Stock Price Prediction for AmEx

📌 Overview

This project predicts the stock prices of American Express (AmEx) using time series forecasting techniques like ARIMA, SARIMA, and VAR. It leverages historical stock market data to provide insights into future price trends.

🚀 Features

• Data preprocessing and cleaning
• Time series modeling with ARIMA, SARIMA, and VAR
• Model evaluation and comparison
• Visualization of stock trends and forecasts
• Performance metrics for model accuracy

📂 Dataset

The project uses previous years' stock market data for AmEx. The dataset includes:

• Date
• Open, High, Low, Close prices
• Volume traded

🛠️ Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Time Series Models (ARIMA, SARIMA, VAR)
• Jupyter Notebook for analysis and visualization

📖 Installation

1. Clone the repository:

   git clone https://github.com/aryadevesh/stock-price-prediction-for-AmEx.git

2. Navigate to repo and install the requirements:

   cd stock-price-prediction-for-AmEx
   pip install -r requirements.txt

3. Run the jupyter notebook:

   jupyter notebook
   

📊 Model Performance

• The models are evaluated based on:

• Mean Absolute Error (MAE)

• Root Mean Squared Error (RMSE)

• Akaike Information Criterion (AIC)

📜 Usage

• Run the Jupyter Notebook to explore data and train models.

• Adjust parameters to optimize predictions.

• Use generated forecasts for analysis and decision-making.

🤝 Contributing

• Contributions are welcome! Feel free to submit issues or pull requests.