house_pricing.ipynb

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  "metadata": {
    "colab": {
      "name": "house_pricing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP+jOdnIEC5/2r2iwF4G7Q2",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SMatusik/house_pricing/blob/main/house_pricing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HD-ldyygpYs_"
      },
      "source": [
        "# import niezbednych bibliotek w jezyku Python\r\n",
        "import pandas as pd\r\n",
        "import sklearn\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "\r\n",
        "# ustawienie ladniejszego wygladu wykresow\r\n",
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N08dZFPPpnnY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "b372e9f8-588d-4ce7-c4e2-abe159a835ef"
      },
      "source": [
        "# pobranie zbioru, mozna z pliku lokalnego ('local') lub prosto z sieci ('web')\r\n",
        "web = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00477/Real%20estate%20valuation%20data%20set.xlsx'\r\n",
        "local = 'Real estate valuation data set.xlsx'\r\n",
        "dataset = pd.read_excel(local)\r\n",
        "#dataset = pd.read_excel(web)\r\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>No</th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012.916667</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "      <td>37.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012.916667</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "      <td>42.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2013.583333</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>47.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2013.500000</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "      <td>54.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012.833333</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "      <td>43.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   No  X1 transaction date  ...  X6 longitude  Y house price of unit area\n",
              "0   1          2012.916667  ...     121.54024                        37.9\n",
              "1   2          2012.916667  ...     121.53951                        42.2\n",
              "2   3          2013.583333  ...     121.54391                        47.3\n",
              "3   4          2013.500000  ...     121.54391                        54.8\n",
              "4   5          2012.833333  ...     121.54245                        43.1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krmcx0y-rC0L",
        "outputId": "712b8d01-fa07-472f-dd44-c5b39459b9f4"
      },
      "source": [
        "# warto sprawdzic czy plik z danymi zawiera wszystkie dane, czy nie ma brakow\r\n",
        "dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 414 entries, 0 to 413\n",
            "Data columns (total 8 columns):\n",
            " #   Column                                  Non-Null Count  Dtype  \n",
            "---  ------                                  --------------  -----  \n",
            " 0   No                                      414 non-null    int64  \n",
            " 1   X1 transaction date                     414 non-null    float64\n",
            " 2   X2 house age                            414 non-null    float64\n",
            " 3   X3 distance to the nearest MRT station  414 non-null    float64\n",
            " 4   X4 number of convenience stores         414 non-null    int64  \n",
            " 5   X5 latitude                             414 non-null    float64\n",
            " 6   X6 longitude                            414 non-null    float64\n",
            " 7   Y house price of unit area              414 non-null    float64\n",
            "dtypes: float64(6), int64(2)\n",
            "memory usage: 26.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zC2eImahrKQq"
      },
      "source": [
        "Zbiór zawiera informacje o 414 domach. \r\n",
        "Zmienne objaśniające:\r\n",
        "\r\n",
        "\r\n",
        "1.   X1 transaction date - data dokonania transakcji.\r\n",
        "2.   X2 house age - wiek domu w latach.\r\n",
        "3.  X3 distance to the nearest MRT - odległość domu od najbliższej stacji metra w metrach.\r\n",
        "4. X4 number of convenience stores - liczba spożywczych sklepów w zasięgu spaceru.\r\n",
        "5. X5 latitude - współrzędne szerokości geograficznej położenia domu w stopniach.\r\n",
        "6. X6 longitude - współrzędne wysokości geograficznej położenia domu w stopniach.\r\n",
        "\r\n",
        "Zmienna objaśniana:\r\n",
        "7. Y house price of unit area - cena za jednostkę powierzchni domu (10000 tajwańskich dolarów/ping, gdzie ping ~3.3m^2.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otjGdsY-FShj"
      },
      "source": [
        "# wyrzucenie nic nie znaczącej dla modelu zmiennej porządkowej 'No'\r\n",
        "dataset = dataset.iloc[: , 1:8]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "3GT5w5B5w_wy",
        "outputId": "7efac845-386f-4f66-b9d9-4a4fc739fb1c"
      },
      "source": [
        "# wyświetlenie podstawowych informacji statystycznych\r\n",
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>X1 transaction date</th>\n",
              "      <th>X2 house age</th>\n",
              "      <th>X3 distance to the nearest MRT station</th>\n",
              "      <th>X4 number of convenience stores</th>\n",
              "      <th>X5 latitude</th>\n",
              "      <th>X6 longitude</th>\n",
              "      <th>Y house price of unit area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "      <td>414.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.148953</td>\n",
              "      <td>17.712560</td>\n",
              "      <td>1083.885689</td>\n",
              "      <td>4.094203</td>\n",
              "      <td>24.969030</td>\n",
              "      <td>121.533361</td>\n",
              "      <td>37.980193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.281995</td>\n",
              "      <td>11.392485</td>\n",
              "      <td>1262.109595</td>\n",
              "      <td>2.945562</td>\n",
              "      <td>0.012410</td>\n",
              "      <td>0.015347</td>\n",
