Camera_Traps3 (1).ipynb

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        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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    {
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      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
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    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"society-ethics/lila_camera_traps\", \"Caltech Camera Traps\")\n"
      ],
      "metadata": {
        "execution": {
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        {
          "execution_count": 3,
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          "data": {
            "text/plain": "DatasetDict({\n    train: Dataset({\n        features: ['file_name', 'width', 'height', 'seq_num_frames', 'date_captured', 'seq_id', 'location', 'rights_holder', 'frame_num', 'annotations', 'bboxes', 'image'],\n        num_rows: 243100\n    })\n})"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "from skimage.feature import local_binary_pattern\n",
        "from skimage import io\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from datasets import load_dataset"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n"
      ],
      "metadata": {
        "execution": {
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "all_labels = []\n",
        "for i, example in enumerate(tqdm(dataset[\"train\"])):\n",
        "\n",
        "        try:\n",
        "\n",
        "            category_name = example[\"annotations\"][\"taxonomy\"][0][\"species\"]\n",
        "            all_labels.append(category_name)\n",
        "        except Exception as e:\n",
        "            print(f\"Error occurred while processing image: {e}\")\n",
        "\n",
        "all_labels = np.array(all_labels)\n",
        "print(all_labels)"
      ],
      "metadata": {
        "execution": {
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      "execution_count": null,
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        {
          "name": "stderr",
          "text": "100%|██████████| 243100/243100 [00:55<00:00, 4343.83it/s]\n",
          "output_type": "stream"
        },
        {
          "name": "stdout",
          "text": "[None None 2 ... None None 8]\n",
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        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "non_none_labels = all_labels[all_labels != None]\n",
        "print(non_none_labels)\n",
        "print(len(non_none_labels))"
      ],
      "metadata": {
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        {
          "name": "stdout",
          "text": "[2 8 5 ... 8 5 8]\n67692\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_labels = np.array(all_labels)\n",
        "print(all_labels)"
      ],
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        {
          "name": "stdout",
          "text": "[None None 2 ... None None 8]\n",
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        }
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    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "\n",
        "import tempfile\n",
        "import os\n",
        "\n",
        "def extract_features(image_array):\n",
        "\n",
        "    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.jpg')\n",
        "    temp_file_path = temp_file.name\n",
        "    cv2.imwrite(temp_file_path, cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB))\n",
        "    temp_file.close()\n",
        "\n",
        "\n",
        "    img = load_img(temp_file_path, target_size=(224, 224))\n",
        "    img_array = img_to_array(img)\n",
        "    img_array = np.expand_dims(img_array, axis=0)\n",
        "    img_array = img_array / 255.0\n",
        "\n",
        "    features = base_model.predict(img_array)\n",
        "\n",
        "\n",
        "    flattened_features = features.flatten()\n",
        "\n",
        "\n",
        "    os.unlink(temp_file_path)\n",
        "\n",
        "    return flattened_features\n",
        "\n",
        "\n",
        "sampling_interval = 100\n",
        "\n",
        "features_list = []\n",
        "labels_list = []\n",
        "\n",
        "for i, (example, label) in enumerate(zip(tqdm(dataset[\"train\"]), all_labels)):\n",
        "    if (i>121550):\n",
        "        break\n",
        "\n",
        "    if i % sampling_interval == 0 and label is not None:\n",
        "        try:\n",
        "            image_url = example[\"image\"]\n",
        "            response = requests.get(image_url)\n",
        "\n",
        "            if response.status_code == 200:\n",
        "                image_array = np.frombuffer(response.content, np.uint8)\n",
        "                image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)\n",
        "\n",
        "                if image is not None:\n",
