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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
| Date | Stars |
|---|---|
| 2026-07-24 | 1239 |
| 2026-07-25 | 1239 |
| 2026-07-28 | 1239 |
| 2026-07-30 | 1239 |
| 2026-08-06 | 1239 |
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# Data Science Portfolio > These are data-science projects I built early in my career (around 2016). If you're starting out — learning the fundamentals or putting together your own portfolio — I hope you find them a useful reference. As of 2026 I've refreshed the whole repository so every notebook still runs top-to-bottom on a current **Python 3.14** stack. For my current work in AI engineering (LLM agents, RAG, and applied ML), see **[sajalsharma.com](https://sajalsharma.com)**. A collection of data-science projects — completed for academic, self-learning, and hobby purposes — presented as Jupyter notebooks, plus a few R analyses published on RPubs. _Data used in these projects (under `data/`) is for demonstration purposes only._ ## Running the notebooks The project uses [uv](https://github.com/astral-sh/uv) for environment management (Python 3.14): ```bash uv sync uv run jupyter lab ``` Or with pip: ```bash pip install -r requirements.txt ``` Most notebooks read the small datasets under `data/`. Two fetch their data on first run and cache it: the digit-recognition notebook downloads MNIST via `torchvision`, and the stock-market notebook pulls tech-stock prices via `yfinance` (with a vendored snapshot as a fallback). ## Contents - ### Machine Learning - [Predicting Boston Housing Prices](https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/boston_housing.ipynb): Building and tuning a decision-tree regressor to predict home values, and evaluating how reliable those predictions are. (Includes a note on the Boston dataset's history and why it was retired from scikit-learn.) - [Supervised Learning: Finding Donors for CharityML](https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/finding_donors.ipynb): Comparing several supervised learning algorithms to predict whether an individual earns more than $50,000 a year, to help a fictional charity identify likely donors. - [Unsupervised Learning: Creating Customer Segments](https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/customer_segments.ipynb): Using PCA and Gaussian-mixture clustering to uncover structure in the annual spending of wholesale customers. - [Reinforcement Learning: Training a Smartcab to Drive](https://github.com/sajal2692/Training-a-Smartcab-to-Drive): Creating an optimized Q-Learning driving agent that navigates a Smartcab through its environment towards a goal. - [Deep Learning: Digit Sequence Recognition with a CNN](https://github.com/sajal2692/data-science-portfolio/blob/main/digit_recognition-mnist-sequence.ipynb): A PyTorch convolutional network that reads sequences of 1-5 digits from a single image, using five classification heads on a shared convolutional trunk and synthetic data built from MNIST. _Tools: PyTorch, scikit-learn, Pandas, Seaborn, Matplotlib, Pygame_ - ### Natural Language Processing - [Disaster Message Classifier](https://github.com/sajal2692/disaster-message-classifier): A multilabel classification model to predict the categories of a disaster message. Includes an ETL pipeline, an ML pipeline, and a web app to classify messages. _Tools: NLTK, Scikit-learn, XGBoost, Flask, Plotly_ - [3-way Sentiment Analysis for Tweets](https://github.com/sajal2692/data-science-portfolio/blob/main/3-Way%20Sentiment%20Analysis%20for%20Tweets.ipynb): 3-way polarity (positive, negative, neutral) classification of tweets from hand-built features and a logistic-regression classifier — with a modern zero-shot transformer baseline (twitter-roberta) alongside for comparison. - [Cross Language Information Retrieval](https://github.com/sajal2692/data-science-portfolio/blob/main/Cross%20Language%20Information%20Retrieval.ipynb): A CLIR system which, given a query in German, searches text documents written in English — with IBM Model 1 alignment and a BM25 / language-model scorer built from scratch. - [Yelp Review Classification](https://github.com/sajal2692/data-science-
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matched fp:9c2c910a7d39bf19, topic:nlp, readme:natural language processing, readme:sentiment analysis
matched fp:9c2c910a7d39bf19, topic:pytorch