bank-churn-prediciton (1).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","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":"nvidiaTeslaT4","dataSources":[{"sourceId":3322096,"sourceType":"datasetVersion","datasetId":2008274}],"dockerImageVersionId":30635,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-01-21T05:30:40.326572Z","iopub.execute_input":"2024-01-21T05:30:40.326902Z","iopub.status.idle":"2024-01-21T05:30:41.313564Z","shell.execute_reply.started":"2024-01-21T05:30:40.326872Z","shell.execute_reply":"2024-01-21T05:30:41.312601Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/bank-customer-churn-prediction/Churn_Modelling.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"# We need to work on Recall Score as Employee which is existing and but predicted is left then providing Bad Service \n# So we need to Increase Recall score or Decrease False Negative as much as possible \n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:41.315402Z","iopub.execute_input":"2024-01-21T05:30:41.315789Z","iopub.status.idle":"2024-01-21T05:30:41.320163Z","shell.execute_reply.started":"2024-01-21T05:30:41.315762Z","shell.execute_reply":"2024-01-21T05:30:41.319164Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as ex\nimport plotly.graph_objs as go \nimport plotly.figure_factory as ff\nfrom plotly.subplots import make_subplots\nsns.set_style('darkgrid')\nfrom sklearn.decomposition import PCA \nfrom sklearn.model_selection import train_test_split,cross_val_score\nfrom sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.metrics import f1_score as f1\nfrom sklearn.metrics import confusion_matrix \nimport scikitplot as skplt \nplt.rc('figure',figsize=(20,10))\nfrom imblearn.over_sampling import SMOTE\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:41.321179Z","iopub.execute_input":"2024-01-21T05:30:41.321423Z","iopub.status.idle":"2024-01-21T05:30:44.803424Z","shell.execute_reply.started":"2024-01-21T05:30:41.321402Z","shell.execute_reply":"2024-01-21T05:30:44.802626Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"df=pd.read_csv(\"/kaggle/input/bank-customer-churn-prediction/Churn_Modelling.csv\")\ndf","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.806136Z","iopub.execute_input":"2024-01-21T05:30:44.806744Z","iopub.status.idle":"2024-01-21T05:30:44.875366Z","shell.execute_reply.started":"2024-01-21T05:30:44.806708Z","shell.execute_reply":"2024-01-21T05:30:44.874517Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n0             1    15634602   Hargrave          619    France  Female   42   \n1             2    15647311       Hill          608     Spain  Female   41   \n2             3    15619304       Onio          502    France  Female   42   \n3             4    15701354       Boni          699    France  Female   39   \n4             5    15737888   Mitchell          850     Spain  Female   43   \n...         ...         ...        ...          ...       ...     ...  ...   \n9995       9996    15606229   Obijiaku          771    France    Male   39   \n9996       9997    15569892  Johnstone          516    France    Male   35   \n9997       9998    15584532        Liu          709    France  Female   36   \n9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n9999      10000    15628319     Walker          792    France  Female   28   \n\n      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n0          2       0.00              1          1               1   \n1          1   83807.86              1          0               1   \n2          8  159660.80              3          1               0   \n3          1       0.00              2          0               0   \n4          2  125510.82              1          1               1   \n...      ...        ...            ...        ...             ...   \n9995       5       0.00              2          1               0   \n9996      10   57369.61              1          1               1   \n9997       7       0.00              1          0               1   \n9998       3   75075.31              2          1               0   \n9999       4  130142.79              1          1               0   \n\n      EstimatedSalary  Exited  \n0           101348.88       1  \n1           112542.58       0  \n2           113931.57       1  \n3            93826.63       0  \n4            79084.10       0  \n...               ...     ...  \n9995         96270.64       0  \n9996        101699.77       0  \n9997         42085.58       1  \n9998         92888.52       1  \n9999         38190.78       0  \n\n[10000 rows x 14 columns]","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>RowNumber</th>\n      <th>CustomerId</th>\n      <th>Surname</th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>15634602</td>\n      <td>Hargrave</td>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>15647311</td>\n      <td>Hill</td>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>15619304</td>\n      <td>Onio</td>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>15701354</td>\n      <td>Boni</td>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>15737888</td>\n      <td>Mitchell</td>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>9996</td>\n      <td>15606229</td>\n      <td>Obijiaku</td>\n      <td>771</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>39</td>\n      <td>5</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>96270.64</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>9997</td>\n      <td>15569892</td>\n      <td>Johnstone</td>\n      <td>516</td>\n      <td>France</td>\n      <td>Male</td>\n      <td>35</td>\n      <td>10</td>\n      <td>57369.61</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101699.77</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>9998</td>\n      <td>15584532</td>\n      <td>Liu</td>\n      <td>709</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>36</td>\n      <td>7</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>42085.58</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>9999</td>\n      <td>15682355</td>\n      <td>Sabbatini</td>\n      <td>772</td>\n      <td>Germany</td>\n      <td>Male</td>\n      <td>42</td>\n      <td>3</td>\n      <td>75075.31</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>92888.52</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>10000</td>\n      <td>15628319</td>\n      <td>Walker</td>\n      <td>792</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>28</td>\n      <td>4</td>\n      <td>130142.79</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>38190.78</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 14 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.876601Z","iopub.execute_input":"2024-01-21T05:30:44.876880Z","iopub.status.idle":"2024-01-21T05:30:44.884006Z","shell.execute_reply.started":"2024-01-21T05:30:44.876856Z","shell.execute_reply":"2024-01-21T05:30:44.883149Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df['Exited'].value_counts()\ndf['Tenure'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.885319Z","iopub.execute_input":"2024-01-21T05:30:44.885657Z","iopub.status.idle":"2024-01-21T05:30:44.903190Z","shell.execute_reply.started":"2024-01-21T05:30:44.885623Z","shell.execute_reply":"2024-01-21T05:30:44.902381Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"Tenure\n2     1048\n1     1035\n7     1028\n8     1025\n5     1012\n3     1009\n4      989\n9      984\n6      967\n10     490\n0      413\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.describe(include='all')\ndf.drop(['RowNumber','CustomerId','Surname'],axis=1,inplace=True)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.904266Z","iopub.execute_input":"2024-01-21T05:30:44.904592Z","iopub.status.idle":"2024-01-21T05:30:44.963408Z","shell.execute_reply.started":"2024-01-21T05:30:44.904560Z","shell.execute_reply":"2024-01-21T05:30:44.962584Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n0          619    France  Female   42       2       0.00              1   \n1          608     Spain  Female   41       1   83807.86              1   \n2          502    France  Female   42       8  159660.80              3   \n3          699    France  Female   39       1       0.00              2   \n4          850     Spain  Female   43       2  125510.82              1   \n\n   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n0          1               1        101348.88       1  \n1          0               1        112542.58       0  \n2          1               0        113931.57       1  \n3          0               0         93826.63       0  \n4          1               1         79084.10       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>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df = pd.get_dummies(df, drop_first=True)\ndf = df.astype(int)\ndf['Exited']","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.964447Z","iopub.execute_input":"2024-01-21T05:30:44.964703Z","iopub.status.idle":"2024-01-21T05:30:44.979866Z","shell.execute_reply.started":"2024-01-21T05:30:44.964680Z","shell.execute_reply":"2024-01-21T05:30:44.978971Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"0       1\n1       0\n2       1\n3       0\n4       0\n       ..