Telco-Churn-Analysis-and-Modeling-XGBoost

Problem statement

• There are promotional charges called as Acquisition Cost and Retention Cost in a telco company. The cost of acquiring new consumers is referred to as acquisition cost. Meanwhile, the expense of retaining existing clients is known as Retention Cost.
• We are frequently inaccurate in our predictions of which customers would churn and which ones will stay due to human limitations. As a result, the allocation of money may be incorrect, resulting in a higher amount of funds being issued.
• Moreover, according to some sources, the acquisition cost is 5x greater than the retantion cost. If we are wrong in predicting a customer who will actually churn, but it turns out that we predict as a customer who will retain, then we need to spend more than it should be.

What to do

I will try to create a Machine Learning model to predict customer churn and retantion.

Goal

Machine Learning has a goal so that cost allocation can be done as precisely as possible.

Value

There is no wasted cost allocation.

Modules needed:

- xgboost
- Pandas
- Scikit-Learn &
- Numpy
- seaborn
- GridsearchCv

Usage

• Just run jupyter notebook in terminal and it will run in your browser.

  Install Jupyter here i've you haven't.

• install xgboost by using pip install xgb in command line prompt/ anconda i've you haven't.

Model performance

• I have acheived model accuracy 78%, but the goal is predict the correctly classified who, left the comapany, means we have more focused on recall, (82%)

           precision    recall  f1-score   support
  
       0       0.92      0.72      0.81      1294
       1       0.52      0.82      0.64       467
  

Model summary

• 77% AUC indicates model is correct on nearly 4 out of 5 customers cases
• 83% Recall means model predicts 83% of customer churn cases correctly
• 52% Precision means model predicts nearly 1 in 2 false positives

Dataset:

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113 https://github.com/rahkum96/Telco-Churn-Analysis-and-Modeling-XGBoost/blob/main/Telco_customer_churn.xlsx