EISystems_Technologies

Insurance Purchase Prediction Using Logistic Regression

This project, developed during my data science internship at Eisystems Technologies, aims to predict insurance purchase likelihood using a logistic regression model. The project includes a fully functional Streamlit app that allows users to interact with the model and visualize predictions.

Project Overview

During my internship at Eisystems Technologies, I received training and practical experience in data science. My primary project involved building a logistic regression model to predict whether a customer would purchase insurance based on a given dataset.

Features

• Interactive Streamlit App: An easy-to-use interface for making predictions and visualizing results.
• Machine Learning Model: Logistic regression model built using scikit-learn.
• Data Visualization: Various plots and charts to understand data distributions and model performance using matplotlib.

Installation and Setup

To run this project, you need to have Python and Streamlit installed on your machine. Follow these steps to get started:

1. Clone this repository:

   git clone https://github.com/yourusername/insurance-purchase-prediction.git
   cd insurance-purchase-prediction

2. Install the required libraries:

   pip install streamlit pandas matplotlib scikit-learn

3. Run the Streamlit app:

   streamlit run app.py

Libraries Used

• Streamlit: For building the interactive web app.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.
• scikit-learn: For building and evaluating the logistic regression model.

Dataset

The dataset used in this project is included in the repository as data.csv. It contains various features relevant to insurance purchase prediction.

Usage

1. Launch the Streamlit app using the command mentioned above.
2. Upload your dataset or use the provided data.csv.
3. Explore the data through visualizations.
4. Make predictions using the logistic regression model.
5. Visualize the prediction results and model performance.

Project Structure

• app.py: Main script to run the Streamlit app.
• data.csv: Sample dataset used for model training and predictions.
• model.py: Script containing the logistic regression model implementation.
• requirements.txt: List of required libraries.

Acknowledgements

I would like to thank Eisystems Technologies for the opportunity to work on this project and gain valuable experience in data science. Feel free to contribute to this project by forking the repository and submitting pull requests. If you encounter any issues, please open an issue on GitHub. Disclaimer: This project is for educational purposes only. The dataset and model are simplified and may not reflect real-world complexities.

For more information, please visit Eisystems Technologies.