📊 Divar Project

🌟 Project Description

This project analyzes data from the Divar platform (an advertising company in Iran), including Exploratory Data Analysis (EDA), statistical analysis, recommender system, and price/rent prediction. The main goal is to use machine learning techniques to better understand the data and provide predictive models. 🚀

👥 Contributors

• Mahdi 👨‍💻
• Ramin 👨‍💻

📂 Dataset

The dataset used in this project is not available in the repository due to its large size (approximately 1 million records 📈). It consists of 64 columns, including:

• 20 numerical columns 🔢
• 44 categorical columns 🏷️

🗂️ Project Structure

The divar_project repository is divided into 5 main sections in src/:

1. EDA (Exploratory Data Analysis) 🔍: Exploratory analysis of data to understand distributions, relationships, and patterns.
2. Statistical_analysis 📊: Advanced statistical analyses such as statistical tests and statistical modeling.
3. recommender_system 🤖: Implementation of a recommender system for suggesting products or ads.
4. prediction_price 💰 and prediction_rent 🏠: Predictive models for purchase price and rent. These two sections are in a shared folder.

🛠️ Technologies and Libraries Used

• Data Processing: pandas 📊, numpy 🔢, scipy 🔍
• Visualization: matplotlib 📈, seaborn 🎨, plotly 📊, geopandas 🗺️
• Machine Learning: sklearn 🤖, scipy 🔬
• Algorithms and Models: k-means 📍, DBSCAN 🔄, LightGBM 🌟, Random Forest Regressor 🌲

📋 Prerequisites

• Python 3.11 🐍
• Install required libraries via pip install -r requirements.txt (the requirements.txt file should be available in the repository).

🚀 How to Run

1. Clone the repository: git clone https://github.com/username/divar_project.git 📥
2. Navigate to the project directory: cd divar_project 📁
3. Create a virtual environment (optional): python -m venv env 🏗️
4. Install libraries: pip install -r requirements.txt 📦
5. For each section, run the corresponding scripts (e.g., for EDA: python eda/main.py ▶️).

⚠️ Important Notes

• The original data is not uploaded to the repository due to its size. Please download the data from the relevant source and place it in the data/ folder. 💾
• For questions or collaboration, use Issues or Pull Requests. 💬

📜 License

This project is released under the MIT License. 📄