14_Mpox_Classification_2159.ipynb

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    "colab": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Loading and Preprocessing the data"
      ],
      "metadata": {
        "id": "eqsHlTjV64ZY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mounting the drive"
      ],
      "metadata": {
        "id": "prSwaW2j7GrF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "YMlg9P5kIR-2",
        "outputId": "d65942af-44f4-479f-aa60-1af97330b0e6"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing required libraries"
      ],
      "metadata": {
        "id": "S0BHagTj7Nvv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "ZFMD3t_F7Qvb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading the data"
      ],
      "metadata": {
        "id": "8w2v8qfi7U8c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to the folders\n",
        "mpox_path = '/content/drive/MyDrive/mpox/Augmented Images/Augmented Images/Monkeypox_augmented'\n",
        "non_mpox_path= '/content/drive/MyDrive/mpox/Augmented Images/Augmented Images/Others_augmented'\n",
        "img_size = (200, 200)\n",
        "\n",
        "# Listing all the files in the two folders\n",
        "mpox_files = os.listdir(mpox_path)\n",
        "non_mpox_files = os.listdir(non_mpox_path)\n",
        "\n",
        "# Empty arrays to store the images and labels\n",
        "images = []\n",
        "labels = []\n",
        "\n",
        "# Loading and preprocessing the images from mpox_files\n",
        "for file in mpox_files:\n",
        "    img_path = os.path.join(mpox_path, file)\n",
        "    img = cv2.imread(img_path)\n",
        "    img = cv2.resize(img,img_size)\n",
        "    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    img_flatten = img_gray.flatten()\n",
        "    images.append(img_flatten)\n",
        "    labels.append(1)\n",
        "\n",
        "# Loading and preprocessing the images from non_mpox_files\n",
        "for file in non_mpox_files:\n",
        "    img_path = os.path.join(non_mpox_path, file)\n",
        "    img = cv2.imread(img_path)\n",
        "    img = cv2.resize(img, img_size) \n",
        "    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    img_flatten = img_gray.flatten()\n",
        "    images.append(img_flatten)\n",
        "    labels.append(0)"
      ],
      "metadata": {
        "id": "z6u1dW7B7jrW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assigning Variables, Normalising and Shuffling"
      ],
      "metadata": {
        "id": "6dHDzJrp8sdq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the images and labels to numpy arrays\n",
        "X = np.array(images)\n",
        "y = np.array(labels)\n",
        "\n",
        "# Normalizing the Vectors\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "# X and y vectors\n",
        "print(\"X values:- \\n\", X)\n",
        "print(\"\\ny values:- \\n\", y)\n",
        "\n",
        "# Shape of the dataset\n",
        "print('\\nX shape:', X.shape)\n",
        "print('y shape:', y.shape)\n",
        "\n",
        "# Binary class counts\n",
        "counts = np.bincount(y)\n",
        "print(\"class count: \", counts)"
      ],
      "metadata": {
        "id": "Y2crVGsz8wJ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "260bb2ac-d77e-4186-c646-0b7339ad03d2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X values:- \n",
            " [[-1.51543451 -1.52141135 -1.5243884  ... -1.54010267 -1.5261383\n",
            "  -1.51450681]\n",
            " [-1.50252319 -1.50846669 -1.51143429 ... -1.54010267 -1.5261383\n",
            "  -1.51450681]\n",
            " [ 1.57037237  1.59825202  1.61050561 ...  1.87868235  1.88294697\n",
            "   1.87730567]\n",
            " ...\n",
            " [ 0.42126437  0.58856844  0.49645237 ...  1.19492534  1.50265061\n",
            "   1.13407983]\n",
            " [ 0.08556989  0.08372665  0.08192093 ... -0.0495124  -0.04569888\n",
            "  -0.02805512]\n",
            " [ 1.31214585  1.31346948  1.31256114 ...  1.68723039  1.65205275\n",
            "   1.62055493]]\n",
            "\n",
            "y values:- \n",
            " [1 1 1 ... 0 0 0]\n",
            "\n",
            "X shape: (3192, 40000)\n",
            "y shape: (3192,)\n",
            "class count:  [1764 1428]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Shuffling and Splitting the dataset into Training and Testing sets"
      ],
      "metadata": {
        "id": "3xj5Chm0OmOX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "# Data Shuffling\n",
        "X, y = shuffle(X, y, random_state=42)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Splittinf the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# X and y vectors\n",
        "print(\"X_train:- \\n\", X_train)\n",
        "print(\"\\nX_test:- \\n\", X_test)\n",
        "print(\"\\ny_train:- \\n\", y_train)\n",
        "print(\"\\ny_test:- \\n\", y_test)\n",
        "\n",
        "# Shape of the dataset\n",
        "print('\\nX_train:', X_train.shape)\n",
        "print('X_test:', X_test.shape)\n",
        "print('y_train:', y_train.shape)\n",
        "print('y_test:', y_test.shape)"
      ],
      "metadata": {
        "id": "Dz-8JF6HOluV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bee557e5-8952-4df6-f7e6-dc4c633ec298"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train:- \n",
            " [[ 1.49290441  1.59825202  1.66232204 ... -1.12984847 -1.14584193\n",
            "  -1.16316368]\n",
            " [-0.72784364 -0.73178701 -0.73418784 ... -0.88369595 -0.87420167\n",
            "  -0.86587335]\n",
            " [-1.20556269 -1.21073948 -1.21348982 ... -0.24096437 -0.23584706\n",
            "  -0.23075308]\n",
            " ...\n",
            " [ 0.51164365  0.44617717  0.3798654  ...  0.770996    0.78280392\n",
            "   0.8097631 ]\n",
