End-to-end data preprocessing pipeline on the Breast Cancer dataset handling duplicates, missing values, outliers (IQR), encoding (Ordinal/OneHot/Label), scaling (MinMax/Standard), and a Logistic Regression classifier.

Feature encoding (OneHot, Ordinal, Label) and scaling (Standardization, Normalization) techniques on the Breast Cancer dataset using scikit-learn with proper train/test data leakage prevention.

The Diabetes Prediction Application serves as a valuable tool for individuals to assess their risk of diabetes based on personal health information. By leveraging machine learning techniques, the application provides accurate predictions, aiding users in making informed decisions about their health.

Using LightGBM and Other Models for Car Prices' Prediction – Study Project for Yandex Practicum

Interconnect : Clients Churn Prediction using ML

Students' Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at last applying Classification Algorithms from Machine Learning to Separate Students with different grades