One hot encoding

What is One-Hot Encoding?

One-Hot Encoding is a technique used to convert categorical data (like labels or categories) into a numerical format that machine learning algorithms can understand. Here's how it works:

If you have a categorical column with multiple possible values (e.g., "Urban", "Suburban", "Rural"), One-Hot Encoding creates a new binary column (0 or 1) for each unique category.
For each row, if the category matches the one represented by the column, it gets a 1; otherwise, it gets a 0.

For example:

Original Column: Location_Category with values ["Urban", "Suburban", "Rural"]
One-Hot Encoded Columns:
- Urban → [1, 0, 0]
- Suburban → [0, 1, 0]
- Rural → [0, 0, 1]

One-Hot Encoding for the "Location_Category" Column:

Let's apply one-hot encoding to the "Location_Category" column in your provided data:

Original Data:

Number_of_Riders	Number_of_Drivers	Location_Category	Customer_Loyalty_Status	Number_of_Past_Rides	Average_Ratings	Time_of_Booking	Vehicle_Type	Expected_Ride_Duration	Historical_Cost_of_Ride
90	45	Urban	Silver	13	4.47	Night	Premium	90	284.26
58	39	Suburban	Silver	72	4.06	Evening	Economy	43	173.87
42	31	Rural	Silver	0	3.99	Afternoon	Premium	76	329.80
89	28	Rural	Regular	67	4.31	Afternoon	Premium	134	470.20
78	22	Rural	Regular	74	3.77	Afternoon	Economy	149	579.68

One-Hot Encoded Data:

Number_of_Riders	Number_of_Drivers	Urban	Suburban	Rural	Customer_Loyalty_Status	Number_of_Past_Rides	Average_Ratings	Time_of_Booking	Vehicle_Type	Expected_Ride_Duration	Historical_Cost_of_Ride
90	45	1	0	0	Silver	13	4.47	Night	Premium	90	284.26
58	39	0	1	0	Silver	72	4.06	Evening	Economy	43	173.87
42	31	0	0	1	Silver	0	3.99	Afternoon	Premium	76	329.80
89	28	0	0	1	Regular	67	4.31	Afternoon	Premium	134	470.20
78	22	0	0	1	Regular	74	3.77	Afternoon	Economy	149	579.68

Explanation:

For the "Location_Category" column, we created three new binary columns: Urban, Suburban, and Rural.
In each row, the respective column for the location category gets a 1, and the others get a 0.

For example:

The first row is in the Urban category, so it gets a 1 in the "Urban" column and 0s in the "Suburban" and "Rural" columns.
The second row is in the Suburban category, so it gets a 1 in the "Suburban" column and 0s in the others.