2. Iris_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML - Flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Importing Libraries\n",
    "2. Getting the Data\n",
    "3. Preprocessing the Data ( Most important, also obtional, depends on the Dataset)\n",
    "4. Creating a model\n",
    "5. (Fitting) Running the alogrithm\n",
    "6. Testing accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T13:43:11.996120Z",
     "start_time": "2020-03-31T13:43:01.198727Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np; import pandas as pd import matplotlib.pyplot as plt;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T13:58:34.449460Z",
     "start_time": "2020-03-31T13:58:33.693101Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:12:43.698333Z",
     "start_time": "2020-03-31T14:12:43.672402Z"
    }
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "0      setosa\n",
       "1  versicolor\n",
       "2   virginica"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(datasets.load_iris()['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:08:21.815920Z",
     "start_time": "2020-03-31T14:08:21.769049Z"
    }
   },
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_Data = pd.DataFrame(data = datasets.load_iris()['data'],columns = datasets.load_iris()['feature_names'])\n",
    "Iris_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:09:52.954636Z",
     "start_time": "2020-03-31T14:09:52.926711Z"
    }
   },
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "..  ..\n",
       "145  2\n",
       "146  2\n",
       "147  2\n",
       "148  2\n",
       "149  2\n",
       "\n",
       "[150 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_Solve = pd.DataFrame(datasets.load_iris()['target'])\n",
    "Iris_Solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:47:13.105710Z",
     "start_time": "2020-03-31T14:47:12.945138Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = Iris_Solve[0].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:11:36.981896Z",
     "start_time": "2020-03-31T14:11:36.940008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_Data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:16:26.562468Z",
     "start_time": "2020-03-31T14:16:26.524566Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 1 columns):\n",
      "0    150 non-null int32\n",
      "dtypes: int32(1)\n",
      "memory usage: 728.0 bytes\n"
     ]
    }
   ],
   "source": [
    "Iris_Solve.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:28:01.934745Z",
     "start_time": "2020-03-31T14:28:01.922780Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train,x_test, y_train,y_test = train_test_split(Iris_Data,Iris_Solve,train_size =0.75, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:28:08.444955Z",
     "start_time": "2020-03-31T14:28:08.413035Z"
    }
   },
   "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 (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>112 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "102                7.1               3.0                5.9               2.1\n",
       "108                6.7               2.5                5.8               1.8\n",
       "94                 5.6               2.7                4.2               1.3\n",
       "105                7.6               3.0                6.6               2.1\n",
       "72                 6.3               2.5                4.9               1.5\n",
       "..                 ...               ...                ...               ...\n",
       "109                7.2               3.6                6.1               2.5\n",
       "69                 5.6               2.5                3.9               1.1\n",
       "74                 6.4               2.9                4.3               1.3\n",
       "121                5.6               2.8                4.9               2.0\n",
       "29                 4.7               3.2                1.6               0.2\n",
       "\n",
       "[112 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:29:57.989651Z",
     "start_time": "2020-03-31T14:29:57.964717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38 entries, 123 to 18\n",
      "Data columns (total 4 columns):\n",
      "sepal length (cm)    38 non-null float64\n",
      "sepal width (cm)     38 non-null float64\n",
      "petal length (cm)    38 non-null float64\n",
      "petal width (cm)     38 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.5 KB\n"
     ]
    }
   ],
   "source": [
    "x_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:34:26.102150Z",
     "start_time": "2020-03-31T14:34:26.096650Z"
    }
   },
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:34:21.487845Z",
     "start_time": "2020-03-31T14:34:21.479867Z"
    }
   },
   "outputs": [],
   "source": [
    "modelFit = model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:35:11.267698Z",
     "start_time": "2020-03-31T14:35:11.258723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.tree.tree.DecisionTreeClassifier"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(modelFit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:37:07.630542Z",
     "start_time": "2020-03-31T14:37:07.574693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 2, 1, 0, 1, 1, 0, 0, 0, 1, 2, 2, 1, 2, 2, 0, 1, 2, 2,\n",
       "       0, 0, 0, 2, 1, 2, 0, 2, 2, 2, 0, 0, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = modelFit.predict(x_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:38:29.632657Z",
     "start_time": "2020-03-31T14:38:29.622684Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 0, 2, 2, 1, 0, 1, 1, 0, 0, 0, 1, 2, 1, 1, 2, 2, 0, 1, 2, 2,\n",
       "        0, 0, 0, 2, 1, 2, 0, 2, 2, 2, 0, 0, 0, 1, 0, 0]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test).reshape(1,38)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-31T14:39:47.118695Z",
     "start_time": "2020-03-31T14:39:47.109717Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.36842105263158\n"
     ]
    }
   ],
   "source": [
    "score = accuracy_score(pred,y_test)\n",
    "print(score*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://user-images.githubusercontent.com/48029688/83121956-988a0a00-a0f0-11ea-8369-b6eccc6b7bbb.png)"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}