🚀 Complete ML Project: Salary Prediction using Linear Regression & Streamlit. 95.6% accuracy, interactive web interface, clean dataset, pre-trained model. Perfect for learning ML, web development, and practical HR applications.

🚗 Predict car prices instantly with Linear & Lasso Regression! Built with Streamlit, scikit-learn, pandas & matplotlib. Compare models, explore data, and learn ML hands-on. Fast, open source, and easy to use for students & developers!

profit estimation of companies with linear regression

This repository showcases a machine learning project that leverages PyTorch to implement a linear regression model for predicting house prices in Boston. It uses the well-known Boston Housing Dataset, incorporating a complete pipeline from data preprocessing and loading to model training, evaluation, and result visualization.

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It's designed to take you on a journey through the fundamental principles and applications of Linear Regression.

Perceptron regressing revenue for an ice cream stand according to temperature.

Machine/Deep Learning metrics implementation in python

Predict sales prices and practice feature engineering, RFs, and gradient boosting

A Preprocessing, Analytical and Modeling Case Study using Supervised ML Models

Machine learning regression pipeline to predict delivery time using feature engineering, GridSearchCV optimization, automated testing and CI with GitHub Actions.

Predicting house price

Utilizando-se a técnica de regressão linear, com o auxílio dos frameworks scikit-learn e statsmodel, foi possível criar um modelo de predição de preços de imóveis, com base em variáveis explanatórias de um database.

A data mining project to analyse Airbnb's data of Berlin for the year 2020 using KDD

A Comparison of Regression Models for Predicting Graduate Admissions

This repository contains a project for predicting house prices using multiple regression techniques and machine learning models, including boosting algorithms. The goal is to train several models on historical house price data and evaluate their performance using the R² score.

This project used various machine learning algorithms to predict rainfall.

Reduce the time that cars spend on the test bench. Work with a dataset representing different permutations of features in a Mercedes-Benz car to predict the time it takes to pass testing. Optimal algorithms will contribute to faster testing, resulting in lower carbon dioxide emissions without reducing Mercedes-Benz’s standards.

This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.

This project builds and optimizes a model on a dataset using Ridge regression and polynomial features. Model accuracy is enhanced through regularization and polynomial transformations. Grid search and cross-validation are used to find the best parameters, and the model's performance is evaluated.