EDA_TRAIN.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"UJRgk0c7MX3i"},"outputs":[],"source":["#importing libraries\n","import numpy as np\n","import pandas as pd \n","\n","import matplotlib.pyplot as plt\n","plt.style.use(\"seaborn-whitegrid\")\n","\n","import seaborn as sns\n","\n","from collections import Counter\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nVQBAsP2A4qp"},"outputs":[],"source":["#input data\n","train_df = pd.read_csv(\"/content/drive/MyDrive/Artifical intelligence/train.csv\")\n","test_df = pd.read_csv(\"/content/drive/MyDrive/Artifical intelligence/train.csv\")\n","test_PassengerId = test_df[\"PassengerId\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26,"status":"ok","timestamp":1677436847156,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"Nmd1_uJ3BauO","outputId":"d2ac5818-b6f9-44d2-b91b-50ca1c9072d0"},"outputs":[{"data":{"text/plain":["Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n","       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n","      dtype='object')"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["train_df.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":415,"status":"ok","timestamp":1677436856515,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"_Oa9Vl4YBv5Y","outputId":"f6d92ddf-efe7-4099-95da-531048d6ae02"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-22c22286-e22f-41d7-b431-c5479fa8e390\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22c22286-e22f-41d7-b431-c5479fa8e390')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-22c22286-e22f-41d7-b431-c5479fa8e390 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1677436866564,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"1KjSDtI4ByNj","outputId":"d47a900a-1ccb-478d-f0bd-3a73890358c5"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-5fa35956-02d2-4a86-8638-355349456750\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>714.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","      <td>891.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>446.000000</td>\n","      <td>0.383838</td>\n","      <td>2.308642</td>\n","      <td>29.699118</td>\n","      <td>0.523008</td>\n","      <td>0.381594</td>\n","      <td>32.204208</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>257.353842</td>\n","      <td>0.486592</td>\n","      <td>0.836071</td>\n","      <td>14.526497</td>\n","      <td>1.102743</td>\n","      <td>0.806057</td>\n","      <td>49.693429</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.420000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>223.500000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>20.125000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>7.910400</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>446.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>28.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>14.454200</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>668.500000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>38.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>31.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>891.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>80.000000</td>\n","      <td>8.000000</td>\n","      <td>6.000000</td>\n","      <td>512.329200</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fa35956-02d2-4a86-8638-355349456750')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["       PassengerId    Survived      Pclass         Age       SibSp  \\\n","count   891.000000  891.000000  891.000000  714.000000  891.000000   \n","mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n","std     257.353842    0.486592    0.836071   14.526497    1.102743   \n","min       1.000000    0.000000    1.000000    0.420000    0.000000   \n","25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n","50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n","75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n","max     891.000000    1.000000    3.000000   80.000000    8.000000   \n","\n","            Parch        Fare  \n","count  891.000000  891.000000  \n","mean     0.381594   32.204208  \n","std      0.806057   49.693429  \n","min      0.000000    0.000000  \n","25%      0.000000    7.910400  \n","50%      0.000000   14.454200  \n","75%      0.000000   31.000000  \n","max      6.000000  512.329200  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["train_df.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":360,"status":"ok","timestamp":1677436999623,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"aC-Nzb0LB00N","outputId":"c94dbd57-c197-46b3-f931-4fb5b742a82d"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 891 entries, 0 to 890\n","Data columns (total 12 columns):\n"," #   Column       Non-Null Count  Dtype  \n","---  ------       --------------  -----  \n"," 0   PassengerId  891 non-null    int64  \n"," 1   Survived     891 non-null    int64  \n"," 2   Pclass       891 non-null    int64  \n"," 3   Name         891 non-null    object \n"," 4   Sex          891 non-null    object \n"," 5   Age          714 non-null    float64\n"," 6   SibSp        891 non-null    int64  \n"," 7   Parch        891 non-null    int64  \n"," 8   Ticket       891 non-null    object \n"," 9   Fare         891 non-null    float64\n"," 10  Cabin        204 non-null    object \n"," 11  Embarked     889 non-null    object \n","dtypes: float64(2), int64(5), object(5)\n","memory usage: 83.7+ KB\n"]}],"source":["train_df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k8-z7AyaCdTI"},"outputs":[],"source":["#Univariate Variable Analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2VGZa1LnC6IX"},"outputs":[],"source":[" #Categorical Variable"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QWHPwGZ1B63-"},"outputs":[],"source":[" def bar_plot(variable):\n","    \"\"\"\n","    input: variable ex: \"Sex\"\n","    output: bar plot & value count\n","    \"\"\"\n","    # get feature\n","    var = train_df[variable]\n","    # count number of categorical variable(value/sample)\n","    varValue = var.value_counts()\n","    \n","    # visualize\n","    plt.figure(figsize = (9,3))\n","    plt.bar(varValue.index, varValue)\n","    plt.xticks(varValue.index, varValue.index.values)\n","    plt.ylabel(\"Frequency\")\n","    plt.title(variable)\n","    plt.show()\n","    print(\"{}: \\n {}\".format(variable, varValue))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1834,"status":"ok","timestamp":1677436955003,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"AZA86RtmCGtr","outputId":"1c05dc1c-d0c9-447b-9ffb-0a7d5630f363"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Survived: \n"," 0    549\n","1    342\n","Name: Survived, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Sex: \n"," male      577\n","female    314\n","Name: Sex, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Pclass: \n"," 3    491\n","1    216\n","2    184\n","Name: Pclass, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Embarked: \n"," S    644\n","C    168\n","Q     77\n","Name: Embarked, dtype: int64\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAiYAAADNCAYAAACM0rsuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcMklEQVR4nO3de3BU5eHG8WchWWNgMSSwweCNKpYoIRgiU6IRwjUQLVGTmKaKlmilhEunQW5ixcEqF4sYZMRBQcRaU1arYbwkRWUGMWxLtqbEpiJglUJIdjEQTbIIYX9/dLq/UhCXuCd7svl+/tp92XP2eTNnhmfOe85Zi8/n8wkAAMAEuoU6AAAAwH9QTAAAgGlQTAAAgGlQTAAAgGlQTAAAgGlQTAAAgGlEhDoAgPBTU1OjFStWqL6+Xj6fTzExMXrggQfU2Nio9957T48//rjuuusu5eTkaPLkyWds/8UXX+ixxx7TZ599Jkm68MILNWPGDI0dO7ajpwKgg1FMAASVz+fTtGnT9Oijj2rUqFGSpIqKChUVFWnbtm0aN27cd+5jzpw5mjx5stauXStJqq6u1t133623335bF198sZHxAYQYSzkAgqqxsVFut1vJycn+sfHjx+uNN97Q22+/rXvuucc/vmfPHuXk5GjkyJFatGiR2tra/OP/vX1ycrLKy8vVr18/OZ1O3XLLLVq6dKkmTJig0aNH66OPPuqo6QEwGMUEQFD17t1bSUlJmjJlijZv3qwDBw5Ikvr163fGZ51OpzZt2qR33nlHf/nLX/T+++9Lkm666SbNmjVLL774ovbt2ydJio+Pl8VikSTt27dPQ4YMUXl5uX7xi19o8eLFHTM5AIajmAAIKovFog0bNmjcuHF68cUXNXbsWGVlZamiouKMz06YMEEXXnihLrzwQo0cOdJ/5mPFihX66U9/qi1btujmm2/W6NGj9fvf/96/XXR0tCZOnCjp32djamtr1dra2iHzA2AsigmAoLPZbJo1a5a2bNmiHTt2aPLkyfrVr34lr9d72udiY2NP26apqUmSdMEFF6iwsFCbN2+W0+nUtGnTtGzZMm3fvl2S1KtXL//Zk169ekmSf1sAnRvFBEBQHT58WLt27fK/79Onj37+85/r6quvPuOsxrFjx057fdFFF+nLL7/Ujh07/OO9evVSXl6e0tPTtWfPHknS0aNHz9hHTEyMAbMB0NEoJgCCqq6uTkVFRaqpqfGP/e1vf9OhQ4fOKCYVFRU6fvy4WlpatH37dqWmpsrr9WrWrFn+syOS9Pnnn6u6ulqpqamSJK/Xq61bt0qSysvLNXjwYF1wwQUdMDsARuN2YQBBdd1112nJkiVavHixvvrqK506dUp9+vTRk08+qbq6utM+m5aWpilTpqi+vl6jRo1Senq6unXrpmeeeUYlJSV69NFH5fP51LNnTy1YsEDJyclyOp3q37+/qqqqtGLFCp04cUKrVq0KzWQBBJ3F5/P5Qh0CAALldDq1aNEi/elPfwp1FAAGYCkHAACYBsUEAACYBks5AADANDhjAgAATKPT3JVTVVUV6ggAACCIhg0bdsZYpykm0tknYEa1tbVKTEwMdYygYT7mxnzMLdzmA/PqbMfat51wYCkHAACYBsUEAACYBsUEAACYBsUEAACYBsUEAACYhqF35ZSVlem5555TRESEZs2apR/+8IeaO3eu2tra1LdvX61YsUJWq1VlZWXauHGjunXrpry8POXm5hoZCwAAmJRhxaSxsVFr1qzRq6++qpaWFq1evVrl5eUqKCjQxIkTtXLlSjkcDmVnZ2vNmjVyOByKjIxUTk6Oxo0bp5iYGKOineaK+W8atOf9Qd3bP5dmBXV/AACYkWFLOZWVlRoxYoR69uwpu92uJUuWyOl0asyYMZKkjIwMVVZWqrq6WklJSbLZbIqKilJKSopcLpdRsQAAgIkZdsbkX//6l7xer6ZNm6ampibNnDlTra2tslqtkqS4uDi53W55PB7Fxsb6t4uNjZXb7T7rPmtra42Ka3qhnLvX6w2rvz3zMTfmA7RPuBxrhl5jcvToUT399NM6dOiQpkyZov/+vcBv++3Ac/2moDFPtAvukotRQvk0v872NMHvwnzMjfkA7dPZjrUOf/JrXFycrrvuOkVEROiyyy5Tjx491KNHD3m9XklSfX297Ha77Ha7PB6Pf7uGhgbZ7XajYgEAABMzrJjceOON2rlzp06dOqXGxka1tLQoLS1N5eXlkqSKigqlp6crOTlZu3fvVlNTk5qbm+VyuZSammpULAAAYGKGLeXEx8drwoQJysvLkyQtWrRISUlJmjdvnkpLS5WQkKDs7GxFRkaquLhYhYWFslgsKioqks1mMyoWAAAwMUOvMcnPz1d+fv5pYxs2bDjjc5mZmcrMzDQyCgAA6AR48isAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADANigkAADCNCKN27HQ6NXv2bA0cOFCSdPXVV+vee+/V3Llz1dbWpr59+2rFihWyWq0qKyvTxo0b1a1bN+Xl5Sk3N9eoWAAAwMQMKyaSNHz4cJWUlPjfL1iwQAUFBZo4caJWrlwph8Oh7OxsrVmzRg6HQ5GRkcrJydG4ceMUExNjZDQAAGBCHbqU43Q6NWbMGElSRkaGKisrVV1draSkJNlsNkVFRSklJUUul6sjYwEAAJMw9IzJ3r17NW3aNB07dkwzZsxQa2urrFarJCkuLk5ut1sej0exsbH+bWJjY+V2u8+6v9raWiPjmloo5+71esPqb898zI35AO0TLseaYcXkiiuu0IwZMzRx4kQdOHBAU6ZMUVtbm//ffT7fWbf7tnFJSkxMDHpOab8B+ww+Y+YemNra2pB+f7AxH3NjPkD7dLZjraqq6qzjhi3lxMfHa9KkSbJYLLrsssvUp08fHTt2TF6vV5JUX18vu90uu90uj8fj366hoUF2u92oWAAAwMQMKyZlZWV6/vnnJUlut1tHjhzRbbfdpvLycklSRUWF0tPTlZycrN27d6upqUnNzc1yuVxKTU01KhYAADAxw5ZyRo8erTlz5ujdd9/ViRMntHjxYiUmJmrevHkqLS1VQkKCsrOzFRkZqeLiYhUWFspisaioqEg2m82oWAAAwMQMKyY9e/bU2rVrzxjfsGHDGWOZmZnKzMw0KgoAAOgkePIrAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDYoJAAAwDUOLidfr1dixY/Xaa6+prq5Od911lwoKCjR79mx98803kqSysjLdfvvtys3N1ebNm42MAwAATM7QYvLMM8/ooosukiSVlJSooKBAL7/8si6//HI5HA61tLRozZo1euGFF7Rp0yZt3LhRR48eNTISAAAwsYCKyalTp857x/v27dPevXs1atQoSZLT6dSYMWMkSRkZGaqsrFR1dbWSkpJks9kUFRWllJQUuVyu8/4uAAAQHiIC+dCECRM0cuRI3XLLLUpOTg5ox8uWLdNDDz2k119/XZLU2toqq9UqSYqLi5Pb7ZbH41FsbKx/m9jYWLnd7m/dZ21tbUDfHY5COXev1xtWf3vmY27MB2ifcDnWAiomb775piorK/Xqq69q+fLlGj58uG6++WZdeeWVZ/3866+/rqFDh+rSSy8967/7fL7zGv+PxMTEQOKep/0G7DP4jJl7YGpra0P6/cHGfMyN+QDt09mOtaqqqrOOB1RMrFarRo4cqRtvvFEffvihSkpK9Oabb+qSSy7RggULNHDgwNM+v23bNh04cEDbtm3T4cOHZbVaFR0dLa/Xq6ioKNXX18tut8tut8vj8fi3a2ho0NChQ9s/SwAA0KkFVEx27typt956Sy6XSzfccIMWL16sa6+9Vp999pmKi4v12muvnfb5VatW+V+vXr1a/fv311//+leVl5dr8uTJqqioUHp6upKTk7Vo0SI1NTWpe/fucrlcWrhwYVAnCAAAOo+Aiklpaamys7P18MMPq3v37v7xAQMGKC8vL6AvmjlzpubNm6fS0lIlJCQoOztbkZGRKi4uVmFhoSwWi4qKimSz2do3EwAA0OkFVEymT5+ut99+WyNHjpQkLVmyRPn5+Ro4cKDy8/PPue3MmTP9rzds2HDGv2dmZiozM/N8MgMAgDAV0O3CixcvVlpamv/97bffrkceecSwUAAAoGsKqJicPHlSqamp/vfXXHPNd95BAwAAcL4CWsoZMmSIZs2apZSUFJ06dUpOp1NDhgwxOhsAAOhiAiomDz74oCorK/Xxxx8rIiJC991332lnUAAAAIIhoKWcw4cP69NPP9Xx48f19ddfa+fOnXr66aeNzgYAALqYgM6YTJs2Tenp6erXr5/ReQAAQBcWUDGJiYlRcXGx0VkAAEAXF1Ax+dGPfqTf/e53GjZsmCIi/n+Tq666yrBgAACg6wmomOzYsUOS9M477/jHLBaLXnzxRWNSAQCALimgYrJp0yZJ0okTJxQZGWloIAAA0HUFdFeO0+nUj3/8Y91yyy2SpCeffFIffPCBocEAAEDXE1AxKSkp0caNG9W3b19J0pQpU7R69WpDgwEAgK4noGISERGh3r17y2KxSJLi4uL8rwEAAIIloGtMLrnkEj311FNqbGzUW2+9pa1bt3JHDgAACLqAismSJUu0ZcsWDRs2TB999JFGjx6tSZMmGZ0NAAB0MQEt5ZSVlcnn82no0KG65pprdPLkSZWVlRmdDQAAdDEBnTH55JNP/K9Pnjyp6upqDRw4UNnZ2UblAgAAXVBAxWTevHmnvW9ra9OsWbPOuU1ra6vmz5+vI0eO6Pjx45o+fboGDRqkuXPnqq2tTX379tWKFStktVpVVlamjRs3qlu3bsrLy1Nubm77ZwQAADqtgIpJa2vrae/dbrf2799/zm3ef/99DR48WPfdd58OHjyoqVOnKiUlRQUFBZo4caJWrlwph8Oh7OxsrVmzRg6HQ5GRkcrJydG4ceMUExPT7kkBAIDOKaBikpWV5X9tsVhks9k0derUc27z3xfH1tXVKT4+Xk6nU4888ogkKSMjQ+vXr9eAAQOUlJQkm80mSUpJSZHL5dLo0aPPezIAAKBzC6iYvPfee+3+gvz8fB0+fFhr167Vz372M1mtVkn/fhaK2+2Wx+NRbGys//OxsbFyu91n3VdtbW27c3R2oZy71+sNq7898zE35gO0T7gcawEVkzFjxpx13OfzyWKx6N133/3WbV955RXV1tbqgQcekM/nO23bb9vnt0lMTAwk7nk695KUWRgz98DU1taG9PuDjfmYG/MB2qezHWtVVVVnHQ+omEyePFlXXXWVhg8fru7du+vDDz/Unj17dP/993/rNjU1NYqLi9PFF1+sxMREtbW1qUePHvJ6vYqKilJ9fb3sdrvsdrs8Ho9/u4aGBg0dOvT8ZgcAAMJCwD/iN2nSJPXp00e9e/dWVlaWXC6XoqOjFR0dfdZtdu3apfXr10uSPB6PWlpalJaWpvLycklSRUWF0tPTlZycrN27d6upqUnNzc1yuVxKTU0N0vQAAEBnEtAZE6vVquXLl2vo0KGyWCz66KOPvvO3cvLz8/Xggw+qoKBAXq9Xv/71rzV48GDNmzdPpaWlSkhIUHZ2tiIjI1VcXKzCwkJZLBYVFRX5L4QFAABdS0DFZPXq1XrjjTfkdDrl8/n0gx/8QNOmTTvnNlFRUfrtb397xviGDRvOGMvMzFRmZmaAkQEAQLgKqJj07NlTiYmJiomJUVZWlhoaGjirAQAAgi6gYrJs2TLV1dXpiy++UFZWlkpLS3Xs2DEtWrTI6HwAAKALCeji15qaGq1atUo9evSQJM2cOVN///vfDQ0GAAC6noCKycmTJ3XixAn/Ba9ffvmljh8/bmgwAADQ9QS0lDN16lTdcccdOnTokO69917t379fCxcuNDobAADoYgIqJv3799dLL72kvXv3KjIyUgMGDFBUVJTR2QAAQBcT0FLO0qVLZbVaNWTIECUmJlJKAACAIQI6YxIdHa3x48dr0KBBioyM9I8/9dRThgUDAABdzzmLyeOPP64FCxZo6tSpkiSXy6WUlJQOCQYAALqecxaT//x88