05. Titanic data competition.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3136,"databundleVersionId":26502,"sourceType":"competition"}],"dockerImageVersionId":30626,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"### Importing the necessary libraries","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:20.174098Z","iopub.execute_input":"2023-12-19T13:28:20.174778Z","iopub.status.idle":"2023-12-19T13:28:22.129538Z","shell.execute_reply.started":"2023-12-19T13:28:20.174728Z","shell.execute_reply":"2023-12-19T13:28:22.128325Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"#### Importing Datasets\n","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/titanic/train.csv')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.136421Z","iopub.execute_input":"2023-12-19T13:28:22.141179Z","iopub.status.idle":"2023-12-19T13:28:22.177123Z","shell.execute_reply.started":"2023-12-19T13:28:22.141121Z","shell.execute_reply":"2023-12-19T13:28:22.175692Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"test_df = pd.read_csv('/kaggle/input/titanic/test.csv')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.185240Z","iopub.execute_input":"2023-12-19T13:28:22.189737Z","iopub.status.idle":"2023-12-19T13:28:22.210839Z","shell.execute_reply.started":"2023-12-19T13:28:22.189674Z","shell.execute_reply":"2023-12-19T13:28:22.209394Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"### test_df.info\nChecking the infomations about the test dataset.","metadata":{}},{"cell_type":"code","source":"test_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.221655Z","iopub.execute_input":"2023-12-19T13:28:22.226741Z","iopub.status.idle":"2023-12-19T13:28:22.268565Z","shell.execute_reply.started":"2023-12-19T13:28:22.226676Z","shell.execute_reply":"2023-12-19T13:28:22.267407Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  418 non-null    int64  \n 1   Pclass       418 non-null    int64  \n 2   Name         418 non-null    object \n 3   Sex          418 non-null    object \n 4   Age          332 non-null    float64\n 5   SibSp        418 non-null    int64  \n 6   Parch        418 non-null    int64  \n 7   Ticket       418 non-null    object \n 8   Fare         417 non-null    float64\n 9   Cabin        91 non-null     object \n 10  Embarked     418 non-null    object \ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Looking into the null values.\n\nLooking into the null values in each column in the test dataset","metadata":{}},{"cell_type":"code","source":"test_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.270423Z","iopub.execute_input":"2023-12-19T13:28:22.271163Z","iopub.status.idle":"2023-12-19T13:28:22.284376Z","shell.execute_reply.started":"2023-12-19T13:28:22.271115Z","shell.execute_reply":"2023-12-19T13:28:22.283217Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nPclass           0\nName             0\nSex              0\nAge             86\nSibSp            0\nParch            0\nTicket           0\nFare             1\nCabin          327\nEmbarked         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Handling missing data.\nFilling in the only null value in the column 'Fare' with mode of the observed values in the column","metadata":{}},{"cell_type":"code","source":"test_df['Fare']","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.286042Z","iopub.execute_input":"2023-12-19T13:28:22.286744Z","iopub.status.idle":"2023-12-19T13:28:22.391616Z","shell.execute_reply.started":"2023-12-19T13:28:22.286698Z","shell.execute_reply":"2023-12-19T13:28:22.390403Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"0        7.8292\n1        7.0000\n2        9.6875\n3        8.6625\n4       12.2875\n         ...   \n413      8.0500\n414    108.9000\n415      7.2500\n416      8.0500\n417     22.3583\nName: Fare, Length: 418, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"test_df['Fare'] = test_df['Fare'].fillna(test_df['Fare'].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.393195Z","iopub.execute_input":"2023-12-19T13:28:22.394094Z","iopub.status.idle":"2023-12-19T13:28:22.403952Z","shell.execute_reply.started":"2023-12-19T13:28:22.394049Z","shell.execute_reply":"2023-12-19T13:28:22.402748Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"test_df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.406185Z","iopub.execute_input":"2023-12-19T13:28:22.410744Z","iopub.status.idle":"2023-12-19T13:28:22.425246Z","shell.execute_reply.started":"2023-12-19T13:28:22.410689Z","shell.execute_reply":"2023-12-19T13:28:22.423724Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nPclass           0\nName             0\nSex              0\nAge             86\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          327\nEmbarked         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"test_df['Fare']","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.427178Z","iopub.execute_input":"2023-12-19T13:28:22.427974Z","iopub.status.idle":"2023-12-19T13:28:22.441366Z","shell.execute_reply.started":"2023-12-19T13:28:22.427929Z","shell.execute_reply":"2023-12-19T13:28:22.439891Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"0        7.8292\n1        7.0000\n2        9.6875\n3        8.6625\n4       12.2875\n         ...   \n413      8.0500\n414    108.9000\n415      7.2500\n416      8.0500\n417     22.3583\nName: Fare, Length: 418, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.446283Z","iopub.execute_input":"2023-12-19T13:28:22.447411Z","iopub.status.idle":"2023-12-19T13:28:22.472912Z","shell.execute_reply.started":"2023-12-19T13:28:22.447361Z","shell.execute_reply":"2023-12-19T13:28:22.471926Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ","text/html":"<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>"},"metadata":{}}]},{"cell_type":"markdown","source":"### train_df.info\nChecking the infomations about the train dataset.","metadata":{}},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.474569Z","iopub.execute_input":"2023-12-19T13:28:22.475753Z","iopub.status.idle":"2023-12-19T13:28:22.490818Z","shell.execute_reply.started":"2023-12-19T13:28:22.475704Z","shell.execute_reply":"2023-12-19T13:28:22.489572Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Looking into the necessary numerical informations of the train dataset","metadata":{}},{"cell_type":"code","source":"train_df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.492811Z","iopub.execute_input":"2023-12-19T13:28:22.493654Z","iopub.status.idle":"2023-12-19T13:28:22.536424Z","shell.execute_reply.started":"2023-12-19T13:28:22.493605Z","shell.execute_reply":"2023-12-19T13:28:22.535083Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"       PassengerId    Survived      Pclass         