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48 Commits

Author SHA1 Message Date
02ae6a2188 Change X to X_train 2023-10-28 14:45:36 +02:00
65d5118fa0 Import mae 2023-10-28 14:34:26 +02:00
Julia Krzemien
01b9d67075 saved predictins in output file1 2023-10-28 14:27:10 +02:00
Julia Krzemien
588ce8889a saved predictins in output file 2023-10-28 14:25:18 +02:00
Julia Krzemien
75f77df1b6 . 2023-10-24 18:56:59 +02:00
f0bb3d56f2 Update 'Jenkinsfile' 2023-10-24 18:55:05 +02:00
Julia Krzemien
0088e5e9e4 model modification 2023-10-24 18:54:39 +02:00
4db79f51e0 Update 'Jenkinsfile' 2023-10-24 18:54:07 +02:00
5d04e2d7de Update 'Jenkinsfile' 2023-10-24 18:52:43 +02:00
c5dbe27db7 Update 'Jenkinsfile' 2023-10-24 18:51:56 +02:00
8cf30759c5 Update 'Jenkinsfile' 2023-10-24 18:51:20 +02:00
6280df0a8e Update 'Jenkinsfile' 2023-10-24 18:49:07 +02:00
a559ece77d Update 'Jenkinsfile' 2023-10-24 18:47:56 +02:00
2a9f8b73a5 Update 'Jenkinsfile' 2023-10-24 18:47:13 +02:00
002112f98e Update 'Jenkinsfile' 2023-10-24 18:43:36 +02:00
33a4bb1a6b Update 'Jenkinsfile' 2023-10-24 18:43:07 +02:00
05d24b1fb5 Update 'Jenkinsfile' 2023-10-24 18:41:27 +02:00
bce58adc1d Update 'Jenkinsfile' 2023-10-24 18:40:20 +02:00
736a5249ab Update 'Jenkinsfile' 2023-10-24 18:39:20 +02:00
5d85dc7e5d add black formater to requirements 2023-10-24 18:38:34 +02:00
84503fc245 Update 'Jenkinsfile' 2023-10-24 18:33:43 +02:00
olgkwo
5da3a2e872 jenkins test 2 2023-10-24 18:24:14 +02:00
olgkwo
e384e57ceb add empty commit to test jenkins 2023-10-24 18:14:23 +02:00
bc18ce19fa Update 'Jenkinsfile' 2023-10-24 18:06:48 +02:00
b7479d6178 Update 'Jenkinsfile' 2023-10-24 17:51:47 +02:00
a8becd1294 Add jenkinsfile 2023-10-24 17:44:18 +02:00
566a6e4199 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:50:08 +02:00
7f318bbb75 Add picture to GUI 2023-10-17 18:49:55 +02:00
de713e32eb Updated README 2023-10-17 18:48:49 +02:00
a0f2f42007 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:41:41 +02:00
72a4ef2e20 Modyfikacje kosmetyczne 2023-10-17 18:41:36 +02:00
1b3ec830e1 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:40:57 +02:00
d1c42a84a4 Add requirements file for pip 2023-10-17 18:40:44 +02:00
d6706a3a78 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:40:40 +02:00
2a315b6aaf Modyfikacja podręczniku użytkownika 2023-10-17 18:40:33 +02:00
3998119cad Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:36:23 +02:00
6d3a4d9dc1 Integrate regression model 2023-10-17 18:36:06 +02:00
olgkwo
5704b9c9e0 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:32:52 +02:00
olgkwo
52bd08a35f delete report file 2023-10-17 18:32:07 +02:00
68409d7488 Merge remote-tracking branch 'origin/poprawki_ML' 2023-10-17 18:18:16 +02:00
524dd9d8c2 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:16:55 +02:00
d4f6dbc400 Zmiana kolorów 2023-10-17 18:10:27 +02:00
cdeece0a59 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 18:08:44 +02:00
4f4bf3c48b Czynności i podrecznik uzytkownika 2023-10-17 18:08:34 +02:00
6407d2b4ef Dodanie zabezpieczen na liczbę cyfr pięter oraz metrażu 2023-10-17 18:04:34 +02:00
cb3ce74c98 Merge branch 'master' of https://git.wmi.amu.edu.pl/s452979/SysInf_grupa_B_lab_2 2023-10-17 17:50:49 +02:00
b9b2f2abd8 first README 2023-10-17 17:50:33 +02:00
c1896a7893 Poprawienie pola wartosci przewidywanej 2023-10-17 17:47:15 +02:00
15 changed files with 3166 additions and 1665 deletions

