EDA using Python & ML Churn Prediction

Exploratory Data Analysis for the telecom Customer Churn analysis has been done using Python(Data cleaning, Univariate, and bivariate analysis, for numerical and categorical variables). The dataset has been trained and fitted using the Decision Tree Classifier and Random forest Classifier model with SMOTEENN and PCA algorithms. The accuracy prediction score has been compared between the models in order to increase the quality of the predicted accuracy. The dataset has been dumped into a .sav file in order to perform API calls.