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machine_learning/decision_tree_9/decision_tree.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "33050e33",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4b8f9c58",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"salaries.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "974d9dcf",
"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>company</th>\n",
" <th>job</th>\n",
" <th>degree</th>\n",
" <th>salary_more_then_100k</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>google</td>\n",
" <td>sales executive</td>\n",
" <td>bachelors</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>google</td>\n",
" <td>sales executive</td>\n",
" <td>masters</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>google</td>\n",
" <td>business manager</td>\n",
" <td>bachelors</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>google</td>\n",
" <td>business manager</td>\n",
" <td>masters</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>google</td>\n",
" <td>computer programmer</td>\n",
" <td>bachelors</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" company job degree salary_more_then_100k\n",
"0 google sales executive bachelors 0\n",
"1 google sales executive masters 0\n",
"2 google business manager bachelors 1\n",
"3 google business manager masters 1\n",
"4 google computer programmer bachelors 0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e55303b1",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7b55fb49",
"metadata": {},
"outputs": [],
"source": [
"le_company = LabelEncoder()\n",
"le_job = LabelEncoder()\n",
"le_degree = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e271294f",
"metadata": {},
"outputs": [],
"source": [
"company_n = le_company.fit_transform(df.company)\n",
"job_n = le_company.fit_transform(df.job)\n",
"degree_n = le_company.fit_transform(df.degree)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a2e7b968",
"metadata": {},
"outputs": [],
"source": [
"salary = df.salary_more_then_100k"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "769ad437",
"metadata": {},
"outputs": [],
"source": [
"df.company, df.job, df.degree = company_n, job_n, degree_n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "00cdea98",
"metadata": {},
"outputs": [],
"source": [
"features = df.drop(\"salary_more_then_100k\", axis=\"columns\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "accdeb52",
"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>company</th>\n",
" <th>job</th>\n",
" <th>degree</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" company job degree\n",
"0 2 2 0\n",
"1 2 2 1\n",
"2 2 0 0\n",
"3 2 0 1\n",
"4 2 1 0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d3592e22",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "46bf28c9",
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(features, salary, test_size=0.2, random_state=10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0d414a37",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(16, 12, 4)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(features), len(x_train), len(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "c5df5983",
"metadata": {},
"outputs": [],
"source": [
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7c36264c",
"metadata": {},
"outputs": [],
"source": [
"dtc = tree.DecisionTreeClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "5655fe44",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier()"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtc.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "7f542c33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.75"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtc.score(x_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3e61b2f3",
"metadata": {},
"outputs": [],
"source": [
"pred = dtc.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "99f4aa4f",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "4087c69d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 0],\n",
" [1, 2]], dtype=int64)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_matrix(y_test, pred)"
]
},
{
"cell_type": "markdown",
"id": "31569bc2",
"metadata": {},
"source": [
"titanic "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "fb427dfd",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"titanic.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "734fbe56",
"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>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "236e74ea",
"metadata": {},
"outputs": [],
"source": [
"df = df.drop([\"PassengerId\", \"Name\", \"SibSp\", \"Parch\", \"Ticket\", \"Cabin\", \"Embarked\"], axis=\"columns\")#删掉这些不会影响是否能够生存"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "a348cb79",
"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>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age Fare\n",
"0 0 3 male 22.0 7.2500\n",
"1 1 1 female 38.0 71.2833\n",
"2 1 3 female 26.0 7.9250\n",
"3 1 1 female 35.0 53.1000\n",
"4 0 3 male 35.0 8.0500"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "1c83ecbb",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "31934ad8",
"metadata": {},
"outputs": [],
"source": [
"le = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "a8c11cb0",
"metadata": {},
"outputs": [],
"source": [
"df.Sex = le.fit_transform(df.Sex)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "0ee729dd",
"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>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>38.0</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>26.0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>35.0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Sex Age Fare\n",
"0 0 3 1 22.0 7.2500\n",
"1 1 1 0 38.0 71.2833\n",
"2 1 3 0 26.0 7.9250\n",
"3 1 1 0 35.0 53.1000\n",
"4 0 3 1 35.0 8.0500"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "6e78bdfc",
"metadata": {},
"outputs": [],
"source": [
"survived = df.Survived"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "06950393",
"metadata": {},
"outputs": [],
"source": [
"features = df.drop(\"Survived\", axis=\"columns\")"
]
},
{
"cell_type": "markdown",
"id": "3703250b",
"metadata": {},
"source": [
"数据中有缺失，需要处理。"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "8df08717",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pclass 0\n",
"Sex 0\n",
"Age 177\n",
"Fare 0\n",
"dtype: int64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "6bbac05e",
"metadata": {},
"outputs": [],
"source": [
"features.Age = features.Age.fillna(features.Age.median())"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "74fdd7ec",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Pclass 0\n",
"Sex 0\n",
"Age 0\n",
"Fare 0\n",
"dtype: int64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.isnull().sum()#已经没有缺失数据"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "ce016346",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "1cd7238f",
"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(features, survived, test_size=0.2, random_state=10)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "fa0b077c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(891, 712, 179)"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(features), len(x_train), len(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "50eba766",
"metadata": {},
"outputs": [],
"source": [
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "eb37b412",
"metadata": {},
"outputs": [],
"source": [
"dct = tree.DecisionTreeClassifier()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "a3e977e4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier()"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dct.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "50f6560c",
"metadata": {},
"outputs": [],
"source": [
"y_pred = dct.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "30e62926",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8268156424581006"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(y_pred == y_test).sum()/len(y_test)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "72259345",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "e46f73ce",
"metadata": {},
"outputs": [],
"source": [
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "e4aeef28",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[102, 15],\n",
" [ 16, 46]], dtype=int64)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cm"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b14a69f2",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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