ML_КР(fork).ipynb

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        "<a href=\"https://colab.research.google.com/github/EugIva/109m_MLcoursework/blob/main/ML_%D0%9A%D0%A0(fork).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DatasetAnalyzer:\n",
        "    def __init__(self):\n",
        "        self.dataset_path = None\n",
        "        self.data = None\n",
        "        self.features = None\n",
        "        self.target = None\n",
        "        self.model = None\n",
        "\n",
        "    def load_dataset(self):\n",
        "        mat = pd.read_csv(\"student-mat.csv\")\n",
        "        por = pd.read_csv(\"student-por.csv\")\n",
        "\n",
        "        self.data = pd.concat([mat, por])\n",
        "\n",
        "    def explore_dataset(self):\n",
        "\n",
        "        # Типо всякие графики, зависимости и тп\n",
        "        # Анализ датасета\n",
        "        print(\"Dataset Info:\")\n",
        "        print(self.data.info())\n",
        "\n",
        "        print(\"Correlation Matrix:\")\n",
        "        correlation_matrix = self.data.corr()\n",
        "        print(correlation_matrix)\n",
        "\n",
        "    def prepare_data(self, test_size=0.2):\n",
        "\n",
        "        # Тут еще должна быть подготовка данных, типо заполнение пропусков\n",
        "        self.data['alcohol'] = (5 * self.data['Dalc'] + 2 * self.data['Walc']) / 7\n",
        "        self.data.drop_duplicates(keep = \"first\")\n",
        "\n",
        "        self.features = self.data.drop(columns=[\"school\", \"sex\", \"address\", \"famsize\", \"Pstatus\",\n",
        "              \"Mjob\", \"Fjob\", \"reason\", \"guardian\", \"schoolsup\",\n",
        "              \"famsup\", \"paid\", \"activities\", \"nursery\", \"higher\",\n",
        "              \"internet\", \"romantic\", \"Dalc\", \"Walc\"])\n",
        "        self.target = self.data['alcohol']\n",
        "\n",
        "        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(\n",
        "            self.features, self.target, test_size=0.2, random_state=17\n",
        "        )\n",
        "\n",
        "    def train_model(self):\n",
        "        # Обучение какой-нибудь модели\n",
        "        self.model = LinearRegression()\n",
        "        self.model.fit(self.X_train, self.y_train)\n",
        "\n",
        "    def predict(self, input_data):\n",
        "        # Предсказание чего-нибудь от пользователя\n",
        "        predictions = self.model.predict(input_data)\n",
        "        return predictions\n",
        "\n",
        "    def quality_model(self):\n",
        "        # Оценка качества (хз, надо или нет) на тестовом куске\n",
        "        y_pred = self.model.predict(self.X_test)\n",
        "        mse = mean_squared_error(self.y_test, y_pred)\n",
        "        print(f\"MSE: {mse}\")"
      ],
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      "execution_count": null,
      "outputs": []
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      "source": [
        "da = DatasetAnalyzer()\n",
        "\n",
        "da.load_dataset()\n",
        "da.prepare_data()\n",
        "\n"
      ],
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              "     age  Medu  Fedu  traveltime  studytime  failures  famrel  freetime  \\\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e86820a9-d5b0-4296-ba94-4b81e3f7a67d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "da.train_model()"
      ],
      "metadata": {
        "id": "mvg3dV2EVco0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "da.quality_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li-tZf3eZiXc",
        "outputId": "12452f5b-ee05-4542-a29a-9c119920b8ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE: 8.667797918820465e-30\n"
          ]
        }
      ]
    }
  ]
}