        "                    features = extract_features(image)\n",
        "                    features_list.append(features)\n",
        "                    labels_list.append(label)\n",
        "                else:\n",
        "                    print(\"Error: Unable to decode image.\")\n",
        "            else:\n",
        "                print(f\"Error: Unable to download image - Status Code: {response.status_code}\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error occurred while processing image: {e}\")\n",
        "\n",
        "X = np.array(features_list)\n",
        "y = np.array(labels_list)\n",
        "\n"
      ],
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          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VmsLAwaqKF0V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:05:11.656535Z",
          "iopub.execute_input": "2024-05-01T11:05:11.656895Z",
          "iopub.status.idle": "2024-05-01T11:05:11.664055Z",
          "shell.execute_reply.started": "2024-05-01T11:05:11.656869Z",
          "shell.execute_reply": "2024-05-01T11:05:11.662954Z"
        },
        "trusted": true,
        "id": "6Y10NT1MKF0W",
        "outputId": "d28ff402-bfe3-4554-e822-ea4874c46199"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[0.0821155 , 0.        , 0.        , ..., 0.        , 0.5785745 ,\n        0.        ],\n       [0.08095409, 0.        , 0.        , ..., 0.        , 0.55827284,\n        0.        ],\n       [0.2590238 , 0.        , 0.        , ..., 0.        , 0.55884117,\n        0.        ],\n       ...,\n       [0.00445279, 0.        , 0.        , ..., 0.09728047, 1.0262372 ,\n        0.        ],\n       [0.8966054 , 0.        , 0.        , ..., 0.        , 1.0643289 ,\n        0.        ],\n       [0.40827644, 0.        , 0.        , ..., 0.        , 0.53847206,\n        0.        ]], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:05:27.246058Z",
          "iopub.execute_input": "2024-05-01T11:05:27.246426Z",
          "iopub.status.idle": "2024-05-01T11:05:27.254147Z",
          "shell.execute_reply.started": "2024-05-01T11:05:27.2464Z",
          "shell.execute_reply": "2024-05-01T11:05:27.253139Z"
        },
        "trusted": true,
        "id": "9vrh8f_FKF0X",
        "outputId": "96e925bc-a9b4-4ec7-dc13-2924d4dd9626"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 51,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 8,  8,  8, 10,  5,  5,  8,  8, 10, 10,  8,  8,  8,  2,  2,  8,  5,\n       10, 10,  1,  8,  4,  8,  8,  8,  8,  4,  1,  4,  4,  8,  8,  2,  1,\n       10,  4,  8,  4,  4, 10,  1,  8, 10,  2,  1,  1,  1,  4, 10,  8,  6,\n        8,  2,  4,  4,  4,  8,  8,  8,  4,  4,  8,  2,  8,  1, 10,  8,  2,\n       10, 10,  1, 10,  4,  8, 10, 10,  4, 10,  4,  8,  6,  2, 10,  8, 10,\n        4,  8,  4,  4,  8,  1,  1,  8,  5,  8,  8, 10,  8,  8,  8,  2,  1,\n        6,  8,  1,  8,  8,  4,  4,  8,  1,  2,  8,  6, 10,  1,  8,  2,  8,\n        6,  8,  4,  8,  8,  1,  8,  1, 10, 10,  1,  4,  2,  8,  8,  8,  2,\n        4,  5,  1,  8,  6,  8,  8,  8,  8, 10,  2,  8,  1,  5,  1,  6,  8,\n        1,  8,  8, 10,  8,  4,  8,  8,  1,  1,  1,  8,  8,  4,  4,  8,  8,\n        2,  1,  8,  1,  5,  8, 10,  4,  4,  8, 10,  2,  4,  4,  2, 10,  8,\n        8,  4,  4,  2, 10,  8,  5,  4,  4,  8,  4,  5, 10,  8,  8,  4,  8,\n        5, 10, 10,  5, 10,  4,  6,  5,  6,  2,  8,  8,  8,  4,  4,  4,  8,\n        4,  8, 10,  8,  8,  5,  1,  5,  8,  1,  4, 10,  8,  6, 10,  8,  2,\n        8,  1,  1,  8,  2, 10,  8,  8, 10,  4,  4, 10,  1,  4, 10,  4,  8,\n        2,  8,  1,  4,  2,  4,  4,  8,  5,  1,  4,  8,  8,  4,  8,  8,  8,\n        2,  2, 10,  4,  4,  5,  5,  2,  8, 10,  8,  4,  5,  4,  1,  1, 10,\n       10,  8, 10,  4,  1,  5,  2, 10,  8,  1,  4,  1,  1,  1,  1, 10, 10,\n        4,  3,  1])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:05:29.341976Z",
          "iopub.execute_input": "2024-05-01T11:05:29.34285Z",
          "iopub.status.idle": "2024-05-01T11:05:29.348687Z",
          "shell.execute_reply.started": "2024-05-01T11:05:29.342817Z",
          "shell.execute_reply": "2024-05-01T11:05:29.347698Z"
        },
        "trusted": true,
        "id": "3FjsNC71KF0X",
        "outputId": "0761559d-ba2d-478b-aa2e-7620b1a2c5cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "text/plain": "309"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:06:28.777676Z",
          "iopub.execute_input": "2024-05-01T11:06:28.778099Z",
          "iopub.status.idle": "2024-05-01T11:06:28.784702Z",
          "shell.execute_reply.started": "2024-05-01T11:06:28.778062Z",
          "shell.execute_reply": "2024-05-01T11:06:28.783641Z"
        },
        "trusted": true,
        "id": "fDrvjja9KF0Y",
        "outputId": "98886581-626a-41c3-e428-570409d6d27c"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "text/plain": "309"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVM ON X and y**"
