Mieszkania.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Ładowanie danych:"
+      ],
+      "metadata": {
+        "id": "coWdAJZAPC1C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bozs99nnO2jv",
+        "outputId": "4119ebc8-eccf-4574-866c-2502176e0fbd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!git clone git://gonito.net/mieszkania5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Importy:"
+      ],
+      "metadata": {
+        "id": "OFaZTYDGQqLQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import csv\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "data = pd.read_table(\"mieszkania5/train/train.tsv\", delimiter='\\t', header=None)\n",
+        "data.rename(columns={0: 'cena', 1: 'stan', 2: 'czynsz', 3: 'x3', 4: 'cenazam', 5: 'link', 6: 'pietro', 7: 'x7', 8: 'metraz', 9: 'rynek', 10: 'liczba pokoi', 11: 'budynek', 12: 'x12', 13: 'x13', 14: 'x14', 15: 'x15', 16: 'x16', 17: 'x17', 18: 'x18', 19: 'x19', 20: 'x20', 21: 'x21', 22: 'x22', 23: 'x23', 24: 'x24', 25: 'x25'}, inplace=True)\n",
+        "\n",
+        "data.drop('x3', inplace=True, axis=1)\n",
+        "data.drop('cenazam', inplace=True, axis=1)\n",
+        "data.drop('link', inplace=True, axis=1)\n",
+        "data.drop('pietro', inplace=True, axis=1)\n",
+        "data.drop('budynek', inplace=True, axis=1)\n",
+        "data.drop('x7', inplace=True, axis=1)\n",
+        "data.drop('x12', inplace=True, axis=1)\n",
+        "data.drop('x13', inplace=True, axis=1)\n",
+        "data.drop('x14', inplace=True, axis=1)\n",
+        "data.drop('x15', inplace=True, axis=1)\n",
+        "data.drop('x16', inplace=True, axis=1)\n",
+        "data.drop('x17', inplace=True, axis=1)\n",
+        "data.drop('x18', inplace=True, axis=1)\n",
+        "data.drop('x19', inplace=True, axis=1)\n",
+        "data.drop('x20', inplace=True, axis=1)\n",
+        "data.drop('x21', inplace=True, axis=1)\n",
+        "data.drop('x22', inplace=True, axis=1)\n",
+        "data.drop('x23', inplace=True, axis=1)\n",
+        "data.drop('x24', inplace=True, axis=1)\n",
+        "data.drop('x25', inplace=True, axis=1)\n",
+        "\n",
+        "data['czynsz'] = data['czynsz'].str.extract('(\\d+)')\n",
+        "data['stan'] = data['stan'].map({'do zamieszkania': 2, 'do remontu': 1, 'do wykończenia': 2})\n",
+        "data['rynek'] = data['rynek'].map({'wtórny': 0, 'pierwotny': 1})\n",
+        "\n",
+        "data.dropna(inplace=True)"
+      ],
+      "metadata": {
+        "id": "K-TUB0pAPCp2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "57zFDlw7PDDb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cena = data['cena']\n",
+        "parametry = data[['stan', 'czynsz', 'liczba pokoi', 'metraz', 'rynek']]"
+      ],
+      "metadata": {
+        "id": "___F5VBeco6H"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression"
+      ],
+      "metadata": {
+        "id": "H1shMEsxTccr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def train_model(cena, parametry):\n",
+        "  model = LinearRegression()\n",
+        "  model.fit(X=parametry, y=cena)\n",
+        "  return model"
+      ],
+      "metadata": {
+        "id": "vT9sCZ2XTjKy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = train_model(cena, parametry)"
+      ],
+      "metadata": {
+        "id": "-DZ-HNMtUBmr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def predict(stan, czynsz, liczba_pokoi, metraz, rynek):\n",
+        "  return model.predict(np.array([[stan, czynsz, liczba_pokoi, metraz, rynek]])).item()"
+      ],
+      "metadata": {
+        "id": "oK_ZW9N9Wg2u"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predict(1, 200, 2, 40.0, 0)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bLmRBRBMgFTg",
+        "outputId": "f94f3691-9a2a-4035-b3ad-dde097631e85"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "217119.72285625804"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "K7eEdZFzgI3n"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

README.md

@@ -1,2 +1,3 @@
 # SI_lab02
 
+Projekt aplikacji wyliczającej ceny mieszkań na podstawie kilku zmiennych podanych przez użytkownika

ceny_mieszkan.py

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+import tkinter.filedialog as filedialog
+import tkinter as tk
+
+master = tk.Tk()
+header_text = "Ceny mieszkań"
+header_label = tk.Label(master, text=header_text, font=("Arial", 25), anchor = "center", justify="center")
+header_label.pack(fill="both", expand=True, padx=20, pady=20)
+
+def output():
+    path = tk.filedialog.askopenfilename()
+    input_entry.delete(1, tk.END)
+    input_entry.insert(0, path)
+
+
+top_frame = tk.Frame(master)
+middle_frame = tk.Frame(master)
+bottom_frame = tk.Frame(master)
+line = tk.Frame(master, height=1, width=400, bg="grey80", relief='groove')
+
+input_path = tk.Label(top_frame, text="Powierzchnia w m2:")
+input_entry = tk.Entry(top_frame, text="", width=40)
+
+input_path2 = tk.Label(middle_frame, text="Liczba pokoi:")
+input_entry2 = tk.Entry(middle_frame, text="", width=40)
+
+input_path3 = tk.Label(middle_frame, text="Czynsz:")
+input_entry3 = tk.Entry(middle_frame, text="", width=40)
+
+input_path4 = tk.Label(middle_frame, text="Rynek:")
+input_entry4= tk.Entry(middle_frame, text="", width=40)
+
+input_path5 = tk.Label(middle_frame, text="Stan:")
+input_entry5= tk.Entry(middle_frame, text="", width=40)
+
+
+begin_button = tk.Button(bottom_frame, text='Oblicz')
+
+output_path = tk.Label(bottom_frame, text="Prognozowana cena:")
+output_entry = tk.Entry(bottom_frame, text="", width=40)
+
+line.pack(pady=4)
+top_frame.pack(side=tk.TOP)
+
+bottom_frame.pack(side=tk.BOTTOM)
+middle_frame.pack(side=tk.TOP)
+
+input_path.pack(pady=5)
+input_entry.pack(pady=5)
+
+input_path2.pack(pady=5)
+input_entry2.pack(pady=5)
+
+input_path3.pack(pady=5)
+input_entry3.pack(pady=5)
+
+input_path4.pack(pady=5)
+input_entry4.pack(pady=5)
+
+input_path5.pack(pady=5)
+input_entry5.pack(pady=5)
+
+output_path.pack(pady=5)
+output_entry.pack(pady=5)
+
+begin_button.pack(pady=30, fill=tk.X)
+
+master.mainloop()