Change X to X_train

.gitignore

@@ -0,0 +1,3 @@
+# ignoruj pliki .bak
+
+*.bak

Jenkinsfile

@@ -0,0 +1,13 @@
+pipeline {
+    agent {
+        docker { image 'python:3.10' }
+    }
+    stages {
+        stage('Test') {
+            steps {
+                sh 'pip install --user black'
+                sh 'black ./'
+            }
+        }
+    }
+}

README.md

@@ -0,0 +1,20 @@
+## 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.

config.txt

@@ -0,0 +1 @@
+--metric MAE --precision 1

dev-0/expected.tsv

@@ -0,0 +1,462 @@
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emptyfile

@@ -0,0 +1 @@
+test

interface.py

@@ -0,0 +1,75 @@
+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, background="#ffffff")
+tytul.grid(column=0, row=1, columnspan=2)
+
+
+metraz = Label()
+metraz.config(text="Metraż", pady=20, background="#ffffff")
+metraz.grid(column=0, row=2)
+
+metraz_entry = Entry()
+metraz_entry.grid(column=0, row=3)
+
+pietro_entry = Entry()
+pietro_entry.grid(column=1, row=3)
+
+
+pietro = Label()
+pietro.config(text="Piętro", pady=20, background="#ffffff")
+pietro.grid(column=1, row=2)
+
+
+przewidywania = Label()
+przewidywania.config(text="Przewidywanie:", pady=20, padx=5, background="#ffffff")
+przewidywania.grid(column=0, row=5, sticky='e')
+
+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", background="#000000", foreground="#ffffff")
+start_button.grid(column=0, row=4, columnspan=2, sticky="s", pady=20)
+
+window.mainloop()
+
+

requirements.txt

@@ -0,0 +1,38 @@
+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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