ds project.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyM8Mygkb21KHFV9ysT4yibO"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["#import libraries\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline"],"metadata":{"id":"7Hkm9WCd779g"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#reading data\n","import pandas as pd\n","train=pd.read_csv(\"train.csv\")\n","train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"dcLHwla8JTPN","executionInfo":{"status":"ok","timestamp":1690100835348,"user_tz":-330,"elapsed":3611,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"b8e9302f-b232-49f4-d57e-1125d5b3029e"},"execution_count":108,"outputs":[{"output_type":"execute_result","data":{"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","886          887         0       2   \n","887          888         1       1   \n","888          889         0       3   \n","889          890         1       1   \n","890          891         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","886                              Montvila, Rev. Juozas    male  27.0      0   \n","887                       Graham, Miss. Margaret Edith  female  19.0      0   \n","888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n","889                              Behr, Mr. Karl Howell    male  26.0      0   \n","890                                Dooley, Mr. Patrick    male  32.0      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  \n","..     ...               ...      ...   ...      ...  \n","886      0            211536  13.0000   NaN        S  \n","887      0            112053  30.0000   B42        S  \n","888      2        W./C. 6607  23.4500   NaN        S  \n","889      0            111369  30.0000  C148        C  \n","890      0            370376   7.7500   NaN        Q  \n","\n","[891 rows x 12 columns]"],"text/html":["\n","\n","  <div id=\"df-abef1bc9-3895-4f19-b141-193c335cb310\">\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","    <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>886</th>\n","      <td>887</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>Montvila, Rev. Juozas</td>\n","      <td>male</td>\n","      <td>27.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>211536</td>\n","      <td>13.0000</td>\n","      <td>NaN</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>887</th>\n","      <td>888</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Graham, Miss. Margaret Edith</td>\n","      <td>female</td>\n","      <td>19.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>112053</td>\n","      <td>30.0000</td>\n","      <td>B42</td>\n","      <td>S</td>\n","    </tr>\n","    <tr>\n","      <th>888</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","    <tr>\n","      <th>889</th>\n","      <td>890</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Behr, Mr. Karl Howell</td>\n","      <td>male</td>\n","      <td>26.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>111369</td>\n","      <td>30.0000</td>\n","      <td>C148</td>\n","      <td>C</td>\n","    </tr>\n","    <tr>\n","      <th>890</th>\n","      <td>891</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>Dooley, Mr. Patrick</td>\n","      <td>male</td>\n","      <td>32.0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>370376</td>\n","      <td>7.7500</td>\n","      <td>NaN</td>\n","      <td>Q</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>891 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abef1bc9-3895-4f19-b141-193c335cb310')\"\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","\n","\n","    <div id=\"df-bbbdd952-2569-4670-8072-4f28160ddf4f\">\n","      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbbdd952-2569-4670-8072-4f28160ddf4f')\"\n","              title=\"Suggest charts.\"\n","              style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","      </button>\n","    </div>\n","\n","<style>\n","  .colab-df-quickchart {\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-quickchart: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-quickchart {\n","    background-color: #3B4455;\n","    fill: #D2E3FC;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart: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","      async function quickchart(key) {\n","        const containerElement = document.querySelector('#' + key);\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      }\n","    </script>\n","\n","      <script>\n","\n","function displayQuickchartButton(domScope) {\n","  let quickchartButtonEl =\n","    domScope.querySelector('#df-bbbdd952-2569-4670-8072-4f28160ddf4f button.colab-df-quickchart');\n","  quickchartButtonEl.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"]},"metadata":{},"execution_count":108}]},{"cell_type":"code","source":["train.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"4shgLc6-JTYY","executionInfo":{"status":"ok","timestamp":1690089868950,"user_tz":-330,"elapsed":834,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"2f9d458e-27f9-48ac-f10f-2beb0009ea42"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"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  "],"text/html":["\n","\n","  <div id=\"df-e00a7cd5-7f1f-4ee1-a67a-325059377486\">\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. 