vDhwyVJTz/99Hc+8RUAAKC9znmNic/nO+d7AACAYDpnMfnfH+r7rh/uAwAA+D7OuZRTU1OjnJwcSf8+W/LZZ58pJydHPp9PFotFDoejQ0ICAICu4ZzFZMuWLR2VAwAA4NzFpH///h2VAwAAILAHrAEAAHSEgB6w1l7Lly9XVVWVTp48qfvvv19JSUmaO3eu2tra1LdvX61YsUJWq1VlZWXauHGjunXrpry8POXm5hoZCwAAmJRhxWTnzp369NNPVVpaqsbGRt16660aMWKECgoKNHHiRK1cuVIOh0PZ2dlas2aNHA6HIiMjlZOTo3HjxikmJsaoaGHtivlvGrTn/UHd2z+XZgV1fwCA8GDYUs7111/vf2R9r1691NraKqfTqTFjxkiSMjIyVFlZqerqaiUlJclmsykqKkopKSlyuVxGxQIAACZm2BmT7t27Kzo6WpLkcDh000036YMPPpDVapUkxcXFye12y+PxKDY21r9dbGys3G73Wff5nyfRdkXhNvdQzsfr9YbV35P5mFu4zQfmFS7HmqHXmEjS1q1b5XA4tH79eo0fP94//m1PkT3X02UTExODni/YSxRGCXzu4Taf4KutrQ3p9wcb8zG3cJsPzKuzHWtVVVVnHTf0rpzt27dr7dq1WrdunWw2m6Kjo+X1eiVJ9fX1stvtstvt8ng8/m0aGhpkt9uNjAUAAEzKsGLy1Vdfafny5Xr22Wf9F7KmpaWpvLxcklRRUaH09HQlJydr9+7dampqUnNzs1wul1JTU42KBQAATMywpZy33npLjY2N+uUvf+kfW7p0qRYtWqTS0lIlJCQoOztbkZGRKi4uVmFhoSwWi4qKimSz2YyKBQAATMywYnLHHXfojjvuOGN8w4YNZ4xlZmYqMzPTqCgAAKCT4MmvAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANCgmAADANAwtJnv27NHYsWP10ksvSZLq6up01113qaCgQLNnz9Y333wjSSorK9Ptt9+u3Nxcbd682chIAADAxAwrJi0tLVqyZIlGjBjhHyspKVFBQYFefvllXX755XI4HGppadGaNWv0wgsvaNOmTdq4caOOHj1qVCwAAGBihhUTq9WqdevWyW63+8ecTqfGjBkjScrIyFBlZaWqq6uVlJQkm82mqKgopaSkyOVyGRULAACYWIRhO46IUETE6btvbW2V1WqVJMXFxcntdsvj8Sg2Ntb/mdjYWLnd7rPus7a21qi4phducw/lfLxeb1j9PZmPuYXbfGBe4XKsGVZMvovP5zuvcUlKTEw0IMl+A/YZfIHPPdzmE3y1tbUh/f5gYz7mFm7zgXl1tmOtqqrqrOMdeldOdHS0vF6vJKm+vl52u112u10ej8f/mYaGhtOWfwAAQNfRocUkLS1N5eXlkqSKigqlp6crOTlZu3fvVlNTk5qbm+VyuZSamtqRsQAAgEkYtpRTU1OjZcuW6eDBg4qIiFB5ebmeeOIJzZ8/X6WlpUpISFB2drYiIyNVXFyswsJCWSwWFRUVyWazGRULAACYmGHFZPDgwdq0adMZ4xs2bDhjLDMzU5mZmUZFAQAAnQRPfgUAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZBMQEAAKZh2I/4AcFwxfw3Ddrz/qDu7Z9Ls4K6PwDoqjhjAgAATINiAgAATIOlHKADhdvSVGeYD8tsQOdimmLy2GOPqbq6WhaLRQsXLtSQIUNCHQkAAHQwUxSTP//5z/r8889VWlqqffv2aeHChSotLQ11LAAA0MFMUUwqKys1duxYSdKVV16pY8eO6euvv1bPnj1DnAxAV8LSFDpKZzjWpNAcbxafz+fr8G/9Hw899JBGjhzpLycFBQX6zW9+owEDBvg/U1VVFap4AADAAMOGDTtjzBRnTP7X2brS2cIDAIDwYorbhe12uzwej/99Q0OD+vbtG8JEAAAgFExRTG644QaVl5dLkj7++GPZ7XauLwEAoAsyxVJOSkqKrr32WuXn58tisejhhx8OdSQAABACprj4NZyE2/NY9uzZo+nTp+uee+7RnXfeGeo439vy5ctVVVWlkydP6v7779f48eNDHaldWltbNX/+fB05ckTHjx/X9OnTlZGREepY35vX69XNN9+s6dOn67bbbgt1nHZzOp2aPXu2Bg4cKEm6+uqr9dBDD4U4FcJVc3Oz5s2bp2PHjunEiRMqKipSenp6qGO1mynOmISLcHseS0tLi5YsWaIRI0aEOkpQ7Ny5U59++qlKS0vV2NioW2+9tdMWk/fff1+DBw/Wfffdp4MHD2rq1KlhUUyeeeYZXXTRRaGOERTDhw9XSUlJqGOgC/jjH/+oAQMGqLi4WPX19br77rv1zjvvhDpWu1FMgijcnsditVq1bt06rVu3LtRRguL666/3n8Hq1auXWltb1dbWpu7du4c42fmbNGmS/3VdXZ3i4+NDmCY49u3bp71792rUqFGhjgJ0Kr1799Ynn3wiSWpqalLv3r1DnOj7McXFr+HC4/GcdkDExsbK7XaHMNH3ExERoaioqFDHCJru3bsrOjpakuRwOHTTTTd1ylLy3/Lz8zVnzhwtXLgw1FG+t2XLlmn+/PmhjhE0e/fu1bRp0/STn/xEO3bsCHUchLGsrCwdOnRI48aN05133ql58+aFOtL3whkTA3H5jjlt3bpVDodD69evD3WU7+2VV15RbW2tHnjgAZWVlclisYQ6Uru8/vrrGjp0qC699NJQRwmKK664QjNmzNDEiRN14MABTZkyRRUVFbJaraGOhjD0xhtvKCEhQc8//7z+8Y9/aOHChXrttddCHavdKCZBxPNYzG/79u1au3atnnvuOdlstlDHabeamhrFxcXp4osvVmJiotra2vTll18qLi4u1NHaZdu2bTpw4IC2bdumw4cPy2q1ql+/fkpLSwt1tHaJj4/3L7dddtll6tOnj+rr68OmeMFcXC6XbrzxRknSoEGD1NDQ0GmXqSWWcoKK57GY21dffaXly5fr2WefVUxMTKjjfC+7du3yn/HxeDxqaWnp1OvKq1at0quvvqo//OEPys3N1fTp0zttKZGksrIyPf/885Ikt9utI0eOhMV1QDCnyy+/XNXV1ZKkgwcPqkePHp22lEjcLhx0TzzxhHbt2uV/HsugQYNCHandampqtGzZMh08eFARERGKj4/X6tWrO+1/6qWlpVq9evVpv8G0bNkyJSQkhDBV+3i9Xj344IOqq6uT1+vVjBkzNHr06FDHCorVq1erf//+nfp24a+//lpz5sxRU1OTTpw4oRkzZmjkyJGhjoUw1dzcrIULF+rIkSM6efKkZs+e3anvpqSYAAAA02ApBwAAmAbFBAAAmAbFBAAAmAbFBAAAmAbFBAAAmAbFBAAAmAbFBAAAmMb/AWOXT0P3H4Q2AAAAAElFTkSuQmCC","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["SibSp: \n"," 0    608\n","1    209\n","2     28\n","4     18\n","3     16\n","8      7\n","5      5\n","Name: SibSp, dtype: int64\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 648x216 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Parch: \n"," 0    678\n","1    118\n","2     80\n","5      5\n","3      5\n","4      4\n","6      1\n","Name: Parch, dtype: int64\n"]}],"source":["category1 = [\"Survived\", \"Sex\", \"Pclass\",\"Embarked\", \"SibSp\", \"Parch\"]\n","for c in category1:\n","    bar_plot(c)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1677437063267,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"fkPjfdQzCJ2s","outputId":"3a295e00-3ee5-4195-c544-e277f67c3a61"},"outputs":[{"name":"stdout","output_type":"stream","text":["B96 B98        4\n","G6             4\n","C23 C25 C27    4\n","C22 C26        3\n","F33            3\n","              ..\n","E34            1\n","C7             1\n","C54            1\n","E36            1\n","C148           1\n","Name: Cabin, Length: 147, dtype: int64 \n","\n","Braund, Mr. Owen Harris                     1\n","Boulos, Mr. Hanna                           1\n","Frolicher-Stehli, Mr. Maxmillian            1\n","Gilinski, Mr. Eliezer                       1\n","Murdlin, Mr. Joseph                         1\n","                                           ..\n","Kelly, Miss. Anna Katherine \"Annie Kate\"    1\n","McCoy, Mr. Bernard                          1\n","Johnson, Mr. William Cahoone Jr             1\n","Keane, Miss. Nora A                         1\n","Dooley, Mr. Patrick                         1\n","Name: Name, Length: 891, dtype: int64 \n","\n","347082      7\n","CA. 2343    7\n","1601        7\n","3101295     6\n","CA 2144     6\n","           ..\n","9234        1\n","19988       1\n","2693        1\n","PC 17612    1\n","370376      1\n","Name: Ticket, Length: 681, dtype: int64 \n","\n"]}],"source":["category2 = [\"Cabin\",\"Name\",\"Ticket\"]\n","for c in category2:\n","    print(\"{} \\n\".format(train_df[c].value_counts()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p8nE-1BACtfR"},"outputs":[],"source":["#Numerical variable"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hxcmisowCkyD"},"outputs":[],"source":["def plot_hist(variable):\n","    plt.figure(figsize = (9,3))\n","    plt.hist(train_df[variable], bins = 50)\n","    plt.xlabel(variable)\n","    plt.ylabel(\"Frequency\")\n","    plt.title(\"{} distribution with hist\".format(variable))\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":674},"executionInfo":{"elapsed":1489,"status":"ok","timestamp":1677437093954,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"kM5Ekeb2Com1","outputId":"ae0f3588-534c-4999-b2bd-c2a300125c86"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure 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","text/plain":["<Figure 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   Pclass  Survived\n","0       1  0.629630\n","1       2  0.472826\n","2       3  0.242363"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["# Pclass vs Survived\n","train_df[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"], as_index = False).mean().sort_values(by=\"Survived\",ascending=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"elapsed":46,"status":"ok","timestamp":1677437269559,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"rl_beKcjDWoP","outputId":"9e1bafa6-7c15-4995-ac71-98aea234bba6"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-18051156-9b3b-4bb1-8437-f47bd032a649\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Sex</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>female</td>\n","      <td>0.742038</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>male</td>\n","      <td>0.188908</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-18051156-9b3b-4bb1-8437-f47bd032a649')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-18051156-9b3b-4bb1-8437-f47bd032a649 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-18051156-9b3b-4bb1-8437-f47bd032a649');\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","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["      Sex  Survived\n","0  female  0.742038\n","1    male  0.188908"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["# Sex vs Survived\n","train_df[[\"Sex\",\"Survived\"]].groupby([\"Sex\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1677437279517,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"gtqvzEFWDXB6","outputId":"f5bd12d6-6c50-4122-a4bd-1332d2895480"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-96f678be-0aa7-4b90-9d34-dd2fde21a697\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>SibSp</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0.535885</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>0.464286</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0.345395</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>0.250000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>0.166667</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>8</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96f678be-0aa7-4b90-9d34-dd2fde21a697')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-96f678be-0aa7-4b90-9d34-dd2fde21a697 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-96f678be-0aa7-4b90-9d34-dd2fde21a697');\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","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   SibSp  Survived\n","1      1  0.535885\n","2      2  0.464286\n","0      0  0.345395\n","3      3  0.250000\n","4      4  0.166667\n","5      5  0.000000\n","6      8  0.000000"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["# Sibsp vs Survived\n","train_df[[\"SibSp\",\"Survived\"]].groupby([\"SibSp\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"executionInfo":{"elapsed":663,"status":"ok","timestamp":1677437291625,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"ZRz3ynKvDZaH","outputId":"b2c9e46a-7fc1-4e27-fb7d-97f81efda518"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-a80a42d1-8263-444c-99ba-9e945e7e6ef3\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Parch</th>\n","      <th>Survived</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>0.600000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>0.550847</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>0.500000</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0.343658</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a80a42d1-8263-444c-99ba-9e945e7e6ef3')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-a80a42d1-8263-444c-99ba-9e945e7e6ef3 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-a80a42d1-8263-444c-99ba-9e945e7e6ef3');\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","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   Parch  Survived\n","3      3  0.600000\n","1      1  0.550847\n","2      2  0.500000\n","0      0  0.343658\n","5      5  0.200000\n","4      4  0.000000\n","6      6  0.000000"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["# Parch vs Survived\n","train_df[[\"Parch\",\"Survived\"]].groupby([\"Parch\"],as_index=False).mean().sort_values(by=\"Survived\",ascending=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sKawhx7bDcYP"},"outputs":[],"source":["#Checking Outlier"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vABnGPhJDoXj"},"outputs":[],"source":["def detect_outliers(df,features):\n","    outlier_indexes = []\n","    \n","    for c in features:\n","        # 1 st quartile\n","        Q1 = np.percentile(df[c],25)\n","        # 3 rd quartile\n","        Q3 = np.percentile(df[c],75)\n","        # IQR\n","        IQR = Q3 - Q1\n","        # outlier step\n","        outlier_step = IQR * 1.5\n","        # detect outlier and their indexes\n","        outlier_list_col = df[(df[c]< Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index\n","        # store indexes\n","        outlier_indexes.extend(outlier_list_col)\n","        \n","    outlier_indexes = Counter(outlier_indexes)\n","    multiple_outliers = list(i for i, v in outlier_indexes.items() if v > 2)\n","    \n","    return multiple_outliers"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":625,"status":"ok","timestamp":1677437366455,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"RTuB0ThfDrpR","outputId":"5617984c-dbfd-453b-8358-398c08d5c3dc"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-b99e2587-36a8-4843-8cbd-1cfa23dd7e49\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>27</th>\n","      <td>28</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>Fortune, Mr. Charles Alexander</td>\n","      <td>male</td>\n","      <td>19.0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>19950</td>\n","      <td>263.00</td>\n","      <td>C23 C25 C27</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Fortune, Miss. Mabel Helen</td>\n","      <td>female</td>\n","      <td>23.0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>19950</td>\n","      <td>263.00</td>\n","      <td>C23 C25 C27</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>159</th>\n","      <td>160</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Master. Thomas Henry</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>180</th>\n","      <td>181</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Miss. Constance Gladys</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>201</th>\n","      <td>202</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Mr. Frederick</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>324</th>\n","      <td>325</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Mr. George John