KamusiiimeImmaculate/Churn-Prediction-Challenge-by-IndabaX-Tanzania

# Description This challenge is for an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. This solution will help this telecom company to better serve their customers by understanding which customers are at risk of leaving. Evaluation The error metric for this competition is the F1 score, which ranges from 0 (total failure) to 1 (perfect score). Hence, the closer your score is to 1, the better your model. F1 Score: A performance score that combines both precision and recall. It is a harmonic mean of these two variables. Formula is given as: 2*Precision*Recall/(Precision + Recall) Precision: This is an indicator of the number of items correctly identified as positive out of total items identified as positive. Formula is given as: TP/(TP+FP) Recall / Sensitivity / True Positive Rate (TPR): This is an indicator of the number of items correctly identified as positive out of total actual positives. Formula is given as: TP/(TP+FN) Where: TP=True Positive FP=False Positive TN=True Negative FN=False Negative