ium_434788/IUM_5_434788_wersja_Jupyter.ipynb

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  "metadata": {
    "colab": {
      "name": "IUM_5_434788.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tY8oIUexCAg2"
      },
      "source": [
        "# 0. Imports and downloading the Data Frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlE65Fo32mGf"
      },
      "source": [
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.layers import Dense\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFXots4qa8Sz"
      },
      "source": [
        "### 0.1. Wyczytanie pliku csv z mojego repo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "8y82qyYTCN3H",
        "outputId": "b1521955-f5f4-4080-ace4-6ce3f03d453e"
      },
      "source": [
        "!curl -OL https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv\n",
        "\n",
        "wine=pd.read_csv('winequality-red.csv')\n",
        "wine"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100   98k  100   98k    0     0  66899      0  0:00:01  0:00:01 --:--:-- 66899\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
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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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.880</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1599 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
              "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
              "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
              "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
              "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
              "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
              "...             ...               ...          ...  ...        ...      ...      ...\n",
              "1594            6.2             0.600         0.08  ...       0.58     10.5        5\n",
              "1595            5.9             0.550         0.10  ...       0.76     11.2        6\n",
              "1596            6.3             0.510         0.13  ...       0.75     11.0        6\n",
              "1597            5.9             0.645         0.12  ...       0.71     10.2        5\n",
              "1598            6.0             0.310         0.47  ...       0.66     11.0        6\n",
              "\n",
              "[1599 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1z5M-qvCanz"
      },
      "source": [
        "# 1. Analiza zbioru"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-qmc_JJbDbY"
      },
      "source": [
        "### 1.1. Heatmap by zbada korelacje. Z początku zastanawiałem się, czy nie wykorzystać tylko kolumn wysoko skorelowanych z 'Quality', jednak koniec końców model będzie się opierać o wszystkie kolumny"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "KGx2vKgO5L1b",
        "outputId": "4dc89448-a6b7-4f2f-919e-c79f58b33be0"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(wine.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJlKPvbbCjiA"
      },
      "source": [
        "# 2. Normalizacja i podział zbioru na Test/Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSb2f0BRbdtI"
      },
      "source": [
        "### 2.1. 'y' to pojedyńcza kolumna z wartościami 'quality'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nASk0bFA25Qs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbf37222-32f9-41e7-df82-46cc98ab13b7"
      },
      "source": [
        "y = wine.quality\n",
        "y.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    5\n",
              "1    5\n",
              "2    5\n",
              "3    6\n",
              "4    5\n",
              "Name: quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Syf1hf1qbvAc"
      },
      "source": [
        "### 2.2. 'x' to wszystkie kolumny poza 'quality'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DRh8_4RaC2eV",
        "outputId": "bca3de72-1368-462c-bf58-e4536fc93b74"
      },
      "source": [
        "x = wine.drop(['quality'], axis= 1)\n",
        "x.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...    pH  sulphates  alcohol\n",
              "0            7.4              0.70         0.00  ...  3.51       0.56      9.4\n",
              "1            7.8              0.88         0.00  ...  3.20       0.68      9.8\n",
              "2            7.8              0.76         0.04  ...  3.26       0.65      9.8\n",
              "3           11.2              0.28         0.56  ...  3.16       0.58      9.8\n",
              "4            7.4              0.70         0.00  ...  3.51       0.56      9.4\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjRN2mU9b0S4"
      },
      "source": [
        "### 2.3. Normalizacja wartości w x (do przedziału 0-1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "aYSFPw7e58uC",
        "outputId": "0462f35a-1343-4fc3-b0f8-83df57951b86"
      },
      "source": [
        "x=((x-x.min())/(x.max()-x.min()))\n",
        "x.head()"
      ],
      "execution_count": 6,
      "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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.247788</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.068493</td>\n",
              "      <td>0.106845</td>\n",
              "      <td>0.140845</td>\n",
              "      <td>0.098940</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.137725</td>\n",
              "      <td>0.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.283186</td>\n",
              "      <td>0.520548</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.116438</td>\n",
              "      <td>0.143573</td>\n",
              "      <td>0.338028</td>\n",
              "      <td>0.215548</td>\n",
              "      <td>0.494126</td>\n",
              "      <td>0.362205</td>\n",
              "      <td>0.209581</td>\n",
              "      <td>0.215385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.283186</td>\n",
              "      <td>0.438356</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.095890</td>\n",
              "      <td>0.133556</td>\n",
              "      <td>0.197183</td>\n",
              "      <td>0.169611</td>\n",
              "      <td>0.508811</td>\n",
              "      <td>0.409449</td>\n",
              "      <td>0.191617</td>\n",
              "      <td>0.215385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.584071</td>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.068493</td>\n",
              "      <td>0.105175</td>\n",
              "      <td>0.225352</td>\n",
              "      <td>0.190813</td>\n",
              "      <td>0.582232</td>\n",
              "      <td>0.330709</td>\n",
              "      <td>0.149701</td>\n",
              "      <td>0.215385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.247788</td>\n",
              "      <td>0.397260</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.068493</td>\n",
              "      <td>0.106845</td>\n",
              "      <td>0.140845</td>\n",
              "      <td>0.098940</td>\n",
              "      <td>0.567548</td>\n",
              "      <td>0.606299</td>\n",
              "      <td>0.137725</td>\n",
              "      <td>0.153846</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  ...        pH  sulphates   alcohol\n",
              "0       0.247788          0.397260         0.00  ...  0.606299   0.137725  0.153846\n",
