I don’t want to confuse here So it’s in placing simply work trying to tell years So we have a formula right Be not plus Bone x one plus B two weeks toe So what does my straight to do if this variable it’s missing We are We know the stall values in stuff taking the mean ordered immediate considering that a stall independent variables what could be the right missing value for that becomes more advanced predictive mean matching their various methods to do that But it begs some time to explain it so I don’t want to go into that region It uh get to just be clear this is not a perfect mission just to impute the missing values some impaired missing values on how my model performance.

But what Right So be if Colon What We don’t make any mistakes if column dot free Indian fill in easy function on I missed Phyllida City Meanness in missing will you So I do not have any single is over here What do they do Because four million variables I used the mean to fill in missing values for the particular column right on dime some raising it on a check for the duplicates that is there any duplicate records presently in my moral in the different So I do not want to have the duplicates All right so how do we remove duplicates How do I remove duplicates Not drop Duplicates it So uh in place again in places he could do before and after So we had so many numbers of duplicates I’m just moving it I do not have any duplicates report here So what is the correlation So we see that the bedrooms and the accommodates How little 0.71 correlation value Some doing person correlated bets and they’re accommodates is 80% created Onda uh bitchin accommodates I think we have discussed 80% correlated.

Yeah So these two variables are highly correlated They since the bitch are having highly positive correlation One of these variables I think that’s had the highest correlation So I want to remove this bet Likewise Accommodates and bedrooms Bedrooms under a bed and bedrooms are also highly correlated Every one day remove some of the other labels right So I wonder more the bets on live bedrooms from this bets on bedrooms are highly correlated This accommodates Betsen bedroom 0.700 0.8 I want to have the accommodate and they want to remove this Betsen bedroom from this morning right This is again an into the way of doing it We have on what are one’s debate to remove the quarterly calling their variables using it that will possibly see in the next session again Right this We’re going within question What about We’re going to use it Mr.

All basics later Far more at once to be that very using currently in industry Hey for understanding What is integration off to understand Indian concepts were just going were textbook concepts Now we’re dropping the highly coordinated videos So I want a drop again Be if drop they have drop I wanted to move bits I’ve bedrooms Access is equal to one in place is equal toe Right now I want to again haven’t hip hop We have the heat map Not many values are really a lot of Praise and accommodating little b are the present The independent variable in independent Let it be highly creative They know I’m checking for the old players There are few outliers present so I finally a lower and upper range on a cab Theo clears You know what it’s in capping me Whatever the values greater than this operates I cap it This your upper ridge else.

Show me the same UDF call Well it, No After capping I did not see most of the outliers They do have any Don’t hear this How to Capitol clear with a small Monica Okay No check for the data distribution I use inspired plot under Okay So they could see whatever the distribution we do in the accommodates accumulating bedrooms about rooms on the review score ratings on the accommodates How the distributions are it Not many are linearly distributed over here We did not find any Livia schools rating on the love race Somewhat I don’t find any party note here that not trained much of the pattern over here Let’s just again go ahead under.

He’s a good regression And the train split I split the eggs and way variables All right so now split Except way How did we do it Killings in big skill on Okay that was all defended Lear B if dot drop work Uh right Okay No I just want and you split it into examined variables Sure Uh three extreme on X test I’m the weight drain Why it just sequitur train first What do the father weren’t good Okay From Miss Kane on North Normally selection input during the split, Yeah No it does come Picks Come on Why Comer Their space that’s physical is zero points through faith Come on Ondo State that says it will do little point of freight The street is equal to one Some random study can get that I think we missed again Kristen Encirclement.

Right Okay So extra in interest I’m just before getting the 75 25% or a ship now a linear regression one How do exactly linear regression water Right now there’s going be if killer it’s killing but that so using the escape letting But that is nothing but a regression model Creation morning sequel to linear regression We’re so linear regression the regression model and benefited regulations mortal dot fit footwork Extreme comma While you drink they know the more the less fit Now I want to find what are only coefficients right Regression mortal not coefficient So this is the coefficient one thing And I had a different far Look for this to show all the coefficients so he could use this to display the only coefficient under Let me just so the coalition for the accommodates 0.1 to the coefficients of bathroom 0.26.

So what does it signify again So what does this coefficient in the linear regression Significant in case recordable on one unit off change Next No vibrating by 0.26 in case of Baku Okay What is the way we live here This lock place They have taken the log rhythm of the place So one unit one bathroom increase one unit Increasing bathroom So they don’t 0.2646 You need change Indy It’ll increase by 0.646 in the wrong place Your place increases bay 0.646 Dusty interpretation from this Now what is the intercept value given ready Regression Morning Again We have uh maybe we call it in person I’m interested me out Zero Okay The industry this 2.917 No the r squared.

How do you find the ice cream Here in the same model The aggression with aggression moralizing morally that I have built have different data in different data Identify Give score So it has a variable intercept It does the coefficient ready task or everything in bold So in the score extreme Come up White train So here it is giving me in value off 0.4877 So is it a good morning letter Bad More So What is this for 480.4877 Can you put it in the terms and Can you tell me Question of determination Just put it in Sometimes some are racist Immigration No just business Report something you want it Plenty of management is What is this Yeah That variation that you’re doing great He explains that he is a prophet That’s what I want So Ford made person dates off Variation in Y variable is nothing with the lock is explained by the predictors in this morning.

So what All this four day burnt 17 mentioned this part imitation did the war on the foreign personage off the variation in the way variable The variable here is that it would not price if you could go here The lot president variable is explained by the predictor’s Credit cards are nothing but all the expiry was They’re actually the accommodates bathrooms countries about the cleaning These are all the predictors 49% of the variance Relacion de la Chris is explained very predicted this morning Is this a good morning Morning Bad Well greater.

I square should be one to be perfect moral But this or less Very less it isn’t were fitting more But you can check again how much the data is balanced on you can again go ahead What doesn’t Poor-fitting with 0.48 is not in good model What it means just there are some more variables to be imputed Are you Just convert the rest of variables Takes some skill on check whether all the assumptions are made before executing this part Okay I forgot extremely important assumptions of the regression model When this is done I’ll just planet What are the assumptions Hard and tribulation wanting to be undergone it.