Customer-Churn-Prediction-using-Machine-Learning

A fictional telco company that provided home phone and Internet services data from customers in California to predict their future churning rate with machine learning

Run this project in Google Colab.

Requirements

• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Seaborn

Dataset

• Telco customer churn: IBM dataset Download link
• Put the Extracted data into the ./data folder.

Data Visualization

Corelation Matrix

Reuslt (Confusion Matrix)

Random Forrenst Model

Logistic Regression Model

Comprehensive summary

From the model evaluation, we can see that out of 3 machine learning algorithms, Random Forest provides the highest accuracy for predicting the number of churn customers, 94% after hyper-tuning the parameters. Before hyper-tuning, it was 92%. of the ROC and AUC scores; we can also interpret that both the Decision Tree and Random forest algorithms are efficient in predicting future churn values. We can state from the overall evaluation outcomes that Random Forest performs slightly better than the other two algorithms.

Contributing

1. Fork the repository.
2. Create a new branch for your feature using git checkout -b feature/your-feature-name.
3. Make your changes and commit them using git commit -m "Add your commit message here".
4. Push your changes to your fork using git push origin feature/your-feature-name.
5. Open a pull request to the main repository.

Credits

This project was created by KaziRamisaRifa. An Standalone Machine Learning Project From Scratch.