Label Encoding: Understanding and Implementation
├── Introduction
│   └── What is Label Encoding?
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Applying Label Encoding
│   ├── Data Preparation
│   ├── Encoding Process
│   └── Understanding Encoded Labels
├── Visualization
│   └── Visualizing Label Encoded Data
└── Conclusion
    └── Pros and Cons of Label Encoding

1. Introduction

What is Label Encoding?

• Label Encoding is a process of converting categorical text data into model-understandable numerical data. In this technique, each unique category value is assigned a numerical value.

2. Setting Up the Environment

Importing Libraries

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

Generating the Dataset

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

3. Applying Label Encoding

Data Preparation

• Ensure that the data is clean and consists of categorical values.

Encoding Process

# Applying Label Encoding
label_encoder = LabelEncoder()
df['Encoded'] = label_encoder.fit_transform(df['Category'])

Understanding Encoded Labels

• Each unique category is assigned an integer value, starting from 0.

4. Visualization

Visualizing Label Encoded Data