14/knn_analytics.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":["# K-Nearest Neighbours Tutorial\n","* Let’s take a look at how we could go about classifying data using the K-Nearest Neighbors algorithm in Python.\n","* For this tutorial, we’ll be using the breast cancer dataset from the sklearn.datasets module. \n","* We need to start by importing the proceeding libraries."]},{"cell_type":"code","execution_count":23,"metadata":{"trusted":true},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from matplotlib import pyplot as plt\n","from sklearn.datasets import load_breast_cancer\n","from sklearn.metrics import confusion_matrix,accuracy_score\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.model_selection import train_test_split\n","import seaborn as sns\n","from math import sqrt"]},{"metadata":{},"cell_type":"markdown","source":["* The dataset classifies tumors into two categories (malignant and benign) and contains something like 30 features. \n","* In the real world, you’d look at the correlations and select a subset of features that plays the greatest role in determining whether a tumor is malignant or not.\n","* We must encode categorical data for it to be interpreted by the model (i.e. malignant = 0 and benign = 1)."]},{"metadata":{"trusted":true},"cell_type":"code","source":["breast_cancer = load_breast_cancer()\n","X = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)\n","#X = X[['mean area', 'mean compactness']]\n","y = pd.Categorical.from_codes(breast_cancer.target, breast_cancer.target_names)\n","y = pd.get_dummies(y, drop_first=True)"],"execution_count":7,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":["* The point of building a model, is to classify new data with undefined labels. \n","* Therefore, we need to put aside data to verify whether our model does a good job at classifying the data. \n","* We have set aside 20% of the samples in the original dataset for testing."]},{"metadata":{"trusted":true},"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=1)"],"execution_count":8,"outputs":[]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[],"source":["# calculate the Euclidean distance between two vectors\n","def euclidean_distance(row1, row2):\n","\tdistance = 0.0\n","\tfor i in range(len(row1)):\n","\t\tdistance += (row1[i] - row2[i])**2\n","\treturn sqrt(distance)"]},{"cell_type":"code","execution_count":58,"metadata":{},"outputs":[],"source":["# Locate the most similar neighbors\n","def get_neighbors(train, test_row, num_neighbors):\n","\tdistances = list()\n","\tfor count,train_row in enumerate(train.values):\n","\t\tdist = euclidean_distance(test_row, train_row)\n","\t\tdistances.append((count, dist))\n","\tdistances.sort(key=lambda tup: tup[1])\n","\tneighbors = list()\n","\tfor i in range(num_neighbors):\n","\t\tneighbors.append(distances[i][0])\n","\treturn neighbors"]},{"cell_type":"code","execution_count":71,"metadata":{},"outputs":[],"source":["# Make a classification prediction with neighbors\n","def predict_classification(train,y_train,test_row, num_neighbors):\n","\tneighbors = get_neighbors(train, test_row, num_neighbors)\n","\toutput_values = list([y_train.iloc[i].values[0] for i in neighbors])\n","\tprediction = max(set(output_values), key=output_values.count)\n","\treturn