Python Developer | Data Science & Machine Learning | Cybersecurity & Network Automation
🎓 B.Tech CSE | Building hands-on projects across data-driven prediction and security automation
- Predicted individual medical insurance charges using EDA, feature engineering, and statistical feature selection (Pearson correlation, Chi-square tests)
- Linear Regression model achieves R² = 0.80 on test data
- Key insight: Smoking status is the single strongest cost driver (r = 0.79) — more predictive than age, BMI, or region
- Built a price prediction model on 18K+ used Ford listings, comparing one-hot vs. label encoding for categorical features
- One-hot encoding improved R² from 0.74 to 0.85, demonstrating the real impact of encoding choice on linear model performance
- Automated host discovery and open port scanning using Python and Nmap
- Flags exposed services and potential vulnerabilities
- Impact: Quickly identifies misconfigured hosts in test environments
- Python automation tool that organizes files into folders based on type (Images, Documents, Videos, Music, etc.)
- Handles duplicate file names and dynamically creates folders if they don't exist
- Includes OOP design and exception handling
- Impact: Saves time and improves file management efficiency; demonstrates Python automation and system-level scripting skills
Languages: Python, Bash
Data Science: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter
Security Tools: Nmap, Wireshark
Platforms & OS: Linux, Git, Virtual Machines
- Machine Learning: Moving from Linear Regression to tree-based models (Random Forest, XGBoost) and model interpretability (SHAP)
- Network Security & Reconnaissance: Building SOC automation scripts, network scanners
- Cloud Security & Compliance (Upcoming): Automating checks in AWS/Azure
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📧 Email: nupurjaiswal914@gmail.com 🔗 LinkedIn: Nupur Jaiswal
⚡ This profile showcases hands-on projects across data science/ML and cybersecurity automation, highlighting continuous learning and practical, end-to-end execution.