pythonDataAnalysis

Portfolio Purpose

Customer Churn Prediction

📌 Overview

This project analyzes customer churn using machine learning techniques. The goal is to identify key factors contributing to customer churn and build a predictive model to help businesses retain customers.

📊 Dataset

• The dataset contains customer information, contract details, and payment methods.
• Key categorical and numerical features were analyzed.
• The target variable is Churn (0: No, 1: Yes).

🔍 Exploratory Data Analysis (EDA)

• Data cleaning and preprocessing (handling missing values, encoding categorical features).
• Correlation analysis using heatmaps.
• Chi-square tests to check dependency of categorical variables on churn.
• Statistical analysis to identify patterns in numerical data.

Key Findings:

✅ Higher churn among:

• Customers without partners or dependents.
• Month-to-month contract holders.
• Customers using electronic checks.
• Senior citizens.

📈 Model Building

Logistic Regression

• Initial model trained to understand feature importance.
• Key predictors: Contract type, payment method, senior citizen status, monthly charges.

Random Forest Classifier

• Improved model performance.
• Feature importance analysis showed Contract Type as the most significant predictor.

🏆 Results & Evaluation

• Accuracy: 80%
• Precision & Recall:
  • Churned customers (1): 68% precision, 48% recall.
  • Non-churned customers (0): 83% precision, 92% recall.
• The model effectively predicts churn but struggles with recall for actual churned customers.

🚀 Next Steps

• Tune hyperparameters for better recall.
• Try advanced models like XGBoost.
• Deploy as a web app for real-time predictions.

📂 Repository Structure

📁 Churn Prediction
 ├── 📜 Churn Prediction.ipynb  # Jupyter Notebook with full analysis
 ├── 📜 README.md               # Project documentation
 ├── 📊 data/                   # Dataset 
 ├── 📂 models/                 # Trained model files
 └── 📈 plots/                   # Visualizations

🛠️ Tech Stack

• Python (pandas, numpy, scikit-learn, seaborn, matplotlib)
• Machine Learning: Logistic Regression, Random Forest

👥 Contributors

• Sabheen Gull

📢 Feedback

Have suggestions? Feel free to open an issue or contribute!

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