KNN (K-Nearest Neighbours Classifier).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f6303a35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "69372d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import scikit-learn dataset library\n",
    "from sklearn import datasets\n",
    "\n",
    "#Load dataset\n",
    "wine = datasets.load_wine()\n",
    "wine.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b33f4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n"
     ]
    }
   ],
   "source": [
    "# print the names of the features\n",
    "print(wine.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34fc5970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class_0' 'class_1' 'class_2']\n"
     ]
    }
   ],
   "source": [
    "# print the label species(class_0, class_1, class_2)\n",
    "print(wine.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "844fab3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(wine.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "75e2b4b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 13)\n"
     ]
    }
   ],
   "source": [
    "# print data(feature)shape\n",
    "print(wine.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "80b505dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3831341c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Accuracy: 0.7962962962962963\n",
      "2\n",
      "Accuracy: 0.6666666666666666\n",
      "3\n",
      "Accuracy: 0.6851851851851852\n",
      "4\n",
      "Accuracy: 0.6851851851851852\n",
      "5\n",
      "Accuracy: 0.7222222222222222\n",
      "6\n",
      "Accuracy: 0.7407407407407407\n",
      "7\n",
      "Accuracy: 0.7222222222222222\n",
      "8\n",
      "Accuracy: 0.7407407407407407\n",
      "9\n",
      "Accuracy: 0.7222222222222222\n",
      "10\n",
      "Accuracy: 0.7407407407407407\n"
     ]
    }
   ],
   "source": [
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "for i in range(1,11):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    print(i)\n",
    "    print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3aa32649",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import knearest neighbors Classifier model\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Create KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "#Train the model using the training sets\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "31690501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      1.00      0.87        13\n",
      "           1       0.73      0.73      0.73        26\n",
      "           2       0.45      0.33      0.38        15\n",
      "\n",
      "    accuracy                           0.69        54\n",
      "   macro avg       0.65      0.69      0.66        54\n",
      "weighted avg       0.66      0.69      0.67        54\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6943db13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preliminary model score:\n",
      "0.7407407407407407\n"
     ]
