Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Life Expectancy Analyzer
  • Category: Regression & Health Analytics
  • Model Algo: Random Forest Regressor
  • Github Repo: Link

Technologies Used

This health analytics project uses a Random Forest Regressor model to predict life expectancy with an accuracy of 92%. The backend is developed using Flask, with the model serialized via Pickle for seamless deployment. The frontend is built using HTML, CSS, and Bootstrap, ensuring a responsive and user-friendly interface for users to input data and receive predictions instantly.

Details of Project

Life Expectancy Analyzer is a machine learning-based application designed to estimate a person's expected lifespan using socio-economic and health-related parameters such as immunization coverage, income levels, schooling, BMI, and mortality rates. The system uses a trained Random Forest Regressor to make accurate predictions based on diverse inputs. This model achieved a 92% accuracy rate on test data, demonstrating its reliability and robustness in real-world use.

By providing valuable insights into health outcomes, the project aims to support government organizations, NGOs, and healthcare analysts in policy planning and awareness initiatives. It also raises awareness for individuals to understand how lifestyle and environmental factors impact longevity.

Project Features

  • Predicts life expectancy based on user inputs using Random Forest Regressor.
  • Achieved 92% model accuracy on test data.
  • Supports public health analysis and awareness planning.
  • Flask-based backend for serving model predictions.
  • Pickle integration for model storage and reuse.
  • Responsive interface using HTML, CSS, and Bootstrap.
  • Quick and clear prediction output for decision support.