              "      <td>13.606488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2012.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.382840</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24.932070</td>\n",
              "      <td>121.473530</td>\n",
              "      <td>7.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.916667</td>\n",
              "      <td>9.025000</td>\n",
              "      <td>289.324800</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.963000</td>\n",
              "      <td>121.528085</td>\n",
              "      <td>27.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.166667</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>492.231300</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>24.971100</td>\n",
              "      <td>121.538630</td>\n",
              "      <td>38.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.416667</td>\n",
              "      <td>28.150000</td>\n",
              "      <td>1454.279000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>24.977455</td>\n",
              "      <td>121.543305</td>\n",
              "      <td>46.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2013.583333</td>\n",
              "      <td>43.800000</td>\n",
              "      <td>6488.021000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>25.014590</td>\n",
              "      <td>121.566270</td>\n",
              "      <td>117.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       X1 transaction date  ...  Y house price of unit area\n",
              "count           414.000000  ...                  414.000000\n",
              "mean           2013.148953  ...                   37.980193\n",
              "std               0.281995  ...                   13.606488\n",
              "min            2012.666667  ...                    7.600000\n",
              "25%            2012.916667  ...                   27.700000\n",
              "50%            2013.166667  ...                   38.450000\n",
              "75%            2013.416667  ...                   46.600000\n",
              "max            2013.583333  ...                  117.500000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjmnX0djHS_n"
      },
      "source": [
        "# wyrzucenie spacji z nazw kolumn co ulatwi dostep do nich oraz wyrzucenie z nazw oznaczen \"X+numer\"\r\n",
        "dataset.columns = [c.replace(' ', '_') for c in dataset.columns.str[3:]]\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zA9rPJcbHdEs"
      },
      "source": [
        "# zmiana nazw kolumn na prostsze\r\n",
        "dataset.rename(columns={dataset.columns[6]: \"price\" }, inplace = True)\r\n",
        "dataset.rename(columns={dataset.columns[3]: \"no_of_stores\" }, inplace = True)\r\n",
        "dataset.rename(columns={dataset.columns[2]: \"metro_distance\" }, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4M6YwbIKxuEP"
      },
      "source": [
        "# podzial danych na dane zmienna X (dane sluzace do nauki) oraz Y (wyniki)\r\n",
        "X = dataset.iloc[: , 0:6]\r\n",
        "X['no_of_stores'] = X['no_of_stores'].astype(float)\r\n",
        "\r\n",
        "Y = dataset.iloc[:, 6]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "LrJcO_h8x8Po",
        "outputId": "b380086f-47cb-410d-da66-d783c9bf1bd6"
      },
      "source": [
        "# wyswietlenie pierwszych 5 wierszy zeby sprawdzic podzial danych\r\n",
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>transaction_date</th>\n",
              "      <th>house_age</th>\n",
              "      <th>metro_distance</th>\n",
              "      <th>no_of_stores</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012.916667</td>\n",
              "      <td>32.0</td>\n",
              "      <td>84.87882</td>\n",
              "      <td>10.0</td>\n",
              "      <td>24.98298</td>\n",
              "      <td>121.54024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012.916667</td>\n",
              "      <td>19.5</td>\n",
              "      <td>306.59470</td>\n",
              "      <td>9.0</td>\n",
              "      <td>24.98034</td>\n",
              "      <td>121.53951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013.583333</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5.0</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2013.500000</td>\n",
              "      <td>13.3</td>\n",
              "      <td>561.98450</td>\n",
              "      <td>5.0</td>\n",
              "      <td>24.98746</td>\n",
              "      <td>121.54391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012.833333</td>\n",
              "      <td>5.0</td>\n",
              "      <td>390.56840</td>\n",
              "      <td>5.0</td>\n",
              "      <td>24.97937</td>\n",
              "      <td>121.54245</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   transaction_date  house_age  ...  latitude  longitude\n",
              "0       2012.916667       32.0  ...  24.98298  121.54024\n",
              "1       2012.916667       19.5  ...  24.98034  121.53951\n",
              "2       2013.583333       13.3  ...  24.98746  121.54391\n",
              "3       2013.500000       13.3  ...  24.98746  121.54391\n",
              "4       2012.833333        5.0  ...  24.97937  121.54245\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QH0Y-z8IyD31",
        "outputId": "ad0ae77b-a7b8-4e41-b356-468c970b99a9"
      },
      "source": [
        "# sprawdzenie czy dane poprawnie sie rozdzielily\r\n",
        "Y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    37.9\n",
              "1    42.2\n",
              "2    47.3\n",
              "3    54.8\n",
              "4    43.1\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diGVEEBOyJuT"
      },
      "source": [
        "# dzieki sci-kit-learn mozna prosto podzielic dane w losowy sposob na dane treningowe (90%) oraz testowe (10%) za pomoca parametru train_size\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "\r\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGwngffmzRj4"
      },
      "source": [
        "# zaimportowanie klasy regresji liniowej\r\n",
        "from sklearn.linear_model import LinearRegression\r\n",
