# Linear Regression in python | python crash course_06

## Linear Regression in Machine learning :

Welcome to machine learning in python crash course, so in this section we will learn different machine learning algorithm. Let's start:
Linear Regressions is usually the first machine learning algorithm. It is a simple model but everyone need to master it as it lays the foundation for other machine learning algorithm.

## It is a very powerful techniques and can be used to understand the factors that influence profitability. It can be used to forecast sale in the coming months by analyzing the sales data for previous month. It can also be used to gain various insights into customers behaviour. By the end of the blog, we will build a model which looks like the below picture i.e, determine a line which best fit the data. Example In this example, we will use Pima Indian Diabetes dataset to select four of the attributes having best features with the help of chi-square statistical test. ``` from pandas import read_csv from numpy import set_printoptions from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2 path = r'C:\pima-indians-diabetes.csv' names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'age', 'class'] dataframe = read_csv(path, names=names) array = dataframe.value ```

Next, we will separate array into inputs and outputs components −
X = array[:,0:8]

Y = array[:,8]
The following line of code will select the best features from dataset −
test = SelectKBest(score_func=chi2, k=4)

fit = test.fit(X,Y)
set_printoptions(precision=2)
print(fit.scores_)
featured_data = fit.transform(X)
print ("\nFeatured data:\n", featured_data[0:4])

OUTPUT:
[ 111.52 1411.89 17.61 53.11 2175.57 127.67 5.39 181.3
Featured data:
[[148.  0. 33.6 50.
[  89. 94. 28.1 21. ]]
[  85.  0. 26.6 31. ]
[ 183.  0. 23.3 32. ]

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1. 2. Really nice and interesting post. I was looking for this kind of information and enjoyed reading this one. Keep posting. Thanks for sharing.  