تصميم جرافيك 3d

This project focuses on building a machine learning model to predict house prices based on various features such as location, size, number of rooms, and other relevant factors. The model is developed using Linear Regression, one of the fundamental algorithms in supervised learning.

The workflow includes data preprocessing, feature selection, and model training to ensure accurate predictions. The project also handles important steps like dealing with missing values, scaling features, and evaluating model performance using metrics such as Mean Squared Error (MSE) and R² score.

This project demonstrates a solid understanding of:

Data cleaning and preprocessing

Exploratory Data Analysis (EDA)

Building and training regression models

Model evaluation and interpretation

Overall, it serves as a practical introduction to machine learning and showcases how linear regression can be applied to solve real-world problems like real estate price prediction.