        "model = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiuIsARszRxd",
        "outputId": "267e422c-6b3d-4052-8c1f-6dbaf7d90faf"
      },
      "source": [
        "# metoda ktora uczy model korzystajac z rozdzielonych danych treningowych\r\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkYjDam6Y-79"
      },
      "source": [
        "# uzycie wyuczonego modelu do prognozowania danych testowych\r\n",
        "Y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ef1WXRpzjnL",
        "outputId": "fb040932-60cf-4d40-df48-b131079ef198"
      },
      "source": [
        "# obliczenia R2-score oraz błędu średniokwadratowego \r\n",
        "from sklearn.metrics import r2_score\r\n",
        "from sklearn.metrics import mean_squared_error\r\n",
        "\r\n",
        "print('R2 Score danych testowych ' +  str(r2_score(Y_test, Y_pred)))\r\n",
        "print('Błąd średniokwadratowy dla danych testowych: ' + str(mean_squared_error(Y_test, Y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2 Score danych testowych 0.379319745127607\n",
            "Błąd średniokwadratowy dla danych testowych: 222.39068182298112\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uJZWiTtzsU4"
      },
      "source": [
        "# utworzenie tabeli z wynikami, etykietą oraz bledem predykcji\r\n",
        "results = pd.DataFrame(data={'Y_test': Y_test, 'Y_pred': Y_pred})\r\n",
        "results['Error'] = results['Y_test'] - results['Y_pred']\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QilSN_MTzqIi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "518ed1f4-bdb9-4ee1-9cba-5f1dd4a2d767"
      },
      "source": [
        "# podejrzenie etykiet, wynikow i bledow\r\n",
        "results.head(25).T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>379</th>\n",
              "      <th>369</th>\n",
              "      <th>123</th>\n",
              "      <th>44</th>\n",
              "      <th>60</th>\n",
              "      <th>387</th>\n",
              "      <th>135</th>\n",
              "      <th>350</th>\n",
              "      <th>125</th>\n",
              "      <th>155</th>\n",
              "      <th>339</th>\n",
              "      <th>348</th>\n",
              "      <th>233</th>\n",
              "      <th>151</th>\n",
              "      <th>343</th>\n",
              "      <th>187</th>\n",
              "      <th>244</th>\n",
              "      <th>119</th>\n",
              "      <th>141</th>\n",
              "      <th>270</th>\n",
              "      <th>390</th>\n",
              "      <th>179</th>\n",
              "      <th>232</th>\n",
              "      <th>372</th>\n",
              "      <th>7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Y_test</th>\n",
              "      <td>69.700000</td>\n",
              "      <td>22.800000</td>\n",
              "      <td>45.500000</td>\n",
              "      <td>53.900000</td>\n",
              "      <td>21.300000</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>20.80000</td>\n",
              "      <td>42.300000</td>\n",
              "      <td>48.600000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>50.400000</td>\n",
              "      <td>53.700000</td>\n",
              "      <td>32.400000</td>\n",
              "      <td>44.700000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>21.700000</td>\n",
              "      <td>59.600000</td>\n",
              "      <td>28.90000</td>\n",
              "      <td>117.50000</td>\n",
              "      <td>38.600000</td>\n",
              "      <td>42.600000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>46.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y_pred</th>\n",
              "      <td>50.664524</td>\n",
              "      <td>28.172536</td>\n",
              "      <td>42.595161</td>\n",
              "      <td>46.909878</td>\n",
              "      <td>33.522292</td>\n",
              "      <td>31.254692</td>\n",
              "      <td>30.66446</td>\n",
              "      <td>40.912199</td>\n",
              "      <td>47.290579</td>\n",
              "      <td>15.710614</td>\n",
              "      <td>50.067631</td>\n",
              "      <td>47.177774</td>\n",
              "      <td>44.330498</td>\n",
              "      <td>54.188758</td>\n",
              "      <td>42.980945</td>\n",
              "      <td>23.533463</td>\n",
              "      <td>38.471734</td>\n",
              "      <td>46.860003</td>\n",
              "      <td>39.40827</td>\n",
              "      <td>41.19357</td>\n",
              "      <td>44.612643</td>\n",
              "      <td>38.680341</td>\n",
              "      <td>13.177762</td>\n",
              "      <td>39.476031</td>\n",
              "      <td>46.382308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Error</th>\n",
              "      <td>19.035476</td>\n",
              "      <td>-5.372536</td>\n",
              "      <td>2.904839</td>\n",
              "      <td>6.990122</td>\n",
              "      <td>-12.222292</td>\n",
              "      <td>-5.654692</td>\n",
              "      <td>-9.86446</td>\n",
              "      <td>1.387801</td>\n",
              "      <td>1.309421</td>\n",
              "      <td>-0.110614</td>\n",
              "      <td>0.332369</td>\n",
              "      <td>6.522226</td>\n",
              "      <td>-11.930498</td>\n",
              "      <td>-9.488758</td>\n",
              "      <td>3.619055</td>\n",
              "      <td>-1.533463</td>\n",
              "      <td>-16.771734</td>\n",
              "      <td>12.739997</td>\n",
              "      <td>-10.50827</td>\n",
              "      <td>76.30643</td>\n",
              "      <td>-6.012643</td>\n",
              "      <td>3.919659</td>\n",
              "      <td>4.222238</td>\n",
              "      <td>2.023969</td>\n",
              "      <td>0.317692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              379        369        123  ...        232        372        7  \n",
              "Y_test  69.700000  22.800000  45.500000  ...  17.400000  41.500000  46.700000\n",
              "Y_pred  50.664524  28.172536  42.595161  ...  13.177762  39.476031  46.382308\n",
