integracja z gui
This commit is contained in:
Szymon Obst
parent
73884bb32b
commit
f6e8f0710a
79
Gui.py
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79
Gui.py
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# Import Module
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from tkinter import *
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import pickle
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import pandas as pd
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import numpy as np
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#Styl
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tlo = "grey"
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guziki = "#60839D"
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# Create Object
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root = Tk()
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root.geometry('1000x600')
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root.configure(bg=tlo)
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def update():
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dane = [address1.__getattribute__,address2.__getattribute__,address3.__getattribute__]
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return None
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#Wczytanie modelu
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model = pickle.load(open('mieszkania_model', 'rb'))
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def cena_mieszkania(pokoje, metraz, rok):
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mieszkanie = pd.DataFrame([[pokoje, metraz, rok]], columns=['pokoje', 'metraz', 'rok'])
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cena = model.predict(mieszkanie).item()
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return np.round(cena, 2)
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# Funkcja tworzy nowe okno ktore wyswietla predykcje ceny
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def Oblicz():
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newWindow = Toplevel(root)
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newWindow.geometry('600x300')
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newWindow.title("wynik")
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newWindow.config(bg=tlo)
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# okno
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T4 = Label(newWindow,bg=tlo,fg="white",height=3,width=37, text = "Potencjalna cena mieszkania:", bd = 0, relief=RAISED).place(x = 100, y= 50)
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address1 = Label(newWindow,width=37,height=3,bg=guziki,text = wyznacz_cene()).place(x = 100, y= 100)
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update()
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return None
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def reset():
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return None
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Button(root,text="Oblicz",font="arial 20 bold",command=Oblicz ).place(x= 800, y=500)
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#Pola do wpisywania
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# metraż
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address1 = Text(root,width=37,height=3,bg=guziki)
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address1.place(x = 100, y= 100)
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T = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Liczba Pokoi", bd = 0, relief=RAISED).place(x = 100, y= 50)
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# liczba pokoi
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address2 = Text(root,width=37,height=3,bg=guziki)
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address2.place(x = 100, y= 200)
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T2 = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Metraż", bd = 0, relief=RAISED).place(x = 100, y= 150)
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# Rok budowy
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address3 = Text(root,width=37,height=3,bg=guziki)
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address3.place(x = 100, y= 300)
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T3 = Label(root,bg=tlo,fg="white",height=3,width=37, text = "Rok Budowy", bd = 0, relief=RAISED).place(x = 100, y= 250)
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Output = Text(root, height = 5,
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width = 25,
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bg = tlo)
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def wyznacz_cene():
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pokoje = address1.get("1.0",END)
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metraz = address2.get("1.0",END)
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rok = address3.get("1.0",END)
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return cena_mieszkania(pokoje,metraz,rok)
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# Execute Tkinter
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root.mainloop()
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164
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},
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{
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"cell_type": "code",
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"execution_count": 118,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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{
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},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"[2547 rows x 26 columns]"
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]
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},
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"execution_count": 121,
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"execution_count": 12,
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"metadata": {},
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}
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},
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{
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"metadata": {},
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{
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"[462 rows x 25 columns]"
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]
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},
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"execution_count": 123,
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"execution_count": 14,
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"metadata": {},
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{
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{
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"cell_type": "code",
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"execution_count": 125,
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"execution_count": 16,
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"metadata": {},
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{
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"[462 rows x 1 columns]"
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]
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},
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"execution_count": 125,
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 126,
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"metadata": {},
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{
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"execution_count": 127,
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{
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"execution_count": 127,
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"metadata": {},
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"outputs": [
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{
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"max 6.000000e+06 11.000000 404.000000 2022.000000"
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]
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},
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"execution_count": 140,
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"execution_count": 31,
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"metadata": {},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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"[1763 rows x 4 columns]"
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]
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},
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"execution_count": 141,
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"execution_count": 32,
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"metadata": {},
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"metadata": {},
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"outputs": [
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{
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"max 2.318580e+06 6.000000 234.200000 2021.000000"
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]
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},
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"execution_count": 142,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"metadata": {},
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{
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"metadata": {},
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"metadata": {},
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"outputs": [
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{
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]
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"execution_count": 148,
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"metadata": {},
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}
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},
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{
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{
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]
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{
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{
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{
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{
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{
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"<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"
|
||||
}
|
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@ -2350,7 +2350,7 @@
|
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},
|
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{
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"cell_type": "code",
|
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"execution_count": 159,
|
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"execution_count": 50,
|
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"metadata": {},
|
||||
"outputs": [],
|
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"source": [
|
||||
@ -2359,7 +2359,7 @@
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},
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{
|
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"cell_type": "code",
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"execution_count": 160,
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"execution_count": 51,
|
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"metadata": {},
|
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"outputs": [],
|
||||
"source": [
|
||||
@ -2368,7 +2368,7 @@
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},
|
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{
|
||||
"cell_type": "code",
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"execution_count": 161,
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"execution_count": 52,
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"metadata": {},
|
||||
"outputs": [
|
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{
|
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@ -2377,7 +2377,7 @@
|
||||
"0.6264197107092035"
|
||||
]
|
||||
},
|
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"execution_count": 161,
|
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"execution_count": 52,
|
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"metadata": {},
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"output_type": "execute_result"
|
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}
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@ -2388,7 +2388,7 @@
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 162,
|
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"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2397,7 +2397,7 @@
|
||||
"22930468691.882328"
|
||||
]
|
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},
|
||||
"execution_count": 162,
|
||||
"execution_count": 53,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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@ -2408,7 +2408,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 163,
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"execution_count": 54,
|
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
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@ -2417,7 +2417,7 @@
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 164,
|
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"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -2426,7 +2426,7 @@
|
||||
"0.5872189061243203"
|
||||
]
|
||||
},
|
||||
"execution_count": 164,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
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"output_type": "execute_result"
|
||||
}
|
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@ -2437,7 +2437,7 @@
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},
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{
|
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"cell_type": "code",
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"execution_count": 165,
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"execution_count": 56,
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"metadata": {},
|
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"outputs": [
|
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{
|
||||
@ -2446,7 +2446,7 @@
|
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"12603444942.321444"
|
||||
]
|
||||
},
|
||||
"execution_count": 165,
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
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}
|
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@ -2464,7 +2464,7 @@
|
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},
|
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{
|
||||
"cell_type": "code",
|
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"execution_count": 166,
|
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"execution_count": 57,
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
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@ -2473,7 +2473,7 @@
|
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},
|
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{
|
||||
"cell_type": "code",
|
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"execution_count": 167,
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [
|
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{
|
||||
@ -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": {
|
||||
|
||||
1
mieszkania5
Submodule
1
mieszkania5
Submodule
@ -0,0 +1 @@
|
||||
Subproject commit 242c10da9f5aa9be2878f9ca555eb4d4833f4710
|
||||
BIN
mieszkania_model
Normal file
BIN
mieszkania_model
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user