One-Hot Encoding: Technique and Practical Application
├── Introduction
│   └── What is One-Hot Encoding?
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating Sample Categorical Data
├── Implementing One-Hot Encoding
│   ├── Data Preparation
│   ├── Applying One-Hot Encoding
│   └── Understanding the Encoded Data
├── Visualization
│   └── Visualizing One-Hot Encoded Data
└── Conclusion
    └── Advantages and Use Cases

1. Introduction

What is One-Hot Encoding?

• One-Hot Encoding is a technique used to convert categorical variables into a form that could be provided to machine learning algorithms to improve predictions.

2. Setting Up the Environment

Importing Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import OneHotEncoder

Generating Sample Categorical Data

# Sample categorical data
data = {'Category': ['Apple', 'Banana', 'Orange', 'Apple', 'Banana']}
df = pd.DataFrame(data)

3. Implementing One-Hot Encoding

Data Preparation

• Prepare categorical data for encoding. Ensure the data is clean and formatted correctly.

Applying One-Hot Encoding

encoder = OneHotEncoder(sparse=False)
encoded_data = encoder.fit_transform(df[['Category']])

Understanding the Encoded Data

• The encoded data is a binary matrix representing the presence of each category in the original data.

4. Visualization

Visualizing One-Hot Encoded Data