Machine Learning Quick Reference

Use case

Let's work on a use case that will help us in understanding the network.

We will work on a time series problem. We have got the Google stock price dataset. One being training and the other being test. We will now look at a use case to forecast the stock prices of Google:

1. Let's start by importing the libraries:

import numpy as npimport matplotlib.pyplot as pltimport pandas as pd

2. Next, import the training set:

dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')training_set = dataset_train.iloc[:, 1:2].values

3.  Feature scaling is done in the next step:

from sklearn.preprocessing import MinMaxScalersc = MinMaxScaler(feature_range = (0, 1))training_set_scaled = sc.fit_transform(training_set)

4. Let's create a data structure ...