87.5 accuracy titanic.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_score, f1_score\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder, LabelEncoder"
      ]
    },
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      "source": [
        "# Save to df\n",
        "train = pd.read_csv('/content/train.csv')\n",
        "test = pd.read_csv('/content/test.csv')\n",
        "\n",
        "# Shape and preview\n",
        "print('Train set shape:', train.shape)\n",
        "print('Test set shape:', test.shape)\n",
        "train.head()"
      ],
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Train set shape: (8693, 14)\n",
            "Test set shape: (4277, 13)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "      <th>Spa</th>\n",
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              "      <td>0001_01</td>\n",
              "      <td>Europa</td>\n",
              "      <td>False</td>\n",
              "      <td>B/0/P</td>\n",
              "      <td>TRAPPIST-1e</td>\n",
              "      <td>39.0</td>\n",
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              "      <td>44.0</td>\n",
              "      <td>Juanna Vines</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0003_01</td>\n",
              "      <td>Europa</td>\n",
              "      <td>False</td>\n",
              "      <td>A/0/S</td>\n",
              "      <td>TRAPPIST-1e</td>\n",
              "      <td>58.0</td>\n",
              "      <td>True</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>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0003_02</td>\n",
              "      <td>Europa</td>\n",
              "      <td>False</td>\n",
              "      <td>A/0/S</td>\n",
              "      <td>TRAPPIST-1e</td>\n",
              "      <td>33.0</td>\n",
              "      <td>False</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>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",
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            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.isna().sum())\n",
        "print(test.isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyNWDT50ThSo",
        "outputId": "58c193ac-0001-402d-a9c1-cd383f9c01f4"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId       0\n",
            "HomePlanet      201\n",
            "CryoSleep       217\n",
            "Cabin           199\n",
            "Destination     182\n",
            "Age             179\n",
            "VIP             203\n",
            "RoomService     181\n",
            "FoodCourt       183\n",
            "ShoppingMall    208\n",
            "Spa             183\n",
            "VRDeck          188\n",
            "Name            200\n",
            "Transported       0\n",
            "dtype: int64\n",
            "PassengerId       0\n",
            "HomePlanet       87\n",
            "CryoSleep        93\n",
            "Cabin           100\n",
            "Destination      92\n",
            "Age              91\n",
            "VIP              93\n",
            "RoomService      82\n",
            "FoodCourt       106\n",
            "ShoppingMall     98\n",
            "Spa             101\n",
            "VRDeck           80\n",
            "Name             94\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Duplicates in train set: {train.duplicated().sum()}, ({np.round(100*train.duplicated().sum()/len(train),1)}%)')\n",
        "print('')\n",
        "print(f'Duplicates in test set: {test.duplicated().sum()}, ({np.round(100*test.duplicated().sum()/len(test),1)}%)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "CfD7WnN9aoPs",
        "outputId": "0120d078-432d-436d-e96a-2c337280baf5"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicates in train set: 0, (0.0%)\n",
            "\n",
            "Duplicates in test set: 0, (0.0%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmWzV_sPUBgv",
        "outputId": "5a43b189-95a3-45a7-95b2-a007a94bc89a"
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      "execution_count": 331,
      "outputs": [
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          "output_type": "execute_result",
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              "0"
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          "metadata": {},
          "execution_count": 331
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      "cell_type": "code",
      "source": [
        "test.duplicated().sum()"
      ],
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        },
        "id": "36WwJNAxUBdU",
        "outputId": "27e6ed32-6230-45d2-8f66-4a3ff3d8ad75"
      },
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.nunique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lBHbx7kgUBbT",
        "outputId": "b135a44c-ff62-4bcd-802e-7af8ee1ea490"
      },
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     8693\n",
              "HomePlanet         3\n",
              "CryoSleep          2\n",
              "Cabin           6560\n",
              "Destination        3\n",
              "Age               80\n",
              "VIP                2\n",
              "RoomService     1273\n",
              "FoodCourt       1507\n",
              "ShoppingMall    1115\n",
              "Spa             1327\n",
              "VRDeck          1306\n",
              "Name            8473\n",
              "Transported        2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7DyWGk-UBZF",
        "outputId": "2280a44d-fbc1-431e-9b8d-b1e2e88794ee"
      },
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      object\n",
              "HomePlanet       object\n",
              "CryoSleep        object\n",
              "Cabin            object\n",
              "Destination      object\n",
              "Age             float64\n",
              "VIP              object\n",
              "RoomService     float64\n",
              "FoodCourt       float64\n",
              "ShoppingMall    float64\n",
              "Spa             float64\n",
              "VRDeck          float64\n",
              "Name             object\n",
              "Transported        bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=['PassengerId', 'Cabin' ,'Name']\n",
        "\n",
        "train[X].head()\n",
        "\n",
        "# Categorical features\n",