Age       SibSp  \\\ncount   891.000000  891.000000  891.000000  714.000000  891.000000   \nmean    446.000000    0.383838    2.308642   29.699118    0.523008   \nstd     257.353842    0.486592    0.836071   14.526497    1.102743   \nmin       1.000000    0.000000    1.000000    0.420000    0.000000   \n25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n75%     668.500000    1.000000    3.000000   38.000000    1.000000   \nmax     891.000000    1.000000    3.000000   80.000000    8.000000   \n\n            Parch        Fare  \ncount  891.000000  891.000000  \nmean     0.381594   32.204208  \nstd      0.806057   49.693429  \nmin      0.000000    0.000000  \n25%      0.000000    7.910400  \n50%      0.000000   14.454200  \n75%      0.000000   31.000000  \nmax      6.000000  512.329200  ","text/html":"<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>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Ploting correlation matrix\n\nCorrelation matrix to see the positive or relation between the features","metadata":{}},{"cell_type":"code","source":"sns.heatmap(train_df.select_dtypes(include=['float64', 'int64']).corr(), cmap='YlGnBu', fmt='.2f', annot_kws={\"size\": 10})\nplt.title('Correlation Matrix of Titanic Dataset')\nplt.show()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.537856Z","iopub.execute_input":"2023-12-19T13:28:22.538575Z","iopub.status.idle":"2023-12-19T13:28:22.993951Z","shell.execute_reply.started":"2023-12-19T13:28:22.538526Z","shell.execute_reply":"2023-12-19T13:28:22.992695Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Heatmap to visualize the missing values\nPloting a heatmap to visualize the missing data for each feature","metadata":{}},{"cell_type":"code","source":"# Create a heatmap using Matplotlib and Seaborn\nplt.figure(figsize=(15, 10))\nsns.heatmap(train_df.isnull(), cmap='YlGnBu', cbar=True)\nplt.title('Missing Values in Titanic Dataset')\nplt.xlabel('Columns')\nplt.ylabel('Rows')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:22.996296Z","iopub.execute_input":"2023-12-19T13:28:22.997041Z","iopub.status.idle":"2023-12-19T13:28:23.870875Z","shell.execute_reply.started":"2023-12-19T13:28:22.996987Z","shell.execute_reply":"2023-12-19T13:28:23.869820Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:23.872205Z","iopub.execute_input":"2023-12-19T13:28:23.873116Z","iopub.status.idle":"2023-12-19T13:28:23.894858Z","shell.execute_reply.started":"2023-12-19T13:28:23.873031Z","shell.execute_reply":"2023-12-19T13:28:23.893515Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Countplot \nCountplots for seeing survived with respect to Sex, Embarked, Pclass columns ","metadata":{}},{"cell_type":"code","source":"sns.set_style('whitegrid')\nsns.countplot(x = 'Survived', hue = 'Sex', data = train_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:23.896908Z","iopub.execute_input":"2023-12-19T13:28:23.897451Z","iopub.status.idle":"2023-12-19T13:28:24.203714Z","shell.execute_reply.started":"2023-12-19T13:28:23.897404Z","shell.execute_reply":"2023-12-19T13:28:24.202663Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Survived', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"sns.set_style('whitegrid')\nsns.countplot(x = 'Survived', hue = 'Embarked', data = train_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.205250Z","iopub.execute_input":"2023-12-19T13:28:24.205649Z","iopub.status.idle":"2023-12-19T13:28:24.583507Z","shell.execute_reply.started":"2023-12-19T13:28:24.205615Z","shell.execute_reply":"2023-12-19T13:28:24.582276Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Survived', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"sns.set_style('whitegrid')\nsns.countplot(x = 'Survived', hue = 'Pclass', data = train_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.584864Z","iopub.execute_input":"2023-12-19T13:28:24.585216Z","iopub.status.idle":"2023-12-19T13:28:24.906701Z","shell.execute_reply.started":"2023-12-19T13:28:24.585183Z","shell.execute_reply":"2023-12-19T13:28:24.905582Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Survived', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.908542Z","iopub.execute_input":"2023-12-19T13:28:24.909441Z","iopub.status.idle":"2023-12-19T13:28:24.922295Z","shell.execute_reply.started":"2023-12-19T13:28:24.909403Z","shell.execute_reply":"2023-12-19T13:28:24.921265Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.923752Z","iopub.execute_input":"2023-12-19T13:28:24.924287Z","iopub.status.idle":"2023-12-19T13:28:24.937609Z","shell.execute_reply.started":"2023-12-19T13:28:24.924252Z","shell.execute_reply":"2023-12-19T13:28:24.936180Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n       'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.939378Z","iopub.execute_input":"2023-12-19T13:28:24.940083Z","iopub.status.idle":"2023-12-19T13:28:24.968194Z","shell.execute_reply.started":"2023-12-19T13:28:24.940036Z","shell.execute_reply":"2023-12-19T13:28:24.967356Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"   PassengerId  Survived  Pclass  \\\n0            1         0       3   \n1            2         1       1   \n2            3         1       3   \n3            4         1       1   \n4            5         0       3   \n\n                                                Name     Sex   Age  SibSp  \\\n0                            Braund, Mr. Owen Harris    male  22.0      1   \n1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n2                             Heikkinen, Miss. Laina  female  26.0      0   \n3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n4                           Allen, Mr. William Henry    male  35.0      0   \n\n   Parch            Ticket     Fare Cabin Embarked  \n0      0         A/5 21171   7.2500   NaN        S  \n1      0          PC 17599  71.2833   C85        C  \n2      0  STON/O2. 