Mushroom_Classification.ipynb

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      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
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      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "DZGtraImwF3D"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp kaggle.json ~/.kaggle"
      ],
      "metadata": {
        "id": "ZIag54cuwF5c"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d uciml/mushroom-classification"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "c8df1291-2790-4d73-b74b-2c292bc464b7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading mushroom-classification.zip to /content\n",
            "  0% 0.00/34.2k [00:00<?, ?B/s]\n",
            "100% 34.2k/34.2k [00:00<00:00, 67.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/mushroom-classification.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNWcgqYXwF-Y",
        "outputId": "7038f7e1-ea38-4cab-e11c-b67c9c016224"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/mushroom-classification.zip\n",
            "  inflating: mushrooms.csv           \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "5j9yR5HSwGAT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/mushrooms.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "vXjRCrQvwGCw",
        "outputId": "824e8987-b071-4dab-f4f0-9319e307b283"
      },
      "execution_count": 9,
      "outputs": [
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          "metadata": {},
          "execution_count": 9
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              "(8124, 23)"
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      "execution_count": 12,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8124 entries, 0 to 8123\n",
            "Data columns (total 23 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   class                     8124 non-null   object\n",
            " 1   cap-shape                 8124 non-null   object\n",
            " 2   cap-surface               8124 non-null   object\n",
            " 3   cap-color                 8124 non-null   object\n",
            " 4   bruises                   8124 non-null   object\n",
            " 5   odor                      8124 non-null   object\n",
            " 6   gill-attachment           8124 non-null   object\n",
            " 7   gill-spacing              8124 non-null   object\n",
            " 8   gill-size                 8124 non-null   object\n",
            " 9   gill-color                8124 non-null   object\n",
            " 10  stalk-shape               8124 non-null   object\n",
            " 11  stalk-root                8124 non-null   object\n",
            " 12  stalk-surface-above-ring  8124 non-null   object\n",
            " 13  stalk-surface-below-ring  8124 non-null   object\n",
            " 14  stalk-color-above-ring    8124 non-null   object\n",
            " 15  stalk-color-below-ring    8124 non-null   object\n",
            " 16  veil-type                 8124 non-null   object\n",
            " 17  veil-color                8124 non-null   object\n",
            " 18  ring-number               8124 non-null   object\n",
            " 19  ring-type                 8124 non-null   object\n",
            " 20  spore-print-color         8124 non-null   object\n",
            " 21  population                8124 non-null   object\n",
            " 22  habitat                   8124 non-null   object\n",
            "dtypes: object(23)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIZcqOavwGPv",
        "outputId": "36b6b4eb-00e1-4423-d538-a5eaadefe769"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class                       0\n",
              "cap-shape                   0\n",
              "cap-surface                 0\n",
              "cap-color                   0\n",
              "bruises                     0\n",
              "odor                        0\n",
              "gill-attachment             0\n",
              "gill-spacing                0\n",
              "gill-size                   0\n",
              "gill-color                  0\n",
              "stalk-shape                 0\n",
              "stalk-root                  0\n",
              "stalk-surface-above-ring    0\n",
              "stalk-surface-below-ring    0\n",
              "stalk-color-above-ring      0\n",
              "stalk-color-below-ring      0\n",
              "veil-type                   0\n",
              "veil-color                  0\n",
              "ring-number                 0\n",
              "ring-type                   0\n",
              "spore-print-color           0\n",
              "population                  0\n",
              "habitat                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zA64PP9awGQi",
        "outputId": "828910b4-135d-4856-9720-fb03e724539a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "e    4208\n",
              "p    3916\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "cbW1SHqtwGT5",
        "outputId": "8e60f1e5-6f73-499a-a59c-b4ec1e12884f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
              "count   8124      8124        8124      8124    8124  8124            8124   \n",
              "unique     2         6           4        10       2     9               2   \n",
              "top        e         x           y         n       f     n               f   \n",
              "freq    4208      3656        3244      2284    4748  3528            7914   \n",
              "\n",
              "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
              "count          8124      8124       8124  ...                     8124   \n",
              "unique            2         2         12  ...                        4   \n",
              "top               c         b          b  ...                        s   \n",
