| Proj No. | A3095-251 |
| Title | Deep Learning-Based Stock Price Prediction Using LSTM |
| Summary | Today, it is easy to access stock and financial information of public companies and Artificial Intelligence (AI) and Machine Learning (ML) techniques can be applied for predicting stock market. Thus, the purpose of this project is to develop a Long Short-Term Memory (LSTM)-based deep learning model to predict stock prices using historical data. In the first stage, student will be required to download stock price data from Yahoo Finance API or Alpha Vantage AP for multinational companies like Apple (AAPL), Tesla (TSLA), Amazon (AMZN), Microsoft (MSFT), etc. The second stage will require Data Preprocessing for Feature Selection (such as Open, High, Low, Close, Volume), Data Normalization (Apply MinMaxScaler to scale prices between 0 and 1) and data conversion to Time-Series Format (Using sliding window technique). In the third stage, student will build the LSTM Model (Model Architecture, Loss Function, Evaluation Metrics). The final stage will include Model Training & Testing, Results & Deployment. Tools & Technologies used will include, Python, TensorFlow/Keras, Pandas, Scikit-learn, Matplotlib. The potential application of this project will be Algorithmic Trading Systems and Short-term Trading Strategies. |
| Supervisor | A/P Mohammed Yakoob Siyal (Loc:S2 > S2 B2A > S2 B2A 28, Ext: +65 67904464) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering I (Loc: S2-B4c-15) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |