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.

Leveraging ColumnTransformer, pipelines, standardization, and encoding, we'll preprocess data. Using Logistic Regression, Decision Trees, Random Forest, and XGBoost, we'll analyze factors like job satisfaction, promotion, and salary to predict churn. This helps companies improve satisfaction, reduce turnover, and enhance stability.

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.

Simple Project Using NASA dataset to classify objects near earth as hazardous or non-hazardous

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

Credit card fraud detection using machine learning. Web application using Streamlit framework

Sklearn, logistic regression, Naive Bayes classifier, K-Nearest Neighbors, decision trees

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

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.

"Flight Price Prediction: GitHub repo for ML-based airline ticket price forecasting. Collect, preprocess data, train models, deploy, and evaluate. Open-source under MIT License."

Predictive Analytics Practice: Random Forest, K-NN, SVM, Regression, Logistic Regression

In this project, fault detection and diagnosis model with decision tree is developed based on ASHRAE RP 1312 data. Rules generated by decision tree can be verified with domain expertise.

Using the Rain in australia dataset for rainfall perdiction

Prevendo a satisfação dos clientes de um e-commerce.

A new method of supervised feature scaling using decision tree

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

The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)

Machine Learning: Classification with SKLearn