Hi, I'm Rushabh!
Building AI-powered solutions that turn data into strategic advantage.

About

I’m a data professional passionate about transforming data into intelligence through AI-powered solutions. With strong foundation in Python, machine learning and cloud platforms like Azure and AWS, I specialize in building predictive models, automating workflows, and delivering actionable insights that solve complex business problems.

From developing anomaly detection systems to deploying LLM-based chatbots and forecasting models, I’ve worked on real-world projects that drive operational efficiency and strategic decision-making. My experience spans end-to-end data science workflows from ETL and model development to data visualization and stakeholder communication.

I’m always excited to take on challenges where data meets impact. If you’re looking for someone who blends analytics with AI innovation, let’s connect.

Skills
Leveraging machine learning and advanced analytics to transform raw data into strategic, actionable insights that drive real-world impact...
Programming Language:

Python (Pandas, NumPy, Matplotlib, Seaborn), PySpark, SQL, R

Data Science & ML:
Scikit-Learn, PyTorch, Regression, Classification, Time-Series Analysis, Hypothesis Testing

AI & Cloud Platforms:
Azure OpenAI, AWS Bedrock, LLMs, Azure, AWS, GCP

Data Engineering & Warehousing:
ETL Pipelines, AWS Redshift, Apache AirFlow, Snowflake

Business Intelligence Tools:
Tableau, Power BI, Looker

Portfolio

Airline Delay Analysis Prediction

In this project, I performed data cleaning, visualization, and applied machine learning models using Python, SciPy, and Scikit-Learn to predict airline delays. I explored trends across airlines, airports, and flight distances. Models like Linear Regression, Decision Tree, Random Forest, and XGBoost were applied, with Random Forest achieving the best performance. This project offers insights to optimize airline operations and predict delays efficiently.

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Twitter Sentiment Analysis

In this project, I built a sentiment analysis pipeline using Python, Tweepy, and TextBlob to classify tweets as positive, negative, or neutral. I performed data collection, cleaning, and text preprocessing on live Twitter data using API. The project outputs structured sentiment results and an interactive Streamlit dashboard for exploration. It demonstrates modular design, secure credential handling, and is ready for cloud deployment.

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Credit Card Fraud Detection

In this project, I analyzed 280K+ credit card transaction data to detect fraudulent activity using machine learning techniques. I performed data cleaning, handled class imbalance using SMOTE, and applied models such as Logistic Regression, Random Forest, and XGBoost. Random Forest delivered the best performance in terms of precision and recall. The project demonstrates how ML can identify rare fraudulent patterns and support real-time fraud detection.

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12 Creative Ways to Visualize Time

I designed a dashboard featuring 12 creative time-series visualizations, including line charts, dot plots, butterfly charts, and calendar heatmaps. This project showcases versatility in data presentation, helping stakeholders identify trends, make comparisons, and track cumulative progress over time.

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View more Projects on GitHub, Tableau Public and Medium

Resume
Looking for a data scientist who can turn complex data into business value? I bring expertise in analytics, machine learning, and cloud technologies to drive data-backed solutions that impact decision-making and business growth.
Contact

Interested in hiring me for your team?

I’m open to new opportunities with the right team. I’m passionate about data and machine learning, and would love to learn more about new opportunities in US.

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