      ],
      "metadata": {
        "id": "2ksHjH97KF0Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "svm_classifier = SVC()\n",
        "\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:06:34.40061Z",
          "iopub.execute_input": "2024-05-01T11:06:34.401013Z",
          "iopub.status.idle": "2024-05-01T11:06:35.966206Z",
          "shell.execute_reply.started": "2024-05-01T11:06:34.400985Z",
          "shell.execute_reply": "2024-05-01T11:06:35.965089Z"
        },
        "trusted": true,
        "id": "zbLWDi7mKF0b",
        "outputId": "e637f528-574d-4540-9a33-e783b403efde"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.3387096774193548\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Applying PCA on X**"
      ],
      "metadata": {
        "id": "BoUV3TB8KF0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=80)\n",
        "\n",
        "X_pca = pca.fit_transform(X)\n",
        "\n",
        "\n",
        "print(\"Explained variance ratio:\", pca.explained_variance_ratio_.sum())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:06:40.961798Z",
          "iopub.execute_input": "2024-05-01T11:06:40.962475Z",
          "iopub.status.idle": "2024-05-01T11:06:41.667345Z",
          "shell.execute_reply.started": "2024-05-01T11:06:40.962445Z",
          "shell.execute_reply": "2024-05-01T11:06:41.666034Z"
        },
        "trusted": true,
        "id": "uWHaaGmoKF0d",
        "outputId": "d3966e51-2373-473f-a978-67908ea1e579"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Explained variance ratio: 0.88570106\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pca"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:06:43.697805Z",
          "iopub.execute_input": "2024-05-01T11:06:43.698445Z",
          "iopub.status.idle": "2024-05-01T11:06:43.705961Z",
          "shell.execute_reply.started": "2024-05-01T11:06:43.698416Z",
          "shell.execute_reply": "2024-05-01T11:06:43.704818Z"
        },
        "trusted": true,
        "id": "r8HnuSKnKF0d",
        "outputId": "31e0c51d-ff2d-4f09-c534-166c423290a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 56,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[-8.013198  , -3.1233034 , -0.9918378 , ...,  0.18636154,\n         0.24951333,  0.19041117],\n       [-8.756813  , -2.7532809 , -1.4878727 , ...,  0.02454937,\n         0.20849699,  0.2572052 ],\n       [-5.745994  , -1.1908338 , -0.33080444, ...,  0.90746754,\n         0.5101695 , -0.25720075],\n       ...,\n       [12.96246   , -1.5082202 ,  6.2663126 , ...,  0.44419333,\n         0.01784462, -1.437298  ],\n       [ 9.235702  , -0.7242223 ,  1.776869  , ..., -0.47258043,\n         0.95005816, -1.4744381 ],\n       [-4.1600456 , -1.3640465 , -2.510024  , ..., -1.2627004 ,\n        -0.69127893, -0.30123982]], dtype=float32)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:06:44.719782Z",
          "iopub.execute_input": "2024-05-01T11:06:44.720529Z",
          "iopub.status.idle": "2024-05-01T11:06:44.724911Z",
          "shell.execute_reply.started": "2024-05-01T11:06:44.720499Z",
          "shell.execute_reply": "2024-05-01T11:06:44.723881Z"
        },
        "trusted": true,
        "id": "g3gJnCI6KF0e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Scaling x**"
      ],
      "metadata": {
        "id": "xT9MzQbOKF0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:07:01.57556Z",
          "iopub.execute_input": "2024-05-01T11:07:01.576626Z",
          "iopub.status.idle": "2024-05-01T11:07:01.588715Z",
          "shell.execute_reply.started": "2024-05-01T11:07:01.576587Z",
          "shell.execute_reply": "2024-05-01T11:07:01.587053Z"
        },
        "trusted": true,
        "id": "ejSYOCsOKF0f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "svm_classifier = SVC(kernel='linear')\n",
        "\n",
        "svm_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "y_pred = svm_classifier.predict(X_test_scaled)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:07:15.660969Z",
          "iopub.execute_input": "2024-05-01T11:07:15.66173Z",
          "iopub.status.idle": "2024-05-01T11:07:15.683944Z",
          "shell.execute_reply.started": "2024-05-01T11:07:15.661699Z",
          "shell.execute_reply": "2024-05-01T11:07:15.682903Z"
        },
        "trusted": true,
        "id": "6dbZ_YraKF0f",
        "outputId": "45a6fa92-c594-4abe-bd4c-fa8bb390eb69"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.5\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PCA + Linear kernel gives 58.2**"
      ],
      "metadata": {
        "id": "j5WzsAWzKF0h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
        "\n",
        "svm_classifier = SVC(kernel='linear')\n",
        "\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:07:18.22724Z",
          "iopub.execute_input": "2024-05-01T11:07:18.227643Z",
          "iopub.status.idle": "2024-05-01T11:07:18.251841Z",
