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One Hot Encoding – Feature Engineering

So, I just started solving the latest Hackathon on Analytics Vidhya, Women in the loop . Be it a real-life Data Science problem or a Hackathon, one-hot encoding is one of the most important part of your data preparation.

If you don’t know about it yet, then you are definitely missing out on something which can boost your rank.

One hot encoding is a representation of categorical variables as binary vectors. What this means is that we want to transform a categorical variable or variables to a format that works better with classification and regression algorithms.

This is how One Hot Encoding works

How not to do a categorical division?
Basically, if you have a column with Course Details like. Data Science, Software Development, Testing, etc. and you want to use these categorical variable in your model, then the best way to do is to make a column with binary variable with all the variables. So, you will have Data Science, Software Development, Testing will be new columns with values as 0, 1, 2, etc.

Now the problem is that 2>1>0 and the model might treat it as this way. So, to get things sorted you need to specify this to the model that ‘bro, these are all categorical numbers and you dare not treat it as numbers’

What to do?
Create new column as binary column. So, Data Science, Software Development, Testing, etc. with 0 and 1. This whole process is called One Hot Encoding.

Example below

There was some JSON error while directly posting the code, so pasting the screenshot

Sales is the name of the column which we need to predict, splitting the sample into 8:2 and putting it into train and test
Initial column names, here Course Domain and Course Type are the two columns which need One Hot Encoding Treatment
ohe <- c("Course_Domain","Course_Type")
train_data = as.data.frame(train_data)

Put the name of the variables which need OHE treatment at one place and convert the training_data into data frame
dummies_train = dummyVars(~ Course_Domain+Course_Type , data = train_data)

df_ohe = as.data.frame(predict(dummies,newdata = train_data))

Here we are creating and converting the variables into dummy variables. Let's see how the columns are names in the data frame df_ohe
colnames(df_ohe)
[1] "Course_Domain.Business" "Course_Domain.Development"
[3] "Course_Domain.Finance & Accounting" "Course_Domain.Software Marketing"
[5] "Course_Type.Course" "Course_Type.Degree"
[7] "Course_Type.Program"

So, all the variables in the two column were given a new name and each have the value 0 or 1..Awesome !!

df_train_ohe = cbind(train_data[,-c(which(colnames(train_data) %in% ohe))],df_ohe)
colnames(df_train_ohe)
The new list of columns in your training data set are below
colnames(df_train_ohe)
[1] "ID" "Day_No"
[3] "Course_ID" "Short_Promotion"
[5] "Public_Holiday" "Long_Promotion"
[7] "User_Traffic" "Competition_Metric"
[9] "Sales" "Course_Domain.Business"
[11] "Course_Domain.Development" "Course_Domain.Finance & Accounting"
[13] "Course_Domain.Software Marketing" "Course_Type.Course"
[15] "Course_Type.Degree" "Course_Type.Program"

You started with 11 variables, and now you have 16 columns, feed it in your XGB or Linear Regression..By the way, you still have 7 more days for the Hacathon..Try it 🙂

Keep Learning 🙂

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About TheDataMonkNewbie

I am the Co-Founder of The Data Monk. I have a total of 4+ years of analytics experience with 3+ years at Mu Sigma and 1 year at OYO. I am an active trader and a logically sarcastic idiot :)

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