prediction"]},{"cell_type":"code","execution_count":72,"metadata":{},"outputs":[],"source":["y_pred=[predict_classification(X_train,y_train,X_test.iloc[i],5) for i in range(X_test.shape[0])]"]},{"cell_type":"code","execution_count":73,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"Given our confusion matrix, our model has an accuracy of 93.85964912280701%\n"}],"source":["result=accuracy_score(y_test, y_pred)\n","print(\"Our model has an accuracy of {}%\".format(result*100))"]},{"metadata":{},"cell_type":"markdown","source":["* The Sklearn library provides a built in implementation of K-Nearest Neighbours. \n","* Therefore, in order to make use of the KNN algorithm, it’s sufficient to create an instance of KNeighborsClassifier.\n","* By default, the KNeighborsClassifier looks for the 5 nearest neighbors. We must explicitly tell the classifier to use Euclidean distance for determining the proximity between neighboring points."]},{"metadata":{"trusted":true},"cell_type":"code","source":["knn = KNeighborsClassifier(n_neighbors=5, metric='euclidean')\n","knn.fit(X_train, y_train.values.ravel())"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":"KNeighborsClassifier(metric='euclidean')"},"metadata":{},"execution_count":9}]},{"metadata":{},"cell_type":"markdown","source":["* Using our newly trained model, we predict whether a tumor is benign or malignant."]},{"metadata":{"trusted":true},"cell_type":"code","source":["y_pred = knn.predict(X_test)"],"execution_count":10,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":["We visually compare the predictions made by our model with the samples inside the testing set."]},{"metadata":{"trusted":true},"cell_type":"code","source":["sns.scatterplot(\n","    x='mean area',\n","    y='mean compactness',\n"," 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p8jpsZIkNWieX9dRtOZ1MDwAiLwM3zFgb9un0td/4IFHKn2NmiZbFFK9pyiApmIoCoaikJNf5CuTJMCzYaE/SPgZHl+5BMgWhdQQ6BovvL+ZTbt8G8Z0bZ/A5Bu6gEzfIeFrQVSkvCGSLQqpXtN1jS17TviDBMBPu9LY/OsJbDa5iZEESnhchcobIhkopHpN0xR2HswqUb7jQGad3GlMqnr2C68D3V68ULf7yiVABgqpnjMMk75dGpcov6RLY7mLoAT4Bqwdl4zztyCU8Dgcl4yrkoHsM3344VKSkkr+W6wLgjpGsXTpUubMmYPX62XcuHGMGjWq2OtffPEFs2fPRghB06ZNmTlzJlFRUSxevJinn36auDjfL27AgAFMmTIlmFWV6inTFDSODeW2azrywZe/IoTghsHtaBIfjmnKMQrJx962T5UHhvokaIEiNTWVWbNmsWjRIux2OzfeeCO9evWiTZs2AOTl5TF9+nQWLlxIYmIi//nPf5g9ezZ/+9vf2Lp1Kw888ABXX311sKonNSDCtOjfJYk+nZIAhZjIEE5m5dd0tSSpzgha19O6devo3bs30dHRhIaGMmTIEJYtW+Z/3ev1Mn36dBITEwFo3749x48fB2Dr1q0sXryYa665hnvvvZfs7OxgVVNqIAyvhWJaKKaJTZc9rpJUEUH7i0lLS8PlOr0SMSEhgdTUVP9xTEwMgwcPBqCwsJB58+b5j10uF3fddRdLliwhKSmJGTNmBKuakiTVQ0LmkT8rISygYhM5gtb1VNovSilllVNubi6TJk2iQ4cODB8+HIAXX3zR//r48eP9AaS84uLCK1jb+sXliqjpKtR68h4FVlfvUW5uKG53LhERUaV+51QlvQ61ToUQmKZBTk4WkZHhFfr9Bi1QJCYmsnHjRv9xWloaCQkJxc5JS0vjtttuo3fv3jz00EOAL3AsXLiQcePGAb4fTtcrVs2MjDwsq2E+UbhcEaSn59Z0NWo1eY8Cq8v3KDQ0hqysdHJySk6LrkqqqmJZdWvmnKpqOJ3hOJ1RxX6/qqqU+YAdtEDRp08fZs+eTWZmJk6nkxUrVvDEE0/4XzdNk4kTJ3LllVcyadIkf3loaCivvPIKXbt2pUuXLrz99ttcdtllwaqmJEn1jKbpxMcnBf1z6nIwraigtiimTJnC2LFj8Xq9jBw5ks6dOzNhwgQmT55MSkoKv/zyC6Zpsnz5cgCSk5N58sknee6555g+fTqFhYW0bNmSp556KljVlCRJkgJQRDlGfQ4fPkyzZs1YsWIFu3fv5uabbyYiovb2XzaEridFUykyLAo9JpFhdjQhME2rQT3lnCt5jwKT9yiw+nSPKt319OijjwJw8803M2PGDC655BIefvhhnn/++aqrpVQxusr/PtnBmp+PAhAfHcLMSX3RZUYKSZKCIOCQ/bZt25g+fTorV65k+PDhzJw5k6NHj1ZH3aRSKIpCVk6RP0gAnDhZyIKVu1G1ujMDQ5KkuiPgN4sQAlVVWbt2Lb179wbA7XYHvWJS6VRVISWjoET5kfRcjHre3SZJUs0IGCiaN2/OhAkTOHLkCBdeeCH33HMP7du3r466SaUwTYt2LWLQfpf5dEDXpthki0KSpCAIOEYxc+ZMVq5cSffu3bHb7fTo0YNhw4ZVR92ks7Ap8PeJfXjl421k5xUxpFdLeic3wpAb8UiSFAQBA0VoaCjdunWjadOmrFixgoyMDAzDqI66SWdhmRZNYp08NPZCAGwaMkhIkhQ0AfsqHn30UV5++WX27t3LjBkzOHr0KA8//HB11E0qg2FYKJbvP8Nbt1aHSpJUt8hZT1KlqKoCmkqRAEPx/X+Q0+tIklTNAnY9nTnraeLEiYCc9SSdJjSNv//ve/Yd9aWC79WxEROHdwJDdoVJUn1RoVlPPXv25J577qFDhw7VUTeplrPZNL7aeMgfJAC+357CgZQcNDkDS5LqjQrNerLZbHLWk+RnCdhzpOSmUvuOZtMmKRK506gk1Q8BH/tCQ0Np1aoV3377LR6Phw4dOuB0OqujblItpyDo361JifILz2+EYcgBdkmqLwIGikWLFvHggw/yyiuv+DcZev/996ujblItZxgW7ZvFMPaq84kOd5AQ4+TeUd2JcOpyhzFJqkcCdj299dZbLFiwgNGjRxMXF8eiRYsYP348N9xwQ3XUT6rlhGEyuFsT+nf1tSxsioIhB7IlqV4JGChUVSU8/HT62aSkJDRNC2qlpLrFMCz/DrxyKaYk1T8Bu56io6PZsWOHf+/Zjz/+mKioqKBXTJIkSaodArYoHnroIf76179y6NAh+vbti8Ph4KWXXqqOukmSJEm1QMBA0bp1a5YsWcKBAwcwTZNWrVphs9mqo26SJElSLRAwULjdbpYtW0Z2djZCCNauXQvALbfcEvTKSZIkSTUvYKCYMmUKaWlptGvXzj9OIUmSJDUcAQPFvn37+Oyzz9D1gKdKkiRJ9VDAWU+NGjWqjnpIkiRJtVTAZkK7du0YO3Ysl1xyCSEhIf5yOUYhSZLUMAQMFPn5+bRo0YJDhw5VR30kSZKkWiZgoBg0aBCDBw8uVrZ48eKgVUiSJEmqXc4aKL766isMw+Cpp55CCOFP8mYYBrNmzZKpxqVKURQFTVMwTQuZP1CSarezBoodO3bw3XffkZGRwZtvvnn6DbrObbfdVi2Vk2qepimA7wu96i6qkuP2sv9YDu2axxCiq2DJtOSSVFspIkA+6HfeeYdRo0YBYJomQohyT5VdunQpc+bMwev1Mm7cOP91Tvniiy+YPXs2QgiaNm3KzJkziYqK4tixY0ybNo2MjAxatWrF008/TVhYWLl/qIyMPCyrYT6mulwRpKfnVvo6mqZiKQr7j+dgWhZtmkSjCgvTrNx9VTSVz787yPtf/uovu/vGrnRtE49ZTVlnq+oe1WfyHgVWn+6RqirExYWf/fVAF2jdujXXXHMN4FtT0b9/f3766aeAH5yamsqsWbOYP38+S5YsYcGCBezZs8f/el5eHtOnT2fevHl8/PHHtG/fntmzZwPw+OOPc9NNN7Fs2TKSk5NlbqkaYKkKD7y0lhmvfs+Tr23gnufXYCqV397UAhZ+vadY2asfb8OQ/U+SVGsF/Mt/6qmnmDlzJgBt27Zl3rx5/uOyrFu3jt69exMdHU1oaChDhgxh2bJl/te9Xi/Tp08nMTERgPbt23P8+HG8Xi8bNmxgyJAhAIwYMaLY+6Tgs9k0vt+WQmpmgb8sM6eQLzccxm6v3MJL0xKYv2vt5RcagFz1L0m1VcBA4fV66dixo/+4Y8eOeDyegBdOS0vD5XL5jxMSEkhNTfUfx8TE+GdTFRYWMm/ePAYPHkxWVhbh4eH+7i2Xy1XsfVLwKYrCiezCEuUZ2e5Kf5/rqkL7FjHFyvr/oQkqskUhSbVVwMdDp9PJN998Q79+/QBYv349oaGhAS9c2tBHabmiTm2v2qFDB4YPH15qUKhojqmy+toaApcrotLXGNyzOYtW7fGP9SgKXHVxK6IiK79f+sPjerJ49V52HMikZ8dELuvZgqhwR6WvWxFVcY/qO3mPAmso9yhgoHj44Yf5y1/+gq7rKIqCoij+sYSyJCYmsnHjRv9xWloaCQkJxc5JS0vjtttuo3fv3jz00EMAxMbGkpeXh2maaJpGenp6ifcFIgezKz/A5tBU/n3XJby7YhemZfGnQe2ICNGrbPDu2r6tuLpPS3QVPG4P6e7ArdSqUp8GIYNF3qPA6tM9CjSYHTBQdOnShVWrVrF79240TaNVq1bY7faAH9ynTx9mz55NZmYmTqeTFStW8MQTT/hfN02TiRMncuWVVzJp0iR/uc1mo0ePHnz22WcMHTqUxYsX+1szUvURpkV8uI2/jOgE+Pooq3KK7KkZToacFStJlaZpCqgqKmAYZpWvTQoYKDweD6tXryY/Px+Abdu2cejQIaZMmVLm+xITE5kyZQpjx47F6/UycuRIOnfuzIQJE5g8eTIpKSn88ssvmKbJ8uXLAUhOTubJJ5/kscce44EHHmDOnDkkJSXx7LPPVsGPKlWUbyqs719c9UxclSSpohRNJeVkIUvX7iMuIoShl5yHTa3aB7uA6yj+8pe/cPjwYdLT07ngggvYvHkzPXv2rNVTVmXXU/1oDgeLvEeByXsUWG24R7quciAtn0fnrfeXRYbZeW5Kf6jAuqRKr6PYsWMHixYtYtCgQTz00EO899575ObKf0CSJEk1zbRKrkvKyfew58hJNK3y655OCXilhIQEdF2nZcuW7N69mzZt2uB2u6usApIkSdI5UsFhK/k1HmLXSp15WomPKVtoaChLly6lQ4cOfP755+zatYuTJ09WWQUkSZKkc6NYglFDOqBrp5cQNE0Ip1lCRJV2vwcMFI8++ig7duzg4osvRlVVRo8eLZMCNiCaphKue4jU3UToRei6XEEtSbWFZQmiQnVeum8gtw7tyD03deOJ2y9CqeIkmwEHs0/Jzs5G0zTCw2v/YjY5mF01Y0iaphJqZpG+6N94TxxBj3LhGnEvhSGNMCqZHLAm1YZByNpO3qPAats9stl83U3GOcw5r/Rg9r59+7juuuu46KKL6NmzJ2PGjOHYsWMVrohU94QohaR/9AzeE0cAMLLTSftgJk61ZHqPc2GzadjsWpUOuklSQ+X1mucUJMoj4F/ogw8+yPXXX8/mzZv5+eefufzyy3n44YeDUhmpdtGw8KYX3wLXzDuJYnordV1FUVBsGmu2pvDqJzvYl5qLostgIUm1VcC/TrfbzY033ojNZsNutzNmzBhOnDhRHXWTapiFii2uSbEyNTQSoQVemV8Woar8682NzFu8lVWbjvD4K9+zZvNx9FJmb0iSVPMC/mU2a9aMTZs2+Y93795N06ZNg1opqXYoJATX8HvRo3y5trTwaBKuux+3qFyg8BgWOw9mFStb+PWveOXyb0mqlQKm8EhNTWXMmDG0b98eXdf55ZdfcLlcDB06FPDtYifVT4ZhUWCLJWH031EsL0LVcYsQDKNyA9lqKY8nWmmFkiTVCgEDxX333Vcd9ZBqKdMU5Jp2wP5bwqfKz3bSVYVu7RPYtCvNX3bTkPbYVJkkUJJqo4CBomfPnqSnp5OdnV2svE2bNkGrlFS/KZbFXTd0YeeBLPYcOclFnZKIjXAEbcaGJEmVEzBQzJw5k3feeYeIiAj/knBFUVi/fn2Ad0rVQVGo8pTCwSYE4DXp2CKaTq1iMQwTqwozXUqSVLUCBoqVK1eyZs0aYmJiAp0qVSdNxbAEJ3OLiI92ognrt7TgdYevBSEDhCTVdgEDRcuWLYmMjKyOukjlpGgqX/54hLeX7QTAYdd4cmIfEiIrtp2oooCiaZhCgMA3RiC7fyRJ+p2AgWLMmDGMHj2aXr16oeunT7/zzjuDWrH66NQK5MpuKGIC85fv9B8XeUxe/HAzj9zSs0LXUXSdd1fuYs3PR0mICWXSdZ1JiAqR3UCSJBUTcE7i7NmzCQ8PJzc3l6ysLP9/Uvlpmkqk3YvtxA5sqduItHt8WxeeoyKPye9TWaVkFKCYRZj52SjluLSuayxZs5fl3x2koNDgwPEc/jZ3HVZ53ixJUoMSsEXhdrt5+eWXq6Mu9Va4VkTKWw9jZPumg2ph0SSN+xfZZsW6ik5xOnRiIhxk5Rb5y/okJ+DdspzU1N3EXHUXeV5bmdfwmBbfbU0pXmZYHD+RT5NYZ4NNqihJUkkBWxRt27Zl586dgU6TzsJm0yjYs8EfJADM/JPkbv4Cu107p2tqQvDkny+mazsX8dEhXNWrKaMujqPwp88o3L8FxVsQ+BqqQtPEktki46JCZJCQJKmYgC2KtLQ0Ro4cSZMmTbDbT6dukCuyy0dRFIy8kl11Vm4m59rJY5oWobrC1BuScR/fD4c2kbfgC4S38NQJECAGKUJw29Bk9h7JJjOnEEWB6we2xaGrCDlGIUnSGQIGiqlTp1ZHPeotr9cgomM/stctAutUMiOF8O5XUlCJGUaWJdA1FWfeETJ//MRfbottDCERECDBq2UJnLrC03ddgrvIwOHQ0EAGCUmSSijXxkWbN29mzZo1eL1eLr74Ynr2rNjsmupW2zYucugWtsIMste8j7AMovpchxmeRKF5bl1PZwqzGZgpv5K/7WvsrjhIn2wAACAASURBVBZEdh9CdpGtVv38tU1t23CmNpL3KLD6dI8qvXHR4sWLmTx5MtnZ2eTn53PPPffw/vvvV2kl67siQ8XtSCD8solEDLmTwvDmVRIkAPK9OkZiR8IG/xmty1D08BgZJCRJqlIBu55ef/11PvjgAxISfKmmJ0yYwG233cYNN9wQ9MrVJ6YpcPsHDqq2e8cwLAyUKr/uuVBVBUtRsIQv+Z8wzVJTjGiagomCx7Cw6RoaQq7fkKRaKmCgsCzLHyQAEhMTUWVKaKkUmqZQYAj+u2gLB1Ny6HF+IjcN6YBiGMWChaoqFHgFj768jvQsN3Zd5c/XdeYPbeLlGIkk1UIBv/Gjo6P54osv/MdffPEFUVFRQa2UVDeZqPxt7jp+/jWdrNwiVv5wiNeWbkf53Z7YFgovfriZ9Cw34Fu/8cIHmxHnPA9MkqRgCtiieOSRR5g0aRJPPPEEADabjRdffDHoFZNOU3QVS/i+RFVErX3qLvSaZGQXFitbt/U4466+oFiZAPYdK5623rQEeW4v4XbZWpWk2iZgoGjbti0fffQRqampmKZJVFQUiYmJ5br40qVLmTNnDl6vl3HjxjFq1KhSz7v//vvp1asXI0aMAHwD6E8//TRxcXEADBgwgClTppT3Z6pXFF3jw6/3sGz9AQRwea/m/GlQO4RR+/YNddhUNFXBPGMwPSk+DCGKtxVUBbq2S2DtlmP+MqdDJzzUhq4IDBN0TcGQe6NKUq0Q8PHts88+Y8SIEbRu3RqbzcawYcP46quvAl44NTWVWbNmMX/+fJYsWcKCBQvYs2dPiXMmTpzIsmXLipVv3bqVBx54gCVLlrBkyZIGGyQ0TWXfsWw+Xbsf0xJYlmDZ+oPsOnQSXa/+J29VVcr8XBW4+Y8X+HNNOewad13fBdvve5QswW3XdKR7+wQUxRdMZtx+Eaqq8OWmYzz3/s+s2HAY1aaj1OPcU5qmgqbhESA0tVL5vyQpmAK2KObOncubb74JQKtWrVi0aBGTJk1i4MCBZb5v3bp19O7dm+joaACGDBnCsmXLimWdXbp0KYMGDfKfc8rWrVs5ePAg8+bNo127djzyyCMNclxE11V+3JlWovzHnal0ahVTvSnBNZUTOUUcSc/j/Jax2DWlRBeYMC0u6ZzERZ2SyM4rIjYyBFWIEvUUQqBaFn8Z2RkUBWEJNE3hlSXb+Hazr5WxZc8JftmfwZ0ju0Ad22ejPFRN5XiWm3+8sYGcfA/REQ4eubUX8eH2SmcXlqSqFvCx1LIsGjVq5D9OSkrCsgL/Q05LS8PlcvmPExISSE1NLXbO+PHjuf7660u81+Vycdddd7FkyRKSkpKYMWNGwM+rjwzDonv7hBLl3TskVuuXiaKpvLlsJ/fOXsNz7/3En//1JQdT80ptXQjTQrMs4sJsUMbOdZYlwLTAMFEsC9OCdWd0RQFs2pVeIktufWECM9/0BQmAk7lFPPXWRoyarZYklSpgiyI2Npb33nuPkSNHoigKH330EfHx8QEvXNqC7/J2I5w5WD5+/HgGDx5crvedUtYKw7qmvV3n6otb8fl63xjFkF4tSG4dR1T42TPPulwRVVqH1MwCVv14xH9sCXjl423MnHQxrpiwKvmMjGw3dptGoef0uISuqei6SlxU1XzGmar6HlVUamY+2Xme35UVoKgqrtiq/3nPRU3fo7qgodyjgIFixowZTJ061T/rqWPHjjzzzDMBL5yYmMjGjRv9x2lpacXWY5xNbm4uCxcuZNy4cYAv4Jy5YVJ51LYUHpV13aWtGT6gDeCb9eRxe0h3e0o9NxhpBQqNkvcyt8CDx2tW2WepusqoKzrw6sfb/WXXD2oLplXlP09tSL2gaioJMU7SfpsiDNCiUQTCqvqf91zUhntU29WnexQohUe5tkJdtGgR2dnZaJpGeHj5ntb79OnD7NmzyczMxOl0smLFCn+wKUtoaCivvPIKXbt2pUuXLrz99ttcdtll5frM6qIoYNcVFCy8lhb0biBhWP5ZQzUR/kJDdJLiwjieke8vG9KrJTZFqbKuEsuwuLhTEl3auNh5MJO2zWKICrPV29XaKoJHbu3Fv9/+kUOpuZzXJIppo7uj4+uWkqTapNyP6hUdTE5MTGTKlCmMHTsWr9fLyJEj6dy5MxMmTGDy5Ml06tSp1PdpmsZzzz3H9OnTKSwspGXLljz11FMV+uxg0jSFMKWAnO8+xszLIKL7VRDdjAKjanI31UaaEPx9Yh8Wfb2HAyk59PtDE3p1TKz66aumRYRDo/f5CZhm7V0vUhUsUxAZovPYbb1AAQToSuW3yZWkYChX9ti6JphdT5F2D8f/dy9WQY6/LOH6B/G6zq/eWUhnEazmsKL4ukvM39Kb1+U1DvWpyyBY5D0KrD7do0pnj5VO0zQFT8q+YkECIOf7JdgpfcygvhACTMMCS9TpICFJUsWVq+vp6NGjZGdnF5vJ1LFjx6BVqrYSAlS7s0S5YnfKPEWSJNVbAQPFv//9b95++21/Og3wTXP98ssvg1qx2siyBFpsY+wJLfCkHfQVajox/f6PfEunZoaaJUmSgitgoPj8889ZsWJFufM71Xf5hh3XDX/Dc/xXzNxMnK27UYgTqwpWD2uaglmB66iqglP1oCkWllAoJKTSdSiNrqvY8GIpGh6DUveXkCSp/goYKJKSkmSQOINlCXI8NrSEZNRGCjlV0F8fohnYRRGe9MOExTfFq4bgNsr+1aiqQrhawImPn6PoyC5ssY2Jv/ZuRGTLStfnTOF2A++Bn8nZthpbXFOiel9LnhUqZ+dIUgMScNbTCy+8QEFBAYMGDSIk5PQTa20eo6hLC+7sOnBwAxmfnl6NHjtkAmrrPhQZZx/3CNW9ZC+dReHhX/xlamgkTSc8S5a7YgsUAVRdw+K3BY6Kb+DablOwfvmCrK/f8p+nR7lIGP0kuV57hT+jtqhPs1WCRd6jwOrTPar0grtFixYBFMvw2lDHKIIhRCni2BevFSvL+upNGrfpQRFnT9Ohq6JYkACwCnIQnkKgYilMVJvGwq/3suy7AzhsGqOv7ECv8xOxW27Sfyye2dfITke4c0APnMalPLTfNjWqrhaKrqvk5hehaCpqNX6uJNVlAQNFeVKKS5UgBKKooHiRtwhE2V9gpkXxQXVAsYeg2BwVWtqr6yobd6Wz9Nt9AHgNi7mLttJhSiyhYQqqMwxy0ou9R7U5Kj1ur2kKlqKy51gOhmnRrlk0qrAqNEZT8Q9V2XU0h49W7cFh1xhzxfnEhNvr9cI+SaoKAQNFZmYmH3/8Mfn5+QghsCyLgwcPlivfkxSYgY6zVRfc+zf7y0KanY8Z4FdTKEKIv+ZuUhc8gZmbiWJ34rr2blRnOBQWlfvzBQrrtx0vUf7z7nQSLmxGzKBxpL47wx+4Qtv1xNSd4C33R5TKUlQeeGktqZm+IBkd7uCZv/ZDCVICC01TOZxRwJOv/eAv27rnBC9OG4gtKJ8oSfVHwEBx9913ExISwp49e+jTpw/r1q2je/fu1VG3BsFt2oj9453kfL+YooPbcDTtQFSf68g17JT12G6aFgW2WBLH/hOMIhTdQaGwoep2oPyBQlXgglaxbPileAr49i1i8HgMbFHNaDJxNoUHt2OLbYwSlUh+gIH2QGw2je92pPmDBMDJvCKWf3eQoRe3wOsJQrBQFD5du79YkWEKNu5IpV/nJLxyEaEknVXAldnHjh1j3rx59OvXj9GjR/Puu+9y6NCh6qhbgyCEbxaV48KRxFz3ECEX/R85Xnu5BuNNU5DrtZMrIsjx2vH8bvBbVRXsdh27/exf7F6vyaXdmpF8nm+djKLAkN4taBQTimUJikyNHDMMq2Vv3OHNyPPaKj09VlEgK7ewRHlGjruUs6uGooAruuRiybiokFJT4kuSdFrAR8NTe0+0bNmS3bt3c80112AYcnuVEN3ErhhgeBC6gwIrpMIDo5qmgqqgAB7DRIiKjS+URdFU0nOKWP79QRrHh3Fpt6aoQpRaR2GYTP2/rpiWQFEVNECYxStSlXmsvF6Tfn9owoKVu/37aysKXH1xK1+akCAwDZNr+p3HNz8d5WSer8XVMimS9i1iZEoSSQogYKCIi4vjlVde4Q9/+AOzZ88mPDycvLy86qhbtVNVpVxP8k7dwLtjFemr3wXLRI9pROKNj5KnhpV7Wq6iqRzLdLP0233ERDgY1r8Ndq38s3AUBUI0E5tiIIAi4cDzW/zWdY3dR7P5+xn98cu/O8g/J11c6rWEEGAKNABTBH19uRAQoqs8Pbkf763chdewuGFQW6JD7eXaPfFcP9OGYNbd/TiYmotd10iKC0UxLbmeXpICKNfGRZ9++ik9evQgOTmZ559/nnvvvbc66lZtQnQTOx6M7DT06ESKLDtF5tl75WxWIWlfv+0/NrJSyPr6LcIHTcBtBc6zqOsqB9LyeHTed/6y1T8d5fl7BpS7zhF2g5NfvEb+zvWoDicxA8fibOkbO/Jagve//LXY+RnZbgoLPcQ6DCxFp8jSa3StiWVaxITq/HlYMgKqZaqqb0aVSfcOib61NoZZpUFCUeSqdal+KleL4oYbbmDXrl3cc8893HXXXcUW3tV1Dl1gHfiRo5/NAQQoKgkj7sWW2BFvKTu7qaqCkXOiRLkndT+q5YFypNEwBSxatbdYWZ7by66DWVzQPDrgF6bdppC/+Uvyd6wFwCrMJ+OzOTS+/XkgBgBNPT1eYddVZt7WBeevy8nYuR5bbBNiLh1DvhYe3OmoAfgCle/zq7vzpyqDpK6rGCicyC4kxK4RHmIDy5RBQ6o3Aj7+/vzzzwwePJg77riDtLQ0+vfvz6ZNm6qjbtXCoXjIXPkq/hlGwuLE53MJUUoOtoLvC0aPaQRq8RjrbNMdUy1fAFUUcDpKxmhfWeBvF114cO8r+TsoOuZrRdhUuGlIe05tUX5FzyZE7v+S7DXv400/TMGu70h5+2+EqeWfHVXs83WVcN1DpK2IMN2LptW+zLnl3Z+98p8DHgvufX4N02av4a5nVjH7w82g1a2NrDRN9S9+lKTfC/gv46mnnuL1118nOjqaRo0a8dRTT/Hkk09WR92qh2X6FridWVSQU2bS8CLLQeKfHkKPSgBVI+yCvkT2GkZhedcWWIKbLm+PTT99+5slRtCiUUS5nvBNxY6j6fklyh2JrQDfwHPT+DCenzqAkQPbMLxPYwp/+ab4NfKyEIUVH2vSNQVnYRonFjzO0RfvIOujfxJGXu0JFpqKF4W0nCLQtaB/+amaysKv95CZc/rBYtOuNI6dyEdVa8k9KYOmKaBrbD+YxS+HTqJUwz2T6p6AXU+FhYW0adPGf9y/f39mzZoV1EpVJ1PRsTdqhSfl9Bz7kJadMMTZnwiLTAU9pjWuUX9HVcFraeR6y/8EaVmCiBCdl+4byIZfUoiJCKFDyxj/wKqigFMz0H9b1WZgo+CMtQtFXkFkjz/iObqLwsM7QNWJ7jsS0xHpP0eYFuF2jWF9z8Mh3BRGxuH53YZLij0ETN+6BlVV8HrNgF0yTrWQ1PefxMzL8tXl+F5OLH6WmOEPkF/TS9c0lU/WHmDRqj2Ar4U2c9LFxITagjb+YVpw7ER+ifKUzAKaxYfW+pxjpqIy9T/fcDLX97AUGxnC05MvqeFaSbVNwECh6zrZ2dn+pvy+ffuCXqnq5LYcuK67n5Nfv03R0d2EtEwm+pIbyTVslNUNZBiCPM49MZ5lWmjAJZ2SEMK3a9ypT4uwGWQt/y8Fv24AFMKSLyFqwBhyPae/iPMMO9FDp6IJL6gahmKn0FAJO+MzhBB4PAaW7iD28gmkvvMYwvQFn4juV2KoDhRVY932FA6n5TGwe7PAKS1Mrz9InFJ0fC+aUvNpMEwLf5AAcBcZzFm0hftGdQ/atlI2TWFwz2Zs/vV0mhNNVejUOq5WbI1bFrtdZ9kPh/xBAiAzp5BvNx9j+IA2ZbxTamgCBoo///nPjB49mhMnTjB16lTWrl3LjBkzqqNu1cKyBLnCSeiAW4kQXkzVTo5XqbZFWL9fEWyzaRTu/+G3IAEgyN/2DaEd+qC5LvA/GVuWIN+yYdN0nKII8/BPhITHYoY0R1G0YvU3DAs1LInGE2fjPXEEPTIe0xaOW9iZ/sp3HEzxZcD85Nv9PDTuQs5vFn3WlcqKZkMNCcMqPP0UbYttjCVqvpslz12y7y81s8DXSgvSZ3q9Jp1axXH7sE58unY/4U4btwy9AJtSff+GzpWi4F9TcqbSyqSGLWCguPTSSznvvPNYu3YtlmUxadKkYl1R9YEQgkJDBapuwdu5UlUoPLi9RHnR4R3YGiUX60JRVYUQTwbH3ngQYfj27HY06UDcsHvI9RbvBvKYCh4zBDWmLYVCgAlZBUX+IHHKeyt38/DNPc76xeq27LiG30P6R89gFeajhcfgGjYVNyFAzT5BR4XbiQi1kVtwOmBc0qUJNlUhmDNvLcOkb6dEel6QgIKCTa3aBYrB4vGYXNG7JZ+t3c+pHjJVVRh8YfOarZhU65QraU9oaCg9e/b0H+/Zs6feBYvawjQFzva9yNu2uli5s013Co3iUcyhGmStescfJACKju7Eyk1HDW1Sav+4dcZK6NL4noIVztbt5jUViG5F0m3P+rqxNDtuy1ErvhhVYfHPSX3575KtHD+RT98ujbn2ktaY3uBnEjC81m8zQwTBuhXlXRBaXkIIwhwaT0/ux/tf7kZB4U+XtcNpk4PZUnEBA8XMmTN55513iIiI8DelFUVh/fr1Qa9cQ2QYFiGN2hF50TByN3wGqkr0RSNQohtjeYt/SSjCwnKX3DjFcuehhJUoLkYIiI5w0LxRBIfOaFXceFl73xNxGV92XlPBazoAx2+NiNrRxWKZgjC7yt03/AFLCF9LohqCRLApqooJHErPIykuDLumVFlqdGFaxIbZmDgs2VdgCSyZdl36nYCBYuXKlaxZs4aYmJjqqI8E5Hl1QrpdS0S3qwAowk5+KVNvPYqDiB5XUfTxf/xlakgY9sSWuD2Bv7xVy+LxCb35blsKh1JzGdSjGXERtaN1UB6qrmIKX64sVRFYhuVfxKeCP4/UudB1DY/lu46m1NwGR7qusvtoDv94Y4O/NXHbNR3p2ympyoKFZQmo5bOzpJoVMFC0bNmSyMjIQKdJVazQq1AYYFaVYVg4mnXCNeJecjctR4+II+aS68kzHZTnKd/3BWHSN7kR6m+ptuvK06Sia3z49R6WfXcAVVG4tl9rrurTEmFUfpBJ0TW+35nGJ9/uI8xp45arOxIf6aiRDY68Al78cHOxLqc3Pt1Bn06NgzZAL0m/FzBQjBkzhtGjR9OrVy90/fTpd955Z1ArJpVPvldHb9SZyKs6IBQNW3QkRgX38a1rezHousr2Q1l84t9fQvDBV7/SuW08zeNDK5WWxGbT2LI/kxc/PL2R1IMvfcuc+wZRE2utFRSyfzcLyTAtDNOq6VUrUgMScNTqVMbY3NxcsrKy/P9J1UfXVUJ1L6GaB5ut5NeVYVi4DZ1Cb8N4xtQ0lR+2p5Qo/3FnGlolU2cYlmDlD8X3WzFMwda9J9D16h/kVRD06phUrKxFowhstWUlvNQgBGxRuN1uXn755XO6+NKlS5kzZw5er5dx48YxatSoUs+7//776dWrFyNGjAB8myVNmzaNjIwMWrVqxdNPP01YWIDR2XoqRLPQcg77ZjeZXqJ6D8eW1KHYSu2GxrIEXdq6+PrHI8XKO7eJr/RYgqooJMaGlihPjK2hVdaWxe3DOxEfHcKmXem0bR7N6Cs6oCFqeia31IAEfERq27YtO3furPCFU1NTmTVrFvPnz2fJkiUsWLCAPXv2lDhn4sSJLFu2rFj5448/zk033cSyZctITk7mpZdeqvDn1weKAnYrj5R3HqPoyE48x/eS/tHTkH20Qefj8XpN/tDWRb+uTVAU33auQ3q3oFVSZKUDhWWaXD+wLdERDn9ZlzbxJMWXf68R+C0poaZiqSpCU1HO8fclBAivwcgBrXl8fC/GXdkBzRI1mvVXangCPpampaUxcuRImjRpgt1+enB16dKlZb5v3bp19O7dm+joaACGDBnCsmXLio1tLF26lEGDBvnPAfB6vWzYsIEXX3wRgBEjRjB69GimTZtWsZ+sHrDZNPK3fg+i+Jdf3qblhA68A7MBP1JaXoNbrjqfsVee7wsWUCUD2UKATRXM+ms/UjILcDp0osLsYFZw7wpdY/aCn/hpdzp2XeWmIR3o94fGnOvKP9PwrdOwSkl9L0nBFjBQTJ069ZwunJaWhsvl8h8nJCSwZcuWYueMHz8egB9//NFflpWVRXh4uH/g3OVykZqaek51qOt8Kc0TSpTrsUlYZSyKayiEafmbxFV5J05tcJQU7dtPu6IBSLepLF23n592+/I/eQyL1z/9he4dEgi3q3KfCqnOCRgozlyRXRGl5bkpzx4B5/q+M8XFhVfo/NpGWCZmQQ7C8EKzDoRe0JeCX74FQIuMJ6r7FegRZx+zcbkiqquqdVYw71FugYdtezNKlO8/lsOlPZoF7XOrmvx3FFhDuUdBGxFNTExk48aN/uO0tDQSEko+Hf9ebGwseXl5mKaJpmmkp6eX631nysjIq/Xpnc/GpoE97wjpHz2NmXcSPTqRxOsfILrPCMzCfPToRuR4HZhnmQLrckWQXsHpsWejqgo2m/ZbFtr6089VlfeoNLqu8Ye2LnYcyCxW3rppFCdO5NaJFkWw71F9UJ/ukaoqZT5gB21EtE+fPqxfv57MzEzcbjcrVqygX79+Ad9ns9no0aMHn332GQCLFy8u1/vqKl3XsNt1f6spRC0ibeFTmHknATBOppK2+FkURxiF4c3J8QRvb4UzOXSTMCMDz3fzsXZ8QZTd06AH0CvCMEyG9G5B746N/LsZ3jG8E6F2rU4ECUn6vaC2KKZMmcLYsWPxer2MHDmSzp07M2HCBCZPnkynTp3O+t7HHnuMBx54gDlz5pCUlMSzzz4brGrWGFVVCNc9FB3YiDfrOOHn98VriwTTi/W7DYa86YdRRfXlLNI0FT3nMMfefpRTvf+5Gz8jccw/yDHlMq/yEIbB7cOSGX9tMooCGtSZVe+S9HuKqO1J889BXeh6irB5OfHhk3hSD/xWopD4f49gi2vG8dfuxczP9p9rT2hB7KCbMeNaU1hGDidNU4mOtJGX58FjiHN+enXqBjmf/YfCA1uLlSfe+AiF0W1q/b0NpD51GQSLvEeB1ad7VGNdT9LZKQqI/MwzggSA4OTqd7FQSBgxDS0yHgBbXBPihoynKPUAoozsPk7dICT3IOlLnsez4UMi9cLas491LVbBeRKS1CA13OW9NUpBWCUHh4VlYJoWekwSrqsmouh2zLwsMlfNx3XNX8nxlt51oesqZOwnZcGT/rK8batJuvVpcsyKb9fqEXai+95AyoFtgMAW35SYvtdjj2+CYhVRqITUWDbVqqJovtTd2XkeYiIcqEKm15aks5GBogYIIdAi4tGjEzFOnl4jEtVnJF7VSZHXIiy+Bd7UfViouK6dQp7pOOvWmnY8ZK9fXKzMKsjBk7ofLf78Cn+pm6aFEdmYxuOfwb33R0JbdyV96Yt4UvdjT2hB/LVTKNBj6uzqYHeRl+93pDLvo61YAhx2jRm3X0SjWCdYArOOpFmXpOoiu56qkFM3ibQVEWkrIkQv+0s033LQaPQTRPe/ifBOA2g05u+ojdrj9ZqYpiDHY8MTfz5GUheyPfYyv5QFCorNUaJctZ09uARSZGjkaXGEdhxA2uLn8KT6MrV60g6S9sFMnErd3Ve5oNDglSXb/FswFHlM/vPeT2zfn8m67alQS2Z3aZqC0FQ8AoSmodaSekkNj2xRVJEIu0HOmvnkbVmFoulEXTSCsM6DyPeWPkvINAXZph1b8pWEqAK3V5RIz1DeloBH6ET3/z/c+7eA5ZsdZYtvhh7bBKscGxidjRACLAPvieLJ94yTqb/Nwqp4t1ZtUOQxMX4XeFMyC4gOD+Gpt37kglaxRDr0cw6yVUFVFQoMwWPz1pGW5cZh0/jzdZ3p0jquRvbFkBo2+YhSBXRdpWj/JvI2fwXCQhgeTq55D7Uwh0ibhwg1jwibB72UwWWv16SoyKrUTCLTFHgccTS54z/EDR5H/LCpJNz4KHlG5b/IBap/YP0ULSwaodbE7gxVw+nQcUU7i5X1vCCRX/b7VlNv35dRIynFz2QpCi9+uJm0LDcARV6T2e//XOaEBkkKFhkoqoCuWLj3/FiszNmyM5Y7h+Ov3cuxuXeS+vp9hBSmoOuV+0PXdbXEnhRhNi8OIwcz/yRhyf1wNGmLYhk41MqvvXATQsLwe1FDIwEF1RmOa8S9uEVIpa9dU6IjHMy44yL+0NZFdLiD/t2aMuLStny8Zh8AbZvF1PhgvQD2H80uVmZagly3p2YqJDVosuvpHJxKa2EYFiG6hUMTWK06U7Dre/85Ub2Gkr7kP/7Fc2b+SdIXPUPCqL+T+1uXTZjNi2Z6EIYHHGEUWCFYlsDh0FBMAwMV728znfwL9A5tx8w/SWS7CykkFLtqkP3V/yjYsd53njOCRtc/QMpHzxLRZSDOzkNw/7Z3haIo6LqKZVnlHog2TQER8TQe90+wTBTdhomOMOtuUkJFUXBqCpNv6IJA4cedqTz+yne4iwyG9W9NXKSjxmdAaYpCl3Yu1m057i8LsWtEhNqhCrLkSlJFyEBRAQ5N4BAFFOzagB4RR3jT9uT8tJL09YtJuvFhQtv3pGDXD6BqaOHR2Bu1IvqiYYCComrkbFqB8tt2M+E2Lzlfv0b+L+sA0GOSSBr1OGZRNoV7tmFPbInD7sQZEU9OkU647iFt/mN4M48BkLXqbRqP+xeoqj9IAFjuXLK/X0pEp36c/PYDmnQagBsdp26gF+Xg3r0JR+J5qPHNKbTsmKZV5sK8cN3LyRUvU7D7n4UqywAAH4RJREFUBwDsjc4j/so7CHOEkq9F1tmZT76uPoGqKPTokEDXdi4UVUGDWjEGIEyL8dck4y40+Gl3OomxoUy5satvGm9NV05qcGSgKCdNU7G5j3P0zYfgtzUQ9oQWxA4cTbbpJeXDp4jpez2xA8ciVBuoGlEX/pHUD/6FVVSAotuJv/IOhKqjqgoiN80fJACcrTqTv/0bMr9+218W1ftabK7mhLbshjftoD9IAGAanPx2AdEXX1+irt7sNEKan+87LT8be0QE1qEtHFv6PAAxA0bhdISgZRzDmdQG0xZe6o55mqZipB/wBwkAT8o+Cnb/gGUahHa7mvw6vnOzEAJM4dsP2xS1po0khEC1LCZf3wWhKAghsCm+bW8lqbrJMYpysiseTq6e7w8S4JsqKrwetIhYRFEBmV++Qe7PX1GohCEsixPLXsYqKgBAGB5OLH8FRVioqoI342ix64ef///t3Xl8VNXd+PHPXWbJZCXLhBACaEBZZJUqLmUVENmXp7gCVdGn9Wfdd6zIT6tFVLRa5Kn96aPQxbrQggpqFVs2WaqACooQ1oTsJJn9Luf3x+DAmDAJSgyE8/6LOfdO7r2Hec13zvY9F1K96m9HChSVcMlOXLmdcGAhjNCRY6qGmpSKiIRRPekoevygdXLXAQR3b0VxutHSsnGJEEblfrJH3UDOuFtA2BT/7/1ULHuO4j/civH1alwNTOdVVSU2LfZokYr9YJloast8raqqgu7QcDi0Vr2y2rZFtHVjWiiWLYOE1GJkoGgiBYF99Jf1YbYRRtGO/Kp2deiOZdkoiLjFdAAiEkRYRnRso6A7KEdVv6pG958AnHmFtJv+KJ7CfoT2fokiTFztu6ImpZFx0WTaXfN/ybnsv8kedQOm4qTttEdxd+iOI7MdbQZdgTOnA1aghrZXzSFku9C06AK8mvVv48zO59CaN+Puq/rjP+FS6g+SmqaNp/O59co9hX2xIyEs+8f/llZUlUpfhBeXfslfPvwGA0WmKpGkZia7npoogpv0ARMpe/3xWJmWnI4zp0M0ICgqKb2HomV3xDJsTEXFXdCN0L5tsfP1tBzQnGBDRPGQO/UBqj54GTtUh20aJHe7EP/2tWRfOpPS1x7H8kdTjVd//GfaXfck7a6bh/+Ljyn+3wcAgaI5yL3i10QyOpE+5nYULNzJKUT8PtqMv4ugcOMiQsXbCwju+hSItmyEEb9YThhhGhqoEEJgujLIGXcLVSsXI8wIaX2HoyVnkHHBBPy44EfsMdc0ldKaEHc/typWtnLTPn535xCg8QFeXVexUTBPgjEISTqVyOyxx8Gjm1BzAN+md9HSckjrPxpTdaLZYUDBwEHQjE5dVVWFFDVI1bsLCO75HFfbM8ka838IaBmxAWBdV3GJEIoiMBQ3LtXAKP4Ko6qY6o8Wx107bcAE0s8fx77