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mirea-projects/Third term/Artificial intelligence systems and big data/5.ipynb

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first commit
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Рабочая тетрадь No 5"
]
},
{
"cell_type": "code",
many
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"execution_count": 1,
first commit
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import tree, metrics\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.3 Задание\n",
"\n",
"Создайте класс по работе с тригонометрическими функциями. В классе \n",
"должны быть реализованы функции вычисления: \n",
"- косинуса; \n",
"- синуса; \n",
"- тангенса; \n",
"- арксинуса; \n",
"- арккосинуса; \n",
"- арктангенса; \n",
"- перевода из градусов в радианы."
]
},
{
"cell_type": "code",
many
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"execution_count": 2,
first commit
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"metadata": {},
"outputs": [],
"source": [
"class TrigFunctions:\n",
" def __init__(self, precision=10):\n",
" # Константа для pi\n",
" self.pi = 3.141592653589793\n",
" # Переменная точности для вычислений\n",
" self.precision = precision\n",
"\n",
" # Факториал для ряда Тейлора\n",
" def factorial(self, n):\n",
" result = 1\n",
" for i in range(2, n + 1):\n",
" result *= i\n",
" return result\n",
"\n",
" # Приближенное вычисление синуса с помощью ряда Тейлора\n",
" def sin(self, angle_radians):\n",
" sin_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n + 1)) / self.factorial(2 * n + 1)\n",
" sin_approx += sign * term\n",
" \n",
" return sin_approx\n",
"\n",
" # Приближенное вычисление косинуса с помощью ряда Тейлора\n",
" def cos(self, angle_radians):\n",
" cos_approx = 0\n",
" \n",
" for n in range(self.precision):\n",
" sign = (-1) ** n\n",
" term = (angle_radians ** (2 * n)) / self.factorial(2 * n)\n",
" cos_approx += sign * term\n",
" \n",
" return cos_approx\n",
"\n",
" # Приближенное вычисление тангенса как sin/cos\n",
" def tan(self, angle_radians):\n",
" return self.sin(angle_radians) / self.cos(angle_radians)\n",
"\n",
" # Приближенное вычисление арксинуса с использованием метода Ньютона\n",
" def arcsin(self, value):\n",
" if value < -1 or value > 1:\n",
" return None # Арксинус определен только на отрезке [-1, 1]\n",
" \n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.sin(x) - value) / self.cos(x)\n",
" \n",
" return x\n",
"\n",
" # Арккосинус как pi/2 - арксинус\n",
" def arccos(self, value):\n",
" return self.pi / 2 - self.arcsin(value)\n",
"\n",
" # Приближенное вычисление арктангенса с использованием метода Ньютона\n",
" def arctan(self, value):\n",
" x = value\n",
" \n",
" for _ in range(self.precision):\n",
" x -= (self.tan(x) - value) / (1 + value ** 2)\n",
" \n",
" return x"
]
},
{
"cell_type": "code",
many
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"execution_count": 3,
first commit
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Угол (рад): 0.7853981633974483\n",
"sin: 0.7071067811865475\n",
"cos: 0.7071067811865475\n",
"tan: 1.0\n",
"arcsin (рад): 0.7853981633974483\n",
"arccos (рад): 0.7853981633974483\n",
"arctan (рад): 0.7853981633974483\n"
]
}
],
"source": [
"trig_functions = TrigFunctions(precision=10)\n",
"\n",
"angle = 3.141592653589793 / 4 # pi/4 радиан (45 градусов)\n",
"\n",
"sin_value = trig_functions.sin(angle)\n",
"cos_value = trig_functions.cos(angle)\n",
"tan_value = trig_functions.tan(angle)\n",
"arcsin_value = trig_functions.arcsin(sin_value)\n",
"arccos_value = trig_functions.arccos(cos_value)\n",
"arctan_value = trig_functions.arctan(tan_value)\n",
"\n",
"print(\"Угол (рад):\", angle)\n",
"print(\"sin:\", sin_value)\n",
"print(\"cos:\", cos_value)\n",
"print(\"tan:\", tan_value)\n",
"print(\"arcsin (рад):\", arcsin_value)\n",
"print(\"arccos (рад):\", arccos_value)\n",
"print(\"arctan (рад):\", arctan_value)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 1\n",
"\n",
"Представьте дерево показанное на рисунке с использованием списка из \n",
"списков. Выведите на печать корень дерева, а также его левое и правое \n",
"поддеревья."
]
},
{
"cell_type": "code",
many
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Корень: a\n",
"Левое поддерево: ['b', ['d', []], ['e', []]]\n",
"Правое поддерево: ['c', ['f', []]]\n"
]
}
],
"source": [
"tr = ['a', ['b', ['d', []], ['e', []]], ['c', ['f', []]]]\n",
"\n",
"print(f\"Корень: {tr[0]}\")\n",
"print(f\"Левое поддерево: {tr[1]}\")\n",
"print(f\"Правое поддерево: {tr[2]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2.2 Задание 2\n",
"\n",
"Дан класс, описывающий бинарное дерево. \n",
"\n",
"\n",
"```python\n",
"class Tree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data \n",
" def PrintTree(self): \n",
" print(self.data) \n",
"```\n",
"Реализуйте в классе функцию для вставки нового элемента в дерево по \n",
"следующим правилам: \n",
" \n",
"- Левое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Правое поддерево узла содержит только узлы со значениями меньше, \n",
"чем значение в узле. \n",
"- Каждое из левого и правого поддеревьев также должно быть \n",
"бинарным деревом поиска. \n",
"- Не должно быть повторяющихся узлов. \n",
"\n",
"Метод вставки сравнивает значение узла с родительским узлом и решает \n",
"куда доваить элемент (в левое или правое поддерево). Перепишите, метод \n",
"PrintTree для печати полной версии дерева."
