KAVINAYA U V

[email protected] • 91-9344804573 • Krishnagiri, Tamil Nadu
LinkedIn: https://www.linkedin.com/in/kavinaya-u-v-93a4162bb?utm_source=share_via&utm_content=profile&utm_medium=member_android • GitHub: github.com/Kavinaya-tech

PROFESSIONAL SUMMARY

Detail-oriented fresher with a solid foundation in Python, machine learning, and data pipelines. Built end-to-end ML projects spanning regression, classification, NLP-based bias detection, and automated data preprocessing, and completed data-focused virtual experience programs with Tata Group and BCG X. Seeking an entry-level Data Scientist role to contribute, learn, and grow.

WORK EXPERIENCE

Data Visualization Job Simulation
Tata Group (via Forage)
• Analyzed a large-scale online retail dataset (~540,000 transactions) to uncover revenue trends, seasonal patterns, and demographic variations
• Designed data visualizations addressing specific business questions posed by the CEO and CMO, and presented insights in a business-facing format
Data Science Job Simulation
BCG X (via Forage)
• Conducted exploratory data analysis on customer data for a utility client to investigate drivers of customer churn
• Engineered features and built a predictive classification model to estimate churn risk, then summarized findings into a business recommendation

EDUCATION

Higher Secondary Certificate
06/2022 - 04/2023
Kingsley Gardens Matric Hr Sec School GPA: 83.17
B.Tech — Artificial Intelligence and Data Science (Pursuing)
P.S.V College of Engineering and Technology , Krishnagiri, Tamil Nadu GPA: 8.37

SKILLS

Technical Skills: Python, SQL, Pandas, NumPy, Scikit-learn, NLP, Data Cleaning, Feature Engineering
Soft Skills: Analytical Thinking, Problem Solving, Critical Thinking, Communication, Time Management
Tools: Git, Docker, Power BI, Excel, Matplotlib, Streamlit, FastAPI
Other: Data Visualization

PROJECTS

Linear Regression — Fuel Consumption & CO2 Prediction 🔗
Technologies: Python, Scikit-learn, Pandas, Matplotlib
• Built simple & multiple linear regression models to predict CO2 emissions from vehicle data; evaluated with R², MAE, and RMSE
• Performed EDA on engine size, cylinders, and city/highway fuel features; visualized regression fit and residuals with train/test split
Automated Data Cleaning Pipeline 🔗
Technologies: Python, Pandas, NumPy, ETL Design
• Built a fully automated, reusable pipeline handling missing values, duplicates, type errors, and outliers across CSV and Excel files
• Designed modular plug-and-play components to eliminate manual preprocessing and ensure consistent ML-ready data output
Customer Churn Prediction using Logistic Regression
Technologies: Python, Scikit-learn, Pandas, Matplotlib
• Developed a binary classification model to predict customer churn with strong accuracy; evaluated using confusion matrix and ROC-AUC
• Applied feature scaling and handled class imbalance to improve model precision and recall on the minority churn class
Weather Data Analysis using Baseline Predictive Model
Technologies: Python, Pandas, NumPy, Matplotlib, Seaborn
• Analyzed historical weather data through EDA and built a baseline predictive model for temperature and rainfall forecasting
EchoBias — AI-Based News Bias Detection & Multi-Perspective Analysis
Technologies: Python, NLP, ML Classification, News APIs
• Designed an AI system to detect political bias in news articles and generate balanced multi-perspective summaries
• Applied NLP techniques including text vectorization and sentiment analysis with ML classifiers to identify bias across news sources

CERTIFICATIONS

Machine Learning with Python
IBM
Python for Data Science
IBM
Introduction to Cloud Computing
IBM

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