Data-Analysis-and-Visualization

This repository contains data analysis and visualization projects using Python. It focuses on exploratory data analysis (EDA) techniques applied to popular datasets, including house price data and the Iris flower dataset. The projects demonstrate data loading, cleaning, statistical analysis, correlation exploration, outlier detection, and various visualizations using libraries like Pandas, NumPy, Matplotlib, and Seaborn.

Repository Structure

• House-Price-Prediction/: EDA on the Ames Housing dataset for house price analysis. README
• Iris-Flower/: EDA on the Iris flower dataset for species analysis. README

Key Features Across Projects

• Data loading from CSV files.
• Dataset overview (shape, head, info, missing values, duplicates).
• Separation of numerical and categorical features.
• Descriptive statistics (mean, median, std, etc.).
• Correlation analysis with heatmaps.
• Visualizations: histograms, boxplots, scatterplots, pairplots, violin plots.
• Outlier detection using Interquartile Range (IQR).
• Custom insights and recommendations summarized in text.

License

This project is licensed under the MIT License - see the LICENSE file for details.