]
},
{
"cell_type": "code",
many
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"class BinaryTree: \n",
" def __init__(self, data): \n",
" self.left = None \n",
" self.right = None \n",
" self.data = data\n",
"\n",
" def insert(self, data):\n",
" if data < self.data:\n",
" if self.left is None:\n",
" self.left = BinaryTree(data)\n",
" else:\n",
" self.left.insert(data)\n",
" elif data > self.data:\n",
" if self.right is None:\n",
" self.right = BinaryTree(data)\n",
" else:\n",
" self.right.insert(data)\n",
"\n",
" def Print(self, level=0):\n",
" if self.right:\n",
" self.right.Print(level + 1)\n",
" \n",
" print(' ' * 4 * level + '->', self.data)\n",
" if self.left:\n",
" self.left.Print(level + 1) "
]
},
{
"cell_type": "code",
many
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"execution_count": 18,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" -> 17\n",
" -> 15\n",
" -> 12\n",
"-> 10\n",
" -> 7\n",
" -> 5\n",
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" -> 3\n",
" -> 1\n"
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]
}
],
"source": [
"root = BinaryTree(10)\n",
"root.insert(5)\n",
many
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"root.insert(1)\n",
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"root.insert(15)\n",
many
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"root.insert(7)\n",
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"root.insert(3)\n",
"root.insert(7)\n",
"root.insert(12)\n",
"root.insert(17)\n",
"\n",
"root.Print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3.1 Задание\n",
"\n",
"Постройте классификатор на основе дерева принятия решений следующего датасета:\n",
"\n",
"```python\n",
"X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
"target = [0, 0, 0, 1, 1, 1]\n",
"```"
]
},
{
"cell_type": "code",
many
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"ds = pd.DataFrame(np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]))\n",
"target = [0, 0, 0, 1, 1, 1]"
]
},
{
"cell_type": "code",
many
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
"x_train, x_test, y_train, y_test = train_test_split(ds, target, test_size=0.2)"
]
},
{
"cell_type": "code",
many
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"[Text(0.5, 0.75, 'x[1] <= 0.0\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
" Text(0.25, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
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" Text(0.375, 0.5, 'True '),\n",
many
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" Text(0.75, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
first commit
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" Text(0.625, 0.5, ' False')]"
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},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
many
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"classifier = DecisionTreeClassifier()\n",
"classifier.fit(x_train, y_train)\n",
"\n",
"tree.plot_tree(classifier)"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
many
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"[1 1]\n",
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"[[2]]\n",
" precision recall f1-score support\n",
"\n",
many
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" 1 1.00 1.00 1.00 2\n",
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"\n",
" accuracy 1.00 2\n",
" macro avg 1.00 1.00 1.00 2\n",
"weighted avg 1.00 1.00 1.00 2\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
many
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"/home/nktkln/.local/lib/python3.12/site-packages/sklearn/metrics/_classification.py:409: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels.\n",
first commit
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" warnings.warn(\n"
]
}
],
"source": [
"y_pred = classifier.predict(x_test)\n",
"\n",
"print(y_pred)\n",
"print(confusion_matrix(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4.1 Задание\n",
"\n",
"Задание. Постройте модель регрессии для данных из предыдущей рабочей \n",
"тетради.Для примера можно взять потребления газа (в миллионах \n",
"галлонов) в 48 штатах США или набор данных о качестве красного вина: \n",
"https://raw.githubusercontent.com/likarajo/petrol_consumption/master/data/petrol_consumption.csv \n",
"https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv \n",
"\n",
"Постройте прогноз. Оцените точность модели."