            " [-0.09518867 -0.11044327 -0.1382989  ...  0.12826442  0.09012125\n",
            "   0.08005045]\n",
            " [ 0.22759448 -0.11044327  0.72962631 ... -1.06147277 -0.9013657\n",
            "  -0.37939825]]\n",
            "\n",
            "X_test:- \n",
            " [[ 0.67949088  0.74390438  0.76848863 ... -0.03583726 -0.05928089\n",
            "  -0.1226475 ]\n",
            " [-1.51543451 -1.52141135 -1.5243884  ... -1.54010267 -1.5261383\n",
            "  -1.51450681]\n",
            " [-0.99898148 -0.96479091 -0.76009605 ...  0.92142254  1.24459236\n",
            "   0.83678949]\n",
            " ...\n",
            " [-1.51543451 -1.52141135 -1.5243884  ... -1.54010267 -1.5261383\n",
            "  -1.51450681]\n",
            " [ 0.49873232  0.49795581  0.48349826 ...  0.52484348  0.55190969\n",
            "   0.56652555]\n",
            " [ 0.29215111  0.31673056  0.32804897 ...  0.14193956  0.15803132\n",
            "   0.22869562]]\n",
            "\n",
            "y_train:- \n",
            " [1 1 0 ... 0 0 1]\n",
            "\n",
            "y_test:- \n",
            " [1 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 0 0 1 0 1 1 1\n",
            " 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1\n",
            " 0 1 1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 1 0 0 1 0\n",
            " 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0\n",
            " 1 0 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 0 1 0 0\n",
            " 1 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
            " 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1\n",
            " 0 1 0 0 0 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 1\n",
            " 1 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1\n",
            " 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0\n",
            " 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 0\n",
            " 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 1 0 1 0 1 1\n",
            " 1 0 0 1 1 1 0 1 1 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 0\n",
            " 0 1 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0\n",
            " 0 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 1 0 0 1 1 0 1 1 0 1 0\n",
            " 1 0 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0\n",
            " 1 0 0 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0\n",
            " 0 0 1 1 1 1 0 1 1 1]\n",
            "\n",
            "X_train: (2553, 40000)\n",
            "X_test: (639, 40000)\n",
            "y_train: (2553,)\n",
            "y_test: (639,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LOGISTIC REGRESSION"
      ],
      "metadata": {
        "id": "LEBlXWdCl5hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Creating a logistic regression model and fitting it to the training data\n",
        "lr = LogisticRegression(random_state=42, max_iter=1500)\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "atEHxU7Pl92x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "581cf891-b02f-4c59-b7c4-6b044d0e8fc9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1500, random_state=42)"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions on the testing data\n",
        "y_pred_lr = lr.predict(X_test)\n",
        "print('Predicted and actual values on y_test')\n",
        "print(np.concatenate((y_pred_lr.reshape(len(y_pred_lr),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dPOLxzrRfSNf",
        "outputId": "4e3b9cbc-8887-4978-c6cf-d2afc8b3e382"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted and actual values on y_test\n",
            "[[0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " ...\n",
            " [0 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the accuracy\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "print('Accuracy:', accuracy_lr)\n",
        "\n",
        "# Printing the confusion matrix\n",
        "cm_lr = confusion_matrix(y_test, y_pred_lr)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_lr)\n",
        "\n",
        "# Graphical display of the confusion matrix\n",
        "ConfusionMatrixDisplay(cm_lr).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "Nx1YksfzgD8j",
        "outputId": "17a20720-1305-4292-8495-2c61357be1c5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.837245696400626\n",
            "Confusion Matrix:\n",
            "[[322  39]\n",
            " [ 65 213]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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gL774QlOnTlV+fr7eeecdSVJJSckJu9/HtjWWR5N9YGCgUlJStGbNGg0bNkzS0X8ha9as0fjx4z0ZmmmUHwzQY/ckqexAgEIjrOrUs0Zzl+1RyhVV+nxTuHZtC5Mkjf59ssNxL2/5WvGJddqQE62KQ6205u0YrXn7v79W4zrUacknX7fodwEaK7ptg6YsKFBMbIOOHPbX3p3BeuDmztq2/uhtdR261Gj09GJFRFtVWthKry6I0zvPtfVw1DhTREZGOiT7xsjMzNRXX32ljRs3Oqy/88477f/cq1cvtW/fXldffbX27NmjLl26uCVe6Qy4z37SpEnKyMjQBRdcoIsuukhPPvmkqqurNXr0aE+HZgqTnjj5UEif31fpg6Ltpzz+1sklunUyrXt4l/l/Tjzl9pceTdBLjya0UDRoKZ56gt748eOVk5Oj9evXq0OHDqfct1+/fpKk3bt3q0uXLoqPj9cnn3zisM+xbvjJxvlPxOPJ/k9/+pN+/PFHzZw5UyUlJerbt69WrVp1XNsCAABXuKuN31iGYWjChAlavny51q5dq06dOp32mO3bt0uS2rdvL0lKTU3V3LlzdeDAAcXGxkqSVq9ercjISCUnJ5/sNMfxeLKXjv7qoW0PAPAlmZmZWrZsmf75z38qIiLCPsYeFRWlkJAQ7dmzR8uWLdPgwYPVpk0bffHFF5o4caIuv/xy9e7dW5I0YMAAJScn69Zbb9W8efNUUlKiBx98UJmZmU7NYTsjkj0AAM2tpZ+N/+yzz0o6+uCcX1u8eLFGjRqlwMBA/fvf/7YPXycmJmrEiBF68MEH7fv6+/srJydH48aNU2pqqsLCwpSRkaE5c+Y4FQvJHgBgCp5o459KYmKi1q1bd9rzJCUl6b333nPq2r/lVffZAwAA51HZAwBMoaUr+zMJyR4AYApmTva08QEA8HFU9gAAUzBzZU+yBwCYgiHnb5/77fHeimQPADAFM1f2jNkDAODjqOwBAKZg5sqeZA8AMAUzJ3va+AAA+DgqewCAKZi5sifZAwBMwTAsMlxI2K4c62m08QEA8HFU9gAAU2jp99mfSUj2AABTMPOYPW18AAB8HJU9AMAUzDxBj2QPADAFM7fxSfYAAFMwc2XPmD0AAD6Oyh4AYAqGi218b67sSfYAAFMwJBmGa8d7K9r4AAD4OCp7AIAp2GSRhSfoAQDgu5iNDwAAfBaVPQDAFGyGRRYeqgMAgO8yDBdn43vxdHza+AAA+DgqewCAKZh5gh7JHgBgCiR7AAB8nJkn6DFmDwCAj6OyBwCYgpln45PsAQCmcDTZuzJm78ZgWhhtfAAAfByVPQDAFJiNDwCAjzPk2jvpvbiLTxsfAABfR2UPADAF2vgAAPg6E/fxSfYAAHNwsbKXF1f2jNkDAODjqOwBAKbAE/QAAPBxZp6gRxsfAAAfR2UPADAHw+LaJDsvruxJ9gAAUzDzmD1tfAAAfByVPQDAHHioDgAAvs3Ms/Eblez/9a9/NfqE1113XZODAQAA7teoZD9s2LBGncxischqtboSDwAAzceLW/GuaNQEPZvN1qiFRA8AOFMda+O7sjgjKytLF154oSIiIhQbG6thw4YpPz/fYZ+amhplZmaqTZs2Cg8P14gRI1RaWuqwT0FBgYYMGaLQ0FDFxsZqypQpamhocCoWl2bj19TUuHI4AAAtx3DD4oR169YpMzNTmzdv1urVq1VfX68BAwaourravs/EiRO1cuVKvfnmm1q3bp2Kioo0fPhw+3ar1aohQ4aorq5OmzZt0ssvv6zs7GzNnDnTqVicTvZWq1UPP/ywzjrrLIWHh+u7776TJM2YMUMvvviis6cDAMCrVFZWOiy1tbUn3G/VqlUaNWqUzj33XPXp00fZ2dkqKChQXl6eJKmiokIvvviinnjiCV111VVKSUnR4sWLtWnTJm3evFmS9OGHH+rrr7/W//3f/6lv374aNGiQHn74YS1cuFB1dXWNjtnpZD937lxlZ2dr3rx5CgwMtK8/77zz9MILLzh7OgAAWojFDYuUmJioqKgo+5KVldWoq1dUVEiSYmJiJEl5eXmqr69XWlqafZ8ePXqoY8eOys3NlSTl5uaqV69eiouLs++Tnp6uyspK7dixo9Hf3Olb75YsWaLnnntOV199te666y77+j59+mjXrl3Ong4AgJbhpvvsCwsLFRkZaV8dFBR02kNtNpvuu+8+XXLJJTrvvPMkSSUlJQoMDFR0dLTDvnFxcSopKbHv8+tEf2z7sW2N5XSy/+GHH9S1a9fj1ttsNtXX1zt7OgAAvEpkZKRDsm+MzMxMffXVV9q4cWMzRXVqTrfxk5OTtWHDhuPWv/XWWzr//PPdEhQAAG7XwhP0jhk/frxycnL08ccfq0OHDvb18fHxqqurU3l5ucP+paWlio+Pt+/z29n5xz4f26cxnK7sZ86cqYyMDP3www+y2Wx65513lJ+fryVLlignJ8fZ0wEA0DJa+K13hmFowoQJWr58udauXatOnTo5bE9JSVGrVq20Zs0ajRgxQpKUn5+vgoICpaamSpJSU1M1d+5cHThwQLGxsZKk1atXKzIyUsnJyY2OxelkP3ToUK1cuVJz5sxRWFiYZs6cqd/97ndauXKlrrnmGmdPBwCAT8rMzNSyZcv0z3/+UxEREfYx9qioKIWEhCgqKkpjxozRpEmTFBMTo8jISE2YMEGpqam6+OKLJUkDBgxQcnKybr31Vs2bN08lJSV68MEHlZmZ2ai5Asc06dn4l112mVavXt2UQwEA8IiWfsXts88+K0nq37+/w/rFixdr1KhRkqT58+fLz89PI0aMUG1trdLT0/XMM8/Y9/X391dOTo7GjRun1NRUhYWFKSMjQ3PmzHEqlia/CGfr1q3auXOnpKPj+CkpKU09FQAAza+F33pnNOLXQXBwsBYuXKiFCxeedJ+kpCS99957zl38N5xO9vv379dNN92k//znP/bbBcrLy/X73/9er732msPkAwAA4HlOz8a/4447VF9fr507d6qsrExlZWXauXOnbDab7rjjjuaIEQAA1x2boOfK4qWcruzXrVunTZs2qXv37vZ13bt319NPP63LLrvMrcEBAOAuFuPo4srx3srpZJ+YmHjCh+dYrVYlJCS4JSgAANyuhcfszyROt/Efe+wxTZgwQVu3brWv27p1q+699149/vjjbg0OAAC4rlGVfevWrWWx/Hesorq6Wv369VNAwNHDGxoaFBAQoNtvv13Dhg1rlkABAHBJCz9U50zSqGT/5JNPNnMYAAA0MxO38RuV7DMyMpo7DgAA0Eya/FAdSaqpqVFdXZ3DOmffBAQAQIswcWXv9AS96upqjR8/XrGxsQoLC1Pr1q0dFgAAzkgeeuvdmcDpZH///ffro48+0rPPPqugoCC98MILmj17thISErRkyZLmiBEAALjA6Tb+ypUrtWTJEvXv31+jR4/WZZddpq5duyopKUlLly7VyJEjmyNOAABcY+LZ+E5X9mVlZercubOko+PzZWVlkqRLL71U69evd290AAC4ybEn6LmyeCunk33nzp21d+9eSVKPHj30xhtvSDpa8R97MQ4AADhzOJ3sR48erc8//1ySNG3aNC1cuFDBwcGaOHGipkyZ4vYAAQBwCxNP0HN6zH7ixIn2f05LS9OuXbuUl5enrl27qnfv3m4NDgAAuM6l++wlKSkpSUlJSe6IBQCAZmORi2+9c1skLa9RyX7BggWNPuE999zT5GAAAID7NSrZz58/v1Ens1gsHkn2/3PNIAX4BbX4dYGW8P0bUZ4OAWg21iM1UsY/W+ZiJr71rlHJ/tjsewAAvBaPywUAAL7K5Ql6AAB4BRNX9iR7AIApuPoUPFM9QQ8AAHgXKnsAgDmYuI3fpMp+w4YNuuWWW5SamqoffvhBkvTKK69o48aNbg0OAAC3MfHjcp1O9m+//bbS09MVEhKizz77TLW1tZKkiooKPfroo24PEAAAuMbpZP/II49o0aJFev7559WqVSv7+ksuuUTbtm1za3AAALiLmV9x6/SYfX5+vi6//PLj1kdFRam8vNwdMQEA4H4mfoKe05V9fHy8du/efdz6jRs3qnPnzm4JCgAAt2PMvvHGjh2re++9V1u2bJHFYlFRUZGWLl2qyZMna9y4cc0RIwAAcIHTbfxp06bJZrPp6quv1pEjR3T55ZcrKChIkydP1oQJE5ojRgAAXGbmh+o4newtFoseeOABTZkyRbt371ZVVZWSk5MVHh7eHPEBAOAeJr7PvskP1QkMDFRycrI7YwEAAM3A6WR/5ZVXymI5+YzEjz76yKWAAABoFq7ePmemyr5v374On+vr67V9+3Z99dVXysjIcFdcAAC4F238xps/f/4J1z/00EOqqqpyOSAAAOBebnvr3S233KKXXnrJXacDAMC9THyfvdveepebm6vg4GB3nQ4AALfi1jsnDB8+3OGzYRgqLi7W1q1bNWPGDLcFBgAA3MPpZB8VFeXw2c/PT927d9ecOXM0YMAAtwUGAADcw6lkb7VaNXr0aPXq1UutW7durpgAAHA/E8/Gd2qCnr+/vwYMGMDb7QAAXsfMr7h1ejb+eeedp++++645YgEAAM3A6WT/yCOPaPLkycrJyVFxcbEqKysdFgAAzlgmvO1OcmLMfs6cOfrzn/+swYMHS5Kuu+46h8fmGoYhi8Uiq9Xq/igBAHCVicfsG53sZ8+erbvuuksff/xxc8YDAADcrNHJ3jCO/qS54oormi0YAACaCw/VaaRTve0OAIAzGm38xunWrdtpE35ZWZlLAQEAAPdyKtnPnj37uCfoAQDgDWjjN9KNN96o2NjY5ooFAIDmY+I2fqPvs2e8HgAA7+T0bHwAALwSlf3p2Ww2WvgAAK/V0s/GX79+va699lolJCTIYrFoxYoVDttHjRoli8XisAwcONBhn7KyMo0cOVKRkZGKjo7WmDFjVFVV5fR3d/pxuQAAeCVXHpXbhK5AdXW1+vTpo4ULF550n4EDB6q4uNi+vPrqqw7bR44cqR07dmj16tXKycnR+vXrdeeddzoXiJrwPnsAAHB6gwYN0qBBg065T1BQkOLj40+4befOnVq1apU+/fRTXXDBBZKkp59+WoMHD9bjjz+uhISERsdCZQ8AMAc3Vfa/fQFcbW1tk0Nau3atYmNj1b17d40bN06HDh2yb8vNzVV0dLQ90UtSWlqa/Pz8tGXLFqeuQ7IHAJiCu8bsExMTFRUVZV+ysrKaFM/AgQO1ZMkSrVmzRn/961+1bt06DRo0yP5CuZKSkuPmygUEBCgmJkYlJSVOXYs2PgAATigsLFRkZKT9c1BQUJPOc+ONN9r/uVevXurdu7e6dOmitWvX6uqrr3Y5zl+jsgcAmIOb2viRkZEOS1OT/W917txZbdu21e7duyVJ8fHxOnDggMM+DQ0NKisrO+k4/8mQ7AEAptDSt945a//+/Tp06JDat28vSUpNTVV5ebny8vLs+3z00Uey2Wzq16+fU+emjQ8AQDOoqqqyV+mStHfvXm3fvl0xMTGKiYnR7NmzNWLECMXHx2vPnj26//771bVrV6Wnp0uSevbsqYEDB2rs2LFatGiR6uvrNX78eN14441OzcSXqOwBAGbRwvfZb926Veeff77OP/98SdKkSZN0/vnna+bMmfL399cXX3yh6667Tt26ddOYMWOUkpKiDRs2OAwLLF26VD169NDVV1+twYMH69JLL9Vzzz3n9FensgcAmEMLPy63f//+p3zU/AcffHDac8TExGjZsmXOXfgEqOwBAPBxVPYAAFOw/LK4cry3ItkDAMzBxG+9I9kDAEzB1dvnmvvWu+bEmD0AAD6Oyh4AYA608QEAMAEvTtiuoI0PAICPo7IHAJiCmSfokewBAOZg4jF72vgAAPg4KnsAgCnQxgcAwNfRxgcAAL6Kyh4AYAq08QEA8HUmbuOT7AEA5mDiZM+YPQAAPo7KHgBgCozZAwDg62jjAwAAX0VlDwAwBYthyGI0vTx35VhPI9kDAMyBNj4AAPBVVPYAAFNgNj4AAL6ONj4AAPBVVPYAAFOgjQ8AgK8zcRufZA8AMAUzV/aM2QMA4OOo7AEA5kAbHwAA3+fNrXhX0MYHAMDHUdkDAMzBMI4urhzvpUj2AABTYDY+AADwWVT2AABzYDY+AAC+zWI7urhyvLeijQ8AgI+jsscJtWn7s0Zn7lTKxQcUFGxV8f4wzZ/bV7t3RUuSJj7wmdKG7Hc4Jm9zO82cdLEHogVOLnL5AYV+UqlWP9TKCLSotluYfrolXg0JQfZ9wv9dprCN5Qrc+7P8frapYHGyjDB/h/O0++s+Be6rkX9lg6xh/qrpFa7ykfGyxrRq6a+EpqKND/xXeESdHvvHf/TFtraaNamfKsqDlJBYparDjv9R25rbTk/O7Wv/XF9PowhnnuCvq3U4vY3quoRIVkPRr5Yq7pG9Knqim4zgo3+zllqbfu4brp/7hqv1stITnqfm3HBV/DFW1tYBCiirV/QrJWr7RIFKH+nSkl8HLmA2voesX79e1157rRISEmSxWLRixQpPhoNfXH/LHv1YGqIn5/bVNztbq7Q4VJ99EquSH8Ic9quv99NPZcH2pepwoIciBk7uwAOdVN2/teoTg1V/dogOZXZQwMF6BX73s32fw0PaqnJYrGrPCT3peQ7/oa3quoXK2i5Qtd3DVDmsnYK+PSI1eHEGMJtj99m7sngpj1b21dXV6tOnj26//XYNHz7ck6HgV/pdWqJtW9pp+iNbdd75h3Tox2C9+87Z+uBfSQ779Tr/kJa++4GqKlvp87y2euW5HjpcScLHmc3viFWSZAv3P82epzhHVYPCNpSrtluoFGBxV2hAs/Fosh80aJAGDRrU6P1ra2tVW1tr/1xZWdkcYZlefMIRDf7j91r+Wme9vuQcdetZrv+d+JUa6v205v1ESVLellhtWtdeJUWhat+hWhn/u0uzn9iiyXdeKpuN//jhDGUz1Dq7WDXdQ1XfMdjpw6P/r1gRHxySX62h2nNCdGDa2e6PEc3GzG18rxqzz8rK0uzZsz0dhs+z+BnavStaS/7RU5L03TdRSup8WIP++L092a//91n2/b//LlL7dkfqxbc+Uq/zD+rzvHYeiRs4nZgXixRYWKOSOU0bZ6+8rp2qropRwME6Rb15QG3+vl8/TkuSLPzA9QomnqDnVTOqpk+froqKCvtSWFjo6ZB80k+HglWwN8JhXeG+cLWL+/kkR0glRWGq+ClQ7TtUN3d4QJO0fvEHhWw7rNJZnWVt07QZ9LbIADUkBKmmd4QO3tdRoZ8dVuC3R9wcKeB+XlXZBwUFKSgo6PQ7wiVffxGjszpWOaw7K7FKP5aEnPSYNu1+VkRUnX465HxrFGhWhqHWLxUp9JNKlT7UWQ2xbppX8stkLUu9F5d7JmPmNr5XVfZoGSte76we5/2kG277Vu3PqtYV1+zXwKEFynn7bElScEiDbs/8Wt3P/Umx8UfUJ+VHzfzrpyreH6a8LbTwcWaJebFI4RvKdfDeRNlC/ORXXi+/8npZ6v77ODS/8nq12vezWpXUSZICC2rUat/P8qtqOPr52yOKWHVQrfb9LP8f6xT8VZXaPlWo+rjAo5P04B2YjQ/817c7o/XItAs1atxO3TT6G5UWh+q5p87V2g87SJJsVovO7lqpqwcXKiy8XmUHg/XZJ+30ynM91FDf9BnOQHOI+LBMkhT/0F6H9Qfv7qDq/q3t+0S/dcC+LX7Wdw77GEF+Ct1Sqag3Dsiv1iZrdIB+7huhgxNjpVbUTDjzeTTZV1VVaffu3fbPe/fu1fbt2xUTE6OOHTt6MDJ8uilOn26KO+G2ujp/zZzIk/LgHb5/o9dp96m4IU4VN5z4712S6jsGq3RWZ3eGBQ8wcxvfo8l+69atuvLKK+2fJ02aJEnKyMhQdna2h6ICAPgkE8/G92iy79+/vwwvHgMBAMAbMGYPADAFM7fxmVkCADAHm+H64oTTvf/FMAzNnDlT7du3V0hIiNLS0vTtt9867FNWVqaRI0cqMjJS0dHRGjNmjKqqHG+NbgySPQDAHAw3LE449v6XhQsXnnD7vHnztGDBAi1atEhbtmxRWFiY0tPTVVNTY99n5MiR2rFjh1avXq2cnBytX79ed955p3OBiDY+AADN4lTvfzEMQ08++aQefPBBDR06VJK0ZMkSxcXFacWKFbrxxhu1c+dOrVq1Sp9++qkuuOACSdLTTz+twYMH6/HHH1dCQkKjY6GyBwCYgkX/Hbdv0vLLeSorKx2WX7+grbH27t2rkpISpaWl2ddFRUWpX79+ys3NlSTl5uYqOjranuglKS0tTX5+ftqyZYtT1yPZAwDMwU1P0EtMTFRUVJR9ycrKcjqUkpISSVJcnOPzHeLi4uzbSkpKFBsb67A9ICBAMTEx9n0aizY+AABOKCwsVGRkpP2zN7yzhcoeAGAKLrXwf3XbXmRkpMPSlGQfHx8vSSotLXVYX1paat8WHx+vAwcOOGxvaGhQWVmZfZ/GItkDAMyhhWfjn0qnTp0UHx+vNWvW2NdVVlZqy5YtSk1NlSSlpqaqvLxceXl59n0++ugj2Ww29evXz6nr0cYHAKAZnO79L/fdd58eeeQRnXPOOerUqZNmzJihhIQEDRs2TJLUs2dPDRw4UGPHjtWiRYtUX1+v8ePH68Ybb3RqJr5EsgcAmITFMGRx4RHtzh57uve/3H///aqurtadd96p8vJyXXrppVq1apWCg4PtxyxdulTjx4/X1VdfLT8/P40YMUILFixwOnaSPQDAHGy/LK4c74TTvf/FYrFozpw5mjNnzkn3iYmJ0bJly5y78AkwZg8AgI+jsgcAmEJLt/HPJCR7AIA58D57AAB83K+egtfk470UY/YAAPg4KnsAgCn8+il4TT3eW5HsAQDmQBsfAAD4Kip7AIApWGxHF1eO91YkewCAOdDGBwAAvorKHgBgDjxUBwAA32bmx+XSxgcAwMdR2QMAzMHEE/RI9gAAczDk2vvsvTfXk+wBAObAmD0AAPBZVPYAAHMw5OKYvdsiaXEkewCAOZh4gh5tfAAAfByVPQDAHGySLC4e76VI9gAAU2A2PgAA8FlU9gAAczDxBD2SPQDAHEyc7GnjAwDg46jsAQDmYOLKnmQPADAHbr0DAMC3cesdAADwWVT2AABzYMweAAAfZzMkiwsJ2+a9yZ42PgAAPo7KHgBgDrTxAQDwdS4me3lvsqeNDwCAj6OyBwCYA218AAB8nM2QS614ZuMDAIAzFZU9AMAcDNvRxZXjvRTJHgBgDozZAwDg4xizBwAAvorKHgBgDrTxAQDwcYZcTPZui6TF0cYHAMDHUdkDAMyBNj4AAD7OZpPkwr3yNu+9z542PgAAPo7KHgBgDrTxAQDwcSZO9rTxAQDwcVT2AABzMPHjckn2AABTMAybDBfeXOfKsZ5GGx8AYA6GcbQ6b+ri5Jj9Qw89JIvF4rD06NHDvr2mpkaZmZlq06aNwsPDNWLECJWWlrr7W0si2QMA0GzOPfdcFRcX25eNGzfat02cOFErV67Um2++qXXr1qmoqEjDhw9vljho4wMAzMFwccz+l8q+srLSYXVQUJCCgoJOeEhAQIDi4+OPW19RUaEXX3xRy5Yt01VXXSVJWrx4sXr27KnNmzfr4osvbnqcJ0BlDwAwB5vN9UVSYmKioqKi7EtWVtZJL/ntt98qISFBnTt31siRI1VQUCBJysvLU319vdLS0uz79ujRQx07dlRubq7bvzqVPQAATigsLFRkZKT988mq+n79+ik7O1vdu3dXcXGxZs+ercsuu0xfffWVSkpKFBgYqOjoaIdj4uLiVFJS4vaYSfYAAHNwUxs/MjLSIdmfzKBBg+z/3Lt3b/Xr109JSUl64403FBIS0vQ4moA2PgDAFAybzeXFFdHR0erWrZt2796t+Ph41dXVqby83GGf0tLSE47xu4pkDwBAC6iqqtKePXvUvn17paSkqFWrVlqzZo19e35+vgoKCpSamur2a9PGBwCYg5va+I01efJkXXvttUpKSlJRUZFmzZolf39/3XTTTYqKitKYMWM0adIkxcTEKDIyUhMmTFBqaqrbZ+JLJHsAgFnYDMnScsl+//79uummm3To0CG1a9dOl156qTZv3qx27dpJkubPny8/Pz+NGDFCtbW1Sk9P1zPPPNP0+E6BZA8AQDN47bXXTrk9ODhYCxcu1MKFC5s9FpI9AMAcDEOSC5PsvPgVtyR7AIApGDZDhgttfINkDwDAGc6wybXKnrfeAQCAMxSVPQDAFGjjAwDg60zcxvfqZH/sV1aDrc7DkQDNx3qkxtMhAM3G9nOtpJapmhtU79IzdRpU775gWpjF8OK+xP79+5WYmOjpMAAALiosLFSHDh2a5dw1NTXq1KmTW94mFx8fr7179yo4ONgNkbUcr072NptNRUVFioiIkMVi8XQ4plBZWanExMTjXvEI+AL+vlueYRg6fPiwEhIS5OfXfHPGa2pqVFfnehc4MDDQ6xK95OVtfD8/v2b7JYhTa+wrHgFvxN93y4qKimr2awQHB3tlknYXbr0DAMDHkewBAPBxJHs4JSgoSLNmzVJQUJCnQwHcjr9v+CqvnqAHAABOj8oeAAAfR7IHAMDHkewBAPBxJHsAAHwcyR6NtnDhQp199tkKDg5Wv3799Mknn3g6JMAt1q9fr2uvvVYJCQmyWCxasWKFp0MC3Ipkj0Z5/fXXNWnSJM2aNUvbtm1Tnz59lJ6ergMHDng6NMBl1dXV6tOnjxYuXOjpUIBmwa13aJR+/frpwgsv1N///ndJR99LkJiYqAkTJmjatGkejg5wH4vFouXLl2vYsGGeDgVwGyp7nFZdXZ3y8vKUlpZmX+fn56e0tDTl5uZ6MDIAQGOQ7HFaBw8elNVqVVxcnMP6uLg4t7wyEgDQvEj2AAD4OJI9Tqtt27by9/dXaWmpw/rS0lLFx8d7KCoAQGOR7HFagYGBSklJ0Zo1a+zrbDab1qxZo9TUVA9GBgBojABPBwDvMGnSJGVkZOiCCy7QRRddpCeffFLV1dUaPXq0p0MDXFZVVaXdu3fbP+/du1fbt29XTEyMOnbs6MHIAPfg1js02t///nc99thjKikpUd++fbVgwQL169fP02EBLlu7dq2uvPLK49ZnZGQoOzu75QMC3IxkDwCAj2PMHgAAH0eyBwDAx5HsAQDwcSR7AAB8HMkeAAAfR7IHAMDHkewBAPBxJHsAAHwcyR5w0ahRozRs2DD75/79++u+++5r8TjWrl0ri8Wi8vLyk+5jsVi0YsWKRp/zoYceUt++fV2Ka9++fbJYLNq+fbtL5wHQdCR7+KRRo0bJYrHIYrEoMDBQXbt21Zw5c9TQ0NDs137nnXf08MMPN2rfxiRoAHAVL8KBzxo4cKAWL16s2tpavffee8rMzFSrVq00ffr04/atq6tTYGCgW64bExPjlvMAgLtQ2cNnBQUFKT4+XklJSRo3bpzS0tL0r3/9S9J/W+9z585VQkKCunfvLkkqLCzUDTfcoOjoaMXExGjo0KHat2+f/ZxWq1WTJk1SdHS02rRpo/vvv1+/fb3Eb9v4tbW1mjp1qhITExUUFKSuXbvqxRdf1L59++wvX2ndurUsFotGjRol6egrhLOystSpUyeFhISoT58+euuttxyu895776lbt24KCQnRlVde6RBnY02dOlXdunVTaGioOnfurBkzZqi+vv64/f7xj38oMTFRoaGhuuGGG1RRUeGw/YUXXlDPnj0VHBysHj166JlnnnE6FgDNh2QP0wgJCVFdXZ3985o1a5Sfn6/Vq1crJydH9fX1Sk9PV0REhDZs2KD//Oc/Cg8P18CBA+3H/e1vf1N2drZeeuklbdy4UWVlZVq+fPkpr3vbbbfp1Vdf1YIFC7Rz50794x//UHh4uBITE/X2229LkvLz81VcXKynnnpKkpSVlaUlS5Zo0aJF2rFjhyZOnKhbbrlF69atk3T0R8nw4cN17bXXavv27brjjjs0bdo0p/+dREREKDs7W19//bWeeuopPf/885o/f77DPrt379Ybb7yhlStXatWqVfrss890991327cvXbpUM2fO1Ny5c7Vz5049+uijmjFjhl5++WWn4wHQTAzAB2VkZBhDhw41DMMwbDabsXr1aiMoKMiYPHmyfXtcXJxRW1trP+aVV14xunfvbthsNvu62tpaIyQkxPjggw8MwzCM9u3bG/PmzbNvr6+vNzp06GC/lmEYxhVXXGHce++9hmEYRn5+viHJWL169Qnj/Pjjjw1Jxk8//WRfV1NTY4SGhhqbNm1y2HfMmDHGTTfdZBiGYUyfPt1ITk522D516tTjzvVbkozly5efdPtjjz1mpKSk2D/PmjXL8Pf3N/bv329f9/777xt+fn5GcXGxYRiG0aVLF2PZsmUO53n44YeN1NRUwzAMY+/evYYk47PPPjvpdQE0L8bs4bNycnIUHh6u+vp62Ww23XzzzXrooYfs23v16uUwTv/5559r9+7dioiIcDhPTU2N9uzZo4qKChUXF6tfv372bQEBAbrggguOa+Ufs337dvn7++uKK65odNy7d+/WkSNHdM011zisr6ur0/nnny9J2rlzp0MckpSamtroaxzz+uuva8GCBdqzZ4+qqqrU0NCgyMhIh306duyos846y+E6NptN+fn5ioiI0J49ezRmzBiNHTvWvk9DQ4OioqKcjgdA8yDZw2ddeeWVevbZZxUYGKiEhAQFBDj+uYeFhTl8rqqqUkpKipYuXXrcudq1a9ekGEJCQpw+pqqqSpL07rvvOiRZ6eg8BHfJzc3VyJEjNXv2bKWnpysqKkqvvfaa/va3vzkd6/PPP3/cjw9/f3+3xQrANSR7+KywsDB17dq10fv/7ne/0+uvv67Y2Njjqttj2rdvry1btujyyy+XdLSCzcvL0+9+97sT7t+rVy/ZbDatW7dOaWlpx20/1lmwWq32dcnJyQoKClJBQcFJOwI9e/a0TzY8ZvPmzaf/kr+yadMmJSUl6YEHHrCv+/7774/br6CgQEVFRUpISLBfx8/PT927d1dcXJwSEhL03XffaeTIkU5dH0DLYYIe8IuRI0eqbdu2Gjp0qDZs2KC9e/dq7dq1uueee7R//35J0r333qu//OUvWrFihXbt2qW77777lPfIn3322crIyNDtt9+uFStW2M/5xhtvSJKSkpJksViUk5OjH3/8UVVVVYqIiNDkyZM1ceJEvfzyy9qzZ4+2bdump59+2j7p7a677tK3336rKVOmKD8/X8uWLVN2drZT3/ecc85RQUGBXnvtNe3Zs0cLFiw44WTD4OBgZWRk6PPPP9eGDRt0zz336IYbblB8fLwkafbs2crKytKCBQv0zTff6Msvv9TixYv1xBNPOBUPgOZDsgd+ERoaqvXr16tjx44aPny4evbsqTFjxqimpsZe6f/5z3/WrbfeqoyMDKWmpioiIkJ//OMfT3neZ599Vtdff73uvvtu9ejRQ2PHjlV1dbUk6ayzztLs2bM1bdo0xcXFafz48ZKkhx9+WDNmzFBWVpZ69uypgQMH6t1331WnTp0kHR1Hf/vtt7VixQr16dNHixYt0qOPPurU973uuus0ceJEjR8/Xn379tWmTZs0Y8aM4/br2rWrhg8frsGDB2vAgAHq3bu3w611d9xxh1544QUtXrxYvXr10hVXXKHs7Gx7rAA8z2KcbGYRAADwCVT2AAD4OJI9AAA+jmQPAICPI9kDAODjSPYAAPg4kj0AAD6OZA8AgI8j2QMA4ONI9gAA+DiSPQAAPo5kDwCAj/v/7cb5PN5kAOIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#KNN"