        "cat_feats=['HomePlanet', 'CryoSleep', 'Destination', 'VIP']\n",
        "exp_feats=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']\n"
      ],
      "metadata": {
        "id": "EyNEojvrUBXL"
      },
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training set\n",
        "train['Age_group']=np.nan\n",
        "train.loc[train['Age']<=12,'Age_group']='Age_0-12'\n",
        "train.loc[(train['Age']>12) & (train['Age']<18),'Age_group']='Age_13-17'\n",
        "train.loc[(train['Age']>=18) & (train['Age']<=25),'Age_group']='Age_18-25'\n",
        "train.loc[(train['Age']>25) & (train['Age']<=30),'Age_group']='Age_26-30'\n",
        "train.loc[(train['Age']>30) & (train['Age']<=50),'Age_group']='Age_31-50'\n",
        "train.loc[train['Age']>50,'Age_group']='Age_51+'\n",
        "\n",
        "# test set\n",
        "test['Age_group']=np.nan\n",
        "test.loc[test['Age']<=12,'Age_group']='Age_0-12'\n",
        "test.loc[(test['Age']>12) & (test['Age']<18),'Age_group']='Age_13-17'\n",
        "test.loc[(test['Age']>=18) & (test['Age']<=25),'Age_group']='Age_18-25'\n",
        "test.loc[(test['Age']>25) & (test['Age']<=30),'Age_group']='Age_26-30'\n",
        "test.loc[(test['Age']>30) & (test['Age']<=50),'Age_group']='Age_31-50'\n",
        "test.loc[test['Age']>50,'Age_group']='Age_51+'"
      ],
      "metadata": {
        "id": "gBRK3w7kUBUg"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exp_feats=['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']\n",
        "\n",
        "train['Expenditure']=train[exp_feats].sum(axis=1)\n",
        "train['No_spending']=(train['Expenditure']==0).astype(int)\n",
        "\n",
        "# test set\n",
        "test['Expenditure']=test[exp_feats].sum(axis=1)\n",
        "test['No_spending']=(test['Expenditure']==0).astype(int)"
      ],
      "metadata": {
        "id": "UQDbLMqjUBSF"
      },
      "execution_count": 337,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['Group'] = train['PassengerId'].apply(lambda x: x.split('_')[0]).astype(int)\n",
        "test['Group'] = test['PassengerId'].apply(lambda x: x.split('_')[0]).astype(int)\n",
        "\n",
        "# New feature - Group size\n",
        "train['Group_size']=train['Group'].map(lambda x: pd.concat([train['Group'], test['Group']]).value_counts()[x])\n",
        "test['Group_size']=test['Group'].map(lambda x: pd.concat([train['Group'], test['Group']]).value_counts()[x])"
      ],
      "metadata": {
        "id": "lWRN-O_hUBPi"
      },
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['Solo']=(train['Group_size']==1).astype(int)\n",
        "test['Solo']=(test['Group_size']==1).astype(int)"
      ],
      "metadata": {
        "id": "rgpL7bTGUBNQ"
      },
      "execution_count": 339,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace NaN's with outliers for now (so we can split feature)\n",
        "train['Cabin'].fillna('Z/9999/Z', inplace=True)\n",
        "test['Cabin'].fillna('Z/9999/Z', inplace=True)\n",
        "\n",
        "# New features - training set\n",
        "train['Cabin_deck'] = train['Cabin'].apply(lambda x: x.split('/')[0])\n",
        "train['Cabin_number'] = train['Cabin'].apply(lambda x: x.split('/')[1]).astype(int)\n",
        "train['Cabin_side'] = train['Cabin'].apply(lambda x: x.split('/')[2])\n",
        "\n",
        "# New features - test set\n",
        "test['Cabin_deck'] = test['Cabin'].apply(lambda x: x.split('/')[0])\n",
        "test['Cabin_number'] = test['Cabin'].apply(lambda x: x.split('/')[1]).astype(int)\n",
        "test['Cabin_side'] = test['Cabin'].apply(lambda x: x.split('/')[2])\n",
        "\n",
        "# Put Nan's back in (we will fill these later)\n",
        "train.loc[train['Cabin_deck']=='Z', 'Cabin_deck']=np.nan\n",
        "train.loc[train['Cabin_number']==9999, 'Cabin_number']=np.nan\n",
        "train.loc[train['Cabin_side']=='Z', 'Cabin_side']=np.nan\n",
        "test.loc[test['Cabin_deck']=='Z', 'Cabin_deck']=np.nan\n",
        "test.loc[test['Cabin_number']==9999, 'Cabin_number']=np.nan\n",
        "test.loc[test['Cabin_side']=='Z', 'Cabin_side']=np.nan\n",
        "\n",
        "# Drop Cabin (we don't need it anymore)\n",
        "train.drop('Cabin', axis=1, inplace=True)\n",
        "test.drop('Cabin', axis=1, inplace=True)\n"
      ],
      "metadata": {
        "id": "uJK55zsyUBK4"
      },
      "execution_count": 340,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# New features - training set\n",
        "train['Cabin_region1']=(train['Cabin_number']<300).astype(int)   # one-hot encoding\n",
        "train['Cabin_region2']=((train['Cabin_number']>=300) & (train['Cabin_number']<600)).astype(int)\n",
        "train['Cabin_region3']=((train['Cabin_number']>=600) & (train['Cabin_number']<900)).astype(int)\n",
        "train['Cabin_region4']=((train['Cabin_number']>=900) & (train['Cabin_number']<1200)).astype(int)\n",
        "train['Cabin_region5']=((train['Cabin_number']>=1200) & (train['Cabin_number']<1500)).astype(int)\n",
        "train['Cabin_region6']=((train['Cabin_number']>=1500) & (train['Cabin_number']<1800)).astype(int)\n",
        "train['Cabin_region7']=(train['Cabin_number']>=1800).astype(int)\n",
        "\n",
        "# New features - test set\n",
        "test['Cabin_region1']=(test['Cabin_number']<300).astype(int)   # one-hot encoding\n",
        "test['Cabin_region2']=((test['Cabin_number']>=300) & (test['Cabin_number']<600)).astype(int)\n",
        "test['Cabin_region3']=((test['Cabin_number']>=600) & (test['Cabin_number']<900)).astype(int)\n",
        "test['Cabin_region4']=((test['Cabin_number']>=900) & (test['Cabin_number']<1200)).astype(int)\n",
        "test['Cabin_region5']=((test['Cabin_number']>=1200) & (test['Cabin_number']<1500)).astype(int)\n",
        "test['Cabin_region6']=((test['Cabin_number']>=1500) & (test['Cabin_number']<1800)).astype(int)\n",
        "test['Cabin_region7']=(test['Cabin_number']>=1800).astype(int)"
      ],
      "metadata": {
        "id": "PxnMcY5pUBIX"
      },
      "execution_count": 341,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace NaN's with outliers for now (so we can split feature)\n",
        "train['Name'].fillna('Unknown Unknown', inplace=True)\n",
        "test['Name'].fillna('Unknown Unknown', inplace=True)\n",
        "\n",
        "# New feature - Surname\n",
        "train['Surname']=train['Name'].str.split().str[-1]\n",
        "test['Surname']=test['Name'].str.split().str[-1]\n",