Jr</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>341</th>\n","      <td>342</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Fortune, Miss. Alice Elizabeth</td>\n","      <td>female</td>\n","      <td>24.0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>19950</td>\n","      <td>263.00</td>\n","      <td>C23 C25 C27</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>792</th>\n","      <td>793</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Miss. Stella Anna</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>846</th>\n","      <td>847</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Mr. Douglas Bullen</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>863</th>\n","      <td>864</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.55</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b99e2587-36a8-4843-8cbd-1cfa23dd7e49')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-b99e2587-36a8-4843-8cbd-1cfa23dd7e49 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["     PassengerId  Survived  Pclass                               Name     Sex  \\\n","27            28         0       1     Fortune, Mr. Charles Alexander    male   \n","88            89         1       1         Fortune, Miss. Mabel Helen  female   \n","159          160         0       3         Sage, Master. Thomas Henry    male   \n","180          181         0       3       Sage, Miss. Constance Gladys  female   \n","201          202         0       3                Sage, Mr. Frederick    male   \n","324          325         0       3           Sage, Mr. George John Jr    male   \n","341          342         1       1     Fortune, Miss. Alice Elizabeth  female   \n","792          793         0       3            Sage, Miss. Stella Anna  female   \n","846          847         0       3           Sage, Mr. Douglas Bullen    male   \n","863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n","\n","      Age  SibSp  Parch    Ticket    Fare        Cabin Embarked  \n","27   19.0      3      2     19950  263.00  C23 C25 C27        S  \n","88   23.0      3      2     19950  263.00  C23 C25 C27        S  \n","159   NaN      8      2  CA. 2343   69.55          NaN        S  \n","180   NaN      8      2  CA. 2343   69.55          NaN        S  \n","201   NaN      8      2  CA. 2343   69.55          NaN        S  \n","324   NaN      8      2  CA. 2343   69.55          NaN        S  \n","341  24.0      3      2     19950  263.00  C23 C25 C27        S  \n","792   NaN      8      2  CA. 2343   69.55          NaN        S  \n","846   NaN      8      2  CA. 2343   69.55          NaN        S  \n","863   NaN      8      2  CA. 2343   69.55          NaN        S  "]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["train_df.loc[detect_outliers(train_df,[\"Age\",\"SibSp\",\"Parch\",\"Fare\"])]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_S9NceSYDuqG"},"outputs":[],"source":["# drop outliers\n","train_df = train_df.drop(detect_outliers(train_df,[\"Age\",\"SibSp\",\"Parch\",\"Fare\"]),axis = 0).reset_index(drop = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iBbrQlBuD1ZJ"},"outputs":[],"source":["train_df_len = len(train_df)\n","train_df = pd.concat([train_df,test_df], axis=0).reset_index(drop=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1677437414822,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"FJnmfK3uD4NA","outputId":"8c0a194f-7c7d-464e-9ddd-b1b7dd08ecf4"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-d7c08c7f-0ca4-4a1b-b60d-07573de93f06\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Braund, Mr. Owen Harris</td>\n","      <td>male</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Heikkinen, Miss. Laina</td>\n","      <td>female</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n","      <td>female</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Allen, Mr. William Henry</td>\n","      <td>male</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7c08c7f-0ca4-4a1b-b60d-07573de93f06')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-d7c08c7f-0ca4-4a1b-b60d-07573de93f06 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   PassengerId  Survived  Pclass  \\\n","0            1         0       3   \n","1            2         1       1   \n","2            3         1       3   \n","3            4         1       1   \n","4            5         0       3   \n","\n","                                                Name     Sex   Age  SibSp  \\\n","0                            Braund, Mr. Owen Harris    male  22.0      1   \n","1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n","2                             Heikkinen, Miss. Laina  female  26.0      0   \n","3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n","4                           Allen, Mr. William Henry    male  35.0      0   \n","\n","   Parch            Ticket     Fare Cabin Embarked  \n","0      0         A/5 21171   7.2500   NaN        S  \n","1      0          PC 17599  71.2833   C85        C  \n","2      0  STON/O2. 3101282   7.9250   NaN        S  \n","3      0            113803  53.1000  C123        S  \n","4      0            373450   8.0500   NaN        S  "]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GL0mgZ6WD6iI"},"outputs":[],"source":["#checking missing values"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1677437461442,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"VlDRHiAPEFQ9","outputId":"d2ca7797-e18a-4229-9475-deecc83571ea"},"outputs":[{"data":{"text/plain":["Index(['Age', 'Cabin', 'Embarked'], dtype='object')"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["train_df.columns[train_df.isnull().any()]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1677437471735,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"KPOZsDS6EF6l","outputId":"aed1fcd1-42d8-4bfb-811e-20a18da057bb"},"outputs":[{"data":{"text/plain":["PassengerId       0\n","Survived          0\n","Pclass            0\n","Name              0\n","Sex               0\n","Age             347\n","SibSp             0\n","Parch             0\n","Ticket            0\n","Fare              0\n","Cabin          1367\n","Embarked          4\n","dtype: int64"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["train_df.isnull().sum()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"elapsed":418,"status":"ok","timestamp":1677437492362,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"ZEJR-xpnEIUx","outputId":"eeec39f1-4672-4e70-d17c-a9cf611df6da"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-e7260c9e-0f3f-451e-9132-554e75803fb5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>60</th>\n","      <td>62</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Icard, Miss. Amelie</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>B28</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>821</th>\n","      <td>830</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n","      <td>female</td>\n","      <td>62.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>B28</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>942</th>\n","      <td>62</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Icard, Miss. Amelie</td>\n","      <td>female</td>\n","      <td>38.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>B28</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1710</th>\n","      <td>830</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n","      <td>female</td>\n","      <td>62.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113572</td>\n","      <td>80.0</td>\n","      <td>B28</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7260c9e-0f3f-451e-9132-554e75803fb5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-e7260c9e-0f3f-451e-9132-554e75803fb5 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["      PassengerId  Survived  Pclass  \\\n","60             62         1       1   \n","821           830         1       1   \n","942            62         1       1   \n","1710          830         1       1   \n","\n","                                           Name     Sex   Age  SibSp  Parch  \\\n","60                          Icard, Miss. Amelie  female  38.0      0      0   \n","821   Stone, Mrs. George Nelson (Martha Evelyn)  female  62.0      0      0   \n","942                         Icard, Miss. Amelie  female  38.0      0      0   \n","1710  Stone, Mrs. George Nelson (Martha Evelyn)  female  62.0      0      0   \n","\n","      Ticket  Fare Cabin Embarked  \n","60    113572  80.0   B28      NaN  \n","821   113572  80.0   B28      NaN  \n","942   113572  80.0   B28      NaN  \n","1710  113572  80.0   B28      NaN  "]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["#filling missing values\n","train_df[train_df[\"Embarked\"].isnull()]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IJ0x-e4jEQ12"},"outputs":[],"source":["train_df[\"Embarked\"] = train_df[\"Embarked\"].fillna(\"C\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"executionInfo":{"elapsed":46,"status":"ok","timestamp":1677437526520,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"3VHqfepQETv1","outputId":"97a20f62-ac08-4f1a-945e-8275e5a23948"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-3e5cc227-abaf-4789-a478-16f92475c575\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e5cc227-abaf-4789-a478-16f92475c575')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-3e5cc227-abaf-4789-a478-16f92475c575 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["Empty DataFrame\n","Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n","Index: []"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Fare\"].isnull()]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1677437543740,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"VuVlB8QiEXst","outputId":"d52cb705-5a21-4f16-c63f-618b62d71a32"},"outputs":[{"data":{"text/plain":["13.274400205128204"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["(train_df[train_df[\"Pclass\"] == 3][\"Fare\"]).mean()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7avEG-tSEZ82"},"outputs":[],"source":["train_df[\"Fare\"] = train_df[\"Fare\"].fillna((train_df[train_df[\"Pclass\"] == 3][\"Fare\"]).mean())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"executionInfo":{"elapsed":41,"status":"ok","timestamp":1677437562865,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"tNh5Fi-KEcqr","outputId":"1a3a8a9a-fac8-4516-df98-33b44837b1ab"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-a25b8fbd-35ef-42ca-b7d6-854b59994507\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a25b8fbd-35ef-42ca-b7d6-854b59994507')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["Empty DataFrame\n","Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n","Index: []"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Fare\"].isnull()]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KAh7nECAEesA"},"outputs":[],"source":["#corelating"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":266},"executionInfo":{"elapsed":32,"status":"ok","timestamp":1677437588146,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"InC5__TaEkRi","outputId":"6f06cdfa-d409-4038-b054-0c1da9974227"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["list1 = [\"SibSp\",\"Parch\",\"Age\",\"Fare\",\"Survived\"]\n","sns.heatmap(train_df[list1].corr(), annot=True, fmt=\".2f\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"executionInfo":{"elapsed":1338,"status":"ok","timestamp":1677437620493,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"EwBgC-OLEk32","outputId":"7537a1a6-c744-4787-eb45-1b7c860caddf"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# SibSp and Survived\n","g = sns.factorplot(x=\"SibSp\", y=\"Survived\", data = train_df, king = \"bar\",size = 6)\n","g.set_ylabels(\"Survived Probability\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"executionInfo":{"elapsed":872,"status":"ok","timestamp":1677437729525,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"RrdEDap6EsZj","outputId":"b331302b-d6d1-47fb-eab1-2b4bac03e373"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#Parch and Survived\n","g = sns.factorplot(x=\"Parch\", y=\"Survived\",kind=\"bar\",data=train_df,size=6)\n","g.set_ylabels(\"survived probability\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"executionInfo":{"elapsed":1245,"status":"ok","timestamp":1677437768879,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"85NAEXxFE4Aw","outputId":"e6cd9026-a665-42d3-a49f-c7941509b94e"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x432 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#Pclass and Survived\n","g = sns.factorplot(x=\"Pclass\",y=\"Survived\",data=train_df, kind=\"bar\", size=6)\n","g.set_ylabels(\"survived probability\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"executionInfo":{"elapsed":1041,"status":"ok","timestamp":1677437807447,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"i_SCeNklFQsS","outputId":"81587de9-aead-4af2-bc81-e38ddc323358"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x216 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Age and Survived\n","g = sns.FacetGrid(train_df, col=\"Survived\")\n","g.map(sns.distplot, \"Age\", bins=25)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1677437882393,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"InwkBYhlFaJ2","outputId":"e66f5931-ba79-45e8-b184-90fcc405c36b"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-0b0d58d5-5e21-4aed-8fec-1769eb70cacc\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Moran, Mr. James</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>330877</td>\n","      <td>8.4583</td>\n","      <td>NaN</td>\n","      <td>Q</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>Williams, Mr. Charles Eugene</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>244373</td>\n","      <td>13.0000</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>20</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>Masselmani, Mrs. Fatima</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2649</td>\n","      <td>7.2250</td>\n","      <td>NaN</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>27</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Emir, Mr. Farred Chehab</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2631</td>\n","      <td>7.2250</td>\n","      <td>NaN</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>29</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>330959</td>\n","      <td>7.8792</td>\n","      <td>NaN</td>\n","      <td>Q</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1740</th>\n","      <td>860</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Razi, Mr. Raihed</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2629</td>\n","      <td>7.2292</td>\n","      <td>NaN</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>1744</th>\n","      <td>864</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>8</td>\n","      <td>2</td>\n","      <td>CA. 2343</td>\n","      <td>69.5500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1749</th>\n","      <td>869</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>van Melkebeke, Mr. Philemon</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>345777</td>\n","      <td>9.5000</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1759</th>\n","      <td>879</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Laleff, Mr. Kristo</td>\n","      <td>male</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>349217</td>\n","      <td>7.8958</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>1769</th>\n","      <td>889</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n","      <td>female</td>\n","      <td>NaN</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>W./C. 6607</td>\n","      <td>23.4500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>347 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b0d58d5-5e21-4aed-8fec-1769eb70cacc')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\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","    [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-0b0d58d5-5e21-4aed-8fec-1769eb70cacc button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Ellen \"Nellie\"   \n","...           ...       ...     ...                                       ...   \n","1740          860         0       3                          Razi, Mr. Raihed   \n","1744          864         0       3         Sage, Miss. Dorothy Edith \"Dolly\"   \n","1749          869         0       3               van Melkebeke, Mr. Philemon   \n","1759          879         0       3                        Laleff, Mr. Kristo   \n","1769          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n","\n","         Sex  Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n","5       male  NaN      0      0      330877   8.4583   NaN        Q  \n","17      male  NaN      0      0      244373  13.0000   NaN        S  \n","19    female  NaN      0      0        2649   7.2250   NaN        C  \n","26      male  NaN      0      0        2631   7.2250   NaN        C  \n","27    female  NaN      0      0      330959   7.8792   NaN        Q  \n","...      ...  ...    ...    ...         ...      ...   ...      ...  \n","1740    male  NaN      0      0        2629   7.2292   NaN        C  \n","1744  female  NaN      8      2    CA. 2343  69.5500   NaN        S  \n","1749    male  NaN      0      0      345777   9.5000   NaN        S  \n","1759    male  NaN      0      0      349217   7.8958   NaN        S  \n","1769  female  NaN      1      2  W./C. 6607  23.4500   NaN        S  \n","\n","[347 rows x 12 columns]"]},"execution_count":57,"metadata":{},"output_type":"execute_result"}],"source":["train_df[train_df[\"Age\"].isnull()]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"elapsed":1001,"status":"ok","timestamp":1677437893219,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"eP8y-1KwFsst","outputId":"a8c0c738-0cdb-41dd-996d-e9ae02cdb397"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.factorplot(x=\"Sex\",y=\"Age\",data=train_df, kind=\"box\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"elapsed":714,"status":"ok","timestamp":1677437905112,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"0aEeBxd3FvHh","outputId":"96319f37-698a-416e-eaca-d0f38e0551cc"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 401.625x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.factorplot(x=\"Sex\",y=\"Age\",hue=\"Pclass\", data=train_df, kind=\"box\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":721},"executionInfo":{"elapsed":1217,"status":"ok","timestamp":1677437916767,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"eYaOzt68FyFY","outputId":"fdef8494-72e4-4d88-ca16-c16264fb1197"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgWElEQVR4nO3de3RU1d3/8Q8khjhBhEQSjCXEGtE8Uir46CpIkAhikloFrSZmJaxWZUmViy7lIqRco5VLWRK0YBFZFYukjZRSBRKxYF2uEH9Qq6XGhqFGIWAMuSAwGS4hvz9o8hBNMiEzZ/aZyfv1D5yTmb2/M2vWJzt7ztm7W2NjY6MAAH7X3XQBANBVEcAAYAgBDACGEMAAYAgBDACG2C6A9+7da7oEAPAL2wUwAHQVBDAAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGGJZAJ88eVKTJ09Wdna2MjIy9P777+uzzz5TRkaGMjIyNG/ePKu6/o6ysjKlpqbK6XT6rU8A8MSyAP7Tn/6kq6++WuvXr9eKFSv07LPP6tlnn9Xs2bO1ceNGnThxQu+9955V3beQm5urkydPauHChX7pDwA6wrIA7tOnj+rq6iRJ33zzjXr37q2KigoNHjxYkpScnKzi4mKrum9WVlam8vJySVJ5eTmjYAC2YVkA//jHP9bhw4d1xx13KCsrSzNmzFCvXr2afx4VFaWqqiqrum+Wm5vb4phRMAC7CLWq4T//+c+KjY3V2rVr9dlnn+nxxx/XZZdd1vzz9vYCLS0t9VkdTaPfC4992T4AeJKYmNjqecsC+O9//7tGjBghSbr++ut16tQpnT17tvnnlZWVio6ObvW5bRXbGfHx8S1COD4+3qftA0BnWTYFMWDAAH388ceSpIqKCkVEROiaa67Rnj17JElFRUVKSkqyqvtmOTk5LY7nzp1reZ8A0BGWjYDT09M1e/ZsZWVl6ezZs5o/f7769u2ruXPn6ty5c/rhD3+o4cOHW9V9s4EDBzaPguPj45WQkGB5nwDQEd0a25uMNWDv3r266aabfNpmWVmZpk2bppUrVxLAAGyjSwQwANgRtyIDgCEEMAAYQgADgCEEMAAYQgADgCEEMAAYQgADgCEEMAAYQgADgCEEMAAYQgADgCFdIoDZlBOAHXWJAGZTTgB2FPQBzKacAOwq6AOYTTkB2FXQB3Brm3ICgB0EfQDHx8e3ewwApgR9ALMpJwC7CvoAbtqUUxKbcgKwlaAPYOn8KDgiIoLRLwBbYVNOADCkS4yAd+zYoZEjR2rnzp2mSwGAZl1iBHz77bfr7NmzCg0N1V//+leftg0AnRX0I+AdO3bo7NmzkqSzZ88yCgZgG0E/Am4a/TZhFAzALoJ+BHxh+LZ2DACmhFrV8B//+Edt2bKl+Xjfvn164403NH/+fEnSddddpwULFljVPQDYnl+mID788ENt27ZNTqdT06dP1+DBg/XUU0/p7rvv1m233dbisb6eghg5cuR3zv3tb3/zWfsA0FmWjYAv9NJLL+lXv/qVsrKyNHjwYElScnKyiouLvxPAvhYaGvqdOWCTtm/frq1bt7b7mJqaGklSZGRku49LS0tTSkqKz2oD4F+Wp9Enn3yiK6+8UiEhIerVq1fz+aioKFVVVbX6nNLSUp/139ocsC/bv1iHDx+Wy+Vq9zFN70t4eLjHtky+FgAdk5iY2Op5ywO4oKBA48eP/8759mY+2iq2M+Lj41ssQRkfH+/T9i9WYmKiHnrooXYfM3XqVElSXl6eP0oCYIjlV0GUlJRoyJAhioyMVF1dXfP5yspKRUdHW909q6EBsC1LA7iyslIREREKCwvTJZdcou9///vas2ePJKmoqEhJSUlWdi+J1dAA2JelAVxVVdXii6TZs2dr+fLlysjIUFxcnIYPH25l981YDQ2AHQX9nXCBiDlgoGsI+jvhAMCuCGAAMIQABgBDCGAAMKRLBHBZWZlSU1PldDpNlwIAzbpEAOfm5urkyZNauHCh6VIAoFnQB3BZWVnzrcjl5eWMggHYRtAHcG5ubotjRsEA7CLoA/jChXhaOwYAU4I+gJvWgWjrGABMCfoAvuOOO1ocp6amGqoEAFoK+gBet25di+M1a9YYqgQAWgr6AGZXZAB2FfQB/O094EzvCQcATQI+jTxtchkbG6svv/yyxXHTco/fxiaXMIGNWruuoB8Bf/sD6+kDDNhRdXW1qqurTZcBHwv4EXBKSorH3/hZWVn68ssvtWDBAiUnJ/upMqBjOvIZZpH+4BTwAdwRkZGRioyMJHwB2ErQT0EAgF0RwABgCAEMAIYQwABgCAEMAIYQwABgCAEMAIYQwABgiKU3YmzZskWvvPKKQkNDNXXqVF133XWaMWOGGhoa1LdvXy1dulRhYWFWlgAAtmXZCLi2tlYvvfSSNmzYoNWrV+vdd99VXl6eMjMztWHDBg0YMEAFBQVWdQ8AtmdZABcXF2vYsGHq2bOnoqOjtWjRIpWUlGj06NGSpOTkZBUXF1vVPQDYnmVTEIcOHZLb7dakSZP0zTffaMqUKaqvr2+ecoiKilJVVZVV3QOA7Vk6B1xXV6cXX3xRhw8f1oQJE9TY2Nj8swv//22lpaU+rcPlclnSrlUCrV5Yj89EYEtMTGz1vGUBHBUVpSFDhig0NFRxcXGKiIhQSEiI3G63wsPDVVlZqejo6IsqtrMcDocl7Vol0OqF9fhMBCfL5oBHjBih3bt369y5c6qtrZXL5dLw4cNVWFgoSSoqKlJSUpJV3QOA7VkWwDExMbrzzjv1wAMPaOLEicrJydGUKVO0efNmZWZmqq6uTuPGjbOqe/hZWVmZUlNT5XQ6TZcCBAxL54AzMjKUkZHR4ty3t4lHcMjNzdXJkye1cOFCvfbaa6bLAQICd8LBa2VlZSovL5cklZeXMwoGOogAhtdyc3NbHC9cuNBQJUBgIYDhtabRb1vHAFpHAMNr8fHx7R4DaB0BDK/l5OS0OJ47d66hSoDAQgDDawMHDmwe9cbHxyshIcFsQUCAIIDhEzk5OYqIiGD0C1wES68DRtcxcOBAbdu2zXQZQEBhBAwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhnAdMILO9u3btXXr1nYfU1NTI0mKjIxs93FpaWlKSUnxWW3AhQhgdEnV1dWSPAcwYCUCGEEnJSXF46h16tSpkqS8vDx/lAS0ijlgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADDEsjvhSkpKNG3aNF177bWSzu8Z9sgjj2jGjBlqaGhQ3759tXTpUoWFhVlVAgDYmqW3It9yyy0tbvV85plnlJmZqdTUVC1fvlwFBQXKzMy0sgQAsC2/TkGUlJRo9OjRkqTk5GQVFxf7s3sAsBVLR8BOp1OTJk3SsWPHNHnyZNXX1zdPOURFRamqqqrV55WWlvq0DpfLZUm7Vgm0egORv97j/Px8HTp0yOt2Dh48KEl65JFHvG7re9/7ntLT071uBx2XmJjY6nnLAjg+Pl6TJ09WamqqDh48qAkTJqihoaH5542NjW0+t61iO8vhcFjSrlUCrd5A5K/3uKamRp8f+EK9HdFetRPaeKkkqfZIvVft1Lm+lsPh4LNlE5YFcExMjNLS0iRJcXFxuuKKK/TPf/5Tbrdb4eHhqqysVHS0dx9KIBD0dkQr+foM02VIknZ+ttF0CbiAZXPAW7Zs0dq1ayVJVVVVqq6u1r333qvCwkJJUlFRkZKSkqzqHgBsz7IR8O23366nn35a7777rs6cOaP58+crMTFRM2fOVH5+vmJjYzVu3DirugcA27MsgHv27KnVq1d/5/y6deus6hIAAgpbEsEjX21y6YsNLvPy8uR0Or1qQ5L2798v6f+2JuqshIQEr9tA10UAwyf8tcml0+nUZ//4h/p52c6l//237h//6HQbX3lZA0AAwyO7bXLZT9LD6mZ5P56sVduXUgIdwWI8AGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAI6wEDCGjbt2/XihUr2n3MqVOndPbsWa/7Cg0NVY8ePdp9zLRp0zq88wsjYAAwhBEwYKGamhrVub7Wzs82mi5FklTn+lqX1pjfTcSXOrJji10xAgYAQxgBAxaKjIxUfW2jkq/PMF2KJGnnZxst3zhVksrKyjRt2jStXLlSCQkJlvcXqDo0Aj59+rQOHTpkdS0AgkRubq5OnjyphQsXmi7F1jwG8Ntvv617771XkyZNknT+jd28ebPVdQEIUGVlZSovL5cklZeXy+l0mi3IxjwG8O9//3tt2rRJffr0kSRNnz5dGzZssLwwAIEpNze3xTGj4LZ5DOCQkBCFhYWpW7fz35yGhYV1uHG3260xY8Zo06ZNOnLkiLKzs5WZmalp06bp9OnTna8agG01jX7bOsb/8RjAQ4cO1fTp01VZWanf/va3evDBBzVs2LAONb5q1SpdfvnlkqS8vDxlZmZqw4YNGjBggAoKCryrHIAtxcfHt3uM/+MxgJ988kmlp6frpz/9qXr06KGZM2fqySef9NjwgQMH5HQ6NWrUKElSSUmJRo8eLUlKTk5WcXGxd5UDsKWcnJwWx3PnzjVUif15DOAXX3xRu3fv1qlTp3T8+HF98MEHWrVqld5+++12b+1bvHixZs2a1XxcX1/fPH0RFRWlqqoqH5QPwG4GDhzYPOqNj4/nMrR2eLwOuKamRp9++qluu+02devWTR988IGuueYaHTlyRO+8845eeOGF7zxn8+bNuvHGG9W/f/9W22xsbGy3z9LS0o5V30Eul8uSdq0SaPVK/qu5qR+7cLlc7b5mu9Urea7ZF7KysrRs2TJlZ2cH1OfYKomJia2e9xjA5eXleuONN5q/hJs4caIef/xxrV69WllZWa0+Z9euXTp48KB27dqlr776SmFhYXI4HHK73QoPD1dlZaWio6MvutjOcjgclrRrlUCrV/JfzQ6HQ3b6+tbhcLT7mh0Oh2pV78eKPPNUsy8kJiZq7NixlvYRDDwGcFVVlf7973/r+uuvlyR9+eWXOnTokA4fPqyTJ0+2+pwLR8UrV67UVVddpY8++kiFhYW65557VFRUpKSkJI/F5eXl+eQawv3790uSpk6d6nVbCQkJ7bbji5r9WS/wbZ5WF/PVymKS71cXCzQeA/iZZ57R7NmzdeTIEUnn53J/8Ytf6PPPP9dTTz3V4Y6mTJmimTNnKj8/X7GxsRo3bpzH5zidTn30z091zuHdrZPdGs6/zL0HvvKqne6uGo+PcTqdKtv3d8X1bOh0P70az/+14S7/f51uQ5K+PBHi1fMBWMtjAA8fPlyrVq3Stm3b9Pbbb+vYsWM6d+6cbr311g51MGXKlOb/r1u37qILPOeIlPt/7rro51kh/NO3OvS4uJ4NyvnfExZX41nunp6mS0AA8sXqYhMmTFB5ebni4+P12muv+aiy4NNmANfV1amwsFBvvfWWvvjiC40dO1bHjx9XUVGRP+sDEGBauxWZKyFa12YAjxgxQnFxcZo5c6aSkpLUvXv3Dk0bAOjaWrsVmVFw69q8Dvj5559XXFyc5syZo3nz5nHjBIAO4VbkjmtzBHzXXXfprrvu0rFjx7R9+3b95je/0X/+8x8tXrxY9913H39SAB3kix0x3GfOX3EUfkmE17VcpSiv2vAkPj6+RehyK3LbPH4Jd/nllys9PV3p6emqrKzUW2+9pRkzZmjTpk3+qA8IaL4aqOzff/4KnKuuifOqnasUZfngKScnR4888kjzMbcit+2idsSIiYnRww8/rIcfftiqeoCg4qtrsJvaycvL80l7Vmq6FbnpKgj+Wm4be8IB8LmcnBxFREQw+vWAPeEA+NzAgQO1bds202XYHiNgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADDEsj3h6uvrNWvWLFVXV+vUqVN67LHHdP3112vGjBlqaGhQ3759tXTpUoWFhVlVAgDYmmUBvHPnTg0aNEgTJ05URUWFHnroIQ0dOlSZmZlKTU3V8uXLVVBQoMzMTKtKAABbsyyA09LSmv9/5MgRxcTEqKSkRAsWLJAkJScn69VXX203gGtqatTdVa3wT9+yqsyL0t1VrZoaRuwAfMPybekzMjL01VdfafXq1fr5z3/ePOUQFRWlqqqqVp9TWloqSTp9+rTV5V2006dPN9fXGpfLZauJdZfL1W69vuxHkuV9NfVjF8H2/sIaiYmJrZ63PIA3btyo0tJSTZ8+XY2Njc3nL/z/tzUV269fP1WclNz/c5fVZXZI+KdvqV+/fm2+mZLkcDjk9mNNnjgcjnbr9WU/UtsfNF/2Y6dfy8H2/sK/LBus7du3T0eOHJF0/kPT0NCgiIgIud3n46myslLR0dFWdQ8AtmfZCHjPnj2qqKjQnDlzdPToUblcLiUlJamwsFD33HOPioqKlJSUZFX3QMDYvn27tm7d2u5j9u/fL0maOnVqu49LS0tTSkqKz2qDtSwL4IyMDM2ZM0eZmZlyu92aO3euBg0apJkzZyo/P1+xsbEaN26cVd0DQSUqKsp0CbCAZQEcHh6uX//61985v27dOqu6BAJSSkoKo9Yuyk5f2ANAl0IAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGGL5esCwt7y8PDmdTq/b6ehqXZ4kJCR43QYQKAjgLs7pdOqjf30k9fayof/+LfVRxUedb6POyxqAAEMAQ+otnRt1znQV6r6LGTF0LQQwAkpNTY0OSspV21tadUTDf/8N8aKN05LO1dR4VQe6NgIYASUmJkbV1dVet3Omvl6S1OPSSzvdRuh/6wE6iwBGQGltkf/OaPqiLy8vzyftAZ3BpBsAGMII2MdqampUdTxEuXt6mi5FXxwPUV/mKAHbYgQMAIYwAvaxyMhIOb45oJz/PWG6FOXu6anwyEjTZQBoAyNgADDE9iPg7q4ahX/6lldtdDtz/pKjxks6f8lRUy1SP6/aAIAmtg7ghIQEn7TTtE7Btdd4G579fFYTANg6gH21KAvXfAKwI+aAAcAQS0fAS5Ys0d69e3X27Fk9+uij+sEPfqAZM2aooaFBffv21dKlSxUWFmZlCQBgW5YF8O7du7V//37l5+ertrZW48eP17Bhw5SZmanU1FQtX75cBQUFyszMtKoEALA1y6Ygbr75Zq1YsUKS1KtXL9XX16ukpESjR4+WJCUnJ6u4uNiq7gHA9iwL4JCQEDkcDklSQUGBRo4cqfr6+uYph6ioKFVVVVnVPQDYnuVXQezYsUMFBQV69dVXNXbs2ObzjY1tr+daWlrq0xpcLpcl7bbVl52+2XS5XO2+7qb3xi481evLfiT/fCaAxMTEVs9bGsDvv/++Vq9erVdeeUWXXXaZHA6H3G63wsPDVVlZqejo6IsqtrOaRuK+bretvtyW99JxDoej3dftcDikWj8W5IGnen3Zj+SfzwTQFssGa8ePH9eSJUv08ssvq3fv3pKk4cOHq7CwUJJUVFSkpKQkq7oHANuzbAS8detW1dbW6oknnmg+9/zzzysnJ0f5+fmKjY3VuHHjrOoeAGzPsgBOT09Xenr6d86vW7fOqi4