\n9995    0\n9996    0\n9997    1\n9998    1\n9999    0\nName: Exited, Length: 10000, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"X=df.drop(['Exited'],axis=1)\ny=df['Exited']","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:44.980958Z","iopub.execute_input":"2024-01-21T05:30:44.981320Z","iopub.status.idle":"2024-01-21T05:30:44.987082Z","shell.execute_reply.started":"2024-01-21T05:30:44.981288Z","shell.execute_reply":"2024-01-21T05:30:44.986163Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nsns.countplot(x='Exited', data=df)  \n\nplt.title('Count of Each Class')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:33:58.633531Z","iopub.execute_input":"2024-01-21T05:33:58.634262Z","iopub.status.idle":"2024-01-21T05:33:58.966683Z","shell.execute_reply.started":"2024-01-21T05:33:58.634232Z","shell.execute_reply":"2024-01-21T05:33:58.965848Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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imblearn.over_sampling import SMOTE\nX_res,y_res=SMOTE().fit_resample(X,y)\ny_res.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:45.361823Z","iopub.execute_input":"2024-01-21T05:30:45.362106Z","iopub.status.idle":"2024-01-21T05:30:45.393889Z","shell.execute_reply.started":"2024-01-21T05:30:45.362071Z","shell.execute_reply":"2024-01-21T05:30:45.393044Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Exited\n1    7963\n0    7963\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"sns.countplot(x=y_res)\nplt.title('Count Plot of y_res')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:45.394917Z","iopub.execute_input":"2024-01-21T05:30:45.395195Z","iopub.status.idle":"2024-01-21T05:30:45.729338Z","shell.execute_reply.started":"2024-01-21T05:30:45.395172Z","shell.execute_reply":"2024-01-21T05:30:45.728397Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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AElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X_res,y_res,test_size=0.20,random_state=42)\nX_train_v1, X_test_v1, y_tain_v1, y_test_v1 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()\nX_train_v1.var(axis=0)\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:45.730809Z","iopub.execute_input":"2024-01-21T05:30:45.731182Z","iopub.status.idle":"2024-01-21T05:30:45.748796Z","shell.execute_reply.started":"2024-01-21T05:30:45.731149Z","shell.execute_reply":"2024-01-21T05:30:45.747995Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"CreditScore          8.362621e+03\nAge                  9.896382e+01\nTenure               7.373204e+00\nBalance              3.749177e+09\nNumOfProducts        3.236094e-01\nHasCrCard            2.347543e-01\nIsActiveMember       2.330407e-01\nEstimatedSalary      3.285799e+09\nGeography_Germany    1.872790e-01\nGeography_Spain      1.444629e-01\nGender_Male          2.427392e-01\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\n\nscaled_X_train_v1 = scaler.fit_transform(X_train_v1)","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:45.749764Z","iopub.execute_input":"2024-01-21T05:30:45.750026Z","iopub.status.idle":"2024-01-21T05:30:45.758166Z","shell.execute_reply.started":"2024-01-21T05:30:45.750003Z","shell.execute_reply":"2024-01-21T05:30:45.757512Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(20,10))\n\nx = X.columns\ny = scaled_X_train_v1.var(axis=0)\n\nax.bar(x, y, width=0.3)\nax.set_xlabel('Features')\nax.set_ylabel('Variance')\nax.set_ylim(0, 0.25)\n\nfor index, value in enumerate(y):\n    plt.text(x=index, y=value+0.001, s=str(round(value, 3)), ha='center')\n    \nfig.autofmt_xdate()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:45.759140Z","iopub.execute_input":"2024-01-21T05:30:45.759516Z","iopub.status.idle":"2024-01-21T05:30:46.293842Z","shell.execute_reply.started":"2024-01-21T05:30:45.759483Z","shell.execute_reply":"2024-01-21T05:30:46.292869Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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Modelling ","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:46.294976Z","iopub.execute_input":"2024-01-21T05:30:46.295649Z","iopub.status.idle":"2024-01-21T05:30:46.299757Z","shell.execute_reply.started":"2024-01-21T05:30:46.295613Z","shell.execute_reply":"2024-01-21T05:30:46.298849Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nmodel = RandomForestClassifier(n_estimators=20)\nmodel.