          "shell.execute_reply.started": "2024-05-01T11:07:18.227614Z",
          "shell.execute_reply": "2024-05-01T11:07:18.250792Z"
        },
        "trusted": true,
        "id": "jmeLlP8SKF0i",
        "outputId": "829280c7-0297-4715-b37c-fe84e3984a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.5806451612903226\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {\n",
        "    'C': [0.001, 0.01, 0.1, 1],\n",
        "    'gamma': [1, 0.1, 0.01, 0.001],\n",
        "    'kernel': [ 'linear', 'rbf']\n",
        "}\n",
        "\n",
        "\n",
        "svm = SVC()\n",
        "\n",
        "\n",
        "grid_search = GridSearchCV(estimator=svm, param_grid=param_grid, cv=5, n_jobs=-1, verbose=0)\n",
        "\n",
        "grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "best_svm = grid_search.best_estimator_\n",
        "\n",
        "accuracy = best_svm.score(X_test_scaled, y_test)\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:15:51.392874Z",
          "iopub.execute_input": "2024-05-01T11:15:51.393636Z",
          "iopub.status.idle": "2024-05-01T11:15:52.107949Z",
          "shell.execute_reply.started": "2024-05-01T11:15:51.393596Z",
          "shell.execute_reply": "2024-05-01T11:15:52.106965Z"
        },
        "trusted": true,
        "id": "v3k039w1KF0j",
        "outputId": "17baaaad-029f-4393-d7f1-c0a752c62fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Best Parameters: {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\nAccuracy: 0.3548387096774194\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "y_encoded = label_encoder.fit_transform(y)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T08:18:56.263547Z",
          "iopub.execute_input": "2024-05-01T08:18:56.26433Z",
          "iopub.status.idle": "2024-05-01T08:18:56.269398Z",
          "shell.execute_reply.started": "2024-05-01T08:18:56.264298Z",
          "shell.execute_reply": "2024-05-01T08:18:56.268349Z"
        },
        "trusted": true,
        "id": "KkMVaYLoKF0k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "label_encoder.fit(y)\n",
        "\n",
        "y_encoded = label_encoder.transform(y)\n",
        "\n",
        "\n",
        "unique_encoded_values = np.unique(y_encoded)\n",
        "print(\"Encoded labels:\", unique_encoded_values)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:08:35.259981Z",
          "iopub.execute_input": "2024-05-01T11:08:35.260957Z",
          "iopub.status.idle": "2024-05-01T11:08:35.269028Z",
          "shell.execute_reply.started": "2024-05-01T11:08:35.260901Z",
          "shell.execute_reply": "2024-05-01T11:08:35.26789Z"
        },
        "trusted": true,
        "id": "aIPuzqL_KF0l",
        "outputId": "1bab20ab-790c-43df-eb9a-4f65c506721c"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Encoded labels: [0 1 2 3 4 5 6 7]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y_encoded, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:08:40.147242Z",
          "iopub.execute_input": "2024-05-01T11:08:40.148094Z",
          "iopub.status.idle": "2024-05-01T11:08:40.154646Z",
          "shell.execute_reply.started": "2024-05-01T11:08:40.14806Z",
          "shell.execute_reply": "2024-05-01T11:08:40.153329Z"
        },
        "trusted": true,
        "id": "Y4Ls1gFBKF0l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:08:44.40325Z",
          "iopub.execute_input": "2024-05-01T11:08:44.403614Z",
          "iopub.status.idle": "2024-05-01T11:08:44.410689Z",
          "shell.execute_reply.started": "2024-05-01T11:08:44.403588Z",
          "shell.execute_reply": "2024-05-01T11:08:44.409722Z"
        },
        "trusted": true,
        "id": "gAQBJQiDKF0l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm_classifier = SVC(kernel='linear')\n",
        "\n",
        "svm_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = svm_classifier.predict(X_test_scaled)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:08:48.1822Z",
          "iopub.execute_input": "2024-05-01T11:08:48.182827Z",
          "iopub.status.idle": "2024-05-01T11:08:48.204299Z",
          "shell.execute_reply.started": "2024-05-01T11:08:48.182797Z",
          "shell.execute_reply": "2024-05-01T11:08:48.20334Z"
        },
        "trusted": true,
        "id": "DQhK-1iyKF0m",
        "outputId": "7d1c6d52-faa7-4970-c432-7bf107020c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.5\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "xgb_classifier = XGBClassifier()\n",
        "\n",
        "\n",
        "xgb_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = xgb_classifier.predict(X_test_scaled)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T11:08:51.719939Z",
          "iopub.execute_input": "2024-05-01T11:08:51.720296Z",
          "iopub.status.idle": "2024-05-01T11:08:52.595767Z",
          "shell.execute_reply.started": "2024-05-01T11:08:51.720267Z",
          "shell.execute_reply": "2024-05-01T11:08:52.594984Z"