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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"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["#info\n","train.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t5Fezny2MJe6","executionInfo":{"status":"ok","timestamp":1690089598634,"user_tz":-330,"elapsed":415,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"aa77c677-4a99-4b0c-cc27-0e21889daa3b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"code","source":["#we can  use seaborn to create a simple heatmap to see where the missing data\n","train.isnull()"],"metadata":{"id":"T4aPY1jzJTfe","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1690089910826,"user_tz":-330,"elapsed":619,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"53a43e48-1351-490d-ca98-2c6889e07226"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n","0          False     False   False  False  False  False  False  False   False   \n","1          False     False   False  False  False  False  False  False   False   \n","2          False     False   False  False  False  False  False  False   False   \n","3          False     False   False  False  False  False  False  False   False   \n","4          False     False   False  False  False  False  False  False   False   \n","..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n","886        False     False   False  False  False  False  False  False   False   \n","887        False     False   False  False  False  False  False  False   False   \n","888        False     False   False  False  False   True  False  False   False   \n","889        False     False   False  False  False  False  False  False   False   \n","890        False     False   False  False  False  False  False  False   False   \n","\n","      Fare  Cabin  Embarked  \n","0    False   True     False  \n","1    False  False     False  \n","2    False   True     False  \n","3    False  False     False  \n","4    False   True     False  \n","..     ...    ...       ...  \n","886  False   True     False  \n","887  False  False     False  \n","888  False   True     False  \n","889  False  False     False  \n","890  False   True     False  \n","\n","[891 rows x 12 columns]"],"text/html":["\n","\n","  <div id=\"df-e238f8c6-98c9-4742-b238-c76bb6dff864\">\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>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</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>886</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>887</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>888</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>889</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>890</th>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>891 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e238f8c6-98c9-4742-b238-c76bb6dff864')\"\n","              title=\"Convert this dataframe to an interactive 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\n"},"metadata":{}}]},{"cell_type":"code","source":["#graphical representation of data\n","sns.set_style('whitegrid')\n","sns.countplot(x='Survived',data=train)"],"metadata":{"id":"o_2e7r2SAxJS","colab":{"base_uri":"https://localhost:8080/","height":466},"executionInfo":{"status":"ok","timestamp":1690090758516,"user_tz":-330,"elapsed":12,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"9f56255d-14da-4c44-fcbb-75c0affb4c77"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"metadata":{},"execution_count":15},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.set_style('whitegrid')\n","sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"aMEE5jbWRHW3","executionInfo":{"status":"ok","timestamp":1690091070175,"user_tz":-330,"elapsed":675,"user":{"displayName":"Manikandan 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\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.set_style('whitegrid')\n","sns.countplot(x='Survived',hue='Pclass',data=train,palette='rainbow')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"68jcSUQSRHZF","executionInfo":{"status":"ok","timestamp":1690091166066,"user_tz":-330,"elapsed":802,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"a5ddcc03-c2e5-49b9-fba0-3a135eaf94d6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Survived', ylabel='count'>"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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4e3urVatW6ty5syZPnqz27dtrxYoVtdqnoWHn3nvvVUJCgu688061bt1azzzzjPz8/JSVlaXz589r3bp1mj59umJjY9W5c2elpKRoz549ysrKkiTt2LFDOTk5evHFF9WhQwclJCRo0qRJevvtt3X58mUjdw0AAFRAeXl5rf/NdpsxOz8dqLR//36VlpYqLi7O2aZNmzYKCwtTVlaWoqKilJWVpXbt2ikkJMTZJj4+XklJScrJyVHHjh0rXQNQV6xWq9EluA2OPaBy7Ha7HA6H8+Mqi8UiT/+mdVLD1X5+3P/Peemll3TPPfeoefPmKioq0qZNm5SZmaklS5bccDtX99Fut1/zu6KivzsMDzs3Gqh08OBBeXl5qVGjRi7tg4ODlZ+fL0kqKChwCTqSnNNX21TGvn37qrgXQOX4+vpWOoybWXZ2tkpKSowuA6hXPD09VVJSovLycklXgo6vj0+ln31THY7ycpVcvFjhwJOXl6dp06apoKBA/v7+