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pipeline {
agent {
docker { image 'python:3.10' }
}
stages {
stage('Test') {
steps {
sh 'pip install --user black'
sh 'black ./'
}
}
}
}

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## Systemy Informatyczne <br />
### Grupa B <br />
Celem projektu jest zaznajomienie się studentów z systemem kontroli wersji Git. <br />
### W ramach projektu przeprowadzone zostaną następujące czynności: <br />
1. Utworzenie repozytorium
2. Sklonowanie repozytorium przez wszystkich członków grupy.
3. Modyfikacja plików będących efektem pracy podczas poprzednich laboratoriów zgodnie z komentarzami prowadzących.
4. Na gałęzi master znajdują się pliki README, gitignore oraz kody źródłowe, natomiast rysunki znajdują się na gałęzi with-pictures.
### Instalacja i uruchamianie aplikacji: <br />
1. W pierwszej kolejności należy skolonować repozytorium
2. Następnie należy wykonać polecenie: pip install -r requirements.txt
3. W celu skorzystania z kalkulatora mieszkań należy uruchomić plik interface.py.
### Podręcznik użytkownika: <br />
- Interfejs programu zawiera dwa pola do wprowadzania parametrów. Użytkownik wpisuje metraż mieszkania oraz piętro, na którym znajduje się mieszkanie.
- Oba pola nie mogą być puste, a wpisywane wartości muszą być typu numerycznego. Wprowadzone wartości metrażu powinny zawierać się w przedziale od 0 do 100 000 metrów kwadratowych, a liczba pięter powinna zawierać sie w przedziale od 0 do 100. Jeśli wprowadzone dane nie zawierją się w tych przedziałach, to wyświetlony zostaje komunikat: "Please provid numbers between 0 to 100 for floors and 0 to 100 000 for metric area.". Jeśli któreś z pól pozostanie puste to pojawi się komunikat: "Please provide valid data".
- Po prawidłowym uzupełnieniu pól można uruchomić kalkulator za pomocą przycisku "start". Program poprosi o potwierdzenie startu. Wynikiem działania programu jest prognozowana cena mieszkania, wyliczona na podstawie działania modelu regresji liniowej.

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--metric MAE --precision 1

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from tkinter import *
from tkinter import messagebox
import pickle
import numpy as np
from sklearn.preprocessing import StandardScaler
window = Tk()
window.minsize(width=600, height=600)
window.config(padx=50, pady=50)
window.config(background="#ffffff")
window.title("Regresja")
canvas = Canvas(width=250, height=150, highlightthickness=0, background="#ffffff")
house = PhotoImage(file="house.png")
canvas.create_image(140,75, image=house)
canvas.grid(column=0, row=0, columnspan=2, padx=60)
tytul = Label()
tytul.config(text="Wycena wartości mieszkania na podstawie danych z serwisu otodom.pl", padx=70, pady=50)
tytul.grid(column=0, row=0, columnspan=2)
tytul.config(text="Wycena wartości mieszkania na podstawie danych z serwisu otodom.pl", padx=70, pady=50, background="#ffffff")
tytul.grid(column=0, row=1, columnspan=2)
metraz = Label()
metraz.config(text="Metraż", pady=20)
metraz.grid(column=0, row=1)
metraz.config(text="Metraż", pady=20, background="#ffffff")
metraz.grid(column=0, row=2)
metraz_entry = Entry()
metraz_entry.grid(column=0, row=2)
metraz_entry.grid(column=0, row=3)
pietro_entry = Entry()
pietro_entry.grid(column=1, row=2)
pietro_entry.grid(column=1, row=3)
pietro = Label()
pietro.config(text="Piętro", pady=20)
pietro.grid(column=1, row=1)
pietro.config(text="Piętro", pady=20, background="#ffffff")
pietro.grid(column=1, row=2)
przewidywania = Label()
przewidywania.config(text="Przewidywanie:", pady=20, padx=5)
przewidywania.grid(column=0, row=4, sticky='e')
przewidywania.config(text="Przewidywanie:", pady=20, padx=5, background="#ffffff")
przewidywania.grid(column=0, row=5, sticky='e')
wartosc_regresji = Entry()
wartosc_regresji.insert(0, "CENA")
wartosc_regresji.grid(column=1, row=4, sticky='w')
wartosc_regresji = Label()
wartosc_regresji.config(text="Wartosć przewidywana", background="#ffffff")
wartosc_regresji.grid(column=1, row=5, sticky='w')
model = pickle.load(open("model.pkl", 'rb'))
def val_numbers():
max_metraz = 100000
max_piętro = 100
metraz = metraz_entry.get()
pietro = pietro_entry.get()
if metraz == "" or not metraz.isdigit() or pietro=="" or not pietro.isdigit():
messagebox.showinfo(title="Error", message=f"Please provide valid data")
elif int(metraz) < 0 or int(metraz) > max_metraz or int(pietro) < 0 or int(pietro) > max_piętro:
messagebox.showinfo(title="Error", message=f"Please provide numbers between 0 to 100 for floors and 0 to 100.000 for metric area.")
else:
confirm = messagebox.askyesno(title="Confirm", message="Do you want to start prediction?")
if confirm == True:
array = np.array([[int(pietro_entry.get()),float(metraz_entry.get())]])
scaler = StandardScaler()
transformed_data = scaler.fit_transform(array)
prediction = model.predict(transformed_data)
wartosc_regresji.config(text=f'{round(prediction[0][0], 2)} PLN')
start_button = Button(command=val_numbers)
start_button.config(text="Start")
start_button.grid(column=0, row=3, columnspan=2, sticky="s", pady=20)
start_button.config(text="Start", background="#000000", foreground="#ffffff")
start_button.grid(column=0, row=4, columnspan=2, sticky="s", pady=20)
window.mainloop()

