Developed an interactive data analysis toolkit designed to streamline and enhance various data analysis tasks, from exploratory data analysis to advanced feature engineering and model evaluation. This project provides a comprehensive suite of tools for data scientists, analysts, and enthusiasts to efficiently clean, transform, visualize, and model their datasets. "Key Accomplishments:" --Data Cleaning: Implemented functionalities for handling missing values, removing duplicates, and converting data types. --Feature Engineering: Enabled users to create new features, select relevant ones, and generate polynomial features to enrich their datasets. --Data Transformation: Applied log transformation, polynomial features, and binning techniques to prepare data for modeling. --Model Building: Facilitated training and evaluation of machine learning models, including hyperparameter tuning and cross-validation. --Interactive Visualizations: Integrated Plotly for generating a wide range of interactive plots such as histograms, scatter plots, bar charts, heatmaps, and pie charts. --Download Modified Data: Provided options for users to download cleaned, transformed, and feature-engineered datasets. --Technologies Used: Python, Streamlit, Plotly, pandas, numpy, scikit-learn GitHub: https://github.com/Divyansh0108/DataAnalysisToolkit This project showcases advanced capabilities in data preprocessing, feature engineering, and visualization, offering an all-in-one solution for comprehensive data analysis and making the data science workflow more accessible and efficient.