Churn Prediction
Customer Churn Model
Function get_data():
url
string
URL of a CSV file
Returns pandas.dataframe
Usage
df = model_churn.get_data(url = 'https://raw.githubusercontent.com/../BankChurners.csv')
Function preprocess_inputs():
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")
Function pretrained():
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")
Function train():
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")
Function predict():
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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