Computer Science Machine Learning

Chhote Lal Bairwa

# ***************.********@****.***.** +91-787******* ð cldadr § cldadr

Education

National Institute Of Technology Karnataka, Surathkal B.Tech in Computer Science and Engineering

2021 – 2025

SHRKJ Government Senior Secondary School, Dausa

Secondary Schooling

2019-2021

Projects

Customer Segmentation and Churn Prediction Mar 2025

Tools and Technologies Used: Python, Pandas, Scikit-learn, K-Means, Logistic Regression, SMOTE, Z-score/IQR, One-Hot Encoding, Elbow Method, Silhouette Score, ROC-AUC.

Analyzed telecom customer data to segment users using K-Means clustering and predicted churn using logistic regression. Identified high-risk customer groups and generated actionable insights to support targeted retention strategies, achieving 86% recall and 80% ROC-AUC for churn prediction. Assessing Fairness and Transparency in Credit Scoring Using Explainable AI Aug 2024

Tools and Technologies Used: Python, SHAP, XGBoost, LightGBM, Random Forest, Pandas, Scikit- learn, NearMiss, Tomek Links, One-Hot Encoding, Kaggle Lending Club Dataset.

Implemented ML models (XGBoost, LightGBM, Random Forest) to predict loan defaults using Lending Club data and applied SHAP for model interpretability. Assessed fairness across demographics (gender, ethnicity) to uncover bias in credit decisions, promoting transparency and ethical AI practices.

The following project has been replicated into a research paper and selected for the CISCON 2025 conference which is the flagship conference of MAHE Manipal

Interactive Data Analysis and AI Automation Dec 2024

Tools and Technologies Used: Python, Streamlit, OpenAI, LangChain, Pandas, Cashfree

Built a Streamlit app for automated data insights and CSV visualizations. Integrated LangChain + OpenAI for natural language queries. Added Cashfree for subscription management. Heart Disease Prediction Using ML Apr 2024

Tools and Technologies Used: Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Scikit-learn, XGBoost, Random Forest, Logistic Regression, Neural Networks.

Developed a heart disease prediction system using clinical datasets and applied machine learning algorithms including Logistic Regression, Random Forest, XGBoost, and Neural Networks. Conducted exploratory data analysis and preprocessing to enhance model performance, achieving over 90% accuracy with Random Forest and identifying key risk factors contributing to heart disease. Technical Skills

Languages: Python, SQL, MySQL, C, C++

Frameworks/Libraries: XGBoost, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn Testing & QA: Selenium, PyTest, NUnit, Postman, JMeter Version Control: Git, GitHub

DevOps & Automation: Azure DevOps, CI/CD

Cloud & Data Integration: MuleSoft, Informatica, SSIS Data Processing & ETL: Excel (queries, transformation), Bulk import tools ML & Visualization: Tableau, Power BI, QlikView

Frameworks & APIs: RESTful APIs, API integration, Data transformation Postions of Responsibility

Head of, Technical Committee, FARC-Club Jan 2022 – May 2025

Committee Co-ordinator, Incident – NITK Annual Tech Fest May 2024 – Mar 2025 Certifications And Achievements

Convolutional Neural Networks – Coursera Certificate,DeepLearning.AI Jan 2025

Improving Deep Neural Networks –Coursera Certificate,DeepLearning.AI Feb 2025

Neural Networks and Deep Learning –Coursera Certificate,DeepLearning.AI Mar 2025

Philosophy and Critical Thinking -NPTEL Certificate,IIT Kharagpur Jan-Apr2024

Gold Level Winner-WorldQuant Brain Challenge Alphathon secured 18th rank nationally 2023

Contact this candidate