diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..df08e96
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+# ignoruj pliki .bak
+
+*.bak
\ No newline at end of file
diff --git a/interface.py b/interface.py
new file mode 100644
index 0000000..4a94b7e
--- /dev/null
+++ b/interface.py
@@ -0,0 +1,57 @@
+from tkinter import *
+from tkinter import messagebox
+
+window = Tk()
+window.minsize(width=600, height=600)
+window.config(padx=50, pady=50)
+window.title("Regresja")
+
+
+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)
+
+
+metraz = Label()
+metraz.config(text="Metraż", pady=20)
+metraz.grid(column=0, row=1)
+
+metraz_entry = Entry()
+metraz_entry.grid(column=0, row=2)
+
+pietro_entry = Entry()
+pietro_entry.grid(column=1, row=2)
+
+
+pietro = Label()
+pietro.config(text="Piętro", pady=20)
+pietro.grid(column=1, row=1)
+
+
+przewidywania = Label()
+przewidywania.config(text="Przewidywanie:", pady=20, padx=5)
+przewidywania.grid(column=0, row=4, sticky='e')
+
+wartosc_regresji = Label()
+wartosc_regresji.config(text="Wartosć przewidywana")
+wartosc_regresji.grid(column=1, row=4, sticky='w')
+
+
+
+def val_numbers():
+ 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")
+ else:
+ confirm = messagebox.askyesno(title="Confirm", message="Do you want to start prediction?")
+
+
+
+start_button = Button(command=val_numbers)
+start_button.config(text="Start")
+start_button.grid(column=0, row=3, columnspan=2, sticky="s", pady=20)
+
+window.mainloop()
+
+
diff --git a/model_regresji_liniowej.ipynb b/model_regresji_liniowej.ipynb
new file mode 100644
index 0000000..88cc4d9
--- /dev/null
+++ b/model_regresji_liniowej.ipynb
@@ -0,0 +1,2012 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_dataset = pd.read_csv(\"J:/Desktop/Projects/mieszkania5/train/train.tsv\", sep = \"\\t\", header=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_dataset = pd.read_csv(\"J:/Desktop/Projects/mieszkania5/test-A/in.tsv\", sep = \"\\t\", header=None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4\n",
+ "7000 42\n",
+ "6750 42\n",
+ "7100 37\n",
+ "7500 29\n",
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+ ]
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[4].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.set_option(\"display.max_columns\", None)"
+ ]
+ },
+ {
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+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [
+ {
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+ "\n",
+ " 25 \n",
+ "0 telewizja kablowa, internet, meble, piwnica, g... "
+ ]
+ },
+ "execution_count": 147,
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+ }
+ ],
+ "source": [
+ "train_dataset.head(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3\n",
+ "pełna własność 1515\n",
+ "spółdzielcze własnościowe 158\n",
+ "spółdzielcze wł. z KW 131\n",
+ "udział 15\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[3].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 15,8, (2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered= train_dataset[[0,8,15,2]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ "> 10 24\n",
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+ "suterena 5\n",
+ "poddasze 3\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "filtered[15].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\s433445\\AppData\\Local\\temp\\ipykernel_3048\\4107925475.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " filtered[15] = filtered[15].replace({\"parter\": 0})\n"
+ ]
+ }
+ ],
+ "source": [
+ "filtered[15] = filtered[15].replace({\"parter\": 0})"
+ ]
+ },
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+ "metadata": {},
+ "outputs": [
+ {
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+ "metadata": {},
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+ }
+ ],
+ "source": [
+ "filtered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "string = [\"> 10\", \"suterena\", \"poddasze\"]\n",
+ "filtered = filtered[~filtered[15].isin(string)]\n",
+ "filtered = filtered[~filtered[15].isna()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ "execution_count": 154,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "filtered[15].value_counts()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered[15] = filtered[15].apply(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "8\n",
+ "38 59\n",
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+ "execution_count": 156,
+ "metadata": {},
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+ "source": [
+ "filtered[8].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def check_value(number):\n",
+ " try:\n",
+ " return float(number)\n",
+ " except:\n",
+ " return str(number)\n",
+ " \n",
+ "def check_str(value):\n",
+ " if isinstance(value,float):\n",
+ " return 1\n",
+ " else:\n",
+ " return 0\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered[8] = filtered[8].apply(check_value)\n",
+ "\n",
+ "filtered[\"isinstance\"] = filtered[15].apply(check_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered = filtered[filtered[\"isinstance\"]==0]"
+ ]
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+ "filtered"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def check_value(number):\n",
+ " try:\n",
+ " return float(number)\n",
+ " except:\n",
+ " return str(number)\n",
+ " \n",
+ "def check_str(value):\n",
+ " if isinstance(value,float):\n",
+ " return 1\n",
+ " else:\n",
+ " return 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\s433445\\AppData\\Local\\temp\\ipykernel_3048\\588287806.py:1: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " filtered[8] = filtered[8].apply(check_value)\n"
+ ]
+ }
+ ],
+ "source": [
+ "filtered[8] = filtered[8].apply(check_value)\n",
+ "filtered[\"check\"] = filtered[8].apply(check_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered = filtered[filtered[\"check\"]==1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 175,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "filtered = filtered[[0, 15, 8]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 188,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 189,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = filtered.drop(0,axis=1)\n",
+ "y = filtered[[0]]\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "trans_data = scaler.fit_transform(X)\n",
+ "\n",
+ "X_train, X_test, y_train, y_test = train_test_split(trans_data,y, test_size=0.33)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 190,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reg = LinearRegression()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reg.fit(X_train, y_train)\n",
+ "results = reg.predict(X_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "metadata": {},
+ "outputs": [
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+ " [ 524500.01037447],\n",
+ " [ 633645.98278715]])"
+ ]
+ },
+ "execution_count": 195,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import r2_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 200,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.5703042723573604"
+ ]
+ },
+ "execution_count": 200,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "r2_score(y_test, results)"
+ ]
+ }
+ ],
+ "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"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "1b132c2ed43285dcf39f6d01712959169a14a721cf314fe69015adab49bb1fd1"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/raport_lab1_grB.ipynb b/raport_lab1_grB.ipynb
new file mode 100644
index 0000000..a37bde7
--- /dev/null
+++ b/raport_lab1_grB.ipynb
@@ -0,0 +1,68 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Raport z zadań laboratorium nr 1 \n",
+ "### grupa B\n",
+ "### 10.10.2023\n",
+ "Skład:
\n",
+ "Piotr Szkudlarek - typ B2
\n",
+ "Katarzyna Kuryło - typ A1
\n",
+ "Julia Krzemień - typ A3
\n",
+ "Olga Kwoczak - typ B3
"
+ ],
+ "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
+}
\ No newline at end of file