        "\n",
        "# New feature - Family size\n",
        "train['Family_size']=train['Surname'].map(lambda x: pd.concat([train['Surname'],test['Surname']]).value_counts()[x])\n",
        "test['Family_size']=test['Surname'].map(lambda x: pd.concat([train['Surname'],test['Surname']]).value_counts()[x])\n",
        "\n",
        "# Put Nan's back in (we will fill these later)\n",
        "train.loc[train['Surname']=='Unknown','Surname']=np.nan\n",
        "train.loc[train['Family_size']>100,'Family_size']=np.nan\n",
        "test.loc[test['Surname']=='Unknown','Surname']=np.nan\n",
        "test.loc[test['Family_size']>100,'Family_size']=np.nan\n",
        "\n",
        "# Drop name (we don't need it anymore)\n",
        "train.drop('Name', axis=1, inplace=True)\n",
        "test.drop('Name', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "nvRczuyaUBGX"
      },
      "execution_count": 342,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Labels and features\n",
        "y=train['Transported'].copy().astype(int)\n",
        "X=train.drop('Transported', axis=1).copy()\n",
        "\n",
        "# Concatenate dataframes\n",
        "data=pd.concat([X, test], axis=0).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "6k9s34D_UBEC"
      },
      "execution_count": 343,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "GHP_gb=data.groupby(['Group','HomePlanet'])['HomePlanet'].size().unstack().fillna(0)"
      ],
      "metadata": {
        "id": "k3jte-mYUBBi"
      },
      "execution_count": 344,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "HP_bef=data['HomePlanet'].isna().sum()\n",
        "\n",
        "# Passengers with missing HomePlanet and in a group with known HomePlanet\n",
        "GHP_index=data[data['HomePlanet'].isna()][(data[data['HomePlanet'].isna()]['Group']).isin(GHP_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[GHP_index,'HomePlanet']=data.iloc[GHP_index,:]['Group'].map(lambda x: GHP_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#HomePlanet missing values before:',HP_bef)\n",
        "print('#HomePlanet missing values after:',data['HomePlanet'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AX6pWwwcUA_i",
        "outputId": "c91ea13e-d665-47c0-fd30-51811cd27f48"
      },
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#HomePlanet missing values before: 288\n",
            "#HomePlanet missing values after: 157\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CDHP_gb=data.groupby(['Cabin_deck','HomePlanet'])['HomePlanet'].size().unstack().fillna(0)\n",
        "\n",
        "# Missing values before\n",
        "HP_bef=data['HomePlanet'].isna().sum()\n",
        "\n",
        "# Decks A, B, C or T came from Europa\n",
        "data.loc[(data['HomePlanet'].isna()) & (data['Cabin_deck'].isin(['A', 'B', 'C', 'T'])), 'HomePlanet']='Europa'\n",
        "\n",
        "# Deck G came from Earth\n",
        "data.loc[(data['HomePlanet'].isna()) & (data['Cabin_deck']=='G'), 'HomePlanet']='Earth'\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#HomePlanet missing values before:',HP_bef)\n",
        "print('#HomePlanet missing values after:',data['HomePlanet'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJFMkiy9UA9M",
        "outputId": "bfa8ef84-76ce-4c18-b0c4-6e20c9760a88"
      },
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#HomePlanet missing values before: 157\n",
            "#HomePlanet missing values after: 94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "HP_bef=data['HomePlanet'].isna().sum()\n",
        "\n",
        "SHP_gb=data.groupby(['Surname','HomePlanet'])['HomePlanet'].size().unstack().fillna(0)\n",
        "# Passengers with missing HomePlanet and in a family with known HomePlanet\n",
        "SHP_index=data[data['HomePlanet'].isna()][(data[data['HomePlanet'].isna()]['Surname']).isin(SHP_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[SHP_index,'HomePlanet']=data.iloc[SHP_index,:]['Surname'].map(lambda x: SHP_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#HomePlanet missing values before:',HP_bef)\n",
        "print('#HomePlanet missing values after:',data['HomePlanet'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XV6j2GVoUA5q",
        "outputId": "ca74a0df-2894-4ed6-f4fb-9825e6ebf543"
      },
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#HomePlanet missing values before: 94\n",
            "#HomePlanet missing values after: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Joint distribution of HomePlanet and Destination\n",
        "HPD_gb=data.groupby(['HomePlanet','Destination'])['Destination'].size().unstack().fillna(0)\n",
        "\n",
        "# Missing values before\n",
        "HP_bef=data['HomePlanet'].isna().sum()\n",
        "\n",
        "# Fill remaining HomePlanet missing values with Earth (if not on deck D) or Mars (if on Deck D)\n",
        "data.loc[(data['HomePlanet'].isna()) & ~(data['Cabin_deck']=='D'), 'HomePlanet']='Earth'\n",
        "data.loc[(data['HomePlanet'].isna()) & (data['Cabin_deck']=='D'), 'HomePlanet']='Mars'\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#HomePlanet missing values before:',HP_bef)\n",
        "print('#HomePlanet missing values after:',data['HomePlanet'].isna().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMhEKxk_UAz2",
        "outputId": "ce1cfda6-fa3f-47e3-f5e2-e4ecf83b567b"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#HomePlanet missing values before: 10\n",
            "#HomePlanet missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "D_bef=data['Destination'].isna().sum()\n",
        "\n",
        "# Fill missing Destination values with mode\n",
        "data.loc[(data['Destination'].isna()), 'Destination']='TRAPPIST-1e'\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Destination missing values before:',D_bef)\n",
        "print('#Destination missing values after:',data['Destination'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Ykrvgx0ddYG",
        "outputId": "08ab7f15-c787-4f8d-bed1-3d24bac4d8c1"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Destination missing values before: 274\n",
            "#Destination missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Joint distribution of Group and Surname\n",
        "GSN_gb=data[data['Group_size']>1].groupby(['Group','Surname'])['Surname'].size().unstack().fillna(0)\n"
      ],
      "metadata": {
        "id": "YAkZOBkkddUu"
