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.

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Feature Engineering with Python

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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.

Alzheimer's Disease Classification using Decision tree

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

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.

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

Прогнозирование рыночной стоимости автомобилей

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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.

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

Data Manipulation of Biopic Dataset

A hybrid machine learning & deep learning pipeline for predicting heartdisease from clinical data. Includes EDA,preprocessing with ColumnTransformer, models (Logistic Regression, Random Forest, XGBoost) and a Keras ANN. Evaluation covers ROC-AUC, Precision-Recall, calibration, with SHAP explainability ensuring transparency and trust in healthcareAI

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.

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MLOps project

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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.

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.

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.