This project aims to predict Customer Lifetime Value (CLV) using machine learning models. The objective is to help businesses estimate the future value of their customers, enabling better decision-making for customer retention and marketing strategies.
Customer Lifetime Value (CLV) is a critical metric for understanding the long-term financial value of a customer to a business. This project focuses on predicting CLV using machine learning techniques. By analyzing customer data, the model can help businesses optimize their marketing strategies and improve customer retention.
To get a local copy up and running, follow these steps.
Ensure you have the following installed on your machine:
- Python 3.x
pip(Python package installer)virtualenv(If you don't have it installed, follow the instructions in the Installation section)
To run this project, you will need to add the following environment variables to your .env file:
MY_SECRET="<YOUR_SECRET_KEY>"
DB_HOST=127.0.0.1
DB_USER="<YOUR_DATABASE_USERNAME>"
DB_PASSWORD="<YOUR_DATABASE_PASSWORD>"
DB_DATABASE="CLV"
CLOUD_NAME="<YOUR_CLOUD_NAME>"
CLOUD_API_KEY="<YOUR_CLOUD_API_KEY>"
CLOUD_API_SECRET="<YOUR_CLOUD_API_SECRET>"-
Clone the Repository and Set Up the Virtual Environment:
First, clone the repository, navigate into the project directory, and set up a virtual environment:
git clone https://github.com/HimanshuBhole2/1.-Better-Customer-Lifetime-Value-CLV-Prediction.git cd 1.-Better-Customer-Lifetime-Value-CLV-Prediction -
** Install virtualenv if you haven't already : **
pip install virtualenv
-
** Create a virtual environment : **
virtualenv venv -
**Activate the virtual environment
source venv/bin/activate # On macOS/Linux # venv\Scripts\activate # On Windows -
Install Dependencies:
With the virtual environment activated, install the required Python packages using the requirements.txt file:
pip install -r requirements.txt