House Price Prediction in Python - Full Machine Learning Project

Today we complete a full machine learning project and we go through the full data science process, to predict housing prices in Python.
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Timestamps:
(0:00) Intro
(0:44) Loading Data Set
(6:32) Data Exploration
(13:24) Data Preprocessing
(19:54) Feature Engineering
(22:40) Linear Regression Model
(30:02) Random Forest Model
(40:06) Outro

[@aituition8336]

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

[@felcycecelia]

I agree with you

[@softwareengineer8923]

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

[@collinskiprop1484]

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

[@mrzfpv7871]

your tutorials are the best thing i found on the internet

[@tathagataray4899]

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

[@mxolisishange7516]

Amazing work man

[@ebek4806]

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.

[@DataBlenda]

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

[@adamshenk9970]

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

[@V.Laz.]

Keep it up bro! Pls do more videos with predictions

[@FrazzledMom]

Best tutorial I've seen.

[@irontv171]

thank you !!! it was really helpful

[@krish4659]

a small summary : for those who are gonna start , he preprocessed the data set 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 )

[@washingtonalmeida75]

🤯 Great video.

[@nelsonberm3910]

Thanks for the vid

[@learn_techie]

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

[@gemon39]

Hi. Very well explained! thank you.

[@PrajwalBs-nh4nc]

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..

[@enes13]

11:47 train_data.corr(numeric_only=True)

[@evolved__ca]

Thanks

[@mohammedirshad2167]

this was really helpful

[@lusc6]

thanks

[@mitchellcook3349]

This saved me, thanks

[@adamshenk9970]

bruh

[@IkaroSampaioDj]

explained better than my instructor xD thanks man

[@adamshenk9970]

boss so appreciated I can't even express it

[@yusufcan1304]

it was great thank you a lot bro.

[@Anonymous-tm7jp]

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

[@clemenza4]

Nice, ty

[@FrostyBoiFN]

love this

[@pratikmane7465]

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

[@muhamed_alashmnty]

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

[@sanskruti2908]

thankkk youuu !!!!

[@user-ob5tm1np8n]

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.

[@giansirait9631]

Just Nice

[@user-xp2ps7xs9p]

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..

[@vishwanathsonu7613]

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

[@user-sd8el5uh3l]

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?

[@gustavosantiago6679]

did u discover that?

[@techsnail8581]

u can create FCT with a model and X as an argument and then u can predict every value u want

[@gustavosantiago6679]

​@@techsnail8581 dattebayo

[@Xrtd62]

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 !

[@samore11]

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?

[@sauravsharma7706]

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

[@freebeast3790]

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

[@princesamuel3951]

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

[@claudiaorduzsiabato2656]

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

[@thephotomedic3254]

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

[@princesamuelkyeremanteng5008]

KNN Regressor

[@Anonymous-tm7jp]

Naive bayes, Gaussian naive bayes, KNN, Decision tree(Randomforest is collection of decision trees), gradient boosting and XGBoost. Try every one of them with different different parameters for each and select the best one with best set of parameters

[@ChickenPurger]

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

[@TimothyMayes]

Because log(0) is undefined. That is, you cannot raise a number to a power to get 0.

[@sudhanshu004]

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?

[@user-kj6vz1qo4h]

the bars should fit in normal distribution curve which generally would be in middle

[@christianjohnson9245]

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.

[@patricks2595]

You mean for instance explaining what the different hyper parameters actually are that were re-scaled in the random forest and such?

[@pavelvlasov9307]

exactly@@patricks2595

[@washingtonalmeida75]

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?

[@HoloqKing]

alt + shift + down arrow key.

[@7ucky7vn37]

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

[@filipozoz6988]

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?

[@victorynwokejiobi1762]

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..?

[@MarcVideoProduction]

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

[@bitterbob30]

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.

[@Ailearning879]

The model/function is made by the algorithm and that cannot be inferred. All we can do is put the values parameters and get the prediction.

[@harshans7712]

@@Ailearning879 but can you please help me where to test the model which is trained? since we only got the model's accuracy or score. And I'm a beginner in ML

[@VedaVoyager]

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

[@TheMisanri]

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.

[@amerispunk]

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 '

[@MatthewXiong-gk8nz]

plt.figure(figsize=(15,8)) sns.heatmap(train_data.loc[:, train_data.columns!='ocean_proximity'].corr(), annot=True, cmap="YlGnBu") I used this code to ignore the column. Hopefully this will help you get through it.

[@vrajbirje5603]

@@MatthewXiong-gk8nz thanks so much buddy

[@rishabh_pahwa]

Nice

[@shashidharvoorugonda7930]

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

[@michaelg9359]

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

[@kensh9000]

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

[@sanaahmed1860]

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?

[@Pumieeee]

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

[@gongxunliu5237]

train_data.corr(numeric_only=True) will do

[@fireguy9931]

@@gongxunliu5237 I didn't even know that was a parameter, tysm

[@jonathanitty5701]

@@gongxunliu5237 wow I rewatched the video 10 times to understand how he was able to get past that error and am still lost... I ended up converting the ocean proximity column into an id column prior to running the model... did corr() used to automatically filter out the string columns or something in the past?

[@morimementos]

@@jonathanitty5701 i think it was either that, or the default value changed from True to False, not sure which

[@user-ow7uu3kn4j]

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

[@zawichrowana]

I have experienced the same issue - how did the author manage to render a heatmap without dropping this column?

[@_KobbyOb]

Try sns.heatmap(train_data.corr(numeric_only = True), annot=True, cmap= "YlGnBu")

[@sosohhu]

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

[@user-oq7ju6vp7j]

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

[@umamihsanilu.2149]

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

[@alisarena8951]

Can you upload the data path over here

[@Harirtaylorversion]

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

[@rogerhartje5964]

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.

