iamharshvardhan / Customer-Churning-Prediction

This project predicts customer churn using scikit-learn (Logistic Regression, KNN, Random Forest) and TensorFlow neural networks. It aims to compare traditional and deep learning models for churn prediction.

Customer-Churning-Prediction

This project predicts customer churn using scikit-learn (LogisticRegression, KNN, RandomForestClassifier) and TensorFlow neural networks. It aims to compare traditional and deep learning models for churn prediction.

Prerequisites

• Python 3
• Jupyter Notebook
• Scikit-learn
• Pandas
• NumPy
• Matplotlib
• TensorFlow
• TensorFlow Hub

Instructions

• Clone the repository.

git clone https://github.com/iamharshvardhan/Customer-Churning-Prediction.git

• Open the churn-prediction.ipynb Jupyter Notebook.
• Run the cells in the notebook to train and evaluate the deep-learning model.

Results

• We compared sklearn's LogisticRegression, KNearestNeighbour and RandomForestClassifier and tune their hyperparameters to find the most accurate model possible.
• The deep learning model we have used is (Sequential) with 3 layers of neural networks.

License

This project is licensed under the MIT License.