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mieszkania5/SysInf.ipynb

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Pierwsza wersja pliku
2023-10-17 17:30:25 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
]
}
],
"source": [
"!git clone git://gonito.net/mieszkania5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'j:\\\\Desktop\\\\SysInf\\\\mieszkania5\\\\train\\\\train.tsv'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = r'j:\\Desktop\\SysInf\\mieszkania5\\train\\train.tsv'\n",
"train"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data=pd.read_csv(train, sep='\\t', header=None)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25],\n",
" dtype='int64')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"x_train = data[[0,6,8,19]]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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" <th>2546</th>\n",
" <td>1990000.0</td>\n",
" <td>więcej niż 10</td>\n",
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" 0 6 8 19\n",
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"3 419000.0 4 88 NaN\n",
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"... ... ... ... ...\n",
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"2545 260000.0 3 62 1930.0\n",
"2546 1990000.0 więcej niż 10 392 NaN\n",
"\n",
"[2547 rows x 4 columns]"
]
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.replace( \"więcej niż 10\", '11')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train[(x_train[19] >= 1800) | (x_train[19] <= 2023)]\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train.rename(columns = {0:\"cena\", 6:\"pokoje\", 8:\"metraz\", 19:\"rok\"})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"x_train = x_train[x_train[\"metraz\"] != '6 909']\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"x_train[\"pokoje\"] = x_train[\"pokoje\"].astype(float)\n",
"x_train[\"metraz\"] = x_train[\"metraz\"].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cena</th>\n",
" <th>pokoje</th>\n",
" <th>metraz</th>\n",
" <th>rok</th>\n",
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" </thead>\n",
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" <td>1985.0</td>\n",
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" <th>9</th>\n",
" <td>260000.0</td>\n",
" <td>3.0</td>\n",
" <td>44.20</td>\n",
" <td>1970.0</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>330682.0</td>\n",
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" <td>1999.0</td>\n",
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" <th>2544</th>\n",
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" <td>3.0</td>\n",
" <td>55.25</td>\n",
" <td>1910.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2545</th>\n",
" <td>260000.0</td>\n",
" <td>3.0</td>\n",
" <td>62.00</td>\n",
" <td>1930.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1767 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" cena pokoje metraz rok\n",
"2 249000.0 2.0 44.30 1960.0\n",
"6 449000.0 4.0 92.00 1930.0\n",
"7 389000.0 3.0 63.60 1985.0\n",
"9 260000.0 3.0 44.20 1970.0\n",
"10 330682.0 3.0 48.99 2019.0\n",
"... ... ... ... ...\n",
"2541 383680.0 3.0 70.40 2016.0\n",
"2542 507600.0 4.0 94.00 1914.0\n",
"2543 342400.0 2.0 53.50 1999.0\n",
"2544 335000.0 3.0 55.25 1910.0\n",
"2545 260000.0 3.0 62.00 1930.0\n",
"\n",
"[1767 rows x 4 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"x = x_train[[\"pokoje\", \"metraz\", \"rok\"]]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"y = x_train[[\"cena\"]]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"model = LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-r
],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-3.54305400e+04, 7.47165498e+03, -2.77294899e+00]])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.coef_"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1ea4fa86bc0>]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x[\"pokoje\"],y, 'o')\n",
"plt.plot(x[\"pokoje\"],model.predict(x), '--')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1ea50bcb940>]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x[\"rok\"],y, 'o')\n",