      },
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "SN_bef=data['Surname'].isna().sum()\n",
        "\n",
        "# Passengers with missing Surname and in a group with known majority Surname\n",
        "GSN_index=data[data['Surname'].isna()][(data[data['Surname'].isna()]['Group']).isin(GSN_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[GSN_index,'Surname']=data.iloc[GSN_index,:]['Group'].map(lambda x: GSN_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Surname missing values before:',SN_bef)\n",
        "print('#Surname missing values after:',data['Surname'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ODVqav1djTY",
        "outputId": "87aa47ee-2af9-4d6c-80d9-c2f4ddffc8cf"
      },
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Surname missing values before: 294\n",
            "#Surname missing values after: 155\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Replace NaN's with outliers (so we can use map)\n",
        "data['Surname'].fillna('Unknown', inplace=True)\n",
        "\n",
        "# Update family size feature\n",
        "data['Family_size']=data['Surname'].map(lambda x: data['Surname'].value_counts()[x])\n",
        "\n",
        "# Put NaN's back in place of outliers\n",
        "data.loc[data['Surname']=='Unknown','Surname']=np.nan\n",
        "\n",
        "# Say unknown surname means no family\n",
        "data.loc[data['Family_size']>100,'Family_size']=0"
      ],
      "metadata": {
        "id": "DxnaJ8mydWWt"
      },
      "execution_count": 352,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "GCD_gb=data[data['Group_size']>1].groupby(['Group','Cabin_deck'])['Cabin_deck'].size().unstack().fillna(0)\n",
        "GCN_gb=data[data['Group_size']>1].groupby(['Group','Cabin_number'])['Cabin_number'].size().unstack().fillna(0)\n",
        "GCS_gb=data[data['Group_size']>1].groupby(['Group','Cabin_side'])['Cabin_side'].size().unstack().fillna(0)\n"
      ],
      "metadata": {
        "id": "CB8sJWZiUAx3"
      },
      "execution_count": 353,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CS_bef=data['Cabin_side'].isna().sum()\n",
        "\n",
        "# Passengers with missing Cabin side and in a group with known Cabin side\n",
        "GCS_index=data[data['Cabin_side'].isna()][(data[data['Cabin_side'].isna()]['Group']).isin(GCS_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[GCS_index,'Cabin_side']=data.iloc[GCS_index,:]['Group'].map(lambda x: GCS_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Cabin_side missing values before:',CS_bef)\n",
        "print('#Cabin_side missing values after:',data['Cabin_side'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hx0XgIdYUAv4",
        "outputId": "cc86e070-e664-43da-87f7-73189c649884"
      },
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Cabin_side missing values before: 299\n",
            "#Cabin_side missing values after: 162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Joint distribution of Surname and Cabin side\n",
        "SCS_gb=data[data['Group_size']>1].groupby(['Surname','Cabin_side'])['Cabin_side'].size().unstack().fillna(0)\n",
        "\n",
        "# Ratio of sides\n",
        "SCS_gb['Ratio']=SCS_gb['P']/(SCS_gb['P']+SCS_gb['S'])\n"
      ],
      "metadata": {
        "id": "oyZxKXyIUAt-"
      },
      "execution_count": 355,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CS_bef=data['Cabin_side'].isna().sum()\n",
        "\n",
        "# Drop ratio column\n",
        "SCS_gb.drop('Ratio', axis=1, inplace=True)\n",
        "\n",
        "# Passengers with missing Cabin side and in a family with known Cabin side\n",
        "SCS_index=data[data['Cabin_side'].isna()][(data[data['Cabin_side'].isna()]['Surname']).isin(SCS_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[SCS_index,'Cabin_side']=data.iloc[SCS_index,:]['Surname'].map(lambda x: SCS_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Drop surname (we don't need it anymore)\n",
        "data.drop('Surname', axis=1, inplace=True)\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Cabin_side missing values before:',CS_bef)\n",
        "print('#Cabin_side missing values after:',data['Cabin_side'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AA0yPoIlUAsP",
        "outputId": "09416441-3e4d-4a13-823a-0e1b3f169d80"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Cabin_side missing values before: 162\n",
            "#Cabin_side missing values after: 66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " data['Cabin_side'].value_counts()\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dm3feHbUAqn",
        "outputId": "afef12b1-9502-4dc9-e1b2-a6401a196d63"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    6504\n",
              "P    6400\n",
              "Name: Cabin_side, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CS_bef=data['Cabin_side'].isna().sum()\n",
        "\n",
        "# Fill remaining missing values with outlier\n",
        "data.loc[data['Cabin_side'].isna(),'Cabin_side']='Z'\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Cabin_side missing values before:',CS_bef)\n",
        "print('#Cabin_side missing values after:',data['Cabin_side'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seLXgUiTUAos",
        "outputId": "ff9748d5-7d52-4371-9a09-9df8f1b00049"
      },
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Cabin_side missing values before: 66\n",
            "#Cabin_side missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CD_bef=data['Cabin_deck'].isna().sum()\n",
        "\n",
        "# Passengers with missing Cabin deck and in a group with known majority Cabin deck\n",
        "GCD_index=data[data['Cabin_deck'].isna()][(data[data['Cabin_deck'].isna()]['Group']).isin(GCD_gb.index)].index\n",
        "\n",
        "# Fill corresponding missing values\n",
        "data.loc[GCD_index,'Cabin_deck']=data.iloc[GCD_index,:]['Group'].map(lambda x: GCD_gb.idxmax(axis=1)[x])\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Cabin_deck missing values before:',CD_bef)\n",
        "print('#Cabin_deck missing values after:',data['Cabin_deck'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dliZXm3LUAnA",
        "outputId": "4484c4fc-d629-49a5-8f0e-c2c566a2288e"
      },
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Cabin_deck missing values before: 299\n",