BIKDY6fsiAOhSCGAAMIQABgBDCGAAMIQABgBDCGAAMIQABgBDCGAAMMTWWxLBejU1NVKd1H2XDX4X10k1l9Z43cz27du1devWdh/TtE+gp22v0tLSlJKS4nVNQGsIYHRJUVFRpksACOCuLjIyUl/Uf6Fzo86ZLkXdd3VXZGSk1+2kpKQwakVAsMHfnQDQNRHAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGCIpQFcVlamMWPG6PXXX5ckHTlyRNnZ2crMzNS0adN0+vRpK7sHAFuzLIBdLpcWLVqkYcOGNZ/Ly8tTZmamNmzYoAEDBqigoMCq7gHA9iwL4LCwMK1Zs0bR0dHN50pKSjR69GhJUnJysoqLi63qHgBsz7I94UJDQxUa2rL5+vp6hYWFSTq/KWJVVVWrzy0tLfVpLS6Xy5J22+rLThPrLper3dfd9N7Yhad6gUCUmJjY6nljm3I2Nja2+bO2iu0sh8NhSbtt9eW2vJeOczgc7b5uh8Mh1fqxIA881QsEE78O1hwOh9zu8/FUWVnZYnoCALoavwbw8OHDVVhYKEkqKipSUlKSP7sHAFuxbApi3759Wrx4sSoqKhQaGqrCwkItW7ZMs2bNUn5+vmJjYzVu3DirugcA27MsgAcNGqT169d/5/y6deus6hIAAoqdvrAHgC6FAAYAQwhgADCEAAYAQwhgADCEAAYAQwhgADDE2FoQwezLEyHK3dOz088/drqbJOnysLbXy+hoHQO9agGAlQhgH0tISPC6jYP790uSYuKv9aqdgT6qB4A1CGAfmzp1qs/ayMvL87otAPbFHDAAGBLwI+Dt27dr69at7T5m/3//pPc0Ok1LS1NKSorPagsYdVL3XV7+Lm5aBDncuzp0lXdlAIEk4AO4I6KiokyXYFu+miNu+iV37VVezFtfxZw1upaAD+CUlJSuOWr1EV/MWV/YDvPWQMcxBwwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGCI3xfjee655/Txxx+rW7dumj17tgYPHuzvEnCRfLXkZ5dd7hNog18D+MMPP9QXX3yh/Px8HThwQLNnz1Z+fr4/SzAuWNcvZslP4OL5NYCLi4s1ZswYSdI111yjY8eO6cSJE+rZs/MbWAYju4UZS34C1vBrAB89elQ33HBD83FkZKSqqqq6VAATZgCaGF2QvbGx9W3XS0tL/VwJAFgnMTGx1fN+DeDo6GgdPXq0+fjrr79W3759v/O4tooFgGDi18vQbr31VhUWFkqS/vWvfyk6OrpLTT8AwIX8OgIeOnSobrjhBmVkZKhbt26aN2+eP7sHAFvp1tjWRKwhe/fu1U033WS6DACwHHfCAYAhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhRteCaMvevXtNlwAAPtXa/Q22uxEDALoKpiAAwBACGAAMIYABwJAuEcDPPfec0tPTlZGRoU8++cR0OR6VlZVpzJgxev31102X0iFLlixRenq67rvvPhUVFZkup1319fWaNm2asrKydP/992vnzp2mS+oQt9utMWPGaNOmTaZL8aikpEQ/+tGPlJ2drezsbC1atMh0SR5t2bJFd999t+69917t2rXLb/3a8ioIXwq0jUBdLpcWLVqkYcOGmS6lQ3bv3q39+/crPz9ftbW1Gj9+vMaOHWu6rDbt3LlTgwYN0sSJE1VRUaGHHnpIycnJpsvyaNWqVbr88stNl9Fht9xyi/Ly8kyX0SG1tbV66aWX9Oabb8rlcmnlypUaNWqUX/oO+gAOtI1Aw8LCtGbNGq1Zs8Z0KR1y8803a/DgwZKkXr16qb6+Xg0NDQoJCTFcWevS0tKa/3/kyBHFxMQYrKZjDhw4IKfT6bdQ6GqKi4s1bNgw9ezZUz179vTriD3opyCOHj2qPn36NB83bQRqV6GhoQoPDzddRoeFhITI4XBIkgoKCjRy5Ejbhu+FMjIy9PTTT2v27NmmS/Fo8eLFmjVrlukyLorT6dSkSZP04IMP6oMPPjBdTrsOHTokt9utSZMmKTMzU8XFxX7rO+hHwN/GZc/W2LFjhwoKCvTqq6+aLqVDNm7cqNLSUk2fPl1btmxRt27dTJfUqs2bN+vGG29U//79TZfSYfHx8Zo8ebJSU1N18OBBTZgwQUVFRQoLCzNdWpvq6ur04osv6vDhw5owYYJ27tzpl89E0AdwRzcCRee9//77Wr16tV555RVddtllpstp1759+xQVFaUrr7xSiYmJamhoUE1NjaKiokyX1qpdu3bp4MGD2rVrl7766iuFhYWpX79+Gj58uOnS2hQTE9M81RMXF6crrrhClZWVtv0lEhUVpSFDhig0NFRxcXGKiIjw22ci6Kcg2AjUWsePH9eSJUv08ssvq3fv3qbL8WjPnj3No/SjR4/K5XK1mKKymxdeeEFvvvmm/vCHP+j+++/XY489Zuvwlc5fUbB27VpJUlVVlaqrq2091z5ixAjt3r1b586dU21trV8/E0E/Ag60jUD37dunxYsXq6KiQqGhoSosLNTKlSttG25bt25VbW2tnnjiieZzixcvVmxsrLmi2pGRkaE5c+YoMzNTbrdbc+fOVffuQT8O8avbb79dTz/9tN59912dOXNG8+fPt/X0Q0xMjO6880498MADkqScnBy/fSZYCwIADOFXPwAYQgADgCEEMAAYQgADgCEEMAAYQgAjaBw6dEhDhgxRdna2srKy9MADD+idd97pdHvZ2dkqKyvzYYVAS0F/HTC6lquvvlrr16+XdP720vHjxyspKSmg1tdA10EAI2j17t1bffv2VXl5uRYsWKDQ0FB1795dK1as0IkTJzR9+nQ5HA5lZWUpLCxMy5cvV0hIiNLS0vSzn/1MkrRt2zY9++yzqqur06pVq2x7gwkCE1MQCFqHDh1SXV2dqqur9ctf/lLr16/X0KFD9Ze//EWSVFpaqmXLlmnUqFFasGCB1qxZozfeeEPFxcVyu92Szq8T8Lvf/U4jR460/WLzCDyMgBFUPv/8c2VnZ6uxsVE9evTQ4sWLdemll2rZsmVyu936+uuv9ZOf/ESS1L9/f/Xp00fV1dXq0aOHIiMjJUkvv/xyc3tNW4nHxMSorq7O768HwY0ARlC5cA64SXZ2tiZOnKiRI0dq7dq1crlckqRLLrlEktS9e3edO3eu1fYuXNuYu/bha0xBIOjV1dUpLi5Op0+f1nvvvaczZ860+HmfPn3U0NCgyspKNTY26tFHH9U333xjqFp0JYyAEfSysrL0+OOPq3///srOztbChQtbbE0kSfPmzdPUqVMlSampqerVq5eJUtHFsBoaABjCFAQAGEIAA4AhBDAAGEIAA4AhBDAAGEIAA4AhBDAAGPL/ASd3AJIp4QXQAAAAAElFTkSuQmCC","text/plain":["<Figure 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","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.factorplot(x=\"Parch\",y=\"Age\", data=train_df, kind=\"box\")\n","sns.factorplot(x=\"SibSp\", y=\"Age\", data=train_df, kind=\"box\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rw7zssvoF03v"},"outputs":[],"source":["train_df[\"Sex\"] = [1 if i == \"male\" else 0 for i in train_df[\"Sex\"]]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":266},"executionInfo":{"elapsed":906,"status":"ok","timestamp":1677437944411,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"L5WQzus1F4V1","outputId":"f7d8c28b-548c-4cf5-aede-03bc847e0b1c"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.heatmap(train_df[[\"Age\",\"Sex\",\"SibSp\",\"Parch\",\"Pclass\"]].corr(), annot=True)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NXjyMD11F7lr"},"outputs":[],"source":["#age\n","index_nan_age = list(train_df[\"Age\"][train_df[\"Age\"].isnull()].index)\n","for i in index_nan_age:\n","    age_pred = train_df[\"Age\"][((train_df[\"SibSp\"] == train_df.iloc[i][\"SibSp\"]) & (train_df[\"Parch\"] == train_df.iloc[i][\"Pclass\"]))].median()\n","    age_med = train_df[\"Age\"].median()\n","    if not np.isnan(age_pred):\n","        train_df[\"Age\"].iloc[i] = age_pred\n","    else:\n","        train_df[\"Age\"].iloc[i] = age_med"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1677437986819,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"Wc5Vzma0GC9r","outputId":"27ff18f5-ca95-4a3b-fd6c-5918615939f7"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-5656b027-3a94-4bbb-943a-e3724a09ccd1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Name</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5656b027-3a94-4bbb-943a-e3724a09ccd1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Laina\n","3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n","4                             Allen, Mr. William Henry\n","5                                     Moran, Mr. James\n","6                              McCarthy, Mr. Timothy J\n","7                       Palsson, Master. Gosta Leonard\n","8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n","9                  Nasser, Mrs. Nicholas (Adele Achem)\n","Name: Name, dtype: object"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["#age\n","train_df[\"Name\"].head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_lzDFKmEGLAv"},"outputs":[],"source":["name = train_df[\"Name\"]\n","train_df[\"Title\"] = [i.split(\".\")[0].split(\",\")[-1].strip() for i in name]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":404,"status":"ok","timestamp":1677438077856,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"DZI2BJlEGZ-u","outputId":"c110a692-fcb5-4ad8-b623-0b2c639d1c41"},"outputs":[{"data":{"text/plain":["0        Mr\n","1       Mrs\n","2      Miss\n","3       Mrs\n","4        Mr\n","5        Mr\n","6        Mr\n","7    Master\n","8       Mrs\n","9       Mrs\n","Name: Title, dtype: object"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Title\"].head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":322},"executionInfo":{"elapsed":1153,"status":"ok","timestamp":1677438088896,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"bZ08NtIFGcUY","outputId":"cc06caec-d874-4f0f-d7b9-b9b1dedd55fa"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x=\"Title\", data = train_df)\n","plt.xticks(rotation = 60)\n","plt.show() "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":369,"status":"ok","timestamp":1677438101498,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"KVqEZhlZGe5o","outputId":"95bcd253-b34b-4a6f-de09-c702d6524f9b"},"outputs":[{"data":{"text/plain":["0     2\n","1     1\n","2     1\n","3     1\n","4     2\n","5     2\n","6     2\n","7     0\n","8     1\n","9     1\n","10    1\n","11    1\n","12    2\n","13    2\n","14    1\n","15    1\n","16    0\n","17    2\n","18    1\n","19    1\n","Name: Title, dtype: int64"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["# convert to categorical\n","train_df[\"Title\"] = train_df[\"Title\"].replace([\"Lady\",\"the Countess\",\"Capt\",\"Col\",\"Don\",\"Dr\",\"Major\",\"Rev\",\"Sir\",\"Jonkheer\",\"Dona\"],\"other\")\n","train_df[\"Title\"] = [0 if i == \"Master\" else 1 if i == \"Miss\" or i == \"Ms\" or i == \"Mlle\" or i == \"Mrs\" else 2 if i == \"Mr\" else 3 for i in train_df[\"Title\"]]\n","train_df[\"Title\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"elapsed":995,"status":"ok","timestamp":1677438117153,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"cMR_YCr0GiHm","outputId":"e50a3afb-30bc-4ef9-b09b-2b8fd5d5a7c2"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x=\"Title\", data = train_df)\n","plt.xticks(rotation = 60)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"elapsed":1280,"status":"ok","timestamp":1677438130301,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"y0izK6U_Glv-","outputId":"ccd1a07e-c3a6-42cb-85eb-a06c79ad8deb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.factorplot(x = \"Title\", y = \"Survived\", data = train_df, kind = \"bar\")\n","g.set_xticklabels([\"Master\",\"Mrs\",\"Mr\",\"Other\"])\n","g.set_ylabels(\"Survival Probability\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vbcUKqSJGpE2"},"outputs":[],"source":["train_df.drop(labels = [\"Name\"], axis = 1, inplace = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1677438149868,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"ugrV7GPbGrri","outputId":"3e7ea4cb-bca8-4b7b-8f5e-1c5017014291"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-4cbf609d-1280-4dae-876a-3f7cca4b824b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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pd.get_dummies(train_df,columns=[\"Title\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":35,"status":"ok","timestamp":1677438173379,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"aIjAZyPcGwBi","outputId":"c1e74cbc-af5e-498a-ec27-701597bc5e8b"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-3c13ec1a-3f3d-40dd-8bbf-8857c121428c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      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1"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":384,"status":"ok","timestamp":1677438192952,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"PJe2JGM6G2rA","outputId":"c295d12d-38ad-402e-efcd-6a40387faf09"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-d783d9df-aa61-4600-b185-def9146a6ee7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      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<tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d783d9df-aa61-4600-b185-def9146a6ee7')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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0            113803   \n","4            5         0       3    1  35.0      0      0            373450   \n","\n","      Fare Cabin Embarked  Title_0  Title_1  Title_2  Title_3  Fsize  \n","0   7.2500   NaN        S        0        0        1        0      2  \n","1  71.2833   C85        C        0        1        0        0      2  \n","2   7.9250   NaN        S        0        1        0        0      1  \n","3  53.1000  C123        S        0        1        0        0      2  \n","4   8.0500   NaN        S        0        0        1        0      1  "]},"execution_count":79,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"elapsed":874,"status":"ok","timestamp":1677438204479,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"cB1ScCsBG4la","outputId":"80f3223f-bcfe-4be0-884f-fbac4cded110"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.factorplot(x = \"Fsize\", y=\"Survived\", data = train_df, kind=\"bar\")\n","g.set_ylabels(\"Survival\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FZR6IgyYG7GW"},"outputs":[],"source":["train_df[\"family_size\"] = [1 if i < 5 else 0 for i in train_df[\"Fsize\"]]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":519},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1677438228214,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"RE6bI0DwG940","outputId":"7e9b085e-858b-492e-ed4f-f5b87e7cb562"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-c119f32d-d2b7-4193-988e-ae80afd890e5\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Embarked</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>Title_2</th>\n","      <th>Title_3</th>\n","      <th>Fsize</th>\n","      <th>family_size</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>C</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>24.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>330877</td>\n","      <td>8.4583</td>\n","      <td>NaN</td>\n","      <td>Q</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>54.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>17463</td>\n","      <td>51.8625</td>\n","      <td>E46</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>2.0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>349909</td>\n","      <td>21.0750</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>27.0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>347742</td>\n","      <td>11.1333</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>14.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>237736</td>\n","      <td>30.0708</td>\n","      <td>NaN</td>\n","      <td>C</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>11</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>4.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>PP 9549</td>\n","      <td>16.7000</td>\n","      <td>G6</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>12</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>58.