    },
    {
     "data": {
      "image/png": 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MfwQufq3cD2+Ml0pypx8sqkqXLl2YN2/eMcu++uor5syZw+eff86jjz7K8uXF3zdWrVoVcC7YefX8gQ6SNmHCBLZu3coHH3wAwObNm1m9ejWqGvAjoxEREeT6PPxS8D2E6Ojo/J/Hjh3LXXfdxQUXXMCsWbPyq6IKO97111/PP//5T2JjY7nmmmsCiqekrC8mX3VawKm3wa+TIN26DTemvFWtWpXMzMz8BJGVlcXy5cvJzc1l06ZNJCUl8dRTT7Fr1y727dtHzZo12bt3b4mO0bdvXyZNmgTAihUr8qtufKWmprJ//35+++030tLSSEtL47777mPixIkMGjSISZMmsX37doD8KqZBgwbx8ssvA07JZ8+ePTRq1IiMjAy2b9/O4cOH+fLLLwuNa/fu3TRt6jyw+c477+TPP+uss3jhhRfyp/NKJb169WLTpk18+OGHjB5d5P14qVmCKKjvOKjRCKbcBzYcqzHlKiwsjE8++YR77rmH+Ph4unfvzg8//EBOTg6XX3453bp14+STT2bcuHHUqVOH888/n8mTJ+c3UgfilltuITMzk7i4OJ588kni4uKoXbv2UetMmDCBCy+88Kh5F198MRMmTKBLly488MAD9O/fn/j4+PzG4eeee47k5GS6detGz549Wb58OZGRkTz44IP06tWLIUOGEBsbW2hcDz/8MMOHD6dfv3751VcAf/3rX9m5cyddu3YlPj6e5OTk/GUjRoygT58++dVOZa1SjUmdkJCgZTJg0E/vwee3wcVvQLdLjn9/xoSolStX0qlTJ6/DKFc5OTlkZWURFRXF2rVrGTRoEKtWrQrosdpQM2TIEMaNG8egQYMCWt/f71tEFqtqgr/1rQThT/dL4aQ4+PYhyDrodTTGmDJ04MAB+vbtS3x8PBdeeCEvv/xyhUsOu3btokOHDlSrVi3g5FAa1purP2HhMPhxePs8mPcCnP5nryMyxpSRmjVrVvihievUqcOqVauCfhwrQRSmVV/odD5892/Ys8XraIwxptxZgijKmX+H3CyY+Q+vIzHGmHJnCaIo9dpArzGw5APYvMTraIwxplxZgijO6XdD9fow9X577NUYc0KxBFGcqNow8AHYMBdWfu51NMZUKsfT3feiRYu4/fbbiz3GaaedVlbhAnDHHXfQtGnTo96QrqzsPYhA5GTDK/2cnl5vWwgRVcv+GMZ4IJTeg3j44YeP6ngPnM72IiJC52HL3NxcWrVqRZMmTXjiiScYMGBAUI6Tk5NDeHh4me/X3oMIhvAIOPsx2LUBfnzZ62iMqdSuvvpq7rrrLpKSkrjnnntYsGABp512GieffDKnnXYaqampgDMWxJAhQwAnuVx77bUMGDCANm3a8Pzzz+fvr0aNGvnrDxgwgEsuuYTY2Fguu+yy/H6Zvv76a2JjY+nbty+33357/n4LSk5OpmvXrtx8881HjRHhr6tv8N8t+NVXX80nn3ziN76kpCQuvfTS/I73hg0bRs+ePenSpQuvvvpq/jZTpkyhR48exMfHM2jQIHJzc2nfvj15g6bl5ubSrl07tm3bVtpfA2DvQQSu7UDoMBjmPOO8SFejodcRGVO2vrkXfj+2X6LjclI3OOeJEm+2atUqpk+fTnh4OHv27GHOnDlEREQwffp07r//fj799NNjtklJSSE5OZm9e/fSsWNHbr75ZiIjI49a5+eff2b58uU0adKEPn36MHfuXBISErjpppuYM2cOrVu3LrJfowkTJjB69GiGDh3K/fffT1ZWFpGRkX67+i6sW/CiLFiwgGXLluX35Prmm29Sr149Dh48yCmnnMLFF19Mbm4uN9xwQ368O3bsICwsjMsvv5wPPviAO++8k+nTpxMfH39Ulx2lYSWIkjjrH5B9EJIf8zoSYyq14cOH51ex7N69m+HDh9O1a1fGjRtXaC+u5513HlWrVqVBgwY0bNgwf8AdX4mJiTRr1oywsDC6d+9OWloaKSkptGnTJv+iXFiCOHLkCF9//TXDhg2jVq1a9OrVi2nTpgFOV98333wz8EdX34V1C16UxMTE/DjAGSQpPj6e3r17s2nTJlavXs2PP/7I6aefnr9e3n6vvfba/O7G33zzzTLp4dVKECXRoD2ccgMseAUSb4RG3nePbEyZKcWdfrD4doP9t7/9jaSkJCZPnkxaWlqh9f55XXvD0d17F7dOoO2wU6ZMYffu3fnVPwcOHKB69eqcd955ftcvrJtu3y7AVfWoxnjf7z1r1iymT5/OvHnzqF69OgMGDODQoUOF7rd58+Y0atSImTNnMn/+/Pxuyo+HlSBKqv9fnCebrLdXY8qFbzfYb7/9dpnvPzY2lnXr1uUPKPTRR/7Hpp8wYQKvv/56fvff69evZ9q0aRw4cMBvV9+FdQveqlUrFi9eDMBnn31GVlaW3+Pt3r2bunXrUr16dVJSUvjxxx8BOPXUU5k9ezbr168/ar/gjBFx+eWXM2LEiDJp5LYEUVLV68GA+2D9bFg1xetojKn0/vKXv3DffffRp08fcnJyynz/1apV46WXXmLw4MH07duXRo0aHdP994EDB5g6depRpYXo6Gj69u3LF1984ber78K6Bb/hhhuYPXs2iYmJzJ8//6hSg6/BgweTnZ1NXFwcf/vb3+jduzcAMTExvPrqq1x00UXEx8cfNSrdBRdckD+GdVmwx1xLIycLXj4NNBdungcRFasnSGPyhNJjrl7at28fNWrUQFW59dZbad++PePGjfM6rBJbtGgR48aNK3RsDHvMtTyER8JZj8H2NbDwda+jMcYcp9dee43u3bvTpUsXdu/ezU033eR1SCX2xBNPcPHFF/P444+X2T6tBFFaqvD+xfDbIrh9iVP1ZEwFYyWIE4uVIMqLiPPy3OF9MKvsMrYx5a0y3SSawpXm9xzUBCEig