fzYzr/tLb5JFzxcOxNRPfzWiZ5ghz4PkbY6+zhv+cwK7PCO78NFaWVNiPtJG/bHCcAiDdbWFV7kNLaYOwbYyyPQR3b8Vz0RXHfE9zUHWVF5Z8zrrPD8aV33p5X/p1zkrYNaPoGjsP1PDBhr0U5KYy6oJOaEK0+DTYk1VryozaXFpTHTWWPVa2KI5DwNTR0s7AM+y/EajUmjbRbSKS6p1r2wIfSaSNuplMxcYSEBDxyfRM08aMrW4WhNFJat8HUb6v3t8TkUA0QHwnmaBZU4aaoANRAGpSCnbQB0D16jfJu3o2Rr+RhA/uQk/NIqnwXGojDo413dUO+SlZPBvV5Ym+DgdAUUm7eOqxL9wMFBSSnPU/su5GNgRyuXS276vhyT/9h2DYZN3nB/nXpwd47JcXNePdSlLrIccojpNl2YQMCBuJp5VCNFgETJ1aw4nfdDb669WhCRyWn5QeF8cPUCsqqedehiVAT8+Je0/y2eeR6L8xLFxkjpwJKKieNHIn30lw1xY0dzJJ7bqAbSGMME712F03ijOJ3Kn34514G96Jt5P7s/vwdO6HJX7csQFh2/zXsC44j1o1nZvp4ewODS+QUxQFRdfYtvcQRcU1zJ45gBHndwSgtCpAdW24VQ+GS9KJIlsUJwldV3DW7af4r4+S1KkneVc+RN2WjxCWSdr544g40tBVlbZX/JrKD14mUrobz5l9SO09FDtUh6K1aTBwRUxwtTuH/F88D2aYyhV/JLXvMKr//dqRjYkUlbZXzcZK69Rg942iQNWHizDK9wLgyOlA26kPUGf9uGMUti3wODV+f/dQPvniICkeB70Ks1GF3eAIhdBU5r6yke17q4Fo8/q+6T9h5/5D7DxQg9Ohyq1JJakJZIviJJFEiIq/P4OIBAl8vZ6Dr/8WHC4yhlyDX8/CQkOp3sfBvz2OO78LmUOvIaX3EGr/swKjrAg1Qf9T2NSotTzgcBEu+QY9pU387nXCpvrDV3BSf1aXw6ER+Hp9LEgAGOV7CezYiKr++D/HbctGtW0G9sqjb2EWwrQaXPSnKHDIF44FCYgGmjc/+obB57bnnMIskt2n9hoQSfqxyBbFSUJRBGZteey1HailbsPbpPcfRYrmRMGmcv3bmJUHqP74LwCoLg+5k+9CTc0mYjfhl72iomgObKP+VFg7FEBp4Oe1qiqEK+qPmUQq9uE6q+X6bQyjsVlOCpEGzgmGTfp0yeHiXu3Ask6aBXaSdDKTLYqThCU0XO3PjivTM3KxfNUcWPBLDv5pNmk9B5Lc/eLYcTscQE3OIKJ5mtSFYgodT9fzcWTl1csIm/aT0Rhq/UF5w7BI6Tm4XnlKzyFN+LJuOUIIvG08ZGfEJy+cOLiQjnlpCNOS3U6S1ETa7NmzZ7f0TZxowWDkpPsSSNJNPFoEl2ahqAqmHR+jLXQyuvbHrNyPVVuJK/8svGNuomL5H7B8VdghH/6vPiHn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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":["* Another way of evaluating our model is to compute the confusion matrix. \n","* The numbers on the diagonal of the confusion matrix correspond to correct predictions whereas the others imply false positives and false negatives."]},{"metadata":{"trusted":true},"cell_type":"code","source":["confusion_matrix(y_test, y_pred)"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":"array([[37,  5],\n       [ 2, 70]])"},"metadata":{},"execution_count":13}]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"output_type":"stream","name":"stdout","text":"Given our confusion matrix, our model has an accuracy of 93.85964912280701%\n"}],"source":["result=accuracy_score(y_test, y_pred)\n","print(\"Given our confusion matrix, our model has an accuracy of {}%\".format(result*100))"]},{"metadata":{},"cell_type":"markdown","source":["# Conclusion\n","\n","* The K-Nearest Neighbors algorithm doesn’t require any additional training when new data becomes available. \n","* The accuracy score for the model implemented by us and the model implemented by sklearn is the same.\n","* Rather it determines the K closest points according to some distance metric (the samples must reside in memory).\n","* Then, it looks at the target label for each of the neighbors and places the new found data point into the same category as the majority. \n","* Given that KNN computes distance, it’s imperative that we scale our data. \n","* In addition, since KNN disregards the underlying features, it’s our responsibility to filter out any features that are deemed irrelevant."]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3.8.2 64-bit","name":"python38264bitc9caf100f5ae416ba9a7bfc7259e69d8"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.8.2-final","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}