skcit-learn

A basic ml project that classifies Iris flower species using the classic Iris dataset with Python, demonstrating data preprocessing, model training, and evaluation with Scikit-learn.

Project in NLP. An Emotion Volatility Index uses sentiment analysis(svm) on financial news articles

Бинарная классификация кредитного риска с помощью Scikit-learn (случайный лес, ROC-AUC 0,936)

End-to-end ML project to predict customer churn with a deployed Streamlit app and actionable retention strategies.

🎓 AI-powered web app that converts PDF notes into smart summaries, study plans & quizzes using LSTM, Scikit-learn & NLTK.

AI-powered email intelligence platform using React, Flask, and NLP for classification, summarization, entity extraction, and analytics.

Machine Learning project to predict online shopper purchase intent

Exhaustive KNN hyperparameter search and evaluation pipeline using cross-validation and multiple classification metrics.

A regression model to predict the housing prices

Multiclass weather classification using KNN, Logistic Regression, Decision Tree, and a hard-voting ensemble.

Machine learning projects for stroke risk prediction using healthcare datasets (kaggle) | Random Forest Algorithm and SMOTE Method (to overcome dataset imbalance)

Machine Learning based Loan Eligibility Prediction System using Gradient Boosting and Flask

End-to-end customer churn prediction project with scikit-learn, FastAPI, Docker, automated testing and GitHub Actions CI.

NLP-based phishing/spam email classifier using real-world email corpora (CEAS, Nazario, Enron, SpamAssassin)

Streamlit app comparing TF-IDF vs encoding strategies for disease classification using KNN, with configurable distance metrics and cross-validated performance metrics.

NBA shot probability modeling using Python and machine learning, with model comparison, calibration, validation, and feature importance analysis.

FlipInsight is an end-to-end Python product analytics platform that cleans Flipkart CSVs, loads data into SQLite/Postgres, trains ML models (price regressor, category classifier, embedding recommender), and serves analytics and inference via FastAPI with a Chart.js dashboard. Pretrained models included; run via pip, ETL, DB init, and uvicorn.

Static malware detector using YARA and Machine Learning

Machine learning disease prediction using Logistic Regression and the Breast Cancer Wisconsin dataset.