              "freq           6812      5612       1728  ...                     4936   \n",
              "\n",
              "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
              "count                    8124                   8124      8124       8124   \n",
              "unique                      9                      9         1          4   \n",
              "top                         w                      w         p          w   \n",
              "freq                     4464                   4384      8124       7924   \n",
              "\n",
              "       ring-number ring-type spore-print-color population habitat  \n",
              "count         8124      8124              8124       8124    8124  \n",
              "unique           3         5                 9          6       7  \n",
              "top              o         p                 w          v       d  \n",
              "freq          7488      3968              2388       4040    3148  \n",
              "\n",
              "[4 rows x 23 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-surface</th>\n",
              "      <th>cap-color</th>\n",
              "      <th>bruises</th>\n",
              "      <th>odor</th>\n",
              "      <th>gill-attachment</th>\n",
              "      <th>gill-spacing</th>\n",
              "      <th>gill-size</th>\n",
              "      <th>gill-color</th>\n",
              "      <th>...</th>\n",
              "      <th>stalk-surface-below-ring</th>\n",
              "      <th>stalk-color-above-ring</th>\n",
              "      <th>stalk-color-below-ring</th>\n",
              "      <th>veil-type</th>\n",
              "      <th>veil-color</th>\n",
              "      <th>ring-number</th>\n",
              "      <th>ring-type</th>\n",
              "      <th>spore-print-color</th>\n",
              "      <th>population</th>\n",
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              "      <th>count</th>\n",
              "      <td>8124</td>\n",
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              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
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              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>e</td>\n",
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              "      <td>f</td>\n",
              "      <td>n</td>\n",
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              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>b</td>\n",
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              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
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              "      <td>o</td>\n",
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              "      <td>w</td>\n",
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              "      <td>d</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>4208</td>\n",
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              "      <td>3244</td>\n",
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              "      <td>6812</td>\n",
              "      <td>5612</td>\n",
              "      <td>1728</td>\n",
              "      <td>...</td>\n",
              "      <td>4936</td>\n",
              "      <td>4464</td>\n",
              "      <td>4384</td>\n",
              "      <td>8124</td>\n",
              "      <td>7924</td>\n",
              "      <td>7488</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 23 columns</p>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7f80adf3-7ac3-4c23-b35b-53d4c55e7442 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-7f80adf3-7ac3-4c23-b35b-53d4c55e7442');\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",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\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-5d5c1e9d-d23f-4ec2-9e3f-de200e0f7723 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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "categorical_columns=[]\n",
        "for i in range(0,23):\n",
        "    if df.iloc[:, i].dtype == \"object\":\n",
        "        categorical_columns.append(df.columns[i])\n",
        "\n",
        "for k in categorical_columns:\n",
        "     df[k]=le.fit_transform(df[k])\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "-PJX-hgPzELI",
        "outputId": "da2d7372-d38b-4524-aada-ec36a5cadd0b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
              "0      1          5            2          4        1     6                1   \n",
              "1      0          5            2          9        1     0                1   \n",
              "2      0          0            2          8        1     3                1   \n",
              "3      1          5            3          8        1     6                1   \n",
              "4      0          5            2          3        0     5                1   \n",
              "\n",
              "   gill-spacing  gill-size  gill-color  ...  stalk-surface-below-ring  \\\n",
              "0             0          1           4  ...                         2   \n",
              "1             0          0           4  ...                         2   \n",
              "2             0          0           5  ...                         2   \n",
              "3             0          1           5  ...                         2   \n",
              "4             1          0           4  ...                         2   \n",
              "\n",
              "   stalk-color-above-ring  stalk-color-below-ring  veil-type  veil-color  \\\n",
              "0                       7                       7          0           2   \n",
              "1                       7                       7          0           2   \n",
              "2                       7                       7          0           2   \n",