              "Error   19.035476  -5.372536   2.904839  ...   4.222238   2.023969   0.317692\n",
              "\n",
              "[3 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GEQlxKEhbnec",
        "outputId": "bfbaeeb5-0b9e-4627-c0ef-806bd9898f5c"
      },
      "source": [
        "# obliczone wartosci modelu\r\n",
        "print('wyraz wolny w równaniu regresji liniowej' + str(model.intercept_))\r\n",
        "print('wspolczynniki dotyczace zmiennych:' + str(model.coef_))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "wyraz wolny w równaniu regresji liniowej-14458.226757904295\n",
            "wspolczynniki dotyczace zmiennych:[ 4.06185317e+00 -2.63090071e-01 -3.88516046e-03  1.21329100e+00\n",
            "  2.37136251e+02  3.30602179e+00]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "__D3hmYpt4zi",
        "outputId": "cfb7ad34-e956-498c-8fcd-3614dbe71bfc"
      },
      "source": [
        "# wyswietlenie wykresu wyników predykcji, danych rzeczywistych i obliczonego błędu\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "\r\n",
        "plt.figure(figsize=(20,8))\r\n",
        "\r\n",
        "plt.scatter(y=Y_test, x=np.arange(0, Y_test.shape[0], 1), c='g', label='prawdziwa cena')\r\n",
        "plt.scatter(y=Y_pred, x=np.arange(0, Y_pred.shape[0], 1), c='r', label='predykcja')\r\n",
        "plt.plot(Y_pred, c='r')\r\n",
        "plt.plot(Y_test.reset_index(drop=True), c='g')\r\n",
        "\r\n",
        "\r\n",
        "plt.xlabel('Domy')\r\n",
        "plt.ylabel('Cena domu')\r\n",
        "plt.title('Regresja liniowa')\r\n",
        "\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "XMuAy-Xxcxkz",
        "outputId": "3aa2b719-b01e-4880-f311-4f54e0033f44"
      },
      "source": [
        "plt.figure(figsize=(20,8))\r\n",
        "\r\n",
        "plt.plot(np.arange(0, results['Error'].shape[0], 1), results['Error'], c='r', label='błąd')\r\n",
        "plt.xlabel('Domy')\r\n",
        "plt.ylabel('Bład predykcji')\r\n",
        "plt.title('Błąd predykcji regresji liniowej')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Błąd predykcji regresji liniowej')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "z5XZfgY2usBP",
        "outputId": "e7d9833f-296e-4da1-fe87-e62a927c8c6d"
      },
      "source": [
        "# histogram wystepowania cen\r\n",
        "chart = dataset['price'].plot(kind='hist', title='Histogram występowania cen', bins=20)\r\n",
        "chart.set_ylabel(\"Częstotliwość\")\r\n",
        "chart.set_xlabel(\"Koszt jednostki powierzchni domu\")\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Koszt jednostki powierzchni domu')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5dbqPFFvbhsjMNNyGk5r4B3xW948hHztDzg0w7Pwqyk0mM6rwQ7VOm5UOHDiAl19+GU2aNAEA2NjYwNXVFVFRUQCAqKgouLq68n4DEZGe6bw4/LNJaenSpfj888/Rv39/fP7551i2bJkuQyIiolLotFkpOjpaY5qzszP27dunyzCIiKgCfEKaiIg0sDgQEZEGFgciItLA4kBERBoMYgxp0o3q9I9ERHUL/9NJa9XtH4mI6g42KxERkQYWByIi0sDiQEREGlgciIhIA4sDERFpYHEgIiINLA5ERKSBxYGIiDSwOBARkQYWByIi0sDiQEREGlgciIhIA4sDERFpYHEgIiINLA5ERKRBZ+M5KJVKrF69Gr///jvMzMzg5uaGFStW4Pr16wgODkZOTg6srKwQFhaGVq1a6SosIiIqhc6Kw/r162FmZobo6GgYGRnh7t27AIDQ0FAEBgbCz88Phw4dQkhICHbt2qWrsIiIqBQ6aVbKz8/HwYMHERQUBCMjIwBA06ZNkZWVhcTERPj6+gIAfH19kZiYiOzsbF2ERUREZdDJlUNycjKsrKzwwQcf4PTp07CwsEBQUBDMzc1hb28PY2NjAICxsTHs7OyQmpoKa2trrddvY2NZW6HrjK1tQ32H8Ex7lvfPsxxbdRlyboBh51fd3HRSHFQqFZKTk9GuXTssXLgQFy5cwLRp0/Dee+/VyPqzsvKgVosaWZc+2No2RGZmrr7DqJA+/5Ge1f1TV45dVRhyboBh51dRbjKZUYUfqnXSrOTg4AATExOp+ahz585o0qQJzM3NkZ6eDpVKBeBxEcnIyICDg4MuwiIiojLopDhYW1vD29sbJ0+eBABcv34dWVlZaNWqFVxdXREVFQUAiIqKgqura6WalIiIqObp7NtKy5Ytw6JFixAWFgYTExOsW7cOjRo1wtKlSxEcHIxt27ahUaNGCAsL01VIRERUBp0VhxYtWuCzzz7TmO7s7Ix9+/bpKgwiItICn5AmIiINLA5ERKSBxYGIiDSwOBARkQYWByIi0sDiQEREGlgciIhIA4sDERFpYHEgIiINLA5ERKSBxYGIiDSwOBARkQYWByIi0sDiQEREGlgciIhIA4sDERFpYHEgIiINLA5ERKRBZ8OEUs1p2Kg+zM2qdugKlMXIffCohiMiIkPD4lAHmZuZYMhbh6q0bORGP+TWcDxEZHh0Vhx8fHxgamoKMzMzAMD8+fPRo0cPnD9/HiEhIVAqlWjevDnWr18PGxsbXYVFRESl0OmVw/vvv4+2bdtKr9VqNRYsWIA1a9ZAoVBg27Zt2LBhA9asWaPLsIiI6B+0viGdnp6O+/fvl5h2//59pKenV3njCQkJMDMzg0KhAACMHj0a3333XZXXR0RENaPcK4eFCxciLCwMADB9+nSsXr0ajRs3luanpaVh8eLF2Ldvn1Ybmz9/PoQQ8PDwwLx585CamgpHR0dpvrW1NdRqNXJycmBlZaV1EjY2llq/91lla9vQILdVU57lmJ/l2KrLkHMDDDu/6uZWbnFo06YN3nrrLWzcuBHXr1+Hi4tLifkuLi74+++/tdrQ7t274eDggMLCQqxatQrLly9H3759qx75U7Ky8qBWixpZlz7Y2jZEZqb2t4mre9Ars62a3G51VDXm2lbZY1eXGHJugGHnV1FuMplRhR+qy21WmjJlCvz8/AAANjY2uHnzZon5N2/e1PoTvoODAwDA1NQUgYGBOHv2LBwcHJCSkiK9Jzs7GzKZrFJXDUREVPMqvOfQs2dPAIC/vz9mzZqF48eP46+//kJMTAxmz56NgICACjfy8OFD5OY+rmJCCBw5cgSurq7o0KEDCgoKEBcXBwDYs2cPBgwYUJ18iIioBmj9baWpU6fCxMQEYWFhSEtLQ7NmzRAQEICJEydWuGxWVhZmzZoFlUoFtVoNZ2dnhIaGQiaTYd26dQgNDS3xVVYiItIvrYuDTCbDlClTMGXKlEpvpEWLFjh48GCp89zd3REZGVnpdRIRUe0pt1np7t270u+nTp1CcnIyACAzMxMLFy7EO++8g8zMzNqNkIiIdK7CG9JPnmNYtmwZjI2NAQBr165FcXExjIyMsGTJktqPkoiIdKrcZqUVK1Zg3rx52L17N9LT0+Ho6Iji4mKcOHECMTExqFevHnr06KGrWImISEfKLQ4dO3ZEeHg4AMDS0hJ3797F1atX4ezsDAsLCxQWFqK4uFgngRIRke5UeEPa3t4eADBu3DiMGDECRUVFWLRoEQDg7NmzaN26de1GSEREOlepr7L27dsXxsbGeO655wA8LhwrV66steCIiEg/KtUra4sWLXDu3DnEx8fD3t4ecrkcJiYcEoKIyNBofWa/du0a3nzzTRQUFMDBwQGpqakwMzNDREQEnJ2dazNGIiLSMa2Lw7JlyzBy5EhMnjwZRkZGAIAdO3Zg6dKl+Oyzz2otQCIi0j2tx3O4fPkyJk6cKBUGAHjttddw+fLlWgmMiIj0R+viYGdnhzNnzpSYFhcXBzs7uxoPioiI9EvrZqW5c+di+vTpeOWVV+Do6IiUlBT89NNP7CiPiMgAaV0cevfujf379+Po0aPIyMhAmzZtMHv2bDz//PO1GR/VsMIiVZ0b/aq6MRcoi5H74FENRkRk+LQuDg8ePMDzzz+P6dOn12Y8VMtM6xljyFuHqrRs5Ea/Go5GO9WJGXgct2GO90VUe7QuDt27d0fr1q3h6ekJLy8vKBQKNGnSpDZjIyIiPdG6OMTGxuLs2bOIi4vDrl278Pbbb8PJyQmenp4ICQmpzRgNTsNG9WFuVnLX17WmHiIybFoXBzMzM3Tp0gVubm6Qy+U4ceIE9u3bh+joaBaHSjI3M6l2MwkRUW3SujisX78ecXFxSE9Ph1wuh0KhwFdffYUXXnihNuMjIiI90Lo4fPHFF2jatCnGjBkDLy8vdOzYkf0qEREZqErdc7h48SJiY2Px4YcfIikpCS+88AI8PT35DSYiIgOj9RPSJiYmkMvlGDVqFAICAtCvXz/Ex8dj27ZtldrgBx98ABcXF1y5cgUAcP78eQwdOhT9+/fHpEmTkJWVVbkMiIioxmldHFauXImhQ4eiZ8+e+PTTT2FpaYn3339fo0uN8ly6dAnnz59H8+bNAQBqtRoLFixASEgIoqOjoVAosGHDhspnQURENUrrZqXGjRtj0aJFkMvlMDMzq/SGCgsLsXz5cmzcuBETJkwAACQkJMDMzAwKhQIAMHr0aPTu3Rtr1qyp9PqJiKjmaF0cZs2aVa0Nvffeexg6dCicnJykaampqXB0dJReW1tbQ61WIycnB1ZWVtXaHhERVV25xWHy5MnYsWMHACAwMLBEd91P2717d7kbOXfuHBISEjB//vwqhlk+GxvLWlkvGY7afMjQkB9gNOTcAMPOr7q5lVschg0bJv0eEBBQ5Y3Exsbi2rVr6N27NwAgLS0NkydPxvjx45GSkiK9Lzs7GzKZrNJXDVlZeVCrRZXj0zVD/oN8VmVm1k7vSra2DWtt3fpmyLkBhp1fRbnJZEYVfqgutzgMGTJE+n348OGVDO//mzp1KqZOnSq