House Price Prediction in Python - Full Machine Learning Project 

a small summary : for those who are gonna start , he preprocessed the dataset a bit ( removing NaN values, adding features and splitting the catogerical value column to binary columns ) and then scaled,splitted and trained & tested on linear , random forest ..finding best estimator at last ( no explaination on what estimators are, so read forest ahead of doing this )

Mate you explain everything so concisely and keep it so interesting! Really enjoyed this video

AT APPROX 31:00 - If ISLAND is not showing I just increased my test_size = 0.2 to 0.25, or until it became large enough that it did include the ISLAND. Not sure of a real fix but this worked to get past this hurdle. Take care

Just found your channel! Im on a journey to become a data scientist and really build a solid understanding. This is a great first project to get under my belt. Having you by my side while going through the steps is awesome. I will try out doing projects all by myself also but first following along is a great start to get more comfortable and see the steps included and how u tackle it! Greetings from Sweden!

If you could brief explain what linear regression did ? Were all the variable taken into account and develop a slop to predict the value based on existing data? What if we removed some negatively correlated data and the response? I fail to understand what we did apart from cool images, if you can make a brief lectures on regression random decision tree cluster with some situation analysis- it would help us Thanks

The good: feature engineering, I liked the one hot encoding explanation, and how easy you made it look. The bad: extremely superficial explanations. E.g., min 29, “we get a score of 66, which is not too bad, but also not too good” great, thanks for the in-depth explanation as to what 66 means and how to interpret. Most of these “tutorials” are just people recording themselves writing code, like it´s a big deal. The real important piece is understanding the business problem, and interpreting results in terms everyone can understand; I can copy/paste code from a hundred different websites. Also, linear regression is not about getting a 66 or whatever score, it´s about predicting a value, in this case, house prices; how is “66” relevant to that goal?? The ugly: speak way too fast for no reason at all. You´re making a tutorial, not speed racing. Thanks anyway.

11:47 train_data.corr(numeric_only=True)

Hi. What I would recommend doing in the hyperparameter tunning phase on the RFR model. Is to use np.range() instead of a list with hard values the model has to use and which are limited to two options or three. Yes this might take a lot of time to run but using randomizedsearchCV would be okay as a starter then if you see the model improving you can use gridsearchcv instead.

Thank you much for the detailed video , everything was explained very feel , i would suggest this could be the best video to start with the machine learning projects as a beginner. And personally this video helped me a lot as i am taking up my first ML project..

One of the best machine learning tutorials on RU-vid, thanks a a lot for lucid and well detailed explanation.

Am impressed,your explanation is so smooth and i can keep tyrack and understand every step or code you input💯

Oh my!! Just amazing!! Make more such videos. Thank you so much.

your tutorials are the best thing i found on the internet

Great video, thank's a lot. But I'm missing the most interesting part: How can I use the model for getting the house value for an object which isn't part of the used data?

This was a great video. Just discovered your channel today. Definitely going to subscribe!

wish you had also showed some graphs that we can produce once the regression is done

Keep it up bro! Pls do more videos with predictions

Great content, but as a Newley founded developer interested in ML I do wish you went into a bit more detial on the key features being leveraged in the walkthrough. I would not mind spending an hour or so more to fully understand the methods and functions your leveraging in this demo. All in all thank you for your hard work and dedication in sharing what I believe to be humans biggest development since the Industrial Revolution. Keep on Techin sir.

Amazing work man

You don't need to normalize data when dealing with linear regression, that's the main advantage of this method, it is based on coefficients, and those coeficients adjust to the order of magnitude of each variable !

Best tutorial I've seen.

explained better than my instructor xD thanks man

boss so appreciated I can't even express it

ya think? I should have cut my losses when you made the test/train split that early, .at around 28:00 the instructions became to confused to be useful. Until then, thanks for the instructions.

Excellent tutorial...

I have two questions 1. Why didnt you use all feature in train_data (many columns were skewed) to convert via log 2. I didnt saw any change in histogram before and after . How did you decided that data is converted to normal distribution?

Saw this as how to build project , this is my first one , let's see where this will take me - 1.

thank you !!! it was really helpful

For those in the comments section, never do inplace=True.

Explained everything perfectly, Your channel is going to be my go to channel, to learn data science!!!

Thank you for nice explanation. Keep this good work. I want to know what is the outcome of this model. What insight I got after run the model.

Every thing was great but the fact that ive to debugg my entire code because we split earlier and had to pre process the test data again was so painfull speacially in jupyter lab

How this channel doesn't get 1M yet !!

I can't get over you sir You are a legend

Randomforest algo takes features at random so if we literally change nothing and fit the model again and again we can see the scores changing(+-2%). Also only one variable median income was strongly related with target(bcoz it had correlation>0.5). If many variables would have been above 0.5 then we might had seen drastic changes during gridsearch min_features

🤯 Great video.

Hi. Very well explained! thank you.

Heatmap cannot be render while there are non-numerical values (ocean_proximity) in the train data

Thanks for the vid! First day on ur chanel really happy found u! And it seems you use a sort of autocompite for typing when on terminal? or ur typing is just soo fast..

Guys please how was he able to copy and paste so fast @26:01min... Where he was trying to change train data to test data..?

