labrijisaad/prediction-du-cours-de-bourse

Forecast Apple stock prices using Python, machine learning, and time series analysis. Compare performance of four models for comprehensive analysis and prediction.

Stock Price Forecasting 📈🔮

In this project, we use Python and machine learning algorithms to perform time series analysis and forecasting on the stock prices of Apple Inc (AAPL). The project is divided into several sections:

1. 📈 Preparing Historic Pricing Data: We begin by preprocessing the data, which involves cleaning and preparing the stock price data for analysis.

2. 🔍 Exploratory Data Analysis: Once the data is prepared, we perform exploratory data analysis to identify trends, patterns, or seasonalities that may be present.

3. 📊 Data Visualization: We use various data visualization techniques to better understand the data and identify patterns or trends.

4. 🤖 Model Development: We develop four different forecasting models using the following algorithms:

   • 🧠 Algorithm 1: Long Short-Term Memory (LSTM)
   • 📈 Algorithm 2: Linear Regression with pandas_ta
   • 💻 Algorithm 3: Support Vector Machines (SVM)
   • 📊 Algorithm 4: Autoregressive Integrated Moving Average (ARIMA)

5. 📈 Model Performance: We compare the performance of the different models and discuss the implications of our findings.

Overall, this project aims to provide a comprehensive analysis of the stock prices of AAPL using both time series analysis and machine learning techniques, and to use that analysis to make informed predictions about the future direction of the stock. 📈

Getting Started

To get started with this project, you will need to have Python 3 and the following packages installed:

• NumPy
• pandas
• matplotlib
• scikit-learn
• pandas_ta
• ta-lib
• Keras (with TensorFlow backend)

You can install these packages using pip. For example, to install NumPy and pandas, you can use the following command:

pip install numpy pandas

Once you have the required packages installed, you can clone this repository and navigate to the project directory. From there, you can run the Jupyter notebook Final document - Technical Analysis of AAPL Stocks.ipynb to see the code and results of the project.

Project Structure

The project is organized as follows:

• data/: This directory contains the stock price data that we will use for the analysis.
• notebooks/: This directory contains the Jupyter notebook with the code and results of the project.

Author

• 🙌 Notebook made by @labriji_saad
• 🔗 Linkedin @labriji_saad
• 📫 Feel free to contact me if anything is wrong or if anything needs to be changed 😎! [email protected]