dicision-tree

A machine learning project that compares five classification algorithms for predicting the presence of heart disease from clinical patient data.

Ever wondered which file your code should be in? Based on this tree you can determine what kind of code you are working with.

Various data structure implementations in Python

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

AI Game experiments using Machine Learning and Reinforcement Learning

AI/ML Task 4 classification evaluation project

House price prediction using scikit-learn — feature scaling, multi-model comparison (Linear, Ridge, Decision Tree), RMSE & R² evaluation on the California Housing Dataset.

Practice dataset for regression or classification modelling

This repository contains R scripts for performing logistic regression and Naive Bayes classification on various datasets. The scripts demonstrate data loading, visualization, model training, prediction, and evaluation.

MDBOT (Graduation Project)

Notebooks of Basics of ML models

Diabetes Dataset This dataset is originally from the N. Inst. of Diabetes & Diges. & Kidney Dis.

Develop and evaluate machine learning models using techniques such as Decision Tree, SVM, K-Nearest Neighbors, and K-Means clustering

multi-class disease risk classification (Low/Medium/High) for SmartCare Hospital. Leakage-free preprocessing, 5 tuned ML models + SHAP explainability, and a Streamlit clinical decision-support prototype.

Application for predicting the flight fares at different dates with different transporters

"Titanic: Machine Learning from Disaster" is a classic Kaggle competition for beginners https://www.kaggle.com/competitions/titanic. The goal is to use machine learning to predict which passengers survived the sinking of the Titanic based on historical data. This teaches data analysis and model building skills in a real-world context.

Panads,Numpy, Scikit learn, Keras, ML Libraries

model to predict the survival in the Titanic disaster, with 98.8 accuracy.