diff --git a/machine_learning/principle_component_analysis.py b/machine_learning/principle_component_analysis.py
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+++ b/machine_learning/principle_component_analysis.py
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+"""
+Principal Component Analysis (PCA) is a dimensionality reduction technique
+commonly used in machine learning. It transforms high-dimensional data into
+lower dimensions while retaining most of the information.
+
+Here,we use a dataset (Iris dataset) and apply PCA to reduce the
+dimensionality. We compute the principal components and transform the dataset
+into a lower-dimensional space.
+
+We reduce the number of columns form 4 to 2
+
+"""
+
+import numpy as np
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import load_iris
+
+
+def collect_dataset():
+    """Collect dataset (Iris dataset)
+    :return: Feature matrix and target values
+    """
+    data = load_iris()
+    return np.array(data.data), np.array(data.target)
+
+
+def apply_pca(data_x, n_components):
+    """Apply Principal Component Analysis (PCA)
+    :param data_x: Original dataset
+    :param n_components: Number of principal components
+    :return: Transformed dataset and explained variance
+    """
+    # Standardizing the features
+    scaler = StandardScaler()
+    data_x_scaled = scaler.fit_transform(data_x)
+
+    # Applying PCA
+    pca = PCA(n_components=n_components)
+    principal_components = pca.fit_transform(data_x_scaled)
+
+    # Explained variance ratio
+    explained_variance = pca.explained_variance_ratio_
+
+    return principal_components, explained_variance
+
+
+def main():
+    """Driver function"""
+    data_x, data_y = collect_dataset()
+    # Set number of principal components
+    n_components = 3
+
+    # Apply PCA
+    transformed_data, variance_ratio = apply_pca(data_x, n_components)
+
+    print("Transformed Dataset (First 5 rows):")
+    print(transformed_data[:5])
+
+    print("\nExplained Variance Ratio:")
+    print(variance_ratio)
+
+
+if __name__ == "__main__":
+    main()