0UkVUTWiMi9fpbXFZHJIrJURBaISFefZWki8quILBGR0Bzdo2EnSLgGFr4BmaleR2NMiUVFRbF9+3ZLEpWcqrJ9+3aioqJKtF3Q3oMQkXDgReBMIB1YKCKfq+oKn9XuB5ao6oUiEuuu7zt+XpKqHt+74sE24H5Y+jFMfQAu/6T49Y0JIc2aNSM9PT2/iwZTeUVFRdGsWbMSbRPMF+USgTWqug5ARCYCQwHfBNEZeBxAVVNEpJWINFLVY1+BDFXR9aH/n2HaX2H1dGh/htcRGROwyMjIo97cNcZXMKuYmgKbfKbT3Xm+fgEuAhCRRKAlkJfiFJgmIotF5MbCDiIiN4rIIhFZ5NldUOJNzuBC0x5wen41xphKIJgJ4th3wZ2Lvq8ngLoisgQYC/wM5F1h+6hqD+Ac4FYROd3fQVT1VVVNUNWEmJiYsom8pCKqwJmPQmYKLH7LmxiMMaaMBTNBpAPNfaabAZt9V1DVPap6jap2B64EYoD17rLN7r8ZwGScKqvQFXsetOoHyf+Egzu9jsYYY45bMBPEQqC9iLQWkSrAKOCoIdlEpI67DOB6YI6q7hGRaBGp6a4TDZwFLAtirMdPBM52k8OcZ7yOxhhjjlvQEoSqZgO3AVOBlcAkVV0uImNEZIy7WidguYik4FQl3eHObwR8LyK/AAuAr1Q19Ds+ahwHPa6A+a/A9rVeR2OMMcfF3qQua3u3wn97QOv+MPpDb2Mxxphi2JvU5almI+j3J0j9CtbN9joaY4wpNUsQwdD7FqjTAqbeD7ll3z2xMcaUB0sQwRAZBWf+HbYug5/f9zoaY4wpFUsQwdJ5GLQ4FWY+Cof2eB2NMcaUmCWIYMl77HV/Jnz/L6+jMcaYErMEEUxNe0D8aJj3IuxM8zoaY4wpEUsQwTboQQiLgG8f8joSY4wpEUsQwVarCfS5E1b8H2z4wetojDEmYJYgysNpY6FWU5hyH+Tmeh2NMcYExBJEeahSHc54GLYsgaUTvY7GGGMCYgmivHS9BJomwIy/O+NYG2NMiLMEUV7CwmDw47B3C8x9zutojDGmWJYgylPzRKck8cPzsGtT8esbY4yHLEGUtzMedv6d8YinYRhjTHEsQZS3Os3h1Nvg149h00KvozHGmEJZgvBC33FQoxFMvQ8q0XgcxpjKxRKEF6rWcN6wTl8Iyz71OhpjjPHLEoRX4i+Fk+KcLjiOHPA6GmOMOYYlCK+EhcHgJ2BPutOZnzHGhBhLEF5q1Qc6XeB0B75ni9fRGGPMUYKaIERksIikisgaEbnXz/K6IjJZRJaKyAIR6RrotpXGmX+H3GxnYCFjjAkhQUsQIhIOvAicA3QGRotI5wKr3Q8sUdU44ErguRJsWznUaw29b4YlH8Lmn72Oxhhj8gWzBJEIrFHVdap6BJgIDC2wTmdgBoCqpgCtRKRRgNtWHv3uhur1Ycr99tirMSZkBDNBNAV8+5NId+f5+gW4CEBEEoGWQLMAt608omrBwL/Cxh9g5edeR2OMMUBwE4T4mVfw9vgJoK6ILAHGAj8D2QFu6xxE5EYRWSQiizIzM48jXI/1uBIadoFpf4OsQ15HY4wxQU0Q6UBzn+lmwGbfFVR1j6peo6rdcdogYoD1gWzrs49XVTVBVRNiYmLKMPxyFhYOg/8JuzbA/PFeR2OMMUFNEAuB9iLSWkSqAKOAo+pPRKSOuwzgemCOqu4JZNtKqc0A6HAOzHkG9mV4HY0x5gQXtAShqtnAbcBUYCUwSVWXi8gYERnjrtYJWC4iKThPLN1R1LbBijWknPUPyD4IyY95HYkx5gQnWomemklISNBFixZ5Hcbxm3KfU81003dwUtfi1zfGmFISkcWqmuBvmb1JHYr6/wWiasNUe+zVGOMdSxChqFpdGHA/rJ/t9NNknfkZYzxgCSJUJVwDzRJh2gPwTAf4fCxsnG8lCmNMuYnwOgBTiPBIuHaq8/Lczx/Ar5/AT+9C/XbQ/VKIHw21mngdpTGmErNG6ori8F5Y8ZmTLDb+ABIGbZLg5Mug43kQGeV1hMaYCqioRmpLEBXR9rXwywRYMsEZTyKqNnS9BLpfBk17gPh7Ed0YY45lCaKyys11GrKXfOj04ZR9CGJinSqouFFQs5HXERpjQpwliBPBod2wfLJTBZW+ACQc2p/pJIsO50BEleL3YYw54RSVIKyRurKIqg09r3Y+21bDkg/gl4mwagpUqwfdhjvtFY3jvY7UGFNBWAmiMsvNgbXJTrJI+QpyDkOjbm4V1AiIbuB1hMYYj1kVk4GDO2HZp04V1OafICwCOgx2Grbbn+k8VmuMOeFYFZNx3s4+5Xrnk7HSrYL6CFK+hOgYiBvplCwadfE6UmNMiLASxIksJxvWTHeSReo3kJsFjbs7pYpul0D1el5HaIwJMqtiMsXbvx1+/dhJFr8vhfAq0PFcJ1m0HQjhVtg0pjI6rgQhIkOAr1U1NxjBlSVLEGXk91+ddyuWfgQHtkONkyB+pJMsYjp6HZ0xpgwdb4J4HzgV+BR4S1VXln2IZcMSRBnLPgKrpzmlilVTQXOgaYLzuGy3EVC1htcRGmOO03FXMYlILWA0cA2gwFvABFXdW5aBHi9LEEG0LwOWTnKSRcYKaNgFrvoCout7HZkx5jgc94BB7jjRnwITgcbAhcBPIjK2zKI0oa1GQzjtNrj5B7j0Y9ixFt69wGm7MMZUSsUmCBE5X0QmAzOBSCBRVc8B4oG7gxyfCTUi0OEsGD0Rtq+Bd4fCgR1eR2WMCYJAShDDgX+rapyqPq2qGQCqegC4NqjRmdDVNglGT4Btq5yShCUJYyqdQBLEQ8CCvAkRqSYirQBUdUaQ4jIVQduBTpLIXGUlCWMqoUASxMeA7yOuOe68YonIYBFJFZE1InKvn+W1ReQLEflFRJaLyDU+y9JE5FcRWSIi1vIcqtoNgtEfQmaqJQljKplAEkSEqh7Jm3B/LrbvaBEJB14EzgE6A6NFpHOB1W4FVqhqPDAAeFZEfPedpKrdC2thNyGi