atu2rRYtWqTo6GgVFxdfd51Lly6ptLRUhw4dqnKdhoedqwOVzp8/r82bNysxMVGrVq0ypJaIiAj+2wYMYLPZjC4BqFcuXryoY8eOydfXVz4+Pi7LKnOmpdosFvn6+la4+bx58yrdhYeHh7y8vBQeHn7Nvtrt9gqdqDA87FwdqCRJnTt31r59+7RixQr96le/Umlpqc6dO+dydqewsFChoaGSrpzF2bt3r8v2rt6tdbVNZVitVsIOYACOO6ByrFarLBaL88PMru5jdf5Gu92LQK8OVOrcubO8vLyUkZHhXHb06FGdPHlSUVFRkqSoqCgdPnxYhYWFzjY7d+6Uv79/rd+zDwAA6gdDz+zcaKDS0qVLFRAQoCFDhigtLU2BgYHy9/fX3LlzFR0d7Qw78fHxCg8P17Rp0zR16lTl5+drwYIFGj58uLy9vY3cNQAA4CYMDTuFhYVKTExUXl6eAgICZLPZtHTpUvXs2VOSNGPGDHl4eGjixIm6fPmy4uPjNWvWLOf6VqtVb7zxhpKSkjRs2DD5+vpq0KBBmjhxolG7BAAA3IyhYSclJeWmyxs0aKBZs2a5BJyfatGihRYvXlzTpQEA4PbqdDCyQWpiH91uzA4AALg5Ly8vSbrh7dpmcnUfr+5zVRh+NxYAAKgcq9Wqxo0bKy8vT5Lk5+dnuruyHA6HiouLlZeXp8aNG1frrk3CDgAA9VCzZs0kyRl4zKpx48bOfa0qwg4AAPWQxWJR8+bN1bRpU5WWlhpdTq3w8vKqkedwEXYAAKjHeCDuz2OAMgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDVDw86bb76pIUOGKDo6WrGxsRo3bpyOHj3q0mbEiBGy2WwuH88//7xLm5MnT2r06NGKjIxUbGys5s2bp7KysrrcFQAA4KY8jew8MzNTw4cPV0REhOx2u+bPn69Ro0bp/fffl5+fn7PdQw89pIkTJzqnfX19nZ/b7XaNGTNGISEhWrt2rfLy8pSYmCgvLy89++yzdbo/AADA/RgadpYuXeoynZaWptjYWB04cEDdunVzzvfx8VFoaOh1t7Fjxw7l5ORo2bJlCgkJUYcOHTRp0iSlp6drwoQJ8vb2rnA9dru9ajsCVIHVajW6BLfBsQegKir6u8PQsPNT58+flyQFBga6zN+4caPee+89hYaGqnfv3ho3bpzz7E5WVpbatWunkJAQZ/v4+HglJSUpJydHHTt2rHD/+/btq4G9AH6er69vpX42zS47O1slJSVGlwHApNwm7JSXlyslJUUxMTFq166dc/6AAQMUFhampk2bKjs7W+np6frmm2/06quvSpIKCgpcgo4k53R+fn6laoiIiOC/bcAANpvN6BIA1EN2u71CJyrcJuwkJyfr66+/1urVq13mDxs2zPm5zWZTaGioHnvsMeXm5uqOO+6o0RqsVithBzAAxx2A2uQWt57Pnj1b27Zt0/Lly9WsWbObto2MjJQkHTt2TNKVszgFBQUuba5O32icDwAAuHUYGnYcDodmz56tLVu2aPny5WrZsuXPrnPw4EFJ/woyUVFROnz4sAoLC51tdu7cKX9/f4WHh9dO4QAAoN4w9DJWcnKyNm3apNdee00NGzZ0jrEJCAiQj4+PcnNztXHjRiUkJKhx48bKzs5WamqqunXrpvbt20u6Mhg5PDxc06ZN09SpU5Wfn68FCxZo+PDhlboTCwAAmJOhYWfNmjWSrjw48MdSU1M1ePBgeXl5KSMjQytWrFBxcbGaN2+uvn37aty4cc62VqtVb7zxhpKSkjRs2DD5+vpq0KBBLs/lAQAAty5Dw052dvZNlzdv3lyrVq362e20aNFCixcvrqmyAACAibjFAGUAAIDaQtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmVqWwM3LkSJ07d+6a+RcuXNDIkSOrXRQAAEBNqVLYyczMVGlp6TXzL126pC+++KLaRQEAANQUz8o0PnTokPPznJwc5efnO6fLy8v16aef6rbbbqu56gAAAKqpUmHnP/7jP2SxWGSxWPToo49es9zHx0czZ86sseIAAACqq1Jh5+9//7scDof69Omj//mf/1FQUJBzmZeXl4KDg2W1Wmu8SAAAgKqqVNhp0aKFJNfLWQAAAO6sUmHnx7799lvt2rVLhYWFKi8vd1k2YcKEahcGAABQE6oUdt59910lJSWpSZMmCgkJkcVicS6zWCyEHQAA4DaqFHZef/11Pf300xo9enRN1wMAAFCjqvScnbNnz+pXv/pVtTt/8803NWTIEEVHRys2Nlbjxo3T0aNHXdpcunRJycnJ6t69u6Kjo/XUU0+poKDApc3Jkyc1evRoRUZGKjY2VvPmzVNZWVm16wMAAPVflcLOAw88oB07dlS788zMTA0fPlzvvvuuli1bprKyMo0aNUrFxcXONikpKfrkk0+0YMECrVy5Unl5eS6Xyex2u8aMGaPS0lKtXbtWaWlpWr9+vV5++eVq1wcAAOq/Kl3GatWqlRYuXKgvv/xS7dq1k6en62Yq+sqIpUuXukynpaUpNjZWBw4cULdu3XT+/HmtW7dO6enpio2NlXQl/PTr109ZWVmKiorSjh07lJOTo2XLlikkJEQdOnTQpEmTlJ6ergkTJsjb27vC+2W32yvcFqguHtPwLxx7AKqior87qhR23nnnHfn5+SkzM1OZmZkuyywWS5Xfj3X+/HlJUmBgoCRp//79Ki0tVVxcnLNNmzZtFBYW5gw7WVlZateunUJCQpxt4uPjlZSUpJycHHXs2LHC/e/bt69KdQOV5evrW6mfTbPLzs5WSUmJ0WUAMKkqhZ2PP/64putQeXm5UlJSFBMTo3bt2kmSCgoK5OXlpUaNGrm0DQ4Odr6qoqCgwCXoSHJO//h1FhURERHBf9uAAWw2m9ElAKiH7HZ7hU5UVPk5OzUtOTlZX3/9tVavXm1YDVarlbADGIDjDkBtqlLY+a//+q+bLk9NTa3U9mbPnq1t27Zp1apVatasmXN+SEiISktLde7cOZezO4WFhQoNDXW22bt3r8v2rt6tdbUNAAC4dVXpbqxz5865fJw+fVq7du3Sli1bnONuKsLhcGj27NnasmWLli9frpYtW7os79y5s7y8vJSRkeGcd/ToUZ08eVJRUVGSpKioKB0+fFiFhYXONjt37pS/v7/Cw8OrsnsAAMBEqnRmZ9GiRdfMKy8vV1JS0jWB5WaSk5O1adMmvfbaa2rYsKFzjE1AQIB8fHwUEBCgIUOGKC0tTYGBgfL399fcuXMVHR3tDDvx8fEKDw/XtGnTNHXqVOXn52vBggUaPnx4pe7EAgAA5mRxOByOmtrY0aNHNXLkyAo/g+dGgxJTU1M1ePBgSVceKpiWlqb3339fly9fVnx8vGbNmuVyieq7775TUlKSMjMz5evrq0GDBmny5MnX3BJ/I3a73Xl3F2MHUJd++McC2c9+Z3QZhrEGtlCTe542ugwA9VRF/37X6ADl48ePV+rJxdnZ2T/bpkGDBpo1a5ZmzZp1wzYtWrTQ4sWLK9wvAAC4dVQp7Px0ALLD4VB+fr62bdumQYMG1UhhAAAANaFKYeerr75ymfbw8FBQUJCmT5+uIUOG1EhhAAAANaFKYWflypU1XQcAAECtqNaYndOnTzvfUn7XXXcpKCioRooCAACoKVUKO8XFxZozZ47++te/qry8XNKVJ6D++te/1u9//3v5+vrWaJEAAABVVaWHCqalpWn37t16/fXX9fnnn+vzzz/Xa6+9pt27dystLa2mawQAAKiyKoWdzZs364UXXlBCQoL8/f3l7++vhIQEzZkzR5s3b67pGgEAAKqsSmHn4sWL17xpXLryNvKLFy9WuygAAICaUqWwExUVpZdfflmXLl1yzrt48aJeffVV52scAAAA3EGVBijPmDFDTzzxhO655x61b99eknTo0CF5e3vrrbfeqtECAQAAqqNKYcdms+lvf/ubNm7c6Lz1fMCAARo4cKB8fHxqtEAAAIDqqFLYefPNNxUcHKyHHnrIZf6f//xnnT59WqNHj66R4gAAAKqrSmN23nnnHd11113XzG/btq3Wrl1b7aIAAABqSpXCTn5+vkJDQ6+ZHxQUpPz8/GoXBQAAUFOqFHaaN2+uf/7zn9fM/+KLL9S0adNqFwUAAFBTqjRmZ+jQoUpJSVFZWZl69OghScrIyNCLL76oxx9/vEYLBAAAqI4qhZ0nnnhCZ86cUXJyskpLSyVJDRo00BNPPKExY8bUaIEAAADVUaWwY7FYNHXqVI0bN05HjhyRj4+P7rzzTnl7e9d0fQAAANVSpbBzVcOGDdWlS5eaqgUAAKDGVWmAMgAAQH1B2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZG2AEAAKZmaNjZvXu3xo4dq/j4eNlsNm3dutVl+fTp02Wz2Vw+Ro0a5dLmzJkzmjx5smJiYtS1a1fNmDFDRUVFdbkbAADAjXka2XlxcbFsNpuGDBmiCRMmXLdNr169lJqa6pz29vZ2WT5lyhTl5+dr2bJlKi0t1YwZM/T888/rpZdeqtXaAQBA/WBo2ElISFBCQsJN23h7eys0NPS6y44cOaJPP/1Uf/7znxURESFJmjlzpkaPHq1p06bptttuq/GaAQBA/WJo2KmIzMxMxcbGqlGjRurRo4eefvppNWnSRJK0Z88eNWrUyBl0JCkuLk4eHh7au3ev7rvvvkr1Zbfba7R24GasVqvRJbgNjj0AVVHR3x1uHXZ69eql++67T7fffruOHz+u+fPn63e/+53eeecdWa1WFRQUKCgoyGUdT09PBQYGKj8/v9L97du3r6ZKB27K19dXHTt2NLoMt5Gdna2SkhKjywBgUm4ddvr37+/8/OoA5T59+jjP9tS0iIgI/tsGDGCz2YwuAUA9ZLfbK3Siwq3Dzk+1bNlSTZo00bFjxxQbG6uQkBCdPn3apU1ZWZnOnj17w3E+N2O1Wgk7gAE47gDUpnr1nJ3vv/9eZ86ccQaZ6OhonTt3Tvv373e2+eyzz1ReXq4uXboYVSYAAHAjhp