Lec_2_LinearRegression.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/Lec_2_LinearRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# Введение в Машинное обучение\n",
        "\n",
        "## Высшая Школа Цифровой Культуры\n",
        "\n",
        "## Материалы к лекции 2. Линейная регрессия"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nB4YxlaV828"
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      "source": [
        "### Примеры"
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Загрузим данные\n",
        "data = {\n",
        "    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "    'X': [9, 4, 17, 2, 14, 10, 13, 1, 11, 3],\n",
        "    'Y': [15, 9, 41, 1, 32, 22, 30, 4, 31, 8]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Вычислим выборочные средние x и y\n",
        "mean_x = df['X'].mean()\n",
        "mean_y = df['Y'].mean()\n",
        "\n",
        "# Вычислим коэффициенты theta0 и theta1 с использованием метода наименьших квадратов\n",
        "x = df['X']\n",
        "y = df['Y']\n",
        "theta1 = np.sum((x - mean_x) * (y - mean_y)) / np.sum((x - mean_x) ** 2)\n",
        "theta0 = mean_y - theta1 * mean_x\n",
        "\n",
        "# Вычислим статистику R**2\n",
        "y_pred = theta0 + theta1 * x\n",
        "ss_total = np.sum((y - mean_y) ** 2)\n",
        "ss_residual = np.sum((y - y_pred) ** 2)\n",
        "r_squared = 1 - (ss_residual / ss_total)\n",
        "\n",
        "print(\"Выборочное среднее x:\", mean_x)\n",
        "print(\"Выборочное среднее y:\", mean_y)\n",
        "print(\"Коэффициент theta1:\", theta1)\n",
        "print(\"Коэффициент theta0:\", theta0)\n",
        "print(\"R**2:\", r_squared)\n"
      ],
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            "Выборочное среднее x: 8.4\n",
            "Выборочное среднее y: 19.3\n",
            "Коэффициент theta1: 2.410128388017118\n",
            "Коэффициент theta0: -0.9450784593437902\n",
            "R**2: 0.9513257196553756\n"
          ]
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      "source": [
        "#чтение данных, так как названия конфет уникальны, используем их в качестве индекса\n",
        "import pandas as pd\n",
        "DATA = pd.read_csv(\"candy-data.csv\", delimiter=',', index_col='competitorname')"
      ]
    },
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 70 entries, 100 Grand to Strawberry bon bons\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   chocolate         70 non-null     int64  \n",
            " 1   fruity            70 non-null     int64  \n",
            " 2   caramel           70 non-null     int64  \n",
            " 3   peanutyalmondy    70 non-null     int64  \n",
            " 4   nougat            70 non-null     int64  \n",
            " 5   crispedricewafer  70 non-null     int64  \n",
            " 6   hard              70 non-null     int64  \n",
            " 7   bar               70 non-null     int64  \n",
            " 8   pluribus          70 non-null     int64  \n",
            " 9   sugarpercent      70 non-null     float64\n",
            " 10  pricepercent      70 non-null     float64\n",
            " 11  winpercent        70 non-null     float64\n",
            " 12  Y                 70 non-null     int64  \n",
            "dtypes: float64(3), int64(10)\n",
            "memory usage: 9.7+ KB\n"
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      "source": [
        "DATA.info()"
      ]
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      "source": [
        "#обучение модели будем проводить на данных, за исключением некоторых конфет\n",
        "# train_data = DATA.drop(['100 Grand','3 Musketeers','One dime','One quarter','Air Heads'])\n",
        "\n",
        "train_data = DATA.drop(['Caramel Apple Pops', 'Hersheys Kisses'])"
      ]
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              "                       chocolate  fruity  caramel  peanutyalmondy  nougat  \\\n",
              "competitorname                                                              \n",
              "100 Grand                      1       0        1               0       0   \n",
              "3 Musketeers                   1       0        0               0       1   \n",
              "One dime                       0       0        0               0       0   \n",
              "One quarter                    0       0        0               0       0   \n",
              "Air Heads                      0       1        0               0       0   \n",
              "...                          ...     ...      ...             ...     ...   \n",
              "Snickers Crisper               1       0        1               1       0   \n",
              "Sour Patch Kids                0       1        0               0       0   \n",
              "Sour Patch Tricksters          0       1        0               0       0   \n",
              "Starburst                      0       1        0               0       0   \n",
              "Strawberry bon bons            0       1        0               0       0   \n",
              "\n",
              "                       crispedricewafer  hard  bar  pluribus  sugarpercent  \\\n",
              "competitorname                                                               \n",
              "100 Grand                             1     0    1         0         0.732   \n",
              "3 Musketeers                          0     0    1         0         0.604   \n",
              "One dime                              0     0    0         0         0.011   \n",
              "One quarter                           0     0    0         0         0.011   \n",
              "Air Heads                             0     0    0         0         0.906   \n",
              "...                                 ...   ...  ...       ...           ...   \n",
              "Snickers Crisper                      1     0    1         0         0.604   \n",
              "Sour Patch Kids                       0     0    0         1         0.069   \n",
              "Sour Patch Tricksters                 0     0    0         1         0.069   \n",
              "Starburst                             0     0    0         1         0.151   \n",
              "Strawberry bon bons                   0     1    0         1         0.569   \n",
              "\n",
              "                       pricepercent  winpercent  Y  \n",
              "competitorname                                      \n",
