This repository contains a project for building a model on various features using Linear Regression and integrating it with a Flask web application.

The goal of this project is to analyze ecommerce customer data, build a predictive model using Linear Regression, and create a Flask web application to interact with the model.

• Data Analysis using Jupyter Notebooks
• Model Building using Linear Regression
• Web Application using Flask

• notebooks/: Contains Jupyter Notebooks for data analysis and model building
• app/: Contains Flask web application code
• static/ and templates/: Contains static files and HTML templates for the Flask app

1. Clone the repository:

   git clone https://github.com/Ankitaghavate/Ecommerce-Customer-Insight-Analysis.git
   cd Ecommerce-Customer-Insight-Analysis

Create and activate a virtual environment:

python3 -m venv env
source env/bin/activate  # On Windows use `env\Scripts\activate`

pip install -r requirements.txt

Run the Jupyter Notebooks to perform data analysis and build the model. The notebooks are located in the notebooks/ directory.

Run the Flask application:

python app.py

Open your web browser and go to http://127.0.0.1:5000 to interact with the web application.

Alternatively, you can access the deployed application at https://ecommerce-customer-insight-analysis.onrender.com/

The model is built using the Linear Regression algorithm. The features used for the model are derived from the ecommerce customer data. The model is trained and evaluated in the Jupyter Notebooks.

The Flask application provides a user interface to interact with the model. Users can input feature values and get predictions from the model.

Contributions are welcome! Please fork the repository and submit a pull request.

This project is licensed under the MIT License.