7ZKSoqUm5urnP6xIkTOnjwoAIDAxUYGKhXX31V999/v0JCQnT8+HG9+OKLatWqlXr16iVJatOmjXr16qXf//73Sk5OVmlpqebMmaP+/ftzJxYAAJBkcNjZv3+/Ro4c6Zy++jydQYMGKSkpSYcPH9aGDRt0/vx5NW3aVD179tSkSZNcnrWTnp6uOXPm6NFHH5WHh4f69u2rmTNn1vm+AAAA92Ro2Onevbuys7NvuHzp0qU/u43GjRvzAEEAAHBD9WrMDgAAQGURdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgCgmuwOh9EluA2+FnBH9erdWADgjqwWiybt/FY5Zy8aXYqhwgN9tDDuTqPLAK5B2AGAGpBz9qL2/1BidBkAroPLWAAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAA1DCHo9zoEtyCu3wdPI0uAAAAs7FYPHT+n6tVdiHP6FIM4+nfVAExvzG6DEmEHQAAakXZhTzZz35ndBkQl7EAAIDJEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpGRp2du/erbFjxyo+Pl42m01bt251We5wOLRw4ULFx8erS5cueuyxx/Ttt9+6tDlz5owmT56smJgYde3aVTNmzFBRUVEd7gUAAHBnhoad4uJi2Ww2zZo167rLFy9erJUrVyopKUnvvvuufH19NWrUKF26dMnZZsqUKcrJydGyZcv0xhtv6PPPP9fzzz9fV7sAAADcnKFhJyEhQc8884zuu+++a5Y5HA6tWLFCTz75pPr06aP27dvrD3/4g/Ly8pxngI4cOaJPP/1Uc+fOVWRkpLp27aqZM2fq/fff1//93//V9e4AAAA35Gl0ATdy4sQJ5efnKy4uzjkvICBAkZGR2rNnj/r37689e/aoUaNGioiIcLaJi4uTh4eH9u7de90QdTN2u73G6gd+jtVqNboEt1Hfjz2+l67q+/ezJvAz8S+1+fNQ0W27bdjJz8+XJAUHB7vMDw4OVkFBgSSpoKBAQUFBLss9PT0VGBjoXL8y9u3bV8Vqgcrx9fVVx44djS7DbWRnZ6ukpMToMqqE7+W16vP3sybwM+HKHX4e3DbsGCEiIoI0DhjAZrMZXQJqEN9P/Fht/jzY7fYKnahw27ATGhoqSSosLFTTpk2d8wsLC9W+fXtJUkhIiE6fPu2yXllZmc6ePetcvzKsVithBzAAx5258P3Ej7nDz4PbPmfn9ttvV2hoqDIyMpzzLly4oC+//FLR0dGSpOjoaJ07d0779+93tvnss89UXl6uLl261HnNqJhyh8PoEgAAtxBDz+wUFRUpNzfXOX3ixAkdPHhQgYGBCgsL08iRI/X666+rVatWuv3227Vw4UI1bdpUffr0kSS1adNGvXr10u9//3slJyertLRUc+bMUf/+/XXbbbcZtVv4GR4Wiz4q+lKny2/d5yHd6RmiON92RpcBALcEQ8PO/v37NXLkSOd0amqqJGnQoEFKS0vT7373O5WUlOj555/XuXPndPfdd2vJkiVq0KCBc5309HTNmTNHjz76qDw8PNS3b1/NnDmzzvcFlXO6vEj59nNGl2GYJh4NjS4BAG4Zhoad7t27Kzs7+4bLLRaLJk2apEmTJt2wTePGjfXSSy/VRnkAAMAE3HbMDgAAQE0g7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAakSojycPDYVbctvXRQAA6pdG3lYeGioeGuqOCDsAgBrFQ0N5aKi74TIWAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcJOHbFzOyYAAIbgbqw6YrVYNGnnt8o5e9HoUgz1i7BGmhoZZnQZAIBbCGGnDuWcvaj9P5QYXYah2jRqYHQJAIBbDJexAACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqbl12HnllVdks9lcPh544AHn8kuXLik5OVndu3dXdHS0nnrqKRUUFBhYMQAAcDeeRhfwc9q2batly5Y5p61Wq/PzlJQUbd++XQsWLFBAQIDmzJmjCRMmaO3atUaUCgAA3JDbhx2r1arQ0NBr5p8/f17r1q1Tenq6YmNjJV0JP/369VNWVpaioqIq3Zfdbq9uuTf045AGwFVtHnt1geMbuLHaPL4rum23DzvHjh1TfHy8GjRooKioKE2ePFlhYWHav3+/SktLFRcX52zbpk0bhYWFVTns7Nu3rwYr/xdfX1917NixVrYNmEF2drZKSkqMLqNKOL6Bm3OH49utw06XLl2Umpqq1q1bKz8/X4sWLdLw4cO1ceNGFRQUyMvLS40aNXJZJzg4WPn5+VXqLyIigv/QAAPYbDajSwBQS2rz+Lbb7RU6UeHWYSchIcH5efv27RUZGanevXvrww8/lI+PT433Z7VaCTuAATjuAPNyh+Pbre/G+qlGjRrpzjvvVG5urkJCQlRaWqpz5865tCksLLzuGB8AAHBrqldhp6ioSMePH1doaKg6d+4sLy8vZWRkOJcfPXpUJ0+erNJ4HQAAYE5ufRlr3rx56t27t8LCwpSXl6dXXnlFHh4eGjBggAICAjRkyBClpaUpMDBQ/v7+mjt3rqKjowk7AADAya3Dzvfff69nn31WZ86cUVBQkO6++269++67CgoKkiTNmDFDHh4emjhxoi5fvqz4+HjNmjXL4KoBAIA7ceuw88c//vGmyxs0aKBZs2YRcAAAwA3VqzE7AAAAlUXYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApmaasPP222/r3nvvVUREhIYOHaq9e/caXRIAAHADpgg7H3zwgVJTUzV+/HitX79e7du316hRo1RYWGh0aQAAwGCmCDvLli3TQw89pCFDhig8PFzJycny8fHRunXrjC4NAAAYzNPoAqrr8uXLOnDggMaMGeOc5+Hhobi4OO3Zs6dC23A4HM5tWa3WWqnTarWqQ6C3GlgctbL9+uLOhl6y2+0KdviZI2lXUaDDR3a7XZaGzWRR7fzM1QeWhqGy2+2y2+1Gl1ItHN9XcHxfwfF9RV0c31e3ffXv+I3U+7Dzww8/XDm4goNd5gcHB+vo0aMV2kZ5ebkk6auvvqrx+n7skQaSQmu1C/dnL1ZW1v8pRFKIGhhdjYEuKEtZktpKDdsaXYyxsrKMrqBGcHyL49uJ49upjo7vq3/Hb6Teh52a4OnpqYiICHl4eMhisRhdDgAAqACHw6Hy8nJ5et48ztT7sNOkSRNZrdZrBiMXFhYqJCSkQtvw8PCQt7d3bZQHAAAMVu8vq3p7e6tTp07KyMhwzisvL1dGRoaio6MNrAwAALiDen9mR5J++9vfKjExUZ07d1aXLl20fPlylZSUaPDgwUaXBgAADGaKsNOvXz+dPn1aL7/8svLz89WhQwctWbKkwpexAACAeVkcP3e/FgAAQD1W78fsAAAA3AxhBwAAmBphBwAAmBphBwAAmBphB7eUt99+W/fee68iIiI0dOhQ7d271+iSANSA3bt3a+zYsYqPj5fNZtPWrVuNLgluhLCDW8YHH3yg1NRUjR8/XuvXr1f79u01atSoa56+DaD+KS4uls1m06xZs4wuBW6IW89xyxg6dKgiIiL0/PPPS7rypO2EhASNGDFCo0ePNrg6ADXFZrNp0aJF6tOnj9GlwE1wZge3hMuXL+vAgQOKi4tzzvPw8FBcXJz27NljYGUAgNpG2MEt4YcffpDdbldwcLDL/ODgYBUUFBhUFQCgLhB2AACAqRF2cEto0qSJrFbrNYORCwsLeYcaAJgcYQe3BG9vb3Xq1EkZGRnOeeXl5crIyFB0dLSBlQEAapsp3noOVMRvf/tbJSYmqnPnzurSpYuWL1+ukpISDR482OjSAFRTUVGRcnNzndMnTpzQwYMHFRgYqLCwMAMrgzvg1nPcUlatWqWlS5cqPz9fHTp00MyZMxUZGWl0WQCqadeuXRo5cuQ18wcNGqS0tDQDKoI7IewAAABTY8wOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOgFvCrl27ZLPZdO7cuVrtZ/r06Ro3blyt9gGgcng3FoA6dfr0aS1cuFDbt29XQUGBAgMD1b59e40bN0533313rfUbHR2tHTt2KCAgoNb6AOCeCDsA6tRTTz2l0tJSpaWlqWXLliosLFRGRobOnDlTpe05HA7Z7XZ5et7815m3t7dCQ0Or1AeA+o3LWADqzLlz5/T5559rypQp6tGjh1q0aKEuXbpozJgx+uUvf6kTJ07IZrPp4MGDLuvYbDbt2rVL0r8uR23fvl2DBw9WRESE1q1bJ5vNpiNHjrj099///d/q06ePy3rnzp3ThQsX1KVLF23fvt2l/ZYtWxQdHa2SkhJJ0qlTpzRp0iR17dpV//Zv/6Ynn3xSJ06ccLa32+1KTU1V165d1b17d/3hD38QrxsE3A9hB0Cd8fPzk5+fn7Zu3arLly9Xa1svvfSSJk+erA8++ED333+/OnfurI0bN7q0ee+99zRgwIBr1vX399cvfvELbdq0yWX+xo0b1adPH/n6+qq0tFSjRo1Sw4YN9fbbb2vNmjXy8/PTE0884az9rbfe0vr165WSkqLVq1fr7Nmz2rJlS7X2C0DNI+wAqDOenp5KS0vThg0b1LVrVz388MOaP3++Dh06VOltTZw4UT179tQdd9yhxo0ba+DAgXr//fedy7/55hsdOHBAAwcOvO76//7v/66tW7c6z+JcuHBB27Ztc7b/4IMPVF5erhdeeEE2m01t2rRRamqqTp06pczMTEnS8uXLNXr0aPXt21dt2rRRcnIyY4IAN0TYAVCn7r//fn366ad6/fXX1atXL2VmZmrw4MH6y1/+UqntREREuEz3799f3333nbKysiRdOUvTqVMntWnT5rrr33PPPfLy8tLHH38sSdq8ebP8/f0VFxcnSTp06JByc3MVExOj6OhoRUdHq3v37rp06ZJyc3N1/vx