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>113783</td>\n","      <td>26.5500</td>\n","      <td>C103</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>20.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>A/5. 2151</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>14</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>39.0</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>347082</td>\n","      <td>31.2750</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>15</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>14.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>350406</td>\n","      <td>7.8542</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c119f32d-d2b7-4193-988e-ae80afd890e5')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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Title_1  Title_2  Title_3  Fsize  \\\n","0    7.2500   NaN        S        0        0        1        0      2   \n","1   71.2833   C85        C        0        1        0        0      2   \n","2    7.9250   NaN        S        0        1        0        0      1   \n","3   53.1000  C123        S        0        1        0        0      2   \n","4    8.0500   NaN        S        0        0        1        0      1   \n","5    8.4583   NaN        Q        0        0        1        0      1   \n","6   51.8625   E46        S        0        0        1        0      1   \n","7   21.0750   NaN        S        1        0        0        0      5   \n","8   11.1333   NaN        S        0        1        0        0      3   \n","9   30.0708   NaN        C        0        1        0        0      2   \n","10  16.7000    G6        S        0        1        0        0      3   \n","11  26.5500  C103        S        0        1        0        0      1   \n","12   8.0500   NaN        S        0        0        1        0      1   \n","13  31.2750   NaN        S        0        0        1        0      7   \n","14   7.8542   NaN        S        0        1        0        0      1   \n","\n","    family_size  \n","0             1  \n","1             1  \n","2             1  \n","3             1  \n","4             1  \n","5             1  \n","6             1  \n","7             0  \n","8             1  \n","9             1  \n","10            1  \n","11            1  \n","12            1  \n","13            0  \n","14            1  "]},"execution_count":82,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head(15)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1677438238805,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"w17n3_4cHBMr","outputId":"d18b0aa7-16cf-4b47-e420-2a453e3c5c24"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEDCAYAAADayhiNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbOklEQVR4nO3df1TV9eHH8dfFy42DXaaX+LjRpjO3Y8wRBZSB4jJhFc6FxxDh4Kqxs7mhtnPoq838TTVJ7ZTG+mFr8oWjU+nHyNPEZbplkjve22HYoZlWk8jg3iZSIoF6v3/k3ie+isPk/kiej7+478/nfu4Lj+fz4vP+fD73Y/P7/X4BACApItQBAADhg1IAABiUAgDAoBQAAAalAAAwKAUAgGEPdYCL4Xa7Qx0BAL6SUlJSzjn+lS4FqfdfDABwbuf7g5rpIwCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAML7yN68Bl6rDyxNDHQFhaPjihoBunyMFAIBBKQAADEoBAGAEtBQOHDigzMxMVVVVSZK6u7tVUlKiO+64Q3feeaeOHTsmSaqpqdG0adOUm5urLVu29Fg3Pz9fhYWFampqCmRUAIACWAodHR0qLS1VWlqaGdu8ebOGDh2q6upqZWdna9++fero6FB5ebnWr1+vyspKVVRUqK2tTVu3blVMTIw2btyoWbNmafXq1YGKCgA4I2Cl4HA4tG7dOlmWZcZ27typH//4x5KkvLw8TZo0SfX19UpMTJTT6VRUVJSSk5Pl8XhUV1enrKwsSVJ6ero8Hk+gogIAzgjYJal2u112e8/NNzc3629/+5tWrlypK664QkuWLJHP55PL5TLruFwueb3eHuMRERGy2Wzq6uqSw+Hosc3GxsZA/QpASA0OdQCEpUDv84J6n4Lf79fIkSM1e/Zs/e53v9NTTz2l733ve2et09t7zyUhIaHfcwLh4HCoAyAs9cc+L2wesnPFFVfo+uuvlySNHz9eBw8elGVZ8vl8Zp3W1lZZliXLsuT1eiV9ftLZ7/efdZQAAOhfQS2FCRMm6LXXXpMkvfXWWxo5cqSSkpLU0NCg9vZ2HT9+XB6PR6mpqRo3bpy2bdsm6fNzEWPHjg1mVAAYkAI2fbR//36VlZWpublZdrtdtbW1WrVqlR588EFVV1crOjpaZWVlioqKUklJiYqKimSz2VRcXCyn06ns7Gzt2bNH+fn5cjgcWrFiRaCiAgDOsPl7m6z/CnC73UpJSQl1DCAg+O4jnEt/fPfR+fad3NEMADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAEZAS+HAgQPKzMxUVVVVj/HXXntNo0ePNq9ramo0bdo05ebmasuWLZI+fy5zSUmJ8vPzVVhYqKampkBGBQAogKXQ0dGh0tJSpaWl9Rj/7LPP9PTTTysuLs6sV15ervXr16uyslIVFRVqa2vT1q1bFRMTo40bN2rWrFlavXp1oKICAM4IWCk4HA6tW7dOlmX1GH/yySdVUFAgh8MhSaqvr1diYqKcTqeioqKUnJwsj8ejuro6ZWVlSZLS09Pl8XgCFRUAcIY9YBu222W399z8e++9p7ffflv33HOPVq5cKUny+XxyuVxmHZfLJa/X22M8IiJCNptNXV1dpkz+o7GxMVC/AhBSg0MdAGEp0Pu8gJXCufz2t7/VwoULz7uO3++/oPGEhISLzgWEo8OhDoCw1B/7PLfb3euyoF191NLSonfffVf33nuvpk+frtbWVhUWFsqyLPl8PrNea2urLMuSZVnyer2SPj/p7Pf7zzpKAAD0r6AdKQwbNkyvvPKKeX3zzTerqqpKnZ2dWrhwodrb2zVo0CB5PB4tWLBAn376qbZt26aMjAzt3LlTY8eODVZUABiwAlYK+/fvV1lZmZqbm2W321VbW6u1a9dqyJAhPdaLiopSSUmJioqKZLPZVFxcLKfTqezsbO3Zs0f5+flyOBxasWJFoKICAM6w+XubrP8KcLvdSklJCXUMICAOL08MdQSEoeGLGy56G+fbd3JHMwDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYAS2FAwcOKDMzU1VVVZKkI0eO6K677lJhYaHuuusu8wzmmpoaTZs2Tbm5udqyZYukz5/LXFJSovz8fBUWFqqpqSmQUQEACmApdHR0qLS0VGlpaWbs0Ucf1fTp01VVVaWsrCz94Q9/UEdHh8rLy7V+/XpVVlaqoqJCbW1t2rp1q2JiYrRx40bNmjVLq1evDlRUAMAZASsFh8OhdevWybIsM7ZkyRLdcsstkqShQ4eqra1N9fX1SkxMlNPpVFRUlJKTk+XxeFRXV6esrCxJUnp6ujweT6CiAgDOsAdsw3a77Paem4+OjpYknTp1Shs2bFBxcbF8Pp9cLpdZx+Vyyev19hiPiIiQzWZTV1eXHA5Hj202NjYG6lcAQmpwqAMgLAV6nxewUujNqVOnNG/ePN14441KS0vTSy+91GO53+8/5/t6G09ISOj3jEA4OBzqAAhL/bHPc7vdvS4L+tVHv/nNbzRixAjNnj1bkmRZlnw+n1ne2toqy7JkWZY5Ed3d3S2/33/WUQIAoH8FtRRqamoUGRmpuXPnmrGkpCQ1NDSovb1dx48fl8fjUWpqqsaNG6dt27ZJknbu3KmxY8cGMyoADEgBmz7av3+/ysrK1NzcLLvdrtraWn388ce67LLLNHPmTEnSqFGjtHTpUpWUlKioqEg2m03FxcVyOp3Kzs7Wnj17lJ+fL4fDoRUrVgQqKgDgDJu/t8n6rwC3262UlJRQxwAC4vDyxFBHQBgavrjhordxvn0ndzQDAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgBHQUjhw4IAyMzNVVVUlSTpy5IhmzpypgoIC3XPPPerq6pL0+WM6p02bptzcXG3ZskXS589lLikpUX5+vgoLC9XU1BTIqAAABbAUOjo6VFpaqrS0NDO2Zs0aFRQUaMOGDRoxYoSqq6vV0dGh8vJyrV+/XpWVlaqoqFBbW5u2bt2qmJgYbdy4UbNmzdLq1asDFRUAcEbASsHhcGjdunWyLMuM7d27V5MmTZIkTZw4UXV1daqvr1diYqKcTqeioqKUnJwsj8ejuro6ZWVlSZLS09Pl8XgCFRUAcIY9YBu222W399z8iRMn5HA4JEmxsbHyer3y+XxyuVxmHZfLddZ4RESEbDaburq6zPv/o7GxMVC/AhBSg0MdAGEp0Pu8gJXCf+P3+/tlPCEhod8yAeHkcKgDICz1xz7P7Xb3uiyoVx9FR0ers7NTktTS0iLLsmRZlnw+n1mntbXVjHu9Xkmfn3T2+/1nHSUAAPpXUEshPT1dtbW1kqTt27crIyNDSUlJamhoUHt7u44fPy6Px6PU1FSNGzdO27ZtkyTt3LlTY8eODWZUABiQAjZ9tH//fpWVlam5uVl2u121tbVatWqV7rvvPm3atEnx8fHKyclRZGSkSkpKVFRUJJvNpuLiYjmdTmVnZ2vPnj3Kz8+Xw+HQihUrAhUVAHCGzd/bZP1XgNvtVkpKSqhjAAFxeHliqCMgDA1f3HDR2zjfvrNP00cfffTRWWOHDh26uFQAgLBz3lL497//rXfeeUdz5szRoUOHdPDgQR08eFBvv/22fvWrXwUrIwAgSM57TuHdd9/Vc889p/fff19Lly414xEREZoyZUqgswEAguy8pZCamqrU1FRNmTJF6enpwcoEAAiRPl199OGHH2rq1Kn65JNPetxEtmPHjoAFAwAEX59K4dlnn9Xjjz+ur3/964HOAwAIoT6Vwre//W1dddVVgc4CAAixPpWCy+VSXl6err32Wg0aNMiMz5s3L2DBAADB16dSSElJOetGB5vNFpBAAIDQ6fPXXFACAHDp61MpHDhwwPx88uRJ1dfX67vf/a5ycnIClQsAEAJ9KoX58+f3eH3q1CnNnTs3IIEAAKHTp1I4ceJEj9der1fvvvtuQAIBAEKnT6UwefJk87PNZpPT6dRPf/rTgIUCAIRGn0rh1VdflSQdO3ZMERERcjqdAQ0FAAiNPpXCnj17tGzZMl122WXq7u5WRESEli9fzrMMAOAS06dSWLNmjSorK2VZliTpyJEjKikp0YYNGy7ow44fP6758+fr2LFj6u7uVnFxseLi4sw3sI4ePVrLli2TJD3zzDPatm2bbDabZs+erR/84AcX9FkAgAvXp1KIjIw0hSBJ3/jGN2S3X/iTPF944QWNHDlSJSUlamlp0Z133qm4uDgtWLBA11xzjUpKSvTXv/5VV111lV5++WX98Y9/1KeffqqCggKNHz++x93UAID+16c9+ze/+U0tW7ZMN9xwg/x+v/bu3avhw4df8IcNHTpU//znPyVJ7e3tGjJkiJqbm3XNNddIkiZOnKi6ujp5vV5lZGTI4XDI5XLpyiuv1MGDBzV69OgL/kwAQN/1qRTmzJmj559/Xm63WzabTcOGDdPUqVMv+MMmT56s559/XllZWWpvb9cTTzyh5cuXm+WxsbHyer0aMmSIXC6XGXe5XPJ6vecshcbGxgvOAXwVDA51AISlQO/z+lQK999/v3Jzc5WdnS1J2rVrl+6//349++yzF/Rhf/rTnxQfH6/f//73evvtt1VcXNzjSqYvPqvhi3obl6SEhIQLygB8VRwOdQCEpf7Y57nd7l6XnfcZzf/R2dlpCkGSbrrpJnV3d19wEI/Ho/Hjx0uSrr76an322Wc6evSoWd7S0iLLsmRZlnw+31njAIDA6lMpxMfHq6ysTH/5y19UW1urZcuWKT4+/oI/bMSIEaqvr5ckNTc3a/DgwRo1apT27dsnSdq+fbsyMjJ04403ateuXerq6lJLS4taW1v1ne9854I/DwBwYfo0fVRWVqYXXnhBe/bs0aBBg5SUlNTjLue+ysvL04IFC1RYWKiTJ09q6dKliouL0+LFi3X69GklJSWZZ0FPnz5dhYWFstlsWrp0qSIi+tRfAICLYPOfb8I+zLndbm6gwyXr8PLEUEdAGBq+uOGit3G+fSd/fgMADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGJQCAMCgFAAABqUAADAoBQCA0acnr/WnmpoaPfPMM7Lb7Zo7d65Gjx6tefPm6dSpU4qLi9PKlSvlcDhUU1OjiooKRUREaPr06crNzQ12VAAYcIJaCkePHlV5ebmee+45dXR0aO3ataqtrVVBQYFuu+02PfLII6qurlZOTo7Ky8tVXV2tyMhI3XHHHcrKytKQIUOCGRcABpygTh/V1dUpLS1Nl19+uSzLUmlpqfbu3atJkyZJkiZOnKi6ujrV19crMTFRTqdTUVFRSk5OlsfjCWZUABiQgnqk8MEHH6izs1OzZs1Se3u75syZoxMnTsjhcEiSYmNj5fV65fP55HK5zPtcLpe8Xu85t9nY2BiU7ECwDQ51AISlQO/zgn5Ooa2tTY8//rg+/PBD/eQnP5Hf7zfLvvjzF/U2LkkJCQn9nhEIB4dDHQBhqT/2eW63u9dlQZ0+io2N1XXXXSe73a7hw4dr8ODBGjx4sDo7OyVJLS0tsixLlmXJ5/OZ97W2tsqyrGBGBYABKailMH78eL3xxhs6ffq0jh49qo6ODqWnp6u2tlaStH37dmVkZCgpKUkNDQ1qb2/X8ePH5fF4lJqaGsyoADAgBXX6aNiwYbrllls0ffp0SdLChQuVmJio+fPna9OmTYqPj1dOTo4iIyNVUlKioqIi2Ww2FRcXy+l0BjMqAAxINv/5JuzDnNvtVkpKSqhjAAFxeHliqCMgDA1f3HDR2zjfvpM7mgEABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYlAIAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDACEkpdHZ2KjMzU88//7yOHDmimTNnqqCgQPfcc4+6urokSTU1NZo2bZpyc3O1ZcuWUMQEgAEnJKXwxBNP6Gtf+5okac2aNSooKNCGDRs0YsQIVVdXq6OjQ+Xl5Vq/fr0qKytVUVGhtra2UEQFgAEl6KVw6NAhHTx4UDfddJMkae/evZo0aZIkaeLEiaqrq1N9fb0SExPldDoVFRWl5ORkeTyeYEcFgAHHHuwPLCsr06JFi/Tiiy9Kkk6cOCGHwyFJio2Nldfrlc/nk8vlMu9xuVzyer3n3F5jY2PAMwOhMDjUARCWAr3PC2opvPjii7r22mv1rW9965zL/X7/BY1LUkJCQr9kA8LN4VAHQFjqj32e2+3udVlQS2HXrl1qamrSrl279NFHH8nhcCg6OlqdnZ2KiopSS0uLLMuSZVny+Xzmfa2trbr22muDGRUABqSglsKjjz5qfl67dq2uvPJKvfnmm6qtrdXtt9+u7du3KyMjQ0lJSVq4cKHa29s1aNAgeTweLViwIJhRAWBACvo5hf9vzpw5mj9/vjZt2qT4+Hjl5OQoMjJSJSUlKioqks1mU3FxsZxOZ6ijAsAlz+Y/34R9mHO73UpJSQl1DCAgDi9PDHUEhKHhixsuehvn23dyRzMAwKAUAAAGpQAAMCgFAIBBKQAADEoBAGBQCgAAg1IAABiUAgDAoBQAAAalAAAwKAUAgEEpAAAMSgEAYFAKAACDUgAAGEF/8trDDz8st9utkydP6he/+IUSExM1b948nTp1SnFxcVq5cqUcDodqampUUVGhiIgITZ8+Xbm5ucGOCgADTlBL4Y033tA777yjTZs26ejRo5o6darS0tJUUFCg2267TY888oiqq6uVk5Oj8vJyVVdXKzIyUnfccYeysrI0ZMiQYMYFgAEnqNNH119/vR577DFJUkxMjE6cOKG9e/dq0qRJkqSJEyeqrq5O9fX1SkxMlNPpVFRUlJKTk+XxeIIZFQAGpKAeKQwaNEjR0dGSpOrqak2YMEG7d++Ww+GQJMXGxsrr9crn88nlcpn3uVwueb3ec26zsbEx8MGBEBgc6gAIS4He5wX9nIIkvfLKK6qurtazzz6rH/7wh2bc7/efc/3exiUpISGh3/MB4eBwqAMgLPXHPs/tdve6LOhXH7322mt68skntW7dOjmdTkVHR6uzs1OS1NLSIsuyZFmWfD6feU9ra6ssywp2VAAYcIJaCp988okefvhhPfXUU+akcXp6umprayVJ27dvV0ZGhpKSktTQ0KD29nYdP35cHo9HqampwYwKAANSUKePXn75ZR09elS//vWvzdiKFSu0cOFCbdq0SfHx8crJyVFkZKRKSkpUVFQkm82m4uJiOZ3OYEYFgAHJ5j/fhH2Yc7vdSklJCXUMICAOL08MdQSEoeGLGy56G+fbd3JHMwDAoBQAAAalAAAwKAUAgEEpAACMkNzRHE5S/ud/Qx0BYci98iehjgCEBEcKAACDUgAAGJQCAMCgFAAABqUAADAoBQCAQSkAAAxKAQBgUAoAAINSAAAYYf01Fw899JDq6+tls9m0YMECXXPNNaGOBACXtLAthb///e/617/+pU2bNunQoUNasGCBNm3aFOpYAHBJC9vpo7q6OmVmZkqSRo0apWPHjunTTz8NcSoAuLSF7ZGCz+fTmDFjzGuXyyWv16vLL7+8x3put/uiPufpGWP++0oYcC72/1W/mLw+1AkQhrwB/r8ZtqXw//n9/rPGenvwNADgywnb6SPLsuTz+czr1tZWxcXFhTARAFz6wrYUxo0bp9raWknSW2+9Jcuyzpo6AgD0r7AtheTkZI0ZM0YzZszQAw88oCVLloQ60iXroYceUl5enmbMmKF//OMfoY4D9HDgwAFlZmaqqqoq1FEGhLA+p3DvvfeGOsIlj0t/Ec46OjpUWlqqtLS0UEcZMML2SAHBwaW/CGcOh0Pr1q2TZVmhjjJgUAoDnM/n09ChQ83r/1z6C4QDu92uqKioUMcYUCgF9HCuS38BDByUwgDHpb8AvohSGOC49BfAF9n8zBcMeKtWrdK+fftks9m0ZMkSXX311aGOBEiS9u/fr7KyMjU3N8tut2vYsGFau3athgwZEupolyxKAQBgMH0EADAoBQCAQSkAAAxKAQBgUAoAAINSwIDU3d2t3NxczZ8//0u9/8EHH1RTU5PWrl3bL9/e+fTTT+vNN9+86O0AFyusvyUVCBSv16uuri6VlZV9qffff//9/Zrn5z//eb9uD/iyuE8BA9KcOXO0e/du3Xrrrfrggw8kSSdPnlRZWZmGDx+uzMxM3Xzzzaqrq1NGRob8fr9ef/11TZgwQffee69mzpypRYsWqba2VkOHDtW+ffuUl5entLQ0dXV1KTs7W9u2bZPdfvbfXbt379ajjz6qqKgoxcbGatWqVVq0aJFuueUWHTlyRH/+858lSe+//74KCwv1s5/9TIsWLVJTU5NOnjypuXPn8lXSCBimjzAgzZ8/XyNHjlR+fr6Ki4tVWVmpadOmacOGDZKkDz74QHl5edq8ebMqKyt16623avPmzXruuefOub3bb79dL7/8sqTPv458woQJ5ywESaqqqtJ9992nqqoqTZ48WW1tbWZZQUGBKisrtXLlSsXGxio/P18vvfSS4uLiVFlZqfLycj300EP9+48BfAHTRxjQ4uLi9MADD2jt2rVqb2/XmDFjJEmXX365Ro0aJUmKjo7WmDFjZLfbdfr06XNuJyMjQytXrlR3d7d27NihqVOn9vqZt956q5YsWaIpU6Zo8uTJZ30B4enTpzV//nwtXLhQMTExevPNN+V2u+XxeCRJn332mbq6uuRwOPrjnwDogVLAgLZmzRqNHz9e+fn52rZtm3bt2iVJGjRoUI/1evur/4vLx40bp7q6Or3zzju67rrrel03JydHGRkZeuWVV/TLX/5Sjz32WI/lTz31lJKTk5WamipJioyM1KxZs/SjH/3oS/yGwIVh+ggD2tGjRzV8+HD5/X7t2LFD3d3dX3pbt99+u9asWaMbbrjhvOuVl5fLbrcrLy9P2dnZOnTokFlWX1+v119/XbNnzzZjSUlJ2rFjhyTp448/1iOPPPKlMwL/DUcKGNDy8vJUWlqqK6+80pw83r1795fa1ve//30dO3ZMU6ZMOe968fHxuvvuuxUTE6OYmBjdfffdevXVVyV9fuRy9OhR3XXXXZKk5ORkzZkzR2+88YZmzJihU6dO9SgMoL9x9RHQT9577z0tW7ZM69evD3UU4EvjSAHoBxs3btTmzZu1YsUKSVJXV5eKiorOWm/kyJFavnx5sOMBfcaRAgDA4EQzAMCgFAAABqUAADAoBQCAQSkAAAxKAQBg/B8WAElUHWp6gAAAAABJRU5ErkJggg==","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x = \"family_size\", data=train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"executionInfo":{"elapsed":718,"status":"ok","timestamp":1677438249656,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"xNU2iRGCHDxt","outputId":"26726546-b100-463f-d340-02685598c6c3"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 360x360 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.factorplot(x = \"family_size\", y= \"Survived\", data = train_df, kind = \"bar\")\n","g.set_ylabels(\"Survival\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":423,"status":"ok","timestamp":1677438257784,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"X23AbFdNHGMs","outputId":"89fbe3e9-33b9-4c2b-d324-26eb08be9ade"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-d84ca599-89c3-4fec-9393-bf2924449177\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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</tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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0            113803   \n","4            