القاهره

Project Description

Item Price Prediction – Machine Learning Model

This project focuses on predicting store item prices using machine learning techniques. The goal of the project is to build and compare different regression models in order to find the most accurate model for price prediction.

The project was developed using a dataset from a Kaggle competition related to item price prediction. The workflow includes several important steps in a typical data science pipeline such as data preprocessing, feature engineering, model training, and model evaluation.

Several regression algorithms were implemented and compared, including:

Linear Regression

Random Forest Regression

Support Vector Regression (SVR)

CatBoost Regression

The dataset was first cleaned and prepared by handling missing values and selecting the most relevant features. After preprocessing, different machine learning models were trained and evaluated to compare their performance and determine the best predictive model.

This project demonstrates practical experience with machine learning workflows, model comparison, and predictive analytics using Python.

Technologies used:

Python

Pandas

NumPy

Scikit-learn

CatBoost