Welcome to my Data Visualization Practice Repository! This repository contains a collection of hands-on Jupyter Notebooks exploring essential plotting libraries in Python, including Matplotlib, Seaborn, and Plotly.
The goal of this repository is to demonstrate practical applications of exploratory data analysis (EDA), statistical visualization, and interactive plotting, concluding with a real-world IPL Dataset Capstone Project.
| Topic / Notebook | Key Concepts Practiced |
|---|---|
| 📊 Matplotlib & Seaborn.ipynb | Customizing figure aesthetics, subplots, grids, legends, and line/scatter plots. |
| 📊 Categoricalplots.ipynb | Categorical distribution analysis using barplot, boxplot, violinplot, and countplot. |
| 📈 Distribution plot.ipynb | Analyzing data distributions via hist, kdeplot, jointplot, and pairplot. |
| 🔲 Matrixplot.ipynb | Correlation matrices and hierarchical clustering using heatmap and clustermap. |
| 📉 regressio.ipynb | Linear regression trend lines and scatter fits using lmplot and regplot. |
| 🌐 plotlyandcufflinks.ipynb | Creating interactive, web-ready visualizations using Plotly and Cufflinks. |
| 🏏 IPL_Capstone_Project.ipynb | End-to-end Exploratory Data Analysis (EDA) on IPL cricket match statistics (IPL.csv). |
- Language: Python 3.x
- Libraries:
matplotlibseabornplotly&cufflinkspandasnumpy