From f6e8f0710a122085e4297e2cb4ade6157f2ec99d Mon Sep 17 00:00:00 2001
From: Szymon Obst <s478993@labs.wmi.amu.edu.pl>
Date: Tue, 17 Oct 2023 18:50:02 +0200
Subject: [PATCH] integracja z gui

---
 Gui.py           |  79 +++++++++++++++++++++++
 SysInf.ipynb     | 164 +++++++++++++++++++++++++----------------------
 mieszkania5      |   1 +
 mieszkania_model | Bin 0 -> 572 bytes
 model.py         |   0
 5 files changed, 168 insertions(+), 76 deletions(-)
 create mode 100644 Gui.py
 create mode 160000 mieszkania5
 create mode 100644 mieszkania_model
 create mode 100644 model.py

diff --git a/Gui.py b/Gui.py
new file mode 100644
index 0000000..9b1074e
--- /dev/null
+++ b/Gui.py
@@ -0,0 +1,79 @@
+# Import Module
+from tkinter import *
+import pickle
+import pandas as pd
+import numpy as np
+
+#Styl
+tlo = "grey"
+guziki = "#60839D"
+
+# Create Object
+root = Tk()
+root.geometry('1000x600')
+root.configure(bg=tlo)
+
+def update():
+    dane = [address1.__getattribute__,address2.__getattribute__,address3.__getattribute__]
+    return None
+
+#Wczytanie modelu
+model = pickle.load(open('mieszkania_model', 'rb'))
+
+def cena_mieszkania(pokoje, metraz, rok):
+    mieszkanie = pd.DataFrame([[pokoje, metraz, rok]], columns=['pokoje', 'metraz', 'rok'])
+    cena = model.predict(mieszkanie).item()
+    return np.round(cena, 2)
+
+# Funkcja tworzy nowe okno ktore wyswietla predykcje ceny
+def Oblicz():
+    newWindow = Toplevel(root)
+    newWindow.geometry('600x300')
+    newWindow.title("wynik")
+    newWindow.config(bg=tlo)
+    
+    # okno
+    T4 = Label(newWindow,bg=tlo,fg="white",height=3,width=37, text = "Potencjalna cena mieszkania:", bd = 0, relief=RAISED).place(x = 100, y= 50)
+    address1 = Label(newWindow,width=37,height=3,bg=guziki,text = wyznacz_cene()).place(x = 100, y= 100)
+    update()
+    return None
+
+def reset():
+    return None
+
+Button(root,text="Oblicz",font="arial 20 bold",command=Oblicz ).place(x= 800, y=500)
+
+#Pola do wpisywania 
+
+
+# metraż
+
+address1 = Text(root,width=37,height=3,bg=guziki)
+address1.place(x = 100, y= 100)
+T = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Liczba Pokoi", bd = 0, relief=RAISED).place(x = 100, y= 50)
+
+
+# liczba pokoi
+address2 = Text(root,width=37,height=3,bg=guziki)
+address2.place(x = 100, y= 200)
+
+T2 = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Metraż", bd = 0, relief=RAISED).place(x = 100, y= 150)
+
+# Rok budowy
+address3 = Text(root,width=37,height=3,bg=guziki)
+address3.place(x = 100, y= 300)
+
+T3 = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Rok Budowy", bd = 0, relief=RAISED).place(x = 100, y= 250)
+
+Output = Text(root, height = 5, 
+              width = 25, 
+              bg = tlo)
+
+def wyznacz_cene():
+    pokoje = address1.get("1.0",END)
+    metraz = address2.get("1.0",END)
+    rok = address3.get("1.0",END)
+    return cena_mieszkania(pokoje,metraz,rok)
+
+# Execute Tkinter
+root.mainloop()
\ No newline at end of file
diff --git a/SysInf.ipynb b/SysInf.ipynb
index 14f6dc1..cb489b8 100644
--- a/SysInf.ipynb
+++ b/SysInf.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +452,7 @@
        "[2547 rows x 26 columns]"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -470,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -865,7 +865,7 @@
        "[462 rows x 25 columns]"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -979,7 +979,7 @@
        "[462 rows x 1 columns]"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1006,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1392,7 +1392,7 @@
        "[418 rows x 25 columns]"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1410,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1419,7 +1419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1428,7 +1428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1437,7 +1437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1453,7 +1453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1462,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1471,7 +1471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1494,7 +1494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1503,7 +1503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1512,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1521,7 +1521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1530,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1540,7 +1540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1643,7 +1643,7 @@
        "max    6.000000e+06    11.000000   404.000000  2022.000000"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1654,7 +1654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1784,7 +1784,7 @@
        "[1763 rows x 4 columns]"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1802,7 +1802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1905,7 +1905,7 @@
        "max    2.318580e+06    6.000000  234.200000  2021.000000"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1916,7 +1916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1925,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1934,7 +1934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1943,7 +1943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1952,7 +1952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1962,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -2092,7 +2092,7 @@
        "[320 rows x 4 columns]"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2110,7 +2110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2119,7 +2119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2128,7 +2128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2138,7 +2138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -2241,7 +2241,7 @@
        "max    6.000000e+06    11.000000   404.000000  2022.000000"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2259,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2275,7 +2275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2285,7 +2285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2295,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2311,7 +2311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2320,19 +2320,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+       "<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": 158,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2350,7 +2350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2359,7 +2359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2368,7 +2368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -2377,7 +2377,7 @@
        "0.6264197107092035"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2388,7 +2388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -2397,7 +2397,7 @@
        "22930468691.882328"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2408,7 +2408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2417,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -2426,7 +2426,7 @@
        "0.5872189061243203"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2437,7 +2437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -2446,7 +2446,7 @@
        "12603444942.321444"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2464,7 +2464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2473,7 +2473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -2567,7 +2567,7 @@
        "[292 rows x 1 columns]"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2586,7 +2586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2602,7 +2602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -2633,7 +2633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2664,7 +2664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -2695,7 +2695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2726,7 +2726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -2757,7 +2757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -2788,7 +2788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2800,7 +2800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2809,7 +2809,7 @@
        "184531.94"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2817,6 +2817,18 @@
    "source": [
     "cena_mieszkania(pokoje=2, metraz=25, rok=2000)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Zapisanie modelu do pliku\n",
+    "import pickle\n",
+    "filename = 'mieszkania_model'\n",
+    "pickle.dump(model, open(filename, 'wb'))\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/mieszkania5 b/mieszkania5
new file mode 160000
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diff --git a/model.py b/model.py
new file mode 100644
index 0000000..e69de29