55efnKzIy0rlNT09Pde7cuVL7AaD2MUAZQJ1r0KCBevbsqZ49e2r8+PF67rnn9Morr+jtt9+WJJdxL2VlZdfdhq+vr8t0aGioevTooU2bNikqKkqbNm3SI488csMavL29df/992vjxo3q37+/Nm3apH79+jkHOhcXF6tTp05KT0+/Zt2goKBK7zMA43BmB4DhwsPDVVxc7AwR+fn5zmU/Hqz8cwYOHKgPPvhAe/bs0fHjx9WvX7+fbb9jxw59/fXX+uyzz1wueXXq1EnHjh1TcHCwWrVq5fIREBCggIAAhYaG6ssvv3SuU1ZWpgMHDlS4XgB1g7ADoM788MMPGjlypP7617/q0KFDOn78uD788EMtWbJEv/zlL+Xj46OoqCj96U9/0pEjR5SZmakFCxZUePv33XefioqKlJSUpO7du+u22267aftu3bopJCREU6ZM0e233+5ySWrgwIFq0qSJnnzySX3++ec6fvy4du3apblz5+r777+XJI0cOVKLFy/W1q1bdeTIESUnJ9f6QwsBVB6XsQDUmYYNGyoyMlLLly9Xbm6uysrK1KxZMw0dOlRjx46VJKWkpOi5557T4MGD1bp1a02dOlWPP/54hbbv7++v3r1768MPP1RKSsrPtrdYLOrfv7+WLFmi8ePHuyzz9fXVqlWrlJ6ergkTJqioqEi33XabYmNj5e/vL0l6/PHHlZ+fr8TERHl4eGjIkCG67777dP78+Up+ZQDUJouDh0IAAAAT4zIWAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtf8HwY89vemSJuIAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=40)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":657},"id":"ZzhcdLAnRHa8","executionInfo":{"status":"ok","timestamp":1690091451255,"user_tz":-330,"elapsed":534,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"81f51290-65e5-48be-c00c-1dc67d5bce6f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-22-ac571356fd3a>:1: UserWarning: \n","\n","`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n","\n","Please adapt your code to use either `displot` (a figure-level function with\n","similar flexibility) or `histplot` (an axes-level function for histograms).\n","\n","For a guide to updating your code to use the new functions, please see\n","https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n","\n","  sns.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=40)\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Age'>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["train['Age'].hist(bins=30,color='darkred',alpha=0.3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"9FkIcYESRHc7","executionInfo":{"status":"ok","timestamp":1690091577087,"user_tz":-330,"elapsed":929,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"bfa6d1f1-6cc5-4d6a-c489-f7de81a5cf10"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":23},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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LAcheXaGQwqFQQmPziouVf9gFJAHADcIHcIIKh0IKNjXJiURcjfPl5Kh0/HjCB4CEET6AE5gTich2GT44VwtgoHgdAQAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEblpLsAIBP4LEt2JKL25uYEBvsUOXQo+UUBgEcRPgBJsiyFOzp0cO9eOZGIu6EFBRpcVpaiwgDAewgfwGGcSES2y/Dhc3l7ADjRMecDAAAYRfgAAABGDSh8PP300xo7dqweeOCB2LKuri4tWrRIU6ZMUU1Nje644w61tbUNuFAAAOANCYePTz75RGvWrNHYsWPjli9ZskTvvvuuli9frtWrV2v//v2aN2/egAsFAADekFD4OHjwoP7pn/5JDQ0NKikpiS1vb2/XunXr9JOf/ERTp05VZWWllixZos2bN2vLli3JqhkAAGSxhD7tsnjxYk2fPl21tbV64oknYsu3bdum7u5u1dbWxpaNHj1a5eXl2rJliyZNmtTvfUSj0URK63NbydxmJvFaf7ZtKxqNyj6sH9u24/7ujS8aldPL2P5I99j+9Hckx+eL3VeJ6O1+TuV+vfY47Y3Xe/R6fxI9JmO7/eE6fGzYsEG///3v9corrxy1rq2tTbm5uSouLo5bXlpaqtbWVlf7aWxsdFtaWraZSbzQn2VZyuvs1L5AQJFw+Kj1gUDgmGPzCwt16qBBatm9W91dXa72m/ax3365WV/9HSknL09dQ4cqHAy6Ci3S8e/nVO1X8sbj9Hi83qPX+5PoMdVchY+9e/fqgQce0C9/+Uvl5+enqiZJUlVVlfx+f1K2FY1G1djYmNRtZhKv9dfR0qL8Awfivm/Dtm0FAgGNGjVKltX72cKcggIVDhki/+mnu/6ujnSP9VVU6LNPP+2zvyNZOTkqq6hQ4ciRrvbZo7f7OZX79drjtDde79Hr/Un0mIzt9oer8LF9+3YFg0FdeeWVcTv7+OOP9eKLL2rlypXq7u5WKBSKe/cjGAyqzOU3QPr9/qQf+FRsM5N4pT/LsuT3++VznGOu63Wc3y9fH2P73Geax/YEjr76622sm9sfNX4ANQ9kv155nPbF6z16vT+JHlPNVfg477zztH79+rhl9957r84880zdcsstOvXUU5Wbm6tNmzbp+9//viRp165d2rNnj6v5HgAAwLtchY/CwkKNGTMmbtngwYN18sknx5bPmTNHy5YtU0lJiQoLC9XQ0KCamhrCBwAAkJSCa7ssXLhQlmVp/vz5CofDqqurU319fbJ3gwzXFQopHAq5H8gVYgHA8wYcPlavXh33c35+vurr6wkcJ7hwKKRgUxNXiAUAHIWr2iJluEIsAKA3XFgOAAAYRfgAYEx/v8MEgLdx2gWAKz7Lkh2JqP3bb2XtL9u2ldfZqXB7uwadfHJqigOQFQgfANyxLIU7OnRw715XE4qj0aha9+xRRUUF4QM4wRE+ACTE7YRiOxpVtLs7hRUByBacgAUAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABG5aS7AACJ8VmW7EhE7c3NCQz2KXLoUPKLAoB+IHwA2cqyFO7o0MG9e+VEIu6GFhRocFlZigoDgL4RPoAs50Qisl2GD5/L2wNAMjHnAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRrsLHSy+9pFmzZmny5MmaPHmyrrnmGr3//vux9V1dXVq0aJGmTJmimpoa3XHHHWpra0t60QAAIHu5Ch8jRozQPffco1dffVXr1q3Teeedp9tvv107d+6UJC1ZskTvvvuuli9frtWrV2v//v2aN29eSgoHAADZydU3nM6cOTPu5x//+Md6+eWXtWXLFo0YMULr1q3TQw89pKlTp0r6Joxccskl2rJliyZNmpS0ogEAQPZKeM5HNBrVhg0b9PXXX6umpkbbtm1Td3e3amtrY7cZPXq0ysvLtWXLlmTUCgAAPMD1tV127NihH/zgB+rq6tLgwYO1YsUKnXXWWWpqalJubq6Ki4vjbl9aWqrW1lbXhUWjUddjjretZG4zk2Rif7ZtKxqNynZZky8aldPLWNu24/52M3Yg+zU1tj/9ZVrNbsce3mMmPVaTKROfi8nk9f4kekzGdvvDdfg444wz9Nprr6m9vV1vvfWWFixYoBdeeMHtZo6rsbEx6dvctm2bBluWol9/ndB4/+DB+tq25ThOkitLjlTcZ4mwLEt5nZ3aFwgoEg67GptfWKhTBw1Sy+7d6u7qOmp9IBBIeOxA9pvysc3NkvruL+NqTmBsTl6e9uzZo8//+EdXQSvbZMpzMVW83p9Ej6nmOnzk5eVp1KhRkqTKyko1Njbq+eef18UXX6zu7m6FQqG4dz+CwaDKErh0d1VVlfx+v+txvYlGo2psbFRlZaU69+1TcN8+11cBtXJyVFpRocKRI5NSUzL19JfM+2ygOlpalH/ggOv7OaegQIVDhsh/+ulxY23bViAQ0KhRo2RZvZ8tPNbYgezX1FhfRYU++/TTPvvLtJrdjrVtWy1796q8vFzFp53map/ZIhOfi8nk9f4kekzGdvvDdfg4km3bCofDqqysVG5urjZt2qTvf//7kqRdu3Zpz549CU029fv9ST/wfr9flmXJ5zjyuXz3wuc4siwrox+MqbjPEtVzX7m9ny2/X74+xvZ1DI43diD7TfXYnsDh5jGW7poTGSu56zFbZdJzMRW83p9Ej6nmKnw8/PDDmjZtmk499VQdPHhQr7/+uj766COtXLlSRUVFmjNnjpYtW6aSkhIVFhaqoaFBNTU1fNIFAADEuAofwWBQCxYs0P79+1VUVKSxY8dq5cqVOv/88yVJCxculGVZmj9/vsLhsOrq6lRfX5+SwpF6XaGQwqGQ+4E+nyKHDiW/IACAJ7gKH0uWLOlzfX5+vurr6wkcHhEOhRRsapLjdn5MQYEGJzDPBwBwYhjwnA94mxOJuJ6Q6HN5ewDAiYULywEAAKMIHwAAwChOuwDIGglPgpaUV1ys/CO+gRlAehA+AGSNRCdB+3JyVDp+POEDyBCEDwBZJZFJ0JxfBjILz0kAAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AGCWz5fuCgCkWU66CwBw4rD8ftnd3WpvbnY/2OdT5NChhPbrsyzZkUhi+5WUV1ys/OLihMYCOJqr8PHUU0/p17/+tXbt2qWCggLV1NTonnvu0Zlnnhm7TVdXl5YtW6Y33nhD4XBYdXV1qq+v17Bhw5JePIDs4vP71d3RoT99+aWcSMTVWKugQIPLyhLbsWUp3NGhg3v3ut6vLydHpePHEz6AJHJ12uWjjz7S9ddfr7Vr1+q5555TJBLRzTffrK+//jp2myVLlujdd9/V8uXLtXr1au3fv1/z5s1LeuEAspcTich2+cdtaMik/QKI5+qdj5UrV8b9vGzZMk2dOlXbt2/Xueeeq/b2dq1bt04PPfSQpk6dKumbMHLJJZdoy5YtmjRpUtIKBwAA2WlAcz7a29slSSUlJZKkbdu2qbu7W7W1tbHbjB49WuXl5a7DRzQaHUhpvW4rGo3Ktu1v/na5fcfni43NNIf3l0yJ3le+aFROksfath33t6n9mhrbn/4yrWa3Y2M9Ok7W1Cy5e+6n6rmYKbzen0SPydhufyQcPmzb1pIlSzR58mSNGTNGktTW1qbc3FwVH3FutLS0VK2tra6239jYmGhpx7R9+3bldXZqXyCgSDjsamxOXp66hg5VOBh09QvCpGTeZ5ZlJXxf5RcW6tRBg9Sye7e6u7qSOjYQCKRlvykf++1EyL76y7iaExz7p6++yqqaE3nup+L1K5N4vT+JHlMt4fCxaNEi7dy5Uy+99FIy64mpqqqS3+9Pyrai0agaGxs1YcIEde7bp/wDB2S7neyWk6OyigoVjhyZlJqSqae/ZN5nktTR0pLQfZVTUKDCIUPkP/30pI21bVuBQECjRo2SZfU+VSkV+zU11ldRoc8+/bTP/jKtZrdjbdvWvmBQJSefnDU1S+6e+6l6LmYKr/cn0WMyttsfCYWPxYsX67333tMLL7ygESNGxJYPGzZM3d3dCoVCce9+BINBlbmcpe73+5N+4P1+vyzLkt/vl89xXI21DhubqZJ9nw3kvvKlaGxfxyCV+0312J7A4eYxlu6aExkrSZbPl1U1J/LcT8XrVybxen8SPaaaq0+7OI6jxYsX6+2339aqVat02mmnxa2vrKxUbm6uNm3aFFu2a9cu7dmzh8mmAABAkst3PhYtWqTXX39dv/jFL3TSSSfF5nEUFRWpoKBARUVFmjNnjpYtW6aSkhIVFhaqoaFBNTU1hA8AACDJZfh4+eWXJUl/93d/F7d86dKluvLKKyVJCxculGVZmj9/ftyXjAEAAEguw8eOHTuOe5v8/HzV19cTOAAAQK+4sBwAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AkESWxcsqcDyuLiwHACcan2XJjkTU3tx83Nvatq28zk51tLTEQkhecbHyi4tTXSaQVQgfANAXy1K4o0MH9+6VE4n0edNoNKp9gYDyDxyQ3++XLydHpePHEz6AIxA+AKAfnEhE9nHChx2NKhIOy45E5HMczmsDx8BzAwAAGEX4AAAARnHaBQAyVFcopHAolPB4JrsiUxE+ACBDhUMhBZuajjvRtTdMdkUmI3wAQAbrz0TX3nBOHZmMxycAADCK8OERfKsiACBbcNqln9x8y2FvBjLx63iTznr7VsVk7BfAwAzodcPnU+TQoeQXBWQAwkd/ufiWwyMNdOLX8SadHfmtisnaL4ABGsDrhlVQoMFlZSkqDEgvwodLiUz+SsYJkb72e+S3KiZzvwAGLpHXDV8Ck0yBbMHvJwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGuw8fHH3+s2267TXV1dRo7dqw2btwYt95xHD366KOqq6tTdXW1brrpJn3++efJqhcAAGQ51+Hj66+/1tixY1VfX9/r+meeeUarV6/W/fffr7Vr12rQoEG6+eab1dXVNeBiAQBA9stxO2D69OmaPn16r+scx9Hzzz+vH/7wh7rwwgslSQ8++KBqa2u1ceNGXXrppQOrFgAAZD3X4aMvzc3Nam1tVW1tbWxZUVGRJk6cqM2bN7sKH9FoNGl19WwrGo3Ktu1v/na5fV80KifBsY7PJ9txEu7peDXbth33d9x+vx2biv0ey0Duq2ONPVaPqd6vqbH96S/TanY7Ntbjt8+FbKjZ7dgjj2O6apYG/vzvzeGvpV5FjwPfbn8kNXy0trZKkkpLS+OWl5aWqq2tzdW2Ghsbk1ZXj+3btyuvs1P7AgFFwmFXY/MLC3XqoEFq2b1b3S5PIeUNGiTfiBFqaW6W4ziuxlp+v3IcR1/0Y7+BQCDu55y8PHUNHapwMOjql5okWZaVlvvqeGOP7NHUflM+trlZUt/9ZVzNCY7901dfZV3Nbsf2HMd01SwN7Pl/PKl4fc409JhaSQ0fyVRVVSW/35+UbUWjUTU2NmrChAnq3LdP+QcOyI5EXG0jp6BAhUOGyH/66YmNzcuTr63N9Vh/QYFOGj5cvj72a9u2AoGARo0aJcv68zQeKydHZRUVKhw50tU+e3S0tKTnvupl7LF6TPV+TY31VVTos08/7bO/TKvZ7VjbtrUvGFTJySdnTc1uxx75OE1XzdLAn/+96XktTebrc6ahx4Fvtz+SGj7KysokScFgUMOHD48tDwaDGjdunKtt+f3+pB94v98vy7Lk9/vlS+AdCN8Ax/ocx/VYn+P0e789vR2+3yOXuao7jffVscb21U8q95vqsT2Bw83xSnfNiYyVJMvny6qaExnbcxzTej8P8Pnfl1S8PmcaekytpH7PR0VFhcrKyrRp06bYso6ODm3dulU1NTXJ3BUAAMhSrt/5OHjwoHbv3h37ubm5WU1NTSopKVF5ebluuOEGPfHEExo1apQqKir06KOPavjw4bFPvwAAsld/TwsCfXEdPrZt26Ybbrgh9vPSpUslSbNnz9ayZct0yy23qLOzU/fdd59CoZDOOeccPfvss8rPz09e1QCAlOoKhRQOheKW2batvM5OdbS09BlC8oqLlV9cnOoSkcVch48pU6Zox44dx1zv8/l055136s477xxQYQCA9AmHQgo2Nck5bLJrNBrVvkBA+QcOHHOugC8nR6XjxxM+0KeM/bQLACC9nEgk/lNn0agi4bDsSOSYk2A5KYP+4HECAACMInwAAACjOO0CAEgan2XJjkTU/u239rrFZNUTA+EDAJA8lqVwR4cO7t0bN1m1P5iseuIgfAAAku7Iyar9wTyAEwfHGgAAGEX4AAAARnHaBQA8aEATP30+RQ4dSn5RwLcIHwDgRQOY+GkVFGjwt1cpB1KB8AEAHpbIxE+fy9sDbjHnAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAOAwlsWvxlTjo7YAgBNeVyikcCgk27aV19mpjpaWfocQrsTrHuEDAHDCC4dCCjY1KdLVpX2BgPIPHJDf7z/uOK7EmxjCBwAA+vMXskXCYdmRiHyOc9wxnKBJDPcbAAAwivABAACM4rQLACAjDOhKvGLiZzYhfAAAMsMArsTLxM/sQvgAAGSURK7EyxyC7MLxAgAARhE+AACAUZx28bABTd7y+RQ5dCj5RQEATniEDy8bwOQtq6BAg8vKUlQYAOBERvg4ASQyecvn8vYAAPQXcz4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAATjA+ny+t++ejtgAAJCidV+LtCoUUDoVcj7NtW4Ot9L73kLLw8eKLL2rlypVqbW3VuHHj9K//+q+qrq5O1e4AADAvjVfiDYdCCjY1ud6v4/Mpmuar/6Yk+rzxxhtaunSpbr/9dv3Xf/2Xxo0bp5tvvlnBYDAVuwMAIK16vszRzR+3oSFZ+3X7pZOpkJLw8dxzz+nqq6/WnDlzdNZZZ2nRokUqKCjQunXrUrE7AACQRZJ+2iUcDmv79u36h3/4h9gyy7JUW1urzZs3H3e84zix7fj9/qTUFI1GY9uMRCKyLUu2y/NdPp9P0Wg0I8c6knIGDZLj98fdJpNrdjv2WD1mcs1uxjp+/3H7y7Sa3Y51JPnz8hSx7ayp2e3YIx+n6ap5oOOz8Xkoy1IkElE4HHY37ls9vxvcPhczoeZE9usoub9npT//ru35Pd4Xn9OfW7nw5Zdfatq0aVqzZo1qampiyx988EF9/PHH+s///M8+x4fDYTU2NiazJAAAYEhVVZXy8vL6vE3GfdolJydHVVVVsr79XwMAAMh8juPItm3l5Bw/WiQ9fAwZMkR+v/+oyaXBYFDDhg077njLso6bmAAAQPZK+oTTvLw8TZgwQZs2bYots21bmzZtijsNAwAATkwpOe0yd+5cLViwQJWVlaqurtaqVavU2dmpK6+8MhW7AwAAWSQl4eOSSy7RgQMH9Nhjj6m1tVXjx4/Xs88+26/TLgAAwNuS/mkXAACAvnBhOQAAYBThAwAAGEX4AAAARhE+AACAUZ4PHy+++KJmzpypqqoq/c3f/I0++eSTdJeUsI8//li33Xab6urqNHbsWG3cuDFuveM4evTRR1VXV6fq6mrddNNN+vzzz9NTbAKeeuopzZkzRzU1NZo6dar+8R//Ubt27Yq7TVdXlxYtWqQpU6aopqZGd9xxh9ra2tJUsXsvvfSSZs2apcmTJ2vy5Mm65ppr9P7778fWZ3t/R3r66ac1duxYPfDAA7Fl2d7j448/rrFjx8b9ueiii2Lrs72/Hl9++aXuueceTZkyRdXV1Zo1a1bcpS+y/fVm5syZRx3HsWPHatGiRZKy/