3101282   7.9250   NaN        S  \n3      0            113803  53.1000  C123        S  \n4      0            373450   8.0500   NaN        S  ","text/html":"<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>"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.969562Z","iopub.execute_input":"2023-12-19T13:28:24.970138Z","iopub.status.idle":"2023-12-19T13:28:24.986602Z","shell.execute_reply.started":"2023-12-19T13:28:24.970100Z","shell.execute_reply":"2023-12-19T13:28:24.985385Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df['Embarked'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:24.988702Z","iopub.execute_input":"2023-12-19T13:28:24.989175Z","iopub.status.idle":"2023-12-19T13:28:25.008958Z","shell.execute_reply.started":"2023-12-19T13:28:24.989129Z","shell.execute_reply":"2023-12-19T13:28:25.007681Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"train_df['Cabin'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.010612Z","iopub.execute_input":"2023-12-19T13:28:25.011799Z","iopub.status.idle":"2023-12-19T13:28:25.022654Z","shell.execute_reply.started":"2023-12-19T13:28:25.011751Z","shell.execute_reply":"2023-12-19T13:28:25.021470Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"array([nan, 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n       'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n       'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n       'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n       'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n       'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n       'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n       'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n       'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n       'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n       'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n       'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n       'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n       'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n       'C148'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"sns.heatmap(train_df.select_dtypes(include=['float64', 'int64']).corr(), cmap='YlGnBu', fmt='.2f', annot_kws={\"size\": 10})\nplt.title('Correlation Matrix of Titanic Dataset')\nplt.show()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.024265Z","iopub.execute_input":"2023-12-19T13:28:25.024985Z","iopub.status.idle":"2023-12-19T13:28:25.494548Z","shell.execute_reply.started":"2023-12-19T13:28:25.024947Z","shell.execute_reply":"2023-12-19T13:28:25.493218Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.496072Z","iopub.execute_input":"2023-12-19T13:28:25.496519Z","iopub.status.idle":"2023-12-19T13:28:25.507868Z","shell.execute_reply.started":"2023-12-19T13:28:25.496478Z","shell.execute_reply":"2023-12-19T13:28:25.507082Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.508846Z","iopub.execute_input":"2023-12-19T13:28:25.509800Z","iopub.status.idle":"2023-12-19T13:28:25.519833Z","shell.execute_reply.started":"2023-12-19T13:28:25.509758Z","shell.execute_reply":"2023-12-19T13:28:25.518461Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"train_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.530437Z","iopub.execute_input":"2023-12-19T13:28:25.530887Z","iopub.status.idle":"2023-12-19T13:28:25.541962Z","shell.execute_reply.started":"2023-12-19T13:28:25.530842Z","shell.execute_reply":"2023-12-19T13:28:25.540802Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge            177\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Distribution Plot\n\nPloting distribution plots droping the null values in the 'Age' column.","metadata":{}},{"cell_type":"code","source":"sns.displot(train_df['Age'].dropna(), kde = False, color = 'darkred', bins =40)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:25.544042Z","iopub.execute_input":"2023-12-19T13:28:25.544517Z","iopub.status.idle":"2023-12-19T13:28:26.207873Z","shell.execute_reply.started":"2023-12-19T13:28:25.544476Z","shell.execute_reply":"2023-12-19T13:28:26.206745Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<seaborn.axisgrid.FacetGrid at 0x79ef9bffef20>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x500 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"sns.countplot(x = 'SibSp', data = train_df)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.209583Z","iopub.execute_input":"2023-12-19T13:28:26.210259Z","iopub.status.idle":"2023-12-19T13:28:26.524665Z","shell.execute_reply.started":"2023-12-19T13:28:26.210213Z","shell.execute_reply":"2023-12-19T13:28:26.523414Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='SibSp', ylabel='count'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"train_df['Age'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.526156Z","iopub.execute_input":"2023-12-19T13:28:26.527161Z","iopub.status.idle":"2023-12-19T13:28:26.535502Z","shell.execute_reply.started":"2023-12-19T13:28:26.527119Z","shell.execute_reply":"2023-12-19T13:28:26.534269Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"array([22.  , 38.  , 26.  , 35.  ,   nan, 54.  ,  2.  , 27.  , 14.  ,\n        4.  , 58.  , 20.  , 39.  , 55.  , 31.  , 34.  , 15.  , 28.  ,\n        8.  , 19.  , 40.  , 66.  , 42.  , 21.  , 18.  ,  3.  ,  7.  ,\n       49.  , 29.  , 65.  , 28.5 ,  5.  , 11.  , 45.  , 17.  , 32.  ,\n       16.  , 25.  ,  0.83, 30.  , 33.  , 23.  , 24.  , 46.  , 59.  ,\n       71.  , 37.  , 47.  , 14.5 , 70.5 , 32.5 , 12.  ,  9.  , 36.5 ,\n       51.  , 55.5 , 40.5 , 44.  ,  1.  , 61.  , 56.  , 50.  , 36.  ,\n       45.5 , 20.5 , 62.  , 41.  , 52.  , 63.  , 23.5 ,  0.92, 43.  ,\n       60.  , 10.  , 64.  , 13.  , 48.  ,  0.75, 53.  , 57.  , 80.  ,\n       70.  , 24.5 ,  6.  ,  0.67, 30.5 ,  0.42, 34.5 , 74.  ])"},"metadata":{}}]},{"cell_type":"markdown","source":"### Boxplot\nBoxplot to see the relation in diagram for Age and Pclass column to identify the relation necessary in handling the missing values in Age column for the trainging dataset","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(15,10))\nsns.boxplot(x='Pclass', y='Age', data=train_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.536903Z","iopub.execute_input":"2023-12-19T13:28:26.537230Z","iopub.status.idle":"2023-12-19T13:28:26.865173Z","shell.execute_reply.started":"2023-12-19T13:28:26.537201Z","shell.execute_reply":"2023-12-19T13:28:26.863895Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Pclass', ylabel='Age'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"def impage(cols):\n    Age = cols[0]\n    Pclass = cols[1]\n\n    if pd.isna(Age):\n\n        if Pclass == 1:\n            return 35\n        elif Pclass == 2:\n            return 27\n        elif Pclass ==3:\n            return 25\n    else:\n        return Age","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.866839Z","iopub.execute_input":"2023-12-19T13:28:26.867200Z","iopub.status.idle":"2023-12-19T13:28:26.873704Z","shell.execute_reply.started":"2023-12-19T13:28:26.867166Z","shell.execute_reply":"2023-12-19T13:28:26.872521Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"train_df['Age'] = train_df[['Age', 'Pclass']].apply(impage, axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.875758Z","iopub.execute_input":"2023-12-19T13:28:26.876129Z","iopub.status.idle":"2023-12-19T13:28:26.899013Z","shell.execute_reply.started":"2023-12-19T13:28:26.876087Z","shell.execute_reply":"2023-12-19T13:28:26.898135Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"train_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.900232Z","iopub.execute_input":"2023-12-19T13:28:26.901372Z","iopub.status.idle":"2023-12-19T13:28:26.920605Z","shell.execute_reply.started":"2023-12-19T13:28:26.901335Z","shell.execute_reply":"2023-12-19T13:28:26.919297Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData 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          891 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 \ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"test_df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.922490Z","iopub.execute_input":"2023-12-19T13:28:26.922880Z","iopub.status.idle":"2023-12-19T13:28:26.941663Z","shell.execute_reply.started":"2023-12-19T13:28:26.922846Z","shell.execute_reply":"2023-12-19T13:28:26.940796Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  418 non-null    int64  \n 1   Pclass       418 non-null    int64  \n 2   Name         418 non-null    object \n 3   Sex          418 non-null    object \n 4   Age          332 non-null    float64\n 5   SibSp        418 non-null    int64  \n 6   Parch        418 non-null    int64  \n 7   Ticket       418 non-null    object \n 8   Fare         418 non-null    float64\n 9   Cabin        91 non-null     object \n 10  Embarked     418 non-null    object \ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Boxplot\nBoxplot to see the relation in diagram for Age and Pclass column to identify the relation necessary in handling the missing values in Age column for the testing dataset","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(15,10))\nsns.boxplot(x='Pclass', y='Age', data=test_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:26.942852Z","iopub.execute_input":"2023-12-19T13:28:26.943200Z","iopub.status.idle":"2023-12-19T13:28:27.261049Z","shell.execute_reply.started":"2023-12-19T13:28:26.943169Z","shell.execute_reply":"2023-12-19T13:28:27.260260Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Pclass', ylabel='Age'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"def impage2(cols):\n    Age = cols[0]\n    Pclass = cols[1]\n\n    if pd.isna(Age):\n\n        if Pclass == 1:\n            return 42\n        elif Pclass == 2:\n            return 25\n        elif Pclass ==3:\n            return 23\n    else:\n        return Age","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.262042Z","iopub.execute_input":"2023-12-19T13:28:27.262840Z","iopub.status.idle":"2023-12-19T13:28:27.269545Z","shell.execute_reply.started":"2023-12-19T13:28:27.262808Z","shell.execute_reply":"2023-12-19T13:28:27.268339Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"test_df['Age'] = test_df[['Age', 'Pclass']].apply(impage2, axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.270984Z","iopub.execute_input":"2023-12-19T13:28:27.271425Z","iopub.status.idle":"2023-12-19T13:28:27.288122Z","shell.execute_reply.started":"2023-12-19T13:28:27.271385Z","shell.execute_reply":"2023-12-19T13:28:27.287172Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"train_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.289607Z","iopub.execute_input":"2023-12-19T13:28:27.290481Z","iopub.status.idle":"2023-12-19T13:28:27.304076Z","shell.execute_reply.started":"2023-12-19T13:28:27.290447Z","shell.execute_reply":"2023-12-19T13:28:27.302695Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nSurvived         0\nPclass           0\nName             0\nSex              0\nAge              0\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          687\nEmbarked         2\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"test_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.305658Z","iopub.execute_input":"2023-12-19T13:28:27.306095Z","iopub.status.idle":"2023-12-19T13:28:27.316531Z","shell.execute_reply.started":"2023-12-19T13:28:27.306048Z","shell.execute_reply":"2023-12-19T13:28:27.315264Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nPclass           0\nName             0\nSex              0\nAge              0\nSibSp            0\nParch            0\nTicket           0\nFare             0\nCabin          327\nEmbarked         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df = pd.concat([train_df, test_df])","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.317973Z","iopub.execute_input":"2023-12-19T13:28:27.318386Z","iopub.status.idle":"2023-12-19T13:28:27.328370Z","shell.execute_reply.started":"2023-12-19T13:28:27.318346Z","shell.execute_reply":"2023-12-19T13:28:27.327284Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.329698Z","iopub.execute_input":"2023-12-19T13:28:27.330152Z","iopub.status.idle":"2023-12-19T13:28:27.350030Z","shell.execute_reply.started":"2023-12-19T13:28:27.330118Z","shell.execute_reply":"2023-12-19T13:28:27.348710Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 1309 entries, 0 to 417\nData columns (total 12 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  1309 non-null   int64  \n 1   Survived     891 non-null    float64\n 2   Pclass       1309 non-null   int64  \n 3   Name         1309 non-null   object \n 4   Sex          1309 non-null   object \n 5   Age          1309 non-null   float64\n 6   SibSp        1309 non-null   int64  \n 7   Parch        1309 non-null   int64  \n 8   Ticket       1309 non-null   object \n 9   Fare         1309 non-null   float64\n 10  Cabin        295 non-null    object \n 11  Embarked     1307 non-null   object \ndtypes: float64(3), int64(4), object(5)\nmemory usage: 132.9+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Droping columns\n\nDroping columns 'Cabin' and 'Name' as they are not needed.","metadata":{}},{"cell_type":"code","source":"df = df.drop(['Cabin', 'Name'], axis =1)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.352212Z","iopub.execute_input":"2023-12-19T13:28:27.352639Z","iopub.status.idle":"2023-12-19T13:28:27.362196Z","shell.execute_reply.started":"2023-12-19T13:28:27.352603Z","shell.execute_reply":"2023-12-19T13:28:27.360503Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.363762Z","iopub.execute_input":"2023-12-19T13:28:27.364198Z","iopub.status.idle":"2023-12-19T13:28:27.382289Z","shell.execute_reply.started":"2023-12-19T13:28:27.364121Z","shell.execute_reply":"2023-12-19T13:28:27.381091Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 1309 entries, 0 to 417\nData columns (total 10 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  1309 non-null   int64  \n 1   Survived     891 non-null    float64\n 2   Pclass       1309 non-null   int64  \n 3   Sex          1309 non-null   object \n 4   Age          1309 non-null   float64\n 5   SibSp        1309 non-null   int64  \n 6   Parch        1309 non-null   int64  \n 7   Ticket       1309 non-null   object \n 8   Fare         