🚀 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.

Machine/Deep Learning metrics implementation in python

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

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

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

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

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 Comparison of Regression Models for Predicting Graduate Admissions

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

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.

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 used various machine learning algorithms to predict rainfall.

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.

This project predicts the next day's stock closing price using Lag Features + Linear Regression (with scikit-learn). Built with public financial data from Yahoo Finance and visualized with cost convergence and prediction analysis.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa

A machine learning web app for Boston house price prediction.

This Repository contains scratch implementations of the famous metrics used to evaluate machine learning models.

Detailed data analysis followed by predictive analytics of crimes in india over a period of 2001-2013.

Utilizando-se a técnica de regressão linear, com o auxílio do framework scikit-learn, foram realizados dois projetos nos quais foram utilizados dois databases diferentes (um de consumo de cerveja, e outro do preço de imóveis). Utlizando-se ambos, foi possível prever o consumo de cerveja e o preço dos imóveis, com base nas variáveis explanatórias.

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All things around ... Regression

TensorFlow deep regression model predicting bicycle rental.