              "1       0.283186          0.520548         0.00  ...  0.362205   0.209581  0.215385\n",
              "2       0.283186          0.438356         0.04  ...  0.409449   0.191617  0.215385\n",
              "3       0.584071          0.109589         0.56  ...  0.330709   0.149701  0.215385\n",
              "4       0.247788          0.397260         0.00  ...  0.606299   0.137725  0.153846\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5w0PCwRDb7Qu"
      },
      "source": [
        "### 2.4. Podział na zbiory testowe i treningowe (1:4)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhN2kywv3psP"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "l0iJsRfe9uqK",
        "outputId": "2f06df73-9583-438a-f634-f56c435d22a8"
      },
      "source": [
        "x_train.head()"
      ],
      "execution_count": 8,
      "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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>0.327434</td>\n",
              "      <td>0.363014</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.136986</td>\n",
              "      <td>0.128548</td>\n",
              "      <td>0.225352</td>\n",
              "      <td>0.120141</td>\n",
              "      <td>0.584435</td>\n",
              "      <td>0.433071</td>\n",
              "      <td>0.131737</td>\n",
              "      <td>0.169231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>370</th>\n",
              "      <td>0.203540</td>\n",
              "      <td>0.441781</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.095890</td>\n",
              "      <td>0.085142</td>\n",
              "      <td>0.478873</td>\n",
              "      <td>0.201413</td>\n",
              "      <td>0.545521</td>\n",
              "      <td>0.653543</td>\n",
              "      <td>0.269461</td>\n",
              "      <td>0.230769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>0.831858</td>\n",
              "      <td>0.198630</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.198630</td>\n",
              "      <td>0.128548</td>\n",
              "      <td>0.070423</td>\n",
              "      <td>0.144876</td>\n",
              "      <td>0.831865</td>\n",
              "      <td>0.212598</td>\n",
              "      <td>0.287425</td>\n",
              "      <td>0.369231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>0.309735</td>\n",
              "      <td>0.482877</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.082192</td>\n",
              "      <td>0.120200</td>\n",
              "      <td>0.056338</td>\n",
              "      <td>0.024735</td>\n",
              "      <td>0.523495</td>\n",
              "      <td>0.496063</td>\n",
              "      <td>0.263473</td>\n",
              "      <td>0.353846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>0.283186</td>\n",
              "      <td>0.291096</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.109589</td>\n",
              "      <td>0.093489</td>\n",
              "      <td>0.140845</td>\n",
              "      <td>0.102473</td>\n",
              "      <td>0.435389</td>\n",
              "      <td>0.472441</td>\n",
              "      <td>0.167665</td>\n",
              "      <td>0.492308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     fixed acidity  volatile acidity  ...  sulphates   alcohol\n",
              "751       0.327434          0.363014  ...   0.131737  0.169231\n",
              "370       0.203540          0.441781  ...   0.269461  0.230769\n",
              "374       0.831858          0.198630  ...   0.287425  0.369231\n",
              "537       0.309735          0.482877  ...   0.263473  0.353846\n",
              "708       0.283186          0.291096  ...   0.167665  0.492308\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eml7m1efDZcB"
      },
      "source": [
        "# 3. Model i jego trening (Tensorflow.Keras)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yw5LC_8g4W_z"
      },
      "source": [
        "def regression_model():\n",
        "    model = Sequential()\n",
        "    model.add(Dense(32,activation = \"relu\", input_shape = (x_train.shape[1],)))\n",
        "    model.add(Dense(64,activation = \"relu\"))\n",
        "    model.add(Dense(1,activation = \"relu\"))\n",
        "    \n",
        "    model.compile(optimizer = \"adam\", loss = \"mean_squared_error\")\n",
        "    return model"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZex-gc2-fma"
      },
      "source": [
        "model = regression_model()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3Soo5N94nYK",
        "outputId": "78df14e3-e63f-4e59-b768-dd3c24a7f8d3"
      },
      "source": [
        "model.fit(x_train, y_train, epochs = 600, verbose = 1)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/600\n",
            "40/40 [==============================] - 1s 1ms/step - loss: 27.8321\n",
            "Epoch 2/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 7.2309\n",
            "Epoch 3/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 1.0122\n",
            "Epoch 4/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.8249\n",
            "Epoch 5/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.8217\n",
            "Epoch 6/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.7261\n",
            "Epoch 7/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.6524\n",
            "Epoch 8/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.6332\n",
            "Epoch 9/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.6085\n",
            "Epoch 10/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5933\n",
            "Epoch 11/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5950\n",
            "Epoch 12/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.6067\n",
            "Epoch 13/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5047\n",
            "Epoch 14/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5503\n",
            "Epoch 15/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5120\n",
            "Epoch 16/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5540\n",
            "Epoch 17/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.5384\n",
            "Epoch 18/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5129\n",
            "Epoch 19/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4923\n",
            "Epoch 20/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.5131\n",
            "Epoch 21/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4585\n",
            "Epoch 22/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4688\n",
            "Epoch 23/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4497\n",
            "Epoch 24/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4347\n",
            "Epoch 25/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4830\n",
            "Epoch 26/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.4474\n",
            "Epoch 27/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4599\n",
            "Epoch 28/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.4428\n",
            "Epoch 29/600\n",
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            "Epoch 447/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3030\n",
            "Epoch 448/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3231\n",
            "Epoch 449/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3107\n",