        },
        "trusted": true,
        "id": "IP6emT1bKF0m",
        "outputId": "d0a029ba-7152-4912-ac69-8f426a8df0d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.532258064516129\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'max_depth': [3, 5, 7],\n",
        "    'learning_rate': [0.1, 0.01, 0.001]\n",
        "}\n",
        "\n",
        "\n",
        "xgb_classifier = XGBClassifier()\n",
        "\n",
        "\n",
        "grid_search = GridSearchCV(estimator=xgb_classifier, param_grid=param_grid, cv=10, n_jobs=-1, verbose=2)\n",
        "\n",
        "\n",
        "grid_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "best_xgb = grid_search.best_estimator_\n",
        "\n",
        "\n",
        "accuracy = best_xgb.score(X_test, y_test)\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-05-01T08:19:17.867326Z",
          "iopub.execute_input": "2024-05-01T08:19:17.867868Z",
          "iopub.status.idle": "2024-05-01T08:28:17.327108Z",
          "shell.execute_reply.started": "2024-05-01T08:19:17.867823Z",
          "shell.execute_reply": "2024-05-01T08:28:17.326083Z"
        },
        "trusted": true,
        "id": "ERjc8YAjKF0n",
        "outputId": "4dd53fa6-8375-4044-8dd4-d1b14578711b"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Fitting 10 folds for each of 27 candidates, totalling 270 fits\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.8s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.8s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.2s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.4s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.5s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.6s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   4.5s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.5s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.6s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   4.5s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.9s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.8s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.5s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   4.5s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   5.2s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   4.8s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   5.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   3.8s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   3.8s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   4.7s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   4.6s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=5, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=200; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END .learning_rate=0.001, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.7s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   2.8s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.6s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   4.2s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time=   3.8s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=300; total time=   4.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.6s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.6s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   4.4s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time=   4.3s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   5.0s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   4.9s\n[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=300; total time=   5.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   3.9s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   3.9s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   4.6s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time=   4.8s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   6.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   0.0s\n[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=300; total time=   5.1s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   3.4s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   3.5s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   7.0s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   7.2s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time=   7.1s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=  10.4s\n[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=300; total time=  10.3s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   6.3s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   0.0s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   6.1s\n[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time=  11.8s\nBest Parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\nAccuracy: 0.41935483870967744\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UXhYkT-0KF0o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}