1309 non-null   float64\n 9   Embarked     1307 non-null   object \ndtypes: float64(3), int64(4), object(3)\nmemory usage: 112.5+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"# Create a heatmap using Matplotlib and Seaborn\nplt.figure(figsize=(15, 10))\nsns.heatmap(df.isnull(), cmap='YlGnBu', cbar=True)\nplt.title('Missing Values in Titanic Dataset')\nplt.xlabel('Columns')\nplt.ylabel('Rows')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:27.383722Z","iopub.execute_input":"2023-12-19T13:28:27.384231Z","iopub.status.idle":"2023-12-19T13:28:28.261849Z","shell.execute_reply.started":"2023-12-19T13:28:27.384194Z","shell.execute_reply":"2023-12-19T13:28:28.260943Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 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541UAAAAAAAAWUmaEjebNm3S+PHjVadOHZ0+fVqNGzfW/fv3tXbtWtnZpf1UH29vb02dOlWTJk3StGnTVKxYMQ0ePFiNGze2Grd+/XqZzWY1bNgwxRy7du3SuXPndO7cOatrwSXp1KlTaY4JAAAAAADAVtiZzWbz4w728vLStm3b9Pzzz0v6K/Hy1VdfyWQyPbUAM89powMAAAAAADxTyhodQKZotOV7o0N4qHX1ahgdwiOlqdQsKSlJ2bNnt7x3cHBQrly5MjwoAAAAAAAApLFUymw2a9CgQXJ0dJT012HFH3/8sZ577jmrcVOnTs24CAEAAAAAAJ5RaUrcNG3a1Or9P8+iAQAAAAAA+Dv7tB+Ji79JU+ImJCQkwwOIiorSJ598ou+//1537txRiRIlNHbsWFWsWFGSdO3aNX366af64YcfFBMTo8qVK2vo0KEqWbKk1TyHDx/WZ599pqNHj8re3l7ly5dXWFiYcubMmeExAwAAAAAAZIY0JW4y2q1bt9SmTRv5+/tr9uzZyp8/v86dOydnZ2dJf5Vm9ezZU9myZdP06dPl5OSkefPmqXPnzlq/fr3lfJ3Dhw+rS5cu6tq1q4YOHSoHBwf98ssvsrfntngAAAAAAJB1pelWqYz26aef6tChQ1qyZEmq/WfPntWrr76qiIgIlSlTRpKUnJys6tWr6/3331eLFi0kSS1btlS1atXUp0+fdETDrVIAAAAAgMz0bNwq1XSr7d4qtbruf+xWqYy2fft2eXl5qXfv3goICNAbb7yh5cuXW/oTEhIkSTly5LC02dvby9HRUQcPHpQkXb9+XT/99JMKFCig1q1bq1q1amrXrp0OHDiQuYsBAAAAAADIYIYmbi5cuKClS5eqZMmSCgsLU5s2bTR69GitXr1akuTh4SE3NzdNnDhRt27dUkJCgmbNmqU//vhD0dHRljmkv26yatGihebMmaMXXnhBnTp1UmRkpFFLAwAAAAAASDdDz7gxm83y8vLS+++/L0l64YUXdObMGS1btkxNmzZV9uzZFRoaqo8++khVq1aVg4ODAgICVLNmTT2o8EpOTpYktWrVSs2bN7fMs3v3bq1cuVL9+vUzZnEAAAAAAIBbpdLJ0MSNq6urPD09rdo8PDy0efNmy3svLy99/fXXiomJUWJiolxcXNSiRQt5eXlZ5pCUYh5PT09dvnz5Ka8AAAAAAADg6TG0VMrPz09nz561aouMjFTRokVTjM2TJ49cXFwUGRmp48ePq06dOpKkYsWKqVChQo89DwAAAAAAQFZh6I6bjh07qk2bNpoxY4Zee+01HT16VMuXL9fIkSMtYzZu3CgXFxe5ubnp1KlTGjt2rOrWravAwEBJkp2dnYKDgxUaGqpy5cqpfPnyWr16tX7//XdNmTLFqKUBAAAAwP9r787jY7z6/4+/JyFiXyJKECUksSeKWEJsbdVSWxVVamlx25UKbS1Bxb7vpGpXFXITtJUqX2rXqNLYlyhKRAVJCMn8/ujP3J0msZTkmvB6eszjketc5zrX50xmJsnHWQBIMpkM28z6hWBo4qZChQqaNWuWpkyZotmzZ6tIkSL69NNP9fbbb1vqREVFady4cYqOjpazs7OaNWumnj17WrXTqVMnJSQkKDAwUDExMfL09NSXX34pV1fX9O4SAAAAAADAc2MyP1zl96V30ugAAAAAAAAvFXejA0gX72z7P6NDSNXaerWNDuGxDB1xAwAAAAAAXmzsKvVsDF2cGAAAAAAAAKkjcQMAAAAAAGCjDJ0qVa9ePV26dClZ+Xvvvad+/fpp5syZ2rVrl65cuaJ8+fKpQYMG6tevn3LmzGmpe/nyZY0cOVL79u1TtmzZ1Lx5cw0cOFCZMjELDAAAAAAAozFi5NkYmt1Yu3atEhMTLcenTp1S586d1bBhQ127dk3Xrl2Tv7+/SpYsqUuXLmnkyJG6du2aZZvvxMREde/eXfnz59fq1ast9TNnzqyPP/7YqG4BAAAAAAA8F4YmbvLly2d1vGDBArm6uqpq1aoymUyaOXOm5Zyrq6v69++vTz75RA8ePFCmTJm0a9cunT59WosXL1b+/PlVunRp9evXT5MmTVLv3r3l4OCQ3l0CAAAAAAB4bmxmxFJCQoI2bNigVq1ayWRKecnpO3fuKEeOHJZpUIcPH5a7u7vy589vqePr66s7d+7o9OnT6RI3AAAAAABInZ3JbLOPjMBmEjdhYWG6ffu2WrRokeL5GzduaM6cOWrTpo2l7Pr161ZJG0mW46ioqLQLFgAAAAAAIB3YTOImODhYtWvX1iuvvJLs3J07d9S9e3e5ubmpd+/eBkQHAAAAAACQ/mwicXPp0iXt3r1b77zzTrJzd+7c0Ycffqjs2bNr9uzZypw5s+Vc/vz5df36dav6D4+dnZ3TNmgAAAAAAPBYdibbfWQENpG4WbdunZycnFSnTh2r8jt37qhr167KnDmz5s6dqyxZslid9/Ly0smTJxUdHW0p2717t3LkyKGSJUumR+gAAAAAAABpxvDETVJSktatW6fmzZtbFh2W/kradOnSRXFxcfriiy90584dRUVFKSoqyrKFuK+vr0qWLKnBgwfr+PHj2rlzp6ZNm6b27duzoxQAAAAAAMjwDN0OXPprhMzly5fVqlUrq/Jjx47pl19+kSS9/vrrVud++OEHFSlSRPb29po3b55GjhypNm3aKGvWrGrRooX69u2bbvEDAAAAAIDUGT5iJIMzmc3mjLH/VZo7aXQAAAAAAICXirvRAaSLjjt2GB1Cqpb6+RkdwmOR+AIAAAAAALBRhk+VAgAAAAAAL66MsnuTrWLEDQAAAAAAgI0yNHFTr149eXh4JHsEBARY1TObzfrwww/l4eGhsLCwFNv6888/Vbt2bXl4eOjWrVvpET4AAAAAAECaMnSq1Nq1ay1be0vSqVOn1LlzZzVs2NCq3pIlS2QyPXps1WeffSYPDw9dvXo1TWIFAAAAAABPz87EnkjPwtARN/ny5ZOzs7Pl8eOPP8rV1VVVq1a11ImIiNCXX36psWPHptrOypUrdfv2bXXp0iU9wgYAAAAAAEgXNrPGTUJCgjZs2KBWrVpZRtfEx8dr4MCBGj58uJydnVO87vTp05ozZ47Gjx8vOzub6Q4AAAAAAMAzs5ldpcLCwnT79m21aNHCUhYYGChvb281aNAgxWsSEhL08ccf65NPPpGLi4suXryYXuECAAAAAIAnwK5Sz8ZmEjfBwcGqXbu2XnnlFUnSDz/8oL1792r9+vWpXjN58mS5ubmpWbNm6RUmAAAAAABAurGJxM2lS5e0e/duzZw501K2d+9eRUZGqkqVKlZ1+/Tpo8qVK2vZsmXau3evTp48qe+++07SX7tPSVK1atXUo0cP9e3bN/06AQAAAAAA8JzZROJm3bp1cnJyUp06dSxl3bp1U+vWra3qNW3aVEOHDlXdunUlSTNnztTdu3ct53/99Vd9+umnWrFihVxdXdMldgAAAAAAkDpWo302hidukpKStG7dOjVv3lyZMv0vnIc7Tf2Ti4uLihYtKknJkjN//vmnJMnNzU25cuVKw6gBAAAAAADSnuGJr927d+vy5ctq1aqV0aEAAAAAAADYFJP54cIwL72TRgcAAAAAAHipuBsdQLro8dOPRoeQqnk16xodwmMZPuIGAAAAAAAAKSNxAwAAAAAAYKMMX5wYAAAAAAC8uOxMRkeQsRmauKlXr54uXbqUrPy9997TiBEjJEnh4eGaOnWqjhw5Ijs7O5UuXVpBQUFydHSUJJ07d04TJkzQzz//rPv378vDw0P9+vVTtWrV0rUvAAAAAAAAz5uhiZu1a9cqMTHRcnzq1Cl17txZDRs2lPRX0ubDDz9U9+7dNWzYMNnb2+v48eOys/vfDK8ePXqoWLFiWrJkiRwdHbVkyRL16NFDW7duTXE7cQAAAAAAgIzC0MRNvnz5rI4XLFggV1dXVa1aVZIUGBioDh06qFu3bpY6JUqUsHx948YNnT9/Xl988YU8PT0lSQMHDtTKlSt16tQpEjcAAAAAABiMqVLPxmYWJ05ISNCGDRvUqlUrmUwmRUdH65dffpGTk5Patm2rGjVq6P3339fBgwct1+TNm1fFixdXSEiI4uLi9ODBA3399ddycnJS2bJlDewNAAAAAADAs7OZxE1YWJhu376tFi1aSJIuXrwoSZo1a5Zat26tRYsWqUyZMurUqZPOnz8vSTKZTPrqq6/022+/qVKlSqpQoYIWL16sRYsWKXfu3EZ1BQAAAAAA4LmwmcRNcHCwateurVdeeUWSlJSUJElq06aNWrVqpTJlyujTTz9V8eLFFRwcLEkym80KCAiQk5OTVqxYoW+++UYNGjRQjx49dO3aNcP6AgAAAAAA/mJnw4+MwCa2A7906ZJ2796tmTNnWsoerk/j5uZmVdfNzU2XL1+WJO3du1fbt2/XgQMHlCNHDklS2bJltXv3boWEhFitjQMAAAAAAJDR2ESCad26dXJyclKdOnUsZUWKFFGBAgV07tw5q7rnz59X4cKFJUnx8fGS/poy9Xcmk8kyYgcAAAAAACCjMnzETVJSktatW6fmzZsrU6b/hWMymdS1a1fNnDlTnp6eKl26tNavX6+zZ89qxowZkiQvLy/lypVLQ4YMUa9evZQlSxatWbNGly5dskoCAQAAAAAAY9iZzEaHkKEZnrjZvXu3Ll++rFatWiU716lTJyUkJCgwMFAxMTHy9PTUl19+KVdXV0l/bSe+aNEiTZs2TR988IHu37+vUqVKafbs2ZbtwQEAAAAAADIqk9lsJvUlSTppdAAAAAAAgJeKu9EBpIv+e7cZHUKqplWrZ3QIj2X4iBsAAAAAAPDisjM9vg5SZxOLEwMAAAAAANi6FStWqF69eipfvrxat26tI0eOPNF1mzZtkoeHh3r27PnU9yRxAwAAAAAA8BibN29WYGCgevXqpfXr18vT01Ndu3ZVdHT0I6/7/fffNX78eFWuXPlf3ZfEDQAAAAAASDN2Nvx4GosXL9a7776rVq1aqWTJkgoICJCjo6OCg4NTvSYxMVGDBg1Snz59VLRo0ae8418MXeOmXr16unTpUrLy9957TyNGjFBkZKTGjx+vQ4cOKSEhQbVq1dKwYcOUP39+SX9lrebMmaO9e/fq+vXrKlCggN5++2316NFDDg4O6d0dAAAAAACQgSQkJCghIcGqzMHBIVlOISEhQceOHVP37t0tZXZ2dqpRo4bCw8NTbX/27NlycnJS69atdejQoX8Vo6GJm7Vr1yoxMdFyfOrUKXXu3FkNGzZUXFycunTpIk9PTy1ZskSSNH36dPXo0UNr1qyRnZ2dzp49K7PZrFGjRqlYsWI6efKkhg0bpvj4ePn7+xvVLQAAAAAAkAHMnz9fs2bNsirr3bu3+vTpY1X2559/KjExUU5OTlblTk5OOnv2bIptHzx4UGvXrlVISMgzxWho4iZfvnxWxwsWLJCrq6uqVq2qn376SZcuXVJISIhy5MghSRo/fryqVKmivXv3qkaNGqpdu7Zq165tub5o0aI6d+6cVq1aReIGAAAAAAAbYMu7SnXv3l2dO3e2KnseM3ju3LmjwYMHa/To0clyH0/LZrYDT0hI0IYNG9S5c2eZTCYlJCTIZDJZPWFZsmSRnZ2dDh06pBo1aqTYzu3bt5U7d+70ChsAAAAAAGRQKU2LSknevHllb2+fbCHi6Ohoy3Iuf3fx4kVdunRJ//nPfyxlSUlJkqQyZcro22+/laur6xPFaDOLE4eFhen27dtq0aKFJMnLy0tZs2bVxIkTFR8fr7i4OI0fP16JiYmKiopKsY0LFy5o+fLlatu2bXqGDgAAAAAAXmAODg4qW7as9uzZYylLSkrSnj175O3tnax+iRIltHHjRoWEhFge9erVk4+Pj0JCQlSwYMEnvrfNjLgJDg5W7