            "Epoch 450/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3288\n",
            "Epoch 451/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3194\n",
            "Epoch 452/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3037\n",
            "Epoch 453/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3168\n",
            "Epoch 454/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2999\n",
            "Epoch 455/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3104\n",
            "Epoch 456/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2976\n",
            "Epoch 457/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3572\n",
            "Epoch 458/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3177\n",
            "Epoch 459/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3102\n",
            "Epoch 460/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3087\n",
            "Epoch 461/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3039\n",
            "Epoch 462/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3127\n",
            "Epoch 463/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3300\n",
            "Epoch 464/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3243\n",
            "Epoch 465/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3106\n",
            "Epoch 466/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2922\n",
            "Epoch 467/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3300\n",
            "Epoch 468/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3051\n",
            "Epoch 469/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2999\n",
            "Epoch 470/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3099\n",
            "Epoch 471/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3122\n",
            "Epoch 472/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3105\n",
            "Epoch 473/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3167\n",
            "Epoch 474/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3227\n",
            "Epoch 475/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3039\n",
            "Epoch 476/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3167\n",
            "Epoch 477/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3083\n",
            "Epoch 478/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3280\n",
            "Epoch 479/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3070\n",
            "Epoch 480/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3247\n",
            "Epoch 481/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3205\n",
            "Epoch 482/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3219\n",
            "Epoch 483/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3158\n",
            "Epoch 484/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3384\n",
            "Epoch 485/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2816\n",
            "Epoch 486/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3286\n",
            "Epoch 487/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3357\n",
            "Epoch 488/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3076\n",
            "Epoch 489/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3206\n",
            "Epoch 490/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3173\n",
            "Epoch 491/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3251\n",
            "Epoch 492/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3083\n",
            "Epoch 493/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3154\n",
            "Epoch 494/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3128\n",
            "Epoch 495/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3146\n",
            "Epoch 496/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3092\n",
            "Epoch 497/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3007\n",
            "Epoch 498/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3117\n",
            "Epoch 499/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3241\n",
            "Epoch 500/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3224\n",
            "Epoch 501/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3187\n",
            "Epoch 502/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3159\n",
            "Epoch 503/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3144\n",
            "Epoch 504/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3196\n",
            "Epoch 505/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3058\n",
            "Epoch 506/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3054\n",
            "Epoch 507/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3240\n",
            "Epoch 508/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3082\n",
            "Epoch 509/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2909\n",
            "Epoch 510/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3181\n",
            "Epoch 511/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3180\n",
            "Epoch 512/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3373\n",
            "Epoch 513/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3207\n",
            "Epoch 514/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3228\n",
            "Epoch 515/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3178\n",
            "Epoch 516/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3052\n",
            "Epoch 517/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3281\n",
            "Epoch 518/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3052\n",
            "Epoch 519/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3119\n",
            "Epoch 520/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2770\n",
            "Epoch 521/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3306\n",
            "Epoch 522/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3159\n",
            "Epoch 523/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3191\n",
            "Epoch 524/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3369\n",
            "Epoch 525/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3080\n",
            "Epoch 526/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3014\n",
            "Epoch 527/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3012\n",
            "Epoch 528/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3150\n",
            "Epoch 529/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3105\n",
            "Epoch 530/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3236\n",
            "Epoch 531/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3102\n",
            "Epoch 532/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3199\n",
            "Epoch 533/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2913\n",
            "Epoch 534/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2913\n",
            "Epoch 535/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3063\n",
            "Epoch 536/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3174\n",
            "Epoch 537/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3165\n",
            "Epoch 538/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3277\n",