3Rkw6kPITIH3hjn9PhljKrxAEkSmiFyQNyEiQ4FtAWyXCKxR1XVuUpkIDC2wjgI1RUSAGsAOIDugyE1oae8miYyV8O4wOLjL64iMMccpkAQxBrhfRDaKyCbgHuCmALZrCmzymU535/l6AegEbAZ+Be7weWNbgWkislhEbizsICJyo4gsEpFFmZmZAYRlgqb9mTDSfU/ivWGWJIyp4IpNEKq6VlV741QTdVbV01R1TQD79jcwcsG38s4GlgBNgO7AC+5LeQB9VLUHThXVrSJyeiHxvaqqCaqaEBMTE0BYJqg6nAUj3oPfl8F7F1qSMKYCC+hFORE5D7gFGCciD4rIgwFslg4095luhlNS8HUN8D91rAHWA7EAqrrZ/TcDmIxTZWUqgo6DYeR7Tp9O71/kDIdqjKlwAnlRbjwwEhiLUyoYDrQMYN8LgfYi0tpteB4FfF5gnY3AIPc4jYCOwDoRiRaRmu78aOAsYFlA38iEho7nwIh3YctSeM+ShDEVUSAliNNU9Upgp6o+gtNxX/NitkFVs4HbgKnASmCSqi4XkTEiMsZd7VHgNBH5FZgB3KOq24BGwPci8gvOOxhfqeqUkn4547HYc2HEO7BlCbx/MRza43VExpgSCKQ31wWqmigiPwIXAduBZaravjwCLAnrrC9ErfwSPr4KmvSAyz+FqFrFb2OMKRfH21nfFyJSB3ga+AlIAyaUWXSm8us0BIa/7YyF/cElcDikOgE2xhSiyAQhImHADFXdpaqf4rQ9xKpqII3Uxvyh0/lwyVuQvgjetyRhTEVQZIJw30l41mf6sKpaa6Mpnc4XwPC3IH0hfDDckoQxIS6QKqZpInKx+7azMcen81C45A3YtAA+GAGH93kdkTGmEIGMRH8XEA1ki8ghnEddVVWtpdGUTpcLQRU+vR4+HAGXTrLhS40JQYG8SV1TVcNUtYqq1nKnLTmY49P1Irj4Ndg4z0kSR/Z7HZExpoBiSxBFdHExp+zDMSeUrhc7JYn/3QAfjoRLP4Iq0V5HZYxxBVLF9Gefn6NwurxYDAwMSkTmxNLtEidJTL7RTRKToEp1r6MyxhBAglDV832nRaQ58FTQIjInnrjhgMLkm2DCSBj9kSUJY0JAQJ31FZAOdC3rQMwJLm4EDBsPad/DhFFw5IDXERlzwgukDeK//NFNdxhOt9y/BDEmc6KKH4lTkhgDE0fD6IkQWc3rqIw5YQXSBuHbuVE2MEFV5wYpHnOiix/ltEn8380wYTSMnmBJwhiPBJIgPgEOqWoOOGNNi0h1VbU6ABMc3UcDCv93C0y81BnK1JKEMeUukDaIGYDv/85qwPTghGOMq/ulMPQFWJsMEy+DrENeR2TMCSeQEkSUqub3h6Cq+0TEHjExwXfy5U510+e3wUeXOeNdR0Z5HVXFpQoHd8LONNi10fmgUKeF+2kJ1euD9apjXIEkiP0i0kNVfwIQkZ7AweCGZYyrxxWAwudj4aPLYeT7liQKk5cA8i7+uzb4/Ox+jhTT91VktE/C8PnUbekkkGp1LYGcQAJJEHcCH4tI3njSjXGGIDWmfPS40rn4fXE7TLrCSRIRVb2OyhtHJQD3s9MnERwp0ENu1VrOhb1ua2jd/+gLfu3mzsV+1yY/yWQDbPrx2KFiq9Q4usRRMJFYAqlUAnlRbqGIxOKMFy1AiqpmBT0yY3z1vAo0F768Ez66Aka+VzmTxKHdx170fT+H/V2w3Qt1637HXrCj6hR/wT6pNpxUyKtNB3f5j2PXBkibe2xCqlLTLW34KYXUaQnV6pTyxBgvBPIexK3AB6q6zJ2uKyKjVfWloEdnjK+EawCFL8fBpCthxLsVL0kc2nPshdb334J37JHRf1xwW5567J17sO/Yq9VxPo3jjl2mCod2+S/J7NwA6+ccW6VVtXYh1Vd5Ca128L6LKbFAxqReoqrdC8z7WVVPDmZgpWFjUp8gFr4BX90FHc5xk0QVryP6w+G9/u+28y6eh3YdvX5kdf9VNXVaQN1WFbvKJr9NpJDS0M4NkFWgF98oN4F0GAwD7oew0nT2YEqiqDGpA2mDCBMRUTeTiEg4EEL/I80J55TrnOqmr++Gj6+C4e+UX5I4vA92b/K5Yy5w8Tu44+j1I6q5F/zm0OyUAtUvlfypIRGoXs/5NPFzP6kKB3YUOIcbIDMV5jwN+zPhvH9bkvBQIAliKjBJRMbjdLkxBvgmkJ2LyGDgOSAceF1VnyiwvDbwPtDCjeUZVX0rkG3NCS7xBuffr++Gj6+G4W+XTZI4st9ttM2r9imQAA5sP3r9iKg/LvhNe/hc/Fs5/0Y3qLwJ4HiJQHR959O0xx/zVWHGI/D9v0HC4NxnLUl4JJAEcQ9wI3AzTiP1zzhPMhXJLWm8CJyJ08HfQhH5XFVX+Kx2K7BCVc8XkRggVUQ+AHIC2Nac6BJvcC4m3/wZPrnGSRLhkUVvc+TAHyWAXQXqzHdthAPbjl4/vKpz91+nJTTufuwTPDUaWgIoayIw6CHndzv3P4DAec/aefZAIE8x5YrIj0AbnMdb6wGfBrDvRGCNqq4DEJGJwFDA9yKvQE13vOsawA6c/p56BbCtMdDrRqe6aco9TkniwvGw93f/1T+7NsL+jKO3D6/iPO5ZpwXEnvfH8/55iSC6od29ekEEznjY+d3+8Lwzfe4zliTKWaEJQkQ6AKOA0cB24CMAVU0KcN9NgU0+0+k4F35fLwCfA5uBmsBINyEFsm1enDfilHBo0aJFgKGZSqX3GEBhyr3weLOjl4VFuiWAFtBxcIGngFpCjUaWAEKVCJz5d0Dhh/8CAuc+bUmiHBVVgkgBvgPOV9U1ACIyrgT79vdbLPjI1NnAEpzR6doC34rIdwFu68xUfRV4FZynmEoQHwB7D2Vx/+RlnNGpIUO7Ny3p5iZU9L4ZajeDjJVHlwBqngRh4V5HZ0pLBM581KlumveC0yZxzpOWJMpJUQniYpwSRLKITAEm4v/CXZh0oLnPdDOckoKva4An3Cek1ojIeiA2wG3LRHSVCFZv3cvyzbsZEteE8DD7w6uwOp3vfEzlIgJn/cNJEj++6EwPfsKSRDkotGytqpNVdSTOBXsWMA5oJCIvi8hZAex7IdBeRFqLSBWcZPN5gXU2AoMARKQRztva6wLctkyEhQl3DGrPusz9fLk0KDnIGHO8RODsx6D3LTB/PEy5z0kYJqiKrXxV1f2q+oGqDsG5k18C3BvAdtnAbTiPya4EJqnqchEZIyJj3NUeBU4TkV9xuhW/R1W3FbZtyb9eYM7uchIdG9XkvzPXkJNrf3TGhCQROPuf0OtmmP8yTH3AkkSQFfsmdUVyPG9Sf7l0M7d9+DP/HX0y58c3KePIjDFlRhW+uQcWvAKn3uZUP1l1U6kV9Sa1Pb7hOrdrY9o3rMF/Z64m10oRxoQuEaehOvFGp+F62l+tJBEkliBcYWHC2EHtWbV1H1OW/+51OMaYoojAOU/BKTc4SeLbBy1JBIElCB/ndWtM25honp9hpQhjQp6470UkXOe8TDf9IUsSZcwShI/wMGHswPak/L6XaSusFGFMyMt7wzrhWpj7nNOHkyWJMmMJooDz45vQpkE0z81YY6UIYyqCMLdDv57XOB38zfi7JYkyYgmigPAw4dakdqzcsofpK7d6HY4xJhBhYXDev6DHVfD9v2DmPyxJlAFLEH4M7d6EVvWr89yM1VSmx4CNqdTCwmDIf5wxzL97BpIfsyRxnCxB+BERHsatSe1YvnkPM1ZmFL+BMSY0hIXBkOfg5CucQYdmPe51RBWaJYhCDDu5Kc3rVeP5mVaKMKZCCQuD85+Hky+H2U9CsiWJ0rIEUYjI8DBuS2rH0vTdzErN9DocY0xJhIXB+f+F7pfB7Cdglg1IWRqWIIpwUY9mNKtbjf9YW4QxFU9YGFzwX4i/1Klqmv2U1xFVOJYgihDptkX8smkXc1ZvK34DY0xoCQuHoS9A/Gin0Xr2015HVKFYgijGxT2a0bRONZ6bvspKEcZURGHhMPRFiBsFyf+AOc94HVHZC9K1yRJEMapEhHHzgLb8tHEX36+xUoQxFVJYOAx7CeJGwsxH4btnvY7o+OXmQtr38H+3wNtDgnKIokaUM67hCc14MXkNz01fTd92DRDrWtiYiicsHIa9DJrrvG2NQL+7vI6q5HZthCUT4JcPYWcaVKkJXS+E7MMQUbVMD2UJIgBVI8K5eUBbHvxsOfPWbue0dg28DskYUxph4TBsvFMlM+MRZ4zrvnd6HVXxjhyAlV/Akvdh/RxnXuv+MOB+Z5jdKtWDclhLEAEakdCcF5PX8J8Zqy1BGFORhUfAha8A6vQAKwJ97vA6qmOpwqb5sOQDWDYZjuyFuq0g6QGIHwV1WgQ9BEsQAYqKDOfm/m15+IsVzFu7nVPb1vc6JGNMaYVHwIWvOhfhbx90ShKnjfU6Ksfu3+CXCbDkQ9ixFiKjocsw552OFqc6j++WE0sQJTAqsQUvzlrL8zNWW4IwpqILj4CLXnPaJKb9FRA47TZvYsk6CClfOaWFtcmAQsu+0O9P0HkoVK3hSViWIEogKjKcMf3b8uiXK1iwfgeJret5HZIx5niER8DFrwMK0x5wqptOvbV8jq0Kvy12ksKvn8Lh3VC7OfT/i1OFVK9N+cRRhKAmCBEZDDwHhAOvq+oTBZb/GbjMJ5ZOQIyq7hCRNGAvkANkFzaodnm7rFcLXp61ludmrOKD63t7HY4x5niFR8LFbzgX7Kn3AwKn3hK84+39HX6Z6FQhbUuFiGrQ+QKnCqlVv3KtQipO0BKEiIQDLwJnAunAQhH5XFVX5K2jqk8DT7vrnw+MU9UdPrtJUtWQevnAKUW04R9frWRR2g4SWlkpwpgKLzwSLnkTPrkGpt7ntEn0HlN2+88+DKnfOKWFNdOdaq3mvZxOBbsMg6jaZXesMhTMEkQisEZV1wGIyERgKLCikPVHAxOCGE+ZuTS/FLGa967r5XU4xpiyEB4Jl7wFH18NU+5xqpt63VT6/anCliVOSeHXj+HgTqjZBPrc6ZQWGrQro8CDJ5gJoimwyWc6HfB7NRWR6sBgwLeFSIFpIqLAK6r6aiHb3gjcCNCiRfAf+wKoXiWCG09vw+PfpPDTxp30aFG3XI5rjAmyvCTxyTXwzV+ckkTiDSXbx75M+HUS/PwBZCyH8KrQaQh0vxTaJDnvYlQQwUwQ/l43LqzDkPOBuQWql/qo6mYRaQh8KyIpqjrnmB06ieNVgISEhHLrLOny3i15Zc46npu+mneuTSyvwxpjgi2iyh8lia/vduYVlyRysmDVVKe0sHoq5GZD057OMKhdL4JqFfMmMpgJIh1o7jPdDNhcyLqjKFC9pKqb3X8zRGQyTpXVMQnCK9FVI7i+X2uempLKkk276N68jtchGWPKSkQVGP42TLrSSRIicMr1x673+zKnXWHpJDiwDWo0gt63OFVIDWPLPeyyFszm8oVAexFpLSJVcJLA5wVXEpHaQH/gM5950SJSM+9n4CxgWRBjLZUrT21FneqRPD9jtdehGGPKWkQVGPEOdBgMX/0JFr7hzN+/Hea/AuP7wfg+sOA1aNUHLp0E41bAWY9WiuQAQSxBqGq2iNwGTMV5zPVNVV0uImPc5ePdVS8Epqnqfp/NGwGT3U7xIoAPVXVKsGItrRpVI7ihXxuenprK0vRdxDWr43VIxpiyFFEVRrwLH10BX90FKz6DDT9AbhY0jodznoZul0D1yvk0o1SmMQ4SEhJ00aJF5XrMvYey6PtkMqe0qsfrV4XEqxrGFGnH/iPMWZXJ7FWZ5KrSv0MMp3eIoUGNsu0JtLLYcyiLH1I202zGLbQ8sIzfmp9Pg77X0KBdT69DKxMisriw98zsTerjVDMqkuv6tuZf365i2W+76do0NJ9nNieu3Fxl+eY9JKdmkJyawZJNu1CF+tFVEBE+W7IZEYhrVocBHWJIim1IXNPahIWdmN3aqyqrtu5zzldKBos37CQ7V6kVNZboKhFsSTkMKb/TqfF3DOgYQ1LHhvRoUYeI8NB5wa2sWAmiDOw5lEXfJ2bSu019Xr3SShHGe7sPZvHd6kxmpTqfbfsOIwLxzerkX9S6uTczK7bsITnFSR4/+ySP/h1iGBDbkNPbN6BO9Soef6Pg2n84m7lrtpGcmsms1Ay27D4EQOfGtUiKjWFAx4ac3LwO4WHC6ox9+edrUVpe8oigXwfnvPbvEENMzYpTGiuqBGEJooz8Z/oq/jN9NV/f3o/OTWp5EoM5cakqKb/vJTk1g1kpmSzeuJOcXKV2tUj6d4ghKTaG09vHUL+YaqQd+4/4JJYMdh7IIkygR4u6JMU2ZEDHGDo3rlXhB81SVdZt209ySgazUjNZsH4HR3JyqVE1gr7tGpAUG0P/Dg05qXZUkfvZcyiLuau3Oec9NZOMvYcBiGtWmwFugo1v5iSWUGUJohzsPpBF3ydn0rd9A16+vHLUTZrQtu9wNt+v3sbsVRkkp2Ty+x7nrrdLk1okdWxIUmwM8c1KX/WRk6ssTd+Vf1e9NH03AA1rVs0vhfRp34BaUZFl9p2C6eCRHH5ct51ZqRkkp2aycccBANo3rJGf/BJa1qNKROnOl6qyYsseZqVmkpySwU8bd5KrULd6XpJuyOntY6gbHVqlMUsQ5eRf01J5fuYaptzZj9iTrBRhypaqsjZzH8kpmSSnZrAwbQdZOUrNqhH0bd/Aqd7oGEOjWkXf9ZZW5t7DzF7lHHvOqkz2HsomIkxIaFXXTUgNad+wRkiVLjZuP5Df9jJv7XYOZ+dSLTKcPu3qM6CjkxSa1Q3OaGy7DhxhzuptzErJYNaqTHbsP0KYQPfmdfLPV+fGtTxv67EEUU52HThC3yeT6d8hhhcv6+FZHKbyOHgkh3nrtuUnhfSdBwHo2KgmA2Kdu/ieLesSWc4NpNk5ufy8aZdbF5/Jyi17AGhapxr93dLFaW3rE121fJ+DOZydw8L1O/OTwrpM5+n51g2i80s9ia3rERVZvt1d5OYqS3/b7VZpZbD0t92oQoMaf5TG+rZvQO1q5V8aswRRjp6emsJLs9Yy9c7T6dCopqexmIopbdt+9wKXyY/rtnMk/663QX6DadM61bwO8yhbdh9kdqqTxL5fvY39R3KoEh5Grzb1GNCxIUkdY2jdIDoopYvNuw461TqpGcxds40DR3KoEhHGqW3qM6Cjc75aN4gu8+Mej237DjNnVSbJqZnMWZXJ7oNZhIcJPVvWza8e7NioZrmUxixBlKOd+4/Q98mZDOzUiP+OPtnTWEzFcCgrhwXrd+Q3dK7f5tz1tmkQ7VxcY2NIbF2PqhEVo5O3I9m5LErbkZ/k1mTsA6Bl/eokudU6vdvUL/VdfFZOLos37MxvkE/duhdwSi8DY53zdWqbBlSrUjHOV3ZOLks27XIfq81khVsaa1w7Kj/B9WnXgBpBKo1ZgihnT05JYfzstXw77nTaNbRShDlW+s4D+U8KzV2znYNZOVSNCOPUtvXzL6It64fWXW9pbdpxgFmrMpmVksHctds4lJVLVKRzh58U25Ckjg1pXq/odoCMPYecfaRm8N2qbew9nE1kuHBKq3r5d9xtY0Kr/aO0ft99iNmrnJuF71ZvY5/7XRNb13P/NhrSNqbsSmOWIMrZ9n2H6ftkMmd3acR/Rlkpwrh31Rt25D/hstq9q25W173r7diQ3m3qV5i73tI6lJXD/PU78t8j2LDdeZKobUx0fsNtQqu6RISFsWTTTpJTMpm1KoNlvzl31SfV8r2rrk/NCvIEVWkdyXZKS7PcNpVVW52/m+b1qjnnqwz+bixBeODxr1fy2nfrmH5Xf9rEeDPguPFexp5DPDttFV/9uiXod4IV0Xr3XYTk1Azmr9/BkexcoquEExkRxq4Dbr18i7r5DfKxJ5VPvXyoKqzkeVrb+rx2ZUKpHmm2BOGBbfsO0+/JZM7pehL/Gtnd63BMOTuUlcObc9fz4sw1ZOUow05uwqBOjYJal1zRHTiSzby120lOzeBwVi79O8bQr10MtatX7lJCaR3KymFh2g6SUzLJ3He41G2eliA88thXK3jj+/XM/NMAWoXYUxQmOFSVqcu38s+vV7JxxwHO7NyIB87tZL9/E7KKShCVr3epEHLD6W2IDA/jheQ1XodiykHK73u47PX5jHl/MVGRYbx/XS9euzLBkoOpsKysG0QNa0ZxWa+WvDMvjbED21Wap1LM0XbsP8K/v13FB/M3UDMqkr8P7cKliS0qZe+e5sRif8FBNqZ/G8LDhBetFFHpZOXk8tbc9Qx4OpkPF2zkit4tmXX3AK48tZUlB1MpWAkiyBrWiuLSxBa8/+MGxg5sX+zz3qZimL0qk0e/XMGajH30bdeAvw3pTMeT7J0XU7nYbU45GNO/LWEivDTLShEV3fpt+7nu7YVc9eYCsnJyee3KBN67LtGSg6mUrARRDk6qHcWoxOZ8OH8jtya1C1rvkSZ49hzK4oWZa3hr7nqqhIdx7zmxXNOnVYXp/sKY0rASRDkZ078tIvDyrLVeh2JKICdX+WjhRgY+M4vXvlvHhSc3JfnPAxjTv60lB1PpWQminDSpU40RCc2ZtGgTtya1o0mI9cZpjrUwbQePfLGcZb/toWfLurx59SnENavjdVjGlJugliBEZLCIpIrIGhG518/yP4vIEvezTERyRKReINtWRLcktQOsFBHqftt1kNs+/Inh4+exfd8RnhvVnU/GnGrJwZxwglaCEJFw4EXgTCAdWCgin6vqirx1VPVp4Gl3/fOBcaq6I5BtK6KmdapxSc9mfLRwE7cktaVxbStFhJKDR3IYP3st42c7CfyOQe25qX8bqlexgrY5MQWzBJEIrFHVdap6BJgIDC1i/dHAhFJuW2HcMqAduaq8Mnud16EYl6ry2ZLfGPjsLJ6bsZozOzdixp/6M+7MDpYczAktmAmiKbDJZzrdnXcMEakODAY+LcW2N4rIIhFZlJmZedxBB1vzetW5uEczPlywka3uIPPGO7+m72b4+HncMXEJ9aKrMOmmU3nh0h72pJkxBDdB+OuTt7CeAc8H5