Churn Prediction

Customer Churn Model

Function get_data():

Arguement	Type	Description
url	string	URL of a CSV file

Returns pandas.dataframe

Usage

    df = model_churn.get_data(url = 'https://raw.githubusercontent.com/../BankChurners.csv')

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Function preprocess_inputs():

Input	Type	Description
dataset	*pandas.dataframe*	Model dataset in dataframe
model_name	string	Model name as a string Default = "Logistic_Regression" | "Support_Vector_Machine" "Support_Vector_Machine_Optimized" "Decision_Tree" "Neural_Network" "Random_Forest"

Returns pandas.dataframe, pandas.dataframe

Usage

    X, y = preprocess_inputs(df, "Neural_Network")

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Function pretrained():

Input	Type	Description
model_name	string	Model name as a string Default = "Logistic_Regression" | "Support_Vector_Machine" "Support_Vector_Machine_Optimized" "Decision_Tree" "Neural_Network" "Random_Forest"

Returns model

Usage

    model = pretrained("Neural_Network")

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Function train():

Input	Type	Description
dataset	*pandas.dataframe*	New training dataset in dataframe
model_name	string	Model name as a string Default = "Logistic_Regression" | "Support_Vector_Machine" "Support_Vector_Machine_Optimized" "Decision_Tree" "Neural_Network" "Random_Forest"

Returns model

Usage

    model = train(df, "Neural_Network")

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Function predict():

Input	Type	Description
test dataset	*pandas.dataframe*	Model test dataset in dataframe
model	function()	Model function from pretrained / train

Returns array

Usage

    print(predict(X_test, model))

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