The Diabetes Prediction project utilizes machine learning techniques to determine the probability of an individual having diabetes based on various health metrics like age, BMI, and blood pressure. The prediction model is developed using the Support Vector Machine (SVM) algorithm, which classifies individuals based on these parameters.

Predicting sales volume at various stores

Прогнозирование рыночной стоимости автомобилей

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.

Electronic Music Classification ML

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

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

Alzheimer's Disease Classification using Decision tree

Data Manipulation of Biopic Dataset

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

MLOps project

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.

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.