diff --git a/pcatutorial.ipynb b/pcatutorial.ipynb
index 67e46d3d424f0ebeec2bd683bc501e6ffc4be8fc..095f8354c60ba12ebe821a2cb94304a8400157a0 100644
--- a/pcatutorial.ipynb
+++ b/pcatutorial.ipynb
@@ -123,6 +123,48 @@
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a6716066",
+   "metadata": {},
+   "source": [
+    "### Mathmatical Explanation\n",
+    "Principal Component Analysis (PCA) is a mathematical technique used for dimensionality reduction and feature extraction in data analysis and machine learning. The primary goal of PCA is to transform the original high-dimensional dataset into a new coordinate system, where the data's variance is maximized along the principal components (PCs). Here's a mathematical explanation of PCA:\n",
+    "\n",
+    "Let's assume you have a dataset with n observations and p variables represented by a matrix X of size n times p, where each row corresponds to an observation, and each column corresponds to a variable. The aim of PCA is to find a set of orthogonal vectors, known as principal components, that capture the maximum variance in the data.\n",
+    "\n",
+    "1. **Centering the Data:**\n",
+    "   - Subtract the mean of each variable from the corresponding column of X to center the data. This is done by subtracting the mean vector $\\bar{x}$ from each row of X.\n",
+    "\n",
+    "   $X_{centered} = X - \\bar{x}$ \n",
+    "\n",
+    "2. **Covariance Matrix:**\n",
+    "   - Calculate the covariance matrix S of the centered data. The covariance matrix measures the relationships between variables and is represented as:\n",
+    "\n",
+    "   $S = \\frac{1}{n-1} \\cdot (X_{\\text{centered}}^T \\cdot X_{\\text{centered}})$\n",
+    "\n",
+    "3. **Eigendecomposition:**\n",
+    "   - Perform eigendecomposition on the covariance matrix S to find its eigenvalues $\\lambda_i$ and corresponding eigenvectors $v_i$.\n",
+    "\n",
+    "   $S \\cdot v_i = \\lambda_i \\cdot v_i$\n",
+    "\n",
+    "   The eigenvectors represent the directions (principal components) of maximum variance in the data, and the corresponding eigenvalues represent the magnitude of that variance along each eigenvector.\n",
+    "\n",
+    "4. **Selecting Principal Components:**\n",
+    "   - Sort the eigenvalues in descending order and choose the top k eigenvectors corresponding to the k largest eigenvalues. These k eigenvectors form the basis for the new subspace.\n",
+    "\n",
+    "   $V_k = [v_1, v_2, ..., v_k]$\n",
+    "\n",
+    "5. **Projection:**\n",
+    "   - Project the original data onto the new subspace spanned by the selected principal components. The transformed dataset is given by:\n",
+    "\n",
+    "   $X_{\\text{transformed}} = X_{\\text{centered}} \\cdot V_k$\n",
+    "\n",
+    "   This projection results in a new dataset with reduced dimensionality, where k represents the desired number of principal components.\n",
+    "\n",
+    "PCA is a powerful technique for reducing dimensionality while retaining as much variance as possible in the data. It is widely used in various fields such as data analysis, image processing, and machine learning."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "998669aa",
@@ -246,6 +288,18 @@
       ]
      },
      "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],