dq19corr0j6axrV9OnTNXLkSC1btkx2dnZq3LixypYtK5Mp+Tirq1ev6sMPP1TDhg317rvvpnf4AAAAAAAgBSaT2egQnovOnTvL399f5cqVU4UKFbRkyRLFx8erZcuWkqTBgwfrlVde0cCBA5UlSxa5u7tbXZ8rVy5JSlb+ODaRuLl06ZJ2796tmTNnWpX7+voqLCxMN27cUKZMmZQrVy7VrFlTjRo1sqp39epVdezYUd7e3ho9enR6hg4AAAAAAF4CjRo10o0bNzRjxgxFRUWpdOnSWrRokWWq1JUrV2Rn9/wnNpnMZrPhqa+ZM2fq66+/1vbt25UpU+q5pD179qhz587avHmzSpQoIel/SZuyZctq4sSJsre3/5dRnPyX1wEAAAAA8G883ciLjMr/wA9Gh5Cq8VXqGx3CYxk+4iYpKUnr1q1T8+bNkyVtgoOD5ebmpnz58ik8PFxjx45Vp06drJI2HTp0kIuLi/z9/XXjxg3Ltc7OzunaDwAAAAAAkJwt7yqVERieuNm9e7cuX76sVq1aJTt37tw5TZkyRTExMSpcuLB69OihTp06Wc7/9NNPunDhgi5cuGC1LbgknThxIq1DBwAAAAAASFM2MVXKNjBVCgAAAACQnl6OqVJDD9ruVKnAykyVAgAAAAAAL7Hnv1zvy4XnDwAAAAAAwEaRuAEAAAAAALBRhiZuEhMTNW3aNNWrV08VKlRQgwYNNHv2bP192Z3vv/9eXbp0kY+Pjzw8PBQREZFiW+Hh4erYsaO8vLxUqVIltW/fXnfv3k2vrgAAAAAAgBTYmcw2+8gIDF3jZuHChVq1apXGjx+vkiVL6ujRoxo6dKhy5sypjh07SpLi4uJUqVIlvfXWW/r8889TbCc8PFwffvihunfvrmHDhsne3l7Hjx+XnR0DigAAAAAAQMZlaOImPDxc9evXV506dSRJRYoU0aZNm3TkyBFLnebNm0uSfv/991TbCQwMVIcOHdStWzdLWYkSJdIkZgAAAAAAgPRi6JAUb29v7d27V+fOnZMkHT9+XIcOHVLt2rWfuI3o6Gj98ssvcnJyUtu2bVWjRg29//77OnjwYFqFDQAAAAAAnpCdyXYfGYGhI266deumO3fu6K233pK9vb0SExM1YMAAvf3220/cxsWLFyVJs2bN0uDBg1W6dGmFhISoU6dOCg0N1auvvppG0QMAAAAAAKQtQxM3W7Zs0caNGzV58mSVLFlSERERCgwMVIECBdSiRYsnaiMpKUmS1KZNG7Vq1UqSVKZMGe3Zs0fBwcEaOHBgmsUPAAAAAACQlgxN3EyYMEHdunVT48aNJUkeHh66fPmy5s+f/8SJG2dnZ0mSm5ubVbmbm5suX778fAMGAAAAAABPJaNMSbJVhq5xc/fuXZlM1t9Be3t7q+3AH6dIkSIqUKCAZZ2ch86fP6/ChQs/lzgBAAAAAACMYOiIm7p162revHlycXGxTJVavHixZcqTJN28eVNXrlzRtWvXJMmSoMmfP7+cnZ1lMpnUtWtXzZw5U56enipdurTWr1+vs2fPasaMGYb0CwAAAAAA4HkwmZ9meMtzdufOHU2fPl1hYWGKjo5WgQIF1LhxY/Xq1UsODg6SpHXr1mno0KHJru3du7f69OljOV6wYIFWrFihmJgYeXp6atCgQapcufJTRHPyWbsDAAAAAMBTcDc6gHQxJjzM6BBS9bl3A6NDeCxDEze2hcQNAAAAACA9kbgxWkZI3Bi6xg0AAAAAAABSZ+gaNwAAAAAA25DVdYTRIbx04iNXGR1CurAzMdHnWZC4AQAAAAAoPjLA6BAApMDQqVKJiYmaNm2a6tWrpwoVKqhBgwaaPXt2qtuBDx8+XB4eHvrqq6+sym/evKmBAweqUqVKqly5sj799FPFxsamQw8AAAAAAADSjqEjbhYuXKhVq1Zp/PjxKlmypI4ePaqhQ4cqZ86c6tixo1XdrVu36pdfflGBAgWStTNo0CBFRUVp8eLFun//vj799FMNHz5ckydPTq+uAAAAAACAFNiZjI4gYzN0xE14eLjq16+vOnXqqEiRImrYsKF8fX115MgRq3pXr17V6NGjNWnSJGXOnNnq3JkzZ7Rz506NGTNGFStWVOXKlfX5559r06ZNunr1anp2BwAAAAAA4LkyNHHj7e2tvXv36ty5c5Kk48eP69ChQ6pdu7alTlJSkj755BN17dpVpUqVStZGeHi4cuXKpfLly1vKatSoITs7u2QJIAAAAAAAgIzE0KlS3bp10507d/TWW2/J3t5eiYmJGjBggN5++21LnYULFypTpkzJpk49dP36deXLl8+qLFOmTMqdO7eioqLSNH4AAAAAAPBoTJV6NoYmbrZs2aKNGzdq8uTJKlmypCIiIhQYGKgCBQqoRYsWOnr0qJYuXap169bJZOI7DQAAAAAAXi6GJm4mTJigbt26qXHjxpIkDw8PXb58WfPnz1eLFi108OBBRUdHq27dupZrEhMTNX78eC1dulTbtm1T/vz5dePGDat2Hzx4oJiYGDk7O6drfwAAAAAAAJ4nQxM3d+/eTTaSxt7e3rIdeLNmzVSjRg2r8127dlWzZs3UsmVLSX+tk3Pr1i0dPXpU5cqVkyTt3btXSUlJqlChQjr0AgAAAAAApMaeCTTPxNDETd26dTVv3jy5uLhYpkotXrxYrVq1kiTlzZtXefPmtbomc+bMyp8/v0qUKCFJcnNzU61atTRs2DAFBATo/v37Gj16tBo3bqxXXnkl3fsEAAAAAADwvBiauPn88881ffp0BQQEKDo6WgUKFFCbNm3Uq1evp2pn0qRJGj16tD744APZ2dnpjTfe0Oeff55GUQMAAAAAAKQPk/nhvKSX3kmjAwAAAAAAvFTcjQ4gXUw9utXoEFI1oNzrRofwWHZGBwAAAAAAAICUkbgBAAAAAACwUYaucQMAAAAAAF5sdiZWaHkWho64SUxM1LRp01SvXj1VqFBBDRo00OzZs/XPZXfOnDmjHj166LXXXpOXl5datWqly5cvJ2vPbDbrww8/lIeHh8LCwtKrGwAAAAAAAGnC0BE3Cxcu1KpVqzR+/HiVLFlSR48e1dChQ5UzZ0517NhRkhQZGan33ntPrVq1Ut++fZUjRw6dOnVKWbJkSdbekiVLZDKxQTwAAAAAAHgxGJq4CQ8PV/369VWnTh1JUpEiRbRp0yYdOXLEUmfq1KmqXbu2Bg8ebClzdXVN1lZERIS+/PJLBQcHy9fXN81jBwAAAAAAj2fH+IpnYuhUKW9vb+3du1fnzp2TJB0/flyHDh1S7dq1JUlJSUnavn27Xn31VXXt2lXVq1dX69atk02Dio+P18CBAzV8+HA5Ozunez8AAAAAAADSgqEjbrp166Y7d+7orbfekr29vRITEzVgwAC9/fbbkqTo6GjFxcVp4cKF6t+/vwYNGqSdO3eqd+/eWrp0qapWrSpJCgwMlLe3txo0aGBkdwAAAAAAAJ4rQxM3W7Zs0caNGzV58mSVLFlSERERCgwMVIECBdSiRQslJSVJkurXr69OnTpJkkqXLq2ff/5Zq1evVtWqVfXDDz9o7969Wr9+vYE9AQAAAAAAKbE3OoAMztDEzYQJE9StWzc1btxYkuTh4aHLly9r/vz5atGihfLmzatMmTLJzc3N6jo3NzcdOnRIkrR3715FRkaqSpUqVnX69OmjypUra9myZenTGQAAAAAAgOfM0MTN3bt3k+0CZW9vb9kO3MHBQeXLl7esgfPQ+fPnVbhwYUl/Tbdq3bq11fmmTZtq6NChqlu3bhpGDwAAAAAAkLYMTdzUrVtX8+bNk4uLi2Wq1OLFi9WqVStLna5du2rAgAGqUqWKfHx8tHPnTv34449aunSpJMnZ2TnFBYldXFxUtGjRdOsLAAAAAABIjl2lno2hiZvPP/9c06dPV0BAgKKjo1WgQAG1adNGvXr1stR5/fXXNXLkSC1YsEBjxoxR8eLFNWPGDFWuXNnAyAEAAAAAANKeyfxwXtJL76TRAQAAAAAAXiruRgeQLuZFfG90CKnqUfoNo0N4LENH3AAAAAAAgBeb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'pandas.core.frame.DataFrame'>\nIndex: 1309 entries, 0 to 417\nData columns (total 10 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   PassengerId  1309 non-null   int64  \n 1   Survived     891 non-null    float64\n 2   Pclass       1309 non-null   int64  \n 3   Sex          1309 non-null   object \n 4   Age          1309 non-null   float64\n 5   SibSp        1309 non-null   int64  \n 6   Parch        1309 non-null   int64  \n 7   Ticket       1309 non-null   object \n 8   Fare         1309 non-null   float64\n 9   Embarked     1307 non-null   object \ndtypes: float64(3), int64(4), object(3)\nmemory usage: 112.5+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Handling missing values in 'Embarked' column\n","metadata":{}},{"cell_type":"code","source":"df['Embarked'].info()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.278480Z","iopub.execute_input":"2023-12-19T13:28:28.278911Z","iopub.status.idle":"2023-12-19T13:28:28.295231Z","shell.execute_reply.started":"2023-12-19T13:28:28.278870Z","shell.execute_reply":"2023-12-19T13:28:28.294285Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"<class 'pandas.core.series.Series'>\nIndex: 1309 entries, 0 to 417\nSeries name: Embarked\nNon-Null Count  Dtype \n--------------  ----- \n1307 non-null   object\ndtypes: object(1)\nmemory usage: 20.5+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df['Embarked'].isna().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.296820Z","iopub.execute_input":"2023-12-19T13:28:28.297211Z","iopub.status.idle":"2023-12-19T13:28:28.312555Z","shell.execute_reply.started":"2023-12-19T13:28:28.297175Z","shell.execute_reply":"2023-12-19T13:28:28.311153Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"df[df['Embarked'].isna()]","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.313678Z","iopub.execute_input":"2023-12-19T13:28:28.314230Z","iopub.status.idle":"2023-12-19T13:28:28.335577Z","shell.execute_reply.started":"2023-12-19T13:28:28.314179Z","shell.execute_reply":"2023-12-19T13:28:28.334429Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  Ticket  Fare  \\\n61            62       1.0       1  female  38.0      0      0  113572  80.0   \n829          830       1.0       1  female  62.0      0      0  113572  80.0   \n\n    Embarked  \n61       NaN  \n829      NaN  ","text/html":"<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>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>61</th>\n      <td>62</td>\n      <td>1.0</td>\n      <td>1</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>NaN</td>\n    </tr>\n    <tr>\n      <th>829</th>\n      <td>830</td>\n      <td>1.0</td>\n      <td>1</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>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(15,10))\nsns.boxplot(x='Embarked', y='Fare', data=test_df, palette = 'YlGnBu')","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.337219Z","iopub.execute_input":"2023-12-19T13:28:28.337570Z","iopub.status.idle":"2023-12-19T13:28:28.741962Z","shell.execute_reply.started":"2023-12-19T13:28:28.337539Z","shell.execute_reply":"2023-12-19T13:28:28.740753Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"<Axes: xlabel='Embarked', ylabel='Fare'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"df['Embarked'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.743327Z","iopub.execute_input":"2023-12-19T13:28:28.743673Z","iopub.status.idle":"2023-12-19T13:28:28.751590Z","shell.execute_reply.started":"2023-12-19T13:28:28.743642Z","shell.execute_reply":"2023-12-19T13:28:28.750359Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q', nan], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"def impage3(cols):\n    Embarked = cols[0]\n    Fare = cols[1]\n\n    if pd.isna(Embarked):\n\n        if Fare == 80:\n            return 'C'\n    else:\n        return Embarked","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.753072Z","iopub.execute_input":"2023-12-19T13:28:28.753578Z","iopub.status.idle":"2023-12-19T13:28:28.761930Z","shell.execute_reply.started":"2023-12-19T13:28:28.753536Z","shell.execute_reply":"2023-12-19T13:28:28.760796Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"df['Embarked'] = df[['Embarked', 'Fare']].apply(impage3, axis=1)\ndf['Embarked'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.763449Z","iopub.execute_input":"2023-12-19T13:28:28.763928Z","iopub.status.idle":"2023-12-19T13:28:28.789744Z","shell.execute_reply.started":"2023-12-19T13:28:28.763860Z","shell.execute_reply":"2023-12-19T13:28:28.788391Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"array(['S', 'C', 'Q'], dtype=object)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Encoding Columns\n\nEncoding columns with labelencoder for further processing","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nlabelencoder=LabelEncoder()\n\ndf['Ticket']=labelencoder.fit_transform(df['Ticket'])\ndf['Embarked']=labelencoder.fit_transform(df['Embarked'])\ndf['Sex']=labelencoder.fit_transform(df['Sex'])","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.791218Z","iopub.execute_input":"2023-12-19T13:28:28.791621Z","iopub.status.idle":"2023-12-19T13:28:28.877429Z","shell.execute_reply.started":"2023-12-19T13:28:28.791588Z","shell.execute_reply":"2023-12-19T13:28:28.876594Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"markdown","source":"### Spliting the train, test data","metadata":{}},{"cell_type":"code","source":"df_test = df[df['Survived'].isnull()]\ndf_train = df[~df['Survived'].isnull()]","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.878572Z","iopub.execute_input":"2023-12-19T13:28:28.879480Z","iopub.status.idle":"2023-12-19T13:28:28.887379Z","shell.execute_reply.started":"2023-12-19T13:28:28.879447Z","shell.execute_reply":"2023-12-19T13:28:28.886226Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"df_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.888827Z","iopub.execute_input":"2023-12-19T13:28:28.889194Z","iopub.status.idle":"2023-12-19T13:28:28.903432Z","shell.execute_reply.started":"2023-12-19T13:28:28.889162Z","shell.execute_reply":"2023-12-19T13:28:28.902299Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"PassengerId    0\nSurvived       0\nPclass         0\nSex            0\nAge            0\nSibSp          0\nParch          0\nTicket         0\nFare           0\nEmbarked       0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_test.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.905376Z","iopub.execute_input":"2023-12-19T13:28:28.906247Z","iopub.status.idle":"2023-12-19T13:28:28.921780Z","shell.execute_reply.started":"2023-12-19T13:28:28.906202Z","shell.execute_reply":"2023-12-19T13:28:28.920812Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"PassengerId      0\nSurvived       418\nPclass           0\nSex              0\nAge              0\nSibSp            0\nParch            0\nTicket           0\nFare             0\nEmbarked         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Model to train and test the dataset","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:28.922802Z","iopub.execute_input":"2023-12-19T13:28:28.923909Z","iopub.status.idle":"2023-12-19T13:28:29.231124Z","shell.execute_reply.started":"2023-12-19T13:28:28.923874Z","shell.execute_reply":"2023-12-19T13:28:29.229556Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"X = df_train.drop('Survived', axis = 1 )\nX.tail()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.232679Z","iopub.execute_input":"2023-12-19T13:28:29.233039Z","iopub.status.idle":"2023-12-19T13:28:29.252572Z","shell.execute_reply.started":"2023-12-19T13:28:29.233006Z","shell.execute_reply":"2023-12-19T13:28:29.251203Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"     PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket   Fare  Embarked\n886          887       2    1  27.0      0      0     130  13.00         2\n887          888       1    0  19.0      0      0      18  30.00         2\n888          889       3    0  25.0      1      2     923  23.45         2\n889          890       1    1  26.0      0      0      11  30.00         0\n890          891       3    1  32.0      0      0     642   7.75         1","text/html":"<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>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>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>886</th>\n      <td>887</td>\n      <td>2</td>\n      <td>1</td>\n      <td>27.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>130</td>\n      <td>13.00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>887</th>\n      <td>888</td>\n      <td>1</td>\n      <td>0</td>\n      <td>19.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>18</td>\n      <td>30.00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>888</th>\n      <td>889</td>\n      <td>3</td>\n      <td>0</td>\n      <td>25.0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>923</td>\n      <td>23.45</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>889</th>\n      <td>890</td>\n      <td>1</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>11</td>\n      <td>30.00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>890</th>\n      <td>891</td>\n      <td>3</td>\n      <td>1</td>\n      <td>32.