ML_algorithms_MitBih.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZwJrQs4rWwOGw3x9+bfgC"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Machine Learning algorithms for MIT-BIH dataset:**"
      ],
      "metadata": {
        "id": "vjTljPEn_TnR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Imports**"
      ],
      "metadata": {
        "id": "wSGaZ0Q4awK7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ext157adfgz",
        "outputId": "4295252f-3082-4b1b-c3fd-61bcf39b6836"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VCypBMxXEAgZ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import csv\n",
        "import itertools\n",
        "import collections\n",
        "\n",
        "import pywt\n",
        "from scipy import stats\n",
        "\n",
        "from sklearn.utils import resample\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**matplotlib Settings**"
      ],
      "metadata": {
        "id": "TlwAKCbebJUZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] = (30,6)\n",
        "plt.rcParams['lines.linewidth'] = 1\n",
        "plt.rcParams['lines.color'] = 'b'\n",
        "plt.rcParams['axes.grid'] = True"
      ],
      "metadata": {
        "id": "_kQoTgemagcZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Methods Definitions**"
      ],
      "metadata": {
        "id": "bo6m8TFibPPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def denoise(data):\n",
        "    w = pywt.Wavelet('sym4')\n",
        "    maxlev = pywt.dwt_max_level(len(data), w.dec_len)\n",
        "    threshold = 0.04 # Threshold for filtering\n",
        "\n",
        "    coeffs = pywt.wavedec(data, 'sym4', level=maxlev)\n",
        "    for i in range(1, len(coeffs)):\n",
        "        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))\n",
        "\n",
        "    datarec = pywt.waverec(coeffs, 'sym4')\n",
        "\n",
        "    return datarec"
      ],
      "metadata": {
        "id": "y_-IFvA6bRH9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Variables Definitions**"
      ],
      "metadata": {
        "id": "39lfqTSxbUuH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/drive/MyDrive/data_mit/'\n",
        "window_size = 180\n",
        "maximum_counting = 10000\n",
        "\n",
        "classes = ['N', 'L', 'R', 'A', 'V']\n",
        "n_classes = len(classes)\n",
        "count_classes = [0]*n_classes\n",
        "\n",
        "X = list()\n",
        "y = list()"
      ],
      "metadata": {
        "id": "p39Gp19LbbJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prepare Input Files**"
      ],
      "metadata": {
        "id": "uqe8jxgbbeZr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filenames = next(os.walk(path))[2]\n",
        "\n",
        "records = list()\n",
        "annotations = list()\n",
        "filenames.sort()"
      ],
      "metadata": {
        "id": "CHJWHR-HbfQj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for f in filenames:\n",
        "    filename, file_extension = os.path.splitext(f)\n",
        "\n",
        "    if(file_extension == '.csv'):\n",
        "        records.append(path + filename + file_extension)\n",
        "\n",
        "    else:\n",
        "        annotations.append(path + filename + file_extension)"
      ],
      "metadata": {
        "id": "rxR7oWsRbj4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Extraction and Preprocessing**"
      ],
      "metadata": {
        "id": "n228wWl2blvL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for r in range(0,len(records)):\n",
        "    signals = []\n",
        "\n",
        "    with open(records[r], 'rt') as csvfile:\n",
        "        spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') # read CSV file\\\n",
        "        row_index = -1\n",
        "        for row in spamreader:\n",
        "            if(row_index >= 0):\n",
        "                signals.insert(row_index, int(row[1]))\n",
        "            row_index += 1\n",
        "\n",
        "    if r is 1:\n",
        "        plt.title(records[1] + \" Wave\")\n",
        "        plt.plot(signals[0:700])\n",
        "        plt.show()\n",
        "\n",
        "    signals = denoise(signals)\n",
        "    if r is 1:\n",
        "        plt.title(records[1] + \" wave after denoised\")\n",
        "        plt.plot(signals[0:700])\n",
        "        plt.show()\n",
        "\n",
        "    signals = stats.zscore(signals)\n",
        "    if r is 1:\n",
        "        plt.title(records[1] + \" wave after z-score normalization \")\n",
        "        plt.plot(signals[0:700])\n",
        "        plt.show()\n",
        "\n",
        "    example_beat_printed = False\n",
        "    with open(annotations[r], 'r') as fileID:\n",
        "        data = fileID.readlines()\n",
        "        beat = list()\n",
        "\n",
        "        for d in range(1, len(data)):\n",
        "            splitted = data[d].split(' ')\n",
        "            splitted = filter(None, splitted)\n",
        "            next(splitted)\n",
        "            pos = int(next(splitted))\n",
        "            arrhythmia_type = next(splitted)\n",
        "            if(arrhythmia_type in classes):\n",
        "                arrhythmia_index = classes.index(arrhythmia_type)\n",
        "\n",
        "                count_classes[arrhythmia_index] += 1\n",
        "                if(window_size <= pos and pos < (len(signals) - window_size)):\n",
        "                    beat = signals[pos-window_size:pos+window_size]\n",
        "\n",
        "                    if r is 1 and not example_beat_printed:\n",
        "                        plt.title(\"A Beat from \" + records[1] + \" Wave\")\n",
        "                        plt.plot(beat)\n",
        "                        plt.show()\n",
        "                        example_beat_printed = True\n",
        "\n",
        "                    X.append(beat)\n",
        "                    y.append(arrhythmia_index)\n",
        "\n",
        "print(np.shape(X), np.shape(y))"
      ],
      "metadata": {
        "id": "RhlyBspdbmVP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "27d62a9b-6256-4c0c-ac0f-8d28d50724a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:12: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:18: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:24: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:47: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:12: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:18: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:24: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<>:47: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "<ipython-input-7-818e2cf6d77e>:12: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "  if r is 1:\n",
            "<ipython-input-7-818e2cf6d77e>:18: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "  if r is 1:\n",
            "<ipython-input-7-818e2cf6d77e>:24: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "  if r is 1:\n",
            "<ipython-input-7-818e2cf6d77e>:47: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
            "  if r is 1 and not example_beat_printed:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZQqfTKa6Ei4uLRUBAgHj44Yfl2x555BEREREh0tLSFM83fvx44e/vr/ibnDlzxmpH7t69u4iIiFB8V8zBc+XPtPLft6SkRHTu3FkRIAtR9RjByo8XomK8rvlEUFsAhF6vV1wUfPbZZwKACA8PFzk5OfLtM2fOVHy/hbAeA1nTuC5b3zFL1QWCY8aMEW3atLG6PT8/XwAQM2bMsPm4ugaCSUlJQqPRiDvuuMMq62U+PixbtqzGYG716tVWWWchhLjppptsvg9L5my2OdN38OBB+YTUp08febtbb71VkTksKiqyanNycrLQ6/WKY6n5b3zo0CHFtpUv1P79738Lb29vcfLkScV2M2bMEFqtVpw/f77a92Hru/r4448LLy8vxXH5tddeEwDEtWvX5NsceWwfOHBgvTIm5mNq5fNQZZmZmQKAeP/996vcpqHOkZW/95s3bxYArLKM5l6QyrdXVttz7/LlywUA8dZbbym2u+uuu4QkSYqkAQCh0Wissvnmv8+oUaMU595+/foJSZLEE088Id9WVlYmoqKirM6/tT2e1hQIVlZYWCgSExNFZGSkYny8re/0qFGjrPbpqmKFyq/bEHFRfdW5s3rFihUYNmwYPD09AQBr1qxBbm4uZsyYAQ8PD8W25hl7u3fvRmpqKp588knFNmPGjEF8fDz++OMPq9d54oknFL8PGjQIZ86cqbF9GRkZWLduHe655x7k5uYiLS0NaWlpSE9Px6hRo5CUlIRLly4pHvPYY48pZhcOGjQIBoMB586dq/H1ANNnkpCQIM96NL/fJ554Au7u7vJ2EydOhL+/v83nmDBhgvyZVkWSJNx9991YsWIF8vLy5NuXLFmCli1bYuDAgQBMf5OsrCzce++98vtPS0uDVqtFnz59sH79eqv2VzXmTavV4p577pFnaH7zzTeIjo7GoEGDbG7/0EMP4fLly4rX+Oabb+Dp6YmxY8dW+/4s+fj4AAByc3MVt7du3RqjRo2q8fHjxo1DaWkpfv75Z/m2P//8E1lZWRg3bhwAQAiBpUuX4pZbboEQQvFZjRo1CtnZ2di7d6/8+D/++AP+/v7y53zlyhXs378fEyZMUPxdb7jhBiQkJFi1yfLvm5mZiezsbAwaNEjxGtWxfHxpaSnS09PRtm1bBAQE1Po5LA0fPlwxU7dPnz4ATLOifX19rW6vzf5Xleq+YzUpLCy0OR7RfCwxjxO11/Lly2E0GvHqq69ajeMxHx/MY9l+//13lJaW2nye66+/HsHBwYqxtZmZmVizZo383atKjx494OPjg02bNgEwjUmNiorCQw89hL1796KgoABCCGzZskWxD+r1ernNBoMB6enp8PHxQYcOHRTfjTvvvBNubm6Kth0+fBhHjx5VtO3HH3/EoEGDEBgYqNgvRowYAYPBILevKpbfVfNxeNCgQSgoKMDx48erfWxV6nNsnzx5MrRabZ1e5+jRo3j44Ydx22234V//+le123p6esLd3R0bNmxAZmamzW0a6hw5ZcoUxe8//vgj/P39ccMNNyj+ZomJifDx8bE67lelpnPvihUroNVq8c9//lOx3fTp0yGEwMqVKxW3DxkyxObxEAAeeeQRxbm3T58+EELgkUcekW/TarXo1auX1fHH3uNpVZ588kkcOnQIS5cuRXh4uM3Xy87ORlpaGoYMGYIzZ84gOzu7zq/TmHFRTeoUCJaWlmLNmjWKA/rp06cBAJ07d67yceaAqkOHDlb3xcfHWwVcHh4eCAkJUdwWGBhY5Y5m6dSpUxBC4JVXXkFISIji57XXXgNQMUDZrFWrVlavBaBWrweYAgTLz8T8ftq1a6fYTqfToU2bNjafo3Xr1rV6rXHjxqGwsBC//vorACAvLw8rVqzA3XffLe9QSUlJAEwnpMqfwZ9//ql4/ykpKdi7d2+1J+n77rsPR48exYEDB/Dtt99i/PjxVZbluOGGGxAREYFvvvkGgGlw8HfffYfbbrtNEVzUxBzoVn5MbT+nbt26IT4+XnHCW7JkCYKDg+UJPdeuXUNWVhYWLFhg9TlNmjQJgPK78scff2DkyJFwc3MDUPXfGbD9Xf/999/Rt29feHh4ICgoCCEhIZg3b16tDyKFhYV49dVXER0dDb1ej+DgYISEhCArK6teB6LK33tzMBsdHW3z9truD5XV5jtWHU9PTxQXF1vdbh50XtMFVG2dPn0aGo2mypMWYDqpjR07Fm+88QaCg4Nx2223YeHChYr2ubm5YezYsfjll1/k23/++WeUlpbWGAhqtVr069cPmzdvBmAKBAcNGoSBAwfCYDBg+/btOHr0KDIyMhSBoNFoxEcffYR27dopvhsHDx5UfDeCg4MxfPhwuYQPYNov3NzccOedd8q3JSUlYdWqVVb7hbmcVOVjaGVHjhzBHXfcAX9/f/j5+SEkJAQPPPAAANTruwrU79he2+OFWU5ODu688060bNkS//vf/+TjXElJCVJSUhQ/BoMBer0e7777LlauXImwsDAMHjwY7733HlJSUuTnbIhzpJubG6KiohS3JSUlITs7G6GhoVafT15eXo1/M6B2595z584hMjLS6tjcsWNHxXsxq+5vUJdjUOXjj73HU1s+++wzLFy4EHPmzEHfvn0V9/39998YMWIEvL29ERAQgJCQELz00ksA6vedbsy4qCZuddl4y5YtyMnJadAaagDqfAVnyVx/7rnnnqsyc9S2bdtavZ4QosbXS05OxvHjxzFv3rw6tlSptiezvn37IjY2Fj/88APuu+8+/PbbbygsLFScYMyfweLFixVXNGbmQAaomO08bNiwKl+zT58+iIuLw7Rp05CcnIz77ruvym21Wi3uu+8+fP755/j000/x999/4/Lly/JJoLYOHz4MwPpvVZeT/rhx4/D2228jLS0Nvr6++PXXX3HvvffK79/8OT3wwAOYMGGCzefo2rUrAFOdvA0bNtT777x582bceuutGDx4MD799FNERERAp9Nh4cKF+Pbbb2v1HP/4xz+wcOFCTJs2Df369YO/vz8kScL48ePrVXexqu+9PfuDLbX5jlUnIiIC69evhxBCcQFy5coVAEBkZGS9nrc+JEnCTz/9hO3bt+O3337D6tWr8fDDD+ODDz7A9u3b5Uz2+PHj8dlnn2HlypW4/fbb8cMPPyA+Pr7a2dFmAwcOxNtvv42ioiJs3rwZL7/8MgICAtC5c2ds3rwZYWFhAKAIBN955x288sorePjhh/Hvf/8bQUFB0Gg0mDZtmtV3Y/z48Zg0aRL279+P7t2744cffsDw4cMRHBwsb2M0GnHDDTfghRdesNnG9u3bV9n+rKwsDBkyBH5+fnjzzTcRFxcHDw8P7N27Fy+++GK9a4TW59he14uEiRMn4vLly9i5cyf8/Pzk27du3Wr1/U1OTkZsbCymTZuGW265BcuXL8fq1avxyiuvYNasWVi3bh169OhRp9evLcsMsJnRaERoaKh8EV5Z5SDCFnvOvVWp7m9Ql2OQ5fHHEcfTynbu3Imnn34ajz76KB577DHFfadPn8bw4cMRHx+PDz/8ENHR0XB3d8eKFSvw0UcfNUrd24b425jVKRD8448/FF2gABAXFwfAdOKuvBOaxcTEAABOnDhhVV7lxIkT8v11UVVGypxx0+l0VRZDro+qXq9ydyFQ8X6TkpIU77e0tBTJycm1OhlU55577sHHH3+MnJwcLFmyBLGxsYqrF/PfJDQ0tMbP4I8//lB09Vfl3nvvxVtvvYWOHTuie/fu1W770EMP4YMPPsBvv/2GlStXIiQkpFbduZYWL14MSZLsKgEybtw4vPHGG1i6dCnCwsKQk5OD8ePHy/eHhITA19cXBoOhxs9p3bp1KC4uxujRo+XbLP/OlZ04cULx+9KlS+Hh4YHVq1crujkXLlxo9diqvms//fQTJkyYgA8++EC+raioCFlZWdW2vTFUV7i5tt+xqnTv3h1ffPEFjh07psjW7dixQ77fEeLi4mA0GnH06NEan7Nv377o27cv3n77bXz77be4//778f333+PRRx8FAAwePBgRERFYsmQJBg4ciHXr1uHll1+uVTsGDRqEkpISfPfdd7h06ZIc8A0ePFgOBNu3by8HhIDpuzFs2DB8+eWXiufKyspSBHgAcPvtt+Pxxx+Xs+UnT57EzJkzrT6LvLy8eh1DN2zYgPT0dPz8888YPHiwfHtycnKtHt/Yx3az2bNnY/ny5fj5558RHx+vuK9bt25Ys2aN4jbLi+y4uDhMnz4d06dPR1JSErp3744PPvgAX3/9daOdI+Pi4rB27VoMGDDAYVlyW2JiYrB27Vrk5uYqsoLmLv/6nM/rqi7H09q4du0a7rrrLnTv3h1z5861uv+3335DcXExfv31V0UW01Z3e22L2DdEXFRfdeoatjXOZ+TIkfD19cWsWbOs6gOZI/hevXohNDQU8+fPV3ShrFy5EseOHatXl5GXlxcAWJ0EQ0NDMXToUHz22WdyxsDStWvX6vxaAODt7W3zhLtixQpFdyFger8hISGYP38+SkpK5NsXLVrkkJP2uHHjUFxcjK+++gqrVq3CPffco7h/1KhR8PPzwzvvvGNzHJP5M7DV1V+VRx99FK+99poiCKlK165d0bVrV3zxxRdYunQpxo8fr/h8ajJ79mz8+eefGDdunM1u19rq2LEjunTpgiVLlmDJkiWIiIhQnJi0Wi3Gjh2LpUuXyhlIS5bflRUrVqBXr16Kk29ERAS6d++Or776StE1sGbNGhw9elTxXFqtFpIkKWrenT171uYKIlV917RarVVWbs6cOYrnVEtV+2NdvmNVue2226DT6fDpp5/KtwkhMH/+fLRs2RL9+/ev1/MeP34c58+fl3+//fbbodFo8Oabb1pd4Zs/98zMTKu/gTlotDy2aTQa3HXXXfjtt9+wePFilJWV1dgtbNanTx/odDq8++67CAoKkuu1Dho0CNu3b8fGjRutxuja+m78+OOPVmPmANM4x1GjRuGHH37A999/D3d3d9x+++2Kbe655x5s27YNq1evtnp8VlYWysrKqmy/OXNh2Z6SkhLF3686jX1sB4C1a9fiX//6F15++WWrzwIwdcGNGDFC8ePh4YGCggKr815cXBx8fX3l70NjnSPvueceGAwG/Pvf/7a6r6yszGEXjDfddBMMBgM++eQTxe0fffQRJElSXCw3lLocT2tiMBgwfvx4lJSUYOnSpYpx/ZavByi/09nZ2TYDz6qO35U1RFxUX7U+OycnJ+PYsWNWXWN+fn746KOP8Oijj6J379647777EBgYiAMHDqCgoABfffWVfFCbNGkShgwZgnvvvRdXr17Fxx9/jNjYWDzzzDN1brinpycSEhKwZMkStG/fHkFBQejcuTM6d+6MuXPnYuDAgejSpQsmT56MNm3a4OrVq9i2bRsuXryIAwcO1Pn1EhMTMW/ePLz11lto27YtQkND0a9fP6xfvx7z589XbKvT6fDWW2/h8ccfx/XXX49x48YhOTkZCxcurHKMYF307NkTbdu2xcsvv4zi4mKrE4yfnx/mzZuHBx98ED179sT48eMREhKC8+fP448//sCAAQPwySefyF39tfnCxcTE4PXXX691Gx966CE899xzAFBlt3BZWRm+/vprAKbM1rlz5/Drr7/i4MGDGDZsGBYsWFDr16vKuHHj8Oqrr8LDwwOPPPKIVXfK7NmzsX79evTp0weTJ09GQkICMjIysHfvXqxduxYZGRkATIGgedygpVmzZmHMmDEYOHAgHn74YWRkZGDOnDno1KmTYkLPmDFj8OGHH+LGG2/Efffdh9TUVMydOxdt27bFwYMHFc+ZmJiItWvX4sMPP0RkZCRat26NPn364Oabb8bixYvh7++PhIQEbNu2DWvXrkWLFi3s/pzsVdX+eO3atSq/YwcPHpTHup46dQrZ2dl46623AJgyMLfccgsAICoqCtOmTcP777+P0tJS9O7dG8uXL8fmzZvxzTffKLpMzp07h8WLFwOAvPSh+TljYmLw4IMPytt27NgRQ4YMkZcyM+9T//73vzFo0CDceeed0Ov12LVrFyIjIzFr1ix89dVX+PTTT3HHHXcgLi4Oubm5+Pzzz+Hn52c1ZGbcuHGYM2cOXnvtNXTp0kUeQ1UTLy8vJCYmYvv27bjlllvkDMPgwYORn5+P/Px8q0Dw5ptvxptvvolJkyahf//+OHToEL755psqjzfjxo3DAw88gE8//RSjRo2yKuj8/PPP49dff8XNN9+MiRMnIjExEfn5+Th06BB++uknnD171irTaNa/f38EBgZiwoQJ+Oc//wlJkrB48eJaDy1o7GM7YOrxCAkJQbt27eRjktkNN9yguAC0dPLkSQwfPhz33HMPEhIS4ObmhmXLluHq1aty70NjnSOHDBmCxx9/HLNmzcL+/fsxcuRI6HQ6JCUl4ccff8THH3+Mu+66q16fj6VbbrkFw4YNw8svv4yzZ8+iW7du+PPPP/HLL79g2rRpcga0IdXleFqT+fPnY926dXjiiSesMnxhYWG44YYbMHLkSLi7u+OWW27B448/jry8PHz++ecIDQ21uiixFSvYWmiiIeKieqvt9OJPPvlE+Pv7i9LSUpv3//rrr6J///7C09NT+Pn5ieuuu85q+aclS5aIHj16CL1eL4KCgqotnFiZucyApa1bt4rExETh7u5uVW7g9OnT4qGHHhLh4eFCp9OJli1biptvvln89NNP8ja2yp8IYXt6eUpKihgzZozw9fWVS8D8/vvvQpIkcfXqVZufyaeffipat24t9Hq96NWrV7UFpW0VwqxumvvLL78sAIi2bdvafG3z40eNGiX8/f2Fh4eHiIuLExMnThS7d+8WQgjx3HPP2awpJkRF+ZjqVPX5CWGqN6nVakX79u1tPrZy4VgvLy8RGxsrxo4dK3766SebRWura1PlEgFmSUlJ8mts2bLF5mOvXr0qpk6dKqKjo4VOpxPh4eFi+PDhYsGCBUIIU+0yAGLnzp02H7906VLRsWNHodfrRUJCQpUFpb/88kvRrl07odfrRXx8vFi4cKHN7/Xx48fF4MGDhaenpwAqCkpnZmaKSZMmieDgYOHj4yNGjRoljh8/XuV7rw7KC71aMheGrlwKw9Z31Nb7s7U/Vvcdq66Qa+X3YzAYxDvvvCNiYmKEu7u76NSpk/j666+tntPcVls/lUs62LpNCFPhWPNxKjAwUAwZMkSsWbNGCGEqhXTvvfeKVq1ayQV7b775ZnmfsmQ0GkV0dLTNUhs1ef755wUA8e677ypub9u2rQAgTp8+rbi9qKhITJ8+XURERAhPT08xYMAAsW3btiqXtDTX/ARg83MUwlSGaubMmaJt27bC3d1dBAcHi/79+4v/+7//q7Em3d9//y369u0rPD09RWRkpHjhhRfksjqWxzNb5WOEaLhje1Wq+s5Udfw1S0tLE1OnThXx8fHC29tb+Pv7iz59+ljVehWi4c+RZgsWLBCJiYnC09NT+Pr6ii5duogXXnhBXL58udrPoC7n3tzcXPHMM8+IyMhIodPpRLt27aotKF1ZVX+fqr4PttpW2+NpTeVjzI+p6Zjx66+/iq5du8pF5N999115EQXL0lq2YgVbr2vm6LioPiQhaneZdtNNN8HHx0cx28zVPfnkk9i9ezd27typdlPqJSEhATfffDPee+89hz93WloaIiIi8Oqrr+KVV15x+PM3pvfeew8ffvghrly5UuvxH2TSkN8xIiKyX627hocOHVpl7ThX1b17d7n7qrkpKSnBuHHjrMYXOsqiRYtgMBgUXXHNVWxsrDz+hWqvob9jRERkv1pnBIlqY926dTh69CheeeUVDBs2TFHQmRqeZf0yWzw9Passak5ERK6HgSA51NChQ7F161YMGDAAX3/9NVq2bKl2k1xKTVnLCRMmYNGiRY3TGCIiavIYCBI5kbVr11Z7f2RkZLUrZxARkWthIEhERETkoupUUJqIiIiInEedlphzRUajEZcvX4avry9njRIRETUTQgjk5uYiMjLSajEBqsBAsAaXL19GdHS02s0gIiKierhw4QKioqLUbkaTxUCwBuZFtS9cuAA/Pz+VW0NERES1kZOTg+joaPk8TrYxEKyBuTvYz8+PgSAREVEzw2Fd1WOnOREREZGLYiBIRERE5KIYCBIRERG5KAaCRERERC6KgSARERGRi2IgSEREROSiGAgSERERuSgGgkREREQuioEgERERkYtiIEhERETkohgIEhEREbkoBoJERERELoqBIDmMwSjw5ZZkHL6UrXZTiIiIqBbc1G4AOY9l+y7h378fBQCcnT1G5dYQERFRTZgRJIdJTstTuwlERERUBwwEyWH8PHRqN4GIiIjqgIEgOYyvRSBYUmZUsSVERERUGwwEyWG89Vr5/1mFJSq2hIiIiGqDgSA1iKyCUrWbQERERDVgIEgOYzAK+f+Z+cwIEhERNXUMBMlhLAPBrEJmBImIiJo6BoLkMKIiDkRWATOCRERETZ3TB4Kvv/46JElS/MTHx6vdLKdksIgEc4vKVGwJERER1YZLrCzSqVMnrF27Vv7dzc0l3najs+watvw/ERERNU0uERG5ubkhPDxc7WY4PaNFRtAyO0hERERNk9N3DQNAUlISIiMj0aZNG9x///04f/58ldsWFxcjJydH8UO1Y5kFNDIjSERE1OQ5fSDYp08fLFq0CKtWrcK8efOQnJyMQYMGITc31+b2s2bNgr+/v/wTHR3dyC1uvhSBIONAIiKiJs/pA8HRo0fj7rvvRteuXTFq1CisWLECWVlZ+OGHH2xuP3PmTGRnZ8s/Fy5caOQWN1+KrmFGgkRERE2eS4wRtBQQEID27dvj1KlTNu/X6/XQ6/WN3CrnYLBYXtjIMYJERERNntNnBCvLy8vD6dOnERERoXZTnA4zgkRERM2L0weCzz33HDZu3IizZ89i69atuOOOO6DVanHvvfeq3TSnoygfw4wgERFRk+f0XcMXL17Evffei/T0dISEhGDgwIHYvn07QkJC1G6a0+GsYSIioubF6QPB77//Xu0muAxl17CKDSEiIqJacfquYWo8yvIxzAgSERE1dQwEyWEMnCxCRETUrDAQJIexTAJysggREVHTx0CQHIaTRYiIiJoXBoLkMIryMQwEiaqUX1yGQxezIZg5JyKVMRAkh1HMGuYJjqhKd83fhls+2YKVh1PUbgoRuTgGguQw7Bomqp1jV3IAAMv3XVK5JUTk6hgIksMoM4IqNoSomdBpeQgmInXxKEQOwzqCRHWj1UhqN4GIXBwDQXIYy9VE2DVMVDM3LQNBIlIXA0FyGCMLShPViU7DQzARqYtHIXIYdg0T1Q0zgkSkNgaC5DBcYo6objhZhIjUxqMQOYzluEDOGiayrdRiMC0nixCR2hgIksOwjiBRzYpKDfL/2TVMRGpjIEgOw8kiRDUrtAgENRIDQSJSFwNBchjL2I9LzBHZVlxa0TXMCyYiUhsDQXIYdg0T1cwyI2g5XpCISA0MBMlhlEvMMRAkssVyjGAZZ1URkcoYCJLDMCNIVLMii67hMu4nRKQyBoLkMAYjM4JENSlUZATZNUxE6mIgSA6jnDWsYkOImjDLrmFOFiEitTEQJIexPKkJZgSJbLIMBEsZCBKRyhgIksNYjntnpoPINsvyMewaJiK1MRAkhzFyjCBRjSz3DU4WISK1MRAkh+GsYaKaWQZ/zAgSkdoYCJLDsI4gUc0sL5KYESQitTEQJIdRZgRVbAhRE2ZQZAQZCBKRuhgIksMYFOVjeIIjssWoGCPIKyYiUhcDQXIYy95gdg0T2WZg1zARNSEMBMlhOFmEqGaKWcPsGiYilTEQJIfhEnNENeNkESJqShgIksMYOUaQqEaWFWNYPoaI1MZAkByGXcNENWNBaSJqShgIksOwjiBRzZRdw8wIEpG6GAiSw7COIFHNylhHkIiaEAaC5DCcLEJUMyO7homoCWEgSA5jGfsZGQgS2WTgWsNE1IQwECSHscwCCgEIBoNEVrjEHBE1JQwEyWEqZwFZQobIGruGiagpYSBIDlN5ggjHCRJZM3DWMBE1IQwEyWEqZwR5jiOyZrmflLJrmIhUxkCQHMaqa5gZQSIrlYdMcCwtEamJgSA5hBAClYc7cYwgkbXKE4W5mxCRmlwuEJw9ezYkScK0adPUbopTsZXU4DJzRNY4qYqImhKXCgR37dqFzz77DF27dlW7KU7HVjcwu4aJrFWeKcyam0SkJpcJBPPy8nD//ffj888/R2BgoNrNcTq2TmY8wRFZq5wp535CRGpymUBw6tSpGDNmDEaMGFHtdsXFxcjJyVH8UM1sdw03fjuImrrKXcHsGSYiNbmp3YDG8P3332Pv3r3YtWtXjdvOmjULb7zxRiO0yrnYGufETAeRtcpDJjhGkIjU5PQZwQsXLuDpp5/GN998Aw8Pjxq3nzlzJrKzs+WfCxcuNEIrmz/LoE+STP/yBEdkrXLXMMvHEJGanD4juGfPHqSmpqJnz57ybQaDAZs2bcInn3yC4uJiaLVa+T69Xg+9Xq9GU5s1y25gnUaDEoPRZncxkatjRpCImhKnDwSHDx+OQ4cOKW6bNGkS4uPj8eKLLyqCQKo/y4ygm1ZCiYGzhols4RhBImpKnD4Q9PX1RefOnRW3eXt7o0WLFla3U/0pAkGNqW+YmQ4ia1ZLMfKCiYhU5PRjBKlxmLN/kgRoywNBjn0ismadEeR+QkTqcfqMoC0bNmxQuwlOx3wu00iSHAiya5jIWuWySsycE5GamBEkhzCfzDSSKRgEWEeQyJaySjsGr5eISE0MBMkhzN1bGkmqCAR5hiOyYqi0WzAjSERqYiBIDmGra5iBIJE1LjFHRE0JA0FyCHNWQ6uRWFCaqBqcLEJETQkDQXIIo41Zw4wDiaxZl49RqSFERGAgSA5iPrlpNRwjSFSdyhlBZs6JSE0MBMkhjBZjBMsTglZjoYjIuqwSL5iISE0MBMkhKmYNV5SPYR1BImtWk0VYZomIVMRAkByioo6gxaxhnuCIrDAjSERNCQNBcgjL8jESxwgSVclqZRHuJ0SkIgaC5BCW5WO05d8qnuCIrFmvLML9hIjUw0CQHEJRPqY8I8gTHJE1g9Vaw+q0g4gIYCBIDmK00TXMExyRNes6grxgIiL1MBAkh7CsI8gl5oiqZjmMAmCZJSJSFwNBcgjzyUySwDqCRNUw7xc6LVfgISL1MRAkhzBPDNFKliuLqNkioqbJvK/oNBrF70REamAgSA5hWT6GBaWJqmbuGta5mQ6/HEJBRGpiIEgOIReUthgjyFnDRNbMgZ8bxwgSURPAQJAcwnKJufKEoBwcElEFOSOoNWcE1WwNEbk6BoLkEJZdwxWzhlVsEFETJISQ9wv38q5hXjARkZoYCJJDWHYNy5NFeIIjUrAM+syzhjmEgojUxECQHMKya5iTRYhss9wn3DhrmIiaAAaC5BBGi/Ix5rWGORuSSMlyl2AdQSJqChgIkkMYbZSPYdcwkZJl17CbebII9xMiUhEDQXIIuWtYYxonaLpNzRYRNT1GRdcwl2IkIvUxECSHkCeLWBaUZiRIpGA0Vvyfs4aJqClgIEgOoSgfY15rmJkOIgVbGUHuJkSkJgaC5BA2y8fwDEekYDlDWMtZw0TUBDAQJIdQlI/hGEEimyz3E86uJ6KmgIEgOYT5XKaVJGi4xByRTeYxgpxdT0RNBQNBcghz95ZkscQcV0wgUqqYXS8xc05ETQIDQXIIyy4vSZ41rGaLiJqeitn14Ox6ImoSGAiSQ5i7t7QaCVouMUdkk+UQCs6uJ6KmgIEgOYTlyiLsGiayzSCs620yECQiNTEQJIewHPskcbIIkU0cI0hETQ0DQXIIy7FPWoknOCJbjIoxgqbbeMFERGpiIEgOoSgfwzVUiWwyx3xaDYdQEFHTwECQHMKyfAzroxHZZs7+SZLE2fVE1CQwECSHUKwsYu7yYqaDSMG8n5hmDTNzTkTqYyBIDiFsdnmp2CCiJsjWBRMDQSJSEwNBcgjbXV48wRFZksssWcwa5n5CRGpiIEgOIXd5acAuL6IqVMyut6wjqGaLiMjVMRAkhzAqTnDltzEQJFIQ8gVTxRAK7idEpCYGguQQliuLsMuLyLaKIRSQC69zdj0RqcnpA8F58+aha9eu8PPzg5+fH/r164eVK1eq3SynY7S5dJaaLSJqeuQ6ghLX5CaipsHpA8GoqCjMnj0be/bswe7du3H99dfjtttuw5EjR9RumlMxWMyG1JZ/q5jpIFKydcHEOJCI1OSmdgMa2i233KL4/e2338a8efOwfft2dOrUSaVWOR/L8jEaThYhssnWWsMcQkFEanL6QNCSwWDAjz/+iPz8fPTr18/mNsXFxSguLpZ/z8nJaazmNWuW5WM0cpeXmi0ianoMNtYa5gUTEanJ6buGAeDQoUPw8fGBXq/HE088gWXLliEhIcHmtrNmzYK/v7/8Ex0d3citbZ4U5WM4G5LIJkXhdWbOiagJcIlAsEOHDti/fz927NiBKVOmYMKECTh69KjNbWfOnIns7Gz558KFC43c2ubJZvkYdnkRKSgy5+YLJq41TEQqcomuYXd3d7Rt2xYAkJiYiF27duHjjz/GZ599ZrWtXq+HXq9v7CY2e+aYT3GCY6aDSKFirWFYDKHgfkJE6nGJjGBlRqNRMQ6Q7GeQT3AWYwSZ6SBSUM4aVt5GRKQGp88Izpw5E6NHj0arVq2Qm5uLb7/9Fhs2bMDq1avVbppTMXcDWy4xJ3iCI1KwXGtYHkvLIRREpCKnDwRTU1Px0EMP4cqVK/D390fXrl2xevVq3HDDDWo3zanIsyE1krxiAru8iJSUs4Y5u56I1Of0geCXX36pdhNcgmXXsJb10YhsMtpaa5j7CRGpyCXHCJLjVXQNc8UEoqooxgjygomImgAGguQQ5u4tnuCIqmYuFaPhWsNE1EQwECSHsMwIslAukW1ck5uImhoGguQQlpNFWBaDyDYhrIdQMCNIRGpiIEgOoagjKBeUVrNFRE2PubamJElw03IIBRGpj4EgOYRlHcGKgtI8wRFZMtosvM79hIjUw0CQHMJgMRvSPPaJBaWJlORZwxqwzBIRNQkMBMkhDBaTRSSOfSKyqaKgNCdVEVHTwECQHEJRKNd8guNaw0QKRpZZIqImhoEgOYRlpkPDTAeRTbbKLHGJOSJSEwNBcghz9k+rkaAp/1Yx00GkZL44kiRwiTkiahIYCJJDWE4WYUaQyDZbZZZ4wUREamIgSA5hOVlEyzqCRDaZr420GgluDASJqAlgIEgOISyWzmJGkMg2c9AnSVxZhIiaBgaC5BBy17DFEnPMdBApVcyu5xhBImoaGAiSQ5iXztJKFV3DTHQQKRkt6wiaJ1VxRyEiFTEQJIewLIvBpbOIbFPUEeR+QkRNAANBcgjOGiaqmXIpRnYNE5H6GAiSQygyguXfKgaCREqWYwQ5WYSImgIGguQQBstB8BLLxxDZohwjaO4aVrNFROTqGAiSQ1guMSdx7BORTfIYQUUdQUaCRKQeBoLkEEabBaUZCBJZqrhgAlcWIaImgYEgOYRysojpNg6CJ1ISFkvMcQgFETUFDATJIeQ6ghqumEBUFfM+ISnGCHI/ISL1MBAkh6iYDcm1homqYt4nTLPrecFEROpjIEgOYTlZRK4jyEiQSMFoMUZQy/2EiJoABoLkEKwjSFQzo+Wa3FxijoiaAAaC5BAGi0HwGotB8IInOSKZeSytxmKyiBDMChKRehgIkkPIXcMWBaUB00mOiEwsZw27aSoOv8wKEpFaGAiSQ1hOFtFYBII8wRFVqJg1DFjEgZw5TESqYSBIDmE+kWklSXGC4zhBogqWs4bNs+tNt3M/ISJ1MBAkuwkhFEtnWWYEuXoWUQWjjdn1ADOCRKQeBoJkN8tzmFZipoOoKhVjaSvtJ7xgIiKVMBAku1lmMzQaCRaJDo4RJLJgtLHEHMD9hIjUw0CQ7GaZ9dNqlCc4lsUgqiDXEZQgrywCsGuYiNTDQJDsZnkSs+4aVqNFRE2TZdcwAIvlGLmjEJE6GAiS3Sy7tTQaQOIgeCKbDOZZw5IyECzjfkJEKmEgSHYTFgPdNZVOcFxZhKiC5VKMANcbJiL1MRAku1lmBM0nNnPvMAfBE1WoqmuYmXMiUgsDQbJb5VnDABTrDRORieWa3AAvmIhIfQwEyW6Wy8uZadjlRWSlomsY5f9yPyEidTEQJLtZLi9nxtmQRNYMcvmYSl3D3E+ISCUMBMluFeOeKm4zx4Qc+0RUofJkEXNAyP2EiNTCQJDsZhTVZQRVaRJRk2S5Jjdg2TWsVouIyNUxECS7VZ4JCVhOFmEkSGQm7ytWdQQZCRKROpw+EJw1axZ69+4NX19fhIaG4vbbb8eJEyfUbpZTqW6yCLu8iCpUzp5zLC0Rqc3pA8GNGzdi6tSp2L59O9asWYPS0lKMHDkS+fn5ajfNaRjKkxmWXcPmmJAnOKIKlcfTauULJrVaRESuzk3tBjS0VatWKX5ftGgRQkNDsWfPHgwePNhq++LiYhQXF8u/5+TkNHgbmztbXcMc+0RkzaqOIAtKE5HKnD4jWFl2djYAICgoyOb9s2bNgr+/v/wTHR3dmM1rlmxNFuEYQSJrVS4xx/2EiFTiUoGg0WjEtGnTMGDAAHTu3NnmNjNnzkR2drb8c+HChUZuZfNjMNoYI1j+zWJ9NKIKch1BDTOCRNQ0OH3XsKWpU6fi8OHD2LJlS5Xb6PV66PX6RmxV81dxcqu4zZzpEAwEiWTGSuNptbxgIiKVuUwg+NRTT+H333/Hpk2bEBUVpXZznIrRxsoiGg6CJ7JSOXuu5VKMRKQypw8EhRD4xz/+gWXLlmHDhg1o3bq12k1yOjbrCLIsBpGVqpaYK2MgSEQqcfpAcOrUqfj222/xyy+/wNfXFykpKQAAf39/eHp6qtw651B5JiRgUT6GJzgimdVkEQ0zgkSkLqefLDJv3jxkZ2dj6NChiIiIkH+WLFmidtOchjzuyebKImq0iKhpMsoZQZT/Wz6EgplzIlKJ02cEOVmh4VXu7rL8P09wRBUqD6PQctYwEanM6TOC1PAqd3dZ/p9dXkQVzLsDl5gjoqaCgSDZzeZkES4xR2Sl8qxhzq4nIrUxECS7VUwWqbiNhXKJrFUuKM3MORGpjYEg2c1m1zAnixBZqVxzk2NpiUhtDATJbuaTmMS1homqVXkVHjfWESQilTEQJLtVHgAPVJzoGAgSmQghIKqaLMJAkIhUwkCQ7Gara7hiEDxPcESAcl+QJ4twLC0RqYyBINnN1qxh84mOCUEiE8txgPJkEc6uJyKVMRAku9maNSwxI0ikYBnrmTPmzAgSkdoYCJLdbM8aNv3LExyRiaJr2DxGkLOGiUhlDATJbraWmNOWzxbhCY7IRNk1bPqXk0WISG0MBMlutjKCLItBpGS0kRGs6BpWpUlERAwEyX42J4uU9w0beIYjAmB71rCbHAhyPyEidTAQJLsZbNQRlE9wTAgSAbAsvF4xmYorixCR2hgIkt1sThZhpoNIwbwraCVb+4kaLSIiYiBIDmBrsgjHCBIpVSwvZx0Iso4gEamFgSDZzSBnBCtuk2cNs2+YCIBF5tzGmtwss0REamEgSHbjrGGimhlsDqFQ3kdE1NgYCJLdbNcRZKaDyJLRYrKImTk7yK5hIlILA0GyGzOCRDUzB3uW+wmXmCMitTEQJLvZzAhqOWuYyJLBxqxhNwaCRKQyBoJkN/kEx4wgUZVsFV5nRpCI1MZAkOxmq8tLnjXMExwRAIv9xDJzzoLSRKQyBoJkNznTwTqCRFWyPWu4fLII9xMiUgkDQbKb7TqC5rWGeYIjAiwLSlfcVrHEnBotIiJiIEgOYKvLixlBIiVbBaWZESQitTEQJLvZGgTPtYaJlDhZhIiaIgaCZDdmBIlqZuBkESJqghgIkt1sZgS1nDVMZMkc69maVMX9hIjUwkCQ7Gas5gTHjCCRCbuGiagpYiBIdjNWN2uYJzgiABZdw4r9xPQv1xomIrUwECS72VpijhlBIiVbs4bl8jHcT4hIJQwEyW7VFcrlrGEik+pn1zMQJCJ1MBAku9laYs6tvGpuGSvlEgGofok5dg0TkVoYCJLdbC0xx0wHkZKhPDnOySJE1JQwECS7mU9wyowgxwgSWaq+jqAqTSIiYiBI9rPZ5aVlpoPIktHWWFotx9ISkboYCJLdbA2CZ0aQSMnmZBF51rAqTSIiYiBI9jParI/GTAeRJaNcZqniNvN+YuQFExGphIEg2c3WZBF51jBPcEQAbA+h0HCtYSJSGQNBslv1dQR5giMCbM8aZkaQiNTGQJDsZivTIY8R5HRIIgBVzBrWKO8jImpsDATJblwxgahmtmYNc4k5IlIbA0Gymznpp8gIajlrmMhSdRdM7BomIrU4fSC4adMm3HLLLYiMjIQkSVi+fLnaTXI6tjIdbpw1TKRQMYSi4jYtyywRkcqcPhDMz89Ht27dMHfuXLWb4rRsZzo4a5jIUrUZQY4RJCKVuKndgIY2evRojB49utbbFxcXo7i4WP49JyenIZrlVKqbLMKxT0Qm1S4xx/2EiFTi9BnBupo1axb8/f3ln+joaLWb1ORVZDoqbmOXF5GSzSXmuJ8QkcoYCFYyc+ZMZGdnyz8XLlxQu0lNnq1MBzOCRErmXcGya1hXXj+GZZaISC1O3zVcV3q9Hnq9Xu1mNCvVZToMRgEhBCSLIJHIFVWswFNxW8Xsek6qIiJ1MCNIdjNnBDWKWcMVXy12exFVNZbWtJ+UGkwXTEREjY2BINnNnMywPMHp3Cr+z24vItuzhnUWtWQ4jIKI1OD0XcN5eXk4deqU/HtycjL279+PoKAgtGrVSsWWOQ9baw2bxz4BQInBCE9oG71dRE2JzbG0WmXm3I27CRE1MqcPBHfv3o1hw4bJvz/77LMAgAkTJmDRokUqtcq5mLt+3bTWk0UAoKSM45+Iqiu8DgClBiM8dIwEiahxOX0gOHToUI69aWDm1UMsT2qSJMFdq0GJwYhSAwNBIvNuYGvWMGAaJ0hE1Ng4RpDsVibPhlTODDaPf2IgSGR7sohWI8mziMu4nxCRChgIkt3MXV6WM4UBwN3NPCOSJzgiW5NFgIpxgqWcLEJEKmAgSHYzZwS12soZQdPXq6SMJzgiW5NFAEBnXl2EF0xEpAIGgmQ3edawVdcwM4JEZuaxytpKR105I8gxgkSkAgaCZLcyG7MhgYqu4RIGgkTyBVPlVXZ0XF2EiFTEQJDsYrQY1+SmqWKyCMvHEMmzhitfMJnH1rLwOhGpgYEg2cVy+bgqxwgyI0hkc9YwULEKD/cTIlIDA0Gyi+WyWJVPcBWzhpnpIKpq1rCOGUEiUhEDQbKL5bimyl1enCxCVKFi1rDydvOKPJw1TERqYCBIdrEc3155jKA7A0Eima0l5oCKMYKsI0hEamAgSHapPiNo+r2Yk0WIqu4aZkaQiFTEQJDsIp/cJFtlMZgRJDKrarII6wgSkZoYCJJdyqpYXg4AdObJIswIElW9xJyGa3ITkXoYCJJdDFWMewIsxwgy00Fk3g2qml3PgtJEpAYGgmQXg5wRrDoQZH00oool5ionzysygrxgIqLGx0CQ7FJWRXcXUFEol11eRJbjaW2PEWQdQSJSAwNBskt1GUF5ZRGOESSqchgF1xomIjUxECS71G6MIE9wRFXOGtZwLC0RqYeBINmlNhlBnuCIqpk1zDqCRKQiBoJkF3N3ls0xgpwsQiSratawTsPMORGph4Eg2aXaWcOsI0gkq2qJuYpJVcycE1HjYyBIdqlujKB5EDwzHUTVFZRmHUEiUg8DQbKLoZqVRfTlGcHCUkOjtkkNqw6n4OY5m/HE4j24lFWodnOoCapqskjFWsPMCBJR43NTuwHUvFVXR9DPUwcAyCksa9Q2Nbajl3Pw1Ld7UWYUOHwpB6uOpOCuxCj8393d1G4aNSEVGUHl7VxrmIjUxIwg2aW6MYLmQDC7sLRR29TYfj94GWVGgeggT3joTLvUT3suIjktX+WWUVNizghWLihdManK+TPnRNT0MBAku1Q3RtDfRQLBLafSAADThrfHfyf2lm9fsuuCWk2iJshonjVcaV/x1GkBAEWlHCNIRI2PgSDZpayajGCA3DXsvIFgZn4JDl3KBgAMbBeM/nHBmP9AIgDg881ncPRyjprNoyakqiXmzFnkIhcYS0tETQ8DQbJLVTMhgYqMYG5xmbyds9l6Oh1CAO3DfBDm5wEAGNUpDDd2CofBKPDGb0cghHO+d6qbqrLnHnJG0PUCwSOXs7Hw72Sc5TAKItUwECS7mEteVDdGEHDerKC5W3hA22D5NkmS8K+bO0LvpsGO5AysOJSiVvOoCalq1rCrdg1nF5Zi/ILteOO3oxjzn83Yez5T7SYRuSQGgmQX+eRWxcoi3u6mk5yzjhPccuoaAGBQu2DF7VGBXnhiSBwA4J0Vx1BY4nrZHlKqatawq3YNL/r7LHKLTBUF8ksMeOqbvcgvdu4KA0RNEQNBsou59pmtjCDg3BNGzqXn40JGIXRaCX1at7C6/4khcYj098ClrEJ8tum0Ci2kpqSqiyZ9eUbQFeptmuUWleLLLWcAAO+O7YLoIE9czi7C/I3cT4gaGwNBskt1s4YB5y4hsznJ1C3co1UgvPXWJTk93bWYeVNHAMD8jadZaNrFyftKlV3DrhMIfrfzPHKKyhAX4o27EqPxcvl+smDTGVzmfkLUqBgIOhkhBMoacUm3shoCQXNGMMsJA8G/y8cHDmwbXOU2N3eNwHWtg1BUasTCLcmN1TSqh8OXsrH+RGqDPX9VE6s8XGyMYEmZEf/dchYA8PjgOGg1EkZ1Csd1rYNQXGbEe6uOq9tAIhfDQNCJHLmcjaH/twG93l6L2SuP4+DFrAZ/TXN3l60l5gDn7Ro2GAW2nk4HYCobUxVJkjB5UBsAwC8HLnPd5SaooKQMb/52FDfP2YJJC3dh/fGGCQblOoIunhH87cBlpOQUIdRXj9t6RAIw7SevjEkAACzffxmnr+Wp2UQil8JA0EkYjALP/XgQ59ILkFVQivkbT+O2uX9j8bazDfq65jGCNWUEnW3W8KFL2cguLIWvhxu6tvSvdtsh7UMQ7KPHtdxifL/zfCO1kGqj1GDEuM+2479/V2RrF28/1yCvVdXKIq40WUQIgc83m8YGThwQC72bVr6vS5Q/RnQMAwB8yew5UaNhIOgkDl7MwrErOdBpJTw8oDWGdQiBEMDrvx3FoYvZDfa6NY0RdNaMoLlbuH9cC3mt2Kq4u2nwz+FtAQDzNpxu1K57qt4fB6/IBcEf6hcDANiSlNYgQVnVs4YrJos4e83JzUlpOJ6SC293Le7vE2N1/+RBrQEAS/dcRFpecWM3j8glMRB0EofLV7AY0DYYr96SgP9O7I3RnU1FjRtyxqqhmvIxgEUgWOBcgeDmJFPZmOrGB1q6p1c0grzdcTm7CGuPXW3IplE1ikoNeHnZIXy19SyMRiHXrnt0YGu8cWsnhPnpUWIwNkhNO3MgqKt04WAOBI0CKDU4dyBozrze3StaPjZYuq51ELpG+aO4zIgfdnOJRqLGwEDQSZiXMkuI8ANgGnPz1PWmLNSqwynIyC9pkNc1VLPEHAD4ezlfRrCgpAx7zpkChYHtQmr1GA+dFvdeFw0AWPj32YZqGtVgw4lr+GbHebz26xF8veMcjpTvN51b+kOSKsoA7TiT4dDXFUJUObHK3DUMAEVlzts9fCo1DxtOXIMkAZMGxNrcRpIkPNDXlCn8fucFGJ10RSKipoSBoJM4esV0QusUWTFerVOkPxIi/FBmFFh1uGFWt6jtGEFnCgR3JGeg1CDQMsATsS28av24B/rGQKuRsCM5A8eucA1iNZxKzZX/v2DTGRwu7xZOiDRdQPVpEwQA2H4m3aGvaxnPVL5octdqYB42WOTEhcfnrEsCAAyPD0NMC+8qt7ulayR8PdxwPqNAXrmHiBoOA0EnIITAmfJZdu3CfBT33dwtAgCw8vCVBnltQ/kSc64UCP6dVFE2RpJsv29bIvw9cWOncAAcDK+W09cq1rS9mFmI4jIjvN21aBNsCkzMGcF9F7IcOk7QvBQjYL2vSJLk9MvMHb6UjV/2XwYATBvRrtptPd21uLNHSwDANzsaZuIOEVVgIOgEMvJLkFtUBkkCWgUpM1SjygOP7WfSkdcAyzfVto6gMwWC5ixFdWVjqvJo+WD4n/deVGSnqHGcSjVdMPUtz/wBQKeW/vKEn7gQbwT7uKOkzIgjlx03ycpgkRK0VWrJw8lXF3m3vDbgbd0j0bmGWfYAcF/5RJK1x1JxNaeoQdtG5OoYCDqBs+kFAIAIPw/5hGIWF+KD1sHeKDUIbD55zeGvba6LV3kAvJmzlY9JzS3C8ZRcSJJpYk5d9WgViBsSwmAUwPurTzRAC6kqBqOQA8E3bu0MdzfTd3ZMlwh5G0mS0DUqAAAcOtu+zCIQtHXR5OnEgeCWpDRsTkqDTith+g0davWYDuG+SIwJhMEo8MMuThpRW6nB6PQz2l0ZA0EncDbN1N0VG2x73M318aEAgL8aoFBuaS3XGs4tLnOKsinmsjGdIv0Q5O1er+d4YVQHaCRg9ZGrDTI7lWxLTstHYakBnjot2ob64M9pg/HSTfG497pWiu3MGatDlxw3jtNgsMwIWu8r5gkjhU42RlAIgdmrjgEA7u8Tg1Z1GFN7X/nfZcnuCwxCVPD7wct4YvEerD16FUPf34DRH2/GhYwCtZtFDYCBoBM4l24KBKsagD28PBBcfzzV4bPwzGOfqqql52dRIiKnyPFd041t3XFz2ZjazRa2pV2YL8b2jAIAzF55nCe5RmAwCszfaCqjFB/hC61GQmywNx4bHCdnBs26lAeCjuwaNmcEJcl6iTkA8lrVhaXNfx+x9NexVBy+lANvdy3+UV7FoLbGdI2At7sWFzMLecHUyHadzcA/v9uHVUdS8Oj/duNSViGOp+Ri2pL9nMnthFwmEJw7dy5iY2Ph4eGBPn36YOfOnWo3yWGSy7uGWwfbvtru3ToIvno3pOeXYL+Dl50zzxrWVZER1Gk18HY3dXs193GCxWUGefmxkZ3C7HquZ25oD3c3DXYmZ2DDCcd32ZPSP7/bh5/2XARgyuZWxxwIJqXmOWzCSE1llrzK95H8YufJCAoh8Mn6UwCAB/rFoIWPvk6P99BpMbJ8jPOv5RNNqHH83+oTsBXv7TmXiW+5OpLTcYlAcMmSJXj22Wfx2muvYe/evejWrRtGjRqF1NSGW2C+MdWUEdRpNRjcwZTB+svBxYzlruFqVtdwlgkjW0+ZJtyE+enRvXwcWX1FBnhiQvlKFu+uOu4U3eZN1YmUXPxxyDRrPjEmUK5TV5UwPz2CfdxhMAq5LJO9ymqYXe/lXp4RdKKu4a2n07H/Qhb0bho8OrBNvZ7j1u6mtYj/OHSF+0gjSc0pwo7kDEgS8NMT/TC2ZxS+ebQPXr/FtBb0u6uOs4vYybhEIPjhhx9i8uTJmDRpEhISEjB//nx4eXnhv//9r9pNs5sQAsnlYwRbVzFGEABGJpgyWL8euOzQrkjzCU6nrbqMip+TBILmWowjE8Jtdu/V1ZND28LfU4fjKbn4ZgevshvKZ+VdwqM7h2PplP6ID68+IyhJkjxO0Fxn0F4VGUHbh1w5I1jiPF3Dn24wZQPH945GiG/dsoFmA9sGI9BLh7S8Emw97djajmTb7vJi+fHhfugVG4QP7umGAW2D8WC/WHSL8kduURmGf7ARM5YeREp2ETLyS1BSxiC9OXP6QLCkpAR79uzBiBEj5Ns0Gg1GjBiBbdu2WW1fXFyMnJwcxU9TZi4dA1iXjrE0MiEcPno3XMgoxK6zjhtvI3cNO3lGsMxgxJrybOqNncMd8pyB3u54bpRpFuX//XkCmQ20+osr25KUhmX7LwEwBd611cXBgWBNZZbMgWCBk2QED1zIwt+n0uGmkTB5cP2ygYDpuDKmq2lW968H2D3cGHaXnx96xQQqbtdqJHw8vgc6RfqhxGDE97suoO+sv9Dz32vQ9Y3VWLKLF7PNldMHgmlpaTAYDAgLU47pCgsLQ0qK9Wobs2bNgr+/v/wTHR3dWE2tF3PpmEh/69IxljzdtbipiymA+XnvRYe9vrl8jFs1GUFnCAR3nc1ERn4JArx0uK51UM0PqKX7rmuF+HBf5BaV4X/bWDzXkS5kFOCp7/ZCCFNWqktUzfXrzBw9c7jmMYKmruECJ8kIzttgysLe2j0SUYG1nylsy63dTMWlVx9OcWiRb7Jt9znT8oq9YgOt7osN9sbv/xiI7x/riw5hvvLtRaVGzFl3ihPfmimnDwTraubMmcjOzpZ/Llxo2jWszKVjqluyyezO8pmqfxy84rADqjnToauiywsAAryafy3BNUdN2cDh8WHVZj/rSquR8OQwU6bqq21neaJzkIKSMkz+325kFZSiW3QAXr+1U50eL08YuZrrkL9JTUsxOlNG8ERKLlYfNV1kTxkSZ/fz9YoJRIS/B3KLy+TJWtQwCkrK5PW3e8XavuCVJAl927TAqmmDcPKt0Tj8xijotBIuZhbioANrb1LjcfpAMDg4GFqtFlevKidJXL16FeHh1l18er0efn5+ip+mzDxRpKoagpauiw1CVKAncovLsPqIY9YedoWMoBACa46ZPq8bEuybLWzLTZ3DERXoiYz8Evy4x3HZWleVV1yGxxfvwfGUXAT7uGP+Az2rzZbbEuHvgUAvHcosilDbo6aMoLl8TEEznzUshMAbvx2BEKYxme0sskb1pdFIuKN8ybl5G08z69SAvtycDINRIMLfAy0DPKvdVpIkuLtp4KN3k2vVcm3o5snpA0F3d3ckJibir7/+km8zGo3466+/0K9fPxVb5hjm0jGxtSjUanlA/f2gY9YeLqvFrOEAL1Ph5bS8Yoe8ZmNLSs3DhYxCuLtpMKgey8rVxE2rwaMDTUvPfbH5jGI5MqobIQSmfrMXm5PS4KHT4NP7ExHhX/0JzRZJktAh3BTEHE+xfylAedZwFRdM5pVFCpp5RnjV4RRsPZ0OvZsGL93U0WHP+8jA1vBy1+LgxWz8dYxZwYaw/ngqPlhzEgDwYL/qZ9ZXdl35Gt27zmY4vF3U8Jw+EASAZ599Fp9//jm++uorHDt2DFOmTEF+fj4mTZqkdtPsduaaKVtRm4wgANxUvpzWppPXHDIeSZ41XM0sWvMklnPpzbPkgLlbeEBcCzlz42j39I5GgJcO59ILHJatbc6EEFhx6AqO1bF8y9pjqdh48ho8dBp8/1g/u8ZzmmcXn7xqfyBY06xhb315INgA64E3lqJSA95ZaVpF5PHBbRBdzeS1umrho8dD/WIBAC8sPYhf9l9iZtCBhBDyetAP9G1V5y7968q7kfecy+SFbDPUMGe1JmbcuHG4du0aXn31VaSkpKB79+5YtWqV1QSS5qbMYERSebdVfHjtumDiw33RKsgL5zMKsPHENYy2WGe1PmpTR9Bc1sbcjd3c/FkeCI5ogG5hMy93NzzULxb/+SsJn208jdGdwyFJ9peoaS6EENh2Jh0bTlzD9jPp8lijqEBPbH5hWK0+C6NR4MPyjMakAa3RPTrArja1D3NkRrCmMYLmySLNNyO4aOtZXMgoRJifHk8MtX9sYGVThsZhc9I1HLmcg6e/34+NJ67hnTu71Lnbn6xtPZ2O4ym58NRp8fzI+DofezpG+MLbXYvcojKcSMlFQg1F26lpcYlAEACeeuopPPXUU2o3w6HOpuejpMwIL3ctoms5M0+SJNzYORwLNp3BqiMpdgeCFUvMVX3giCnvtk7LK0FuUSl8PXRVbtvUXMwswIELWZAkUwmehjShXww+23gaBy5mY2dyBvq0adGgr1eZ0Sgw/ccDSMkuwtMj2uF8egFOXM1FfnEZ9G4ahPl7INzP9BPm74EwPw+k5xUjOtCr3nUVC0sMWH0kBV9tO4t957Os7r+YWYhTqXm1Gmu2+kgKjl3JgY/eDY8Nqn/JEjNz1/BJBwSCtV1ZpLnOGk7LK8Yn60x1A18YFS8Hto7k76nDT0/0x2ebTmPOulP4ed8l7EjOwPt3d0X/OMcP2WgOsgtKsT05Xb54im3hjUcHtUbHiLoFYp9vPgMAuKdXFPy96n58dtNq0DMmEJuT0rD7XAYDwWbGZQJBZ7Q5yTQwt0O4b51OxKM6mQLBdcdSUVxmgN6t/lfUFUvMVZ0R9PXQIdjHHWl5JVi8/RweHdjGan3XpspcRPq62KB6F8WtrRY+eoxNjMK3O87j881nGj0Q3H0uE8v2mWrubVtQ++K9MS280KWlP05ezYVGkqDTaqDTSgj0ckeQt+kn0NsdBcVlKCgx4FJWITLyS2AUAkcv5yC/PAvmrtXgtu6RSMkpwplr+biUVQgAeO7HA8goKMGrN3eqcrKO0Sjw0VpTNvDhAbEI9Ha356MAALQP8wEApOQUIbugtF4nSDNnzwh+uOYk8orL0KWlvzwOuSF4umsxbUR7dIsOwCOLduFSViEe+nIn3r+7K+7oEVXr5ykzGLFk9wVsO52OrIJS+HvqEOitQ7ifB2JaeCO2hTdigr3gV8VFq9EocCWnCOfS8nEmLR+nr+Xh9LV8nE7NQ0pOEXRaCR46LTx1Wvh76uR9ID2vGP6eOozuHAF3Nw2MQqCkzIiUnCLkF5chu7AUuUVlKC41otRgRInBiJKyin/LDAKe7lr4erghPa8Ex1JyYNlDvudcJn7ZfwnTR3bA44Pb1Oq8kHQ1FxtOXIMkmTLp9dUrJgibk9KwMzlD7san5oGBYDO1/ngqZq00jekY1iG0To/tER2AUF89UnOLsfV0ep0fb6k2s4YBoE/rFvjj0BW8t+oEhACmDqvbAvRqMS9NZi5q29AeHdga3+08j7XHUnEqNRdtQ+2fdVkbBqPAsn3KGcthfnqM6RKJIG8dCkoMSMkpwtWcIlzJLsLV7CI5gDuXXmDX+M/oIE/c1TMa9/VppQi2564/hfdXn8CB8m7iyf/bjU3PD0O4v4fVhcQfh67g5NU8+Hq44ZF6LmdWma+HDi0DPHEpqxAnrubaNd7QYM6cVzlr2HQxltcMxwievpaH78vXn33l5gSHrLpTk2EdQrFq2mC8/ccxbDx5Dc8sOYCrOcV4fHCbGrs1t55Kw5u/H61Vl3+QtztiWnghtoU3WgZ44nJ2IY5fycWpa3nVrqZhMAoUlRqRhVJcyS6yun/1Ecct9RkX4o1+cS3QpaU/1h5LxZqjV/HuquN4d9VxtAn2xqJJ16FVNZMJP9tkygbe0DGs1mPNbend2lR3cNfZDAghXGpoS3PHQLAZKigpwwtLD6KkzIiRCWF4so7jcTQaCSM7heHr7eex+nCKXYGgXEewhkBwytA4Oahafzy1WQSCp1Jzse+8qVt4VKeG7RY2axPigxs6huHPo1fx+aZkvHtX1wZ9veS0fLyz4pg8IQYAPn+oF/w83NAtOqDa8Ve5RaXYcSYDfx2/ihBfD/SIDoCbVkKZQaC4zIDMglJk5JcgM78EGfkl0Ou08HbXomWgJ4J99HDTSAjzNz3O1knDXMvP0uD31yO2hRf+mj5Uzq4ZjAL/rzwbOHlQG7syd5V1CPfFpaxCHLqUbVcgWFMdQfNwibyi5hcIzl1/CkYBjOgY6tBi6zVpH+aLhRN7450Vx/DFlmTMXnkc+85nYvadXa0ywgajwLrjqfjvlmRsO2PKdgd46fDIgNZoGeiJnELTd/VSVhHOpefjbHoB0vKKkVH+3bU1bMFNI6FVkBdig70RF+KNuBAfxIX6ICrQU94HCkuMyCwwPceV7CJsOJGK9PwSBHrpIEkSJJi+ExH+nvD1cIOfhxv8PHXQ67Rw15rKs+i0GrhrNdC5aaDTaFBYakBecSnctVr0jg1EqJ+H3KZ7ekVjya4LeOO3oygsNeBMWj4e/3oP/vfwdTZ7NM6l58u9AFPsHNfZIzoQbhoJV3OKcTGz0KGThahhMRBshr7YnIxrucVoFeSFOff1qHaiRlVu7BSBr7efx5qjV/H2HaLKE1RN5PIx1XQNA6aVGj57MBGPL96Dw5ezUWowOrQws6PlFJXileVHAJiulMMsDrYN7bHBbfDn0atYtu8SnrmhPcL9Hf/al7IK8eXmZCzeflae8AMA3aIDMKJjaK2u5n09dBiRENZgk2hsBYKAaTWdqzlFiCyvc7Zk1wWcvpaPAC8dJg2IdWgbrmsdhHXHU7E56RoeGVj/brOaZg37lM9Gzyspg9EoGiWr5gjHruTgl/2mpd/+Obxdo7++RiPhXzcnoGWgJ95ZcQyrj1zFvvObcHevKAR4uiMtrxhJqXk4eDELaXmmJRx1Wgn3XtcKz4xoX+0QgrziMpxLz8f59AKcTS/AxcwChPp6ID7CF/HhvmgZ4FnnY6+9wVZNJEnC+OtaoVdsED5aexJ/HDTNvJ+4cCeWTulvdWE3d/0pGIwCQ9qHoEcr65VE6sLTXYtOkX44cDEbBy5mMRBsRhgINjNFpQZ8Xp7Kf35Uh3qP7+vTJgh+Hm5Izy/BvvOZVVaRr0ltu4YBU0Dl76lDdmEpTqTkyst4NTV7zmXi6e/34WJmITx0Gvzj+sY9wfWKDULPVgHYez4Ljy/ejRmjO6JrlL9cuiaroAQbTlxD3zYtsHBrMn7dfxk5haUI8dUj0NsdYb4eiAv1RqCXu+nHWwd/T3cEeOlw5lo+ft57EauPpMBc5WFohxD84/q2SMsrQY9WtrNzagj0dkfPVgFIupqHn6b0x7J9lzB/o2npshs+3Ij1zw2Fu5sG7682DZH45/XtHD4RaWiHEMxeeRzbz6SjqNRQ7xmqNY0R9PUw/W2FAPJLyprFhKqiUgMmLtwJg1FgWIcQdI0KUK0tkwa0Ru/YIPzzu304k5aPuetPW20T4KXDuN7ReKhfbI3FkgFTcN4p0h+dIpvmcao6bUN9MPe+nnhuZD7unr8VRy7n4D9/JeGFG+Plbc6l52PpXlM28OkRjjnGdYwwBYLHr+Ti5obtzCAHYiDYzOxIzkBucVn5+K36j1vTaTUY3D4Evx+8gg0nrtU7EKzoGq75ylijkZAQ4YdtZ9Jx7EpOkwwEf9pzES8uPQiDUSAq0BNz7u1RpzVqHeXdsV0xdt5WHLiYjXs/344W3u64uWsETl3Lw9+nbE/kyC/PXAAAjtT8Gv3jWuDxIXEY0j7EgS13rO8e64uiEiP8vXSYMToee89nYmdyBvJLDHhx6UH4e+qQWVCK9mE+dS6CWxsdwnwR7ueBlJwi7EjOqPdnJWcEq7hg0ruZJtiUGgRyi5pHILj7bCau5hTD18MN793VTe3moHNLf6x4ehB+3nsJBy9mobDUgGAfPVoFeaFzS390ivRzuVIzrYO98fYdXfD44j2Yt/E0ruUW474+rdCjVSA+/isJBqPA0A4h6GlnNtDMPFv5eIpj1uimxsFAsJkxr7V5fXyo3d1HQzuEmgLBk6l4blSHej2HnBGsZVs6yoGg/SU5HEkIgU83nMb7q08AAG7uGoF37uxS5azBhtYuzBe/PDUQ768+jk0n05CeX4Kvtp2z2i4hwg//HN4WHcL9cC49H1eyi3AttxjpecXILChFZkEJMgtKkF1Yiqz8UgT5uGNo+xDc1ydGLo/SlOndtIqst+XfY/2JawBMWba37+jSIEMNJEnC0A4h+H7XBWw4kVrvQLCmjKAkSfD10CEjv6TZTBgxLyc2MiG8wWfU15aHTov7+rTCfX1aqd2UJmNUp3Dc2Ckcq46k4Mc9F7HtTDrmP5CI5eVjA58Z0d5hr2WuZ9vUju9UPQaCzYgQAn8dNw3qt2eCh5n5pHb4Ug5Sc4sQ6lv3sWhy+ZhanoTjI8wHiqZ1xfjWH8fw5ZZkAMDkQa0xc3RH1cdptQ72xqf3J6Ko1ID3V5/A7nOZ6BUTiJxCU0DXr00LDG4XIreztR0z/pqL7MISq9teGNUBveuZ0a6NYfGh+H7XBaw4dAX/GpNQr/G0Nc0aBkzdwxn5plqbTV1ecRl+2nMBADC4vWvW8GtO/t/47vjftrN4Z8VxXMwsxM1ztgAwrQfdzc7C65bMq/FcyipEdmGpvM58dX7Zfwmnr+XjmRHtmsywFFfDQLAZOWWx5u2AtvYffEN89ejS0h+HLmVj44lruLtXdJ2fozYFpS11Lh9vc/BiVpOZMLL1VBq+3JIMSQJev6UTJvSPVbtJCh46LV65OUHtZjQJIxPCsetsJrzdtbizZxS6Rwfgzp4NV7cOMF10BXrpcDWnGJuTrmFoPS7CasoIAhUTRnKbwczhLzafQVpeCWJbeMnLVlLT5aHT4rHBccgvNuDjv5LKb9Pg5TGOWw8aAPy9LEoupdRcckkIgae/3w/ANFSlbyPXTiUT9c/CVGvf7TRdgQ9qG+ywNW+HdjBlBTecvFbnxwohKpaYq2HWsFl8uC/8PXXILzHg8KXsOr+moxWUlOGlZYcAAA/0iWlyQSApTegfiw/u7oZ1zw3Fv2/vjLGJUQ2eRXB30+C27qZg86c9F21uk11Qin98tw/rjtuuD2eoRSBonjDS1APBa7nFFhPW4pvExRzVzkP9YuDn4QZJAmaO7oioWq5IVRcV3cM19/pkFVRkv69kFzq8LVQ73IObAYNR4PClbHy/y1S09QEHDoo3Zzc2n7yGMkPVBVKrapdZTXUEzTQaCX3KrxK3n8mo0+s1hNd/PYKz6QUI9/PACzfWb5wkNR53Nw3GJkY1ajkfALgr0bRqxZ9HryK7wLrr9p0Vx/Dbgct4eNFum4+vqCNY/Qo8QNMPBD9Zl4T8EgO6Rfnjpi6NU1+THKOFjx6rpg3GpueHNdhFr3n4T20mjFzIrChEn5Jd3CDtoZqxa7iJEUJg/sYz+HLLGRSVGqF30yCvuAzF5VXsu0X5Y0g7x83y7B4dAF+9G3KKypCUmlenNSrLLALButTT6tumBf48ehXbz6Q3eF2t6izYdBo/7L4IjWQaQ9McZmqSOjpF+iE+3BfHU3Lx64FLeLDSElrmiRMAkF9cZpWxr2mtYQDwLX/M36fSMLJTGIJ9msYEDEtn0/LxzQ7TBemLo+M5pqsZiqxF6Rx7mM8htiaM7DmXCb2bRq4YcTGzIgt4PiO/QdtFVWNGUEWrDl9BdmFFdqGo1IBXfzmCd1cdR1qeafZgen4JisuMkCTTyej/7u7m0EkMWo0k75SHLtatq7bUIoNY21nDAORxILvPZiieo7GcS8/HA1/swDsrTPXnnr2hPcemULUkSZKzgsvLCyibpeUV47JFt9ZBG/tRbcYIBniZihv/cegK/vndPrvb3BBmrzyOsvICxP3jOEmErJknjJxIyVX0Gl3MLMDYeVtx29y/kZFvmvR1IaMiI2jPMpVkH2YEVXL6Wh6e/GYvvPVuuL17S+SXlGFncoZ8hfTijfG4sXM4SsqM8NSZluWq7+ofNekS5Y9tZ9Jx6FI27uld+wkjZQbLruHaX1OYxwlmF5bi8KVsuyva14XRKPDglztxvvwA9OKN8XhiiGPWpiXndnPXSLy94hj2nMvEpaxCuSjxuuOpEBW7AnYkp6NfnPLCojazhvvHtcB//zbNXN96Oh25RaVNKku940w6Vh1JgUYCXrrJsZMMyHm0DvaG3s20FN75jAK5msHSPaZyNQajwI+7L+DxIXGKjCADQfUwI6iS7MJStA31QW5RGRZvP4ef917CxcxChPjq8cVDvTBlaBxaB3ujQ7gvWrXwarAgEDB1DwPA1tNp1W9YSWn5yU2Sqs90VKbmOMHzGQVyELh62mBMGRrH7i2qlXB/D7lMzR8HK7KCa8vXaY4sXwpwwwnriVe1yQgObKfMsO06q/4YWrPM/BI8+8MBAMC43q2aRQ1KUodWI8nfj+PlE0aEEFi6t2Ki1byNp5FVUCKPEZzQLwarpg1q/MYSAAaCqunZKhCrnh6Meff3xKQBsZh+Q3vMf6An1k0f0mBrt1ZlULtguGs1OH0tH0lXa18IVK4hWMsZw5bMGZPtZ2yvkuFoQghcyy2WZ7J1aenPkxnV2S3dIgEAvx4wBYJFpQZsTjJdQL15W2cAwIGLWcjMV9Y7rM0YQQ+dFt882kcOFnefzXRs4+3w4tKDuJRViJgWXpgxOr7mB5BLqzxz+NcDl3E+owB6Nw0i/T2QVVCK9SdS5YzgyE7hTSr77WoYCKpIo5EwuksEXrulE/4xvB1u7Byhys7g66HDoPJsxIpDKbV+nDkQrG0NQUvmzMq+85kwWowjaSjzN55B77fXYso3ewFUHKiI6uKmzuFw12pw+FIO9p3PxLbT6SgsNSDC3wPDO4YiPtwXQgCbkpRZwYqMYPWH3AFtg/HW7aaA0tZYQzV8vf0c/jx6FW4aCZ/e37NWRYLJtckTRlJycfRyDp7/6SAA4P4+Mbixs6nu5IEL2bhYnhGMCmzYCSxUPQaCBAAYXV4UdsWhK7V+TGktxj1VpUO4Lzx0GuQUleFMWl6dH18XBSVl+HT9KcVtjqymT66jhY9ezgq+vOwwlu83jXsa0TEMkiTJq/VsrFSXs6a1hi11LV/beuvpNJxXedzUmqNX8a/lhwEA46+LRqfIprc+ODU9CeWB4M7kDDz34wGUlBkxrEMIXropHt2iTd+hv45fRVGpERoJiPBnIKgmBoIEALihYxjctRqcuJpb69nDhSUGAICne90XctdpNegaFQAA2Hs+q86Pr4uley4it3z91pYBnrite6Q8A5Sorp4d2R4tvN1x9EoOfimfQWwezmEOBDedvKbIdNemoLRZ+zBfBHjpYBTA4PfXY1M9ir07wt7zmXjuR9O4wDt7tuTqNlRrvWKDEB3kiezCUhy9kgO9mwbvju0KN60G3cqP+xcyTN3CEf6ecHdjKKImfvoEwLQ00I2dTcVhv9lxrlaPySsPruq7ykmPVgEAgH0NGAieuZaHj/8yZQPfuLUT/p5xPT4e3wMeuroHr0SA6WJi/oOJ8pJwIzqGYkD5mNdesUHwctciLa8ERy1WVqjNGEEznVaDT+/vKf/+6YZT1WzdMHYmZ2D8Z9uRXViK7tEBeOeOLtC7cZ+h2tFqJDw3sgPM8/CevaE9QsuLwMe08FIMLxjSwXF1cal+WD6GZPf3aYVfD1zGL/sv46UxHeFXw3jF/PJA0Le+gWC0qWzMvvNVD4ovMxjx3c7zOJdegHt6R6N9WO3G9hmMpllq75XXZIwP98XdvZgFJMfoHRuErTOvR1Z+KaKDPOWZ5+5uGnSN8sf2MxlISs2Va3Sa1+Su7ez6/nHB2Drjegx6bz22n8nAsSs5dSr2bq+3/ziKEoMRQzuE4ONxvHCiurute0u0CvJCVmEphlmszy1JEoJ93OUaureWD7Ug9TAjSLLrWgehbagPCksN+OuY7TVTLdmbEexZnhE8cTUXOUXWy3YBwH/WncIrvxzBF1uScfN/tsilOqqz73wmRn+8CS/8dFAOAr95tA+83HndQ47j56FDqxZeVuWHQnxNmY/0vIqZw/LEqjqMp40M8JSz9Iu31y5L7wiXsgpx4GI2JAl4766u8Pfi5BCqnx6tAhVBoNmt3Uxrd8eFeMulxEg9DARJJkkShpWn6Xcm11zDzN5AMNTPA62DvSEEsKuK11t12DR5RauRUGIw4slv9+LvU9b1Do1GgVWHU/D7wct45KvdOHk1D/6eOrx8U0csnzoALZrgcl3knFp4m1YISbcoIVNUahpPW9fM2l09TVnsLUl1q/FZH+fTC3Dj/9uEAbPXAQB6xwQh1Ldx13Qm1/DooNZ46/bOWDqlP+u4NgEMBEnhutamsU47qgkERfkyCuauYZ96BoJAxXJzf5+yrieYmV+Ck1dNM4q3zbgeIxPCUFJmxMOLdim6k0vKjJi2ZD+e+HoPnvp2HzLyS9AhzBebnh+GyYPbsFuLGpU5EMzIswwETV3D+jp+F3vFBkIjmQqhX84qrPkBdpi18hiOp1TUER1/Xe1XGSKqC2+9Gx7oGyMvq0jqYiBICtfFBkGSgDPX8pGaW6S4TwiB+z7fjnGfbYfRKJBXbMpyeOvrH2j1bWPqFthzzjrw3H3OFOzFhXgj1M8Dc+7rgWEdQlBcZsSUr/fitwOXMWvlMfSfvU4u8AsAYX56zHugJ7u0SBXm7HN6frF8W2F5RtCzjoGgr4cOXcrHGf689yIuZhbgUlahfDHmKPsvZGHlYVMNUXetBt2i/HFzV47dInIFHDRFCv5eOnQI88XxlFzsSs7EmK4R8n2Xs4uw9XR6+f8L5YxgfbuGAdMKKwBw5HIOikoNiuydeYktc/FpvZsW/7m3B2795G8kp+XjH9/tk7cN9tHj/bu7ol95hpFZQFJLULVdw3W/9r6vTyscuHgI//fnSfzfnycBAP++vTMe7BvjgNaaZtY/sXgPAOCuxCi8f1dXdtcRuRBmBMmKubt2Z7Kyu/bMtYrCz1dziiu6hu2YhBEV6IlgHz3KjAJHLivrF5rHKZoDQcCUIfliQi8MaNsCcSHeuKVbJOY/kIhtM6/HsA6h8NBpGQSSqoJ9ygPBPOtAsK4ZQQC4o0cUhscrB9z/sOuCHS2scCo1D+MXbEdKThHahfpg5uh4BoFELoYZQbKSGBOIRVvPWhV6Pp1aEQheyS6UizTbkxGUJAk9WgVgzdGr2Hc+C4kxpqAvq6AEhy6ZAsPrKs0qiwvxwTeP9q33axI1JLlrOK+ia9g8RrA+Fynubhp8ObE3MvNLUGo0ou87f+HQJdPyXFGBXvVuZ9LVXNz7+Q6k5RUjPtwXXz/ah5OqiFwQM4JkpWeMqbv22JUcefUQADh9LV/+/5WsooqMoId91xO2CkuvO54Kg1EgPtwX0UH1P9kRNTZz13B+iUHOBBbWc9awpUBvd4T6eqB7+fKIW21MsKqtXWczcPdn25CWV4yOEX74dnJfBDMIJHJJDATJSqS/B0J9Td21By9mybefvFoxo9ByjKA9s4aBisLSey1mAv91LBUAcEP50l1EzYWfhxt05WsKm8cJ2jNGsLL+ccEAgLkbTmHx9nO4VIfZxEII/LTnIu7/YgeyCkyrhnw3uY8cvBKR62EgSFYkSZIncZi7h4UQiiWzrmQVIaewfGUROzOCXaP8odVIuJJdhEtZhSgzGLE5ybS+6rB462KkRE2ZJElyYGUuIVPfWcO2DIs31fo8l16AV5YfxrTv99XwCFMguupwCiYu3IXnfjyAkjIjRnUKw3eT+7KEB5GL4xhBsqlnTABWHUnBnnOZ2HU2Ay/+dBC5RWXy/Wl5xbicbcpEhPvZV3TWW++GTpF+OHgxG7vPZiAywBM5RWUI9NLJC5QTNSctvPW4mlOMtPISMsV2jBGsLDEmCB+N64ZFf5/FgYvZ2H0uE2l5xYqu3eIyA2YuPYQNJ69Bp5WQmlsMc8UZN42Ep4e3w5PD2tZ6yTsicl4MBMmmxPJxgmuPXcVaG8vNnU0vkAPDiABPu1+vd2wQDl7MxvYzGQgsr/83qF0IT1TULLXwqcgIGowCJQZTIOiIjCBgmkl8R48ojPnPZhy5nIMfd1/ElKFxSM0pwrGUXFzMLMDP+y4pHhPh74Fbu0Xi7l7RaBvq45B2EFHzx0CQbOrZKhAjE8Lw59GrkCRgaPsQRAd5oWOEH2b+fAhp5TMi/T11do8RBICBbYPx5ZZkbDyRCl8PUyA4tHy5O6LmpmKZuWJ5fCDg+PqWD/aNwYyfD+GjNScR4KXD/1t7EldzKmYr39MrCuN6t0JsCy8EebuzNAwRWWEgSDZJkoRP7++JPecy0S7MVx7zlF1Qipk/H5K3i3RANhAA+sW1gIdOg8vZRUB2EdzdNDYXKydqDipWFymRxwcCgN7NscOyx/WOLs/apyr2S7OXburIMYBEVC1OFqEquWk16NOmhWJGoZ9nxYxIwDTD2BE8dFrc3r2l/PuYLhEI5ExGaqZCfE2B4OWsIhy/Ypptr3fTQOPgoQ6SJOH9u7phTJcI+Hm4oV+bFnjppni08HbHx+O7MwgkohoxI0h1IkkSWnjrkZJjWofYkTX+Xr+1k9x19uzI9g57XqLG1j7MNAbv+JUcPPDlDgBAcZmxQV4r0Nsdc+/vqbjtscFxDfJaROR8GAhSnbXwcZcDwfZhvg57Xg+dFq/f2slhz0ekloQIfwBAksVqPERETRG7hqnOYlt4y/83Zz6IqEKYn16e/U5E1JQxEKQ6u7lrhPz/dg7MCBI5C0mS0L9tsOK2SQNi1WkMEVE12DVMdXZDQhju7NESwb56+Hsy60Fky/19WuGPg1cAAE8MicOM0fEqt4iIyJokhLnePNmSk5MDf39/ZGdnw8/PT+3mEFEzsnTPRaTkFOHe61pxPV+iRsbzd+0wI0hE1EDGJkap3QQiompxjCARERGRi3L6QPDtt99G//794eXlhYCAALWbQ0RERNRkOH0gWFJSgrvvvhtTpkxRuylERERETYrTjxF84403AACLFi1StyFERERETYzTB4J1VVxcjOLiYvn3nJwcFVtDRERE1HCcvmu4rmbNmgV/f3/5Jzo6Wu0mERERETWIZhkIzpgxA5IkVftz/Pjxej33zJkzkZ2dLf9cuHDBwa0nIiIiahqaZdfw9OnTMXHixGq3adOmTb2eW6/XQ6/X1+uxRERERM1JswwEQ0JCEBISonYziIiIiJq1ZhkI1sX58+eRkZGB8+fPw2AwYP/+/QCAtm3bwsfHR93GEREREanI6QPBV199FV999ZX8e48ePQAA69evx9ChQ1VqFREREZH6JCGEULsRTRkXrSYiImp+eP6unWY5a5iIiIiI7Of0XcP2MidMWViaiIio+TCft9nxWT0GgjXIzc0FABaWJiIiaoZyc3Ph7++vdjOaLI4RrIHRaMTly5fh6+sLSZIc+tw5OTmIjo7GhQsXXHL8gqu/f4Cfgau/f4Cfgau/f4CfQUO9fyEEcnNzERkZCY2GI+GqwoxgDTQaDaKiohr0Nfz8/Fxy5zdz9fcP8DNw9fcP8DNw9fcP8DNoiPfPTGDNGCITERERuSgGgkREREQuioGgivR6PV577TWXXdvY1d8/wM/A1d8/wM/A1d8/wM/A1d+/2jhZhIiIiMhFMSNIRERE5KIYCBIRERG5KAaCRERERC6KgSARERGRi2IgSEREROSiGAiqZO7cuYiNjYWHhwf69OmDnTt3qt0kh9m0aRNuueUWREZGQpIkLF++XHG/EAKvvvoqIiIi4OnpiREjRiApKUmxTUZGBu6//374+fkhICAAjzzyCPLy8hrxXdTfrFmz0Lt3b/j6+iI0NBS33347Tpw4odimqKgIU6dORYsWLeDj44OxY8fi6tWrim3Onz+PMWPGwMvLC6GhoXj++edRVlbWmG+lXubNm4euXbvKqwT069cPK1eulO935vduy+zZsyFJEqZNmybf5uyfweuvvw5JkhQ/8fHx8v3O/v7NLl26hAceeAAtWrSAp6cnunTpgt27d8v3O/OxMDY21uo7IEkSpk6dCsB1vgPNgqBG9/333wt3d3fx3//+Vxw5ckRMnjxZBAQEiKtXr6rdNIdYsWKFePnll8XPP/8sAIhly5Yp7p89e7bw9/cXy5cvFwcOHBC33nqraN26tSgsLJS3ufHGG0W3bt3E9u3bxebNm0Xbtm3Fvffe28jvpH5GjRolFi5cKA4fPiz2798vbrrpJtGqVSuRl5cnb/PEE0+I6Oho8ddff4ndu3eLvn37iv79+8v3l5WVic6dO4sRI0aIffv2iRUrVojg4GAxc+ZMNd5Snfz666/ijz/+ECdPnhQnTpwQL730ktDpdOLw4cNCCOd+75Xt3LlTxMbGiq5du4qnn35avt3ZP4PXXntNdOrUSVy5ckX+uXbtmny/s79/IYTIyMgQMTExYuLEiWLHjh3izJkzYvXq1eLUqVPyNs58LExNTVX8/desWSMAiPXr1wshXOM70FwwEFTBddddJ6ZOnSr/bjAYRGRkpJg1a5aKrWoYlQNBo9EowsPDxfvvvy/flpWVJfR6vfjuu++EEEIcPXpUABC7du2St1m5cqWQJElcunSp0druKKmpqQKA2LhxoxDC9H51Op348ccf5W2OHTsmAIht27YJIUzBtEajESkpKfI28+bNE35+fqK4uLhx34ADBAYGii+++MKl3ntubq5o166dWLNmjRgyZIgcCLrCZ/Daa6+Jbt262bzPFd6/EEK8+OKLYuDAgVXe72rHwqefflrExcUJo9HoMt+B5oJdw42spKQEe/bswYgRI+TbNBoNRowYgW3btqnYssaRnJyMlJQUxfv39/dHnz595Pe/bds2BAQEoFevXvI2I0aMgEajwY4dOxq9zfbKzs4GAAQFBQEA9uzZg9LSUsVnEB8fj1atWik+gy5duiAsLEzeZtSoUcjJycGRI0casfX2MRgM+P7775Gfn49+/fq51HufOnUqxowZo3ivgOv8/ZOSkhAZGYk2bdrg/vvvx/nz5wG4zvv/9ddf0atXL9x9990IDQ1Fjx498Pnnn8v3u9KxsKSkBF9//TUefvhhSJLkMt+B5oKBYCNLS0uDwWBQfLkBICwsDCkpKSq1qvGY32N17z8lJQWhoaGK+93c3BAUFNTsPiOj0Yhp06ZhwIAB6Ny5MwDT+3N3d0dAQIBi28qfga3PyHxfU3fo0CH4+PhAr9fjiSeewLJly5CQkOAS7x0Avv/+e+zduxezZs2yus8VPoM+ffpg0aJFWLVqFebNm4fk5GQMGjQIubm5LvH+AeDMmTOYN28e2rVrh9WrV2PKlCn45z//ia+++gqAax0Lly9fjqysLEycOBGAa+wDzYmb2g0gcmZTp07F4cOHsWXLFrWb0qg6dOiA/fv3Izs7Gz/99BMmTJiAjRs3qt2sRnHhwgU8/fTTWLNmDTw8PNRujipGjx4t/79r167o06cPYmJi8MMPP8DT01PFljUeo9GIXr164Z133gEA9OjRA4cPH8b8+fMxYcIElVvXuL788kuMHj0akZGRajeFbGBGsJEFBwdDq9VazY66evUqwsPDVWpV4zG/x+ref3h4OFJTUxX3l5WVISMjo1l9Rk899RR+//13rF+/HlFRUfLt4eHhKCkpQVZWlmL7yp+Brc/IfF9T5+7ujrZt2yIxMRGzZs1Ct27d8PHHH7vEe9+zZw9SU1PRs2dPuLm5wc3NDRs3bsR//vMfuLm5ISwszOk/g8oCAgLQvn17nDp1yiW+AwAQERGBhIQExW0dO3aUu8hd5Vh47tw5rF27Fo8++qh8m6t8B5oLBoKNzN3dHYmJifjrr7/k24xGI/766y/069dPxZY1jtatWyM8PFzx/nNycrBjxw75/ffr1w9ZWVnYs2ePvM26detgNBrRp0+fRm9zXQkh8NRTT2HZsmVYt24dWrdurbg/MTEROp1O8RmcOHEC58+fV3wGhw4dUpwE1qxZAz8/P6uTS3NgNBpRXFzsEu99+PDhOHToEPbv3y//9OrVC/fff7/8f2f/DCrLy8vD6dOnERER4RLfAQAYMGCAVdmokydPIiYmBoBrHAsBYOHChQgNDcWYMWPk21zlO9BsqD1bxRV9//33Qq/Xi0WLFomjR4+Kxx57TAQEBChmRzVnubm5Yt++fWLfvn0CgPjwww/Fvn37xLlz54QQppIJAQEB4pdffhEHDx4Ut912m82SCT169BA7duwQW7ZsEe3atWsWJROEEGLKlCnC399fbNiwQVE+oaCgQN7miSeeEK1atRLr1q0Tu3fvFv369RP9+vWT7zeXThg5cqTYv3+/WLVqlQgJCWkWpRNmzJghNm7cKJKTk8XBgwfFjBkzhCRJ4s8//xRCOPd7r4rlrGEhnP8zmD59utiwYYNITk4Wf//9txgxYoQIDg4WqampQgjnf/9CmEoHubm5ibffflskJSWJb775Rnh5eYmvv/5a3sbZj4UGg0G0atVKvPjii1b3ucJ3oLlgIKiSOXPmiFatWgl3d3dx3XXXie3bt6vdJIdZv369AGD1M2HCBCGEqWzCK6+8IsLCwoRerxfDhw8XJ06cUDxHenq6uPfee4WPj4/w8/MTkyZNErm5uSq8m7qz9d4BiIULF8rbFBYWiieffFIEBgYKLy8vcccdd4grV64onufs2bNi9OjRwtPTUwQHB4vp06eL0tLSRn43dffwww+LmJgY4e7uLkJCQsTw4cPlIFAI537vVakcCDr7ZzBu3DgREREh3N3dRcuWLcW4ceMU9fOc/f2b/fbbb6Jz585Cr9eL+Ph4sWDBAsX9zn4sXL16tQBg9Z6EcJ3vQHMgCSGEKqlIIiIiIlIVxwgSERERuSgGgkREREQuioEgERERkYtiIEhERETkohgIEhEREbkoBoJERERELoqBIBEREZGLYiBIRERE5KIYCBIRERG5KAaCRERERC6KgSARERGRi/r/kftXXw50Un4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100012, 360) (100012,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(0,len(X)):\n",
        "        X[i] = np.append(X[i], y[i])\n",
        "\n",
        "print(np.shape(X))"
      ],
      "metadata": {
        "id": "XJRA7ewhbuaX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "415b09fc-cc2d-4b0d-e332-2d53e1607952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100012, 361)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classes Distribution**"
      ],
      "metadata": {
        "id": "PwYn7oiUbq_K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_df = pd.DataFrame(X)\n",
        "per_class = X_train_df[X_train_df.shape[1]-1].value_counts()\n",
        "print(per_class)"
      ],
      "metadata": {
        "id": "OzQhhQFgb1SN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Rebalancing Classes**"
      ],
      "metadata": {
        "id": "Wm7y9ffYb5TK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_1=X_train_df[X_train_df[X_train_df.shape[1]-1]==1]\n",
        "df_2=X_train_df[X_train_df[X_train_df.shape[1]-1]==2]\n",
        "df_3=X_train_df[X_train_df[X_train_df.shape[1]-1]==3]\n",
        "df_4=X_train_df[X_train_df[X_train_df.shape[1]-1]==4]\n",
        "\n",
        "df_0=(X_train_df[X_train_df[X_train_df.shape[1]-1]==0]).sample(n=5000,random_state=42)\n",
        "\n",
        "df_1_upsample=resample(df_1,replace=True,n_samples=5000,random_state=122)\n",
        "df_2_upsample=resample(df_2,replace=True,n_samples=5000,random_state=123)\n",
        "df_3_upsample=resample(df_3,replace=True,n_samples=5000,random_state=124)\n",
        "df_4_upsample=resample(df_4,replace=True,n_samples=5000,random_state=125)\n",
        "\n",
        "X_train_df=pd.concat([df_0,df_1_upsample,df_2_upsample,df_3_upsample,df_4_upsample])"
      ],
      "metadata": {
        "id": "6igFmLzCb7Zg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "per_class = X_train_df[X_train_df.shape[1]-1].value_counts()\n",
        "print(per_class)"
      ],
      "metadata": {
        "id": "9cSzF4Owb9dC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train-Test Split**"
      ],
      "metadata": {
        "id": "kGYapzIWcB5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(X_train_df, test_size=0.20)\n",
        "\n",
        "print(\"X_train : \", np.shape(train))\n",
        "print(\"X_test  : \", np.shape(test))"
      ],
      "metadata": {
        "id": "Dr9ojkzKcCyX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f44cbb9-ae1e-48f4-f043-7f6623123fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train :  (20000, 361)\n",
            "X_test  :  (5000, 361)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_train=train[train.shape[1]-1]\n",
        "target_test=test[test.shape[1]-1]\n",
        "train_y=to_categorical(target_train)\n",
        "test_y=to_categorical(target_test)\n",
        "print(np.shape(train_y), np.shape(test_y))"
      ],
      "metadata": {
        "id": "nRE6Ab0_cEoh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03a3109f-2628-4b1f-f30b-0d29caaf9f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 5) (5000, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_x = train.iloc[:,:train.shape[1]-1].values\n",
        "test_x = test.iloc[:,:test.shape[1]-1].values\n",
        "train_x = train_x.reshape(len(train_x), train_x.shape[1],1)\n",
        "test_x = test_x.reshape(len(test_x), test_x.shape[1],1)\n",
        "print(np.shape(train_x), np.shape(test_x))"
      ],
      "metadata": {
        "id": "_Nl2Ab4ocH1i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67743ea7-b69d-402e-e5b5-4c0bdeb0379d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 360, 1) (5000, 360, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nsamples, nx, ny = train_x.shape\n",
        "d2_train_x = train_x.reshape((nsamples,nx*ny))"
      ],
      "metadata": {
        "id": "5iVrti41cIau"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encoding labels properly for use in classifiers**"
      ],
      "metadata": {
        "id": "yFLA4VagcK7U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def label_encoder(array):\n",
        "    rows, cols = array.shape\n",
        "    label = np.zeros(rows)\n",
        "    rows, cols = array.shape\n",
        "    for i in range(rows):\n",
        "        for j in range(cols):\n",
        "            if(array[i][j] == 1):\n",
        "                label[i] = j\n",
        "\n",
        "    return label"
      ],
      "metadata": {
        "id": "sdZTMtHCcM9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d1_train_y = label_encoder(train_y)"
      ],
      "metadata": {
        "id": "lMyDr8yPcRAE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**KNN**"
      ],
      "metadata": {
        "id": "of8lmx3vcTGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model = KNeighborsClassifier(n_neighbors=3)\n",
        "model.fit(d2_train_x,d1_train_y)"
      ],
      "metadata": {
        "id": "lIZSU3uucVXw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "208ab46d-565a-4c09-df4d-0df5ae40e66f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1_test_y = label_encoder(test_y)"
      ],
      "metadata": {
        "id": "Y7ad_xZ2caWM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nsamples, nx, ny = test_x.shape\n",
        "d2_test_x = test_x.reshape((nsamples,nx*ny))"
      ],
      "metadata": {
        "id": "DV-luQZWccdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "-jDYk-z_cc-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print(metrics.accuracy_score(d1_test_y, pred))"
      ],
      "metadata": {
        "id": "_lHfYouWcfFR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0a952ef-01a1-4185-88ae-dc25b9e43c86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9722\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "print(metrics.confusion_matrix(d1_test_y, pred))\n",
