====== Frequently Used Libraries for Machine Learning ======

==== Numpy ====
Numpy is a library typically used for mathematical operations. When used in machine learning, the Numpy library is typically used for array modification such as transposing, converting from 1-D to 2-D arrays, etc.

=== Importing Numpy ===
import numpy as np

==== Matplotlib ====
Matplotlib is a library that is used for visualization. When used in machine learning we can see the regression models and scatter plots of the datasets.

=== Importing Matplotlib ===
import matplotlib.pyplot as plt

==== Pandas ====
Pandas is a data analysis and manipulation tool. Typically we use pandas as a means to import data from csv files (i.e. spreadsheets) using the read_csv function.

=== Importing Pandas ===
import pandas as pd

==== SciKitLearn ====
The SciKitLearn learn library contains basically all of the tools that we need for creating our regression models.

=== Examples of importing SciKitLearn Libraries ===

__Encoding Categorical Data__

|from sklearn.compose import ColumnTransformer|


__Splitting the dataset into Training and Test sets__

|from sklearn.model_selection import train_test_split|


__Linear Regression__

|from sklearn.linear_model import LinearRegression|


__Polynomial Regression__

|from sklearn.preprocessing import PolynomialFeatures|


__Feature Scaling__

|from sklearn.preprocessing import StandardScaler|


__Support Vector Regression__

|from sklearn.svm import SVR|