diff --git a/Prateek-python.md b/Prateek-python.md
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--- /dev/null
+++ b/Prateek-python.md
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+### Introduce Yourself
+My name is Prateek Verma
+
+### Tech Stack I use
+C++,Python
+### How did I discover Zoop?
+Youtube
diff --git a/Prateek-python2.md b/Prateek-python2.md
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+++ b/Prateek-python2.md
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+
diff --git a/matrix_determinant.ipynb b/matrix_determinant.ipynb
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+++ b/matrix_determinant.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr1 = np.array([[16, 1, 2, 3],\n",
+    "                 [4, 5, 6, 7],\n",
+    "                 [8, 9, 17, 11],\n",
+    "                 [12, 13, 14, 15]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16,  1,  2,  3],\n",
+       "       [ 4,  5,  6,  7],\n",
+       "       [ 8,  9, 17, 11],\n",
+       "       [12, 13, 14, 15]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1792.0000000000032"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "det = np.linalg.det(arr1)\n",
+    "det\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-4.23921761+0.j        ,  4.11960881+0.97808983j,\n",
+       "         4.11960881-0.97808983j]),\n",
+       " array([[-0.29340298+0.j        , -0.29794644-0.14168804j,\n",
+       "         -0.29794644+0.14168804j],\n",
+       "        [ 0.94705879+0.j        , -0.17502754-0.05555916j,\n",
+       "         -0.17502754+0.05555916j],\n",
+       "        [ 0.13036236+0.j        , -0.92597568+0.j        ,\n",
+       "         -0.92597568-0.j        ]]))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.eig(arr1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.12000000e+02,  1.68000000e+02,  1.24344979e-14,\n",
+       "        -5.60000000e+01],\n",
+       "       [ 1.68000000e+02,  1.36400000e+03,  1.28000000e+02,\n",
+       "        -7.64000000e+02],\n",
+       "       [-4.24296933e-15,  1.28000000e+02, -2.56000000e+02,\n",
+       "         1.28000000e+02],\n",
+       "       [-5.60000000e+01, -1.43600000e+03,  1.28000000e+02,\n",
+       "         4.68000000e+02]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adj = np.linalg.inv(arr1)*det\n",
+    "adj\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.33333333,  0.66666667, -0.66666667],\n",
+       "       [-0.66666667,  0.66666667,  0.33333333],\n",
+       "       [ 0.66666667,  0.33333333,  0.66666667]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ortho = np.array([[1/3, 2/3, -2/3],\n",
+    "                  [-2/3, 2/3, 1/3],\n",
+    "                  [2/3, 1/3, 2/3]])\n",
+    "ortho\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.33333333, -0.66666667,  0.66666667],\n",
+       "       [ 0.66666667,  0.66666667,  0.33333333],\n",
+       "       [-0.66666667,  0.33333333,  0.66666667]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transpose1 = ortho.transpose()\n",
+    "transpose1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.00000000e+00, -1.54197642e-17,  2.46716228e-17],\n",
+       "       [-1.54197642e-17,  1.00000000e+00, -1.23358114e-17],\n",
+       "       [ 2.46716228e-17, -1.23358114e-17,  1.00000000e+00]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.matmul(ortho, transpose1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M1 = np.array([[1, 0, -1],\n",
+    "               [3, 1, 2]])\n",
+    "M2 = np.array([[1],\n",
+    "               [2],\n",
+    "               [3]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2],\n",
+       "       [11]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.matmul(M1, M2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M3 = np.array([[5, -3],\n",
+    "               [1, 0],\n",
+    "               [-7, 4],\n",
+    "               [0, 2]])\n",
+    "M4 = np.array([[1],\n",
+    "               [0]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5],\n",
+       "       [ 1],\n",
+       "       [-7],\n",
+       "       [ 0]])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.matmul(M3,M4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M5=np.array([[2,1,3],\n",
+    "[-1,1,0],\n",
+    "[-2,4,1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2.999999999999998"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.linalg.det(M5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-750599937895083/2251799813685248,\n",
+       "        -4128299658422957/1125899906842624,\n",
+       "        2251799813685249/2251799813685248],\n",
+       "       [-750599937895083/2251799813685248,\n",
+       "        -6004799503160665/2251799813685248,\n",
+       "        4503599627370499/4503599627370496],\n",
+       "       [6004799503160663/9007199254740992,\n",
+       "        7505999378950831/2251799813685248,\n",
+       "        -4503599627370499/4503599627370496]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I=np.linalg.inv(M5)\n",
+    "I\n",
+    "# np.linalg.pinv(I)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.5 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a9e1fd7a2e46eb840a8adc7a2cabae756acfcbc34a8c7d4b0aee6413053f8c96"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}