[@trusttheprocess4775]

Exactly lmao, i for the life of me could not understand why he would not completely preprocess the data first and then split the data

[@vudumulanagasairahul1298]

where can i get total code

[@sivakrishanayadav9825]

how to get same dataset? where?

[@rollinas1]

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

[@skripandthes]

why?

[@olanrewajuatanda533]

What should we do to substitute that?

[@ebek4806]

True

[@ebek4806]

​@@skripandthes You are making changes into the dataframe you can't reverse unless you restart the whole runtime on your workspace. Like jupyter notebook.

[@ebek4806]

​​@@olanrewajuatanda533 Just define a new dataframe. Instead of doing this: Df.dropna(col, axis=1, inplace=true) Do this: Df = Df.dropna(col, axis=1) This way you don't hard code new changes to the dataframe and you can just edit the cell and run it again to correct any mistakes.

[@alimuhammadnathani7859]

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

[@dewanshpillare8459]

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. 😅

[@codingworld-programmerslif430]

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#.

[@ndosh1man]

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.

[@meenupatel1256]

isnull().sum()?

[@PatientInAffliction]

is there a link to the pyhton notebook?

[@gilbertopoku8944]

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

[@yasoou]

2330 """ ValueError: columns overlap but no suffix specified: Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'ocean_proximity'], dtype='object') i got this error when i tried to join the train data ,that goses like this ( train_data= x_train.join(y_train)). now how do i solve this.

[@aituition8336]

There should be no overlap, your X data are your 'features' - the attributes that your model uses to make a prediction of y 'labels'. In this scenario, the features are things like long, lat, bedrooms, population etc.. the label is the median house price because that is the value you want to predict. You have to drop the median house prices column from the X data frame and assign that column to the y variable. Then once you join X and y, you shouldn't have any overlaps

[@varuncharan9109]

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

[@ferocious_lad2031]

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!

[@shriyapallam7575]

What to do if I get notified error

[@user-mh9nu5jr6m]

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

[@ivanquncu2292]

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

[@mohammadmahdimovahedfar3245]

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.

[@elbishmaharjan4721]

Thanks bro

[@ksix7804]

what if im missing a column ISLAND?

[@adamshenk9970]

I found that I could increase the test_size from 0.2 to 0.25 or until it became large enough that it included the island by change. Not a real fix but works for this. Take care

[@ShubhamShinde-yn9jz]

can you provide the code please

[@rishabhmishra7192]

at minute 16:53 I am facing this issue were it suppose to provide the output with binary values instead it is displaying bool values is there anyway I can covert the values from boolean to binary?

[@georgeharris6151]

I'm having the same issue is there any fix?

[@Vedanti_koli]

df = pd.get_dummies(train_data.ocean_proximity) print(df) df=df.replace({True:1, False:0}) print(df)

[@retrogamer947]

Timestamp : 20:00

[@suryanshtomar5907]

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

[@parth1211]

Add one more blank column / variable to test which gonna be your target variable

[@suryanshtomar5907]

@@parth1211 how to do that?

[@raghunathraoarcot8744]

Hey hav u solved this error

[@jesusgodinho5247]

respect -= 100

[@sinan8036]

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

[@samarthamera]

run all cells again

[@imansaid2321]

@@samarthamera doesn't work

[@noraalharik9488]

@@imansaid2321did you figure it out? It’s not working with me

[@azizurrehman8328]

no matter what i do i cant get the join method

[@Smylesss]

same here

[@PulakKabir]

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

[@aurum18247]

same here

[@Borolad116]

I wouldn't waste your time. This code doesn't work and he races through everything. Much better tutorials out there.

[@sumankumarsahu9711]

Bro preprocess the data properly

[@PulakKabir]

@@sumankumarsahu9711 i followed the eaxct way he showed here

[@evolved__ca]

@@PulakKabir .corr(numeric_only=True) Fixed the correlation portion at least

[@user-lj2qw6jo6p]

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

[@andrijasente]

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

[@illusion7795]

How did you fix it

[@justinx5381]

I am done with the project understood what It does, But a main question still arises in my mind what predictions did we made? Where are the prices that are predicted. Can someone please Explain I am new into data science

[@anel4980]

if you find out the answer to your question, please let me know

[@milosz7]

Let's say that for example you have made a web application that has a form in which you can input the data about the house you own. The user inputs the data about the house and then the data is passed to a model and it evaluates the price of a house according to the data provided by the uses related to the data that it was trained on. A real life use case would be like a website used to sell properties, such model could encourage a person to sell his property if the estimated price satisfies them, also it could help people that do not have enough knowledge about estate market to estimate their property price. Hope that helps ;)

[@selimreza9565]

Please Source Code Link.Please Please Please,

[@thushanmadhulaskshitha5871]

bro can i have the code please

[@thorfast_hawks_191]

plt.figure(figsize=(15,8)) sns.scatterplot(x='latitude', y='longitude', df = train_df, hue='median_house_value', palette='coolwarm') this line of code is note working. its showing ValueError: Could not interpret value `latitude` for parameter `x` how can i fix this?

[@h007]

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

[@kunal3547]

remove rooms ratio and get 0.96 random forest

[@illusion7795]

Do you have the code ?

[@tofaratifolayan]

I don't have the ISLAND column when i do the X_test join y_test and so i get errors. how do i fix that?

[@RelaMuse]

Also having this issue

[@swarnimvarshneya6944]

'could not convert string to float: 'INLAND'' my code keeps showing this

[@samore11]

do you still have INLAND string field in your data?

[@swarnimvarshneya6944]

@@samore11 in the video its showing binary but my code shows false and true for some reason i think maybe thats the issue

[@carlosleite6226]

@@swarnimvarshneya6944 I have the same issue