5         0       3    1  35.0      0      0            373450   \n","\n","      Fare Cabin Embarked  Title_0  Title_1  Title_2  Title_3  Fsize  \\\n","0   7.2500   NaN        S        0        0        1        0      2   \n","1  71.2833   C85        C        0        1        0        0      2   \n","2   7.9250   NaN        S        0        1        0        0      1   \n","3  53.1000  C123        S        0        1        0        0      2   \n","4   8.0500   NaN        S        0        0        1        0      1   \n","\n","   family_size_0  family_size_1  \n","0              0              1  \n","1              0              1  \n","2              0              1  \n","3              0              1  \n","4              0              1  "]},"execution_count":85,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns = [\"family_size\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":531,"status":"ok","timestamp":1677438325337,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"OaptV3gyHIZ0","outputId":"fe0ec809-40f6-4f5f-c474-6c6d9244c862"},"outputs":[{"data":{"text/plain":["0    S\n","1    C\n","2    S\n","3    S\n","4    S\n","Name: Embarked, dtype: object"]},"execution_count":86,"metadata":{},"output_type":"execute_result"}],"source":["#embarked\n","train_df[\"Embarked\"].head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"elapsed":44,"status":"ok","timestamp":1677438333540,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"v0QlBFTeHYv8","outputId":"2be30ca8-23ef-45a6-a53b-502dc34866dc"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x=\"Embarked\", data=train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":287},"executionInfo":{"elapsed":53,"status":"ok","timestamp":1677438343603,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"pTO9s2-3Ha4O","outputId":"cdead013-c294-4f6b-ee1c-427f5da03433"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ab9cc5ec-2d4d-4b63-8835-5887ceaa86c8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>Title_2</th>\n","      <th>Title_3</th>\n","      <th>Fsize</th>\n","      <th>family_size_0</th>\n","      <th>family_size_1</th>\n","      <th>Embarked_C</th>\n","      <th>Embarked_Q</th>\n","      <th>Embarked_S</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A/5 21171</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>PC 17599</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>STON/O2. 3101282</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>113803</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>373450</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab9cc5ec-2d4d-4b63-8835-5887ceaa86c8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n","0            1         0       3    1  22.0      1      0         A/5 21171   \n","1            2         1       1    0  38.0      1      0          PC 17599   \n","2            3         1       3    0  26.0      0      0  STON/O2. 3101282   \n","3            4         1       1    0  35.0      1      0            113803   \n","4            5         0       3    1  35.0      0      0            373450   \n","\n","      Fare Cabin  Title_0  Title_1  Title_2  Title_3  Fsize  family_size_0  \\\n","0   7.2500   NaN        0        0        1        0      2              0   \n","1  71.2833   C85        0        1        0        0      2              0   \n","2   7.9250   NaN        0        1        0        0      1              0   \n","3  53.1000  C123        0        1        0        0      2              0   \n","4   8.0500   NaN        0        0        1        0      1              0   \n","\n","   family_size_1  Embarked_C  Embarked_Q  Embarked_S  \n","0              1           0           0           1  \n","1              1           1           0           0  \n","2              1           0           0           1  \n","3              1           0           0           1  \n","4              1           0           0           1  "]},"execution_count":88,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns=[\"Embarked\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1677438357393,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"xonJQmQWHdR_","outputId":"a3db0950-f897-4295-f61c-21bc85fcf221"},"outputs":[{"data":{"text/plain":["0            A/5 21171\n","1             PC 17599\n","2     STON/O2. 3101282\n","3               113803\n","4               373450\n","5               330877\n","6                17463\n","7               349909\n","8               347742\n","9               237736\n","10             PP 9549\n","11              113783\n","12           A/5. 2151\n","13              347082\n","14              350406\n","15              248706\n","16              382652\n","17              244373\n","18              345763\n","19                2649\n","Name: Ticket, dtype: object"]},"execution_count":90,"metadata":{},"output_type":"execute_result"}],"source":["#ticket\n","train_df[\"Ticket\"].head(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"executionInfo":{"elapsed":458,"status":"ok","timestamp":1677438370650,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"HholmdTpHfsq","outputId":"add148aa-650d-4fcb-a83f-6273ecf244e5"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'A5'"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":["a = \"A/5. 2151\"\n","a.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"asVg49iZHj8Q"},"outputs":[],"source":["tickets = []\n","for i in list(train_df.Ticket):\n","    if not i.isdigit():\n","        tickets.append(i.replace(\".\",\"\").replace(\"/\",\"\").strip().split(\" \")[0])\n","    else:\n","        tickets.append(\"x\")\n","train_df[\"Ticket\"] = tickets"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":382,"status":"ok","timestamp":1677438395377,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"DBpTCLtMHnCv","outputId":"bbb9d5fb-a739-4cfc-a359-01a32d840d0e"},"outputs":[{"data":{"text/plain":["0         A5\n","1         PC\n","2     STONO2\n","3          x\n","4          x\n","5          x\n","6          x\n","7          x\n","8          x\n","9          x\n","10        PP\n","11         x\n","12        A5\n","13         x\n","14         x\n","Name: Ticket, dtype: object"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Ticket\"].head(15)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270},"executionInfo":{"elapsed":51,"status":"ok","timestamp":1677438402411,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"gzujbAYIHo4z","outputId":"295917e5-8855-43fa-ead1-633494b1c0d8"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-21e90c64-ded8-4482-8668-f450373150ef\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Ticket</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>Title_2</th>\n","      <th>Title_3</th>\n","      <th>Fsize</th>\n","      <th>family_size_0</th>\n","      <th>family_size_1</th>\n","      <th>Embarked_C</th>\n","      <th>Embarked_Q</th>\n","      <th>Embarked_S</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>A5</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      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    <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>x</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>x</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","   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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch  Ticket     Fare  \\\n","0            1         0       3    1  22.0      1      0      A5   7.2500   \n","1            2         1       1    0  38.0      1      0      PC  71.2833   \n","2            3         1       3    0  26.0      0      0  STONO2   7.9250   \n","3            4         1       1    0  35.0      1      0       x  53.1000   \n","4            5         0       3    1  35.0      0      0       x   8.0500   \n","\n","  Cabin  Title_0  Title_1  Title_2  Title_3  Fsize  family_size_0  \\\n","0   NaN        0        0        1        0      2              0   \n","1   C85        0        1        0        0      2              0   \n","2   NaN        0        1        0        0      1              0   \n","3  C123        0        1        0        0      2              0   \n","4   NaN        0        0        1        0      1              0   \n","\n","   family_size_1  Embarked_C  Embarked_Q  Embarked_S  \n","0              1           0           0           1  \n","1              1           1           0           0  \n","2              1           0           0           1  \n","3              1           0           0           1  \n","4              1           0           0           1  "]},"execution_count":95,"metadata":{},"output_type":"execute_result"}],"source":["train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":392},"executionInfo":{"elapsed":77,"status":"ok","timestamp":1677438410175,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"-wrZSSFUHrul","outputId":"bfb2833c-31be-4aa5-8857-220898729535"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ebf1ec02-6543-46b3-86c5-a25bea7e8c3f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Pclass</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Title_0</th>\n","      <th>...</th>\n","      <th>T_SOPP</th>\n","      <th>T_SOTONO2</th>\n","      <th>T_SOTONOQ</th>\n","      <th>T_SP</th>\n","      <th>T_STONO</th>\n","      <th>T_STONO2</th>\n","      <th>T_SWPP</th>\n","      <th>T_WC</th>\n","      <th>T_WEP</th>\n","      <th>T_x</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      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T_SOPP  T_SOTONO2  T_SOTONOQ  T_SP  T_STONO  T_STONO2  \\\n","0        0  ...       0          0          0     0        0         0   \n","1        0  ...       0          0          0     0        0         0   \n","2        0  ...       0          0          0     0        0         1   \n","3        0  ...       0          0          0     0        0         0   \n","4        0  ...       0          0          0     0        0         0   \n","5        0  ...       0          0          0     0        0         0   \n","6        0  ...       0          0          0     0        0         0   \n","7        1  ...       0          0          0     0        0         0   \n","8        0  ...       0          0          0     0        0         0   \n","9        0  ...       0          0          0     0        0         0   \n","\n","   T_SWPP  T_WC  T_WEP  T_x  \n","0       0     0      0    0  \n","1       0     0      0    0  \n","2       0     0      0    0  \n","3       0     0      0    1  \n","4       0     0      0    1  \n","5       0     0      0    1  \n","6       0     0      0    1  \n","7       0     0      0    1  \n","8       0     0      0    1  \n","9       0     0      0    1  \n","\n","[10 rows x 50 columns]"]},"execution_count":96,"metadata":{},"output_type":"execute_result"}],"source":["train_df = pd.get_dummies(train_df, columns = [\"Ticket\"], prefix = \"T\")\n","train_df.head(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1677438425371,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"SwfE9pfwHtgf","outputId":"68e7dbbf-eed6-499c-d969-5ed6a463a8cc"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["#Pclass\n","sns.countplot(x=\"Pclass\", data = train_df)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":24,"status":"ok","timestamp":1677438437500,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"I-f4ngcNHxUz","outputId":"24322465-9010-45f3-d687-ce3db97f67eb"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-e467ac00-b22a-4365-bb7e-eb2fed66c664\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Sex</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>...</th>\n","      <th>T_SP</th>\n","      <th>T_STONO</th>\n","      <th>T_STONO2</th>\n","      <th>T_SWPP</th>\n","      <th>T_WC</th>\n","      <th>T_WEP</th>\n","      <th>T_x</th>\n","      <th>Pclass_1</th>\n","      <th>Pclass_2</th>\n","      <th>Pclass_3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>C85</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 52 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e467ac00-b22a-4365-bb7e-eb2fed66c664')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["   PassengerId  Survived  Sex   Age  SibSp  Parch     Fare Cabin  Title_0  \\\n","0            1         0    1  22.0      1      0   7.2500   NaN        0   \n","1            2         1    0  38.0      1      0  71.2833   C85        0   \n","2            3         1    0  26.0      0      0   7.9250   NaN        0   \n","3            4         1    0  35.0      1      0  53.1000  C123        0   \n","4            5         0    1  35.0      0      0   8.0500   NaN        0   \n","\n","   Title_1  ...  T_SP  T_STONO  T_STONO2  T_SWPP  T_WC  T_WEP  T_x  Pclass_1  \\\n","0        0  ...     0        0         0       0     0      0    0         0   \n","1        1  ...     0        0         0       0     0      0    0         1   \n","2        1  ...     0        0         1       0     0      0    0         0   \n","3        1  ...     0        0         0       0     0      0    1         1   \n","4        0  ...     