"plt.plot(x[\"rok\"],model.predict(x), '-')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1ea50b570a0>]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGsCAYAAACB/u5dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABAtklEQVR4nO3deXxU9b3/8fdMVrYkBAxJFEIAESOCymZArApqkKJoF691oWqxYqiithfRWqReG/219lpbS1sXLKVK1SuIiHFBAZcgClLAKAKGpZCAEEgISEhmvr8/xgmZZCaZSc5MZnk9H488hDNnZr7HSTjvfJfP12aMMQIAALCAvaMbAAAAogfBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYpsOCxapVqzRp0iRlZ2fLZrNp8eLFAb+GMUa/+93vNHDgQCUlJenkk0/WQw89ZH1jAQCAX+I76o2PHDmioUOH6qabbtJVV13Vpte444479Oabb+p3v/udzjzzTFVWVqqystLilgIAAH/ZwmETMpvNpkWLFmny5MkNx2pra3Xffffp+eef16FDhzR48GA98sgjuuCCCyRJn3/+uYYMGaJNmzbptNNO65iGAwAAD2E7x2L69OkqKSnRwoULtWHDBv3gBz9QQUGBtmzZIkl69dVX1a9fPy1dulS5ubnq27evfvKTn9BjAQBABwrLYLFz507NmzdPL774osaOHav+/fvr5z//uc477zzNmzdPkvTVV19px44devHFFzV//nw9++yzWrt2rb7//e93cOsBAIhdHTbHoiUbN26Uw+HQwIEDPY7X1taqR48ekiSn06na2lrNnz+/4bynn35aw4YN0+bNmxkeAQCgA4RlsKipqVFcXJzWrl2ruLg4j8e6du0qScrKylJ8fLxH+Dj99NMluXo8CBYAAIReWAaLs88+Ww6HQ/v27dPYsWO9njNmzBjV19dr27Zt6t+/vyTpyy+/lCTl5OSErK0AAOCEDlsVUlNTo61bt0pyBYnf//73uvDCC5Wenq4+ffrouuuu0wcffKBHH31UZ599tr7++mstX75cQ4YM0cSJE+V0OjVixAh17dpVjz32mJxOpwoLC5WSkqI333yzIy4JAICY12HBYsWKFbrwwgubHZ8yZYqeffZZ1dXV6X/+5380f/587d69Wz179tS5556rOXPm6Mwzz5Qk7dmzRz/72c/05ptvqkuXLpowYYIeffRRpaenh/pyAACAwqSOBQAAiA5hudwUAABEJoIFAACwTMhXhTidTu3Zs0fdunWTzWYL9dsDAIA2MMbo8OHDys7Olt3uu18i5MFiz5496t27d6jfFgAAWGDXrl065ZRTfD4e8mDRrVs3Sa6GpaSkhPrtAQBAG1RXV6t3794N93FfQh4s3MMfKSkpBAsAACJMa9MYmLwJAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFgm5AWyAADRx+E0WlNWqX2HjymjW7JG5qYrzs5+ULEo4GCxe/duzZw5U6+//rqOHj2qAQMGaN68eRo+fHgw2gcACHPFm8o159VSlVcdaziWlZqs2ZPyVDA4qwNbho4Q0FDIwYMHNWbMGCUkJOj1119XaWmpHn30UXXv3j1Y7QMAhLHiTeWatmCdR6iQpIqqY5q2YJ2KN5V3UMvQUQLqsXjkkUfUu3dvzZs3r+FYbm6u5Y0CAIQ/h9NozqulMl4eM5Jskua8WqqL8zIZFokhAfVYLFmyRMOHD9cPfvADZWRk6Oyzz9aTTz7Z4nNqa2tVXV3t8QUAiHxryiqb9VQ0ZiSVVx3TmrLK0DUKHS6gYPHVV19p7ty5OvXUU/XGG29o2rRpuv322/X3v//d53OKioqUmpra8NW7d+92NxoA0PH2HfYdKtpyHqKDzRjjrRfLq8TERA0fPlwffvhhw7Hbb79dH3/8sUpKSrw+p7a2VrW1tQ1/d+/nXlVVxbbpABDBSrYd0DVPrm71vOennqv8/j1C0CIEU3V1tVJTU1u9fwfUY5GVlaW8vDyPY6effrp27tzp8zlJSUlKSUnx+AIARL6RuenKSk2Wr9kTNrlWh4zMTQ9ls9DBAgoWY8aM0ebNmz2Offnll8rJybG0UQCA8Bdnt2n2JNcvm03DhfvvsyflMXEzxgQULO68806tXr1av/nNb7R161Y999xz+tvf/qbCwsJgtQ8AEMYKBmdp7nXnKDM12eN4Zmqy5l53DnUsYlBAcywkaenSpZo1a5a2bNmi3Nxc3XXXXZo6darfz/d3jAYAEDmovBn9/L1/Bxws2otgAQBA5AnK5E0AAICWECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYJqBg8cADD8hms3l8DRo0KFhtAwAAESY+0CecccYZevvtt0+8QHzALwEAAKJUwKkgPj5emZmZwWgLAACIcAHPsdiyZYuys7PVr18/XXvttdq5c2eL59fW1qq6utrjCwAARKeAgsWoUaP07LPPqri4WHPnzlVZWZnGjh2rw4cP+3xOUVGRUlNTG7569+7d7kYDAIDwZDPGmLY++dChQ8rJydHvf/973XzzzV7Pqa2tVW1tbcPfq6ur1bt3b1VVVSklJaWtbw0AAEKourpaqamprd6/2zXzMi0tTQMHDtTWrVt9npOUlKSkpKT2vA0AAIgQ7apjUVNTo23btikrK8uq9gAAgAgWULD4+c9/rpUrV2r79u368MMPdeWVVyouLk7XXHNNsNoHAAAiSEBDIf/5z390zTXX6MCBAzrppJN03nnnafXq1TrppJOC1T4AABBBAgoWCxcuDFY7AABAFGCvEAAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMsQLAAAgGUIFgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBYAAMAyBAsAAGAZggUAALAMwQIAAFiGYAEAACxDsAAAAJYhWAAAAMu0K1g8/PDDstlsmjFjhkXNAQAAkazNweLjjz/WX//6Vw0ZMsTK9gAAgAjWpmBRU1Oja6+9Vk8++aS6d+9udZsAAECEalOwKCws1MSJEzV+/PhWz62trVV1dbXHFwAAiE7xgT5h4cKFWrdunT7++GO/zi8qKtKcOXMCbhgAAIg8AfVY7Nq1S3fccYf++c9/Kjk52a/nzJo1S1VVVQ1fu3btalNDAQBA+LMZY4y/Jy9evFhXXnml4uLiGo45HA7ZbDbZ7XbV1tZ6POZNdXW1UlNTVVVVpZSUlLa3HAAAhIy/9++AhkLGjRunjRs3ehy78cYbNWjQIM2cObPVUAEAAKJbQMGiW7duGjx4sMexLl26qEePHs2OAwCA2EPlTQAAYJmAV4U0tWLFCguaAQAAogE9FgAAwDIECwAAYBmCBQAAsAzBAgAAWIZgAQAALEOwAAAAliFYAAAAyxAsAACAZQgWAADAMgQLAABgGYIFAACwDMECAABYhmABAAAsQ7AAAACWIVgAAADLECwAAIBlCBY
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x[\"metraz\"],y, 'o')\n",
"plt.plot(x[\"metraz\"],model.predict(x), 'o')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[330020.67101959],\n",
" [615640.72224431],\n",
" [438723.7484912 ],\n",
" ...,\n",
" [398651.75187026],\n",
" [376543.40054119],\n",
" [426921.61270958]])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(x)"
]
}
],
"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": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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