Feature Engineering with Python

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.

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.

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

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.

MLOps project

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.

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.

Electronic Music Classification ML

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

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

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.

Data Manipulation of Biopic Dataset

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.

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

Alzheimer's Disease Classification using Decision tree