From ed5808d8b4083717c2988cb58afc561c6719703c Mon Sep 17 00:00:00 2001
From: Adrian Szczeszek <adrszc8@st.amu.edu.pl>
Date: Tue, 17 Oct 2023 17:30:25 +0200
Subject: [PATCH] Pierwsza wersja pliku

---
 SysInf.ipynb | 656 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 656 insertions(+)
 create mode 100644 SysInf.ipynb

diff --git a/SysInf.ipynb b/SysInf.ipynb
new file mode 100644
index 0000000..d0c42a2
--- /dev/null
+++ b/SysInf.ipynb
@@ -0,0 +1,656 @@
+{
+ "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": [
+    {
+     "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>0</th>\n",
+       "      <th>6</th>\n",
+       "      <th>8</th>\n",
+       "      <th>19</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>309000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>43.44</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>314900.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>42.60</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>249000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44.30</td>\n",
+       "      <td>1960.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>419000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>88</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>499000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>77</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>507600.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1914.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>342400.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>53.50</td>\n",
+       "      <td>1999.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>335000.0</td>\n",
+       "      <td>3</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</td>\n",
+       "      <td>62</td>\n",
+       "      <td>1930.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2546</th>\n",
+       "      <td>1990000.0</td>\n",
+       "      <td>więcej niż 10</td>\n",
+       "      <td>392</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2547 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             0              6      8       19\n",
+       "0      309000.0              2  43.44     NaN\n",
+       "1      314900.0              2  42.60     NaN\n",
+       "2      249000.0              2  44.30  1960.0\n",
+       "3      419000.0              4     88     NaN\n",
+       "4      499000.0              3     77     NaN\n",
+       "...         ...            ...    ...     ...\n",
+       "2542   507600.0              4     94  1914.0\n",
+       "2543   342400.0              2  53.50  1999.0\n",
+       "2544   335000.0              3  55.25  1910.0\n",
+       "2545   260000.0              3     62  1930.0\n",
+       "2546  1990000.0  więcej niż 10    392     NaN\n",
+       "\n",
+       "[2547 rows x 4 columns]"
+      ]
+     },
+     "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": [
+    {
+     "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>cena</th>\n",
+       "      <th>pokoje</th>\n",
+       "      <th>metraz</th>\n",
+       "      <th>rok</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>249000.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>44.30</td>\n",
+       "      <td>1960.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>449000.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>92.00</td>\n",
+       "      <td>1930.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>389000.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>63.60</td>\n",
+       "      <td>1985.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>260000.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>44.20</td>\n",
+       "      <td>1970.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>330682.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>48.99</td>\n",
+       "      <td>2019.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2541</th>\n",
+       "      <td>383680.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>70.40</td>\n",
+       "      <td>2016.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2542</th>\n",
+       "      <td>507600.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>94.00</td>\n",
+       "      <td>1914.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2543</th>\n",
+       "      <td>342400.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>53.50</td>\n",
+       "      <td>1999.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2544</th>\n",
+       "      <td>335000.0</td>\n",
+       "      <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-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "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": 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",
+      "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
+}