zhGo1EtX75cM2fOVHV1tS688EKtWLEi7roraT2Gjodt2LDBmTBhgvPKK684O3fudP7lX/7F+cu//Eunra0t3aUl5L333nMeeeQR59e//rUzZswY5+23345b/9RTTznnnHOO8/bbbztNTU3Obbfd5sycOdM5dOhQmip25+///u+ddevWOX/4wx+cpqYm55ZbbnEuuOAC5+DBg7Hb3Hfffc706dOdDz74wGlsbHSuvvpq55prrklj1e785je/cd577z3ns88+c3bt2uU88sgjzoQJE5w//OEPjuNkf3+H27p1qzNjxgxn1qxZTkNDQ2x5tvf42GOPOZdeeqmzf//+2J9gMBhbn+39OY7jfPXVV86MGTOcn/zkJ87WrVud3bt3O7/97W+dQCAQu022v94Eg8G4Y/i73/3OGTNmjPPhhx86jpP9x/GJJ55wvvvd7zrvvvuu88UXXzhvvvmmM2nSJGfVqlWx26TzGHo6fFx11VXOokWLYj9Ho1Gnrq7Oeeqpp9JYVXIcGT5s23bOP/9859lnn40tC4VCTmVlpfP666+no8QBCwaDzpgxY5yPPvrIcZxv+pkwYYLz5ptvxm7zf//3f86YMWOczZs3p6nKgTv33HOdtWvXeqq/jo4O56//+q+d3/3ud87f/u3fxsKHF3p87LHHnMsvv7zXdV7oz3Ec5+c//7lz7bXXHnO9F19vGhoanAsvvNCxbdsTx/HWW2917r333rhl8+bNc+6++27HcdJ/DD172iUcDmv79u2qra2NLbMsS7W1tdq8eXMaK0uN5uZmtba2xvVbVFSkiRMnZm2/7e3tkqSSkhJJ0rZt29Td3R3X4+jRo1VeXq4tW7ako8QBiUaj2rBhg77++mvV1NR4qr/Fixdr+vTpcb1I3jmGgUBAdXV1+t73vqe7775be/bskeSd/t555x1VVlZq/vz5mjp1qq644gqtXbs2tt5rrzfhcFi/+tWvNGfOHPl8Pk8cx5qaGn344Yf67LPPJEn/+7//q//5n//RtGnTJKX/GGbcVW2T5Y9//KOi0ahKS0vjlpeWlh41j8ALWltbJanXfrPpPGUP27a1ZMkSTZ48WWPGjJEktbW1KTc3V8XFxXG3LS0tjfWfDXbs2KEf/OAH6urq0uDBg7VixQqdddZZampq8kR/GzZs0O9//3u98sorR63zwjGsrq7W0qVLdcYZZ6i1tVUrVqzQ9ddfr/Xr13uiP0n64osv9PLLL2vu3Lm67bbb1NjYqIaGBuXm5mr27Nmee73ZuHGj2tvbNXv2bEneeJzeeuut6ujo0MUXXyy/369oNKof//jHuvzyyyWl/3eGZ8MHstuiRYu0c+dOvfTSS+kuJenOOOMMvfbaa2pvb9dbb72lBQsW6IUXXkh3WUmxd+9ePfDAA/rlL3+p/Pz8dJeTEtOnT4/9e9y4cZo4caJmzJihN998UwUFBWmsLHkcx1FlZaXuuusuSdLZZ5+tnTt3as2aNbFf0F6ybt06TZs2Taecckq6S0maN998U+vXr9fDDz8c+8/N0qVLNXz48Iw4hp497TJkyBD5/X4Fg8G45cFg0JPXmCkrK5MkT/S7ePFivffee1q1apVGjBgRWz5s2DB1d3crFArF3T4YDMb6zwZ5eXkaNWqUKisrdffdd2vcuHF6/vnnPdHf9u3bFQwGdeWVV+rss8/W2WefrY8++kirV6/W2Wef7Ykej1RcXKzvfOc72r17t2f6Kysr0+jRo+OWnXnmmbHTS156vWlpadEHH3ygq666KrbMC8fxwQcf1K233qpLL71UY8eO1RVXXKEbb7xRTz31lKT0H0PPho+8vDxNmDBBmzZtii2zbVubNm1STU1NGitLjYqKCpWVlcX129HRoa1bt2ZNv47jaPHixXr77be1atUqnXbaaXHrKysrlZubG9fjrl27tGfPHk2aNMlwtclj27bC4bAn+jvvvPO0fv16vfbaa7E/lZWVmjVrVuzf2d7jkQ4ePKgvvvhCZWVlnulv8uTJsbkCPT7//HONHDlSkjdeb3q8+uqrKi0t1QUXXBBb5oXjeOjQIfl8vrhlfr8/9lHbdB9DT592mTt3rhYsWKDKykpVV1dr1apV6uzs1JVXXpnu0hJy8OBB7d69O/Zzc3OzmpqaVFJSovLyct1www164oknNGrUKFVUVOjRRx/V8OHDdeGFF6ax6v5btGiRXn/9df3iF7/QSSedFDsnWVRUpIKCAhUVFWnOnDlatmyZSkpKVFhYqIaGBtXU1GTNC8LDDz+sadOm6dRTT9XBgwf1+uuv66OPPtLKlSs90V9hYWFsjk6PwYMH6+STT44tz/Yef/azn2nGjBkqLy/X/v379fjjj8uyLF122WWeOIaSdOONN+raa6/Vk08+qYsvvliffPKJ1q5dq8WLF0uSfD5f1r/eSN8E/1dffVVXXHGFcnL+/OvQC8dxxowZevLJJ1VeXh477fLcc89pzpw5kjLgGKb88zRptnr1aueCCy5wJkyY4Fx11VXOli1b0l1Swj788ENnzJgxR/1ZsGCB4zjffHRq+fLlTm1trVNZWenceOONzq5du9Jcdf/11tuYMWOcdevWxW5z6NAh5/7773fOPfdcZ+LEic7tt9/u7N+/P41Vu3Pvvfc6M2bMcCZMmOCcd955zo033uj893//d2x9tvfXm8M/aus42d/jj370I+f88893JkyY4PzVX/2V86Mf/Sju+y+yvb8e77zzjnPZZZc5lZWVzkUXXeT8x3/8R9z6bH+9cRzH+e1vf+uMGTOm17qz/Ti2t7c7DQ0NzgUXXOBUVVU53/ve95xHHnnE6erqit0mncfQ5ziHfd0ZAABAinl2zgcAAMhMhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABG/T/nrp3iW8TWLQAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["sns.countplot(x='SibSp',data=train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"mU-pgX4KRHgl","executionInfo":{"status":"ok","timestamp":1690091654701,"user_tz":-330,"elapsed":905,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"24acdd78-a1dd-4c4c-97df-742ffaa03dfc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='SibSp', ylabel='count'>"]},"metadata":{},"execution_count":24},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["train['Fare'].hist(color='green',bins=40,figsize=(8,4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":387},"id":"tImcS5LGUHmB","executionInfo":{"status":"ok","timestamp":1690091746146,"user_tz":-330,"elapsed":471,"user":{"displayName":"Manikandan 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\n"},"metadata":{}}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.figure(figsize=(12,7))\n","sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":636},"id":"ORuDIDWUUHoE","executionInfo":{"status":"ok","timestamp":1690091924673,"user_tz":-330,"elapsed":511,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"1cf215db-08d4-4435-990a-da0f9776f6ce"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Pclass', ylabel='Age'>"]},"metadata":{},"execution_count":27},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x700 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#create a function\n","def impute_Age(cols):\n","  Age=cols[0]\n","  Pclass=cols[1]\n","  if pd.isnull(Age):\n","    if Pclass==1:\n","      return 37\n","    elif Pclass==2:\n","      return 29\n","    else:\n","      return 24\n","  else:\n","    return Age\n","\n"],"metadata":{"id":"lxVdc8NeUHp7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train['Age']=train[['Age','Pclass']].apply(impute_Age,axis=1)\n"],"metadata":{"id":"9OdVwIlKUHsA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517},"id":"a-cIR9w0UHvY","executionInfo":{"status":"ok","timestamp":1690093376203,"user_tz":-330,"elapsed":842,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"7f25c472-3dd3-49eb-d672-51b20ea1aedc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":55},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["train.drop('Cabin',axis=1,inplace=True)"],"metadata":{"id":"v53Y_Z3wbAm8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"DjM5PywAbc18","executionInfo":{"status":"ok","timestamp":1690093613936,"user_tz":-330,"elapsed":736,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"0fa0010a-2ce3-49b2-d52e-8d151f61dd6b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"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 Embarked  \n","0      0         A/5 21171   7.2500        S  \n","1      0          PC 17599  71.2833        C  \n","2      0  STON/O2. 