profit estimation of companies with linear regression
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Updated
Feb 6, 2021 - Jupyter Notebook
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.
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Predict sales prices and practice feature engineering, RFs, and gradient boosting
Predicting house price
Numerical Methods: "Life Expectancy & Linear Regression" Group Project - 2nd Semester 2021 - Computer Science, UBA
A Preprocessing, Analytical and Modeling Case Study using Supervised ML Models
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.
Perceptron regressing revenue for an ice cream stand according to temperature.
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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 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.
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.
A machine learning web app for Boston house price prediction.
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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