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Commit5d59dba

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1 parent012df3e commit5d59dba

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‎Random_Forest_project/src/kuchbhi.py

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Original file line numberDiff line numberDiff line change
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importsys
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importos
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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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fromsrc.model_evaluationimportevaluate_model
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fromsrc.data_preprocessingimportpreprocess_data
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fromsrc.model_trainingimporttrain_model
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# Add the parent directory of 'src' to the Python path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),'..')))
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7-
data=pd.read_csv('Churn_Modelling.csv')
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# Specify the path to the CSV file
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csv_file_path=os.path.join(os.path.dirname(__file__),'..','Churn_Modelling.csv')
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# Load the data
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try:
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data=pd.read_csv(csv_file_path)
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exceptFileNotFoundError:
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print(f"The file{csv_file_path} was not found.")
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exit()
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# Preprocess the data
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X_train,X_test,y_train,y_test=preprocess_data(data)
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# Train the model
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model=train_model(X_train,y_train)
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evaluate_model(model,X_test,y_test)
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# Evaluate the model
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evaluate_model(model,X_test,y_test)
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# model_evaluation.py
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fromsklearn.metricsimportaccuracy_score,precision_score,recall_score,f1_score,confusion_matrix,roc_curve,roc_auc_score
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defevaluate_model(model,X_test,y_test):
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y_pred=model.predict(X_test)
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accuracy=accuracy_score(y_test,y_pred)
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precision=precision_score(y_test,y_pred)
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recall=recall_score(y_test,y_pred)
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f1=f1_score(y_test,y_pred)
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print("Accuracy:",accuracy)
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print("Precision:",precision)
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print("Recall:",recall)
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print("F1-score:",f1)
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# Confusion matrix
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cm=confusion_matrix(y_test,y_pred)
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print("Confusion Matrix:\n",cm)
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# ROC and AUC
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y_pred_proba=model.predict_proba(X_test)
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fpr,tpr,thresholds=roc_curve(y_test,y_pred_proba[:,1])
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auc=roc_auc_score(y_test,y_pred_proba[:,1])
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print("AUC:",auc)
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fromsklearn.ensembleimportRandomForestClassifier
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deftrain_model(X_train,y_train):
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rf_model=RandomForestClassifier(random_state=42)
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rf_model.fit(X_train,y_train)
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returnrf_model

‎Random_Forest_project/src/modelevelution.py

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