REGRESSION ANALYSIS with LINEAR REGRESSION and XGBOOST

This repository contains two Jupyter Notebook implementations showcasing regression analysis using:

1. Linear Regression

2. XGBoost Regression

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Overview

The notebooks analyze the Ecommerce Customers dataset, performing predictive modeling to determine customer behavior and associated trends.

Files

• linear_reg.ipynb: Demonstrates regression using the LinearRegression model from Scikit-Learn.

• xgboost_reg.ipynb: Implements regression using the XGBoost library’s XGBRegressor.

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Prerequisites

Before running the notebooks, ensure you have the following installed:

• Python 3.8+
• Jupyter Notebook
• Required Python ilbraries:
  • pandas
  • numpy
  • seaborn
  • sci-kit
  • xgboost

You can install the necessary libraries using:

pip install pandas numpy seaborn scikit-learn xgboost

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Data

Both notebooks use the Ecommerce Customers dataset. Ensure the dataset is available in the working directory before running the notebooks.

Dataset name: Ecommerce Customers

Columns include:

• Email
• Address
• Avatar
• Avg. Session Length
• Time on App
• Time on Website
• Length of Membership
• Yearly Amount Spent

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Usage

1. Linear Regression

The linear_reg.ipynb notebook covers:

• Data preprocessing and exploration.

• Building a regression model using LinearRegression from Scikit-Learn.

• Evaluating model performance with metrics like Mean Squared Error and R-squared.

2. XGBoost Regression

The xgboost_reg.ipynb notebook includes:

• Data preprocessing and exploration.

• Building a regression model using XGBRegressor from the XGBoost library.

• Fine-tuning the model with hyperparameter optimization.

• Evaluating performance metrics for comparison with the linear regression model.

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Running the Notebooks

1. Clone this repository:

git clone <repository_url>
cd <repository_folder>

2. Start Jupyter Notebook:

jupyter notebook

3. Open and run either linear_reg.ipynb or xgboost_reg.ipynb.

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Results

The outputs include:

• Insights into dataset relationships and trends using visualizations.

• Model performance metrics and predictions.

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Contributions

Feel free to fork the repository, submit issues, or create pull requests to enhance the notebooks.

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License

This project is licensed under the MIT License.