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:46.301176Z","iopub.execute_input":"2024-01-21T05:30:46.301521Z","iopub.status.idle":"2024-01-21T05:30:46.657365Z","shell.execute_reply.started":"2024-01-21T05:30:46.301492Z","shell.execute_reply":"2024-01-21T05:30:46.656486Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(n_estimators=20)","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>RandomForestClassifier(n_estimators=20)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=20)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_predicted = model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:46.658579Z","iopub.execute_input":"2024-01-21T05:30:46.658937Z","iopub.status.idle":"2024-01-21T05:30:46.681666Z","shell.execute_reply.started":"2024-01-21T05:30:46.658905Z","shell.execute_reply":"2024-01-21T05:30:46.680983Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_test, y_predicted))","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:46.682750Z","iopub.execute_input":"2024-01-21T05:30:46.683080Z","iopub.status.idle":"2024-01-21T05:30:46.701824Z","shell.execute_reply.started":"2024-01-21T05:30:46.683051Z","shell.execute_reply":"2024-01-21T05:30:46.700981Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.85      0.85      0.85      1633\n           1       0.84      0.84      0.84      1553\n\n    accuracy                           0.85      3186\n   macro avg       0.85      0.85      0.85      3186\nweighted avg       0.85      0.85      0.85      3186\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.metrics import classification_report\nfrom sklearn.model_selection import train_test_split\nmodel = AdaBoostClassifier(n_estimators=100, random_state=42)  # You can adjust n_estimators as needed\nmodel.fit(X_train, y_train)\n\n# Make predictions on the test set\ny_pred = model.predict(X_test)\n\n# Print the classification report\nprint(classification_report(y_test, y_pred))\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:30:46.702853Z","iopub.execute_input":"2024-01-21T05:30:46.703153Z","iopub.status.idle":"2024-01-21T05:30:47.832237Z","shell.execute_reply.started":"2024-01-21T05:30:46.703127Z","shell.execute_reply":"2024-01-21T05:30:47.831356Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.83      0.82      0.83      1633\n           1       0.81      0.82      0.82      1553\n\n    accuracy                           0.82      3186\n   macro avg       0.82      0.82      0.82      3186\nweighted avg       0.82      0.82      0.82      3186\n\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import recall_score, f1_score\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Assuming X_train, X_test, y_train, y_test are your training and testing sets\n\n# Models\nmodels = {\n    'Random Forest': RandomForestClassifier(n_estimators=20, random_state=42),\n    'AdaBoost': AdaBoostClassifier(n_estimators=50, random_state=42),\n    'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42),\n    'Logistic Regression': LogisticRegression(random_state=42),\n}\n\nrecall_scores = []\nf1_scores = []\n\n# Loop through models\nfor name, model in models.items():\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n    \n    recall = recall_score(y_test, y_pred)\n    f1 = f1_score(y_test, y_pred)\n    \n    recall_scores.append(recall)\n    f1_scores.append(f1)\n\n# Plotting\nfig, ax = plt.subplots(figsize=(10, 6))\nwidth = 0.20\nind = np.arange(len(models))\n\nbar1 = ax.bar(ind, recall_scores, width, label='Recall')\nbar2 = ax.bar(ind + width, f1_scores, width, label='F1 Score')\n\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(models.keys())\nax.legend()\n\n# Add values on top of the bars\nfor i, rect in enumerate(bar1):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nfor i, rect in enumerate(bar2):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nplt.title('Recall and F1 Score Comparison for Different Models')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:32:55.609071Z","iopub.execute_input":"2024-01-21T05:32:55.609499Z","iopub.status.idle":"2024-01-21T05:32:58.885386Z","shell.execute_reply.started":"2024-01-21T05:32:55.609469Z","shell.execute_reply":"2024-01-21T05:32:58.884494Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"#Removing Age ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X1=df.drop(['Exited','Age'],axis=1)\ny1=df['Exited']","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:35:26.443985Z","iopub.execute_input":"2024-01-21T05:35:26.444403Z","iopub.status.idle":"2024-01-21T05:35:26.450827Z","shell.execute_reply.started":"2024-01-21T05:35:26.444369Z","shell.execute_reply":"2024-01-21T05:35:26.449863Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"from imblearn.over_sampling import