Stock Price Prediction

1. Built a basic model to predict price of a stock using its past historical data.

2. Then added a variety of technical indicators like EMA, SMA, RSI, MACD, Stochastic oscillator, VWAP etc. to the dataframe.

3. Performed feature selection using p-value (stastical modelling) to extract the most relevant features.

4. Consequently trained an LSTM network in the enhanced dataframe to improve the accuracy of predictions.