3101282   7.9250        S  \n","3      0            113803  53.1000        S  \n","4      0            373450   8.0500        S  "],"text/html":["\n","\n","  <div id=\"df-28724088-de46-45e8-aa48-c25a38f23a7b\">\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>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>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>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. 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M","userId":"09131001697984495612"}},"outputId":"8b3c9950-3dec-4a1a-d349-bb1f7486802e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n","0            1       3  22.0      1      0   7.2500     1  0  1\n","1            2       1  38.0      1      0  71.2833     0  0  0\n","2            3       3  26.0      0      0   7.9250     0  0  1\n","3            4       1  35.0      1      0  53.1000     0  0  1\n","4            5       3  35.0      0      0   8.0500     1  0  1"],"text/html":["\n","\n","  <div id=\"df-04a149d1-25da-48ca-bbfc-a97f857d4687\">\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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M","userId":"09131001697984495612"}},"outputId":"14f12ac0-2c70-490b-e2f3-3365b3cf8cde"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0    0\n","1    1\n","2    1\n","3    1\n","4    0\n","Name: Survived, dtype: int64"]},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split"],"metadata":{"id":"IcxonC7nh5uK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X_train,X_test,Y_train,Y_test =train_test_split(train.drop('Survived',axis=1),train['Survived'],test_size=0.30,random_state=101)"],"metadata":{"id":"0s80OMlph5xd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression"],"metadata":{"id":"0lYRX8Fog-MA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["logmodel=LogisticRegression()\n","logmodel.fit(X_train,Y_train)"],"metadata":{"id":"SYnntnxWg-Nw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["predictions=logmodel.predict(X_test)"],"metadata":{"id":"G-3yrNkfg-RO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix"],"metadata":{"id":"hNVKfBpSj_L3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["accuracy=confusion_matrix(Y_test,predictions)"],"metadata":{"id":"XHoUphJnk5yR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\"accuracy is:\",accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y23eNyFHk6nd","executionInfo":{"status":"ok","timestamp":1690096477529,"user_tz":-330,"elapsed":523,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"0c7fb222-85f1-4fd7-91b6-0b8709b12d7e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('accuracy is:', 0.8014981273408239)"]},"metadata":{},"execution_count":101}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score"],"metadata":{"id":"SpK45x3Yk7Ze"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#accuracy\n","accuracy=accuracy_score(Y_test,predictions)\n","accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uTQVzR__lfQs","executionInfo":{"status":"ok","timestamp":1690096263405,"user_tz":-330,"elapsed":440,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"68a4d5e3-4476-4e8a-ade9-3fd6566b55e7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8014981273408239"]},"metadata":{},"execution_count":96}]},{"cell_type":"code","source":["#prediction\n","predictions"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y0x1vsamlfUT","executionInfo":{"status":"ok","timestamp":1690096663485,"user_tz":-330,"elapsed":1867,"user":{"displayName":"Manikandan M","userId":"09131001697984495612"}},"outputId":"68d54f86-09dd-41f7-df5d-4d775a6c0b2d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n","       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n","       0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n","       0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n","       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n","       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n","       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n","       0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n","       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n","       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n","       0, 1, 1])"]},"metadata":{},"execution_count":104}]},{"cell_type":"code","source":["#major project completed successfully\n","#acuuracy is 0.8014981273408239\n","#Done by MANIKANDAN M\n","#Thank you.."],"metadata":{"id":"BQbbYGngl_gb"},"execution_count":null,"outputs":[]}]}