bhattmaulik1991/Stock-Price-Prediction

stock price prediction using supervised learning methods

CMPE 257 - Machine Learning - Individual Project

Stock Market Predictor using Supervised Learning

Aim

To examine a number of different techniques to predict future stock prices. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.

Setup Instructions

$ pip install -r requirements.txt
$ python3 src/preprocessing.py
$ python3 src/featureSelection.py
$ python3 src/normalizeInterpolate.py
$ python3 src/regression.py

Methodology

1. Pre-processing and Cleaning
2. Feature Selection
3. Data Normalization
4. Analysis of various supervised learning methods
5. Conclusions

Datasets used

1. http://www.nasdaq.com/
2. https://in.finance.yahoo.com
3. https://www.google.com/finance

References

• Scikit-Learn
• Theano
• Recurrent Neural Networks - LSTM Models
• ARIMA Models
• https://github.com/dv-lebedev/google-quote-downloader
• Book Value
• http://www.investopedia.com/articles/basics/09/simplified-measuring-interpreting-volatility.asp
• Volatility
• https://github.com/dzitkowskik/StockPredictionRNN
• https://github.com/scorpionhiccup
• http://cs229.stanford.edu/proj2013/DaiZhang-MachineLearningInStockPriceTrendForecasting.pdf
• http://cs229.stanford.edu/proj2015/009_report.pdf