SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>ID</th>\n",
       "      <th>Clump</th>\n",
       "      <th>UnifSize</th>\n",
       "      <th>UnifShape</th>\n",
       "      <th>MargAdh</th>\n",
       "      <th>SingEpiSize</th>\n",
       "      <th>BareNuc</th>\n",
       "      <th>BlandChrom</th>\n",
       "      <th>NormNucl</th>\n",
       "      <th>Mit</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Clump  UnifSize  UnifShape  MargAdh  SingEpiSize BareNuc  \\\n",
       "0  1000025      5         1          1        1            2       1   \n",
       "1  1002945      5         4          4        5            7      10   \n",
       "2  1015425      3         1          1        1            2       2   \n",
       "3  1016277      6         8          8        1            3       4   \n",
       "4  1017023      4         1          1        3            2       1   \n",
       "\n",
       "   BlandChrom  NormNucl  Mit  Class  \n",
       "0           3         1    1      2  \n",
       "1           3         2    1      2  \n",
       "2           3         1    1      2  \n",
       "3           3         7    1      2  \n",
       "4           3         1    1      2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/cell_samples.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data[data['Class'] == 4][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='DarkBlue', label='malignant');\n",
    "data[data['Class'] == 2][0:50].plot(kind='scatter', x='Clump', y='UnifSize', color='Yellow', label='benign', ax=ax);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              int64\n",
       "Clump           int64\n",
       "UnifSize        int64\n",
       "UnifShape       int64\n",
       "MargAdh         int64\n",
       "SingEpiSize     int64\n",
       "BareNuc        object\n",
       "BlandChrom      int64\n",
       "NormNucl        int64\n",
       "Mit             int64\n",
       "Class           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a NEED to change object to int!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             int64\n",
       "Clump          int64\n",
       "UnifSize       int64\n",
       "UnifShape      int64\n",
       "MargAdh        int64\n",
       "SingEpiSize    int64\n",
       "BareNuc        int64\n",
       "BlandChrom     int64\n",
       "NormNucl       int64\n",
       "Mit            int64\n",
       "Class          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[pd.to_numeric(data['BareNuc'], errors='coerce').notnull()]\n",
    "data['BareNuc'] = data['BareNuc'].astype('int')\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = data[['Clump','UnifSize', 'UnifShape', 'MargAdh', 'SingEpiSize', 'BareNuc', 'BlandChrom', 'NormNucl', 'Mit']]\n",
    "X = np.asanyarray(feature_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.asanyarray(data['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (457, 9) (457,)\n",
      "Test set: (226, 9) (226,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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()</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()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = clf.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       0.95      0.98      0.96       142\n",
      "           4       0.96      0.90      0.93        84\n",
      "\n",
      "    accuracy                           0.95       226\n",
      "   macro avg       0.95      0.94      0.95       226\n",
      "weighted avg       0.95      0.95      0.95       226\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[139   3]\n",
      " [  8  76]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix = confusion_matrix(y_test, yhat, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kernel='linear'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2 = svm.SVC(kernel='linear')\n",
    "clf2.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 2, 2, 2])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat2 = clf2.predict(X_test)\n",
    "yhat2[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           2       0.95      0.98      0.96       142\n",
      "           4       0.96      0.90      0.93        84\n",
      "\n",
      "    accuracy                           0.95       226\n",
      "   macro avg       0.95      0.94      0.95       226\n",
      "weighted avg       0.95      0.95      0.95       226\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[140   2]\n",
      " [  8  76]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_matrix2 = confusion_matrix(y_test, yhat2, labels=[2,4])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix2, classes=['Benign(2)','Malignant(4)'],normalize= False,  title='Confusion matrix')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}