      ],
      "metadata": {
        "id": "xAbm_C_5okVC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "qHdcUFFMomTA",
        "outputId": "9d87769c-228c-4d54-999c-bf7003a20493"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "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>KNeighborsClassifier()</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\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_knn = knn.predict(X_test)\n",
        "print('Predicted vs actual values on y_test')\n",
        "print(np.concatenate((y_pred_knn.reshape(len(y_pred_knn),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ksw1UdbGf9Zz",
        "outputId": "f3010829-721b-4c14-ac72-5d052a75b3ef"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted vs actual values on y_test\n",
            "[[0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " ...\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
        "print('Accuracy:', accuracy_knn)\n",
        "\n",
        "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_knn)\n",
        "\n",
        "ConfusionMatrixDisplay(cm_knn).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "5m1PtANZf-Be",
        "outputId": "9859e35c-e2a3-4718-d821-2e7a3583d963"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8450704225352113\n",
            "Confusion Matrix:\n",
            "[[334  27]\n",
            " [ 72 206]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SVM"
      ],
      "metadata": {
        "id": "pnUb6cDTwQx4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svm = SVC(kernel='linear', random_state=42)\n",
        "svm.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BvtvKSCNwUB2",
        "outputId": "272de603-53e2-405e-c025-aa659005a083"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_svm = svm.predict(X_test)\n",
        "print('Predicted vs actual values on y_test')\n",
        "print(np.concatenate((y_pred_svm.reshape(len(y_pred_svm),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3HFOlisEf3hV",
        "outputId": "20d5b7c7-8f2e-4c80-fa13-8e4c3a321b2f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted vs actual values on y_test\n",
            "[[1 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " ...\n",
            " [0 1]\n",
            " [1 1]\n",
            " [1 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
        "print('Accuracy:', accuracy_svm)\n",
        "\n",
        "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_svm)\n",
        "\n",
        "ConfusionMatrixDisplay(cm_svm).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "yzWPMpNff5s6",
        "outputId": "24e89c07-a04c-46d9-e034-90d68d7fb2d1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8247261345852895\n",
            "Confusion Matrix:\n",
            "[[315  46]\n",
            " [ 66 212]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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tiRPd3oCme/7h9jrv4iqd1qFBh6uD9O6KNvp0a5QeWrpHklS+v5V+2N9axfuOVOn7docpItKu0zrUK6aNTTu3RWj3x5Hq8+uDioiya1depBben6SLR/6g6DjbL3014BOHa4L1dX64077aQ0E6+MNP+19ekKAbppRq785w7d0RrozflSv5jDo9ODb+eJeEn2jpt94tWLBA0pEH5/yvRYsW6aabblJISIjeeecdzZ07VzU1NUpOTtbIkSN13333OcYGBwdr9erVGjdunNLT0xUZGamsrCyn+/Jd4dNkHxISon79+mndunUaPny4pCMrFtetW6cJEyb4MjTTqPiulR65PUXl+1spItqmzj1r9dDSPep3UbUk6fXF7fSvx376w2vKlUcWNN31eKEGXVOu1iGGNr4Wp3/9zaqGeousyfUaccsBjbjlgE9+HsAbVjx7mlqH2XXrrGJFx9m0d2eYpl3XRSVfU2zAdYbxy8/XTU5O1saNG096nZSUFL3xxhsexWIxThZNM1u2bJmysrL09NNP61e/+pXmzp2r5cuXa/fu3cfM5f9cVVWVYmNj9cMXXRQTzf2vCEyZSX19HQLQbBqNBm3Qa6qsrHR7hburjuaKK9eOVuvIpq8laqip14pLFzVrrM3F53P211xzjQ4cOKAZM2aotLRUffv21Zo1a06a6AEAcEdLt/FPJT5P9tKR2xJo2wMA0DxOiWQPAEBz89az8f0RyR4AYApmbuOzqg0AgABHZQ8AMAUzV/YkewCAKZg52dPGBwAgwFHZAwBMwcyVPckeAGAKhjy7fc6nj5v1EMkeAGAKZq7smbMHACDAUdkDAEzBzJU9yR4AYApmTva08QEACHBU9gAAUzBzZU+yBwCYgmFYZHiQsD0519do4wMAEOCo7AEApsD77AEACHBmnrOnjQ8AQICjsgcAmIKZF+iR7AEApmDmNj7JHgBgCmau7JmzBwAgwFHZAwBMwfCwje/PlT3JHgBgCoYkw/DsfH9FGx8AgABHZQ8AMAW7LLLwBD0AAAIXq/EBAEDAorIHAJiC3bDIwkN1AAAIXIbh4Wp8P16OTxsfAIAAR2UPADAFMy/QI9kDAEyBZA8AQIAz8wI95uwBAAhwVPYAAFMw82p8kj0AwBSOJHtP5uy9GEwLo40PAECAo7IHAJgCq/EBAAhwhjx7J70fd/Fp4wMAEOio7AEApkAbHwCAQGfiPj7JHgBgDh5W9vLjyp45ewAAAhyVPQDAFHiCHgAAAc7MC/Ro4wMAEOCo7AEA5mBYPFtk58eVPckeAGAKZp6zp40PAECAo7IHAJgDD9UBACCwmXk1vkvJ/j//+Y/LF7ziiiuaHAwAAPA+l5L98OHDXbqYxWKRzWbzJB4AAJpPC7bis7Oz9eqrr2r37t0KDw/Xr3/9a/31r39V9+7dHWNqa2t111136cUXX1RdXZ0yMzP11FNPKTEx0TGmsLBQ48aN07vvvquoqChlZWUpOztbrVq53px3aYGe3W53aSPRAwBOVUfb+J5s7ti4caPGjx+v9957T2vXrlVDQ4MGDRqkmpoax5hJkyZp1apVeumll7Rx40YVFxdrxIgRjuM2m02XXXaZ6uvrtXXrVr3wwgvKycnRjBkz3IrFozn72tpahYWFeXIJAABahpcW6FVVVTntDg0NVWho6DHD16xZ4/Q5JydHCQkJysvLU//+/VVZWannnntOS5cu1cUXXyxJWrRokXr27Kn33ntP559/vt5++23t3LlT77zzjhITE9W3b1898MADmjp1qmbOnKmQkBCXQnf71jubzaYHHnhAHTp0UFRUlPbu3StJmj59up577jl3LwcAgF9JTk5WbGysY8vOznbpvMrKSklSfHy8JCkvL08NDQ3KyMhwjOnRo4c6deqk3NxcSVJubq569erl1NbPzMxUVVWVduzY4XLMbif7hx56SDk5OZozZ47TXxRnnXWWnn32WXcvBwBAC7F4YZOKiopUWVnp2KZNm3bSb7bb7brzzjt1wQUX6KyzzpIklZaWKiQkRHFxcU5jExMTVVpa6hjzv4n+6PGjx1zldrJfvHix/vGPf2jUqFEKDg527O/Tp492797t7uUAAGgZhhc2STExMU7b8Vr4Pzd+/Hh9/vnnevHFF738Q7nG7WT/7bffqmvXrsfst9vtamho8EpQAAAEigkTJmj16tV699131bFjR8d+q9Wq+vp6VVRUOI0vKyuT1Wp1jCkrKzvm+NFjrnI72aempmrz5s3H7H/55Zd19tlnu3s5AABahpcqe5e/zjA0YcIErVixQuvXr1fnzp2djvfr10+tW7fWunXrHPvy8