Boston House Price Prediction Analysis (Regression) | Machine Learning | Python

⭐️ Content Description ⭐️
In this video, I have explained about boston house price prediction using various analysis and also explained some important concepts like overfitting vs underfitting (or) bias-variance tradeoff. I have done basic pre-processing, EDA, correlation matrix, feature importance, etc.,
Text-based Tutorial: www.hackersrealm.net/post/boston-house-price-prediction-analysis-using-python
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🕒 Timeline
00:00 Introduction to Boston House Price Prediction Analysis
03:33 Import modules and load data using pandas
05:43 Statistics data information
07:30 Preprocessing the boston house price data
08:01 Exploratory Data Analysis of boston house price
15:52 Min max normalization for attributes
18:48 Standardization for attributes
22:47 Overfitting vs Underfitting
26:49 Correlation matrix for boston house price
30:33 Splitting the data for training & testing
31:21 Model Training & Testing for boston house price
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[@HackersRealm]

Hey Everyone, In EDA, I have mistakenly mentioned left skewed as right skewed & vice versa, the distribution plot is also normal (not uniform). Apologies for the inconvenience!!! I will try to avoid these minor mistakes in the future

[@xlardar8947]

2:22 p.s. thx 4 the video, finally some good description of the features

[@yashrawat1157]

When I'm trying to create the boxplot it is showing positional argument follows keyword argument

[@user-vu1ii5lr4y]

Why are we removing "rad" along with "medv" for X in the Input Split section?

[@sandarsha6354]

you saved my life bro thank you so much

[@HackersRealm]

Happy to help!!!

[@YASHKUMARJAIN]

Your videos are reallly helpful but i m really confused how to follow your playlist for ml? Can you please set the sequence or make new playlist in arranged order , it would be really helpful. Thank you

[@HackersRealm]

Glad the videos are helpful!!! The project playlist are in the arranged order only. follow the order for learning from beginner to expert level

[@azadehsahelii4729]

hi. I have question. when do xgboost algorithm one error shows that says "no module named xgboost" .what should I do ?

[@HackersRealm]

you have to install xgboost, !pip install xgboost, run this in a seperate cell

[@afrahnayeem5027]

Thankyou

[@HackersRealm]

You’re welcome 😊

[@BuiMinhHieuN]

Excuse me, i have a question? Why you choose min max normalization? Thank you so much

[@HackersRealm]

If the value ranges are very high, we have to do this preprocessing, we could also use standard scalar, log transformation. Just for teaching purpose I have used that.

[@bigboodayy1345]

fig, ax = plt.subplots(ncols=7, nrows=2, figsize=(20, 10)) index = 0 ax = ax.flatten() for col, value in df.items(): sns.boxplot(y=col, data=df, ax=ax[index]) index += 1 plt.tight_layout(pad=0.5, w_pad=0.7, h_pad=5.0) When I write this code I get this error. IndexError: index 14 is out of bounds for axis 0 with size 14 Any help?

[@HackersRealm]

Can you rerun the notebook from start, it may work!!!

[@syedhyderalizaidi3012]

why are not we removing outliers here? why we did normalization instead of removing them?

[@HackersRealm]

To avoid losing values, i did normalization, if the outliers are only few, we can implement that also

[@paraskushwaha8453]

I have been given the Boston house dataset which is already split into train.csv and test.csv ..... and now I am confused about how to proceed with this condition......if u can make a video about it will be more helpful or please help me here itself

[@HackersRealm]

Please refer titanic dataset analysis project that i have posted recently, in that you can get idea on how to proceed

[@paraskushwaha8453]

@@HackersRealm thanks bro really helped a lot

[@HackersRealm]

@@paraskushwaha8453 Glad it was helpful!!!

[@sumayyakousar7144]

Hi ashwin.. I went through ur playlist of hacker ranks algorithms solutions using python. Its amazing Is it possible to crack hacker rank interview or should go through more problems? Please reply

[@HackersRealm]

Yes, If you solved the first 100 problems, that is enough to clear the online hackerrank round

[@fi8379]

when i am trying to create distplot. it is showing me runtime error:Selected KDE bandwidth is 0. Cannot estimate density. what should i do?

[@HackersRealm]

Are you applying it in numeric variable?

[@HackersRealm]

@@ishwarigadagi8000 for which feature you are trying distplot

[@HackersRealm]

@@ishwarigadagi8000 try reinstalling statsmodels

[@bishutoshsharma5949]

Sir in 21:00 u r using Sklearn but I'm using it show attributeError why pls help to find error

[@HackersRealm]

you have to install that module using pip or install anaconda

[@bishutoshsharma5949]

@@HackersRealm yes

[@athiradu1154]

Bro were you predicting the house price??

[@HackersRealm]

yes

[@athiradu1154]

@@HackersRealm no sir... I have doubt... At last you are not predicting the house price.....is this video incomplete

[@HackersRealm]

@@athiradu1154 No, we have check how our model predicting the house price, you can use model.predict to see the results

[@pesallway]

Can we keep such projects(regression) in resume???

[@muskankhattri9919]

dataset link?

[@HackersRealm]

It's in the description

[@PassportAndPixels]

Why are we normalizing only TAX and not other similar distribution columns like - INDUS, RAD etc

[@HackersRealm]

if the distribution is more skewed, we can do that. I just shared it for example

[@PassportAndPixels]

@@HackersRealm Thanks for replying. Just another quick question - how can we decide if we need to have a functional form for the model testing. How do select the Variable?. And how do we know if we are not overfitting the model? Do you have any video which I can refer to?

[@HackersRealm]

@@PassportAndPixels you can refer the ml concepts playlist to get some idea on this parts

[@yashkhochare6335]

TypeError: LinearRegression.__init__() got an unexpected keyword argument 'normalize' We are facing this error. What to do?

[@HackersRealm]

try removing that normalize parameter and check again!!!