Astrolytics is a time-series forecasting web application that leverages machine learning to analyze and predict stock market trends using real-world financial data retrieved via REST API. The project integrates data preprocessing, exploratory data analysis (EDA), and advanced forecasting models to generate insightful predictions.
👉 Live Deployment – Access the web application here: Astrolytics.
✅ Real-Time Data Retrieval – Fetch stock price data using Yahoo Finance API.
✅ Data Preprocessing – Handle missing values, ensure data consistency, and perform stationarity checks.
✅ Exploratory Data Analysis (EDA) – Visualize trends, seasonality, and correlations.
✅ Machine Learning Models – Implement ARIMA and SARIMA models for forecasting.
✅ Performance Evaluation – Analyze model accuracy using MSE, RMSE, and MAE.
✅ Interactive Web App – Built using Flask for user-friendly interaction.
- Programming Language: Python 🐍
- Framework: Flask 🌐
- Libraries Used:
- Pandas (Data Processing)
- NumPy (Numerical Computations)
- yFinance (Stock Data API)
- Matplotlib (Data Visualization)
- Statsmodels (Time-Series Analysis)
- Scikit-Learn (Machine Learning)
1️⃣ Retrieve Data: Fetch historical stock data based on user input (e.g., company name, time range).
2️⃣ Preprocess Data: Clean missing values, format timestamps, and ensure chronological order.
3️⃣ Analyze Data: Perform trend analysis, seasonal decomposition, and stationarity checks.
4️⃣ Train Models: Implement ARIMA and SARIMA to predict future stock prices.
5️⃣ Forecast & Evaluate: Predict stock trends and measure model performance using standard metrics.
6️⃣ Web Deployment: Deploy as a Flask-based interactive web app.
Ensure you have Python 3.8+ and the required dependencies installed.
# Clone the repository
git clone https://github.com/Parikshit-0210/Astrolytics.git
cd Astrolytics
# Install dependencies
pip install -r requirements.txt
# Run the application
python app.pyOnce the server is running, open your browser and go to:
🔗 http://127.0.0.1:5000/
Astrolytics/
│── static/ # Static assets (CSS, JS, images)
│── templates/ # HTML templates for UI
│── app.py # Flask web application
│── utils.py # Helper functions for data processing & modeling
│── requirements.txt # Project dependencies
│── README.md # Project documentation
🔹 Integrate LSTM and Prophet models for better accuracy.
🔹 Enhance UI/UX for a seamless experience.
🔹 Add more interactive visualizations.
👨💻 Dhiyanesh B - 23PD08
👨💻 Parikshit V - 23PD24
👨💻 Ramvignesh R - 23PD31
📧 Contact: [Astrolytics]
This project is licensed under the MIT License – feel free to modify and use it!
🔗 GitHub Repo: https://github.com/Parikshit-0210/Astrolytics