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scikit-learn-pipelines

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Data Science case for windmill power prediction based on weather. Based on Data Challenge of Air Liquide (@AirLiquide) and TotalEnergies(@total-sa, @Total-RD) companies in 2021. Real Data Science model was much more complicated to get 6 place. The link of the competition - https://datascience.total.com/fr/challenge/19/details#.

  • Updated Nov 4, 2022
  • Python

Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.

  • Updated Aug 7, 2021
  • Jupyter Notebook
Global-Crop-Yield-Analysis

Analysis on effect of temperature, rainfall and pesticide on Global food crops yield along with Crop Yield prediction using scikit-learn ML algorithms. Interactive website for outcomes using HTML, Bootstrap, JS, CSS, d3 and so on.

  • Updated Aug 19, 2022
  • Jupyter Notebook

Unsupervised anomaly detection on 3-year multivariate sensor data from a cyclone preheater using Python. Detected 437 abnormal timestamps using Isolation Forest, One-Class SVM, and Z-score filtering. Includes visual timeline, anomaly impact summary, and Excel highlights.

  • Updated Aug 1, 2025
  • Jupyter Notebook

A production-grade Explainable AI (XAI) framework for Credit Risk Assessment. Uses LightGBM and SHAP to transform "black-box" loan default predictions into transparent, human-readable insights. High-performance FastAPI implementation designed for scalable financial risk modeling and real-time interpretability.

  • Updated Apr 19, 2026
  • Python

Laptop Cost Evaluator A fast, user-friendly web tool that predicts the market price of a laptop based on key specs like brand, processor, RAM, and storage. Just enter the details on a sleek, single-page form, and get an instant price estimate thanks to a smart backend powered by machine learning.

  • Updated Mar 29, 2026
  • Jupyter Notebook

A Jupyter notebook project to predict customer conversions in digital marketing using Random Forest (0.97 accuracy). Includes EDA, preprocessing, model building, evaluation, and Streamlit deployment to enhance campaign targeting, boost conversion rates, and maximize ROAS.

  • Updated Jan 28, 2026
  • Jupyter Notebook

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