200727_PriyaGole.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e132468a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29756896",
   "metadata": {},
   "source": [
    "# Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fd8b10a",
   "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>ACTION</th>\n",
       "      <th>RESOURCE</th>\n",
       "      <th>MGR_ID</th>\n",
       "      <th>ROLE_ROLLUP_1</th>\n",
       "      <th>ROLE_ROLLUP_2</th>\n",
       "      <th>ROLE_DEPTNAME</th>\n",
       "      <th>ROLE_TITLE</th>\n",
       "      <th>ROLE_FAMILY_DESC</th>\n",
       "      <th>ROLE_FAMILY</th>\n",
       "      <th>ROLE_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>39353</td>\n",
       "      <td>85475</td>\n",
       "      <td>117961</td>\n",
       "      <td>118300</td>\n",
       "      <td>123472</td>\n",
       "      <td>117905</td>\n",
       "      <td>117906</td>\n",
       "      <td>290919</td>\n",
       "      <td>117908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17183</td>\n",
       "      <td>1540</td>\n",
       "      <td>117961</td>\n",
       "      <td>118343</td>\n",
       "      <td>123125</td>\n",
       "      <td>118536</td>\n",
       "      <td>118536</td>\n",
       "      <td>308574</td>\n",
       "      <td>118539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>36724</td>\n",
       "      <td>14457</td>\n",
       "      <td>118219</td>\n",
       "      <td>118220</td>\n",
       "      <td>117884</td>\n",
       "      <td>117879</td>\n",
       "      <td>267952</td>\n",
       "      <td>19721</td>\n",
       "      <td>117880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>36135</td>\n",
       "      <td>5396</td>\n",
       "      <td>117961</td>\n",
       "      <td>118343</td>\n",
       "      <td>119993</td>\n",
       "      <td>118321</td>\n",
       "      <td>240983</td>\n",
       "      <td>290919</td>\n",
       "      <td>118322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>42680</td>\n",
       "      <td>5905</td>\n",
       "      <td>117929</td>\n",
       "      <td>117930</td>\n",
       "      <td>119569</td>\n",
       "      <td>119323</td>\n",
       "      <td>123932</td>\n",
       "      <td>19793</td>\n",
       "      <td>119325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACTION  RESOURCE  MGR_ID  ROLE_ROLLUP_1  ROLE_ROLLUP_2  ROLE_DEPTNAME  \\\n",
       "0       1     39353   85475         117961         118300         123472   \n",
       "1       1     17183    1540         117961         118343         123125   \n",
       "2       1     36724   14457         118219         118220         117884   \n",
       "3       1     36135    5396         117961         118343         119993   \n",
       "4       1     42680    5905         117929         117930         119569   \n",
       "\n",
       "   ROLE_TITLE  ROLE_FAMILY_DESC  ROLE_FAMILY  ROLE_CODE  \n",
       "0      117905            117906       290919     117908  \n",
       "1      118536            118536       308574     118539  \n",
       "2      117879            267952        19721     117880  \n",
       "3      118321            240983       290919     118322  \n",
       "4      119323            123932        19793     119325  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"train.csv\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b30f657a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32769 entries, 0 to 32768\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   ACTION            32769 non-null  int64\n",
      " 1   RESOURCE          32769 non-null  int64\n",
      " 2   MGR_ID            32769 non-null  int64\n",
      " 3   ROLE_ROLLUP_1     32769 non-null  int64\n",
      " 4   ROLE_ROLLUP_2     32769 non-null  int64\n",
      " 5   ROLE_DEPTNAME     32769 non-null  int64\n",
      " 6   ROLE_TITLE        32769 non-null  int64\n",
      " 7   ROLE_FAMILY_DESC  32769 non-null  int64\n",
      " 8   ROLE_FAMILY       32769 non-null  int64\n",
      " 9   ROLE_CODE         32769 non-null  int64\n",
      "dtypes: int64(10)\n",
      "memory usage: 2.5 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da520f54",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv(\"test.csv\")\n",
    "test_data.drop('id',inplace=True, axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e2330ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 58921 entries, 0 to 58920\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   RESOURCE          58921 non-null  int64\n",
      " 1   MGR_ID            58921 non-null  int64\n",
      " 2   ROLE_ROLLUP_1     58921 non-null  int64\n",
      " 3   ROLE_ROLLUP_2     58921 non-null  int64\n",
      " 4   ROLE_DEPTNAME     58921 non-null  int64\n",
      " 5   ROLE_TITLE        58921 non-null  int64\n",
      " 6   ROLE_FAMILY_DESC  58921 non-null  int64\n",