            "#Cabin_deck missing values after: 162\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['HomePlanet','Destination','Solo','Cabin_deck'])['Cabin_deck'].size().unstack().fillna(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "tcJTAQvyUAk8",
        "outputId": "02c3358f-023f-4f82-855c-dbff69b2512f"
      },
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin_deck                         A      B      C      D      E       F  \\\n",
              "HomePlanet Destination   Solo                                              \n",
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              "Europa     55 Cancri e   0      96.0  377.0  313.0   59.0   35.0     0.0   \n",
              "                         1      67.0  141.0  159.0   46.0   34.0     0.0   \n",
              "           PSO J318.5-22 0       2.0    5.0   11.0    0.0    0.0     0.0   \n",
              "                         1       0.0    0.0   10.0    0.0    0.0     0.0   \n",
              "           TRAPPIST-1e   0     152.0  459.0  428.0  120.0   53.0     0.0   \n",
              "                         1      44.0  179.0  201.0   84.0   82.0     0.0   \n",
              "Mars       55 Cancri e   0       0.0    0.0    0.0   32.0   15.0   104.0   \n",
              "                         1       0.0    0.0    0.0   40.0   16.0    92.0   \n",
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              "                         1       0.0    0.0    0.0    9.0    7.0    21.0   \n",
              "           TRAPPIST-1e   0       0.0    0.0    0.0  168.0  219.0   798.0   \n",
              "                         1       0.0    0.0    0.0  164.0  263.0   743.0   \n",
              "\n",
              "Cabin_deck                          G    T  \n",
              "HomePlanet Destination   Solo               \n",
              "Earth      55 Cancri e   0      272.0  0.0  \n",
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              "           PSO J318.5-22 0      230.0  0.0  \n",
              "                         1      466.0  0.0  \n",
              "           TRAPPIST-1e   0     1075.0  0.0  \n",
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              "Europa     55 Cancri e   0        0.0  2.0  \n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Cabin_deck</th>\n",
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              "    <tr>\n",
              "      <th>HomePlanet</th>\n",
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              "      <th rowspan=\"6\" valign=\"top\">Europa</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">55 Cancri e</th>\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-54d75a7c-355a-4365-aa77-69c8a8a377f0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-54d75a7c-355a-4365-aa77-69c8a8a377f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CD_bef=data['Cabin_deck'].isna().sum()\n",
        "\n",
        "# Fill missing values using the mode\n",
        "na_rows_CD=data.loc[data['Cabin_deck'].isna(),'Cabin_deck'].index\n",
        "data.loc[data['Cabin_deck'].isna(),'Cabin_deck']=data.groupby(['HomePlanet','Destination','Solo'])['Cabin_deck'].transform(lambda x: x.fillna(pd.Series.mode(x)[0]))[na_rows_CD]\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Cabin_deck missing values before:',CD_bef)\n",
        "print('#Cabin_deck missing values after:',data['Cabin_deck'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z65sFODKUAis",
        "outputId": "b3e6ccef-0956-4a71-cc7a-63968ae70f13"
      },
      "execution_count": 361,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Cabin_deck missing values before: 162\n",
            "#Cabin_deck missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One-hot encode cabin regions\n",
        "data['Cabin_region1']=(data['Cabin_number']<300).astype(int)\n",
        "data['Cabin_region2']=((data['Cabin_number']>=300) & (data['Cabin_number']<600)).astype(int)\n",
        "data['Cabin_region3']=((data['Cabin_number']>=600) & (data['Cabin_number']<900)).astype(int)\n",
        "data['Cabin_region4']=((data['Cabin_number']>=900) & (data['Cabin_number']<1200)).astype(int)\n",
        "data['Cabin_region5']=((data['Cabin_number']>=1200) & (data['Cabin_number']<1500)).astype(int)\n",
        "data['Cabin_region6']=((data['Cabin_number']>=1500) & (data['Cabin_number']<1800)).astype(int)\n",
        "data['Cabin_region7']=(data['Cabin_number']>=1800).astype(int)"
      ],
      "metadata": {
        "id": "YXgMX3UZUAhN"
      },
      "execution_count": 362,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['VIP'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33MutPcPUAfl",
        "outputId": "6023796f-1709-41bc-e09f-ecdca45e74aa"
      },
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    12401\n",
              "True       273\n",
              "Name: VIP, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 363
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "V_bef=data['VIP'].isna().sum()\n",
        "\n",
        "# Fill missing values with mode\n",
        "data.loc[data['VIP'].isna(),'VIP']=False\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#VIP missing values before:',V_bef)\n",
        "print('#VIP missing values after:',data['VIP'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwSaJH9YUAdR",
        "outputId": "ee493832-8716-44ff-ffbe-e94d0623ecd6"
      },
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#VIP missing values before: 296\n",
            "#VIP missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby(['HomePlanet','No_spending','Solo','Cabin_deck'])['Age'].median().unstack().fillna(0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "JCNy0bjYYBdV",
        "outputId": "588d8e69-dd56-4249-8ad1-55b69b7d9de2"
      },
      "execution_count": 365,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin_deck                      A     B     C     D     E     F     G     T\n",
              "HomePlanet No_spending Solo                                                \n",
              "Earth      0           0      0.0   0.0   0.0   0.0  24.0  25.0  28.0   0.0\n",
              "                       1      0.0   0.0   0.0   0.0  25.0  24.0  26.0   0.0\n",
              "           1           0      0.0   0.0   0.0   0.0   7.0  16.0   8.0   0.0\n",
              "                       1      0.0   0.0   0.0   0.0  21.0  21.5  23.0   0.0\n",
              "Europa     0           0     35.0  34.0  33.0  33.0  34.0   0.0   0.0  44.0\n",
