  Predicting the stock market trend has always been challenging since its
movement is affected by many factors. Here, we approach the future trend
prediction problem as a machine learning classification problem by creating
tomorrow_trend feature as our label to be predicted. Different features are
given to help the machine learning model predict the label of a given day;
whether it is an uptrend or downtrend, those features are technical indicators
generated from the stock's price history. In addition, as financial news plays
a vital role in changing the investor's behavior, the overall sentiment score
on a given day is created from all news released on that day and added to the
model as another feature. Three different machine learning models are tested in
Spark (big-data computing platform), Logistic Regression, Random Forest, and
Gradient Boosting Machine. Random Forest was the best performing model with a
63.58% test accuracy.
