Notebooks for Kaggle competition

A meticulously curated collection of hands-on Jupyter notebooks, designed to illuminate the comprehensive application of MLflow across a spectrum ranging from foundational Machine Learning principles to pioneering Generative AI paradigms.

PaymentCardCo Fraud Patrol Python Project: Jupyter Notebook and Python script

In this Kaggle competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t.

A hands-on, structured PyTorch learning repository that documents my journey from foundational concepts like tensors and autograd to building CNNs, RNNs, and performing hyperparameter tuning. Each notebook contains real experiments, clean code, and practical insights.

This repo contains the notebooks regarding our deep learning based image recognition projects with my students in Spelman College

Practice notebook on heart-disease risk with a small/noisy dataset: EDA → preprocessing → classic ML baselines (scikit-learn). Not for clinical use

📊 Explore panel time-series forecasting techniques for sales using popular Python libraries like ARIMA, Prophet, and AutoTS in Jupyter notebooks.

❤️ Predict heart disease risk using classic machine learning techniques with this Jupyter notebook project, featuring data exploration and model building.

PyTorch notebooks covering fundamentals (tensors, autograd, pipelines) and deep learning projects (ANN, CNN, RNN/LSTM) with GPU optimization, transfer learning, and Optuna-based hyperparameter tuning.

Panel time-series forecasting notebooks (daily sales across stores × items). Clean validation (holdout + rolling-origin backtest), strong statistical baselines (SARIMAX/TBATS/ARIMA), and automated models (AutoTS), with optional Prophet/Darts/NeuralProphet. Primary metric: SMAPE.

A beginner-to-intermediate friendly project exploring machine learning models for regression and classification tasks. Includes a fully documented Jupyter Notebook, Python script, and step-by-step guidance for experimenting with preprocessing, model selection, and evaluation metrics

This repo contains 3 notebooks for implementing PyTorch workflow for simple image classification problem. It contains basic machine learning project flow and also gets improved by using machine learning platforms like Weights & Biases and Optuna. Dataset that is used in CIFAR10. Model is pretrained ResNet18 neural network.