Kernel_Support_Vector Machine.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernel Vector Support Machine \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary  Purchased\n",
       "0     19            19000          0\n",
       "1     35            20000          0\n",
       "2     26            43000          0\n",
       "3     27            57000          0\n",
       "4     19            76000          0\n",
       "..   ...              ...        ...\n",
       "395   46            41000          1\n",
       "396   51            23000          1\n",
       "397   50            20000          1\n",
       "398   36            33000          0\n",
       "399   49            36000          1\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"Social_Network_Ads.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[: , :-1].values\n",
    "y = dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import  train_test_split\n",
    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size=0.2 , random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(320,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 8.53631867e-01, -1.44652121e+00],\n",
       "       [-1.96547978e+00,  4.65635512e-01],\n",
       "       [-3.12897090e-01,  2.62831011e-01],\n",
       "       [ 1.82573933e+00, -1.06988428e+00],\n",
       "       [-4.10107836e-01,  6.00265106e-02],\n",
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       "       [-1.09058306e+00, -1.12782842e+00],\n",
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       "       [ 7.59458956e-02,  2.62831011e-01],\n",
       "       [-1.18779381e+00,  3.20775154e-01],\n",
       "       [-1.28500455e+00,  2.91803083e-01],\n",
       "       [-9.93372315e-01,  4.36663440e-01],\n",
       "       [ 1.63131784e+00, -8.96051849e-01],\n",
       "       [ 1.14526411e+00,  5.23579655e-01],\n",
       "       [ 1.04805336e+00,  5.23579655e-01],\n",
       "       [ 1.33968560e+00,  2.31984809e+00],\n",
       "       [-3.12897090e-01, -1.42777990e-01],\n",
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       "       [-1.18475597e-01, -5.19414919e-01],\n",
       "       [ 9.50842613e-01, -1.15680049e+00],\n",
       "       [-8.96161568e-01, -7.80163563e-01],\n",
       "       [-2.15686344e-01, -5.19414919e-01],\n",
       "       [-1.09058306e+00, -4.61470776e-01],\n",
       "       [-1.18779381e+00,  1.39274180e+00]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel  = 'rbf' ,  random_state = 0)\n",
    "classifier.fit(X_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(classifier.predict(sc.transform([[30 , 85000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = y_pred.reshape(len(y_pred) , 1)\n",
    "y_test = y_pred.reshape(len(y_test) , 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
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      " [1 1]\n",
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      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
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      " [1 1]\n",
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      " [1 1]\n",
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      " [1 1]\n",
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      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((y_pred , y_test) , axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[80]] 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix  , accuracy_score\n",
    "cm = confusion_matrix(y_pred , y_test)\n",
    "acc = accuracy_score(y_pred , y_test)\n",
    "print(cm , acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sana\\AppData\\Local\\Temp\\ipykernel_9128\\2809096271.py:11: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "  plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = sc.inverse_transform(X_train) , y_train \n",
    "x1 , x2 = np.meshgrid(np.arange(start = X_set[: , 0].min() - 10, stop = X_set[: , 0].max() + 10 , step = 1), \n",
    "                      np.arange(start = X_set[: , 0].min() - 1000, stop = X_set[: , 0].max() + 1000 , step = 1))\n",
    "\n",
    "plt.contourf(x1 , x2 , classifier.predict(sc.transform(np.array([x1.ravel() , x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75 , cmap = ListedColormap(('red' , 'green')))\n",
    "plt.xlim(x1.min() , x1.max())\n",
    "plt.ylim(x2.min() , x2.max())\n",
    "for i , j in enumerate(np.unique(y_set)):\n",
    "      plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j) \n",
    "      plt.title('SVM -Linear(Training set)')\n",
    "      plt.xlabel('Age')\n",
    "      plt.ylabel('Estimated Salary')\n",
    "      plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 2-dimensional, but 3 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m plt\u001b[39m.\u001b[39mylim(x2\u001b[39m.\u001b[39mmin() , x2\u001b[39m.\u001b[39mmax())\n\u001b[0;32m     10\u001b[0m \u001b[39mfor\u001b[39;00m i , j \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(np\u001b[39m.\u001b[39munique(y_set)):\n\u001b[1;32m---> 11\u001b[0m       plt\u001b[39m.\u001b[39mscatter(X_set[y_set \u001b[39m==\u001b[39;49mj , \u001b[39m0\u001b[39;49m ] , X_set[y_set \u001b[39m==\u001b[39mj , \u001b[39m1\u001b[39m] , c \u001b[39m=\u001b[39m ListedColormap((\u001b[39m'\u001b[39m\u001b[39mred\u001b[39m\u001b[39m'\u001b[39m , \u001b[39m'\u001b[39m\u001b[39mgreen\u001b[39m\u001b[39m'\u001b[39m)) (i), label \u001b[39m=\u001b[39m j) \n\u001b[0;32m     12\u001b[0m       plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mSVM -Linear(Training set)\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m     13\u001b[0m       plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mAge\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 2-dimensional, but 3 were indexed"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set , y_set = sc.inverse_transform(X_test) , y_test \n",
    "x1 , x2 = np.meshgrid(np.arange(start = X_set[: , 0].min() - 10, stop = X_set[: , 0].max() + 10 , step = 1), \n",
    "                      np.arange(start = X_set[: , 0].min() - 1000, stop = X_set[: , 0].max() + 1000 , step = 1))\n",
    "\n",
    "plt.contourf(x1 , x2 , classifier.predict(sc.transform(np.array([x1.ravel() , x2.ravel()]).T)).reshape(x1.shape),\n",
    "             alpha = 0.75 , cmap = ListedColormap(('red' , 'green')))\n",
    "plt.xlim(x1.min() , x1.max())\n",
    "plt.ylim(x2.min() , x2.max())\n",
    "for i , j in enumerate(np.unique(y_set)):\n",
    "      plt.scatter(X_set[y_set ==j , 0 ] , X_set[y_set ==j , 1] , c = ListedColormap(('red' , 'green')) (i), label = j) \n",
    "      plt.title('SVM -Linear(Training set)')\n",
    "      plt.xlabel('Age')\n",
    "      plt.ylabel('Estimated Salary')\n",
    "      plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}