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", + "7800 20\n", + " ..\n", + "9674 1\n", + "7565 1\n", + "8352 1\n", + "7511 1\n", + "5077 1\n", + "Name: count, Length: 1452, dtype: int64" + ] + }, + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"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": [ + { + "data": { + "text/plain": [ + "array([[ 588386.57221532],\n", + " [ 347185.77610695],\n", + " [ 333667.85491179],\n", + " [ 350580.90494961],\n", + " [ 483551.82037238],\n", + " [ 248402.31355669],\n", + " [ 295901.04373419],\n", + " [ 323316.55226325],\n", + " [ 514285.10329076],\n", + " [ 569442.34898305],\n", + " [ 299242.73507231],\n", + " [ 381314.18786798],\n", + " [ 394953.7443501 ],\n", + " [ 892738.5148359 ],\n", + " [ 272138.69489957],\n", + " [ 378488.55823341],\n", + " [ 302083.12498475],\n", + " [ 439267.54263911],\n", + " [ 377860.01791385],\n", + " [ 305395.29576713],\n", + " [ 262051.02670974],\n", + " [ 419504.94601791],\n", + " [ 317934.53105975],\n", + " [ 747521.51913496],\n", + " [ 266848.78842728],\n", + " [ 231279.0665647 ],\n", + " [ 343488.33557891],\n", + " [ 268743.56605691],\n", + " [ 415383.55851754],\n", + " [ 248464.90773216],\n", + " 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+ "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