This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.

Decision Tree implementation without any library on Iris dataset

Email Spam detection using KNN and Decision Trees Models Trained using the Spambase database

Analyzing NYC Green Taxi data (Jan 2022-Jan 2023) to enhance service coverage using ML. Aims: 1) Predict fares with a regression model using trip data. 2) Locate profitable pickups with a clustering model for driver earnings optimization.

Using Machine learning algorithms on the famous Titanic Disaster Dataset for Predicting the survival of the passenger.

Create a decision tree, plot it, convert the rules into IF-THEN format, and utilize cost-complexity pruning for minimal tree and interpretable rules.

This repository consists of ML projects including data modeling, clustering, classification, and dimensionality reduction models

This project emphasizes on predicting churn of an employee within an organization such that whether the employee will leave the company or continue with it. It uses the data of previous employees who have worked for the company and by finding a pattern it predicts the churn in the form of yes or no.

Udacity Data Scientist Nanodegree Project - Employ supervised algorithms to accurately model individuals income

An implementation of Random Forests in the Python programming language (accuracy tested with 100, 300, and 500 trees)

Machine Learning Algorithms uses LATEX for Documentation

Regression algorithms to predict the median house prices in California districts

Project for Kannada digits recognition using Kannada-MNIST dataset. Employ PCA for dimensionality reduction to 10 components, and apply Decision Trees, Random Forest, Naive Bayes, K-NN Classifier, and SVM for prediction. Evaluate models using Precision, Recall, F1-Score, Confusion Matrix, and RoC-AUC curve.

Hands on over different problems using various ML algorithms

Using Supervised Machine learning predicted big mart sales item outlet sales

This repository contains several task given by the Sparks Foundation under Graduate Research Internship Program July2022

机器学习实战学习笔记

Predicting churn customers using last 3 months data using PCA and multiple classification models like Logistic Regression, Decision Tree and Random Forest and finding the features influencing the churn.

Machine learning course homework