/NVWFio9PR0SVJ6ero+++wz7d+/3zFm7dq1iomJUWpqqsuxuL0af8aMGcrKytK3334ru92uV199Vfn5+Vq8eLFWr17t7uUAAGgZLfzWu/Hjx2vp0qV67bXXFB0d7Zhjj42NVXh4uGJjYzVmzBhNnjxZ8fHxiomJ0cSJE5Wenq7zzz9fkjRo0CClpqbqhhtu0Jw5c1RaWqr77rtP48ePd2n64Ci3K/thw4Zp1apVeueddxQZGakZM2Zo165dWrVqlS699FJ3LwcAQEBasGCBKisrNWDAALVv396xLVu2zDHm8ccf129/+1uNHDlS/fv3l9Vq1auvvuo4HhwcrNWrVys4OFjp6em6/vrrdeONN2r27NluxWIxDP99aV9VVZViY2P1wxddFBPNC/wQmDKT+vo6BKDZNBoN2qDXVFlZqZiYmGb5jqO5ouOTsxQU3vRnw9gP1+qbCfc3a6zNpckP1dm2bZt27dol6cg8fr9+/bwWFAAAXsdb71z3zTff6LrrrtN///tfx72BFRUV+vWvf60XX3zRaaUhAADwPbd73zfffLMaGhq0a9culZeXq7y8XLt27ZLdbtfNN9/cHDECAOC5owv0PNn8lNuV/caNG7V161ant/Z0795dTzzxhC688EKvBgcAgLdYjCObJ+f7K7eTfXJy8nEfnmOz2ZSUlOSVoAAA8DoTz9m73cZ/5JFHNHHiRG3bts2xb9u2bbrjjjv06KOPejU4AADgOZcq+zZt2shi+WmuoqamRmlpaWrV6sjpjY2NatWqlf7whz9o+PDhzRIoAAAeaeGH6pxKXEr2c+fObeYwAABoZiZu47uU7LOyspo7DgAA0Eya/FAdSaqtrVV9fb3TPn97qhAAwCRMXNm7vUCvpqZGEyZMUEJCgiIjI9WmTRunDQCAU1ILv/XuVOJ2sr/nnnu0fv16LViwQKGhoXr22Wc1a9YsJSUlafHixc0RIwAA8IDbbfxVq1Zp8eLFGjBggEaPHq0LL7xQXbt2VUpKipYsWaJRo0Y1R5wAAHjGxKvx3a7sy8vL1aVLF0lH5ufLy8slSb/5zW+0adMm70YHAICXHH2Cniebv3I72Xfp0kX79u2TJPXo0UPLly+XdKTiP/piHAAAcOpwO9mPHj1an3zyiSTp3nvv1fz58xUWFqZJkybp7rvv9nqAAAB4hYkX6Lk9Zz9p0iTHf2dkZGj37t3Ky8tT165d1bt3b68GBwAAPOfRffaSlJKSopSUFG/EAgBAs7HIw7feeS2SludSsp83b57LF7z99tubHAwAAPA+l5L9448/7tLFLBaLT5L97wZcqlZBoS3+vUBL+Hp5W1+HADQb26FaKeu1lvkyE99651KyP7r6HgAAv8XjcgEAQKDyeIEeAAB+wcSVPckeAGAKnj4Fz1RP0AMAAP6Fyh4AYA4mbuM3qbLfvHmzrr/+eqWnp+vbb7+VJP3zn//Uli1bvBocAABeY+LH5bqd7F955RVlZmYqPDxcH3/8serq6iRJlZWVevjhh70eIAAA8Izbyf7BBx/UwoUL9cwzz6h169aO/RdccIE++ugjrwYHAIC3mPkVt27P2efn56t///7H7I+NjVVFRYU3YgIAwPtM/AQ9tyt7q9WqgoKCY/Zv2bJFXbp08UpQAAB4HXP2rhs7dqzuuOMOvf/++7JYLCouLtaSJUs0ZcoUjRs3rjliBAAAHnC7jX/vvffKbrfrkksu0aFDh9S/f3+FhoZqypQpmjhxYnPECACAx8z8UB23k73FYtGf//xn3X333SooKFB1dbVSU1MVFRXVHPEBAOAdJr7PvskP1QkJCVFqaqo3YwEAAM3A7WQ/cOBAWSwnXpG4fv16jwICAKBZeHr7nJkq+759+zp9bmho0Pbt2/X5558rKyvLW3EBAOBdtPFd9/jjjx93/8yZM1VdXe1xQAAAwLu89ta766+/Xs8//7y3LgcAgHeZ+D57r731Ljc3V2FhYd66HAAAXsWtd24YMWKE02fDMFRSUqJt27Zp+vTpXgsMAAB4h9vJPjY21ulzUFCQunfvrtmzZ2vQoEFeCwwAAHiHW8neZrNp9OjR6tWrl9q0adNcMQEA4H0mXo3v1gK94OBgDRo0iLfbAQD8jplfcev2avyzzjpLe/fubY5YAABAM3A72T/44IOaMmWKVq9erZKSElVVVTltAACcskx4253kxpz97Nmzddddd2no0KGSpCuuuMLpsbmGYchischms3k/SgAAPGXiOXuXk/2sWbN066236t13323OeAAAgJe5nOwN48ifNBdddFGzBQMAQHPhoTou+qW33QEAcEqjje+abt26nTThl5eXexQQAADwLreS/axZs455gh4AAP6ANr6Lrr32WiUkJDRXLAAANB8Tt/Fdvs+e+XoAAPyT26vxAQDwSyau7F1O9na7vTnjAACgWTFnDwBAoDNxZe/2s/EBAMDJbdq0SZdffrmSkpJksVi0cuVKp+M33XSTLBaL0zZ48GCnMeXl5Ro1apRiYmIUFxenMWPGqLq62u1YSPYAAHPw5CU4TegK1NTUqE+fPpo/f/4JxwwePFglJSWO7d///rfT8VGjRmnHjh1au3atVq9erU2bNumWW25xLxDRxgcAmERLz9kPGTJEQ4YM+cUxoaGhslqtxz22a9curVmzRh9++KHOPfdcSdITTzyhoUOH6tFHH1VSUpLLsVDZAwDghp+/2r2urq7J19qwYYMSEhLUvXt3jRs3Tt9//73jWG5uruLi4hyJXpIyMjIUFBSk999/363vIdkDAMzBS2385ORkxcbGOrbs7OwmhTN48GAtXrxY69at01//+ldt3LhRQ4YMcbwqvrS09JgH2bVq1Urx8fEqLS1167to4wMATMFbbfyioiLFxMQ49oeGhjbpetdee63jv3v16qXevXvrjDPO0IYNG3TJJZc0PdDjoLIHAMANMTExTltTk/3PdenSRe3atVNBQYEkyWq1av/+/U5jGhsbVV5efsJ5/hMh2QMAzKGFV+O765tvvtH333+v9u3bS5LS09NVUVGhvLw8x5j169fLbrcrLS3NrWvTxgcAmEMLP1SnurraUaVL0r59+7R9+3bFx8crPj5es2bN0siRI2W1WrVnzx7dc8896tq1qzIzMyVJPXv21ODBgzV27FgtXLhQDQ0NmjBhgq699lq3VuJLVPYAADSLbdu26eyzz9bZZ58tSZo8ebLOPvtszZgxQ8HBwfr00091xRVXqFu3bhozZoz69eunzZs3O00LLFmyRD169NAll1yioUOH6je/+Y3+8Y9/uB0LlT0AwBQsP26enO+OAQMG/OJL5N56662TXiM+Pl5Lly5185uPRbIHAJiDiZ+NT7IHAJiCmd96x5w9AAABjsoeAGAOtPEBADABP07YnqCNDwBAgKOyBwCYgpkX6JHsAQDmYOI5e9r4AAAEOCp7AIAp0MYHACDQ0cYHAACBisoeAGAKtPEBAAh0Jm7jk+wBAOZg4mTPnD0AAAGOyh4AYArM2QMAEOho4wMAgEBFZQ8AMAWLYchiNL089+RcXyPZAwDMgTY+AAAIVFT2AABTYDU+AACBjjY+AAAIVFT2AABToI0PAECgM3Ebn2QPADAFM1f2zNkDABDgqOwBAOZAGx8AgMDnz614T9DGBwAgwFHZAwDMwTCObJ6c76dI9gAAU2A1PgAACFhU9gAAc2A1PgAAgc1iP7J5cr6/oo0PAECAo7LHcbU9rVajJ+arX/oBhYbZVPJNhB6f3VsFu2IdY5JPr9boifk665xyBQcbKtwXpYfvOVsHysJ9GDngLGbFfkV8UKXW39bJCLGorlukfrjeqsakUMeYqHfKFbmlQiH7DivosF2Fi1JlRAY7jgfvr1fcK/sV9nm1gioaZYtvrZoL41Q54jSpFTWT36CND/wkKrpBjzz7nj7Ni9f9d5yryooQJSXXqLrqp18Xa4cazXnmPb39n47619NddaimlVLOqFZ9Pf/w4dQStrNGBzPbqv6McMlmKO7fZUp8cJ+KH+smI+zI76ulzq7DfaN0uG+U2iwtO+YarYvrJEP6/pYOarSGqnVRrdo+/a0stXZV3Ni+pX8kNJGZV+P7NNlv2rRJjzzyiPLy8lRSUqIVK1Zo+PDhvgwJkq7K2qsDZWGaO7u3Y19ZcYTTmBtv+1Lbtp6mRU/0cOwr/TayxWIEXLX/z52dPn8/vqOSb96lkL2HVZd65Hf24GXtJEmhO6qPe43avtGq7Rvt+NyYGKKq4jpFvf09yd6fmPg+e5+WYTU1NerTp4/mz5/vyzDwM2kXlqlgV6ymZX+sJW+t07x/bVHm8CLHcYvF0HkX7Ne3hZGaPe9DLXlrnR5btFXnX3RsRQScaoIO2SRJ9qjgk4w8+XXsUTRH4R98+ps6ZMgQDRkyxOXxdXV1qqurc3yuqqpqjrBMz9rhsIaOLNSKpadr2aIu6vZ/lfrjXTvV2GDRutc7Ki6+XhGRNv0ua6/+ueBM5TzZXf3SD+jPcz7StHG/0ucftfX1jwAcn91Qm5wS1XaPUEOnsCZfplVpnaLf/F4/3EBV709o4/uJ7OxszZo1y9dhBDxLkKGCXbFa/FR3SdLeL2KV0qVaQ0YUad3rHWX58Tf+vY0JWvnvzj+OiVHP3hUaOqKIZI9TVvxzxQopqlXp7DOafI3g8gYlPPSVatJjVZ0R78Xo0OxMvEDPr1ZTTZs2TZWVlY6tqKjo5CfBbT98F6rCvVFO+4q+itRp1sOSpKqKEDU2WlS472dj9v00BjjVtHnuW4V/dFBl93eRrW3rJl0juLxBibP2qq57hMpv6eDlCIHm41eVfWhoqEJDQ08+EB7Z+UkbdUipcdrXodMhHSg9cktdY2OQvtwZq44/G5PU6ZD2l3DbHU4xhqE2zxcr4oMqlc3sosaEkCZd5miir+8cru9v6ygFWbwcKJqbmdv4flXZo2Ws/Pfp6tGrQlfftEftO9boosxiDb6ySKtf6uQY88o/O+vCS0uUObxI7TvW6Le/+1ppF+7X6y93+oUrAy0v/rliRW2u0Hd3JMseHqSgigYFVTTIUv/T49CCKhrU+qvDal1aL0kKKaxV668OK6i6UdKPiX7mXjW2a60fbmyvoKpGx3XgR46uxvdk81N+VdmjZXy5M04P3n2Obhqfr+tuLlBZcbj+8VhPbVjzU9syd4NV87P/T7+7aa/+eNdOfVsYqYennq2dnzCHiVNL9NvlkiTrzH1O+7+7raNqBrRxjIl7eb/jmPX+vU5jwj6tVuvSerUurVfHW3c7Xefr5b2aM3zAK3ya7Kurq1VQUOD4vG/fPm3fvl3x8fHq1IkK0Zc+3JKgD7ck/OKYtauStXZVcgtFBDSNK8m48upEVV6deMLjNQPaOP4wgP8ycxvfp8l+27ZtGjhwoOPz5MmTJUlZWVnKycnxUVQAgIBk4tX4Pk32AwYMkOHHcyAAAPgD5uwBAKZAGx8AgEBnN45snpzvp0j2AABzMPGcPffZAwAQ4KjsAQCmYJGHc/Zei6TlkewBAObA++wBAECgItkDAEzh6K13nmzu2LRpky6//HIlJSXJYrFo5cqVTscNw9CMGTPUvn17hYeHKyMjQ19++aXTmPLyco0aNUoxMTGKi4vTmDFjVF1d7fbPTrIHAJiD4YXNDTU1NerTp4/mz59/3ONz5szRvHnztHDhQr3//vuKjIxUZmamamtrHWNGjRqlHTt2aO3atVq9erU2bdqkW265xb1AxJw9AADNYsiQIRoyZMhxjxmGoblz5+q+++7TsGHDJEmLFy9WYmKiVq5cqWuvvVa7du3SmjVr9OGHH+rcc8+VJD3xxBMaOnSoHn30USUlJbkcC5U9AMAULIbh8SZJVVVVTltdXZ3bsezbt0+lpaXKyMhw7IuNjVVaWppyc3MlSbm5uYqLi3MkeknKyMhQUFCQ3n//fbe+j2QPADAHuxc2ScnJyYqNjXVs2dnZbodSWloqSUpMdH7bYmJiouNYaWmpEhKc3z7aqlUrxcfHO8a4ijY+AABuKCoqUkxMjONzaGioD6NxDZU9AMAUvNXGj4mJcdqakuytVqskqayszGl/WVmZ45jVatX+/fudjjc2Nqq8vNwxxlUkewCAObTwavxf0rlzZ1mtVq1bt86xr6qqSu+//77S09MlSenp6aqoqFBeXp5jzPr162W325WWlubW99HGBwCYQws/Qa+6uloFBQWOz/v27dP27dsVHx+vTp066c4779SDDz6oM888U507d9b06dOVlJSk4cOHS5J69uypwYMHa+zYsVq4cKEaGho0YcIEXXvttW6txJdI9gAANItt27Zp4MCBjs+TJ0+WJGVlZSknJ0f33HOPampqdMstt6iiokK/+c1vtGbNGoWFhTnOWbJkiSZMmKBLLrlEQUFBGjlypObNm+d2LCR7AIApNOUpeD8/3x0DBgyQ8QvdAIvFotmzZ2v27NknHBMfH6+lS5e698XHQbIHAJgDL8IBAACBisoeAGAKFvuRzZPz/RXJHgBgDrTxAQBAoKKyBwCYg6cPxvHfwp5kDwAwh/995G1Tz/dXtPEBAAhwVPYAAHMw8QI9kj0AwBwMOd5J3+Tz/RTJHgBgCszZAwCAgEVlDwAwB0Meztl7LZIWR7IHAJiDiRfo0cYHACDAUdkDAMzBLsni4fl+imQPADAFVuMDAICARWUPADAHEy/QI9kDAMzBxMmeNj4AAAGOyh4AYA4mruxJ9gAAc+DWOwAAAhu33gEAgIBFZQ8AMAfm7AEACHB2Q7J4kLDt/pvsaeMDABDgqOwBAOZAGx8AgEDnYbKX/yZ72vgAAAQ4KnsAgDnQxgcAIMDZDXnUimc1PgAAOFVR2QMAzMGwH9k8Od9PkewBAObAnD0AAAGOOXsAABCoqOwBAOZAGx8AgABnyMNk77VIWhxtfAAAAhyVPQDAHGjjAwAQ4Ox2SR7cK2/33/vsaeMDABDgqOwBAOZAGx8AgABn4mRPGx8AgABHZQ8AMAcTPy6XZA8AMAXDsMvw4M11npzrayR7AIA5GIZn1Tlz9gAA4FRFZQ8AMAfDwzl7P67sSfYAAHOw2yWLB/PufjxnTxsfAIAAR2UPADAH2vgAAAQ2w26X4UEb359vvaONDwBAgKOyBwCYg4nb+FT2AABzsBueb26YOXOmLBaL09ajRw/H8draWo0fP15t27ZVVFSURo4cqbKyMm//1JJI9gAANJv/+7//U0lJiWPbsmWL49ikSZO0atUqvfTSS9q4caOKi4s1YsSIZomDNj4AwBwMQ5In99kfqeyrqqqcdoeGhio0NPS4p7Rq1UpWq/WY/ZWVlXruuee0dOlSXXzxxZKkRYsWqWfPnnrvvfd0/vnnNz3O46CyBwCYgmE3PN4kKTk5WbGxsY4tOzv7hN/55ZdfKikpSV26dNGoUaNUWFgoScrLy1NDQ4MyMjIcY3v06KFOnTopNzfX6z87lT0AwBwMuzyr7I+cW1RUpJiYGMfuE1X1aWlpysnJUffu3VVSUqJZs2bpwgsv1Oeff67S0lKFhIQoLi7O6ZzExESVlpY2PcYTINkDAOCGmJgYp2R/IkOGDHH8d+/evZWWlqaUlBQtX75c4eHhzRniMWjjAwBMwVtt/KaKi4tTt27dVFBQIKvVqvr6elVUVDiNKSsrO+4cv6dI9gAAczDsnm8eqK6u1p49e9S+fXv169dPrVu31rp16xzH8/PzVVhYqPT0dE9/0mP4dRvf+HFlZKO93seRAM3HdqjW1yEAzcZ+uE7ST/+eN6dGNXj0TJ1GNbg1fsqUKbr88suVkpKi4uJi3X///QoODtZ1112n2NhYjRkzRpMnT1Z8fLxiYmI0ceJEpaene30lviTJ8GNFRUVHH4fExsbGxubHW1FRUbPlisOHDxtWq9UrcVqtVuPw4cMufe8111xjtG/f3ggJCTE6dOhgXHPNNUZBQYFTXLfddpvRpk0bIyIiwrjyyiuNkpKSZvnfwGIY/vv8P7vdruLiYkVHR8tisfg6HFOoqqpScnLyMatRgUDA73fLMwxDBw8eVFJSkoKCmm9muba2VvX1nneBQ0JCFBYW5oWIWpZft/GDgoLUsWNHX4dhSq6uRgX8Eb/fLSs2NrbZvyMsLMwvk7S3sEAPAIAAR7IHACDAkezhltDQUN1///0nfGIU4M/4/Uag8usFegAA4OSo7AEACHAkewAAAhzJHgCAAEeyBwAgwJHs4bL58+fr9NNPV1hYmNLS0vTBBx/4OiTAKzZt2qTLL79cSUlJslgsWrlypa9DAryKZA+XLFu2TJMnT9b999+vjz76SH369FFmZqb279/v69AAj9XU1KhPnz6aP3++r0MBmgW33sElaWlpOu+88/Tkk09KOvJeguTkZE2cOFH33nuvj6MDvMdisWjFihUaPny4r0MBvIbKHidVX1+vvLw8ZWRkOPYFBQUpIyNDubm5PowMAOAKkj1O6rvvvpPNZlNiYqLT/sTERJWWlvooKgCAq0j2AAAEOJI9Tqpdu3YKDg5WWVmZ0/6ysjJZrVYfRQUAcBXJHicVEhKifv36ad26dY59drtd69atU3p6ug8jAwC4opWvA4B/mDx5srKysnTuuefqV7/6lebOnauamhqNHj3a16EBHquurlZBQYHj8759+7R9+3bFx8erU6dOPowM8A5uvYPLnnzyST3yyCMqLS1V3759NW/ePKWlpfk6LMBjGzZs0MCBA4/Zn5WVpZycnJYPCPAykj0AAAGOOXsAAAIcyR4AgABHsgcAIMCR7AEACHAkewAAAhzJHgCAAEeyBwAgwJHsAQAIcCR7wEM33XSThg8f7vg8YMAA3XnnnS0ex4YNG2SxWFRRUXHCMRaLRStXrnT5mjNnzlTfvn09iuurr76SxWLR9u3bPboOgKYj2SMg3XTTTbJYLLJYLAoJCVHXrl01e/ZsNTY2Nvt3v/rqq3rggQdcGutKggYAT/EiHASswYMHa9GiRaqrq9Mbb7yh8ePHq3Xr1po2bdoxY+vr6xUSEuKV742Pj/fKdQDAW6jsEbBCQ0NltVqVkpKicePGKSMjQ//5z38k/dR6f+ihh5SUlKTu3btLkoqKinT11VcrLi5O8fHxGjZsmL766ivHNW02myZPnqy4uDi1bdtW99xzj37+eomft/Hr6uo0depUJScnKzQ0VF27dtVzzz2nr776yvHylTZt2shiseimm26SdOQVwtnZ2ercubPCw8PVp08fvfzyy07f88Ybb6hbt24KDw/XwIEDneJ01dSpU9WtWzdFRESoS5cumj59uhoaGo4Z9/TTTys5OVkRERG6+uqrVVlZ6XT82WefVc+ePRUWFqYePXroqaeecjsWAM2HZA/TCA8PV319vePzunXrlJ+fr7Vr12r16tVqaGhQZmamoqOjtXnzZv33v/9VVFSUBg8e7Djvb3/7m3JycvT8889ry5YtKi8v14oVK37xe2+88Ub9+9//1rx587Rr1y49/fTTioqKUnJysl555RVJUn5+vkpKSvT3v/9dkpSdna3Fixdr4cKF2rFjhyZNmqTrr79eGzdulHTkj5IRI0bo8ssv1/bt23XzzTfr3nvvdft/k+joaOXk5Gjnzp36+9//rmeeeUaPP/6405iCggItX75cq1at0po1a/Txxx/rtttucxxfsmSJZsyYoYceeki7du3Sww8/rOnTp+uFF15wOx4AzcQAAlBWVpYxbNgwwzAMw263G2vXrjVCQ0ONKVOmOI4nJiYadXV1jnP++c9/Gt27dzfsdrtjX11dnREeHm689dZbhmEYRvv27Y05c+Y4jjc0NBgdO3Z0fJdhGMZFF11k3HHHHYZhGEZ+fr4hyVi7du1x43z33XcNScYPP/zg2FdbW2tEREQYW7dudRo7ZswY47rrrjMMwzCmTZtmpKamOh2fOnXqMdf6OUnGihUrTnj8kUceMfr16+f4fP/99xvBwcHGN99849j35ptvGkFBQUZJSYlhGIZxxhlnGEuXLnW6zgMPPGCkp6cbhmEY+/btMyQZH3/88Qm/F0DzYs4eAWv16tWKiopSQ0OD7Ha7fv/732vmzJmO47169XKap//kk09UUFCg6Ohop+vU1tZqz549qqysVElJidLS0hzHWrVqpXPPPfeYVv5R27dvV3BwsC666CKX4y4oKNChQ4d06aWXOu2vr6/X2WefLUnatWuXUxySlJ6e7vJ3HLVs2TLNmzdPe/bsUXV1tRobGxUTE+M0plOnTurQoYPT99jtduXn5ys6Olp79uzRmDFjNHbsWMeYxsZGxcbGuh0PgOZBskfAGjhwoBYsWKCQkBAlJSWpVSvnX/fIyEinz9XV1erXr5+WLFlyzLVOO+20JsUQHh7u9jnV1dWSpNdff90pyUpH1iF4S25urkaNGqVZs2YpMzNTsbGxevHFF/W3v/3N7VifeeaZY/74CA4O9lqsADxDskfAioyMVNeuXV0ef84552jZsmVKSEg4pro9qn379nr//ffVv39/SUcq2Ly8PJ1zzjnHHd+rVy/Z7XZt3LhRGRkZxxw/2lmw2WyOfampqQoNDVVhYeEJOwI9e/Z0LDY86r333jv5D/k/tm7dqpSUFP35z3927Pv666+PGVdYWKji4mIlJSU5vicoKEjdu3dXYmKikpKStHfvXo0aNcqt7wfQcligB/xo1KhRateunYYNG6bNmzdr37592rBhg26//XZ98803kqQ77rhDf/nLX7Ry5Urt3r1bt9122y/eI3/66acrKytLf/jDH7Ry5UrHNZcvXy5JSklJkcVi0erVq3XgwAFVV1crOjpaU6ZM0aRJk/TCCy9oz549+uijj/TEE084Fr3deuut+vLLL3X33XcrPz9fS5cuVU5Ojls/75lnnqnCwkK9+OKL2rNnj+bNm3fcxYZhYWHKysrSJ598os2bN+v222/X1VdfLavVKkmaNWuWsrOzNW/ePH3xxRf67LPPtGjRIj322GNuxQOg+ZDsgR9FRERo06ZN6tSpk0aMGKGePXtqzJgxqq2tdVT6d911l2644QZlZWUpPT1d0dHRuvLKK3/xugsWLNBVV12l2267TT169NDYsWNVU1MjSerQoYNmzZqle++9V4mJiZowYYIk6YEHHtD06dOVnZ2tnj17avDgwXr99dfVuXNnSUfm0V955RWtXLlSffr00cKFC/Xwww+79fNeccUVmjRpkiZMmKC+fftq69atmj59+jHjunbtqhEjRmjo0KEaNGiQevfu7XRr3c0336xnn31WixYtUq9evXTRRRcpJyfHESsA37MYJ1pZBAAAAgKVPQAAAY5kDwBAgCPZAwAQ4Ej2AAAEOJI9AAABjmQPAECAI9kDABDgSPYAAAQ4kj0AAAGOZA8AQIAj2QMAEOD+H4eGX+zUrpMCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#DECISION TREE"
      ],
      "metadata": {
        "id": "Qi8_9-YWwVIh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(max_depth=4, random_state=42)\n",
        "dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "XT2JZKncwXoE",
        "outputId": "11800689-adac-4b8c-f4bd-cc4ba01e9741"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=4, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4, random_state=42)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=4, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_dt = dt.predict(X_test)\n",