SMOTE\nX_res,y_res=SMOTE().fit_resample(X1,y1)\ny_res.value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:35:32.331999Z","iopub.execute_input":"2024-01-21T05:35:32.332566Z","iopub.status.idle":"2024-01-21T05:35:32.361208Z","shell.execute_reply.started":"2024-01-21T05:35:32.332527Z","shell.execute_reply":"2024-01-21T05:35:32.360342Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Exited\n1    7963\n0    7963\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X_res,y_res,test_size=0.20,random_state=42)\nX_train_v1, X_test_v1, y_train_v1, y_test_v1 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()\nX_train_v1.var(axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:36:54.268441Z","iopub.execute_input":"2024-01-21T05:36:54.268814Z","iopub.status.idle":"2024-01-21T05:36:54.285334Z","shell.execute_reply.started":"2024-01-21T05:36:54.268784Z","shell.execute_reply":"2024-01-21T05:36:54.284473Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"CreditScore          8.326369e+03\nTenure               7.406352e+00\nBalance              3.737589e+09\nNumOfProducts        3.246470e-01\nHasCrCard            2.352736e-01\nIsActiveMember       2.331632e-01\nEstimatedSalary      3.292143e+09\nGeography_Germany    1.858156e-01\nGeography_Spain      1.451757e-01\nGender_Male          2.437092e-01\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import recall_score, f1_score\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Assuming X_train, X_test, y_train, y_test are your training and testing sets\n\n# Models\nmodels = {\n    'Random Forest': RandomForestClassifier(n_estimators=20, random_state=42),\n    'AdaBoost': AdaBoostClassifier(n_estimators=50, random_state=42),\n    'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42),\n    'Logistic Regression': LogisticRegression(random_state=42),\n}\n\nrecall_scores = []\nf1_scores = []\n\n# Loop through models\nfor name, model in models.items():\n    model.fit(X_train_v1, y_train_v1)\n    y_pred = model.predict(X_test_v1)\n    \n    recall = recall_score(y_test_v1, y_pred)\n    f1 = f1_score(y_test_v1, y_pred)\n    \n    recall_scores.append(recall)\n    f1_scores.append(f1)\n\n# Plotting\nfig, ax = plt.subplots(figsize=(10, 6))\nwidth = 0.20\nind = np.arange(len(models))\n\nbar1 = ax.bar(ind, recall_scores, width, label='Recall')\nbar2 = ax.bar(ind + width, f1_scores, width, label='F1 Score')\n\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(models.keys())\nax.legend()\n\n# Add values on top of the bars\nfor i, rect in enumerate(bar1):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nfor i, rect in enumerate(bar2):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nplt.title('Recall and F1 Score Comparison for Different Models')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:37:06.953869Z","iopub.execute_input":"2024-01-21T05:37:06.954569Z","iopub.status.idle":"2024-01-21T05:37:09.910896Z","shell.execute_reply.started":"2024-01-21T05:37:06.954540Z","shell.execute_reply":"2024-01-21T05:37:09.909735Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:37:54.009542Z","iopub.execute_input":"2024-01-21T05:37:54.010541Z","iopub.status.idle":"2024-01-21T05:37:54.016869Z","shell.execute_reply.started":"2024-01-21T05:37:54.010506Z","shell.execute_reply":"2024-01-21T05:37:54.015955Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Index(['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n       'IsActiveMember', 'EstimatedSalary', 'Exited', 'Geography_Germany',\n       'Geography_Spain', 'Gender_Male'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\ncolumns_to_plot = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n                   'IsActiveMember', 'EstimatedSalary', 'Exited', 'Geography_Germany',\n                   'Geography_Spain', 'Gender_Male']\n\n# Plot boxplots for each column\nfig, axes = plt.subplots(nrows=3, ncols=4, figsize=(16, 12))\nfig.suptitle('Boxplots for Each Column', fontsize=16)\n\nfor i, column in enumerate(columns_to_plot):\n    sns.boxplot(x=df[column], ax=axes[i // 4, i % 4])\n\nplt.tight_layout(rect=[0, 0, 1, 0.96])\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:39:20.898924Z","iopub.execute_input":"2024-01-21T05:39:20.899328Z","iopub.status.idle":"2024-01-21T05:39:24.001914Z","shell.execute_reply.started":"2024-01-21T05:39:20.899294Z","shell.execute_reply":"2024-01-21T05:39:24.000983Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x1200 with 12 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pandas as pd\n\n# Assuming df is your DataFrame\ncolumns_to_filter = ['Age', 'CreditScore']\n\n# Calculate the interquartile range (IQR) for each specified