Hi, I'm Chaitanya Patil

I'm a

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About

Data Analytics

Currently pursuing a master's program in Information Technology and Analytics at Rochester Institute of Technology (CGPA: 3.6/4). Skilled in data manipulation, machine learning, and web development, leveraging tools like Python, SQL, Tableau, and more.

I have interned as a Full-Stack Web Developer, Data Analyst Intern, and Machine Learning Analyst Intern, where I built and launched innovative applications, architected scalable data pipelines, and customized ML implementations that significantly boosted project efficiency and data-driven decision-making.

I'm passionate about leveraging advanced technologies—such as logistic regression, Random Forest, Gradient Boosting, and neural networks—to solve real-world challenges. My experiences have equipped me with strong analytical, problem-solving, and collaboration skills, making me eager to contribute in roles that drive impactful innovation.

Phone: +1 (585)910-7221
City: Rochester, New York

Degree: MS – Information Technology and Analytics
Email: cp4734@g.rit.edu

Fun Fact: I believe a good cup of coffee fuels creativity—I never miss my morning brew, which kick-starts my day and inspires innovative solutions!

Projects

EV Stock Analysis

Developed a dynamic Flask web app to analyze and predict EV stock performance using LSTM models and interactive Plotly charts.

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EPL Player Wage & Performance

Built an interactive Tableau dashboard analyzing wages & performance for 20 Premier League teams over 4 seasons.

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Kidney Disease Chatbot – AI-Powered Medical Assistant

Developed an AI-powered chatbot that provides medical insights on kidney disease using Natural Language Processing (NLP).

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Telecom Churn Prediction

Designed and tuned churn prediction models based on cutting-edge machine learning methods involving Natural Language Processingwith Term Frequency-Inverse Document Frequency (TF-IDF) and word embedding on more than 50,000 review texts.

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LLM-Powered Data Risk Auditor

Built an LLaMA 2-based tool to detect data risks and generate Streamlit dashboards.

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Solar Forecasting Using ARIMA

Analyzed and forecasted solar energy generation trends using ARIMA models. Covered top global contributors and visualized real vs predicted generation using RMSE evaluation.

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Instacart Analytics Dashboard

Built an end-to-end analytics dashboard to explore customer behavior, segmentation, and demand forecasting using the Instacart dataset. Includes RFM analysis, A/B testing simulation, recommender systems, and time series forecasting.

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Fraud Detection API

Developed a machine learning-powered fraud detection system with a robust FastAPI backend. Supports real-time transaction scoring, model retraining, and Streamlit-based visualization for anomaly insights.

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Professional Experience

12/2022 – 02/2023

Full-Stack Web Developer Intern

Null Class, Mumbai

Developed three dynamic web applications using advanced JavaScript features for intuitive navigation and interactive data visualizations.
Improved team dynamics and workflows by implementing strategic action plans, boosting project integration efficiency by 30%.
Identified and solved critical productivity challenges, enhancing development process agility.

09/2021 – 10/2021

Data Analytics Intern

Let’s Grow More, Mumbai

Designed and optimized full-stack data pipelines using Python and Pandas, improving internal data processing speed and reliability by 40% across varied analytics workflows.
Automated complex ETL routines across Excel and SQL, reducing manual preparation time by 35% while boosting data accuracy.
Delivered 10+ Matplotlib and Seaborn visualizations that shaped 5+ strategic decisions across product planning efforts.

07/2021 – 09/2021

Machine Learning Intern

Dev Incepts, Mumbai

Architected predictive maintenance pipelines leveraging Random Forest and Gradient Boosting, enhancing fault forecast accuracy by 18% and reducing downtime risk across weekly ops cycles.
Automated data cleaning, training, and testing steps, saving the analytics team 15+ hours of manual work each week.
Enhanced model performance via hyperparameter tuning with GridSearchCV, increasing precision by 12%.