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Commit012df3e

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importpandasaspd
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importnumpyasnp
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fromsklearn.model_selectionimporttrain_test_split
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fromsklearn.preprocessingimportLabelEncoder,StandardScaler
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defpreprocess_data(data):
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data.fillna(method='ffill',inplace=True)
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data['Age_Tenure_Product']=data['Age']*data['Tenure']*data['NumOfProducts']
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data['Balance']=np.log1p(data['Balance'])
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le=LabelEncoder()
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data['Geography']=le.fit_transform(data['Geography'])
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data['Gender']=le.fit_transform(data['Gender'])
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scaler=StandardScaler()
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data[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']]=scaler.fit_transform(data[['CreditScore','Age','Tenure','Balance','NumOfProducts','EstimatedSalary']])
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X=data.drop('Exited',axis=1)
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y=data['Exited']
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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
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returnX_train,X_test,y_train,y_test

‎Random_Forest_project/src/kuchbhi.py

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importpandasaspd
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fromdatapreprocimportpreprocess_data
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frommodel_trainingimporttrain_model
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frommodelevelutionimportevaluate_model
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data=pd.read_csv('Churn_Modelling.csv')
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X_train,X_test,y_train,y_test=preprocess_data(data)
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model=train_model(X_train,y_train)
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evaluate_model(model,X_test,y_test)

‎Random_Forest_project/src/model_training.py

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‎Random_Forest_project/src/modelevelution.py

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