column\nfor column_name in columns_to_filter:\n    Q1 = df[column_name].quantile(0.25)\n    Q3 = df[column_name].quantile(0.75)\n    IQR = Q3 - Q1\n\n    # Define upper and lower bounds\n    lower_bound = Q1 - 1.5 * IQR\n    upper_bound = Q3 + 1.5 * IQR\n\n    # Remove rows where the specified column is outside the bounds\n    df_f = df[(df[column_name] >= lower_bound) & (df[column_name] <= upper_bound)]\n\n# Display the filtered DataFrame","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:53:21.621339Z","iopub.execute_input":"2024-01-21T05:53:21.622187Z","iopub.status.idle":"2024-01-21T05:53:21.634703Z","shell.execute_reply.started":"2024-01-21T05:53:21.622153Z","shell.execute_reply":"2024-01-21T05:53:21.633730Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\ncolumns_to_plot = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n                   'IsActiveMember', 'EstimatedSalary', 'Exited', 'Geography_Germany',\n                   'Geography_Spain', 'Gender_Male']\n\n# Plot boxplots for each column\nfig, axes = plt.subplots(nrows=3, ncols=4, figsize=(16, 12))\nfig.suptitle('Boxplots for Each Column', fontsize=16)\n\nfor i, column in enumerate(columns_to_plot):\n    sns.boxplot(x=df_f[column], ax=axes[i // 4, i % 4])\n\nplt.tight_layout(rect=[0, 0, 1, 0.96])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:53:26.271981Z","iopub.execute_input":"2024-01-21T05:53:26.272616Z","iopub.status.idle":"2024-01-21T05:53:29.441975Z","shell.execute_reply.started":"2024-01-21T05:53:26.272582Z","shell.execute_reply":"2024-01-21T05:53:29.441018Z"},"trusted":true},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1600x1200 with 12 Axes>","image/png":"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imblearn.over_sampling import SMOTE\nX_res,y_res=SMOTE().fit_resample(X2,y2)\ny_res.value_counts()\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(X_res,y_res,test_size=0.20,random_state=42)\nX_train_v2, X_test_v2, y_train_v2, y_test_v2 = X_train.copy(), X_test.copy(), y_train.copy(), y_test.copy()\nX_train_v2.var(axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:55:57.656750Z","iopub.execute_input":"2024-01-21T05:55:57.657493Z","iopub.status.idle":"2024-01-21T05:55:57.694536Z","shell.execute_reply.started":"2024-01-21T05:55:57.657458Z","shell.execute_reply":"2024-01-21T05:55:57.693675Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"CreditScore          8.101018e+03\nAge                  9.866629e+01\nTenure               7.424928e+00\nBalance              3.750212e+09\nNumOfProducts        3.260290e-01\nHasCrCard            2.344424e-01\nIsActiveMember       2.328355e-01\nEstimatedSalary      3.334254e+09\nGeography_Germany    1.850175e-01\nGeography_Spain      1.443098e-01\nGender_Male          2.423593e-01\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"models = {\n    'Random Forest': RandomForestClassifier(n_estimators=20, random_state=42),\n    'AdaBoost': AdaBoostClassifier(n_estimators=50, random_state=42),\n    'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42),\n    'Logistic Regression': LogisticRegression(random_state=42),\n}\n\nrecall_scores = []\nf1_scores = []\n\n# Loop through models\nfor name, model in models.items():\n    model.fit(X_train_v2, y_train_v2)\n    y_pred = model.predict(X_test_v2)\n    \n    recall = recall_score(y_test_v2, y_pred)\n    f1 = f1_score(y_test_v2, y_pred)\n    \n    recall_scores.append(recall)\n    f1_scores.append(f1)\n\n# Plotting\nfig, ax = plt.subplots(figsize=(10, 6))\nwidth = 0.20\nind = np.arange(len(models))\n\nbar1 = ax.bar(ind, recall_scores, width, label='Recall')\nbar2 = ax.bar(ind + width, f1_scores, width, label='F1 Score')\n\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(models.keys())\nax.legend()\n\n# Add values on top of the bars\nfor i, rect in enumerate(bar1):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nfor i, rect in enumerate(bar2):\n    height = rect.get_height()\n    ax.text(rect.get_x() + rect.get_width() / 2, height + 0.01, f'{height:.3f}', ha='center', va='bottom')\n\nplt.title('Recall and F1 Score Comparison for Different Models')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-21T05:56:13.574787Z","iopub.execute_input":"2024-01-21T05:56:13.575541Z","iopub.status.idle":"2024-01-21T05:56:16.741191Z","shell.execute_reply.started":"2024-01-21T05:56:13.575508Z","shell.execute_reply":"2024-01-21T05:56:16.740165Z"},"trusted":true},"execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Hence Random Forest Turns out with Highest Recall and F1 score among all","metadata":{},"execution_count":null,"outputs":[]}]}