              "100 Grand                     0.860   66.971725  1  \n",
              "3 Musketeers                  0.511   67.602936  1  \n",
              "One dime                      0.116   32.261086  0  \n",
              "One quarter                   0.511   46.116505  0  \n",
              "Air Heads                     0.511   52.341465  1  \n",
              "...                             ...         ... ..  \n",
              "Snickers Crisper              0.651   59.529251  1  \n",
              "Sour Patch Kids               0.116   59.863998  1  \n",
              "Sour Patch Tricksters         0.116   52.825947  1  \n",
              "Starburst                     0.220   67.037628  1  \n",
              "Strawberry bon bons           0.058   34.578991  0  \n",
              "\n",
              "[68 rows x 13 columns]"
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              "      <th>chocolate</th>\n",
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              "      <th>competitorname</th>\n",
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              "      <th>100 Grand</th>\n",
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              "      <th>3 Musketeers</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0.604</td>\n",
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              "      <td>67.602936</td>\n",
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              "      <th>One dime</th>\n",
              "      <td>0</td>\n",
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              "      <td>32.261086</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>One quarter</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.511</td>\n",
              "      <td>46.116505</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>Air Heads</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.906</td>\n",
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              "      <th>Snickers Crisper</th>\n",
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              "      <td>1</td>\n",
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              "      <td>59.529251</td>\n",
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              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-db2249f4-7f69-4022-af2e-96d3ff0b676f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-db2249f4-7f69-4022-af2e-96d3ff0b676f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wo72mdq3V83A"
      },
      "outputs": [],
      "source": [
        "#отбираем данные для предикторов, удаляя два последних столбца, индекс не включается в данные.\n",
        "X = pd.DataFrame(train_data.drop(['winpercent', 'Y'], axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9S3eep0BV83A"
      },
      "outputs": [],
      "source": [
        "#указываем столбец отклика\n",
        "y = pd.DataFrame(train_data['winpercent'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aZdz0G8CV83B"
      },
      "outputs": [],
      "source": [
        "#подключаем модель линейной регрессии из библиотеки sklearn\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LJUTD6X9V83B"
      },
      "outputs": [],
      "source": [
        "#обучение модели\n",
        "reg = LinearRegression().fit(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_mYCDoDV83B",
        "outputId": "563cf954-1c48-440a-9e1b-a884963e2041"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[64.69721336]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#предсказание для конфет введеных вручную\n",
        "reg.predict([[1, 0, 0, 1, 1, 0, 0, 1, 1, 0.624, 0.438]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Oju1q9cV83C",
        "outputId": "34a109dc-815d-443e-e937-e226f151c115"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[54.48866347]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#предсказание для конфет из таблицы\n",
        "\n",
        "#выбираем строку из таблицы\n",
        "AirHeads = DATA.loc['Air Heads',:].to_frame().T\n",
        "\n",
        "#отбираем данные для предикторов и выполняем предсказание с помощью модели\n",
        "reg.predict(AirHeads.drop(['winpercent', 'Y'], axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#предсказание для конфет из таблицы\n",
        "\n",
        "#выбираем строку из таблицы\n",
        "AirHeads = DATA.loc['Caramel Apple Pops',:].to_frame().T\n",
        "\n",
        "#отбираем данные для предикторов и выполняем предсказание с помощью модели\n",
        "reg.predict(AirHeads.drop(['winpercent', 'Y'], axis=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bCTvCYM0kOSH",
        "outputId": "0688caf0-d5cd-4343-d614-cff85780ff66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[53.68280247]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#предсказание для конфет из таблицы\n",
        "\n",
        "#выбираем строку из таблицы\n",
        "AirHeads = DATA.loc['Hersheys Kisses',:].to_frame().T\n",
        "\n",
        "#отбираем данные для предикторов и выполняем предсказание с помощью модели\n",
        "reg.predict(AirHeads.drop(['winpercent', 'Y'], axis=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joBS2kfVkYdC",
        "outputId": "7595cb63-b8a1-4037-cf4c-9c4e834b4d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[58.8607841]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbWa2wklV83C"
      },
      "source": [
        "Значение коэффициента $\\theta_0$:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eljvd2sV83C",
        "outputId": "eb62c4c9-177d-4fe0-dd73-79d6fb95b130"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([38.01814174])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "reg.intercept_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dfKmCuQV83C"
      },
      "source": [
        "Значение коэффициентов $\\theta_1, \\ldots, \\theta_p$:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjsUJlGzV83C",
        "outputId": "9d910d2b-9a7e-4a03-b9bf-5ecfad97b4a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[25.43606016, 11.35180477,  0.74567182,  7.8106962 ,  2.28534939,\n",
              "         9.47304367, -8.52752816, -6.42882564, -5.09991795, 11.21941295,\n",
              "        -9.8748955 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "reg.coef_"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "colab": {
      "provenance": [],
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    }
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  "nbformat_minor": 0
}