Stock price prediction is a crucial topic in the world of finance. This project explores the use of advanced machine learning techniques to forecast stock prices, moving beyond traditional methods. We leverage deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and attention mechanisms to predict stock prices with a high degree of accuracy.
The dataset used for this project is historical stock price data for Microsoft (MSFT), sourced from Kaggle. We focus on feature engineering and model development to improve predictive performance and uncover hidden patterns.
In this project, we perform extensive feature engineering to prepare the financial data for machine learning. We create lag features, calculate moving averages (SMA and EMA), compute the Relative Strength Index (RSI), and determine Bollinger Bands to capture essential patterns in the time series data. Missing values are handled as well.
The project demonstrates model training with several machine learning algorithms, including Linear Regression, Random Forest, Naive Bayes, K-Means, and k-Nearest Neighbors (KNN). We provide code snippets and evaluation metrics for each algorithm to assess predictive accuracy.
For each model, we evaluate its performance using relevant metrics such as Mean Squared Error for regression models and Accuracy, Confusion Matrix, and Classification Report for classification models. Silhouette Score and Davies-Bouldin Index are used for clustering.
Clone the repository to your local machine. Install the required Python libraries by running pip install -r requirements.txt. Download the MSFT stock price dataset from Kaggle (or use your own financial dataset) and place it in the project directory. Run the Jupyter notebooks or Python scripts for feature engineering, model training, and model evaluation.
Aishwarya Devi(https://github.com/AishwaryaDevi003) - Project Lead
This project is licensed under the MIT License - see the LICENSE file for details.
Kaggle for providing the financial dataset. Scikit-learn for machine learning libraries. Matplotlib for data visualization. Feel free to contribute to this project and help improve stock price prediction using machine learning techniques.