Building the first Machine Learning model using Jupyter Notebook and Scikit Learn| Linear Regression
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What is Linear Regression and When to Use it? :-
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Types of Machine Learning:-
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Let’s say we want to predict the cost of the house given the size of the house. So, the machine is still not that intelligent to predict the cost of the house just by giving one data point on the size of the house, So it needs past data where a number of a data point are given i.e. a number of different size of the house and their respective cost of the house is also given.
As you can see in the image size of the house vs cost of the house.
On the x-axis, we have given the size of the house and the Y-axis cost of the house.
So, the machine’s task is to draw a best-fitting line over the data.
Let’s see a demo in Jupyter notebook.
Data and Terms used:-
X = [[300], [500], [600], [800], [1200],[1500],[1750],[2000],[2200]]
y = [[80], [130], [180], [260], [390],[380],[450],[380],[420]]
Test ID Size in Sq. Fts. (X) Actual Price in 1000$ (Y)
1 300 80
2 500 130
3 600 180
4 800 260
5 1200 390
6 1500 380
7 1750 450
8 2000 380
9 2200 420
Size in Sq. feet is our X column here
Actual Price in 1000$ is our Y column here
We are considering these terms like this:
X= input variables/features that we feed to model
Y= output variables / target
m = number of training examples
(X(i), Y(i)) = i th training sample
#Linearregression #machinelearning #jupyternotebook #anaconda