⚙️ Data Preprocessing Tools

This repository contains a Google Colab notebook showcasing foundational data preprocessing techniques for machine learning. The notebook covers essential steps to clean, transform, and prepare datasets for modeling.

---

✨ Key Features

🛠️ 1. Importing Libraries

Python libraries used include:

import numpy as np  
import matplotlib.pyplot as plt  
import pandas as pd  

📄 2. Importing the Dataset

The dataset is loaded and split into independent (X) and dependent (y) variables:

dataset = pd.read_csv('Data.csv')  
X = dataset.iloc[:, :-1].values  
y = dataset.iloc[:, -1].values  

Example Output (X):

[['France' 44.0 72000.0]  
 ['Spain' 27.0 48000.0]  
 ['Germany' 30.0 54000.0]  
 ['Spain' 38.0 61000.0]  
 ['Germany' 40.0 nan]  
 ['France' 35.0 58000.0]  
 ['Spain' nan 52000.0]  
 ['France' 48.0 79000.0]  
 ['Germany' 50.0 83000.0]  
 ['France' 37.0 67000.0]]  

Example Output (y):

['No' 'Yes' 'No' 'No' 'Yes' 'Yes' 'No' 'Yes' 'No' 'Yes']  

---

🚧 3. Data Preprocessing Steps

• Handling Missing Data: Identifying and replacing missing values.
• Encoding Categorical Data: Converting text labels into numerical representations for analysis.
  • Encoding Independent Variables.
  • Encoding Dependent Variables.
• Splitting the Dataset: Dividing the dataset into Training and Test subsets.
• Feature Scaling: Normalizing data to enhance model performance.

---

🚀 How to Use

1. Open in Google Colab: Click the animated badge above to launch the notebook.
2. Replace Dataset: Use your dataset by replacing the file path (Data.csv).
3. Run Cells: Execute cells step by step to preprocess the data.

---

🎯 Highlights

• Comprehensive data preprocessing pipeline.
• Handles missing values, categorical data, and scaling.
• Example dataset included for quick testing.

---

🖥️ Animation Note

The Colab badge animation uses a pulsing effect to grab attention. Add this custom animation in your webpage or Markdown styling:

@keyframes pulse {
  0% { transform: scale(1); }
  50% { transform: scale(1.05); }
  100% { transform: scale(1); }
}