        "print(metrics.classification_report(d1_test_y, pred))"
      ],
      "metadata": {
        "id": "1j2T4afxcmgr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3da373d8-636e-43c8-b6ea-585518a64091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 983    5   14   30    5]\n",
            " [   3  977    0    0    4]\n",
            " [   6    2  933    2    0]\n",
            " [  25    1    7  960    4]\n",
            " [  20    5    5    1 1008]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      0.95      0.95      1037\n",
            "         1.0       0.99      0.99      0.99       984\n",
            "         2.0       0.97      0.99      0.98       943\n",
            "         3.0       0.97      0.96      0.96       997\n",
            "         4.0       0.99      0.97      0.98      1039\n",
            "\n",
            "    accuracy                           0.97      5000\n",
            "   macro avg       0.97      0.97      0.97      5000\n",
            "weighted avg       0.97      0.97      0.97      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVM**"
      ],
      "metadata": {
        "id": "2cmWWiJycpoV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc = SVC(kernel = 'rbf')\n",
        "svc.fit(d2_train_x, d1_train_y)"
      ],
      "metadata": {
        "id": "SOJ13XKOcqPf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "a7f5327a-034e-46bb-9d8b-8315bb531c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "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>SVC()</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\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred1 = svc.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "G5ajE_YwctZg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, pred1))"
      ],
      "metadata": {
        "id": "56ERoQPjcuQM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d952204-5b89-463c-8f4d-07414c840720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9618\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, pred1))\n",
        "print(metrics.classification_report(d1_test_y, pred1))"
      ],
      "metadata": {
        "id": "eStpK8Tfcyde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9279d90a-3fa4-44e4-8e5d-763d152137da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1003    9    1   19    5]\n",
            " [   7  971    0    0    6]\n",
            " [  15    0  927    0    1]\n",
            " [  74    1    9  907    6]\n",
            " [  26    7    3    2 1001]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.89      0.97      0.93      1037\n",
            "         1.0       0.98      0.99      0.98       984\n",
            "         2.0       0.99      0.98      0.98       943\n",
            "         3.0       0.98      0.91      0.94       997\n",
            "         4.0       0.98      0.96      0.97      1039\n",
            "\n",
            "    accuracy                           0.96      5000\n",
            "   macro avg       0.96      0.96      0.96      5000\n",
            "weighted avg       0.96      0.96      0.96      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest**"
      ],
      "metadata": {
        "id": "UQT3Lbomc1AV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators = 100)\n",
        "rf.fit(d2_train_x, d1_train_y)"
      ],
      "metadata": {
        "id": "JxGJfCwWc1hG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "19efd551-201b-42fd-de36-6fd5fae93544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "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>RandomForestClassifier()</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred2 = rf.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "LLos7L7Uc44k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, pred2))"
      ],
      "metadata": {
        "id": "pK2Igmc9c6ci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f60b9bf7-370b-4006-adc9-1ba448dc0f36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, pred2))\n",
        "print(metrics.classification_report(d1_test_y, pred2))"
      ],
      "metadata": {
        "id": "u81iiu3sc8F8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd4ec8be-da59-4357-a2d8-869d4fd40feb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1011    2    5   14    5]\n",
            " [  11  970    0    0    3]\n",
            " [   8    0  933    2    0]\n",
            " [  12    0    1  981    3]\n",
            " [  16    2    3    1 1017]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.96      0.97      0.97      1037\n",
            "         1.0       1.00      0.99      0.99       984\n",
            "         2.0       0.99      0.99      0.99       943\n",
            "         3.0       0.98      0.98      0.98       997\n",
            "         4.0       0.99      0.98      0.98      1039\n",
            "\n",
            "    accuracy                           0.98      5000\n",
            "   macro avg       0.98      0.98      0.98      5000\n",
            "weighted avg       0.98      0.98      0.98      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(d2_train_x, d1_train_y)"
      ],
      "metadata": {
        "id": "AIc0-W-Ic-Lg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "725a556d-6b31-44d1-842c-8acb8bdd2bac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred3 = gnb.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "eD6GKw88c_sW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, pred3))"
      ],
      "metadata": {
        "id": "k8CULVHBdBMJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e976d1e7-f0ba-4d38-d8a6-809bccf92372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, pred3))\n",
        "print(metrics.classification_report(d1_test_y, pred3))"
      ],
      "metadata": {
        "id": "9mUV3-I-dCvq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dabd414a-0eb9-48ca-fe12-02ff45b10d9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[606  93 219  66  53]\n",
            " [ 79 762  49  31  63]\n",
            " [ 17 104 738  45  39]\n",
            " [ 89  18 172 592 126]\n",
            " [ 34 192  26 137 650]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.73      0.58      0.65      1037\n",
            "         1.0       0.65      0.77      0.71       984\n",
            "         2.0       0.61      0.78      0.69       943\n",
            "         3.0       0.68      0.59      0.63       997\n",
            "         4.0       0.70      0.63      0.66      1039\n",
            "\n",
            "    accuracy                           0.67      5000\n",
            "   macro avg       0.68      0.67      0.67      5000\n",
            "weighted avg       0.68      0.67      0.67      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "dt = tree.DecisionTreeClassifier(random_state=0)\n",
        "dt.fit(d2_train_x, d1_train_y)\n",
        "pred4 = dt.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "S_OU5ITTdEgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, pred4))"
      ],
      "metadata": {
        "id": "Jm4zUO1jdGA7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1ee6fea-41a4-47ff-ae8d-2d5c50bb5f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, pred4))\n",
        "print(metrics.classification_report(d1_test_y, pred4))"
      ],
      "metadata": {
        "id": "v0EzOZ40dHkj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30464d38-c085-4ccd-d612-afd56a2d04e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[912  25  19  49  32]\n",
            " [ 16 956   3   2   7]\n",
            " [ 14   4 900  20   5]\n",
            " [ 19   2   8 960   8]\n",
            " [ 10  20   6  11 992]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.88      0.91      1037\n",
            "         1.0       0.95      0.97      0.96       984\n",
            "         2.0       0.96      0.95      0.96       943\n",
            "         3.0       0.92      0.96      0.94       997\n",
            "         4.0       0.95      0.95      0.95      1039\n",
            "\n",
            "    accuracy                           0.94      5000\n",
            "   macro avg       0.94      0.94      0.94      5000\n",
            "weighted avg       0.94      0.94      0.94      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 2)\n",
        "\n",
        "X_train = pca.fit_transform(d2_train_x)\n",
        "X_test = pca.transform(d2_test_x)\n",
        "\n",
        "explained_variance = pca.explained_variance_ratio_"
      ],
