AqarSphere 🏡📊

AqarSphere is a machine learning project designed to predict house prices in Saudi Arabia based on various property features. The project uses data analysis, preprocessing, and model training to estimate property prices with high accuracy.

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📁 Repository Structure

File	Description
AqarSphere.ipynb	Main Jupyter notebook with data analysis, preprocessing, and model training
SA_Aqar.csv	Raw real estate data used in the project
cleaned_aqar_data.csv	Cleaned version of the dataset
processed_features.csv	Final dataset with encoded and scaled features
house_price_model.pkl	Trained machine learning model (baseline)
xgboost_model.pkl	XGBoost regression model
scaler.pkl	Scaler used to normalize data
house_price_scaler.pkl	Scaler specific to target variable
README.md	Project documentation (you're reading it)

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🧠 Technologies Used

• Python 🐍
• Pandas for data wrangling
• NumPy for numerical computation
• Matplotlib & Seaborn for data visualization
• Scikit-learn for preprocessing and model evaluation
• XGBoost for advanced regression modeling
• Jupyter Notebook for development and demonstration

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📊 Data Overview

The dataset contains real estate listings from Saudi Arabia, with features such as:

• Property type
• Area (m²)
• Location
• Price
• Number of rooms
• Number of bathrooms
• Age of property

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🔍 Exploratory Data Analysis (EDA)

📸 Example:

📸 Example:

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🧪 Model Training & Evaluation

The models trained include:

• Linear Regression
• XGBoost Regressor

Evaluation metrics used:

• R² Score
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)

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🚀 How to Run the Project

1. Clone the repository

git clone https://github.com/AbdulmalikDS/AqarSphere.git
cd AqarSphere