This project utilizes machine learning techniques to predict stock prices, providing insights into potential market trends. Leveraging deep learning and statistical analysis, we aim to build a robust model capable of making accurate predictions based on historical stock market data.
Data Analysis: In-depth exploration and analysis of historical stock market data to identify patterns and trends.
Machine Learning Models: Implementation of state-of-the-art machine learning models, including deep learning algorithms, for stock price prediction.
Evaluation Metrics: Comprehensive evaluation using metrics such as Mean Squared Error (MSE) and accuracy to assess the performance of the predictive models.
Interactive Visualization: Engaging visualizations to illustrate predicted vs. actual stock prices, aiding in result interpretation.
Python
Scikit-learn
TensorFlow
Pandas
Matplotlib
Jupyter Notebooks
Contributions are welcome! Feel free to open issues, submit pull requests, or provide feedback to enhance the capabilities and accuracy of stock price prediction. License
This project is licensed under the Apache 2.0 License.
This project is for educational and research purposes only. Stock market predictions are inherently uncertain, and users should exercise caution when making financial decisions based on model predictions.