99vHxQUREBF544QXs3bsXcXFxUCgU2LNnDwYMGFDl7RARUc0otzh8/fXXWq1kxIgRVdq4TCbDunXrEBoaCqVSiebNm3N8CCKiZ0C5xeHQoYr7/zEyMqp0cYiJiZF+d3d3R2RkZKWWJyKi2lVucfjss890FQcRET1DtH4I7umb00979dVXaywYIiJ6NmhdHG7evKkxTQiB27dv12hARESkfxU+BPf2228DAIqKiqTfn7hz5w677CYiMkAVFofnnnuu1N+BxzeT+dVTIiLDU2FxmDlzJgCgc+fO6NGjR60HRERE+qd130o9evTA6dOncfDgQWRkZMDOzg5+fn546aWXajM+omorLFJV+an0AmUxch88quGIiJ59WheHffv2ITw8HAEBAejcuTNSU1Px1ltvISgoCCNHjqzNGImqxbSecZXH7I7c6AfD7GCBqHxaF4ft27dj586dePHFF6VpAwcOxOzZs1kciIgMjNZfZc3JyYGzs3OJaa1bt8b9+/drPCgiItIvrYuDu7s71q5di0ePHre/Pnz4EOvWrYNcLq+14IiISD+0blZatmwZ5s6dC4VCgcaNG+P+/fuQy+XYuHFjbcZHRER6oHVxsLOzw+7du5GWliZ9W6lZs2a1GRsREelJpftWatasGTp16iQVBvatRERkeNi3EhERaWDfSkREpIF9KxERkQb2rURERBq0vudQr149JCcnAwAyMzOxcOFCvPPOO8jMzKy14IiISD+0Lg7Lli2DsbExAGDt2rUoLi6GkZERlixZUmvBERGRfmj9nEN6ejocHR1RXFyMEydOICYmBvXq1dO6qWn69Om4ffs2ZDIZGjRogCVLlsDV1RXXr19HcHAwcnJyYGVlhbCwMLRq1aqq+RARUQ3QujhYWlri7t27uHr1KpydnWFhYYHCwkIUFxdrtXxYWBgaNnzcbfIPP/yARYsW4cCBAwgNDUVgYCD8/Pxw6NAhhISEYNeuXVXLhoiIaoTWzUrjxo3DiBEjMH/+fIwdOxYAcPbsWbRu3Vqr5Z8UBgDIy8uDkZERsrKykJiYCF9fXwCAr68vEhMTkZ2dXZkciIiohml95TB16lT07dsXxsbG0lda7e3tsXLlSq039u677+LkyZMQQmD79u1ITU2Fvb29dC/D2NgYdnZ2SE1NhbW1dSVTISKimqJ1cQCA559/HsnJyfjll1/w4MEDDBgwAEqlUuvlV61aBQA4ePAg1q1bh6CgoMpFWwYbG8saWQ9RaSoaRa6qo8zVBYacG2DY+VU3N62LQ1paGt555x3Ex8fDyckJt27dgkKhwMqVK/HBBx8AeNz09Pnnn1e4rmHDhiEkJATNmjVDeno6VCoVjI2NoVKpkJGRAQcHh0olkZWVB7VaVGoZfTLkP0hDlJlZ9lhwtrYNy51flxlyboBh51dRbjKZUYUfqrW+57BgwQK0b98eJ0+exKFDh1CvXj00a9YMP/zwA9544w2EhITgwYMHpS6bn5+P1NRU6XVMTAwaN24MGxsbuLq6IioqCgAQFRUFV1dXNikREemZ1lcOly5dwn//+1/Uq1evxPTGjRtj8+bNSEpKwrx580pd9tGjRwgKCsKjR48gk8nQuHFjREREwMjICEuXLkVwcDC2bduGRo0aISwsrHoZERFRtWldHJ577jn88MMPGDhwoDQtOzsbLVq0gIWFBRQKRZnLNm3aFHv37i11nrOzM/bt21eJkImIqLZpXRyCgoIwa9YsREZGQi6Xo7CwEEuXLq2xm8pERPTs0PqeQ69evbB3717Y2dkhNjYWQ4cOxdSpU9kZHxGRAdL6yiElJQUuLi5YunRpielpaWkcLpSIyMBofeXg4+OD119/HTk5OSWmDxo0qMaDIiIi/dK6ONSvXx/u7u7w9/fH5cuXpelC1J3nC4iISDtaFwcjIyPMnTsXb731FiZOnIjo6GhpOhERGZZKdZ8BPG5GatWqFWbOnImkpCReORARGSCtrxyedI4HAO3atcO+ffsQFxeHgoKCWgmMiIj0p8Irhz/++AMxMTGIjY0tMd3GxgYdOnTA/Pnzay04IiLSjwqvHD766CN4enqWOq9r166IiIio8aCIiEi/KiwOSUlJZT7o1rVrVyQkJNR4UEREpF8VFoe8vDwUFRWVOq+4uBj5+fk1HhQREelXhcWhdevWOHHiRKnzTpw4ofUwoUREVHdUWBxef/11hIaG4tixY1Cr1QAAtVqNY8eOYenSpZg4cWKtB0lERLpV4beVhgwZgrt372LhwoUoKiqClZUVcnJyUK9ePcyePRu+vr66iJOIiHRIq4fgJk6ciICAAJw7dw45OTmwsrKCXC6HpSXHbiYiMkRaPyFtaWnJ7rmJiP4ltH5CmoiI/j1YHIiISAOLAxERaah0r6xVce/ePbz99tu4desWTE1N0bJlSyxfvhzW1tY4f/48QkJCoFQq0bx5c6xfvx42Nja6CIuIiMqgkysHIyMjTJkyBdHR0YiMjESLFi2wYcMGqNVqLFiwACEhIYiOjoZCocCGDRt0ERIREZVDJ8XBysoK3t7e0g3ET8cAABW4SURBVGs3NzekpKQgISEBZmZmUCgUAIDRo0fju+++00VIRERUDp3fc1Cr1fjyyy/h4+OD1NRUODo6SvOsra2hVqs1xqkmIiLd0sk9h6etWLECDRo0wLhx4/D999/XyDptbHT/MF5hkQqm9YwrfiPVeba2Das1vy4z5NwAw86vurnptDiEhYXh5s2biIiIgEwmg4ODA1JSUqT52dnZkMlksLKyqtR6s7LyoFbrdrhSW9uGGPLWoSotG7nRr4ajodqUmZlb5jxb24blzq/LDDk3wLDzqyg3mcyowg/VOmtWCg8PR0JCArZu3QpTU1MAQIcOHVBQUIC4uDgAwJ49ezBgwABdhURERGXQyZXD1atX8dFHH6FVq1YYPXo0AMDJyQlbt27FunXrEBoaWuKrrEREpF86KQ5t2rTBn3/+Weo8d3d3REZG6iIMIiLSEp+QJiIiDSwORESkgcWBiIg0sDgQEZEGFgciItLA4kBERBpYHIiISIPO+1YiqksKi1RV7lupQFmM3AePaiMsolrH4kBUDtN6xtXqQ8swe+6hfwM2KxERkQYWByIi0sDiQEREGlgciIhIA4sDERFpYHEgIiINLA5ERKSBxYGIiDSwOBARkQYWByIi0sDiQEREGnRSHMLCwuDj4wMXFxdcuXJFmn79+nWMGjUK/fv3x6hRo3Djxg1dhENERBXQSXHo3bs3du/ejebNm5eYHhoaisDAQERHRyMwMBAhISG6CIeIiCqgk+KgUCjg4OBQYlpWVhYSExPh6+sLAPD19UViYiKys7N1ERIREZVDb/ccUlNTYW9vD2NjYwCAsbEx7OzskJqaqq+QiIjo/xjEeA42Npb6DoGoVBUNFPSsq+vxV8SQ86tubnorDg4ODkhPT4dKpYKxsTFUKhUyMjI0mp+0kZWVB7Va1EKUZTPkPyqqOZmZdXe4H1vbhnU6/ooYcn4V5SaTGVX4oVpvzUo2NjZwdXVFVFQUACAqKgqurq6wtrbWV0hERPR/dHLlsHLlShw7dgx3797FxIkTYWVlhcOHD2Pp0qUIDg7Gtm3b0KhRI4SFhekiHCIiqoBOisPixYuxePFijenOzs7Yt2+fLkIgIqJK4BPSRESkwSC+rVRVDRvVh7nZv3oXEBGV6l99ZjQ3M8GQtw5VadnIjX41HA0R0bODzUpERKSBxYGIiDSwOBARkQYWByIi0vCvviFNVJsKi1RV7malQFmM3AePajgiIu2xOBDVEtN6xtX6Npxh9vpDdQWblYiISAOLAxERaWBxICIiDSwORESkgcWBiIg0sDgQEZEGFgciItLA5xyInkHVeYAOAJSFKpiZGldpWT6ARwCLA9EzqToP0AGPH6LjA3hUHWxWIiIiDSwORESkgcWBiIg0PBP3HK5fv47g4GDk5OTAysoKYWFhaNWqlb7DIiKqVdUZx762vzjwTBSH0NBQBAYGws/PD4cOHUJISAh27dql77CIiGpVdcexr80vDui9OGRlZSExMRE7d+4EAPj6+mLFihXIzs6GtbW1VuuQyYyqvH27JvXr3LL63HZdXFaf266rOT/5n6rO/1Zd8CzkVxPHqSbnPWEkhBBViqqGJCQkYOHChTh8+LA0bdCgQVi/fj3at2+vx8iIiP69eEOaiIg06L04ODg4ID09HSqVCgCgUqmQkZEBBwcHPUdGRPTvpffiYGNjA1dXV0RFRQEAoqKi4OrqqvX9BiIiqnl6v+cAANeuXUNwcDAePHiARo0aISwsDK1bt9Z3WERE/1rPRHEgIqJni96blYiI6NnD4kBERBpYHIiISAOLAxERaWBx0KF79+7hjTfeQP/+/TFkyBDMnDkT2dnZAIDz589j6NCh6N+/PyZNmoSsrCw9R1t1H3zwAVxcXHDlyhUAhpObUqlEaGgo+vXrhyFDhmDJkiUAHnccOWrUKPTv3x+jRo3CjRs39BtoFRw/fhzDhg2Dn58fhg4dimPHjgGou7mFhYXBx8enxN8hUH4+dSXX0nIr79wCVPF/UJDO3Lt3T5w6dUp6vXbtWvHOO+8IlUol+vTpI2JjY4UQQmzdulUEBwfrK8xqSUhIEJMnTxa9evUSf/75p0HltmLFCrFq1SqhVquFEEJkZmYKIYQYP368OHjwoBBCiIMHD4rx48frLcaqUKvVQqFQiD///FMIIURSUpJwc3MTKpWqzuYWGxsrUlJSpL/DJ8rLp67kWlpuZZ1bhBBV/h9kcdCj7777Trz22mviwoULYvDgwdL0rKws4ebmpsfIqkapVIqRI0eK5ORk6Q/XUHLLy8sTHh4eIi8vr8T0u3fvCg8PD1FcXCyEEKK4uFh4eHiIrKwsfYRZJWq1Wnh5eYm4uDghhBBnzpwR/fr1M4jcnj6BlpdPXcz1n4XvaU/OLUKIKv8P6r1X1n8rtVqNL7/8Ej4+PkhNTYWjo6M0z9raGmq1Whrfoq547733MHToUDg5OUnTDCW35ORkWFlZ4YMPPsDp06dhYWGBoKAgmJubw97eHsbGxgAAY2Nj2NnZITU1tc485W9kZITNmzdj+vTpaNCgAfLz8/Hxxx8jNTW1zuf2tPLyEUIYTK5Pn1uAqv8P8p6DnqxYsQINGjTAuHHj9B1KjTh37hwSEhIQGBio71BqhUqlQnJyMtq1a4f9+/dj/vz5mDVrFh4+fKjv0KqtuLgYH330EbZt24bjx4/jww8/xJw5cwwit3+jmjq38MpBD8LCwnDz5k1ERERAJpPBwcEBKSkp0vzs7GzIZLI69ck6NjYW165dQ+/evQEAaWlpmDx5MsaPH1/ncwMedxBpYmICX19fAEDnzp3RpEkTmJubSx1HGhsb18mOI5OSkpCRkQEPDw8AgIeHB+rXrw8zM7M6n9vTnu7k85/5CCEMItd/nlsAVPn8wisHHQsPD0dCQgK2bt0KU1NTAECHDh1QUFCAuLg4AMCePXswYMAAfYZZaVOnTsWJEycQExODmJgYNGvWDDt27MCUKVPqfG7A40txb29vnDx5EsDjb7ZkZWWhVatWdb7jyGbNmiEtLQ1///03gMd9nWVlZaFly5Z1PrenldfJpyF0AFrauQWo+vmFfSvp0NWrV+Hr64tWrVrB3NwcAODk5IStW7fi7NmzCA0NhVKpRPPmzbF+/Xo0bdpUzxFXnY+PDyIiItC2bVuDyS05ORmLFi1CTk4OTExMMGfOHLz88ssG0XHkt99+i08++QRGRo9HCJs9ezb69OlTZ3NbuXIljh07hrt376JJkyawsrLC4cOHy82nruRaWm6bN28u89wCoEr/gywORESkgc1KRESkgcWBiIg0sDgQEZEGFgciItLA4kBERBpYHKjGxcXFoX///lVa9vTp0+jZs2cNR1QxHx8f/Pbbb6XOGzx4ME6fPq3jiPS3/f3792PMmDFVWjYlJQVyuRwqlUqr9wcHB2PTpk1V2hbVLhYHA/HPk9vhw4fh6emJM2fO1Pq2/kmhUCA6OrrGt1tTKntCOnz4MLy9vWsxomd7+5Xh6OiIc+fOSX0UUd3F7jMM0IEDB7B27Vp89NFHcHd313c4pCfFxcUwMeG/OFUNrxwMzJ49e7B27Vps375dKgzp6emYNm0avLy80LdvX+zdu1d6f3x8PF599VW4u7uja9euWLNmDQBg+fLlkMvl0k+7du2wZcsWLFiwACkpKZg2bRrkcjk++eQTjRj+2TSUnp6OWbNm4aWXXoKPjw927dolzSsoKEBwcDA8PT0xaNAgXLx4scS6fHx8sGPHDgwZMgQeHh6YM2cOlEqlNH/v3r3o27cvvLy8MG3aNKSnpwMAhBBYvXo1unTpAnd3dwwZMgRXrlzBV199hcjISOzYsQNyuRzTpk3TiP/atWvw8fGRulIo70opODgYISEhmDhxIuRyOcaNG4c7d+5I88+ePQt/f394eHjA398fZ8+eBQCcOnUKQ4YMkd43ceJE+Pv7S68DAwPxww8/aGxfrVbj448/Rp8+feDt7Y2goCDk5OQAAG7fvg0XFxfs27cPr7zyCl577bUyjyPwuLfOmTNn4qWXXoK3tzeWL19eIrewsDB4enrCx8cHP//8szR9/Pjx2Lx5M0aPHg25XI5JkyZJA8s8iaG4uLjU/ZWYmIjhw4dDLpdrHMvyjicAuLi4YPfu3ejXrx/kcjk2b96MW7duYfTo0XB3d0dQUBAKCwsBlN405uLigps3b5YaF5Wiej2K07OiV69eYubMmaJLly4iKSmpxLzAwEARGhoqCgoKRGJiovD29ha//fabEEKIkSNHigMHDgghHo9ZcO7cOY11P1nm0qVL0rZOnjxZZiynTp0SPXr0EEI8Hmhk+PDhYsuWLUKpVIpbt24JHx8f8csvvwghhFi/fr0YM2aMuHfvnkhJSRGDBw+Wln2yLX9/f5GWlibu3bsnBgwYIL744gshhBC//fab8PLyEgkJCUKpVIrly5eLwMBAIYQQv/zyixg+fLi4f/++UKvV4q+//hLp6elCCCEWLlwowsPDNfbfyZMnRUJCgnj55ZdFTEyMxrzSLFy4ULi5uYkzZ84IpVIpVqxYIUaPHi2EeDwAi0KhEAcOHBBFRUUiMjJSKBQKkZ2dLR49eiQ6dOggsrKyRGFhoejSpYvo3r27yM3NFY8ePRIdO3YU2dnZGtv/9NNPRUBAgEhNTRVKpVIsWbJEzJ07VwghRHJysmjbtq1YsGCByM/PF48ePSrzOBYXF4shQ4aIVatWifz8fFFQUCANBvPNN9+Idu3aia+++koUFxeL3bt3i27dukmDHI0bN0707t1b/P333+LRo0di3LhxYv369SViKCoq0thXSqVSvPLKK2Lnzp2isLBQHD16VLRr1046FuUdTyGEaNu2rZg2bZrIzc0VV65cEe3btxcTJkwQt27dEg8ePBADBw4U+/fvl3J4chyeXv7GjRulHkfSxCsHA3Ly5El07twZbdu2laalpqbi7NmzmD9/PszMzODq6oqAgAAcOnQIAGBiYoJbt24hOzsbFhYWcHNzK7HO7OxszJgxA0uWLEG7du0qHdPFixeRnZ2NmTNnwtTUFC1atMDIkSNx5MgRAMDRo0cxbdo0WFlZwcHBAePHj9dYx/jx42Fvbw8rKyv06tULSUlJAIDIyEj4+/ujffv2MDU1xbx583D+/Hncvn0bJiYmyM/Px99//w0hBJydnWFnZ1durHFxcXjzzTcRFhaGXr16aZ3jK6+8Ak9PT5iammLu3Lk4f/48UlNT8dNPP6Fly5YYNmyY1KNr69atcfz4cZibm6Njx46Ii4vDpUuX8OKLL8Ld3R1nz57F+fPn0bJlSzRp0kRjW3v27MHcuXPRrFkzmJqaYubMmYiOji7xSX3WrFlo0KCB1McOoHkc4+PjkZGRgbfffhsNGjSAmZkZFAqF9H5HR0eMHDkSxsbGGD58ODIzM3H37l1p/quvvornn38e5ubmGDBggHRMynPhwgUUFRXhtddeQ7169TBgwAB07NhRml/e8XxiypQpsLS0RJs2bdC2bVt069YNLVq0QMOGDdGzZ08kJiZqfdyofCwOBmTp0qW4ceMG3n33XYj/6zIrIyMDjRs3hqWlpfQ+R0dH6XJ91apVuHHjBgYOHAh/f38cP35cel9RURFmz54NX19fDB48uEox3blzBxkZGVAoFNJPRESEdKL5Z7fITw9K8oStra30e/369aVxBjIyMtC8eXNpnoWFBaysrJCeno4uXbpg7NixWL58Obp06YIlS5YgLy+v3Fj37NkDuVxe6Zu/zZo1KxFD48aNkZGRgYyMDI18nt73T74wEBsbC09PT3h5eSE2NhaxsbHw8vIqdVspKSmYMWOGtC8HDRoEmUxWYkzgp+MBSj+OTwaAKeuexNOdstWvXx8ASozvUNYxKU9GRgbs7e2lzv2e7I+n55d1PEuLy8zMTOM1x6CoOSwOBqRp06b49NNP8ccff2Dp0qUAADs7O9y/f7/EifHJiFgA0KpVK4SHh+P333/HG2+8gdmzZ0v/YCtWrIClpSXmzJlT5ZgcHBzg5OSEuLg46efcuXPSvQpbW1ukpqaWiE1bdnZ2Jdr3Hz58iJycHCm3CRMmYP/+/Thy5Ahu3LiB7du3A0CJk9PTli1bhtTUVKxevbpSOaalpUm/5+fn4/79+7Czs4OdnV2JfvSBkvvey8sLp0+fRlxcXInicObMGXh6epa6rWbNmuGTTz4psT8vXrworbO0/Eo7jg4ODkhNTS3z3kBtsLW1RXp6uvTBBUCJ/VPR8ayM+vXro6CgQHqdmZlZxaj/vVgcDIy9vT0+/fRT/Prrr1i9ejUcHBwgl8sRHh4OpVKJy5cv4+uvv8bQoUMBAIcOHZIG/2jUqBEAQCaTYc+ePYiNjcWGDRukQUOeaNq0KZKTk7WKp1OnTrCwsMDHH3+MgoICqFQqXLlyBfHx8QCAgQMH4uOPP8b9+/eRlpaGzz77TOtcfX19sX//fiQlJaGwsBDh4eHo1KkTnJycEB8fLzVj1K9fH6amplIeNjY2JZoqnrCwsMD27dsRFxeHDRs2aB3Hzz//jLi4OBQWFuK9995D586d4eDggJdffhk3btxAZGQkiouLceTIEfz111945ZVXAAByuRzXr19HfHw8OnXqhDZt2uDOnTuIj48vsziMGTMGmzdvlk6i2dnZ0o3r0pR1HDt16gRbW1ts3LgRDx8+hFKpxB9//KF1zlXh5uYGExMT7Nq1C0VFRTh27FiJLyCUdzwr68UXX8TVq1eRlJQEpVIp3YQn7bE4GCBHR0f873//Q3R0NDZu3Ijw8HDcuXMHPXr0wMyZMzFr1ix07doVAPDrr79i8ODBkMvlWLVqFTZt2gRzc3McPnwYycnJ6NGjh/RNl4iICACPB/b58MMPoVAosGPHjnJjMTY2RkREBC5fvozevXvjpZdewuLFi6UrmZkzZ8LR0RG9e/fGpEmT4Ofnp3WeXbt2RVBQEGbNmoXu3bsjOTlZen4hPz8fixcvhpeXF3r16gUrKytMnjwZADBixAj89ddfUCgUmD59eol1NmrUCP/973/xyy+/YPPmzVrF4evri61bt8Lb2xuXLl3C+vXrAQBNmjRBREQEdu7cCW9vb2zfvh0RERHSADINGjRA+/bt8cILL0iDs8jlcjg6OsLGxqbUbU2YMAE+Pj6YNGkS5HI5Ro4cKRXa0pR1HJ8cl5s3b6JXr17o2bMnjh49qlW+VWVqaootW7bgwIED8PLywpEjR9C3b19pfnnHs7Kef/55zJgxA6+//jr69esnjXJH2uN4DlTjfv/9dyxevBg//vijvkOpdcHBwbC3t8fcuXP1HQpRjeKVA9W4q1evVqkpgIieHXx8kmrUypUrERMTg7CwMH2HQkTVwGYlIiLSwGYlIiLSwOJAREQaWByIiEgDiwMREWlgcSAiIg0sDkREpOH/AZDFiyWfnDVLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYw_t8oGg4qo",
        "outputId": "a4420111-ea33-4131-91b5-abbb27bacde1"
      },
      "source": [
        "# obliczenie korelacji kolumny 'price' z innymi kolumnami\r\n",
        "dataset.corr()['price'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               1.000000\n",
              "no_of_stores        0.571005\n",
              "latitude            0.546307\n",
              "longitude           0.523287\n",
              "transaction_date    0.087529\n",
              "house_age          -0.210567\n",
              "metro_distance     -0.673613\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "lTh9mh9Fg4xw",
        "outputId": "ed31bce4-9d83-4429-a7a7-b52e760094c7"
      },
      "source": [
        "dataset.corr()['price'].sort_values(ascending=False).plot(kind='bar')\r\n",
        "\r\n",
        "# korelacja zmiennej ceny domu z innymi. Widać, że głównie wpływ na cenę domu ma liczba sklepów w okolicy i jego położenie"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7ada74d358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HVtnv0KFDWL9+Pe7duweDwYBx48bhzTffBADExsZi2bJlaNu2LQDgb3/7G9atW1fXaETUABXfLkWxBftrNC64dcuS7yCjOhfEli1boFarcfDgQWRmZiIkJAQpKSlwdnY22U+j0WDDhg1wd3dHcXExxo4di+7du6NPnz4AgAEDBuDzzz+vaxwiIrKSOl+D2LdvH4KCggAAHh4e6Nq1K44ePVplvx49esDd3R0A4OLiAk9PT2RlZdX1xxMRUT2p8xlEdna2MDQEAFqtFrm5udV+z2+//YZ///vfWLRokbDt9OnT8Pf3h1qtRlhYGIYMGWJxFjc3tcXfYwmNxqVej1/fmF9acs4v5+yA/PNbylq/b40FMWbMGGRnZ4u+d+LECYt/YH5+PqZOnYoFCxYIZxRDhgzB6NGj0bRpU1y6dAlhYWHYsWMHPD09LTq2TleCykqDxZnMIfdxTOaXlpzzyzk7YH/5bVFW5v6+SqWi2g/WNRZEXFxcte+3adMGWVlZcHV1BQDk5OSgX79+ovvqdDqEhoZi8uTJGDVqlLDd+L0A8PTTT6N37944f/68xQVBRETWU+drED4+PoiOjgYAZGZmIjU1FYMGDaqyX2FhIUJDQxESEoLAwECT9/Ly8oSvs7Ky8O9//xtPPfVUXaMREVEd1PkaxKRJkxAREYHhw4dDqVRi8eLFUKvvn7KsXbsWrVu3RnBwML788ktkZmYiOjpaKJSJEydi3Lhx2LlzJ77//nuoVCoAwHvvvYenn366rtGIiKgOFAaDoX4G7SXAaxAPx/zSknN+OWcH7C+/RuMCv9kJ9Xb8xNX+VrsGwaU2iIhIFAuCiIhEsSCIiEgUC4KIiESxIIiISBQLgoiIRLEgiIhIFAuCiIhEsSCIiEgUC4KIiESxIIiISBQLgoiIRLEgiIhIFAuCiIhEsSCIiEgUC4KIiESxIIiISBQLgoiIRLEgiIhIFAuCiIhEOdT1AKWlpZg3bx4uXrwIlUqFuXPnYujQoVX2O3XqFKZMmQIPDw8AgJOTE2JiYoT3161bh7i4OADAmDFjMG3atLpGIyKiOqhzQWzZsgVqtRoHDx5EZmYmQkJCkJKSAmdn5yr7enp6IjY2tsr2n3/+Gfv370dSUhIAIDAwEH379sWzzz5b13hERFRLdR5i2rdvH4KCggAAHh4e6Nq1K44ePWrRMZKTkxEQEICmTZuiadOmCAgIQHJycl2jERFRHdS5ILKzs9G2bVvhtVarRW5urui+mZmZGDNmDAIDA4XhJADIyclBmzZtTI6Rk5NT12hERFQHNQ4xjRkzBtnZ2aLvnThxwuwf1KVLFxw5cgQuLi64efMmQkND4e7ujgEDBpiftgZubmqrHUuMRuNSr8evb8wvLTnnl3N2QP75LWWt37fGgnjwk76YNm3aICsrC66urgDunw3069evyn5q9f//x7tdu3bw9vbG2bNnMWDAAGi