            "Epoch 539/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3098\n",
            "Epoch 540/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3196\n",
            "Epoch 541/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3007\n",
            "Epoch 542/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3001\n",
            "Epoch 543/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3018\n",
            "Epoch 544/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2872\n",
            "Epoch 545/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2685\n",
            "Epoch 546/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3197\n",
            "Epoch 547/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3055\n",
            "Epoch 548/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3225\n",
            "Epoch 549/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3167\n",
            "Epoch 550/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3164\n",
            "Epoch 551/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3104\n",
            "Epoch 552/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3093\n",
            "Epoch 553/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3062\n",
            "Epoch 554/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3225\n",
            "Epoch 555/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3169\n",
            "Epoch 556/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.2989\n",
            "Epoch 557/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2997\n",
            "Epoch 558/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3211\n",
            "Epoch 559/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3092\n",
            "Epoch 560/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3128\n",
            "Epoch 561/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3047\n",
            "Epoch 562/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3100\n",
            "Epoch 563/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3143\n",
            "Epoch 564/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2766\n",
            "Epoch 565/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3003\n",
            "Epoch 566/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3040\n",
            "Epoch 567/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2720\n",
            "Epoch 568/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3156\n",
            "Epoch 569/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3182\n",
            "Epoch 570/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3047\n",
            "Epoch 571/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3074\n",
            "Epoch 572/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3250\n",
            "Epoch 573/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.2953\n",
            "Epoch 574/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2828\n",
            "Epoch 575/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2882\n",
            "Epoch 576/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2914\n",
            "Epoch 577/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3143\n",
            "Epoch 578/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2871\n",
            "Epoch 579/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2677\n",
            "Epoch 580/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3053\n",
            "Epoch 581/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2921\n",
            "Epoch 582/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3074\n",
            "Epoch 583/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3053\n",
            "Epoch 584/600\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 0.2888\n",
            "Epoch 585/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3111\n",
            "Epoch 586/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3032\n",
            "Epoch 587/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2958\n",
            "Epoch 588/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3009\n",
            "Epoch 589/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3176\n",
            "Epoch 590/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2913\n",
            "Epoch 591/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2803\n",
            "Epoch 592/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2720\n",
            "Epoch 593/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2856\n",
            "Epoch 594/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3113\n",
            "Epoch 595/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2881\n",
            "Epoch 596/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.3043\n",
            "Epoch 597/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2897\n",
            "Epoch 598/600\n",
            "40/40 [==============================] - 0s 1ms/step - loss: 0.3105\n",
            "Epoch 599/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2815\n",
            "Epoch 600/600\n",
            "40/40 [==============================] - 0s 2ms/step - loss: 0.2928\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fafd9388bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRsJLVS2cOfM"
      },
      "source": [
        "# 4. Predykcje, Pokrycie, Precyzja i F-Score (+ Zapisanie y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KlMEBYj4zYC",
        "outputId": "dc6a27fe-5ff7-4614-9f94-abdf140ae073"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "\n",
        "y_pred[:5]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.852079 ],\n",
              "       [5.9662743],\n",
              "       [5.219407 ],\n",
              "       [5.5860786],\n",
              "       [6.314252 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7QI0nqhBKeq"
      },
      "source": [
        "y_pred = np.around(y_pred, decimals=0)\n",
        "\n",
        "y_pred[:5]\n",
        "\n",
        "pd.DataFrame(y_pred).to_csv(\"preds.csv\")"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPDHtbA6AC-P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b7bed7f-6431-4458-c9b4-d1b1f61471b9"
      },
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.603125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUAaNOJaAlsk",
        "outputId": "3bbc97d1-df61-4e2e-aec9-dc75407df371"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,y_pred)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         1.0       0.00      0.00      0.00         0\n",
            "         3.0       0.00      0.00      0.00         1\n",
            "         4.0       0.00      0.00      0.00         6\n",
            "         5.0       0.75      0.62      0.68       152\n",
            "         6.0       0.49      0.70      0.58       115\n",
            "         7.0       0.66      0.47      0.55        40\n",
            "         8.0       0.00      0.00      0.00         6\n",
            "\n",
            "    accuracy                           0.60       320\n",
            "   macro avg       0.27      0.26      0.26       320\n",
            "weighted avg       0.61      0.60      0.60       320\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8OqcubbIIJU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}