data_model_building.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Data set loading*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Information about the data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Splitting the data into a training set and testing set*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Creating the set for training and testing the data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.drop('species',axis=1)\n",
    "y,levels = pd.factorize(iris['species'])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *SVC*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Model training*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC()\n",
    "\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Model Prediction & Evaluation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Results*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " \n",
      " [[13  0  0]\n",
      " [ 0 19  1]\n",
      " [ 0  0 12]]\n",
      "\n",
      "\n",
      "Classification Report: \n",
      " \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       1.00      0.95      0.97        20\n",
      "   virginica       0.92      1.00      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix:','\\n','\\n', confusion_matrix(y_test,predictions))\n",
    "print('\\n')\n",
    "print('Classification Report:','\\n','\\n',classification_report(y_test,predictions,target_names=levels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The outputs are great. We have results over 90 percent.*\n",
    "*The SVC model worked really well.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *GridSearch*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Create a GridSearchCV object and fit it to the training data.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ................................... C=0.1, gamma=1, total=   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
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      "[CV] C=0.1, gamma=0.1 ................................................\n",
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      "[CV] C=0.1, gamma=0.1 ................................................\n",
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      "[CV] C=0.1, gamma=0.1 ................................................\n",
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      "[CV] C=0.1, gamma=0.1 ................................................\n",
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      "[CV] C=0.1, gamma=0.1 ................................................\n",
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      "[CV] C=0.1, gamma=0.01 ...............................................\n",
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      "[CV] C=1, gamma=1 ....................................................\n",
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      "[CV] C=10, gamma=1 ...................................................\n",
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      "[CV] C=10, gamma=0.1 .................................................\n",
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      "[CV] C=10, gamma=0.1 .................................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=10, gamma=0.001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................................ C=10, gamma=0.001, total=   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
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      "[CV] C=100, gamma=1 ..................................................\n",
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      "[CV] C=100, gamma=0.1 ................................................\n",
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      "[CV] C=100, gamma=0.1 ................................................\n",
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      "[CV] C=100, gamma=0.1 ................................................\n",
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      "[CV] C=100, gamma=0.1 ................................................\n",
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      "[CV] C=100, gamma=0.01 ...............................................\n",
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      "[CV] C=100, gamma=0.001 ..............................................\n",
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      "[CV] C=100, gamma=0.001 ..............................................\n",
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      "[CV] C=100, gamma=0.001 ..............................................\n",
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      "[CV] C=100, gamma=0.001 ..............................................\n",
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      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ............................... C=100, gamma=0.001, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.1, 1, 10, 100],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)\n",
    "\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Model Prediction & Evaluation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " \n",
      " [[13  0  0]\n",
      " [ 0 19  1]\n",
      " [ 0  0 12]]\n",
      "\n",
      "\n",
      "Classification Report: \n",
      " \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       1.00      0.95      0.97        20\n",
      "   virginica       0.92      1.00      0.96        12\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix:','\\n','\\n', confusion_matrix(y_test,grid_predictions))\n",
    "print('\\n')\n",
    "print('Classification Report:','\\n','\\n',classification_report(y_test,grid_predictions,target_names=levels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Random Forest*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Creating the set for training and testing the data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.drop('species',axis=1)\n",
    "y,levels = pd.factorize(iris['species'])\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Model training*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "\n",
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Model Prediction & Evaluation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Results*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      " \n",
      " [[13  0  0]\n",
      " [ 0 19  1]\n",
      " [ 0  1 11]]\n",
      "\n",
      "\n",
      "Classification Report: \n",
      " \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       0.95      0.95      0.95        20\n",
      "   virginica       0.92      0.92      0.92        12\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.96      0.96      0.96        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix:','\\n','\\n', confusion_matrix(y_test,rfc_pred))\n",
    "print('\\n')\n",
    "print('Classification Report:','\\n','\\n',classification_report(y_test,rfc_pred,target_names=levels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Random Forest model worked well too, but SVC model had a better overall perfomance result for this particular case*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Confusion Matrix heatmap comparison among the models*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *SVC*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_svc = confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_svc = pd.DataFrame(cf_svc,\n",
    "                     index = ['setosa','versicolor','virginica'], \n",
    "                     columns = ['setosa','versicolor','virginica'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5,4))\n",
    "sns.heatmap(hm_svc, annot=True,cmap='YlGnBu')\n",
    "plt.title('SVC \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test, predictions)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *GridSearchCV*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_grid = confusion_matrix(y_test,grid_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_grid = pd.DataFrame(cf_grid,\n",
    "                     index = ['setosa','versicolor','virginica'], \n",
    "                     columns = ['setosa','versicolor','virginica'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5,4))\n",
    "sns.heatmap(hm_grid, annot=True,cmap='YlGnBu')\n",
    "plt.title('GridSearchCV \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test, grid_predictions)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Random Forest*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf_rfc = confusion_matrix(y_test,rfc_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "hm_rfc = pd.DataFrame(cf_rfc,\n",
    "                     index = ['setosa','versicolor','virginica'], \n",
    "                     columns = ['setosa','versicolor','virginica'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5,4))\n",
    "sns.heatmap(hm_rfc, annot=True,cmap='YlGnBu')\n",
    "plt.title('Random Forest \\nAccuracy:{0:.3f}'.format(accuracy_score(y_test, rfc_pred)))\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}