Project 1

(a) The data set consists of six variables namely-date, Open, High, Low, Close, Volume

(b) The stock market opens at 9:15 am and closes at 3:30 pm. Each stock is defined by an opening price and closing price i.e. the price at which it open and the price at which it closes. During operating regime, the stock prices touches maximum and minimum price. You have an access to tens years of monthly stock price data with the open, high, low, close and the volume that signifies the number of stocks traded. On some day there is no trading, the open, high, low, close remains constant and the volume is zero.

Now, you go to your manager after data visualization and exploratory analysis and model building (do not build a model, assume you built it for sake), your manager says the model predictions are poor as the data is polluted (reported by manager that that instant). Now try to impress your boss by doing some data preprocessing. Assume that you fill missing values by mean of the data corresponding to each feature.

Remenber: please merge the data before preprocessing. You can use pandas.concat (read) to merge your data