Data Visualization Practice Repository 📊

This repository contains my practice and exploration of data visualization concepts using Python libraries.
I have covered both fundamental and advanced visualizations, explored multiple types of plots, and finally applied them to a hands-on Netflix Dataset Project.

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📌 Topics Covered

🔹 Matplotlib

• Line plots (single and multiple series)
• Scatter plots & bubble charts
• Bar charts (vertical, horizontal, grouped, stacked)
• Pie charts with annotations
• Subplots and figure layout
• Styling (colors, markers, annotations, grid, ticks, etc.)

🔹 Seaborn

• Distribution Plots: histogram, KDE, rugplot, displot
• Categorical Plots: barplot, countplot, boxplot, violinplot, stripplot, swarmplot
• Regression & Linear Plots: regplot, lmplot
• Matrix Plots: heatmap, pairplot, clustered heatmaps
• Relational Plots: relplot, catplot
• Themes and color palettes

🔹 Plotly & Cufflinks (Interactive Visualizations)

• Interactive line, bar, scatter plots
• Tooltips, hover information, zooming
• Saving interactive visuals as HTML
• Dashboards-ready interactive charts

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📌 Netflix Dataset Project 🎬

As a complete project, I worked on Netflix Titles Dataset to apply my visualization and analysis skills:

Steps Taken

• Data cleaning and preprocessing with Pandas
  • Handling missing values
  • Extracting top_genre from listed_in
  • Extracting numeric durations from duration
  • Parsing release_year and date_added
• Feature engineering for genres, directors, actors, and type (Movie/TV Show)

Visualizations

• Content releases per year (line chart)
• Most popular genres and their trends over time
• Distribution of movie durations and TV show seasons
• Top 10 directors and actors by number of titles
• Ratings and countries distribution
• Genre vs Release Year heatmaps
• Interactive Plotly visuals for dynamic exploration

Insights

• Growth of Netflix content over years
• Shift in popular genres
• TV Shows vs Movies pattern in terms of durations
• Key players (directors/actors) contributing most titles
• Geographic and ratings-based content analysis

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🎯 Summary

This repository is a complete playground of data visualization where I:

• Practiced Matplotlib, Seaborn, Plotly, and Cufflinks
• Covered all major plot categories: distribution, categorical, regression, matrix, and interactive
• Applied everything on a real-world dataset (Netflix) for insights

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