The aim of this project to see to do the prediction of the weather using the different types of machine learning model.

A data science challenge where you have to predict the human sentiment scores based on the Headline and the title, and according to the date given and the Social media platforms

Stock and Crypto Price Prediction with TensorFlow, LSTM, Linear Regression and Streamlit

KC House Data Analysis

In this regression project, We will make use of different features like age, BMI, region, sex, smoker, etc to predict the medical insurance cost for an individual.

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

A partir do dataset Real Estate Saint Petersburg 2014 - 2019 encontrado no Kaggle foi realizado um projeto para construir um modelo de regressão para prever preços de imóveis

A Machine Learning REGRESSION model to predict the math test score of those who participated in ENEM 2016.

Predict laptop price using XGBRegressor

Worked with Movies Meta Data to predict movies with higher revenue, analyzed the data to detect relationships between different variables impacting movie revenue. 🎬

O projeto analisa os investimentos de uma empresa fictícia no YouTube, Facebook e Jornal, identificando os melhores retornos.

Explore wind turbine power curve analysis techniques on Gitup. Find algorithms, datasets, and models for optimizing wind energy efficiency. Collaborate and innovate in renewable energy

Using Machine Learning Regression Algorithms, Predicting the Energy Used by the Appliance.

It is the detailed collection of house listings from various cities and regions in Bangladesh, with a specific focus on Dhaka and Chittagong. It encompasses essential details such as location, property type, size, amenities, and pricing.

Superstore Data Analysis

Make prediction of big mart sales data using XGBRegressor machine learning model.

Laptop_Price_Prediction

This repository contains the code for a machine learning project that predicts the number of calories burnt based on various factors such as age, weight, height, gender, and physical activity level. The project uses a variety of regression models and data preprocessing techniques to make accurate predictions.