        "print('Predicted vs actual values on y_test')\n",
        "print(np.concatenate((y_pred_dt.reshape(len(y_pred_dt),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZeFBSdWmfzs5",
        "outputId": "25fbf568-0bac-45a7-8e76-121954eb3bef"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted vs actual values on y_test\n",
            "[[0 1]\n",
            " [0 0]\n",
            " [0 0]\n",
            " ...\n",
            " [0 1]\n",
            " [1 1]\n",
            " [0 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "# Plotting the Decision tree\n",
        "print('\\nDecision Tree:- ')\n",
        "plt.figure(figsize=(15,10))\n",
        "tree.plot_tree(dt, filled=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rlM66yT5jhpr",
        "outputId": "010d5335-166c-45bc-9743-b9bce0e01267"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Tree:- \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
        "print('Accuracy:', accuracy_dt)\n",
        "\n",
        "cm_dt = confusion_matrix(y_test, y_pred_dt)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_dt)\n",
        "\n",
        "ConfusionMatrixDisplay(cm_dt).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "4ebgp7yzf1bz",
        "outputId": "0b35083c-1d3c-443a-e0a5-26b993622519"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6948356807511737\n",
            "Confusion Matrix:\n",
            "[[281  80]\n",
            " [115 163]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Random Forest"
      ],
      "metadata": {
        "id": "VtOfhf-vI7VX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "9KtYjB02I6f4",
        "outputId": "6992df40-0b33-4fdc-8ee4-d40c419dbd37"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf = rf.predict(X_test)\n",
        "print('Predicted vs actual values on y_test')\n",
        "print(np.concatenate((y_pred_rf.reshape(len(y_pred_rf),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PgsHrQiifwps",
        "outputId": "7d68d0f2-145c-45b9-f729-98367c173441"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted vs actual values on y_test\n",
            "[[1 1]\n",
            " [1 0]\n",
            " [0 0]\n",
            " ...\n",
            " [1 1]\n",
            " [1 1]\n",
            " [0 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
        "print('Accuracy:', accuracy_rf)\n",
        "\n",
        "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_rf)\n",
        "\n",
        "ConfusionMatrixDisplay(cm_rf).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "I2J1BYuoftXT",
        "outputId": "821fc592-89a3-4a41-a66b-40e22fb609c3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8544600938967136\n",
            "Confusion Matrix:\n",
            "[[323  38]\n",
            " [ 55 223]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Naive Bayes"
      ],
      "metadata": {
        "id": "dXBTtLf_JBvT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "mTAwkakaJHSo",
        "outputId": "398f959e-fc9e-4100-b7f0-e0bbf24eff14"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_nb = nb.predict(X_test)\n",
        "print('Predicted vs actual values on y_test')\n",
        "print(np.concatenate((y_pred_nb.reshape(len(y_pred_nb),1), y_test.reshape(len(y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "AK8UU5HDfihq",
        "outputId": "1880c192-2a45-411b-badc-187b92e62173"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted vs actual values on y_test\n",
            "[[0 1]\n",
            " [1 0]\n",
            " [0 0]\n",
            " ...\n",
            " [1 1]\n",
            " [0 1]\n",
            " [0 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
        "print('Accuracy:', accuracy_nb)\n",
        "\n",
        "cm_nb = confusion_matrix(y_test, y_pred_nb)\n",
        "print('Confusion Matrix:')\n",
        "print(cm_nb)\n",
        "\n",
        "ConfusionMatrixDisplay(cm_nb).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "qU92QwHdflxf",
        "outputId": "0cf93df2-4b80-4a00-88b2-0a223bbdd928"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6604068857589984\n",
            "Confusion Matrix:\n",
            "[[260 101]\n",
            " [116 162]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Accuracy Comparision"
      ],
      "metadata": {
        "id": "AlV0jd-daHkp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "accuracies = {'KNN': accuracy_knn,\n",
        "              'Logistic Regression': accuracy_lr,\n",
        "              'SVM': accuracy_svm,\n",
        "              'Decision Tree': accuracy_dt,\n",
        "              'Random Forest': accuracy_rf,\n",
        "              'Naive Bayes': accuracy_nb}\n",
        "\n",
        "df = pd.DataFrame(accuracies.items(), columns=['Model', 'Accuracy'])\n",
        "\n",
        "df = df.sort_values('Accuracy', ascending=False)\n",
        "\n",
        "print(df)\n",
        "\n",
        "max_accuracy = df.iloc[0]['Accuracy']\n",
        "best_model = df.iloc[0]['Model']\n",
        "\n",
        "print(\"\\nMax accuracy is given by ML model\", best_model, \"with accuracy:\", max_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "pMnZ29J-aN-o",
        "outputId": "f7e63615-19cb-4fdc-99bd-a1f119eee865"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Model  Accuracy\n",
            "4        Random Forest  0.854460\n",
            "0                  KNN  0.845070\n",
            "1  Logistic Regression  0.837246\n",
            "2                  SVM  0.824726\n",
            "3        Decision Tree  0.694836\n",
            "5          Naive Bayes  0.660407\n",
            "\n",
            "Max accuracy is given by ML model Random Forest with accuracy: 0.8544600938967136\n"
          ]
        }
      ]
    }
  ]
}