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

Build ColumnTransformers (Scikit or DaskML) for feature transformation by specifying configs.

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).

Alzheimer's Disease Classification using Decision tree

Predicting sales volume at various stores

This application predicts the likelihood of obesity and diabetes in a person based on various inputs. It utilizes machine learning models, pipelines, and column transformers to efficiently handle data and provide predictions.

Прогнозирование рыночной стоимости автомобилей

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

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

This project predicts whether a person survived the Titanic disaster based on various features using machine learning. It utilizes pipelines, ColumnTransformer, and model serialization for efficient processing and prediction.

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

Car Price Prediction

Diabetes Prediction using Machine Learning is a classification project that predicts the likelihood of diabetes based on health parameters such as age, BMI, and blood pressure. The model is built using the Support Vector Machine (SVM) algorithm.