      " 7   ROLE_FAMILY       58921 non-null  int64\n",
      " 8   ROLE_CODE         58921 non-null  int64\n",
      "dtypes: int64(9)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "908523f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "cor = data.corr()\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6045cf",
   "metadata": {},
   "source": [
    "### We can see that the features are not closely corelated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428030ad",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e2b4e5d",
   "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>RESOURCE</th>\n",
       "      <th>MGR_ID</th>\n",
       "      <th>ROLE_ROLLUP_1</th>\n",
       "      <th>ROLE_ROLLUP_2</th>\n",
       "      <th>ROLE_DEPTNAME</th>\n",
       "      <th>ROLE_TITLE</th>\n",
       "      <th>ROLE_FAMILY_DESC</th>\n",
       "      <th>ROLE_FAMILY</th>\n",
       "      <th>ROLE_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39353</td>\n",
       "      <td>85475</td>\n",
       "      <td>117961</td>\n",
       "      <td>118300</td>\n",
       "      <td>123472</td>\n",
       "      <td>117905</td>\n",
       "      <td>117906</td>\n",
       "      <td>290919</td>\n",
       "      <td>117908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17183</td>\n",
       "      <td>1540</td>\n",
       "      <td>117961</td>\n",
       "      <td>118343</td>\n",
       "      <td>123125</td>\n",
       "      <td>118536</td>\n",
       "      <td>118536</td>\n",
       "      <td>308574</td>\n",
       "      <td>118539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36724</td>\n",
       "      <td>14457</td>\n",
       "      <td>118219</td>\n",
       "      <td>118220</td>\n",
       "      <td>117884</td>\n",
       "      <td>117879</td>\n",
       "      <td>267952</td>\n",
       "      <td>19721</td>\n",
       "      <td>117880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36135</td>\n",
       "      <td>5396</td>\n",
       "      <td>117961</td>\n",
       "      <td>118343</td>\n",
       "      <td>119993</td>\n",
       "      <td>118321</td>\n",
       "      <td>240983</td>\n",
       "      <td>290919</td>\n",
       "      <td>118322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42680</td>\n",
       "      <td>5905</td>\n",
       "      <td>117929</td>\n",
       "      <td>117930</td>\n",
       "      <td>119569</td>\n",
       "      <td>119323</td>\n",
       "      <td>123932</td>\n",
       "      <td>19793</td>\n",
       "      <td>119325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RESOURCE  MGR_ID  ROLE_ROLLUP_1  ROLE_ROLLUP_2  ROLE_DEPTNAME  ROLE_TITLE  \\\n",
       "0     39353   85475         117961         118300         123472      117905   \n",
       "1     17183    1540         117961         118343         123125      118536   \n",
       "2     36724   14457         118219         118220         117884      117879   \n",
       "3     36135    5396         117961         118343         119993      118321   \n",
       "4     42680    5905         117929         117930         119569      119323   \n",
       "\n",
       "   ROLE_FAMILY_DESC  ROLE_FAMILY  ROLE_CODE  \n",
       "0            117906       290919     117908  \n",
       "1            118536       308574     118539  \n",
       "2            267952        19721     117880  \n",
       "3            240983       290919     118322  \n",
       "4            123932        19793     119325  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data.iloc[:,1:]\n",
    "y=data.iloc[:,0]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44024b24",
   "metadata": {},
   "source": [
    "### Normalizing the data by feature scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dfa2b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X)\n",
    "X = scaler.transform(X)\n",
    "test_data = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb50eb1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30872"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isin([1]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a3223ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1897"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isin([0]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da12f4e8",
   "metadata": {},
   "source": [
    "### We can see that the data is not balanced, observations corresponding to zero are way less than observations corresponding to one. So I used stratified train-test split for modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32af5de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.9,stratify=y,random_state=42,shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80288207",
   "metadata": {},
   "source": [