      "metadata": {
        "id": "CZ9BL-LCdJbn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "classifier = LogisticRegression(max_iter = 5000, random_state = 0)\n",
        "classifier.fit(X_train, d1_train_y)"
      ],
      "metadata": {
        "id": "W96eDtgJdMSF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "d555f56d-0adb-4c64-8a38-cc09f74daf7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000, random_state=0)"
            ],
            "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>LogisticRegression(max_iter=5000, random_state=0)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "rU7ViEG6dOQY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "gW2G_9LKdPk_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "755aa710-7d53-4f73-a3d9-8dcab89f2494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2042\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, y_pred))\n",
        "print(metrics.classification_report(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "38Dg_vWddRY6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc3b21b7-bc60-42d5-94b7-34d75c5bd6ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[303 219 253 154 108]\n",
            " [512  51  62 216 143]\n",
            " [141 117 165 335 185]\n",
            " [ 60 131 124 413 269]\n",
            " [514  31  24 381  89]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.20      0.29      0.24      1037\n",
            "         1.0       0.09      0.05      0.07       984\n",
            "         2.0       0.26      0.17      0.21       943\n",
            "         3.0       0.28      0.41      0.33       997\n",
            "         4.0       0.11      0.09      0.10      1039\n",
            "\n",
            "    accuracy                           0.20      5000\n",
            "   macro avg       0.19      0.20      0.19      5000\n",
            "weighted avg       0.19      0.20      0.19      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "lsvm = LinearSVC(random_state=0, tol=1e-5)\n",
        "lsvm.fit(d2_train_x, d1_train_y)"
      ],
      "metadata": {
        "id": "75RSxv55ddH-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "cf830731-055d-4138-caa4-a63c524ac2e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC(random_state=0, tol=1e-05)"
            ],
            "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>LinearSVC(random_state=0, tol=1e-05)</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\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(random_state=0, tol=1e-05)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred5 = dt.predict(d2_test_x)"
      ],
      "metadata": {
        "id": "ctt-hmsiddsH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.accuracy_score(d1_test_y, pred5))"
      ],
      "metadata": {
        "id": "QwLUnqfUdgIF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5600651-ce94-4d58-d894-714034d9e704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, pred5))\n",
        "print(metrics.classification_report(d1_test_y, pred5))"
      ],
      "metadata": {
        "id": "K7bJ_MKQdiw0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6386bcce-4f86-400e-f4b3-3785df115f9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[912  25  19  49  32]\n",
            " [ 16 956   3   2   7]\n",
            " [ 14   4 900  20   5]\n",
            " [ 19   2   8 960   8]\n",
            " [ 10  20   6  11 992]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.94      0.88      0.91      1037\n",
            "         1.0       0.95      0.97      0.96       984\n",
            "         2.0       0.96      0.95      0.96       943\n",
            "         3.0       0.92      0.96      0.94       997\n",
            "         4.0       0.95      0.95      0.95      1039\n",
            "\n",
            "    accuracy                           0.94      5000\n",
            "   macro avg       0.94      0.94      0.94      5000\n",
            "weighted avg       0.94      0.94      0.94      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
        "\n",
        "lda = LDA(n_components=2)\n",
        "X_train = lda.fit_transform(d2_train_x, d1_train_y)\n",
        "X_test = lda.transform(d2_test_x)"
      ],
      "metadata": {
        "id": "Ll0oDXycdlSp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "classifier = LogisticRegression(max_iter = 5000, random_state = 0)\n",
        "classifier.fit(X_train, d1_train_y)"
      ],
      "metadata": {
        "id": "hVwIedGGdmsx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "bd48045d-7f1b-45f8-fbc5-dc191d006907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000, random_state=0)"
            ],
            "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>LogisticRegression(max_iter=5000, random_state=0)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(metrics.accuracy_score(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "g27KUTnkdwTv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d21dc284-cd06-4411-ae9f-80fbaf4c0118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, y_pred))\n",
        "print(metrics.classification_report(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "QP_OX1oNdxtd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62811a7f-ba65-453c-d9f0-4e51a90d886c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[577  94  64 263  39]\n",
            " [129 576   3   0 276]\n",
            " [ 28   7 866  41   1]\n",
            " [203  19  65 708   2]\n",
            " [ 97 275  20   3 644]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.56      0.56      0.56      1037\n",
            "         1.0       0.59      0.59      0.59       984\n",
            "         2.0       0.85      0.92      0.88       943\n",
            "         3.0       0.70      0.71      0.70       997\n",
            "         4.0       0.67      0.62      0.64      1039\n",
            "\n",
            "    accuracy                           0.67      5000\n",
            "   macro avg       0.67      0.68      0.68      5000\n",
            "weighted avg       0.67      0.67      0.67      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import FastICA\n",
        "\n",
        "ica = FastICA(n_components=2)\n",
        "X_train = ica.fit_transform(d2_train_x, d1_train_y)\n",
        "X_test = ica.transform(d2_test_x)"
      ],
      "metadata": {
        "id": "Kfxus7Midzcn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c79d7c1-28d1-432b-9647-7ed4bc33bf71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/decomposition/_fastica.py:542: FutureWarning: Starting in v1.3, whiten='unit-variance' will be used by default.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "classifier = LogisticRegression(max_iter = 5000, random_state = 0)\n",
        "classifier.fit(X_train, d1_train_y)"
      ],
      "metadata": {
        "id": "Msh5NmjXd0-E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "0d7a54e4-9802-4e6c-d49e-0d4d796f3557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5000, random_state=0)</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "print(metrics.accuracy_score(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "1-Vd-n98d2Wn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ec075c9-85d1-4bc5-c66a-450c7223f317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2158\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(metrics.confusion_matrix(d1_test_y, y_pred))\n",
        "print(metrics.classification_report(d1_test_y, y_pred))"
      ],
      "metadata": {
        "id": "t17__P6Ld3qP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b0c10dc-2d06-4023-c3a7-76428a4cc50a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  3 299 642  83  10]\n",
            " [ 74 247 417 212  34]\n",
            " [  2 216 520 195  10]\n",
            " [  6 288 402 287  14]\n",
            " [349 126 142 400  22]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.01      0.00      0.00      1037\n",
            "         1.0       0.21      0.25      0.23       984\n",
            "         2.0       0.24      0.55      0.34       943\n",
            "         3.0       0.24      0.29      0.26       997\n",
            "         4.0       0.24      0.02      0.04      1039\n",
            "\n",
            "    accuracy                           0.22      5000\n",
            "   macro avg       0.19      0.22      0.17      5000\n",
            "weighted avg       0.19      0.22      0.17      5000\n",
            "\n"
          ]
        }
      ]
    }
  ]
}