Simple Linear Regression Example in Python Machine Learning

This script is a simple demonstration of machine learning in python. This uses Linear Regression to predict pizza prices vs diameter
The LinearRegression class is an estimator. Estimators predict a value based onobserved data. In scikit-learn, all estimators implement the fit methods (used to learn the model) and predict (used to predict the value of a response variable).

Simple linear regression assumes that a linear relationship exists between the responsevariable and the explanatory variable; it models this relationship with a linear surface calleda hyperplane. A hyperplane is a subspace that has one dimension less than the ambientspace that contains it. In simple linear regression, there is one dimension for the responsevariable and another dimension for the explanatory variable, for a total of two dimensions.The regression hyperplane thus has one dimension; a hyperplane with one dimension is aline.

Linear Regression Equation

Simple NumPy Array Tutorial

Simple introduction of Numpy

Numpy is the most powerful Python package for working with data.

Knowledge of Numpy is a must for Data analytics, machine learning. Numpy is a core library for scientific computing in Python. Its tools are used to solve computing problem (specifically mathematical models) of Science and Enginering. 

The most important aspect of Numpy is its n-dimensional array having significant advantage over Python Lists

1. More compact 
2. Faster access in reading and writing items 
3. More convenient
4. More efficient.

1. Create a Numpy Array

There are multiple ways of creating a Numpy Array

• array()
• ones()
• zeros()
• logspace()
• linspace()
• arange()

1.1 Creating from a Python List

# Create a one dimensional array from a list
import numpy as np
lst = [0,1,2,3,4]  #Create a List
np_arr = np.array(lst)  #Convert list to np array

1.2. Create a Two Dimensional Array(Matrix )

lst2 = [[0,1,2], [3,4,5], [6,7,8]]
numpy_2darr = np.array(lst2)

1.3. Create a Three Dimensional Array

from numpy import zeros
np.zeros((2,3,2))

1.4. Create an array using Array function 

from numpy import *
arr=array([1,2,3,4,5],int)

1.5 Create an array using linspace function

numpy.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None) : Returns number spaces evenly w.r.t interval.

from numpy import *
arr=linspace(0,15,5)

1.6 Create using arange function

 This is not "arrange" with double r. Its more like A Range.

from numpy import *
arr=arange(0,15,5)
arr
#will print array(0,5,10)

1.7 Create using logspace function

logspace creates array with log values. The first parameter specifies the starting point, the second ending point and the third the number of steps to reach the ending point

>>> arr=logspace(10,20,3)
>>> arr
array([1.e+10, 1.e+15, 1.e+20])

1.8 Create using one and zeros function

>>> arr=ones(10)
>>> arr
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
>>> arr=zeros(10)
>>> arr
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

3. Provide a datatype for the Array

numpy_arr_2d = np.array(lst2, dtype='float')