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>642</td>\n      <td>7.75</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y = df_train['Survived']\ny.tail()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.255131Z","iopub.execute_input":"2023-12-19T13:28:29.255964Z","iopub.status.idle":"2023-12-19T13:28:29.268110Z","shell.execute_reply.started":"2023-12-19T13:28:29.255915Z","shell.execute_reply":"2023-12-19T13:28:29.266540Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"886    0.0\n887    1.0\n888    0.0\n889    1.0\n890    0.0\nName: Survived, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"# Split the dataset into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.269450Z","iopub.execute_input":"2023-12-19T13:28:29.269800Z","iopub.status.idle":"2023-12-19T13:28:29.280226Z","shell.execute_reply.started":"2023-12-19T13:28:29.269768Z","shell.execute_reply":"2023-12-19T13:28:29.278937Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"xgb_classifier = XGBClassifier(\n    objective='binary:logistic',\n    learning_rate=0.15,\n    n_estimators=100,\n    max_depth=4,\n    random_state=8\n)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.281660Z","iopub.execute_input":"2023-12-19T13:28:29.283875Z","iopub.status.idle":"2023-12-19T13:28:29.295106Z","shell.execute_reply.started":"2023-12-19T13:28:29.283836Z","shell.execute_reply":"2023-12-19T13:28:29.292077Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"markdown","source":"### Training the model","metadata":{}},{"cell_type":"code","source":"xgb_classifier.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.296841Z","iopub.execute_input":"2023-12-19T13:28:29.298248Z","iopub.status.idle":"2023-12-19T13:28:29.415253Z","shell.execute_reply.started":"2023-12-19T13:28:29.298200Z","shell.execute_reply":"2023-12-19T13:28:29.414509Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.15, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=4, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None,\n              num_parallel_tree=None, random_state=8, ...)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.15, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=4, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None,\n              num_parallel_tree=None, random_state=8, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=0.15, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=4, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=100, n_jobs=None,\n              num_parallel_tree=None, random_state=8, ...)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = xgb_classifier.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.416418Z","iopub.execute_input":"2023-12-19T13:28:29.417220Z","iopub.status.idle":"2023-12-19T13:28:29.433182Z","shell.execute_reply.started":"2023-12-19T13:28:29.417185Z","shell.execute_reply":"2023-12-19T13:28:29.432317Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"code","source":"# Calculate accuracy\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.437168Z","iopub.execute_input":"2023-12-19T13:28:29.440136Z","iopub.status.idle":"2023-12-19T13:28:29.447239Z","shell.execute_reply.started":"2023-12-19T13:28:29.440093Z","shell.execute_reply":"2023-12-19T13:28:29.446090Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"Accuracy: 0.8268156424581006\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Model on the test dataset","metadata":{}},{"cell_type":"code","source":"Xt = df_test.drop('Survived', axis = 1)\nXt.tail()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.448882Z","iopub.execute_input":"2023-12-19T13:28:29.449928Z","iopub.status.idle":"2023-12-19T13:28:29.472263Z","shell.execute_reply.started":"2023-12-19T13:28:29.449790Z","shell.execute_reply":"2023-12-19T13:28:29.470644Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"     PassengerId  Pclass  Sex   Age  SibSp  Parch  Ticket      Fare  Embarked\n413         1305       3    1  23.0      0      0     711    8.0500         2\n414         1306       1    0  39.0      0      0     834  108.9000         0\n415         1307       3    1  38.5      0      0     872    7.2500         2\n416         1308       3    1  23.0      0      0     579    8.0500         2\n417         1309       3    1  23.0      1      1     262   22.3583         0","text/html":"<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>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>Embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>413</th>\n      <td>1305</td>\n      <td>3</td>\n      <td>1</td>\n      <td>23.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>711</td>\n      <td>8.0500</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>414</th>\n      <td>1306</td>\n      <td>1</td>\n      <td>0</td>\n      <td>39.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>834</td>\n      <td>108.9000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>415</th>\n      <td>1307</td>\n      <td>3</td>\n      <td>1</td>\n      <td>38.5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>872</td>\n      <td>7.2500</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>416</th>\n      <td>1308</td>\n      <td>3</td>\n      <td>1</td>\n      <td>23.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>579</td>\n      <td>8.0500</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>417</th>\n      <td>1309</td>\n      <td>3</td>\n      <td>1</td>\n      <td>23.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>262</td>\n      <td>22.3583</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"yt = df_test['Survived']\nyt.tail()","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.474701Z","iopub.execute_input":"2023-12-19T13:28:29.475222Z","iopub.status.idle":"2023-12-19T13:28:29.487865Z","shell.execute_reply.started":"2023-12-19T13:28:29.475165Z","shell.execute_reply":"2023-12-19T13:28:29.486704Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"413   NaN\n414   NaN\n415   NaN\n416   NaN\n417   NaN\nName: Survived, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Exporting results","metadata":{}},{"cell_type":"code","source":"predictions = xgb_classifier.predict(Xt)\n\n# Create a DataFrame containing 'PassengerId' and 'Survived' columns\npred_df = pd.DataFrame({\n    'PassengerId': df_test['PassengerId'],  # Assuming 'PassengerId' column exists in df_test\n    'Survived': predictions\n})\n\n# Export predictions to a CSV file\npred_df.to_csv('predictions.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-12-19T13:28:29.489416Z","iopub.execute_input":"2023-12-19T13:28:29.490117Z","iopub.status.idle":"2023-12-19T13:28:29.514121Z","shell.execute_reply.started":"2023-12-19T13:28:29.490072Z","shell.execute_reply":"2023-12-19T13:28:29.513125Z"},"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}