4.One Hot Encoding to process Categorical variables (Python) | Process Categorical Features

This video titled "One Hot Encoding to process Categorical variables (Python) | Process Categorical Features" explains categorical variables and how to encode it i.e. converting variables with values in string or text form to numeric value as well how to do one hot encoding using OneHotEncoder() function. There is LabelBinarizer() function as well which is out of the scope of this video. This is a machine learning & deep learning Bootcamp series of data science. You will also get some flavor of data engineering as well in this Bootcamp series. Through this series, you will be able to learn each aspect of the Data science lifecycle right from collecting data from disparate data sources, data preprocessing to doing visualization as well as model deployment in production. You will also see how to perform data preprocessing and build, regression, classification, clustering as well as a recurrent neural network, convolution neural network, autoencoders, etc. Through this series, you will be able to learn everything pertaining to Machine and Deep Learning in one place. Content & Playlist will be updated regularly to add videos with new topics.

********Git Hub Link for DataSet and Python Code*********

[nabeelnaseer]

I really like the content and explanations..Don't really mind the background music..to me it was kinda relaxing..Just started watching your playlist, I was going through eda materials for my assignment . Could you explain how to tackle the cardinality situation when encoding..

[shailmodi]

I guess sklearn has removed categorical_features parameter. So, what is the alternative now?

[gupta]

Hello Sir....I didn't find which video of yours contains explanation regarding loc and iloc....request you to please share the link.

[nettogrowthpartners]

Hi Nitin! I' m in trouble with OneHotEncoder. I get the categorical features encoded using OneHotEncoder, everything works sweet, the problem comes in place when I try to use predict (I'm using RandomForestRegressor) because I have to encode the entry. I really can't figure it out, I have an entry like this ['Honda', 'White', 71934.0, 4.0] (Make, Color, Odometer, Doors). I use a ColumnTransformer so I try to do something like this model.predict(transformer.transform([['Honda', 'White', 71934.0, 4.0]]) it thrws an error. I really can't solve it out. Would you be so kind to shed some light on that? Thanks in advance.

[ambujmittal]

Hello sir,
For a model *in production*, how do you encode categorical variables that were not present in the training dataset?

[NikhilR]

Hello Nice work going through your playlist one by one.
I am stuck in this encoding though getting error in label as well as one hot encoding
for label encoding error is
"'(slice(None, None, None), 1)' is an invalid key"
for onehotencoding error is
"could not convert string to float: 'Male'"
same data same code as you.
appreciate the help.

[mehulkaushal]

TypeError : __init__ got an unexpected keyword argument 'categorical_featured '

[JainmiahSk]

very Nice Tutorial but what if there are multiple variables and need to encode all those.

[dhusor]

would u plz tell me the version of sklearn?

[nikitakocharin]

The background music distracts from the author's explanations, it's better remove the music from the tutorial. The content itself is good, thanks!