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Sklearn LinearRegression in Python (simple linear regression in Scikit-Learn)
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Nov 19, 2024
In this tutorial you will learn how to make a simple LinearRegression with Scikit-learn and Python. ----- Install Python: - MacOS: https://www.jcchouinard.com/install-python-on-macos/ - Windows: https://www.jcchouinard.com/install-python-on-windows/ - Anaconda: https://www.jcchouinard.com/install-python-with-anaconda-on-windows/ Subscribe to my Python Course waitlist: https://ba995fe7.sibforms.com/serve/MUIEAPv50TFkNuoknCNvzRUKLLhvvZd5jzCwEZvi9BNjtkhVuEtLpEG58-Khdquyg9v5V-1qeGDwIXy739I4eFIVcCqeqqV3UUisW_-hAd5ljC1dGMrQvXHC7JvORh9TbnLA1CHqWro4N7YVZ4730-D5dXGxqd3CbaVHSJpS5fyylPMVzCe1_y9xOTl2-CsvEuhO01E0Ytv59HEJ Support my work: https://www.buymeacoffee.com/jcchouinard Subscribe to this channel: https://www.youtube.com/channel/UCthJJqyUGdI5EA0YjnjXthw?sub_confirmation=1 Follow me: https://twitter.com/ChouinardJC https://www.linkedin.com/in/jeanchristophechouinard/ https://www.buymeacoffee.com/jcchouinard
View Video Transcript
0:00
hi everyone my name is Ja and today I'm
0:03
going to teach you how to use psyed
0:05
learn linear regression to make a simple
0:09
linear regression in
0:11
Python so linear regression is using
0:14
ordinary least Square linear regression
0:17
from Psychic learn to predict
0:19
values so the it is found in the SK
0:23
learn linear model module and the linear
0:26
regression class fits a linear model to
0:29
minimize is the residual sum of squares
0:33
between your observation and your
0:35
predict
0:36
prediction so as a nutshell a linear
0:40
regression it is trying to fit a line on
0:45
data
0:46
points so that you minimize the sum the
0:50
residual sums of square so we have an
0:53
intercept we fit a slope uh we fit a
0:56
line using the slope and then we
0:59
calculate the residuals here each of
1:02
them and we sum of we make the sum of it
1:05
squared and that's what the linear
1:09
regression tries to minimize the average
1:12
error that you get from RSS so the first
1:15
thing you have to do is you need to pip
1:17
install psychic learn M clot clib for
1:19
this tutorial and what we'll do is an
1:22
example on the diabetes data set so the
1:25
diabetes data set it is a few phological
1:29
variables
1:30
that preer that creates a feature so you
1:33
can have the X say sex age body mass
1:36
index blood pressure and we try to
1:38
predict uh the disease of diabetes uh on
1:42
this after a one year so we will load
1:46
the data and by loading the data what
1:48
we'll find here is that we have a few
1:51
features and these are the ones that
1:53
I've told you uh about and here we want
1:57
to do a simple linear regression so we
1:59
will only do it on the body mass index
2:02
so we will only use the body mass index
2:04
in order to try to predict uh
2:07
diabetes um here generally when we do
2:11
machine learning we try to have a test
2:14
set set apart so that you can look at
2:16
your accuracy score and that's what we
2:18
do with train test split I'm not going
2:20
into this train test split uh for this
2:24
tutorial I have a video on it but
2:27
essentially we are getting training
2:29
training data and testing data uh from
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this so we're splitting and keeping one
2:36
part for testing later now what we want
2:39
to do is actually train a linear
2:43
regression so linear regression is found
2:46
in in uh from SK learn so you go into
2:50
the SK learn Library uh and you go from
2:54
uh linear
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model uh Li uh module and and then you
3:00
import linear
3:03
regression and to train the model we set
3:07
up a variable to do linear and we use
3:11
linear
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regression and then we use that
3:17
variable to fit the data so fitting
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means training so we train the data on
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the X Trin and Y trining data so you can
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see I'm not using X and Y I'm just using
3:31
the training data from X and Y and I'm
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trying to predict y here and I use the
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Lin reg trained model and I'm using this
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to
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predict using X test so I will take X
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test and try to predict y based on our
3:50
linear
3:54
regression I told you a linear
3:56
regression tries to fit the lines so we
3:58
are going to look at The Intercept which
4:01
was a star on my graph and
4:04
linreg and we will get the
4:10
intercept attribute from our uh train
4:14
model and we will use get the
4:18
coefficient uh of the Lin
4:22
R and we get the coefficient attribute
4:26
and we select the first coefficient and
4:29
and essentially we are going to print
4:31
the
4:32
intercept and at Point z uh the
4:35
intercept and the BMI uh so essentially
4:39
the disease progression of one unit of
4:42
BMI is
4:45
9988 so let's try to plot this so I'm
4:49
going to use Seaborn reg plot I have a
4:52
tutorial on Seaborn reg plot that you
4:53
can look at but essentially I'm going to
4:55
use caborn reg
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plot to uh set x and x will be defined
5:03
as X test so we're going to use test and
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we will use the BME BMI and we will set
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y equal y
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test so essentially we will do a reg
5:17
plug and then we can add some
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information like the title The X label
5:22
and the Y label to put some a access on
5:25
our on our chart and then we do Cloud
5:28
show and we run
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this so here I have my linear regression
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with intercept 150 at 0 0 and the slope
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of this is
5:41
988 so here if you can see it stops only
5:44
at 350 because our data but our axis is
5:47
zero from 0 to 0.15 but if we were to
5:51
expand this to 0 to one we would see
5:55
988 here so that's what uh The Intercept
