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Hello, everyone, and last lecture.

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We created a simple linear regression model in Python.

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We use a site, Model API and a Skillern to create that model.

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Now let's start building them or deepen the integration model.

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And by then.

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First, we will build our model using Start Smardon, and then we will again be using Eskinder.

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I have already imported my estate's model and Escalon liabilities.

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So if you are planning this.

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Don't forget to import the required libraries.

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Now, similar to last thing, we need to first create X and Y variables for over more than.

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So let's first clear dollar, swift even.

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Last time, we only use rule number as our x ray, but this time we need all the variables except the

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price variable.

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Since Bizer table is our dependent variable, we need all the variables except price variable from a

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lot B if as our X would even write X.

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Underscore my date.

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This is our variable name for dependent variables.

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We will write B if not go.

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And then record will right price.

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And after a coma, we'll write Exis Equal the one.

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Let me explain you discovery first.

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We are creating a variable X underscore morality.

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Then we want all the variables of our beef data frame in this variable except the price variable.

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So we are using graph.

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Come on, we'll grab the guys column name.

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And since we are dropping a call them, we need to mention access equally to one.

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If we were dropping any rules, so, for example, if I wanted to drop my first school.

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I would use Grop and then in record I will write one.

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And in excess I would mention zero.

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X is zero is for dropping rose and X is equal to one is for dropping columns.

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So and this is statement.

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We are just dropping price and saving out all the variables and Door X multivariable.

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If we take a head of this X, my D.

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DUFFING.

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This is a sample of what it's my duty to frame.

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And you can see price sweaty when they scrub and desert.

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We only have our independent variable in this is a.

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Now, let's clear Tuller dependent variable name.

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Why are indiscriminately.

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And since our prize is over, the Bindoon video, but will right be off?

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And in this good record with mentioned price.

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We can also take a sample of this.

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You can see now we have created a what dependent and independent variables.

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If you remember and Isaak's Smardon, we have to act concerned, deliver data by default, what be done.

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Which is consent is zero.

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And they said smart.

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Let's add a constant to the word dependent variable x underscore Moradi.

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We'll create another debate.

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But X underscore my date under this good Korn's.

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All right, write Essene dot edcon spent.

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And then record will mention our x ray was, which is x mightly.

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If we take a sample of this, X might be Korn's.

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You can see, Ali, at our first columbus' crime rate.

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Now, our first column is this concept and this is taking Relu one.

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So this is a kind of proxy for what would be dunnart.

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Now to for Total Waman.

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Will create an alarm object, will write alarm, and that's called Moradi.

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Again, this is our variable name.

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You can say a word, object name, and we want to fit ordinary Lisa Square.

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So we'll write Esson Dot or as or unless a sense for ordinary Lisa Square, it is a method to make a

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patient model.

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We'll mention why underscore might be.

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And our X is X underscore may be underscored Conn's.

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There's the one in which we edit consent.

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Then we like big.

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Good on this.

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We have today a lot more that in this Elmendorf's got a mighty good view, this Smardon, right?

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I mean, let's go to my day then.

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Right.

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Got somebody.

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This is the result of a lot more than.

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You can see the artist square value or model is zero points.

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I went to one.

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And it just said artists good value is zero points, I wouldn't want to.

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Which is pretty good.

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Then about probability of F statistics is very low.

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It's in the range of eight to the bottom minus 125.

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So we can say with confidence that our independent variables have some impact on our dependent variable.

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The total number of observation network data are 506 and degrees of freedom is 490, since the formula

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for calculating the degrees of freedom is an.

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Minus B, minus one zero.

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And this five zero six would be.

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That is number of variables is 15 since we have been independent variables.

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And then minus one, that is five hundred and six, minus 16, which will give us four 90.

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So what degree of freedom is for 90?

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Now, let's look at individual independent variables.

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You can see all of what independent variables are listed on the left.

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The next column is, of course, fishing.

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These are the values of be done or be done with two, three and so on.

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Bita fifteen.

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Next is the standard edit.

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After that, we have a D.

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Statistics associated with this variables.

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Then we have a P-value of these variables.

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So remember.

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The lower the up you add, Lou, the more significant the variable is in determining way.

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So if you see the lowest.

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Will lose out in the case of inequality.

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Room them.

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The jet, which is 30 turps ratio.

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Or, Rob, it is the proportion of our population.

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And evidence distance.

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You can say that these are the most significant variables.

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What a what more than.

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These are the variables which have the highest impact on the worldwide variable.

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So how to interpret this data?

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First, you should look at the sign of this patient and.

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If the sign is positive, that means if you're increasing your independent variable, your dependent

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variable will increase with it.

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So, for example.

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I'm on the looking at the significant variable, for example, this room, non variable, the coefficient,

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dispositive, positive.

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And the value of coefficient is for.

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So in a way, I'm seeing that if I am increasing are growing numb by one, my price is going to increase

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by 40 units.

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So in a way, you can say that if all other variables are consent for two houses, if one of the house

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has more room numbers than the other house, then the price is going to be 40.

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Onex more.

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Now, let's look at the air quality here.

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My sign is negative.

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So in a way, I'm saying that if we are increasing the value of air quality, the price is going to

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decrease.

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Now, the p value for airport variable is also very less.

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That is zero point zero one three.

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And here the coefficient is one point one.

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So in a way, we're seeing that if the airport is present in any city, the prize is going to be increased

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by one unit.

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We are also somewhat confident that this variable have a significant effect on our dependent variable.

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So important takeaways from this research is identify the variables which have P value less than zero

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point zero five.

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And try to observe their coefficient and their signs to make business sense out of your data.

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This is the desert.

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From where you will Assad be driving your business intelligence?

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So in this case, you can say that you had house price is dependent on this five or six variables,

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which is room air quality and so on, and you can also tell that all this variables are impacting your

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price.

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Now, we will also create this similar model using a Escalon library.

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Our X and Y variables are ready with us.

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So it will just create another object.

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L m three l m three.

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Is the word object equal to linear regression?

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Alem three is our variable and linear regression is a function of Escalon Library.

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We have already imported Escalon.

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Now, to fit this model we let them through don't fit.

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Now our X was X underscore my D..

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We don't need another variable with the consent sign in case of Escalon.

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So we'll just write a Eskander score.

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Might be another way variable this way, underscore maybe.

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We have for today, at least there, more done with a.

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Now, to get the coefficient, we will just, I think, Alem three.

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Don't intercept the school.

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Coma three.

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Well, if endoscope intercept underscored attributes represent we dunnart and quiff underscore a tribute

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group resing we dulan with our group with the three other.

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You can see first we have the value of intercept.

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And then we have the value of be done with that, too.

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You can also compare this result with the.

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Result we got from Start Smardon.

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You can see.

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It's not possible to get at somebody of a Linnean more than in a Skillern.

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But Escalon is much more useful and on other aspects by fitting the model and while predicting the values

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and running other advanced linear regression models.

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That's why we are also teaching you how to run this Eskinder.

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That is how we run a building integration, Martin and Biton.