Predict the lifetime value of customers for a business based on their historical interactions.

This project applies regression techniques to estimate the future value that a customer will bring to the business. Using a dataset of customer interactions, we preprocess the data, train a Random Forest Regressor model, and evaluate its performance. The model can then be used to predict the lifetime value of new customers.

• Python
• Pandas
• Scikit-learn

• Installation
• Usage
• Features
• Configuration
• Contributing
• Acknowledgements

To run this project, you will need Python installed on your machine. Additionally, you need to install the following Python libraries:

pip install pandas scikit-learn

1. Clone the repository:

   git clone https://github.com/your-username/customer-lifetime-value-prediction.git

2. Navigate to the project directory:

   cd customer-lifetime-value-prediction

3. Ensure you have a dataset named customer_data.csv in the project directory.
4. Run the script to train and evaluate the model:

   python Customer_lifetime_value_prediction.py

5. Review the output, including the Mean Squared Error (MSE), R² score, and predicted lifetime value for new customers.

• Data Loading: Load the dataset from customer_data.csv.
• Preprocessing: Convert dates, handle missing values, and encode categorical features.
• Feature Extraction: Extract and preprocess features for modeling.
• Model Training: Train a Random Forest Regressor model using Scikit-learn.
• Model Evaluation: Evaluate model performance using Mean Squared Error (MSE) and R² score.
• Prediction: Predict the lifetime value of new customers based on historical data.

• Scikit-learn for the machine learning tools and algorithms used.
• Pandas for data manipulation and analysis.
• The Python community for providing a robust ecosystem for data science.