FirasKahlaoui/tunisia-stock-market

This project aims to develop a predictive model for forecasting stock prices in the Tunisian stock market using historical data and machine learning techniques. # Tunisia_Stock_Market This project aims to develop a predictive model for forecasting stock prices in the Tunisian stock market using historical data and machine learning techniques. ## Project Structure The project is organized as follows: - `notebooks/`: Contains Jupyter notebooks for data analysis and preprocessing. - `check_data.ipynb`: Notebook for initial data checking. - `Data_Preprocessing.ipynb`: Notebook for data cleaning and preprocessing. - `data/`: Directory containing various stages of stock market data. - `weekly_stock_market.csv`: Raw weekly stock market data. - `checked_weekly_stock_market.csv`: Data after initial checks. - `cleaned_weekly_stock_market.csv`: Data after cleaning. - `normalized_weekly_stock_market.csv`: Data after normalization. - `stock_scraper/`: Contains the web scraping scripts to collect stock market data. - `companies_data/`: JSON files with data for individual companies. - `companies.json`: List of companies to scrape. - `import_test.py`: Script for testing data import functionality. - `scrapy.cfg`: Configuration file for Scrapy. - `README.md`: This file, containing project documentation. - `requirements.txt`: List of Python libraries required for the project. ## Requirements To ensure you have all the necessary dependencies for the Tunisia Stock Market Prediction project, you can use the `requirements.txt` file provided in the repository. This file includes all the required libraries and frameworks for data analysis, machine learning, deep learning, web scraping, and web development. ### Installation 1. **Clone the Repository:** First, clone the repository to your local machine: ```bash git clone github.com cd tunisia-stock-market-prediction ``` 2. **Create a Virtual Environment:** Next, create a new virtual environment using Python 3. You can create a new virtual environment using `venv`: ```bash python3 -m venv env source env/bin/activate ``` 3. **Instal …