              "                       1     35.0  36.0  35.5  34.0  35.0   0.0   0.0  35.0\n",
              "           1           0     30.0  27.0  28.5  27.0  28.0   0.0   0.0   0.0\n",
              "                       1     37.5  34.0  35.0  31.0  33.0   0.0   0.0   0.0\n",
              "Mars       0           0      0.0   0.0   0.0  30.0  29.0  29.0   0.0   0.0\n",
              "                       1      0.0   0.0   0.0  31.0  28.5  30.0   0.0   0.0\n",
              "           1           0      0.0   0.0   0.0  29.5  26.0  18.0   0.0   0.0\n",
              "                       1      0.0   0.0   0.0  33.0  30.0  29.5   0.0   0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-caf00e49-4493-41af-9e92-06e9265be485\" 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></th>\n",
              "      <th>Cabin_deck</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "      <th>HomePlanet</th>\n",
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              "      <th>Solo</th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Earth</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>25.0</td>\n",
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              "      <td>26.0</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Europa</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>35.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>35.5</td>\n",
              "      <td>34.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>30.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>27.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37.5</td>\n",
              "      <td>34.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Mars</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>28.5</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>26.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>33.0</td>\n",
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              "      <td>29.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? 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",
              "\n",
              "\n",
              "<div id=\"df-fc3eecdc-4066-40c9-b410-4bd715ec8317\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc3eecdc-4066-40c9-b410-4bd715ec8317')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fc3eecdc-4066-40c9-b410-4bd715ec8317 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 365
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "A_bef=data[exp_feats].isna().sum().sum()\n",
        "\n",
        "# Fill missing values using the median\n",
        "na_rows_A=data.loc[data['Age'].isna(),'Age'].index\n",
        "data.loc[data['Age'].isna(),'Age']=data.groupby(['HomePlanet','No_spending','Solo','Cabin_deck'])['Age'].transform(lambda x: x.fillna(x.median()))[na_rows_A]\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Age missing values before:',A_bef)\n",
        "print('#Age missing values after:',data['Age'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HW4MBy35fYPX",
        "outputId": "8e30b27e-7751-49bb-a33a-e99a978ea493"
      },
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Age missing values before: 1410\n",
            "#Age missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gH3c_UydfYMC"
      },
      "execution_count": 366,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D6cXbtyHfYIw"
      },
      "execution_count": 366,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bl0kMDFwfYGv"
      },
      "execution_count": 366,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Update age group feature\n",
        "data.loc[data['Age']<=12,'Age_group']='Age_0-12'\n",
        "data.loc[(data['Age']>12) & (data['Age']<18),'Age_group']='Age_13-17'\n",
        "data.loc[(data['Age']>=18) & (data['Age']<=25),'Age_group']='Age_18-25'\n",
        "data.loc[(data['Age']>25) & (data['Age']<=30),'Age_group']='Age_26-30'\n",
        "data.loc[(data['Age']>30) & (data['Age']<=50),'Age_group']='Age_31-50'\n",
        "data.loc[data['Age']>50,'Age_group']='Age_51+'"
      ],
      "metadata": {
        "id": "dBP1UdcGYBbG"
      },
      "execution_count": 367,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing values before\n",
        "CSL_bef=data['CryoSleep'].isna().sum()\n",
        "\n",
        "# Fill missing values using the mode\n",
        "na_rows_CSL=data.loc[data['CryoSleep'].isna(),'CryoSleep'].index\n",
        "data.loc[data['CryoSleep'].isna(),'CryoSleep']=data.groupby(['No_spending'])['CryoSleep'].transform(lambda x: x.fillna(pd.Series.mode(x)[0]))[na_rows_CSL]\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#CryoSleep missing values before:',CSL_bef)\n",
        "print('#CryoSleep missing values after:',data['CryoSleep'].isna().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T66WlEfkYBYo",
        "outputId": "a6b5f979-eb83-4830-e44b-39dc8a894b35"
      },
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#CryoSleep missing values before: 310\n",
            "#CryoSleep missing values after: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Joint distribution\n",
        "data.groupby(['HomePlanet','Solo','Age_group'])['Expenditure'].mean().unstack().fillna(0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "9GlWvE-Kf6y5",
        "outputId": "85384f10-0ddc-4430-9e06-e77339b301c6"
      },
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age_group        Age_0-12    Age_13-17    Age_18-25    Age_26-30    Age_31-50  \\\n",
              "HomePlanet Solo                                                                 \n",
              "Earth      0          0.0   724.902222   789.700555   841.093596   736.655773   \n",
              "           1          0.0   693.014898   779.395942   795.420690   794.818627   \n",
              "Europa     0          0.0  1153.160256  2652.013298  3534.668246  3975.774005   \n",
              "           1          0.0     0.000000  2489.888889  3806.000000  3949.939929   \n",
              "Mars       0          0.0  1176.839286  1161.808333  1247.098361  1143.671916   \n",
              "           1          0.0  1687.261538  1075.341146  1107.122677  1110.392045   \n",
              "\n",
              "Age_group            Age_51+  \n",
              "HomePlanet Solo               \n",
              "Earth      0      733.649573  \n",
              "           1      826.366972  \n",
