Machine Learning Tutorial Python - 6: Dummy Variables & One Hot Encoding

#MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #OneHotEncoding #sklearntutorials #scikitlearntutorials

Topics that are covered in this Video:
0:00 Introduction
0:47 How to handle text data in machine learning model?
1:38 Nominal vs Ordinal Variables
2:44 Theory (Explain one hot encoding using home prices in different townships)
3:39 Coding (Start)
3:51 Pandas get_dummies method
7:48 Create a model that uses dummy columns
12:45 Label Encoder
13:29 fit_transform() method
15:40 sklearn OneHotEncoder
19:59 Exercise (To predict prices of car based on car model, age, mileage)

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#️⃣ Social Media #️⃣

[celestineokpataku]

I have watched only 4 mins so far i had to pulse and write this comment. I will say this is one of the best tutorial i have seen in data science. Sir you need to take this to another level. What a great teacher you are

[venkatesanrf]

Hi,
Your explanation is very simple and effective
Ans for practice session A)Price of Mercedes Benz -4Yr old--mileage 45000= 36991.31721061

B)Price of BMW_X5 -7Yr old--mileage 86000=11080.74313219
C) percent)

[TheSignatureGuy]

For anyone stuck with the categorical features error.

from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("town", OneHotEncoder(), [0])], remainder = 'passthrough')


X = ct.fit_transform(X)


X


Then you should be able to continue the tutorial without further issue.

[jhagaurav]

Sir pls continue your machine learning tutorials, yours tutorials are one of the best I have seen so far .

[sreenufriendz]

Anyone can be a teacher, but real teacher eliminates the fear from students .. you did the same !! Excellent knowledge and skills

[Genz-or]

I was confuse from where to start studying ml and then my friend suggested this series.... It's great :-)

[noubaddi]

This guy is AMAZING! I have spent 2 days trying decenes of other methods and this is the only one that worked for my data and didnøt come as an error, this guy totally saved my mental sanity, I was growing desperate as in DESPERATE! Thank you, thank you, thank you!

[ankitparashar]

Merc: 36991.317
BMW: 11080.743
Score: 94.17%

[vaishalibisht]

Wonderful Video.
This so far the easiest explanation I have seen for one hot encoding. I have been struggling from very long to find a proper video on this topic and my quest ended today.
Thanks a lot, sir.

[programmingwithraahim]

15:50 write your code like this:

ct = ColumnTransformer(
[('one_hot_encoder', OneHotEncoder(categories='auto'), [0])],
remainder='passthrough'
)
X = ct.fit_transform(X)
X

Ok so it will work fine otherwise it will give an error.

[tech-n-data]

Your ability to simplify things is amazing, thank you so much. You are a natural teacher.

[shrutijain]

this ML tutorial is by far the best one i have seen it is so easy to learn and understand and your exersise also helps me to apply what i have learn so far thank you.

[tushargahtori]

Even in 23 your video is such a relief..kudos to your teaching.

[HashimAli-tzfw]

I achieved the same result using a different method that doesn't require dropping columns or concatenating dataframes. This alternative approach can lead to cleaner and more efficient code

df=pd.get_dummies(df,
columns=['CarModel'], drop_first=True)

[wangangcwayi]

You have gift of explaining things even to the layman. Big Up to you

[ymoniem]

you really made it very easy to understand such new concepts, Thanks a lot
starting from mint 12:30 about OneHotEncoder . Some udpates in Sklearn prevent using categorical_features=[0]

here is the code update as of April 2020
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [0])], remainder='passthrough')
X = np.array(columnTransformer.fit_transform(x), dtype = np.str)
X= X[:, 1:]
model.fit(X, y)
model.predict([[1, 0, 2800]])
model.predict([[0, 1, 3400]])

[bandhammanikanta]

First of all, 1000*Thanks for sharing such content on youtube..
I got an accuracy of 94.17% on training data.

[mk]

I was shocked after the first 5 minutes of the video and have never thought it would be so easy and fast! Thanks ALOT1

[hiver]

the god of data explanation sir..kudos to your patience in explanation

[snomad]

This was really well done! Kudos to you! It's hard to find clear and concise free tutorials nowadays. Subscribed and hope to see more awesome stuff!