1WpMSysnJgVarNfuXMNLpSlBZabD4+8yh0bjg1q3iejm2LTC/tOScX87ZAfvLb4uyMvf3VSoV1X6wrvMQk4+PD6KjowHcH0JKTU3FoEGDquyXn58Pg+H+P95FRUU4fvw4OnfuLBwjPj4eZWVlKCsrQ3x8PEaNGlXXaEREVAd1nsU0adIkREREYPjw4VAqlVi8eLFwtrB27Vq0bt0awcHBSElJwe7du+Hg4AC9Xo+AgAB4e3sDAPr164cRI0bgxRdfBAAEBASgb9++dY1GRER1oDAYP9Y3ABxiejjml5ac88s5O2B/+TUaF/jNTqi34yeu9refISYiImqYWBBERCSKBUFERKJYEEREJIoFQUREolgQREQkigVBRESiWBBERCSKBUFERKJYEEREJIoFQUREolgQREQkigVBRESiWBBERCSKBUFERKLq/MAgIiIyX9ndCiSu9q/X41sLC4KIyIaKb5fCkscXSfnAIw4xERGRKBYEERGJYkEQEZEoFgQREYmq80Xq0tJSzJs3DxcvXoRKpcLcuXMxdOjQKvvt2LEDe/bsEV7fvHkTgYGBmDdvHk6dOoUpU6bAw8MDAODk5ISYmJi6RiMiojqoc0Fs2bIFarUaBw8eRGZmJkJCQpCSkgJnZ2eT/SZOnIiJEycCAMrLyzF48GD4+voK73t6eiI2NraucYiIyErqPMS0b98+BAUFAQA8PDzQtWtXHD16tNrv+eGHH6DRaNCtW7e6/ngiIqondT6DyM7ORtu2bYXXWq0Wubm51X7Pnj17MHbsWJNtmZmZGDNmDBwcHDBhwgSMGTPG4ixubmqLv8cSGo1LvR6/vjG/tOScX87ZAeavrRoLYsyYMcjOzhZ978SJExb/wPz8fPz0009Yvny5sK1Lly44cuQIXFxccPPmTYSGhsLd3R0DBgyw+PhERGQdNRZEXFxcte+3adMGWVlZcHV1BQDk5OSgX79+D90/Pj4ef//734X9AUCt/v+f/Nu1awdvb2+cPXuWBUFEJKE6X4Pw8fFBdHQ0gPvDRKmpqRg0aNBD99+zZw/GjRtnsi0/Px8GgwEAUFRUhOPHj6Nz5851jUZERHVQ52sQkyZNQkREBIYPHw6lUonFixcLZwRr165F69atERwcDAD45Zdf8Oeff+L55583OUZKSgp2794NBwcH6PV6BAQEwNvbu67RiIioDhQG40d3IiKiB/BOaiIiEsWCICIiUSwIIiISxYIgIiJRLAgiIhLFgiAiIlEsCCIiEsWCMENBQYHUEUimTp48if/7v/8DAPzxxx/IyMiQOJH55JydrIMFUY1z585h6NChwsqyqamp+OijjyROZb7k5GSUlJQAuH9X+6RJk3DhwgWJU5lPp9Nhzpw5CAkJAQCkp6dj9+7dEqcy35dffokvvvgCO3bsAABUVFTggw8+kDiVeeSc3ai0tBRr1qzB7NmzAQC//fYbDh06JHEq82VmZiI4OBjDhg0DAFy8eBFRUVE2zcCCqMby5cuxadMmtGrVCgDQrVs3nD17VuJU5tuwYQPUajXOnz+PY8eOISAgAEuXLpU6ltk+/PBDPPPMM7h9+zYA4PHHH8euXbskTmW+pKQkbN++Hc2bNwcAPPbYY0Jh2zs5ZzdauHAh9Ho90tPTAdz/Hb744guJU5lv4cKFeOedd+Dicn+pby8vL+zfv9+mGVgQ1SgvL8cTTzxhss3R0VGiNJZzcLi/1Nbx48cRGBgIPz8/3L17V+JU5svLy0NwcDBUKhWA+4+iVSrl81e2adOmVf6+KBQKidJYRs7ZjS5fvow5c+YIv4ezszMqKyslTmW+4uJiDB48WPhzVyqVNv/3Rz7/tUnAyckJd+7cEf4PunbtGpo0aSJxKvMpFAokJycjOTkZ/fv3B3C/9OTCWHBGt2/fhpyWDnvsscdw5swZKBQKVFZWYv369XjyySeljmUWOWc3cnJyMnl99+5dWf39UalUKC8vF/79ycvLs/kHJNXChQsX2vQnyohGo8FHH32EnJwcXL16FWvXrsVHH32Edu3aSR3NLJ06dcLXX3+NESNGYPDgwcjMzIROp8Pf//53qaOZpaioCAkJCbh+/TrUajWWLl2KgIAA9OzZU+poZunTpw+WL1+O1NRUbNy4EQCwZMkSYdjGnollX7x4sSyyG2VkZCA1NRWXLl3CE088geXLl+PZZ58VPizZO0dHR3z++ef4/fffUVxcjI8//hhTp05Fp06dbJaBq7nW4ObNm/jxxx9hMBjw/PPPo0OHDlJHalS+++47HD58GAaDAcOGDYO/v7/UkSxWWlqKyspKODs7Sx3FYnLOXl5ejs2bN5v8/ZkyZUqVM1N7dubMGfzwww9C/j59+tj057MgqlFQUAC1Wi2cqt67dw8lJSUmT8OzZ5mZmZg3bx7y8vJw+PBhXLx4EYcPH8aMGTOkjtagHTlypNr35XAGFxwcXGXGmNg2atjkU6USeOutt4RpfsD9qX5vv/02vvnmGwlTmc84C2L16tUA7s+CeP/99+2+IGbOnFntBdG1a9faMI3lNm/eDOD+B4rU1FRhSODKlSvo3r27LAqirKzM5LVer8d///tfidLUzsqVK6tsc3FxQc+ePWUxzBQcHIyNGzeiZcuWAO4PuU6bNg07d+60WQZepK7GvXv30KxZM+F18+bNZTULyB5mQdTG0KFDMWTIELi6uuL3339H79690bt3b2RnZ+PRRx+VOl6NvvrqK3z11Vdo27Ytdu/ejfj4eMTHx+Prr79G27ZtpY5Xrc2bN+O5557D1atX0b9/f+F/ffr0sfnwRl3pdDocOHAAer0eer0eKSkpuHLlCpYvX44NGzZIHa9Gf/75p1AOAPDII4/gzp07Ns3AM4gaFBQUCENKOp1OVtPk7GEWRG0Yb0yMjo7Gzp070bRpUwBAUFAQ3njjDQmTWebq1avo0aOH8Lp79+64cuWKhIlqFhQUBB8fHyxZsgSRkZHCdrVabfKPlRzk5+cjNjZWyD1t2jTMmDEDu3btwiuvvIJ33nlH4oTVq6ysRGlpqfAh9c6dO6ioqLBpBhZENV577TUEBwcLF0YTEhIwZcoUiVOZb8KECZg+fToKCwsRFRWF+Ph4zJo1S+pYZissLDSZqujo6IjCwkIJE1mmWbNmSEhIEP7+fPfddyZnpPbIxcUFLi4u+Oc//yl1lDrLy8szKbUWLVrg1q1bJtcV7Zmvry9CQ0MRHBwMANi9ezdeeuklm2bgReoanDp1SrjoOGTIEPTt21fiRJaRehZEXURGRiIrK0s4o0hISIBWq8XixYslTmae3377DeHh4bh69SoUCgU6deqEFStWwNPTU+poNcrJycGqVauQnp5uMqz6/fffS5jKMjNnzkTLli0xduxYAEBcXBwKCwuxevVqjB8/HrGxsRInrFlcXBz+9a9/Abg/9BoQEGDTn8+CaKD0ej1efvllxMXFSR2l1srLy/H111/j9OnTAIDnnnsOr7zyiiyuozzIuESFWq2WOIn5QkNDMXr0aGzduhXLli3D7t270b59e0yfPl3qaGYrKSnBunXrcOrUKQBA3759MW3aNDRr1gy3b9+WzWxEKbEgRKxatQrh4eEPnU1j77NojEJCQrB161ZZ3f3dkDxsuqscZjEFBAQgPj4efn5+SExMRGVlJYKCghATEyN1tEZDp9Phq6++ws2bN02uPdjy3x9egxDxzDPPALh/SidnHTt2REhICEaOHGlyB6xxdVR7J/eCNk53Be7PiEtLS8PTTz8ti4IwnqU1b95cmD0mx2Xvjx07hrS0NJNhMrmcBc2YMQOenp7o37+/sB6ZrbEgRAwbNgx6vR43b97EzJkzpY5Ta3q9Hk8++SSuX78udZRaebCg7969iwMHDshi/N7oq6++Mnl97do1bNmyRaI0lunTpw+KiooQHByMsWPHwsnJCSNHjpQ6lkU+/fRTpKam4tq1a3jhhRfw/fffy+L+B6Pbt29jyZIl0oYw0EONGzdO6gj0gLt37xpeffVVqWPUiZ+fn9QRLJaVlWW4fPmy1DEs5uvraygvLxf+zHNzcw2TJ0+WOJX5/vGPfxhyc3MlzcAziGoMGTIEW7ZsQUBAgMkQjb1PVTQyGAyIjo7GiRMnAADPP/88AgMDZbdss5FCoUBeXp7UMcz24DWIyspKpKam2v06QNeuXRPdrlQqce3atSrL39szJycnODg4QKFQoLy8HO7u7sjNzZU6ltlu376Nl156Cb169TK5jshrEHbC+HCRVatWCdsUCgXS0tKkimSRlStXIi0tTZjmFx8fj8zMTLz//vsSJzPPg9cgDAYDLl++LKshggevQTg4OKB9+/Z2f/1kypQpUCgUMBgMyMnJgVqthkKhQHFxMbRaLQ4fPix1RLM5OzujtLQUvXr1QkREBDQajXDTpRz4+vrC19dX0gycxdSA+fn5IS4uTvjUWl5ejrFjxyIxMVHiZOZ5cIquSqVChw4dTO5MtnfFxcXC08CMSkpKZDHddcmSJejTpw9GjRoFANi/fz/OnDmDDz/8UOJk5vvjjz/QokUL6PV6bNu2DcXFxXjttdfQpk0bqaPJBs8galBQUIBz584BAHr27Ck8flQuHhxOktvQklKprLK894N3Jtu7iRMnVrkP5bXXXpPFvSk///yzyfPXfXx8ZKYBLzIAABKRSURBVLF+0YMeXLdr6tSpVd6fOnUq1q9fb8tIFqmoqMCePXuqzMJavny5zTLY/8I8EkpJScGoUaOExddGjx4tq4eeP//88wgLC0NiYiISExPx1ltv4fnnn5c6ltm2b99u1jZ7U1FRITxHoaysDKWlpSgtLUV+fj5KS0uljmcWg8GAM2fOCK9/+eUXWa1DZo7s7GypI1QrMjISZ8+exb/+9S94eHjgwoULNh8i4xlENdasWYOvv/4aHTt2BHD/+QrvvPMOvL29JU5mnvDwcERHR+PgwYMAAG9vb4wfP17iVDVLTU3F+fPnUVhYaLK0cUlJiSwembpx40Z88cUXUCgUJk+/U6vVCA0NlTCZ+RYsWID33ntPmJBx9+5dYdn4hsLez6hTU1ORmJgIPz8/vPXWW5gwYYLomVB9YkFUo0mTJkI5AICHh4esLnKdOnUKwcHBwmJfAHDy5Em7v9Cbl5eHCxcuoLS0FBcuXBC2Ozs72/T0uramT5+O6dOnY/HixSYrospJnz59cOjQIWRkZAC4f9Plgwvcpaeno3PnzlLFaxSMM5dUKhVKS0vh4uICnU5n0wy8SF2NqKgoODg44OWXX4bBYEBsbCwqKiowefJkGAwGu5/uOmbMmCrj3WLb7NWxY8dkNSTWmMjp79HD2PvvMGnSJHz22WfYvHkzfv31V7Rq1QolJSXYtm2bzTLwDKIa69atA1B13rFx+MBep7v+5z//QWZmJkpKSkzm4hcXF8tiDPyXX37BM888A71eL7qekb0vVfH666/jf//3f/Hcc8+ZDGMYDAYoFAqcPHlSwnTW0RA+Vz722GNSR6jWl19+CZVKhVmzZiExMRHFxcU2X82VBVGN9PR0qSPUytmzZxEbG4s//vjDZC6+Wq1GRESEhMnMExcXh2eeecYku5FCobD7gjDeN7Nnzx6Jk9Qfex+/B+6X2LfffovMzEyEh4fj999/R35+Pnr37g0Adj8ra+vWrQgLCzOZzbdp0yaEhYXZLAOHmBqw2NhY4SY5Imuy9+EZAFi2bBl0Oh0uXryI/fv3o7CwEGFhYfj222+ljmYWexgi5hlEA9auXTvcuXMHzs7OiImJQWpqKsLCwtCuXTupo5klODgYu3fvrnGbvfrrEBNw/4ltPXv2RHh4ODQajUTJ6k4OnytPnTqF+Ph44YFTrVq1ksUz5Y8fP45jx44hPz8fK1euFLaXlJTY/M+d90E0YIsXL0bz5s1x9epVbNu2DW3atMH8+fOljmW2srIyk9d6vR7//e9/JUpjuZCQEPj6+mLbtm3Ytm0b/P39MXz4cLRv397kJjQ5ksOS8U2aNDEpaLncx+Ho6AhnZ2coFAo0b95c+N/jjz8uLP9jKzyDaMCMC5UdPXoUwcHBeO2117B//36pY9Vo8+bN2Lx5M0pKSkym5JaVlcHPz0/CZJY5evSoyQN2IiIiMG7cOOzZswcvvviihMlqVtPDagIDA6WKZrZOnTrhu+++g8FgwO+//44vv/xSeNaLPevbty/69u2LESNGoFOnTpJmYUE0YBUVFTh37hwOHjworCuv1+slTlWzoKAg+Pj4YMmSJSb3EajVapOH0Nu727dvo6ioCI888ggAoLCwUHj8qL0/