qrqjpJuq6qvqmqCqibExMSUIszyd2tSO3JyNb8qw5S/jL2H+Msnv3DBi9+Ttn0/T17cjc9v60ti63peh2ZMyAhm+TkdaO4z3QzYXMi6o/ijeqmk21Y4LepX58KTm/Lh/I3cPKAtDWtGeR3SCeNwdg5vzU3jhZlrOJydww392nDbwHbUquQDzxhTGsEsQSwE2otIaxGpgpMEPi+4kojUBvoDn5V024rstqR2ZOcqr1pbRLlQVb5dsZWz/j2HJ75JoXebekwb15/7z+1kycGYQgStBKGq2SJyGzAVCAfeVNXlIjLGXT7eXfVCYJqq7i9u22DF6oVWDaIZ2r0J78/fwE392xJTs6rXIVVaq7bu5dEvV/Dd6m20a1iDd65NpH+HilEdaYyXbMAgD63L3McZ/5rNDf3acN+5nbwOp9LZdcDphvv9+RuJrhLOuDM7cHnvlkRaT6vG5CtqwCB7hs9DbWJqcEF8E96dt4EbT29D/RpWiigL2Tm5fLhgI//6dhV7DmZxaa8W3HVmR+pFV/E6NGMqFLuV8thtA9tzKDuH175b73UolcL3q7dx7vPf8eBny+l0Ui2+vqMf/xjWzZKDMaVgJQiPtWtYgyFxTXh3Xho3nt4mZC9kqsrazH0kp2Qye1Um2/Yd9jqkY2Tl5LI2cz/N61Vj/OU9ObtLI0T8PTFtjAmEJYgQcPvAdny5dDNvfL+OP58d63U4+Q4eyWHeum0kp2SSnJpB+s6DAHRsVJOW9UPzRbIRCc256rRWREVaT6vGHC9LECGgfaOanNutMe/8sIEb+rWhTnXvShFp2/YzKzWD5NRM5q3bzpHsXKpXCadPuwbcMqAdAzrGWE+0xpwgLEGEiNsHtuerpVt44/v1/OmsjuV23ENZOSxYv4Pk1AxmpWayfpvztHGbmGiu6N2SpI4NOaV1XRv7wJgTkCWIENHxpJqc0/Uk3p6bxvV921C7evBe3krfeYBZqZnMSs1g7prtHMzKoWpEGKe2rc/Vp7ViQMcYWtaPDtrxjTEVgyWIEHL7oPZ8s+x33py7nnFndiiz/R7JzmXRhh35SWHV1n0ANK9XjREJzRgQ25BT29S3entjzFEsQYSQTo1rcXaXRrw5dz3X9m1N7WqlL0Vs3XPIaUtIyeT7NdvYdzibyHChV+v6jEhoTlJsQ9o0iLanfIwxhbIEEWLGDmzP1OVbeeeHNG4f1D7g7bJzclmyaRfJblJYsWUPAI1rR3F+fBOSOsbQp10Doqvar9wYExi7WoSYrk1rc0anRrzx/Xqu6dOKmkV0JLdt32HmrMokOTWTOasy2X0wi/AwIaFlXe49J5akjg3p0KiGlRKMMaViCSIE3TGoPee/8D3v/JDGbQP/KEXk5ipLf9tNckoGs1IzWPrbblQhpmZVzurciKTYhvRp1+C4qqaMMSaPJYgQ1K1ZbQbGNuT179cz7OSm/LRxF7NSMpi9KpPt+48QJtC9eR3uOqMDSbEN6dy4FmFhVkowxpQtSxAh6o5B7Rn64lz6PpkMQL3oKvTvEMOAjjGc3j6GuiHaJYcxpvKwBBGi4pvX4d5zYjlwOJuk2IbENatDuJUSjDHlyBJECBvTv63XIRhjTmDW3bcxxhi/LEEYY4zxyxKEMcYYvyxBGGOM8csShDHGGL+CmiBEZLCIpIrIGhG5t5B1BojIEhFZLiKzfeaniciv7rJFwYzTGGPMsYL2mKuIhAMvAmcC6cBCEflcVVf4rFMHeAkYrKobRaRhgd0kqeq2YMVojDGmcMEsQSQCa1R1naoeASYCQwuscynwP1XdCKCqGUGMxxhjTAkE80W5psAmn+l0oFeBdToAkSIyC6gJPKeq77rLFJgmIgq8oqqv+juIiNwI3OhO7hOR1FLG2wAIxdKKxVUyFlfJWFwlUxnjalnYgmAmCH/9Qqif4/cEBgHVgHki8qOqrgL6qOpmt9rpWxFJUdU5x+zQSRx+k0eJghVZpKoJx7ufsmZxlYzFVTIWV8mcaHEFs4opHWjuM90M2OxnnSmqut9ta5gDxAOo6mb33wxgMk6VlTHGmHISzASxEGgvIq1FpAowCvi8wDqfAf1EJEJEquNUQa0UkWgRqQkgItHAWcCyIMZqjDGmgKBVMalqtojcBkwFwoE3VXW5iIxxl49X1ZUiMgVYCuQCr6vqMhFpA0x2R0KLAD5U1SnBitV13NVUQWJxlYzFVTIWV8mcUHGJasFmAWOMMcbepDbGGFMISxDGGGP8OuEThIi8KSIZIhIyjeAi0lxEkkVkpdsFyR1exwQgIlEiskBEfnHjesTrmHyJSLiI/CwiX3odi69Q7TZGROqIyCcikuL+rZ0aAjF1dM9T3mePiNzpdVwAIjLO/btfJiITRCTK65gAROQON6blZX2uTvg2CBE5HdgHvKuqXb2OB0BEGgONVfUn92muxcAw325KPIpLgGhV3ScikcD3wB2q+qOXceURkbuABKCWqg7xOp48IpIGJIRatzEi8g7wnaq+7j5pWF1Vd3kcVj63u57fgF6qusHjWJri/L13VtWDIjIJ+FpV3/Y4rq44vVQkAkeAKcDNqrq6LPZ/wpcg3Jfvdngdhy9V3aKqP7k/7wVW4ryZ7il17HMnI91PSNxhiEgz4Dzgda9jqQhEpBZwOvAGgKoeCaXk4BoErPU6OfiIAKqJSARQnWPf6/JCJ+BHVT2gqtnAbODCstr5CZ8gQp2ItAJOBuZ7HAqQX42zBMgAvlXVkIgL+A/wF5zHpUNNXrcxi92uYUJBGyATeMutlnvdfecolIwCJngdBICq/gY8A2wEtgC7VXWat1EBzvthp4tIffddsnM5+gXl42IJIoSJSA3gU+BOVd3jdTwAqpqjqt1x3oxPdIu4nhKRIUCGqi72OpZC9FHVHsA5wK1utabXIoAewMuqejKwH/DbJb8X3CqvC4CPvY4FQETq4nQ22hpoAkSLyOXeRgWquhJ4EvgWp3rpFyC7rPZvCSJEuXX8nwIfqOr/vI6nILc6YhYw2NtIAOgDXODW9