This repository showcases my work from the prestigious IBM SkillsBuild 4-Week Virtual Internship conducted in collaboration with AICTE and Edunet Foundation. The internship focused on hands-on learning in:
- ✅ Artificial Intelligence (AI)
- ✅ Machine Learning (ML)
- ✅ Cloud Computing
- ✅ Data Analytics
- ✅ IBM Watsonx & Cloud Services
| Attribute | Details |
|---|---|
| Intern Name | Poorva Jain |
| Institute | Amity University |
| AICTE ID | STU6858141271edf1750602770 |
| Internship ID | INTERNSHIP_1748937226683eaa0a58abc |
| Duration | 15th July 2025 – 7th August 2025 |
| Title | IBM SkillsBuild 4-Weeks Internship on AI & Cloud Technologies |
| Mode | Remote (Virtual) |
| Organization | IBM SkillsBuild × AICTE × Edunet Foundation |
| Mentorship | Weekly mentor-led sessions |
| Stipend | None (Certification-based) |
Title: Analyzing Demographic and Regional Disparities in Tele-Law Case Registrations for Inclusive Legal Access
This project focuses on analyzing the inclusivity and accessibility of India's Tele-Law services. Using machine learning and cloud-based analytics, we identify disparities in access to legal aid based on gender, caste, and region.
Despite the rollout of Tele-Law services, not all communities benefit equally. The goal of this project is to analyze:
- Gender-wise usage patterns
- Caste-based representation (SC/ST/OBC/General)
- Regional disparities across districts and states
These insights aim to help policymakers enhance outreach and improve legal access equity.
There is limited visibility into which groups benefit most (or least) from Tele-Law services. CSC distribution, population diversity, and social barriers make it difficult to assess fairness in legal aid delivery.
The challenge: Analyze official Tele-Law case registration data to identify who is being underserved and why.
A data science pipeline was implemented using IBM Cloud and AI tools to:
- Collect and clean registration data
- Normalize counts based on CSC availability
- Apply regression to discover trends and inequities
- Visualize findings for better understanding
-
Data Source
Gov dataset: 2021–2025 Tele-Law Registrations -
Tools & Cloud Platform
- IBM Watson Studio
- IBM Cloud Object Storage
- Python, Pandas, Scikit-learn
- Jupyter Notebook
-
Model
- Linear Regression to predict registrations based on:
- Gender ratio
- Caste distribution
- Number of CSCs per region
- Linear Regression to predict registrations based on:
-
Evaluation Metrics
- R² Score
- RMSE & MAE
- Disparity patterns vs. national averages
-
Visualization Tools
seaborn,matplotlib,plotly,geopandas
- Gender Disparity: Men are significantly more represented in most regions.
- Caste Gap: SC/ST categories show lower registration numbers despite need.
- Regional Inequality: Several districts have low participation despite CSC presence.
These insights help target awareness campaigns and resource deployment more efficiently.
| Category | Tools/Tech Stack |
|---|---|
| Programming | Python (pandas, numpy, scikit-learn) |
| Cloud Services | IBM Cloud Lite, Watson Studio, Cloud Storage |
| ML Platform | IBM AutoAI, Watsonx.ai |
| Visualization | matplotlib, seaborn, plotly, geopandas |
| Notebook IDE | Jupyter Notebook (IBM Watson Studio) |
- Integrate Census & Socioeconomic data for deeper insights
- Use Clustering & AutoML to classify underserved zones
- Deploy mobile tools to push outreach in local languages
- Forecast legal aid demand using time-series AI models
- Expand to similar e-Governance domains (healthcare, pensions, etc.)
- 🎓 IBM Getting Started with AI
- ☁️ IBM Journey to Cloud
- 🧠 IBM RAG Lab Completion
- ✅ Project Submission & Evaluation Clearance
- 📜 Internship Certificate (IBM × AICTE × Edunet Foundation)
All supporting evidence is included in the /screenshots/ folder.
- 📊 Dataset: data.gov.in – Tele-Law Registrations
- 💻 IBM Watson Studio: Docs
- ☁️ IBM Cloud Storage: Docs
- 🧠 IBM Watsonx: watsonx.ai
- 🧾 My GitHub: Poorva Jain
Poorva Jain
👩🎓 B.Tech CSE | Amity University, Madhya Pradesh
🧠 Passionate about AI, Cloud, and Data for Social Good
🌐 GitHub: github.com/poorva26-ai
"Data isn't just numbers – it's a lens to justice, equality, and impact."