              "3                       7                       7          0           2   \n",
              "4                       7                       7          0           2   \n",
              "\n",
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          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,1:]\n",
        "x.head()"
      ],
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          "height": 307
        },
        "id": "ILvGH7fJ4dzO",
        "outputId": "c502f24d-4ef9-411f-d32c-e80b7a3b3643"
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      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
              "0          5            2          4        1     6                1   \n",
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              "4          5            2          3        0     5                1   \n",
              "\n",
              "   gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
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              "3             0          1           5            0  ...   \n",
              "4             1          0           4            1  ...   \n",
              "\n",
              "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
              "0                         2                       7                       7   \n",
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              "2                         2                       7                       7   \n",
              "3                         2                       7                       7   \n",
              "4                         2                       7                       7   \n",
              "\n",
              "   veil-type  veil-color  ring-number  ring-type  spore-print-color  \\\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c322daf5-fb9f-4e8d-b6b4-7c09d1b1d544')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c322daf5-fb9f-4e8d-b6b4-7c09d1b1d544 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-c322daf5-fb9f-4e8d-b6b4-7c09d1b1d544');\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",
              "\n",
              "\n",
              "<div id=\"df-23b79da1-4f75-43ac-b1aa-c9b38369e972\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-23b79da1-4f75-43ac-b1aa-c9b38369e972')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\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-23b79da1-4f75-43ac-b1aa-c9b38369e972 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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['class']\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jDvBQnOw4mwA",
        "outputId": "6f8e4c05-608d-4dbb-c757-6e6973e73c62"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    0\n",
              "3    1\n",
              "4    0\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "rnwob24Fz6K-"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "model building"
      ],
      "metadata": {
        "id": "bG54lPkR490r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model=LogisticRegression()\n"
      ],
      "metadata": {
        "id": "VxVDc97sz6NR"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "8vJbV6ydz6Pk",
        "outputId": "1db0e72e-31c2-4a95-a408-dea16a775f2d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x_test)"
      ],
      "metadata": {
        "id": "mpprYic4z6SA"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OUPSZYkz6VD",
        "outputId": "5338c4fc-c06a-4b67-b4fe-f563e5d7e5a7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
      ],
      "metadata": {
        "id": "KSZ4OfYmz6Wk"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPaoZbykz6Zq",
        "outputId": "9f4b0c34-f13f-4725-869b-41e255fde148"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.955076923076923"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5Vfplqmz6b4",
        "outputId": "13a02029-ec0c-41c7-df46-d5b11f5e5e74"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[820,  41],\n",
              "       [ 32, 732]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(y_pred,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "lhH1UVP8z6gP",
        "outputId": "fd61afc9-42bb-4d2a-da36-e10c9fe85356"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class    0    1\n",
              "row_0          \n",
              "0      820   41\n",
              "1       32  732"
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-1c0260a6-c862-42ea-8465-099e130c8be6 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-1c0260a6-c862-42ea-8465-099e130c8be6');\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    fill: var(--fill-color);\n",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      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-603759e0-5c9f-4484-b61c-c7b1959de5b7 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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_pred,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8tyA4atz6iX",
        "outputId": "29ecedea-55d1-4704-b223-557a6af1df59"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.96       861\n",
            "           1       0.95      0.96      0.95       764\n",
            "\n",
            "    accuracy                           0.96      1625\n",
            "   macro avg       0.95      0.96      0.95      1625\n",
            "weighted avg       0.96      0.96      0.96      1625\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ANN_CLASSIFICATION"
      ],
      "metadata": {
        "id": "wjQG3tOo7ScI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "metadata": {
        "id": "QyCQVWgO7SDa"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()"
      ],
      "metadata": {
        "id": "AZlFp8uTz6lG"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Dense(23,activation=\"relu\",kernel_initializer=\"random_uniform\"))\n",
