Customer Churn Prediction using Flask & Machine Learning

Project Overview

This project predicts customer churn (whether a customer will leave the service or not) using machine learning models. The model is deployed using Flask, allowing users to upload a CSV file and get predictions.

Technologies Used

• Python (For model training and prediction)
• Flask (For deployment)
• Pandas, NumPy (For data processing)
• Scikit-learn, XGBoost, DecisionTree, RandomForest (For machine learning)
• SMOTE & ENN (For handling imbalanced data)

Project Workflow

1. Data Preprocessing:
   • Handled missing values
   • Converted categorical data using One-Hot Encoding
   • Scaled numerical data
   • Applied SMOTEENN to balance the dataset
2. Model Training:
   • Tried different models (Decision Tree, XGBoost, Random Forest)
   • Selected Random Forest as the final model (best accuracy ~98%)
   • Saved the trained model using Pickle
3. Flask Web App:
   • Users can upload a CSV file
   • The model processes the data and returns predictions
4. Deployment:
   • Flask app can be tested locally using Anaconda/Command Prompt
   • Can be hosted on cloud platforms (like Render, Heroku, or AWS)

How to Run the Project Locally

Step 1: Clone the Repository

git clone https://github.com/sayed-ashfaq/Customer-Churn-Prediction.git
cd Customer-Churn-Prediction

Step 2: Create a Virtual Environment (Recommended)

conda create --name churn_env python=3.9
conda activate churn_env

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Run Flask App

python app.py

The app will start running at http://127.0.0.1:5000/

Step 5: Upload a CSV File

• Go to the browser and open http://127.0.0.1:5000/
• Upload a CSV file with customer data
• The model will predict whether the customer will churn or not

Sample Test Data

• It is uploaded as test_data in the git repo

Next Steps

• Deploy the Flask app to Render, AWS, or Heroku
• Improve the UI using HTML & CSS
• Experiment with more ML models for better accuracy