diff --git a/DIRECTORY.md b/DIRECTORY.md
index f0a34a553946..0dc79258d428 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -602,6 +602,7 @@
   * [Mfcc](machine_learning/mfcc.py)
   * [Multilayer Perceptron Classifier](machine_learning/multilayer_perceptron_classifier.py)
   * [Polynomial Regression](machine_learning/polynomial_regression.py)
+  * [Principal Component Analysis](machine_learning/principal_component_analysis.py)
   * [Scoring Functions](machine_learning/scoring_functions.py)
   * [Self Organizing Map](machine_learning/self_organizing_map.py)
   * [Sequential Minimum Optimization](machine_learning/sequential_minimum_optimization.py)
diff --git a/machine_learning/principal_component_analysis.py b/machine_learning/principal_component_analysis.py
new file mode 100644
index 000000000000..bd5bbf2d872a
--- /dev/null
+++ b/machine_learning/principal_component_analysis.py
@@ -0,0 +1,127 @@
+"""
+Principal Component Analysis (PCA) is a linear dimensionality reduction technique used
+commonly as a data preprocessing step in unsupervised and supervised machine learning pipelines.
+The principle behind PCA is the reduction in the number of variables in the dataset, while
+preserving as much as information as possible.
+
+Here, the  principal components represent the directions of the data that explain a
+maximal amount of variance. Here, the data is projected onto a new coordinate system
+such that the direction cqpturing the largest variation in data can be easily identified.
+
+This implementation of PCA consists of the following steps:
+1. Data Standardization (Z-score Normalization): This step involved the centering of the
+data by subtracting the mean and dividing it by the standard deviation
+so that it has unit variance.
+
+2. Covariance Matrix Calculation: This step involved the calculation of the covariance matrix
+of the standardized data. The covariance matrix allows us to measure how different
+features vary together, capturing the relationships between them.
+
+3. Singular Value Decomposition: In this step, we use Singular Value Decomposition or SVD to
+Decomposes the covariance matrix into its singular eignvectors and singular eigenvalues,
+ which help identify the principal components.
+
+4. Selection of Principal Components: Here, we choose the top k principal components
+that explain the most variance in the data.
+
+5. Projection of Data: Here, we transform the original standardized data into
+the new lower-dimensional space defined by the selected principal components.
+
+REFERENCE: en.wikipedia.org/wiki/Principal_component_analysis
+"""
+
+import numpy as np
+
+
+def svd(matrix):
+    """
+    Perform Singular Value Decomposition (SVD) on the given matrix.
+    Args:
+        matrix (np.ndarray): The input matrix.
+    Returns:
+        tuple: The U, S, and VT matrices from SVD.
+    >>> matrix = np.array([[1, 2], [3, 4]])
+    >>> u, s, vt = svd(matrix)
+    >>> np.allclose(np.dot(u, np.dot(s, vt)), matrix)
+    True
+    """
+    m, n = matrix.shape
+    u = np.zeros((m, m))
+    s = np.zeros((m, n))
+    vt = np.zeros((n, n))
+    eigvals, eigvecs = np.linalg.eig(np.dot(matrix.T, matrix))
+    vt = eigvecs.T
+
+    singular_values = np.sqrt(eigvals)
+    s[:n, :n] = np.diag(singular_values)
+
+    for i in range(n):
+        u[:, i] = np.dot(matrix, vt[i, :]) / singular_values[i]
+
+    return u, s, vt
+
+
+def main(data: list[int], k: int):
+    """
+    Perform Principal Component Analysis (PCA) on the given data.
+
+    Args:
+        data (list[int]): The input data.
+        k (int): The number of principal components to retain.
+
+    Returns:
+        np.ndarray: The transformed data with reduced dimensionality.
+
+    >>> data = np.array([[1, 2], [3, 4], [5, 6]])
+    >>> main(data, 1)
+    array([[-2.82842712],
+           [ 0.        ],
+           [ 2.82842712]])
+    """
+    z_score = data - data.mean(axis=0) / data.std(axis=0)
+    cov_matrix = np.cov(z_score, ddof=1, rowvar=False)
+
+    u, s, vt = svd(cov_matrix)
+    principal_components = vt[:k]
+    transformed_data = np.dot(z_score, principal_components.T)
+    return transformed_data
+
+
+if __name__ == "__main__":
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from sklearn.datasets import make_blobs
+
+    data, _ = make_blobs(n_samples=100, n_features=4, centers=5, random_state=42)
+    k = 2
+
+    transformed_data = main(data, k)
+    print("Transformed Data:")
+    print(transformed_data)
+
+    assert transformed_data.shape == (
+        data.shape[0],
+        k,
+    ), "The transformed data does not have the expected shape."
+
+    # Visualize the original data and the transformed data
+    plt.figure(figsize=(12, 6))
+
+    plt.subplot(1, 2, 1)
+    plt.scatter(data[:, 0], data[:, 1], c="blue", edgecolor="k", s=50)
+    plt.title("Original Data")
+    plt.xlabel("Feature 1")
+    plt.ylabel("Feature 2")
+
+    plt.subplot(1, 2, 2)
+    plt.scatter(
+        transformed_data, np.zeros_like(transformed_data), c="red", edgecolor="k", s=50
+    )
+    plt.title("Transformed Data")
+    plt.xlabel("Principal Component 1")
+    plt.yticks([])
+
+    plt.tight_layout()
+    plt.show()
+
+    print("All tests passed.")