Interactive Data Science Dashboard with Streamlit

This project is a comprehensive end-to-end data science application built with Streamlit, designed to guide users through the entire data analysis pipeline in an interactive and intuitive way. It combines preprocessing, visualization, and machine learning into a single, user-friendly dashboard.

Key features of the application include:

Dataset Upload & Preprocessing: Users can upload their own datasets and apply essential preprocessing steps such as handling missing values, normalization, and feature selection.

Exploratory Data Analysis (EDA): Interactive charts, summary statistics, and correlation heatmaps allow users to quickly understand the structure and relationships within their data.

Machine Learning Integration: The app supports multiple supervised learning models, enabling users to train, evaluate, and compare model performance directly from the dashboard.

Real-Time Insights: Predictions, accuracy metrics, and visualizations are generated instantly, providing immediate feedback for decision-making.

This project demonstrates proficiency in Python, pandas, scikit-learn, data visualization, and machine learning workflows, all seamlessly integrated into a deployable Streamlit app. It serves as a practical showcase of how raw data can be transformed into actionable insights through modern data science practices.