              "Europa     0     3483.639004  \n",
              "           1     3952.085526  \n",
              "Mars       0     1345.419643  \n",
              "           1     1100.298387  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbcdd0e0-a73c-466b-ba7f-27b66377f8a2\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age_group</th>\n",
              "      <th>Age_0-12</th>\n",
              "      <th>Age_13-17</th>\n",
              "      <th>Age_18-25</th>\n",
              "      <th>Age_26-30</th>\n",
              "      <th>Age_31-50</th>\n",
              "      <th>Age_51+</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HomePlanet</th>\n",
              "      <th>Solo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Earth</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>724.902222</td>\n",
              "      <td>789.700555</td>\n",
              "      <td>841.093596</td>\n",
              "      <td>736.655773</td>\n",
              "      <td>733.649573</td>\n",
              "    </tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>693.014898</td>\n",
              "      <td>779.395942</td>\n",
              "      <td>795.420690</td>\n",
              "      <td>794.818627</td>\n",
              "      <td>826.366972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Europa</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1153.160256</td>\n",
              "      <td>2652.013298</td>\n",
              "      <td>3534.668246</td>\n",
              "      <td>3975.774005</td>\n",
              "      <td>3483.639004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2489.888889</td>\n",
              "      <td>3806.000000</td>\n",
              "      <td>3949.939929</td>\n",
              "      <td>3952.085526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Mars</th>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1176.839286</td>\n",
              "      <td>1161.808333</td>\n",
              "      <td>1247.098361</td>\n",
              "      <td>1143.671916</td>\n",
              "      <td>1345.419643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1687.261538</td>\n",
              "      <td>1075.341146</td>\n",
              "      <td>1107.122677</td>\n",
              "      <td>1110.392045</td>\n",
              "      <td>1100.298387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbcdd0e0-a73c-466b-ba7f-27b66377f8a2')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cbcdd0e0-a73c-466b-ba7f-27b66377f8a2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cbcdd0e0-a73c-466b-ba7f-27b66377f8a2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? 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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-76b6fd57-22e8-4c77-9009-a30335a780c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76b6fd57-22e8-4c77-9009-a30335a780c2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-76b6fd57-22e8-4c77-9009-a30335a780c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CryoSleep has no expenditure\n",
        "for col in exp_feats:\n",
        "    data.loc[(data[col].isna()) & (data['CryoSleep']==True), col]=0\n",
        "E_bef=data[exp_feats].isna().sum().sum()\n",
        "\n",
        "# Print number of missing values left\n",
        "print('#Expenditure missing values before:',E_bef)\n",
        "print('#Expenditure missing values after:',data[exp_feats].isna().sum().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItKgSyIIYBXQ",
        "outputId": "0fca9dfc-b4f7-403a-c7a8-80c4132383ab"
      },
      "execution_count": 370,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#Expenditure missing values before: 866\n",
            "#Expenditure missing values after: 866\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ow1c5q0OYBUk",
        "outputId": "7de61e98-6c8c-42f4-f044-f180dc54f17c"
      },
      "execution_count": 371,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId        0\n",
              "HomePlanet         0\n",
              "CryoSleep          0\n",
              "Destination        0\n",
              "Age                0\n",
              "VIP                0\n",
              "RoomService      170\n",
              "FoodCourt        179\n",
              "ShoppingMall     172\n",
              "Spa              173\n",
              "VRDeck           172\n",
              "Age_group          0\n",
              "Expenditure        0\n",
              "No_spending        0\n",
              "Group              0\n",
              "Group_size         0\n",
              "Solo               0\n",
              "Cabin_deck         0\n",
              "Cabin_number     299\n",
              "Cabin_side         0\n",
              "Cabin_region1      0\n",
              "Cabin_region2      0\n",
              "Cabin_region3      0\n",
              "Cabin_region4      0\n",
              "Cabin_region5      0\n",
              "Cabin_region6      0\n",
              "Cabin_region7      0\n",
              "Family_size        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 371
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: in the all null values replace it in mean value of the columns\n",
        "\n",
        "data.fillna(data.mean(), inplace=True)\n"
      ],
      "metadata": {
        "id": "G_jEK5qlYBSx"
      },
      "execution_count": 372,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8GF6PYmYBRW",
        "outputId": "3b3db5a8-869d-4e80-8843-82600c4c6e1a"
      },
      "execution_count": 373,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "HomePlanet       0\n",
              "CryoSleep        0\n",
              "Destination      0\n",
              "Age              0\n",
              "VIP              0\n",
              "RoomService      0\n",
              "FoodCourt        0\n",
              "ShoppingMall     0\n",
              "Spa              0\n",
              "VRDeck           0\n",
              "Age_group        0\n",
              "Expenditure      0\n",
              "No_spending      0\n",
              "Group            0\n",
              "Group_size       0\n",
              "Solo             0\n",
              "Cabin_deck       0\n",
              "Cabin_number     0\n",
              "Cabin_side       0\n",
              "Cabin_region1    0\n",
              "Cabin_region2    0\n",
