🚀 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

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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.

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

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.

A machine learning web app for Boston house price prediction.

A Comparison of Regression Models for Predicting Graduate Admissions

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.

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

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 project used various machine learning algorithms to predict rainfall.