The document discusses different encoding techniques for representing categorical variables in machine learning models, including label encoding, one-hot encoding, and dummy variable encoding. It provides examples of applying each technique to a sample dataset with variables for state, age, salary, and purchase. Label encoding assigns numeric values to each unique category. One-hot encoding creates a new binary variable for each possible category. Dummy variable encoding is similar but can result in collinearity if all dummy variables are included.

1. Whatis
Encodingandwhyitis
required?
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2. State Age Salary Purchase
California 21 21000 No
Texas 25 25000 No
Arizona 23 25000 Yes
Utah 33 30000 Yes
California 45 35000 Yes
California 25 23000 No
Texas 26 27000 Yes
Arizona 27 29000 No
Texas 30 32000 No
Texas 32 31000 Yes
Utah 33 35000 Yes
Utah 34 40000 Yes
Texas 36 42000 Yes
Texas 34 35000 No
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LabelEncoding
State Age Salary Purchase
1 21 21000 No
2 25 25000 No
0 23 25000 Yes
3 33 30000 Yes
1 45 35000 Yes
1 25 23000 No
2 26 27000 Yes
0 27 29000 No
2 30 32000 No
2 32 31000 Yes
3 33 35000 Yes
3 34 40000 Yes
2 36 42000 Yes
2 34 35000 No

3. State_1 State_2 State_3 State_4 Age Salary Purchase
0 1 0 0 21 21000 No
0 0 1 0 25 25000 No
1 0 0 0 23 25000 Yes
0 0 0 1 33 30000 Yes
0 1 0 0 45 35000 Yes
0 1 0 0 25 23000 No
0 0 1 0 26 27000 Yes
1 0 0 0 27 29000 No
0 0 1 0 30 32000 No
0 0 1 0 32 31000 Yes
0 0 0 1 33 35000 Yes
0 0 0 1 34 40000 Yes
0 0 1 0 36 42000 Yes
0 0 1 0 34 35000 No
State Age Salary Purchase
1 21 21000 No
2 25 25000 No
0 23 25000 Yes
3 33 30000 Yes
1 45 35000 Yes
1 25 23000 No
2 26 27000 Yes
0 27 29000 No
2 30 32000 No
2 32 31000 Yes
3 33 35000 Yes
3 34 40000 Yes
2 36 42000 Yes
2 34 35000 No
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OneHotEncoding
𝑦 = 𝑚1 𝑥1 + 𝑚2 𝑥2 + 𝑚3 𝑥3 𝑦 = 𝑚 𝐷1 𝐷1 + 𝑚 𝐷2 𝐷2 + 𝑚 𝐷3 𝐷3 + 𝑚 𝐷4 𝐷4 + 𝑚2 𝑥2 + 𝑚3 𝑥3

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DummyVariableTrap
Multicollinearity

5. State_2 State_3 State_4 Age Salary Purchase
1 0 0 21 21000 No
0 1 0 25 25000 No
0 0 0 23 25000 Yes
0 0 1 33 30000 Yes
1 0 0 45 35000 Yes
1 0 0 25 23000 No
0 1 0 26 27000 Yes
0 0 0 27 29000 No
0 1 0 30 32000 No
0 1 0 32 31000 Yes
0 0 1 33 35000 Yes
0 0 1 34 40000 Yes
0 1 0 36 42000 Yes
0 1 0 34 35000 No
State Age Salary Purchase
1 21 21000 No
2 25 25000 No
0 23 25000 Yes
3 33 30000 Yes
1 45 35000 Yes
1 25 23000 No
2 26 27000 Yes
0 27 29000 No
2 30 32000 No
2 32 31000 Yes
3 33 35000 Yes
3 34 40000 Yes
2 36 42000 Yes
2 34 35000 No
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DummyVariableTrap
𝑦 = 𝑚1 𝑥1 + 𝑚2 𝑥2 + 𝑚3 𝑥3 𝑦 = 𝑚 𝐷2 𝐷2 + 𝑚 𝐷3 𝐷3 + 𝑚 𝐷4 𝐷4 + 𝑚2 𝑥2 + 𝑚3 𝑥3

6. ThankYou!!!
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