best_model.predict(x_valid, batch_size=1, verbose=0)\n","pred[:5]"]},{"cell_type":"markdown","metadata":{"id":"VyyO1S2PNgjd"},"source":["We see that our predictions are there but they are continuous values and not in the binary form that we want. That's because we have sigmoid as our activation function in our last layer. It yields the value between 0 & 1.\n","\n","So, in order to make it in binary form, we can define a point above which that value will be made 1 and below which it will be made 0.\n","\n","Here, we take that point as 0.5."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":57,"status":"ok","timestamp":1673615815958,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"X5jbvYLzGJ4D","outputId":"b454511e-4b5a-448c-c905-abe657f9ca8d"},"outputs":[{"data":{"text/plain":["[1, 0, 0, 1, 0]"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":["y_pred = [1 if x > 0.5 else 0 for x in pred]\n","y_pred[:5]"]},{"cell_type":"markdown","metadata":{"id":"nldif1EcNjXi"},"source":["### PLOTTING ACCURACY AND LOSS PLOTS"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":360},"executionInfo":{"elapsed":1234,"status":"ok","timestamp":1673615817143,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"pWm7IpQeGM-l","outputId":"572f0a9a-e888-431b-bf2d-f31cfe8bdda5"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1296x576 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["f, ax = plt.subplots(1, 2, figsize=[18, 8])\n","ax[0].plot([None] + hist.history['accuracy'], 'o-')\n","ax[0].plot([None] + hist.history['val_accuracy'], 'x-')\n","# Plot legend and use the best location automatically: loc = 0.\n","ax[0].legend(['Train acc', 'Validation acc'], loc = 0)\n","ax[0].set_title('Training/Validation acc per Epoch')\n","ax[0].set_xlabel('Epoch')\n","ax[0].set_ylabel('acc')\n","\n","ax[1].plot([None] + hist.history['loss'], 'o-')\n","ax[1].plot([None] + hist.history['val_loss'], 'x-')\n","\n","# Plot legend and use the best location automatically: loc = 0.\n","ax[1].legend(['Train loss', \"Val loss\"], loc = 0)\n","ax[1].set_title('Training/Validation Loss per Epoch')\n","ax[1].set_xlabel('Epoch')\n","ax[1].set_ylabel('Loss')\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"cpdyIMAmNq16"},"source":["### CONFUSION MATRIX"]},{"cell_type":"markdown","metadata":{"id":"ZbaBXtjTNvsD"},"source":["A confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X7Fd1hyMGPrp"},"outputs":[],"source":["cm = confusion_matrix(y_true=y_valid, y_pred=y_pred)"]},{"cell_type":"markdown","metadata":{"id":"Fqb82W-RN4tx"},"source":["Now, in order to plot confusion matrix, sklearn now provides a very efficient way. Rather than just going over lines of matplotlib code, we can just import ConfusionMatrixDisplay from the metrics module to directly plot it."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"elapsed":63,"status":"ok","timestamp":1673615817146,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"nYC1sQcUGh3P","outputId":"b636fc3a-43d4-4c73-ad5c-1aba5130ce57"},"outputs":[{"data":{"text/plain":["<Figure size 720x576 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.figure(figsize=[10, 8])\n","ConfusionMatrixDisplay.from_predictions(y_valid, y_pred)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Mz9BlOp5OIEX"},"source":["## FINAL PREDICTIONS"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":576,"status":"ok","timestamp":1673615817665,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"689OZyG7GlLF","outputId":"7a4d5312-e37a-45c6-b903-37446c4af384"},"outputs":[{"name":"stdout","output_type":"stream","text":["134/134 [==============================] - 0s 1ms/step\n"]},{"data":{"text/plain":["array([[0.707603  ],\n","       [0.16177617],\n","       [0.707603  ],\n","       [0.707603  ],\n","       [0.707603  ]], dtype=float32)"]},"execution_count":93,"metadata":{},"output_type":"execute_result"}],"source":["preds = best_model.predict(test)\n","preds[:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1673615817667,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"cJs_9ElrGn6N","outputId":"25ef034b-eeea-467d-df7e-b5adf42c1417"},"outputs":[{"data":{"text/plain":["[1, 0, 1, 1, 1]"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["survived = [1 if x > 0.5 else 0 for x in preds]\n","survived[:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33,"status":"ok","timestamp":1673615817668,"user":{"displayName":"Edward Oyieko","userId":"03046916977857693557"},"user_tz":-180},"id":"IUklUVfvGqQH","outputId":"95fb8be9-8577-409f-e2cc-91d670b308b4"},"outputs":[{"data":{"text/plain":["[True, False, True, True, True]"]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["for i in range(len(survived)):\n","    survived[i] = bool(survived[i])\n","\n","survived[:5]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AcQuJopkTa-W"},"outputs":[],"source":["subs = pd.DataFrame({'PassengerId': ids, 'Transported': survived})\n","subs.to_csv('submission.csv', index=False)"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyM3GmDu6aKYeF56cHmawoGa","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}