MArketing Prediction

Bank Deposits Prediction Model

Function get_data():

Input	Type	Description
training dataset	url<string>	URL of a training CSV file
testing dataset	url<string>	URL of a testing CSV file

Returns pandas.dataframe, pandas.dataframe

Usage

    data_train, data_test = get_data(train = 'https://raw.githubusercontent.com/../banking_dataset_train.csv', test = 'https://raw.githubusercontent.com/../banking_dataset_test.csv')

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Function preprocess_inputs():

Input	Type	Description
training dataset	*pandas.dataframe*	Model training dataset in dataframe
testing dataset	*pandas.dataframe*	Model testing 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(data_train, data_test, "Random_Forest")

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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("Random_Forest")

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Function train():

Input	Type	Description
training dataset	*pandas.dataframe*	Model training dataset in dataframe
testing dataset	*pandas.dataframe*	Model testing 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(data_train, data_test, "Random_Forest")

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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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