Customer Churn Prediction (ML + Power BI)

An end-to-end Customer Churn Prediction project using Python (ML) and Power BI. The goal is to identify customers likely to churn and provide business insights for decision-making.

Project Highlights

Full ML pipeline: preprocessing → modeling → evaluation

Random Forest model with saved artifacts (model.pkl, scaler.pkl, features.pkl)

Customer risk segmentation (Low / Medium / High)

Power BI dashboard with KPIs and visuals

7000+ processed customer records with engineered features

Folder Structure

Folder / File
data/
data/raw/
data/processed/
src/
src/data_prep.py
src/train_model.py
src/evaluate.py
models/
models/rf_model.pkl
models/scaler.pkl
models/features.pkl
reports/
reports/visuals/
dashboard/
dashboard/churn_dashboard.pbix
README.md

ML Workflow

• Data cleaning & feature engineering

• One-hot encoding

• Scaling numeric features

• Random Forest training

Classification metrics (AUC, F1, recall, precision)

Power BI Dashboard

Includes:

• Churn Rate KPI

• Total Customers & Churned

• Tenure insights

• Charges distribution

• Filters (Contract, Gender, Internet, Tenure Group)

Tech Stack

Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, Joblib, Power BI

Outcome

A complete business-focused churn prediction solution combining ML + Analytics, ideal for resumes and portfolios.