        "model.add(Dense(46,activation=\"relu\",kernel_initializer=\"random_uniform\"))\n",
        "model.add(Dense(1,activation=\"sigmoid\"))"
      ],
      "metadata": {
        "id": "37aEB0KezENB"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "uC2sW0srzEQF"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train,batch_size=32,epochs=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wP0ZVEjzEWj",
        "outputId": "389ff2ee-3884-4991-e83c-b6d35a2f3726"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "204/204 [==============================] - 1s 6ms/step - loss: 5.5139e-06 - accuracy: 1.0000\n",
            "Epoch 2/30\n",
            "204/204 [==============================] - 1s 6ms/step - loss: 5.0447e-06 - accuracy: 1.0000\n",
            "Epoch 3/30\n",
            "204/204 [==============================] - 1s 5ms/step - loss: 4.7314e-06 - accuracy: 1.0000\n",
            "Epoch 4/30\n",
            "204/204 [==============================] - 2s 8ms/step - loss: 4.5351e-06 - accuracy: 1.0000\n",
            "Epoch 5/30\n",
            "204/204 [==============================] - 1s 6ms/step - loss: 4.3249e-06 - accuracy: 1.0000\n",
            "Epoch 6/30\n",
            "204/204 [==============================] - 1s 5ms/step - loss: 4.1078e-06 - accuracy: 1.0000\n",
            "Epoch 7/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.9936e-06 - accuracy: 1.0000\n",
            "Epoch 8/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.8179e-06 - accuracy: 1.0000\n",
            "Epoch 9/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.6018e-06 - accuracy: 1.0000\n",
            "Epoch 10/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.4863e-06 - accuracy: 1.0000\n",
            "Epoch 11/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.4028e-06 - accuracy: 1.0000\n",
            "Epoch 12/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.8681e-06 - accuracy: 1.0000\n",
            "Epoch 13/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.7247e-06 - accuracy: 1.0000\n",
            "Epoch 14/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.6933e-06 - accuracy: 1.0000\n",
            "Epoch 15/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.5497e-06 - accuracy: 1.0000\n",
            "Epoch 16/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.2075e-06 - accuracy: 1.0000\n",
            "Epoch 17/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.6825e-06 - accuracy: 1.0000\n",
            "Epoch 18/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.1582e-06 - accuracy: 1.0000\n",
            "Epoch 19/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 1.9058e-06 - accuracy: 1.0000\n",
            "Epoch 20/30\n",
            "204/204 [==============================] - 1s 4ms/step - loss: 1.6717e-06 - accuracy: 1.0000\n",
            "Epoch 21/30\n",
            "204/204 [==============================] - 1s 4ms/step - loss: 1.8802e-06 - accuracy: 1.0000\n",
            "Epoch 22/30\n",
            "204/204 [==============================] - 1s 4ms/step - loss: 0.0072 - accuracy: 0.9978\n",
            "Epoch 23/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 6.0963e-05 - accuracy: 1.0000\n",
            "Epoch 24/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 4.1112e-05 - accuracy: 1.0000\n",
            "Epoch 25/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 3.1771e-05 - accuracy: 1.0000\n",
            "Epoch 26/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.6384e-05 - accuracy: 1.0000\n",
            "Epoch 27/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 2.2337e-05 - accuracy: 1.0000\n",
            "Epoch 28/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 1.9064e-05 - accuracy: 1.0000\n",
            "Epoch 29/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 1.6374e-05 - accuracy: 1.0000\n",
            "Epoch 30/30\n",
            "204/204 [==============================] - 1s 3ms/step - loss: 1.4406e-05 - accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f03e69bd0f0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAVnANSN95yI",
        "outputId": "2855c4a9-261e-4f2c-d7fe-e207f1183c86"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51/51 [==============================] - 0s 4ms/step - loss: 1.7738e-05 - accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "testing data"
      ],
      "metadata": {
        "id": "Xn1hMRTHBa9x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pre=model.predict([[5,2,4,1,6,1,0,1,4,0,1,2,2,7,7,0,2,1,4,2,3,5]])\n",
        "binary_predictions = (pre > 0.5).astype(int)\n",
        "if(binary_predictions==1):\n",
        "  print(\"poisonous\")\n",
        "else:\n",
        "  print(\"edible\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ISMzFVr-qOH",
        "outputId": "bee0c064-ff30-4da9-a61b-c2d2dcea6f70"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 65ms/step\n",
            "poisonous\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre=model.predict([[5,2,4,1,3,1,0,1,2,0,1,2,2,5,5,0,2,1,4,2,3,5]])\n",
        "binary_predictions = (pre > 0.5).astype(int)\n",
        "if(binary_predictions==1):\n",
        "  print(\"poisonous\")\n",
        "else:\n",
        "  print(\"edible\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3fyVynzAwC5",
        "outputId": "81985f1f-817c-4d8c-f4d8-002ea2fde704"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 40ms/step\n",
            "poisonous\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OdVU5IlJByU3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qBvYI1rnByXu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FVHFgINXBya7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v0HuIBJXBydy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LZi6Pu7sBygq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NWBT938iBykX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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