Thanks for the vid

Continuity issue apparently: did you drop the ocean_proximity column before you ran the correlation matrix? My train_data.corr() fails due to values like '

I am stuck at "reg.score". please resolve my error

it was great thank you a lot bro.

Great video. Apart from Linear Regression and Random Forest, are there any other algorithms that might be suitable for this type of problem?

Nice, ty

love this

Informative video, quick question why would you not want the values to be zero when taking the log of the values?

sir i am getting -1.25 score! what to do now!

What's the interpretation of the "score"? Is it R-squared for regression? How about for random forests? Do they compare from one model to another?

thankkk youuu !!!!

tahts a great video, but how do i get the predicted values now? I mean i built the model and how would i get predictions?

Gracias, es díficil encontrar buen contenido en mi idioma, así que lo asisto aquí, mismo que me toca con subtitulos. Thanks so much !

hello, what should I do if my X_test doesn't have any value in ISLAND? I can't perfom the reg.score thanks for your help

So how do you find the working details of the model? It's great to know the 'score' is 0.8 or whatever but what parameters are used to get that 0.8? In other words, I train a model with a score of 0.8 then get some new data points (lat, long, #bedrooms, total_bedrooms, etc (all except house price)) What's the equation I use to generate an expected house value and where do I get it? Great video though.

Man! Your computer runs effortlessly😅 It's soo smooth... What are the specs? 😅 I need to get one like that.😂

Great tutorial! One correction at 12:45 - longitude is inveresely correlated with latitude rather than the median house income.

Just Nice

11:50 I got an error using corr() because of non-numeric column 'ocean_proximity'. How did you do it? Did you change the code of pandas? Edit: I found it myself. Go to python installation path/libraries/pandas/core/frame.py Go to corr function definition and set numeric_only: bool = True.

In X test I am getting 14 col while in X train I am getting 15 cols what should I do?

what if im missing a column ISLAND?

guys while training the data always remember to write train and then test the data, like x_train,x_test,y_train,y_test like that otherwise target variable in this case will give NaN values

How did you get 0.66 score? I made similar data transformations and got only 0.25 score and 0.78 MSE

How did you get the .corr() method to ignore the ocean_proximity column even though it had non-numeric values in the beginning??

Hey bro! Can you please guide me in number prediction in a specific position by reading existing excel data!? I wanted to generate 6 numbers with this logic

great video. and o my wat is the intro music. im a music artist and would love to hear the full thing.

BTW, how do you copy and paste so quickly around minute 14 when you were doing the 'log' adjustment on the train_data? Which shortcut are you using?

my ISLAND column gets deleted when creating test_data - any way to fix this?

Hi NeuralNine. I am having doubt in executing the corr() function. How can I move forward?

Can you add custom code so that model predict saleprice when input code is given

hey, broh where is the datase of california house price, i didn't get yet here or in your githab. or you haven't share with us alhough you said the link of the dataset is on the description.

Is it just me who's getting the error "Input contains NaN, infinity or a value too large for dtype('float64')"? For both linear as well as random forest

7:27 wouldn't you rather use data.isna().sum()? If you have a missing value in the whole row you might not catch that.

when I ran x_test_s, I got: could not convert string to float: 'INLAND'. how to solve it?

I don't know, but errors are generated in my code, though I write exactly same thing as you do . And I have no idea what to do. 😅

why do we need normal distribution in total-rooms, population...?

where can i get total code

May I ask why the longitude and longitude are not applied encoding?

sorry to say but in my code "ocean proximity"is not shown.

Can you upload the data path over here

Timestamp : 20:00

11:45 use the test_data.corr(numeric_only=True) instead as this will return an error if you do so. I do not understand how did you not get an error? I got this and had to apply the function above to solve it " ValueError: could not convert string to float: 'NEAR OCEAN'"

respect -= 100

As of this writing, I am not able to find the exact data set (.csv file ) for Californian house prices. If some one can provide me with the link for the same one used in this video this will be greatly appreciated!

where can i get the notebook? i tried searching your gihub repository but dont see any related to house price prediction. Can you please share the notebook?

why you said this is classification at 39:39 when it is regression problem ?

hello there, can i ask for your help to make data preprocessing for a specific dataset. it have 53884 rows and 8 columns..

Nice

At 13:00 why didn't you apply np.log to 'median_income' and 'median_house_value'? They seem pretty skewed as well

Hey how come your channel is much more interesting, and you have less followers. I think you need to make more series on different languages mainly on c#.

when you define the X_test_s ?? when i want to scaling i should use the X_test_s AS your code but i gets error i have not X_test_s

x_test_s = scaler.transform(x_test) is not working .Can anyone help me to resolve

tf my linear regression model score is coming -433.35

no matter what i do i cant get the join method

i got a value error when I used .corr() on my train data. something along the lines of not being able to convert the str into int. so I am unable to make a heat map. I am an absolute beginner so can someone please help me out. anything will be well appreciated

Hi! How did you get those Vim bindings in jupyter?

is there a link to the pyhton notebook?

where is the source code of this project I get an some error

At minute 28:40 line "31" I typed the same "reg.score(X_test, y_test)" but it does'nt work. The ValueError is "Input X contains NaN." What I did wrong? Can anyone help me? I would like to complete this project. Thank you

how to get same dataset? where?