Heart Disease prediction using 5 algorithms

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.

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.

Practice dataset for regression or classification modelling

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

MDBOT (Graduation Project)

This is python for DS and ML bootcamp

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.

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

Graduation Project