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+# Principal Component Analysis (PCA) with Digits Dataset
+
+This is a demonstration of Principal Component Analysis (PCA) using the Digits dataset from scikit-learn.
+
+## About the Project
+
+This project demonstrates how to apply Principal Component Analysis (PCA) on the Digits dataset using scikit-learn. PCA is a dimensionality reduction technique that helps visualize and reduce the dimensionality of a dataset while preserving its important information. It's widely used in machine learning and data analysis.
+
+## Getting Started
+
+### Prerequisites
+
+To run this project, you need the following Python libraries installed:
+
+- scikit-learn
+- numpy
+- matplotlib
+
+You can install them using pip:
+
+```bash
+
+
+pip install scikit-learn numpy matplotlib
+
+
+```
+
+#### Dataset:
+[Digits Dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html)
+
+
+### Usage
+Run the provided Jupyter Notebook (digits_pca.ipynb) to see PCA applied to the Digits dataset. The code will load the Digits dataset, apply PCA, and display the reduced-dimension data as output.
+
+
+### Author
+
+[Aswin P Kumar](https://github.com/AswinPKumar01)
+<br>
+[Connect with me](https://www.linkedin.com/in/aswinpkumarvit/)
+
+
+
+
+
+
+
diff --git a/digital_image_processing/Principal_Component_Analysis/pca_code.py b/digital_image_processing/Principal_Component_Analysis/pca_code.py
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+# -*- coding: utf-8 -*-
+"""PCA_Code.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1lzFebVtljdyiu1hHlRfE4mpxITUmH8bV
+
+### **1. Installing Packages**
+"""
+
+!pip install -U scikit-learn
+
+"""### **2. Importing Necessary Modules**"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.datasets import load_digits
+
+"""### **3. Loading the datatset**"""
+
+# Load the Digits dataset
+digits = load_digits()
+X = digits.data
+y = digits.target
+
+"""### **4. Applying PCA and reducing the image**"""
+
+# Randomly select an image for demonstration
+random_image_index = np.random.randint(0, X.shape[0])
+
+# Define the number of principal components you want to keep
+n_components = int(input("Enter the number of components you want to keep (in the range of 0 to 64): "))
+
+# Apply PCA to the image data
+pca = PCA(n_components=n_components)
+X_pca = pca.fit_transform(X)
+
+# Inverse transform to get the reduced-dimension image
+X_inverse = pca.inverse_transform(X_pca)
+
+"""### **5. Displaying the reduced image**"""
+
+# Original image
+original_image = X[random_image_index].reshape(8, 8)
+
+# Reduced-dimension image
+reduced_image = X_inverse[random_image_index].reshape(8, 8)
+
+# Plot the original and reduced images
+plt.figure(figsize=(8, 4))
+plt.subplot(1, 2, 1)
+plt.imshow(original_image, cmap='gray')
+plt.title('Original Image')
+plt.axis('off')
+
+plt.subplot(1, 2, 2)
+plt.imshow(reduced_image, cmap='gray')
+plt.title(f'Reduced to {n_components} Components')
+plt.axis('off')
+
+plt.show()