NtUeHlZTVxEREfjkk09w69YtBAYGYtiwYZg7d67Uscx2/PhxaLVauLi4IDw8HKmpqfjwww9tOvWbBdGAvfvuu4iMjET//v3x5JNPIiMjAx06dJA6Vo1cXFzg4uKCf/7zn1JHqZPXXnsN/v7+wqyro0ePYvLkybhz544wk8Ze2cXDaupIrVZj6dKlUseotdjYWISGhuKnn35CQUEBli1bhqVLl9q0IDiLqRFbv369zW/dt0ROTg5WrVqF9PR0k4uL33//vYSpLJOeno6ff/4ZAPDss8/K5u7jWbNmISIiAu7u7lJHqbXk5GQMHjwYarUaa9euxfnz5zFr1ix07dpV6mhmMT4XfO3atejQoQMCAgI4i4ls5+DBg3ZdEB988AFGjx6NtLQ0fPrpp9i9ezfat28vdSyLdO7cWTal8CB7eFhNXW3YsAGjR4/G+fPncezYMUycOBFLly7F119/LXU0szRt2hRffvkl9u7di507d8JgMNh8LTIWRCNm7yePhYWFCAwMxI4dO9CrVy/06NEDQUFBsnno/NmzZ7Fq1SrcvHkTer1eVndS28PDaurK+ByU48ePIzAwEH5+fti6davEqcy3fPly7Nq1C3PmzIFGo8GNGzdsPkmDBdGI2fvdsMYLuc2bN0d2djYeffRRFBQUSJzKfPPnz8fUqVPRs2dPKJXymlFuvHdAzhQKBZKTk5GcnCw890EOqwEbdezY0WRaevv27fHWW2/ZNAMLguxWnz59UFRUhODgYIwdOxZOTk7w8fGROpbZmjZtKqtpuQ8qKCjAkiVLhLOdgQMHYv78+XB1dZU4mfk+/PBDbN68GS+//DLatWuHzMxM9OvXT+pYNVq1ahXCw8NNHrn7IFsO8/EidQOUmZkJDw+PGvczXgSTg+zsbJSUlEg+L9wSa9asQe/eve1+7SgxM2bMwBNPPIHx48fDYDDgm2++wZUrV2x+o1ZjdPjwYQwbNuyhF6NteXbHgmiAxo4di9jYWGFV0YcpKCiwy0+ENa04a+/LrBs999xzKCoqgrOzM5ycnGR1DcLf3x8JCQk1brNn9vAJXO44xNQAlZWV4cCBA8jOzq52uWx7LAcA6NWrFxQKhclFdONre15m/a/kvJprZWUldDod3NzcANy/s1ouS1UYDR06VPj67t27OHDgADw9PSVMZJ6HFZuRLQuOBdEAvffee4iOjq6y3Dcgj+Wy5brM+l+1bdsWFRUVJk9lM86ssXeTJk1CQEAAhgwZAgA4cuQIZs+eLW0oC/11KGbs2LGYNGmSRGnMZyy28+fP4/z583jppZcAAElJSejevbtNs3CIqQFbvnw55s2bJ3WMRis1NRUzZ84UhpcqKioQFRWFLl26SB3NLFeuXMHp06cBAP369cOTTz4pcaK6KS8vx4svvoiUlBSpo5hl/Pjx2L59u/CY47KyMrzxxhs2vY9DHh9nqFbmzZuHwsJCnDt3DgDQo0cPtGrVSuJUjcfHH3+MZcuWCQsOnjx5EkuWLJHNjVqdOnWS1aSAv3pwqMZgMODy5csYMGCAxKnMV1hYaPIccEdHRxQWFto0AwuiAfvxxx8RHh6Op59+WvgPZNWqVRg4cKDU0RqF0tJSk9Vo+/fvj08++UTCRDULDw/HqlWrMG7cONFxcLk8bAcwvQahUqkwadIk9OjRQ8JElunXrx/CwsKEobKEhASbT9NlQTRga9aswc6dO4ULc7/99hvCw8NZEDbSrFkznDp1SviP+vTp03Y/A+v1118HAFmtevowxn9Y//zzTwD3b7iUk48++gjR0dE4cOAAAGDIkCF45ZVXbJqBBdGAVVRUmMza8PT0NFnbn+rXBx98gHfffVcYJigvL8fnn38ucarqGReyy8nJEZ6DbCSnKa4AcPPmTcyePVuY9PD0009j1apVsnmioqOjI1599VW8+uqrou8vXLgQCxcurNcM8rr/nyzi6uqK2NhY4XVcXJzdTm1tiLp3746UlBRERUUhKioKBw4ckM1Kotu3bzdrmz2LjIzEK6+8gnPnzuHcuXMIDAw0eb6I3BmvLdYnnkE0YIsXL8acOXOETxleXl749NNPpQ3VCPz1Rj/jJ9aKigpUVFTY9TBTamoqzp8/j8LCQuzcuVPYXlJSIqt1jID7N4K+/PLLwutx48Zhx44dEiaSHxZEA9a+fXt88803uHPnDgDA2dnZ5P2jR49i8ODBUkRr0P56o9+DM2ns/Ua/vLw8XLhwAaWlpbhw4YKw3dnZGcuXL5cwmeWUSiWuX7+Oxx9/HACQkZEh26fjSYUF0Qj8tRiM1qxZw4KoB3K+0c/b2xve3t44duyYTZ9cVh9mzZqFkJAQeHl5CbP4Vq5cKXUsWWFBNGK8R5Ie5tKlS+jatavJ87T37NmDyZMnS5zMfIMHD0ZSUhLOnz8P4P59QA3pGpwtlpDnRepGzN6fB0HS2bt3r1AOANCqVSskJSVJmKh23NzcMHDgQDz33HNo1qxZjQtB2ptr165h586d2LlzJ3777TeT92yx1hcLgoiqEDu71Ov1EiSpvZSUFAwePBg9evRA79690atXL/Tu3VvqWGaLj49HaGgo0tLSkJaWhtDQUHz33Xc2zcAhpkaMQ0z0MB4eHti2bRveeOMNGAwGbN++XXbPA1+1ahX+53/+R5ZP9AOArVu3IjY2FhqNBgBw69YtTJo0SVi8zxbk96dGFvvzzz+Fu0kfNGvWLAnSkBzMnz8fP/zwA7p3746ePXviyJEjsruHoGXLlujdu7csy8HIWA5//dpWuJprA3bjxg3MmTMHaWlpUCgUsruTlKQnx2UqjNcZduzYARcXF4wePRpNmjQR3rfn+1AeNH36dHTq1AlBQUEAgJiYGKSlpWHdunU2y8CCaMBCQ0Px4osvYty4cQCA2NhYJCUlYdu2bRInIzkoLi5GRkYG7t69K2x79tlnJUxkns6dOzeIB07pdDosXboUJ06cgEKhwIABAzB//nzhIU62wGsQDRjvJKXaSk5OxooVK3D79m20bt0aN27cQOfOnR/6nGR7Iuf7UIz0ej127tyJNWvWSJpDvoNzVCPjnaRGvJOUzLVx40bExsaiQ4cOOHDgADZv3oxu3bpJHavRUKlUOHr0qNQxeAbRkPFOUqotBwcHuLm5CVNbBw4cyHW8bGzIkCHYsmULAgICTK4B2fIaCguiAWvod5JS/TE+JrVDhw746quv0LZtW9GZcFR/vvjiCwD3p+tKdQ2FF6kbgXv37pnc5CSXWRwknZMnT6Jr167Q6XRYuHAhbt++jTlz5sjqkZ1UdyyIBiwlJQVLly7FrVu3AMhjNVEiuu/jjz/G/Pnza9xWn3iRugEz3kl68eJFpKWlIT09neVAZtm2bRuKi4sBAO+//z58fHxw7NgxiVM1LmfOnKmy7eeff7ZpBl6DaMCMd5ISWSo2NhahoaH46aefoNPpsGzZMixdulT2S4DLwb59+7Bv3z5kZWXh3XffFbaXlJSgadOmNs3CgmjAhg8fjl27dsn2TlKSjnE69KlTp+Dn54fevXtz7S4b6dixI4YMGYLU1FQMGTJE2K5Wq9G/f3+bZuE1iAasc+fOwtdyvJOUpDN+/HgMGzYM3377LXbu3IlHH30Ufn5+slzyW66KiopMllyXAguCiKrIyMjArl278Oyzz2LEiBG4ceMG9u3bh7feekvqaI1GZmYm5s2bh7y8PBw+fBgXL17E4cOHMWPGDJtlYEEQEdmhN954A2+++SZWr16NhIQEVFZWws/PD3v37rVZBl6DIKIqrl+/jg0bNuDmzZuoqKgQtn/77bcSpmpciouLMXjwYHz22WcA7i+d4+joaNMMLAgiquK9996Dj48Pxo4dy/W7JKJSqVBeXi48GjgvL8/mz7ZgQRBRFZWVlXj77beljtGoTZgwAdOnT0dhYSGioqIQHx9v84d8sSCIqIqePXsiPT3dZCYc2VZAQAD+9re/4YcffkBpaSlWrFiBPn362DQDL1ITURUBAQG4du0aOnbsaHIPDa9BNC4sCCKq4vTp06Lb+/bta+Mkjdf169exceNG3LhxQ7KJAiwIIiI75OfnB39/f3Tp0sVkooAtS5rXIIioiuLiYmzatAlpaWkmz6TmI2ttx8HBAZMnT5Y0A1dzJaIqPvjgAyiVSmRmZuKVV16BSqVC9+7dpY7VqAwaNAhHjhyRNAPPIIioiv/85z+IiorC999/D19fX4wYMQITJ06UOlaj0r9/f0ydOhVKpVJ4wp9CocDJkydtloEFQURVODk5AQAcHR1RVFSEli1boqCgQOJUjUtkZCSWL1+OLl262PwGOSMWBBFV4eHhgaKiIvj5+SEoKAguLi7o0qWL1LEalZYtW8LHx0fSDJzFRETVOnPmDIqLizFo0CA4OPAzpa1s3LgRLVu2xKhRoyR7ngsLgohM6PV6vPzyy4iLi5M6SqNmD89z4ccBIjKhUqnQvHlz3L171+STK9lWenq61BFYEERUVceOHRESEoKRI0eiefPmwvaQkBAJU5GtsSCIqIrCwkI8+eSTuH79urCtuLiYBdHIsCCIqIrs7GysW7fOZNuYMWMkSkNSYUEQkaCiogLl5eWorKxEWVkZjHNYiouLUVpaKnE6sjUWBBEJNm7ciC+++ALA/WdCGKnVaoSGhkoViyTCaa5EVMXixYsRGRkpdQySGAuCiIhEcTVXIiISxYIgIiJRLAgiIhLFgiAiIlEsCCIiEvX/ABaFEP5X/o/4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "3lfTOaJIT33X",
        "outputId": "8ab3a105-0580-4912-e963-b9dde6f46ce4"
      },
      "source": [
        "chart = dataset.plot.scatter(figsize=(16, 8), x='metro_distance', y='no_of_stores', title = 'Wykres ilości sklepów w zależności od odległości od metra')\r\n",
        "chart.set_xlabel(\"Odległość od metra\")\r\n",
        "chart.set_ylabel(\"Liczba sklepów w okolicy\")\r\n",
        "\r\n",
        "# jak widac w okolicach metra jest o wiele więcej sklepów"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Liczba sklepów w okolicy')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6AGaNlNvUdH5",
        "outputId": "19e5398b-e778-4ce0-d298-5ddba0fe7361"
      },
      "source": [
        "# mapa z lokalizacja omawianych domow\r\n",
        "# zaimportowanie biblioteki plotly express, ktora dosyc latwo wyrysuje wspolrzedne na mapie\r\n",
        "import plotly.express as px\r\n",
        "\r\n",
        "chart = px.scatter_mapbox(dataset, lat='latitude', lon='longitude', color='price', size='house_age', zoom=12)\r\n",
        "chart.update_layout(mapbox_style='carto-positron', margin={'l': 10, 'r': 10, 't':10, 'b':10})\r\n",
        "chart.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"093ce7ca-fafd-4a1c-b002-90bcb55a4e02\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"093ce7ca-fafd-4a1c-b002-90bcb55a4e02\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '093ce7ca-fafd-4a1c-b002-90bcb55a4e02',\n",
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