    "### I tested few models like Decision Tree, Logistic Regression, Naive Bayes, etc. and Decision Tree Classifier performed the best."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f00a40f",
   "metadata": {},
   "source": [
    "## Optimising the Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd741057",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "depth_range = [50,100,150,200,250,300,350,400]\n",
    "auc_value = []\n",
    "for depth in depth_range:\n",
    "    clf = DecisionTreeClassifier(criterion=\"entropy\",max_depth = depth)\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred,pos_label=1)\n",
    "    score = metrics.auc(fpr,tpr)\n",
    "    auc_value.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6fe8f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Area Under ROC Curve')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(depth_range, auc_value)\n",
    "\n",
    "plt.xlabel('depth of tree')\n",
    "plt.ylabel('Area Under ROC Curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b414d80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "300 0.7053415852556562\n"
     ]
    }
   ],
   "source": [
    "max_y = max(auc_value)  \n",
    "max_x = depth_range[auc_value.index(max_y)]  \n",
    "print(max_x,max_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a7ca2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21ddc86c310>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fractions = [0,0.1,0.2,0.3,0.4,0.5]\n",
    "\n",
    "min_weight_fraction_leaf = []\n",
    "acc_gini = []\n",
    "acc_entropy = []\n",
    "\n",
    "for i in fractions:\n",
    "    clf = DecisionTreeClassifier(criterion=\"gini\", min_weight_fraction_leaf=i,max_depth=max_x)\n",
    "    clf.fit(X_train, y_train)\n",
    "    pred = clf.predict(X_test)\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test,pred,pos_label=1)\n",
    "    score = metrics.auc(fpr,tpr)\n",
    "    acc_gini.append(score)\n",
    " \n",
    "    clf = DecisionTreeClassifier(criterion='entropy',min_weight_fraction_leaf=i,max_depth=max_x)\n",
    "    clf.fit(X_train, y_train)\n",
    "    pred = clf.predict(X_test)\n",
    "    fpr, tpr, thresholds = metrics.roc_curve(y_test,pred,pos_label=1)\n",
    "    score = metrics.auc(fpr,tpr)\n",
    "    acc_entropy.append(score)\n",
    "    \n",
    "plt.plot(fractions,acc_gini, label='gini')\n",
    "plt.plot(fractions,acc_entropy,  label='entropy')\n",
    "plt.xlabel('min_weight_fraction_leaf')\n",
    "plt.ylabel('Area under ROC Curve')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82518df",
   "metadata": {},
   "source": [
    "### The tree performs slightly better for criterion=\"entropy\" and best for min_weight_fraction_leaf=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7cb15546",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(criterion=\"entropy\",max_depth=max_x,min_weight_fraction_leaf=0)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2051b6e2",
   "metadata": {},
   "source": [
    "## Results on Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50a8d2d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6966370006649276\n"
     ]
    }
   ],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred , pos_label=1)\n",
    "auc = metrics.auc(fpr,tpr)\n",
    "print(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8d709dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "array=confusion_matrix(y_test,y_pred)\n",
    "\n",
    "df_cm = pd.DataFrame(array, index = [i for i in range(2)],columns = [i for i in range(2)])\n",
    "ax=plt.figure(figsize = (5,4))\n",
    "sns.heatmap(df_cm, annot=True,fmt='g') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3ee3bf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.43      0.43       190\n",
      "           1       0.96      0.97      0.97      3087\n",
      "\n",
      "    accuracy                           0.94      3277\n",
      "   macro avg       0.70      0.70      0.70      3277\n",
      "weighted avg       0.93      0.94      0.94      3277\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "report=classification_report(y_test,y_pred)\n",
    "\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eb92f3",
   "metadata": {},
   "source": [
    "## Submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "275f7127",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predicted = clf.predict(test_data)\n",
    "y_predicted = pd.DataFrame(Y_predicted)\n",
    "y_predicted.columns = ['ACTION']\n",
    "y_predicted.insert(loc=0, column='id', value=(y_predicted.index))\n",
    "y_predicted['id'] += 1\n",
    "y_predicted.to_csv(\"Submission.csv\",index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8faee8f",
   "metadata": {},
   "source": [
    "### After Submission on kaggle, I got area under ROC curve around 0.69"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af7271e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}