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model.pkl

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@ -1,68 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"source": [
"## Raport z zadań laboratorium nr 1 \n",
"### grupa B\n",
"### 10.10.2023\n",
"Skład: <br>\n",
"Piotr Szkudlarek - typ B2 <br>\n",
"Katarzyna Kuryło - typ A1 <br>\n",
"Julia Krzemień - typ A3 <br>\n",
"Olga Kwoczak - typ B3 <br>"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Zadanie 2\n",
"\n",
"Program uruchamia się za pomocą notatnika Jupyter."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Zadanie 3\n",
"\n"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Podręcznik użytkowania kalkulatora cen mieszkań\n",
"\n",
"Interfejs programu zawiera dwa pola do wprowadzania parametrów. Użytkownik wpisuje metraż mieszkania oraz piętro, na którym znajduje się mieszkanie. Oba pola nie mogą być puste, a wpisywane wartości muszą być typu numerycznego. Jeśli wprowadzone dane są niepoprawne, wyskoczy okienko z prośbą o uzupełnienie danych - \"Please provide valid data\". Po prawidłowym uzupełnieniu pól można uruchomić kalkulator za pomocą przycisku \"start\".Program poprosi o potwierdzenie startu. Wynikiem działania programu jest prognozowana cena mieszkania, wyliczona na podstawie działania modelu regresji liniowej."
],
"metadata": {}
}
],
"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": "3.11.5"
},
"nteract": {
"version": "nteract-front-end@1.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

38
requirements.txt Normal file
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asttokens==2.4.0
backcall==0.2.0
comm==0.1.4
debugpy==1.8.0
decorator==5.1.1
exceptiongroup==1.1.3
executing==2.0.0
ipykernel==6.25.2
ipython==8.16.1
jedi==0.19.1
joblib==1.3.2
matplotlib-inline==0.1.6
nest-asyncio==1.5.8
numpy==1.26.0
packaging==23.2
pandas==2.1.1
parso==0.8.3
pexpect==4.8.0
pickleshare==0.7.5
platformdirs==3.11.0
prompt-toolkit==3.0.39
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
Pygments==2.16.1
python-dateutil==2.8.2
pytz==2023.3.post1
pyzmq==25.1.1
scikit-learn==1.3.1
scipy==1.11.3
six==1.16.0
stack-data==0.6.3
threadpoolctl==3.2.0
tornado==6.3.3
traitlets==5.11.2
tzdata==2023.3
wcwidth==0.2.8
black==23.10.1

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