0        0         0       0     0      0    1         0   \n","\n","   Pclass_2  Pclass_3  \n","0         0         1  \n","1         0         0  \n","2         0         1  \n","3         0         0  \n","4         0         1  \n","\n","[5 rows x 52 columns]"]},"execution_count":98,"metadata":{},"output_type":"execute_result"}],"source":["train_df[\"Pclass\"] == train_df[\"Pclass\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns = [\"Pclass\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":394,"status":"ok","timestamp":1677438458135,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"k6fU5t-eH0Oo","outputId":"885107a2-f816-4c79-9b17-58b09ebbd084"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-2844e8eb-eb39-4ec8-8fa7-c5177c593917\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>PassengerId</th>\n","      <th>Survived</th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Cabin</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>Title_2</th>\n","      <th>...</th>\n","      <th>T_STONO2</th>\n","      <th>T_SWPP</th>\n","      <th>T_WC</th>\n","      <th>T_WEP</th>\n","      <th>T_x</th>\n","      <th>Pclass_1</th>\n","      <th>Pclass_2</th>\n","      <th>Pclass_3</th>\n","      <th>Sex_0</th>\n","      <th>Sex_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      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  <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>C123</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 53 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2844e8eb-eb39-4ec8-8fa7-c5177c593917')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 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T_STONO2  T_SWPP  T_WC  T_WEP  T_x  Pclass_1  Pclass_2  \\\n","0        1  ...         0       0     0      0    0         0         0   \n","1        0  ...         0       0     0      0    0         1         0   \n","2        0  ...         1       0     0      0    0         0         0   \n","3        0  ...         0       0     0      0    1         1         0   \n","4        1  ...         0       0     0      0    1         0         0   \n","\n","   Pclass_3  Sex_0  Sex_1  \n","0         1      0      1  \n","1         0      1      0  \n","2         1      1      0  \n","3         0      1      0  \n","4         1      0      1  \n","\n","[5 rows x 53 columns]"]},"execution_count":99,"metadata":{},"output_type":"execute_result"}],"source":["#sex\n","train_df[\"Sex\"] = train_df[\"Sex\"].astype(\"category\")\n","train_df = pd.get_dummies(train_df, columns=[\"Sex\"])\n","train_df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qHcV33atH5Qj"},"outputs":[],"source":["#removing\n","train_df.drop(labels = [\"PassengerId\", \"Cabin\"],axis = 1, inplace=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":52,"status":"ok","timestamp":1677438485053,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"Kqd4KzX6H9zT","outputId":"1aa1eace-f90f-4cb5-fd1d-4e2615e8373a"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ca44e0a3-d21b-4426-a85a-53e2fc64dcda\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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  <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 51 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca44e0a3-d21b-4426-a85a-53e2fc64dcda')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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sklearn.linear_model import LogisticRegression\n","from sklearn.svm import SVC\n","from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.metrics import accuracy_score"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":396,"status":"ok","timestamp":1677438523963,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"8qIZYXBGIGZ1","outputId":"1c72edf7-893a-4786-8638-9cef59dc9f38"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-5f914f23-095f-446d-88d3-642d4fa92447\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n"," 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<td>0</td>\n","      <td>1</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 51 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f914f23-095f-446d-88d3-642d4fa92447')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":44,"status":"ok","timestamp":1677438546705,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"7jmZU6PzIM0x","outputId":"f49bf13a-0040-430e-a61d-f6c2ad30a6c2"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-c76f5307-5794-45ec-b023-1af56b60d72d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Age</th>\n","      <th>SibSp</th>\n","      <th>Parch</th>\n","      <th>Fare</th>\n","      <th>Title_0</th>\n","      <th>Title_1</th>\n","      <th>Title_2</th>\n","      <th>Title_3</th>\n","      <th>Fsize</th>\n","      <th>family_size_0</th>\n","      <th>...</th>\n","      <th>T_STONO2</th>\n","      <th>T_SWPP</th>\n","      <th>T_WC</th>\n","      <th>T_WEP</th>\n","      <th>T_x</th>\n","      <th>Pclass_1</th>\n","      <th>Pclass_2</th>\n","      <th>Pclass_3</th>\n","      <th>Sex_0</th>\n","      <th>Sex_1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>881</th>\n","      <td>22.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>7.2500</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>882</th>\n","      <td>38.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>71.2833</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>883</th>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7.9250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>884</th>\n","      <td>35.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>53.1000</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>885</th>\n","      <td>35.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>8.0500</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 50 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c76f5307-5794-45ec-b023-1af56b60d72d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  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T_STONO2  T_SWPP  T_WC  T_WEP  T_x  Pclass_1  \\\n","881              0  ...         0       0     0      0    0         0   \n","882              0  ...         0       0     0      0    0         1   \n","883              0  ...         1       0     0      0    0         0   \n","884              0  ...         0       0     0      0    1         1   \n","885              0  ...         0       0     0      0    1         0   \n","\n","     Pclass_2  Pclass_3  Sex_0  Sex_1  \n","881         0         1      0      1  \n","882         0         0      1      0  \n","883         0         1      1      0  \n","884         0         0      1      0  \n","885         0         1      0      1  \n","\n","[5 rows x 50 columns]"]},"execution_count":105,"metadata":{},"output_type":"execute_result"}],"source":["test.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1677438830152,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"ocqcgHCsIO8P","outputId":"85a24c2a-adc6-44ce-e7c5-dafda898aa82"},"outputs":[{"name":"stdout","output_type":"stream","text":["x_train 590\n","x_test 291\n","y_train 590\n","y_test 291\n","test 891\n"]}],"source":["train = train_df[:train_df_len]\n","x_train = train.drop(labels = \"Survived\", axis = 1)\n","y_train = train[\"Survived\"]\n","x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size = 0.33, random_state = 42)\n","print(\"x_train\",len(x_train))\n","print(\"x_test\",len(x_test))\n","print(\"y_train\",len(y_train))\n","print(\"y_test\",len(y_test))\n","print(\"test\",len(test))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":394,"status":"ok","timestamp":1677438853017,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"Kc8qf9CWJUEp","outputId":"77eead2b-9ccc-4df3-f8c4-17358e6c979a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Training accuracy: % 82.54\n","Testing accuracy: % 82.13\n"]}],"source":["#logistic regression\n","logreg = LogisticRegression()\n","logreg.fit(x_train, y_train)\n","acc_log_train = round(logreg.score(x_train, y_train)*100,2)\n","acc_log_test = round(logreg.score(x_test,y_test)*100,2)\n","\n","print(\"Training accuracy: % {}\".format(acc_log_train))\n","print(\"Testing accuracy: % {}\".format(acc_log_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mS--Z0tPJZRA"},"outputs":[],"source":["#classifier\n","random_state = 42\n","classifier = [\n","    DecisionTreeClassifier(random_state = random_state),\n","    SVC(random_state = random_state),\n","    RandomForestClassifier(random_state = random_state),\n","    LogisticRegression(random_state = random_state),\n","    KNeighborsClassifier()\n","    \n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iP-FlnC1JgoI"},"outputs":[],"source":["dt_param_grid = {\"min_samples_split\": range(10,500,20),\n","                \"max_depth\": range(1,20,2)}\n","svc_param_grid = {\"kernel\": [\"rbf\"],\n","                 \"gamma\": [0.001,0.01,0.1,1],\n","                 \"C\": [1,10,50,100,200,300,1000]}\n","rf_param_grid ={\"max_features\": [1,3,10],\n","               \"min_samples_split\": [2,3,10],\n","               \"min_samples_leaf\": [1,3,10],\n","               \"bootstrap\": [False],\n","               \"n_estimators\": [100,300],\n","               \"criterion\": [\"gini\"]}\n","logreg_param_grid = {\"C\":np.logspace(-3,3,7),\n","                    \"penalty\": [\"l1\",\"l2\"]}\n","knn_param_grid = {\"n_neighbors\": np.linspace(1,19,10, dtype = int).tolist(),\n","                 \"weights\": [\"uniform\",\"distance\"],\n","                 \"metric\": [\"euclidean\",\"manhattan\"]}\n","classifier_param = [dt_param_grid,\n","                   svc_param_grid,\n","                   rf_param_grid,\n","                   logreg_param_grid,\n","                   knn_param_grid]\n","        "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":163984,"status":"ok","timestamp":1677439149065,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"Hrx2gy59JlAK","outputId":"0391737e-1b22-4f78-976a-7c868a0b5321"},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 10 folds for each of 250 candidates, totalling 2500 fits\n","0.8355932203389831\n","Fitting 10 folds for each of 28 candidates, totalling 280 fits\n","0.7983050847457627\n","Fitting 10 folds for each of 54 candidates, totalling 540 fits\n","0.8440677966101695\n","Fitting 10 folds for each of 14 candidates, totalling 140 fits\n","0.8152542372881356\n","Fitting 10 folds for each of 40 candidates, totalling 400 fits\n","0.7813559322033898\n"]}],"source":["cv_result = []\n","best_estimators = []\n","for i in range(len(classifier)):\n","    clf = GridSearchCV(classifier[i], param_grid = classifier_param[i], cv = StratifiedKFold(n_splits = 10), scoring = \"accuracy\", n_jobs = -1, verbose = 1)\n","    clf.fit(x_train, y_train)\n","    cv_result.append(clf.best_score_)\n","    best_estimators.append(clf.best_estimator_)\n","    print(cv_result[i])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"elapsed":1366,"status":"ok","timestamp":1677439191403,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"YvhVwHiUJn1c","outputId":"9dd26d02-ec77-428e-92a8-2083c8f5e011"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cv_results = pd.DataFrame({\"Cross Validation Means\": cv_result,\n","                          \"ML Models\": [\"DecisionTreeClassifier\",\n","                                        \"SVM\",\"RandomForestClassifier\",\n","                                        \"LogisticRegression\",\n","                                        \"KNeighborsClassifier\"]})\n","\n","g = sns.barplot(\"Cross Validation Means\",\"ML Models\", data = cv_results)\n","g.set_xlabel(\"Mean Accuracy\")\n","g.set_title(\"Cross Validation Score\")\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1687,"status":"ok","timestamp":1677439222063,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"c8bxobVCKsAc","outputId":"bfe7c1aa-a305-446c-e1b0-56c9b50ceb6e"},"outputs":[{"name":"stdout","output_type":"stream","text":["0.8213058419243986\n"]}],"source":["#essembling\n","votingC = VotingClassifier(estimators = [(\"dt\", best_estimators[0]),\n","                                        (\"rcf\", best_estimators[2]),\n","                                        (\"lr\", best_estimators[3])],\n","                          voting = \"soft\", n_jobs = -1)\n","votingC = votingC.fit(x_train, y_train)\n","print(accuracy_score(votingC.predict(x_test), y_test))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pm5BdlCYKzZE"},"outputs":[],"source":["test_survived = pd.Series(votingC.predict(test), name = \"Survived\").astype(int)\n","results = pd.concat([test_PassengerId, test_survived], axis = 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"elapsed":414,"status":"ok","timestamp":1677439420643,"user":{"displayName":"Navya","userId":"17420818855499753946"},"user_tz":-330},"id":"lO8EJk8lK9VG","outputId":"ced3fee9-e51e-4756-ed8a-bfa9a007b88b"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-afa6116c-3632-4555-b726-e3ccd5d4df15\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: 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