5:59
is now if we want to compute the
6:01
accuracy score for this uh simple linear
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regression we will import metric from
6:09
psyched learn so we use psych learn
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import
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metrics and we will import
6:16
numpy as NP so here I'm not going too
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deep into what these metrics are uh
6:24
there are tutorials on the topic but
6:27
essentially to fit our model we will try
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to figure out the mean absolute error
6:33
the root mean square error and the
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coefficient of determination the R2
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score so in order to get these metrics
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we take metrics uh we use mean
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absolute
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Matrix dot mean
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absolute error and then we pass in the Y
6:56
test and the Y PR so it will compare uh
7:00
our
7:01
test and our prediction of Y and it will
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look at this absolute error we will want
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to look at the range of Y so we look at
7:12
the minimum value and we use MP Max of Y
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uh to Showcase what is the range so that
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we can can compare the mean absolute
7:23
error to AR range and then we will look
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at metrics that
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root mean
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squared error uh initially we were
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passing mean squared error and the
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attribute was
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squared equal true but that's not uh
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true anymore so we have the root mean
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squared it was dicated so now we use the
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root mean squared error and we use y
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test and Y PR and then we calculate the
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R2 score we can use again the metrics R2
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score so there are multiple uh
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convenient way of the uh calculating
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this without us having to go through all
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the mths behind it and then if we print
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all of that we get that the absolute
8:16
error here is 50 units uh so if we
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compare so essentially uh what the mean
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absolute error is it will look at how
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much the prediction deviate from the
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actual value so if you compare your
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prediction and your actual value how
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much on average are we far off in this
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case we are far off by 50 units and of
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BMI and if we look at the range of the
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disease progression uh so not BMI but
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the the disease progression and if we
8:49
look at that range we go from 25 to 346
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so if we are on average Wrong by 50 it's
8:57
quite significant it's not like
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completely out of it but it's quite
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significant if we look at the root mean
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squared error then that's the magnitude
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of error so what we're saying here is we
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have 62 unit uh so it's somewhat uh
9:16
accurate but it's still high error and
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we have a coefficient of determination
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of 28 so it says how much of the
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variation variance in the Target can we
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explain with BMI and it's only
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28% so it's quite uh weak so in this
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case uh that is a value from 0er to one
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and 28% signifies a weak
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relationship so in order to fix this we
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can add more features to get a better
9:49
performance so we can improve score by
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doing some data processing that we
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haven't done in this tutorial we can do
9:55
feature engineering to generate new
9:58
features and try to improve the score we
10:00
can do hyper parameter tuning and cross
10:02
validation to select better our uh our
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um our uh hyperparameters and then we
10:12
can per use different models instead of
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linear regression or even select better
10:18
features so this is it for the simple
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linear regression with psyit learn so
10:23
feel free to follow me and help me by
10:25
subscribing to this channel uh or ask me
10:28
any questions that you want in the
10:30
comments see you next time
#Blood Sugar & Diabetes
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