]
},
{
"cell_type": "code",
many
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
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" <td>1599.000000</td>\n",
" <td>1599.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>8.319637</td>\n",
" <td>0.527821</td>\n",
" <td>0.270976</td>\n",
" <td>2.538806</td>\n",
" <td>0.087467</td>\n",
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" <td>46.467792</td>\n",
" <td>0.996747</td>\n",
" <td>3.311113</td>\n",
" <td>0.658149</td>\n",
" <td>10.422983</td>\n",
" <td>5.636023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.741096</td>\n",
" <td>0.179060</td>\n",
" <td>0.194801</td>\n",
" <td>1.409928</td>\n",
" <td>0.047065</td>\n",
" <td>10.460157</td>\n",
" <td>32.895324</td>\n",
" <td>0.001887</td>\n",
" <td>0.154386</td>\n",
" <td>0.169507</td>\n",
" <td>1.065668</td>\n",
" <td>0.807569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.600000</td>\n",
" <td>0.120000</td>\n",
" <td>0.000000</td>\n",
" <td>0.900000</td>\n",
" <td>0.012000</td>\n",
" <td>1.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.990070</td>\n",
" <td>2.740000</td>\n",
" <td>0.330000</td>\n",
" <td>8.400000</td>\n",
" <td>3.000000</td>\n",
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" <td>1.900000</td>\n",
" <td>0.070000</td>\n",
" <td>7.000000</td>\n",
" <td>22.000000</td>\n",
" <td>0.995600</td>\n",
" <td>3.210000</td>\n",
" <td>0.550000</td>\n",
" <td>9.500000</td>\n",
" <td>5.000000</td>\n",
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" <td>7.900000</td>\n",
" <td>0.520000</td>\n",
" <td>0.260000</td>\n",
" <td>2.200000</td>\n",
" <td>0.079000</td>\n",
" <td>14.000000</td>\n",
" <td>38.000000</td>\n",
" <td>0.996750</td>\n",
" <td>3.310000</td>\n",
" <td>0.620000</td>\n",
" <td>10.200000</td>\n",
" <td>6.000000</td>\n",
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" <tr>\n",
" <th>75%</th>\n",
" <td>9.200000</td>\n",
" <td>0.640000</td>\n",
" <td>0.420000</td>\n",
" <td>2.600000</td>\n",
" <td>0.090000</td>\n",
" <td>21.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.997835</td>\n",
" <td>3.400000</td>\n",
" <td>0.730000</td>\n",
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" <td>289.000000</td>\n",
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" <td>4.010000</td>\n",
" <td>2.000000</td>\n",
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many
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" fixed acidity volatile acidity citric acid residual sugar \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
first commit
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"mean 8.319637 0.527821 0.270976 2.538806 \n",
"std 1.741096 0.179060 0.194801 1.409928 \n",
"min 4.600000 0.120000 0.000000 0.900000 \n",
"25% 7.100000 0.390000 0.090000 1.900000 \n",
"50% 7.900000 0.520000 0.260000 2.200000 \n",
"75% 9.200000 0.640000 0.420000 2.600000 \n",
"max 15.900000 1.580000 1.000000 15.500000 \n",
"\n",
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" chlorides free sulfur dioxide total sulfur dioxide density \\\n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
first commit
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"mean 0.087467 15.874922 46.467792 0.996747 \n",
"std 0.047065 10.460157 32.895324 0.001887 \n",
"min 0.012000 1.000000 6.000000 0.990070 \n",
"25% 0.070000 7.000000 22.000000 0.995600 \n",
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"75% 0.090000 21.000000 62.000000 0.997835 \n",
"max 0.611000 72.000000 289.000000 1.003690 \n",
"\n",
" pH sulphates alcohol quality \n",
"count 1599.000000 1599.000000 1599.000000 1599.000000 \n",
"mean 3.311113 0.658149 10.422983 5.636023 \n",
"std 0.154386 0.169507 1.065668 0.807569 \n",
"min 2.740000 0.330000 8.400000 3.000000 \n",
"25% 3.210000 0.550000 9.500000 5.000000 \n",
"50% 3.310000 0.620000 10.200000 6.000000 \n",
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]
},
many
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = 'https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv'\n",
"\n",
"ds = pd.read_csv(url)\n",
"\n",
"ds.describe()"
]
},
{
"cell_type": "code",
many
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"execution_count": 12,
first commit
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"metadata": {},
"outputs": [],
"source": [
"X = ds.iloc[:, :-1].values\n",
"y = ds.iloc[:, -1].values\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
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first commit
2024-09-23 23:22:33 +00:00
]
},
many
2024-09-27 05:31:03 +00:00
"execution_count": 13,
first commit
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"regressor = DecisionTreeRegressor()\n",
"regressor.fit(X_train, y_train)\n",
"tree.plot_tree(regressor)"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Actual Predicted\n",
"0 6 5.0\n",
"1 5 6.0\n",
"2 7 7.0\n",
"3 6 5.0\n",
"4 5 5.0\n",
".. ... ...\n",
"315 6 6.0\n",
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"316 4 6.0\n",
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"317 5 5.0\n",
"318 4 5.0\n",
"319 6 7.0\n",
"\n",
"[320 rows x 2 columns]\n"
]
}
],
"source": [
"y_pred = regressor.predict(X_test)\n",
"df = pd.DataFrame({'Actual':y_test, 'Predicted':y_pred})\n",
"print(df)"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"MSE: 0.675\n",
"MAE: 0.475\n"
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]
},
{
"data": {
"text/plain": [
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"np.float64(8.427929427430094)"
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]
},
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"execution_count": 15,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('MSE:', metrics.mean_squared_error(y_test, y_pred))\n",
"print('MAE:', metrics.mean_absolute_error(y_test, y_pred))\n",
"\n",
"metrics.mean_absolute_error(y_test, y_pred) / np.average(y) * 100"
]
}
],
"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",
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"version": "3.12.5"
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}
},
"nbformat": 4,
"nbformat_minor": 2
}
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