              "Cabin_region3    0\n",
              "Cabin_region4    0\n",
              "Cabin_region5    0\n",
              "Cabin_region6    0\n",
              "Cabin_region7    0\n",
              "Family_size      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=data[data['PassengerId'].isin(train['PassengerId'].values)].copy()\n",
        "X_test=data[data['PassengerId'].isin(test['PassengerId'].values)].copy()"
      ],
      "metadata": {
        "id": "A6MUzGjbYBP8"
      },
      "execution_count": 374,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop qualitative/redundant/collinear/high cardinality features\n",
        "X.drop(['PassengerId', 'Group', 'Group_size', 'Age_group', 'Cabin_number'], axis=1, inplace=True)\n",
        "X_test.drop(['PassengerId', 'Group', 'Group_size', 'Age_group', 'Cabin_number'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "WpyPTR-0YBOe"
      },
      "execution_count": 375,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply log transform\n",
        "for col in ['RoomService','FoodCourt','ShoppingMall','Spa','VRDeck','Expenditure']:\n",
        "    X[col]=np.log(1+X[col])\n",
        "    X_test[col]=np.log(1+X_test[col])"
      ],
      "metadata": {
        "id": "wrjBW-g0YBMJ"
      },
      "execution_count": 376,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install catboost\n",
        "\n",
        "from catboost import CatBoostClassifier\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4cjyXCoYBAj",
        "outputId": "69ec8c72-3ec7-43db-a0a8-bed9462c4bc7"
      },
      "execution_count": 384,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from catboost) (1.11.4)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2023.3.post1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.1)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.2.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classifiers\n",
        "classifiers = {\n",
        "    \"CatBoost\" : CatBoostClassifier(random_state=0, verbose=False)\n",
        "}\n"
      ],
      "metadata": {
        "id": "2YeakrphYBG3"
      },
      "execution_count": 385,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-validation split\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X,y,stratify=y,train_size=0.2,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "0rFcuWVlYBJB"
      },
      "execution_count": 398,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {'iterations': [100, 200, 300],\n",
        "        'learning_rate': [0.01, 0.1, 0.5],\n",
        "        'depth': [4, 6, 8]}\n"
      ],
      "metadata": {
        "id": "DlpY_dSOYBD_"
      },
      "execution_count": 399,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from catboost import CatBoostClassifier, Pool, cv\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n"
      ],
      "metadata": {
        "id": "rnvbtVx5YA-l"
      },
      "execution_count": 400,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Creating a CatBoostClassifier\n",
        "catboost_classifier = CatBoostClassifier(loss_function='Logloss', random_state=42)\n",
        "\n",
        "# Defining the parameter grid for hyperparameter tuning\n",
        "grid = {'iterations': [100, 200, 300],\n",
        "        'learning_rate': [0.01, 0.1, 0.5],\n",
        "        'depth': [4, 6, 8]}\n",
        "\n"
      ],
      "metadata": {
        "id": "kIOjBk8-YA8D"
      },
      "execution_count": 401,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing GridSearchCV\n",
        "catboost_classifier_grid = GridSearchCV(estimator=catboost_classifier, param_grid=grid, cv=3, scoring='accuracy')\n",
        "catboost_classifier_grid.fit(X_train, y_train)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hz0D-h9IYA5w",
        "outputId": "a620cfd3-47fb-409c-cff3-8e7c968b3a54"
      },
      "execution_count": 402,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=<catboost.core.CatBoostClassifier object at 0x7ead5e1097e0>,\n",
              "             param_grid={'depth': [4, 6, 8], 'iterations': [100, 200, 300],\n",
              "                         'learning_rate': [0.01, 0.1, 0.5]},\n",
              "             scoring='accuracy')"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=&lt;catboost.core.CatBoostClassifier object at 0x7ead5e1097e0&gt;,\n",
              "             param_grid={&#x27;depth&#x27;: [4, 6, 8], &#x27;iterations&#x27;: [100, 200, 300],\n",
              "                         &#x27;learning_rate&#x27;: [0.01, 0.1, 0.5]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=&lt;catboost.core.CatBoostClassifier object at 0x7ead5e1097e0&gt;,\n",
              "             param_grid={&#x27;depth&#x27;: [4, 6, 8], &#x27;iterations&#x27;: [100, 200, 300],\n",
              "                         &#x27;learning_rate&#x27;: [0.01, 0.1, 0.5]},\n",
              "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x7ead5e1097e0&gt;</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x7ead5e1097e0&gt;</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 402
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the best parameters and model\n",
        "best_params = catboost_classifier_grid.best_params_\n",
        "best_model = catboost_classifier_grid.best_estimator_\n",
        "\n"
      ],
      "metadata": {
        "id": "00fR-QwMYA4D"
      },
      "execution_count": 403,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting on the test set using the best model\n",
        "y_pred = best_model.predict(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "e-4URJ34YA0-"
      },
      "execution_count": 404,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Best Parameters: {best_params}\")\n",
        "print(f\"Accuracy with best model: {accuracy:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxBRM0BLYAyf",
        "outputId": "6180d6c7-e974-4713-cac7-27e8701b2ed9"
      },
      "execution_count": 405,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'depth': 4, 'iterations': 300, 'learning_rate': 0.5}\n",
            "Accuracy with best model: 0.8750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting the best parameters and model\n",
        "best_params = catboost_classifier_grid.best_params_\n",
        "best_model = catboost_classifier_grid.best_estimator_\n"
      ],
      "metadata": {
        "id": "Lfn6pAqzYAwk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}