diabetes_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "884bdd31-6d77-4374-b255-6ba7fd6f5fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install -r requirements.txt;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd0eb7b-de8f-4762-908a-ce70e1e4f1d8",
   "metadata": {},
   "source": [
    "### Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f3a3420-f3e9-477a-84fd-ff088cc5662e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f7a4fe-2fcf-4c72-9342-73480da774f4",
   "metadata": {},
   "source": [
    "### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ad69a1a-8d91-4a3b-b82c-d9d466287492",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Skin thickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Inheritance</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has Diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>28.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>168.00</td>\n",
       "      <td>43.10</td>\n",
       "      <td>2.29</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>543.00</td>\n",
       "      <td>30.50</td>\n",
       "      <td>0.16</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>189.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>846.00</td>\n",
       "      <td>30.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Number of pregnancies  Glucose  Blood Pressure  Skin thickness  Insulin  \\\n",
       "3                       1    89.00           66.00           23.00    94.00   \n",
       "4                       0   137.00           40.00           35.00   168.00   \n",
       "6                       3    78.00           50.00           32.00    88.00   \n",
       "8                       2   197.00           70.00           45.00   543.00   \n",
       "13                      1   189.00           60.00           23.00   846.00   \n",
       "\n",
       "     BMI  Inheritance  Age  Has Diabetes  \n",
       "3  28.10         0.17   21             0  \n",
       "4  43.10         2.29   33             1  \n",
       "6  31.00         0.25   26             1  \n",
       "8  30.50         0.16   53             1  \n",
       "13 30.10         0.40   59             1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Skin thickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Inheritance</th>\n",
       "      <th>Age</th>\n",
       "      <th>Has Diabetes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.30</td>\n",
       "      <td>122.63</td>\n",
       "      <td>70.66</td>\n",
       "      <td>29.15</td>\n",
       "      <td>156.06</td>\n",
       "      <td>33.09</td>\n",
       "      <td>0.52</td>\n",
       "      <td>30.86</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.21</td>\n",
       "      <td>30.86</td>\n",
       "      <td>12.50</td>\n",
       "      <td>10.52</td>\n",
       "      <td>118.84</td>\n",
       "      <td>7.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>18.20</td>\n",
       "      <td>0.09</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>99.00</td>\n",
       "      <td>62.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>76.75</td>\n",
       "      <td>28.40</td>\n",
       "      <td>0.27</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>119.00</td>\n",
       "      <td>70.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>125.50</td>\n",
       "      <td>33.20</td>\n",
       "      <td>0.45</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.00</td>\n",
       "      <td>143.00</td>\n",
       "      <td>78.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>190.00</td>\n",
       "      <td>37.10</td>\n",
       "      <td>0.69</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.00</td>\n",
       "      <td>198.00</td>\n",
       "      <td>110.00</td>\n",
       "      <td>63.00</td>\n",
       "      <td>846.00</td>\n",
       "      <td>67.10</td>\n",
       "      <td>2.42</td>\n",
       "      <td>81.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of pregnancies  Glucose  Blood Pressure  Skin thickness  \\\n",
       "count                 392.00   392.00          392.00          392.00   \n",
       "mean                    3.30   122.63           70.66           29.15   \n",
       "std                     3.21    30.86           12.50           10.52   \n",
       "min                     0.00    56.00           24.00            7.00   \n",
       "25%                     1.00    99.00           62.00           21.00   \n",
       "50%                     2.00   119.00           70.00           29.00   \n",
       "75%                     5.00   143.00           78.00           37.00   \n",
       "max                    17.00   198.00          110.00           63.00   \n",
       "\n",
       "       Insulin    BMI  Inheritance    Age  Has Diabetes  \n",
       "count   392.00 392.00       392.00 392.00        392.00  \n",
       "mean    156.06  33.09         0.52  30.86          0.33  \n",
       "std     118.84   7.03         0.35  10.20          0.47  \n",
       "min      14.00  18.20         0.09  21.00          0.00  \n",
       "25%      76.75  28.40         0.27  23.00          0.00  \n",
       "50%     125.50  33.20         0.45  27.00          0.00  \n",
       "75%     190.00  37.10         0.69  36.00          1.00  \n",
       "max     846.00  67.10         2.42  81.00          1.00  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_columns = ['Number of pregnancies', 'Glucose', 'Blood Pressure', 'Skin thickness', 'Insulin', 'BMI', 'Inheritance', 'Age']\n",
    "y_column = 'Has Diabetes'\n",
    "\n",
    "# For all columns except 'Number of pregnanices' 0 indicates a missing value\n",
    "na_values = {col: 0 for col in X_columns if col != 'Number of pregnancies'}\n",
    "data = pd.read_csv('data.csv', names=[*X_columns, y_column], na_values=na_values)\n",
    "\n",
