I’m excited to share my recent data analysis and visualization project on Tele-Law Services in India — a dataset-driven deep dive into how legal aid is accessed across states, districts, social categories, and genders.

🔍 Key Objectives of the Project:

✅ 1️⃣ Analyzing State-wise and District-wise Trends in Tele-Law case registrations.

✅ 2️⃣ Exploring Gender-based Legal Advice Patterns to highlight disparities.

✅ 3️⃣ Studying Social Category Representation (General, OBC, SC, ST) for legal service usage.

✅ 4️⃣ Evaluating CSC (Common Service Centers) Performance in delivering legal advice.

✅ 5️⃣ Using Predictive Analysis to model future Tele-Law case trends.

✅ 6️⃣ Visualizing the Relationship between CSC Count and Total Cases using Scatter Plots.

✅ 7️⃣ Presenting State-wise Distribution of Total Cases via Pie Charts.

💡 The project combined:

📊 Pandas & Seaborn for data cleaning and analysis

📈 Matplotlib & Seaborn for insightful visualizations

🎯 Takeaway:

This project gave me hands-on experience with real-world datasets, trend spotting, and visual storytelling — key skills for any aspiring data analyst or data scientist.