U8EBorI+96G9IcQ7TYmHUj3KQF+gpMwQsU5wE+qutXrQFxnAOtVNVNVs4D/Aad5HBMAqvqGqvZQ1dNxqsvLpP0BLEGEJLcx+A1gpar+y+t48ohIjDuGByJSDec/TYqnQQGqep+qNlPVVjjVEjNV1fO7O3C6ignFbmNU9Xdgk4h0dGcNAjx9CKKA0YRI9ZJrI9BbRKq7/z8H4bQNei5vHB0RaQFcRBmet2D25lohiMgEYADQQETSgYdU9Q1vo6IPcAXwq1vfD3C/qn7tXUgANAbecZ8uCQMmqWpIPVIaghpR/t3GBGos8IFbnbMOuMbjeABw69LPBG7yOpY8qjpfRD4BfsKpwvmZ0Ol241MRqQ9kAbeq6s6y2vEJ/5irMcYY/6yKyRhjjF+WIIwxxvhlCcIYY4xfliCMMcb4ZQnCGGOMX5YgTEgSERWRZ32m7xaRh8to32+LyCVlsa9ijjPc7SU1ucD8Vu73G+sz7wURubqY/Y0RkSuLWedqEXmhkGX7/M03pjCWIEyoOgxcJCINvA7El/sOSKCuA25R1SQ/yzKAO9x3EALiDtP7bgmOX2bcDurMCcYShAlV2TgvIo0ruKBgCSDvzlhEBojIbBGZJCKrROQJEbnMHcPiVxFp67ObM0TkO3e9Ie724SLytIgsFJGlInKTz36TReRD4Fc/8Yx2979MRJ505z0I9AXGi8jTfr5fJjADuMrP/tqKyBS3c7/vRCTWnf+wiNzt/nyKG+M8N2bft7ObuNuvFpGnCuz7WRH5SURmiEiMO6+7iPzo7m+y2+8QIjJLRP4pIrNxktlw9zv+IiJz/HwnU8lYgjCh7EXgMhGpXYJt4oE7gG44b6N3UNVEnG7Ax/qs1wroj9NF+HhxBn+5DqeXzlOAU4AbRKS1u34i8ICqdvY9mIg0weksbSDQHThFRIap6t+BRcBlqvrnQmJ9AviTn1LJq8BYVe0J3A285Gfbt4AxqnoqkFNgWXdgpHsORopIXu+e0Tj9G/XA6Rb6IXf+u8A9qhqHkwAf8tlXHVXtr6rPAg8CZ6tqPE5HeqaSswRhQpbbg+27wO0l2GyhO57GYWAtkNcl8684SSHPJFXNdQdWWQfE4vSTdKXbvcl8oD7Q3l1/gaqu93O8U4BZbidu2cAHOOMsBPL91gMLgEvz5rk9+J4GfOzG8QpOFyf4rFMHqKmqP7izPiyw6xmqultVD+H0r9TSnZ8LfOT+/D7Q102+dVR1tjv/nQLxf+Tz81zgbRG5AShJVZupoKxe0YS6/+D0f/OWz7xs3Jsbt+M033r8wz4/5/pM53L033vBPmYUEJw796m+C0RkAE532P5IMfEX5584PanmVdmEAbvcLtULU9wxfc9BDoX/Pw+kn538762qY0SkF06pa4mIdFfV7QHsw1RQVoIwIU1VdwCTcKp/8qQBPd2fh+KMbFdSw0UkzG2XaAOkAlOBm8Xpah0R6SDFD6IzH+gvIg3cqqLRONU3AVHVFJy7/CHu9B5gvYgMd2MQEYkvsM1OYK+I9HZnjQrwcGFAXtvNpcD3qrob2Cki/dz5VxQWv4i0VdX5qvogsI0yHJjGhCYrQZiK4FngNp/p14DPRGQBTkNvYXf3RUnFuRA2wqnLPyQir+NUQ/3klkwygWFF7URVt4jIfUAyzp3916r6WQljeQynd9A8lwEvi8hfcZLfRJyBYHxdB7wmIvtxxuXYHcBx9gNdRGSxu/5Id/5VOO0w1Sm6V9enRaQ9zvec4ScmU8lYb67GVEAiUiNvfHARuRdorKp3eByWqWSsBGFMxXSeW3KJADYAV3sbjqmMrARhjDHGL2ukNsYY45clCGOMMX5ZgjDGGOOXJQhjjDF+WYIwxhjj1/8DLVYbCuXfXMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Preliminary model score:\")\n",
    "print(knn.score(X_test,y_test))\n",
    "\n",
    "no_neighbors = np.arange(1, 10)\n",
    "train_accuracy = np.empty(len(no_neighbors))\n",
    "test_accuracy = np.empty(len(no_neighbors))\n",
    "\n",
    "for i, k in enumerate(no_neighbors):\n",
    "    # We instantiate the classifier\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    # Fit the classifier to the training data\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    # Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "\n",
    "    # Compute accuracy on the testing set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)\n",
    "\n",
    "# Visualization of k values vs accuracy\n",
    "\n",
    "plt.title('k-NN: Varying Number of Neighbors')\n",
    "plt.plot(no_neighbors, test_accuracy, label = 'Testing Accuracy')\n",
    "plt.plot(no_neighbors, train_accuracy, label = 'Training Accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of Neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00e886c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}