    "# Dropping all missing values (for simplicity)\n",
    "data = data.dropna()\n",
    "X = data.iloc[:, 0:-1]\n",
    "y = data.iloc[:, -1]\n",
    "display(data.head())\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de097908-79f9-490b-86fb-d834284ab5eb",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e505ad2-cbf8-44f0-bdc9-8a7d3230b7b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "X_scaled = MinMaxScaler().fit(X).transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49cf4cdd-bc8f-4636-9f73-3a7a5473dcc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_model = KNeighborsClassifier(n_neighbors=6)\n",
    "knn_model.fit(X_train, y_train)\n",
    "y_pred_knn = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe33ea58-88a4-4313-8fc5-d9305d53f7ae",
   "metadata": {},
   "source": [
    "### Niave Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9482e1ee-4424-4ba3-981e-201616862dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import CategoricalNB\n",
    "\n",
    "def transform_to_bins(col):\n",
    "    percentiles = [25, 50, 75]\n",
    "    boundaries = np.percentile(col, percentiles)\n",
    "    return np.digitize(col, boundaries, right=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27ab45d1-3055-4696-adf0-ba4ee18b404c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>Blood Pressure</th>\n",
       "      <th>Skin thickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Inheritance</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>392.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.30</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.49</td>\n",
       "      <td>1.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.19</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of pregnancies  Glucose  Blood Pressure  Skin thickness  \\\n",
       "count                 392.00   392.00          392.00          392.00   \n",
       "mean                    1.30     1.48            1.47            1.45   \n",
       "std                     1.19     1.13            1.12            1.10   \n",
       "min                     0.00     0.00            0.00            0.00   \n",
       "25%                     0.00     0.00            0.00            0.00   \n",
       "50%                     1.00     1.00            1.00            1.00   \n",
       "75%                     2.00     2.00            2.00            2.00   \n",
       "max                     3.00     3.00            3.00            3.00   \n",
       "\n",
       "       Insulin    BMI  Inheritance    Age  \n",
       "count   392.00 392.00       392.00 392.00  \n",
       "mean      1.50   1.48         1.49   1.46  \n",
       "std       1.12   1.12         1.11   1.12  \n",
       "min       0.00   0.00         0.00   0.00  \n",
       "25%       0.75   0.00         0.75   0.00  \n",
       "50%       1.50   1.00         1.50   1.00  \n",
       "75%       2.00   2.00         2.00   2.00  \n",
       "max       3.00   3.00         3.00   3.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_nb = X.copy(deep = True)\n",
    "\n",
    "for column in X_columns:\n",
    "    col = np.array(X_nb[column])\n",
    "    X_nb[column] = transform_to_bins(col)\n",
    "\n",
    "X_nb.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a774a5b-37c6-420f-a189-9a3ee5868a5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_nb_train, X_nb_test, _, _ = train_test_split(X_nb, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4df4d0fb-d439-48a7-a6ff-aa5c7f27e32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_model = CategoricalNB(force_alpha=True, min_categories=4)\n",
    "nb_model.fit(X_nb_train, y_train)\n",
    "y_pred_naive_bayes = nb_model.predict(X_nb_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bceda6f-b4a1-4326-b5c4-891b55a0a09d",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b184bc88-3fdb-41d8-8abb-c67af93c91fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg_model = LogisticRegression()\n",
    "logreg_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_logreg = logreg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52132c54-3748-4893-84fe-908ac05fa1cc",
   "metadata": {},
   "source": [
    "## Evaluation of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c8de8cb-d440-4da5-a9e4-df35ab7e3a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, ConfusionMatrixDisplay\n",
    "\n",
    "def display_model_evaluation(y_true, y_pred, model_name=\"\"):\n",
    "    print (f'Accuracy score is {accuracy_score(y_test, y_pred)}')\n",
    "    print (f'F1 score is {f1_score(y_test, y_pred)}')\n",
    "    print ('Confusion Matrix')\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    ConfusionMatrixDisplay(cm).plot(cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3fda09-0caa-48db-aee1-febb5998690c",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d06a3a4-60bb-42fd-9f16-103abdf20039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is 0.7974683544303798\n",
      "F1 score is 0.5789473684210527\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_model_evaluation(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39746e9-6b9e-4629-9f28-56c590beb16f",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e9fa6f2-8b1d-4ce7-8117-52b42687152e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is 0.7341772151898734\n",
      "F1 score is 0.27586206896551724\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_model_evaluation(y_test, y_pred_naive_bayes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18fc0927-1169-4b16-9fff-d39b9f3b6fcc",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b222c24-f055-4d59-b4a1-d375568e4b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is 0.8227848101265823\n",
      "F1 score is 0.6666666666666666\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_model_evaluation(y_test, y_pred_logreg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}