MLND - Stock Prediction Using Twitter Sentiment Analysis

Problem Statement

This project will use supervised learning to predict the next day's stock price status(i.e, whether it will higher or lower than current day's closing price).

This project is a python and scikit learn based implementation of the paper: (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.4517&rep=rep1&type=pdf)

Dataset

The dataset includes:

1. The DJIA stock data from June 2009 to December 2009. It has Open, Close, High and Low values for each day.
2. A collection of 350k tweets between the above mentioned daterange, tagged via a self-written sentiment analysis classifier.

Scripts

Some useful python scripts and jupyter notebook that helped me achieve my goals:

• missing_record.py: fills in interpolated data for missing dates(eg. sundays, holidays etc.) in the DJIA dataset
• SentimentAnalysis.ipynb: Sentiment analysis project to train on kaggleDataset and prepare classifier which can successfully tag the collected tweets.

Report

A final report explaining this project and the surrounding problem domain is available as report.pdf.