The IPL Win Probability Predictor is a web application built using Streamlit. It uses a machine learning model to predict the probability of a team winning an IPL match based on various factors such as batting team, bowling team, host city, target, score, overs completed, and wickets.

Feature Engineering with Python

Electronic Music Classification ML

Alzheimer's Disease Classification using Decision tree

Applying Advanced Machine Learning techinques such as pipelines and text mining, as well as advanced data engeneering methods like column transformers and estimators.

This project uses the famous housing price prediction dataset and employs the two supervised ml algorithms (classification and regression).

House prices dataset exploration and prediction. Workflow includes useful examples of Tensorflow pipelines including k-Nearest Neighbors imputer, Decision Tree Regression and XGBoost Regression

Identify scam messages with level of surety

A machine learning project that predicts car prices based on a dataset.

Machine Learning course of Piero Savastano 5: ColumnTransformer, SimpleImputer, numpy

Predicting sales volume at various stores

Developed an end-to-end ML pipeline to predict Titanic passenger survival using Decision Tree and Random Forest classifiers with automated preprocessing in Scikit-learn.

This project builds an end-to-end machine learning pipeline to predict customer churn using the Telco dataset. It applies real-world data preprocessing, feature engineering, and multiple ML models with recall optimization for business impact. The final system is production-ready with model serialization using Joblib for deployment.

Built an end-to-end regression pipeline to predict house prices using Linear Regression with automated preprocessing (PowerTransform, StandardScaling) via Scikit-learn's Pipeline and ColumnTransformer.

Прогнозирование рыночной стоимости автомобилей

Data Manipulation of Biopic Dataset