6.6-KNNClassifier.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"KNNClassifier.ipynb","provenance":[],"authorship_tag":"ABX9TyO73yBx+Qi/V1rnjvZQSoh0"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"M9E7xA3Pg1lY","colab_type":"code","colab":{}},"source":["import itertools\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.ticker import NullFormatter\n","import pandas as pd\n","import numpy as np\n","import matplotlib.ticker as ticker\n","from sklearn import preprocessing\n","%matplotlib inline\n","\n","from sklearn import metrics\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import confusion_matrix,accuracy_score"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"fy8aseibg3oY","colab_type":"code","outputId":"cbe4eff6-fbaf-4324-97ad-71a41bc92f68","executionInfo":{"status":"ok","timestamp":1589894209229,"user_tz":-360,"elapsed":1011,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":200}},"source":["url='https://raw.githubusercontent.com/FazlyRabbiBD/Data-Science-Book/master/data-telecom-customer.csv'\n","df = pd.read_csv(url)\n","df.head()"],"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>region</th>\n","      <th>tenure</th>\n","      <th>age</th>\n","      <th>marital</th>\n","      <th>address</th>\n","      <th>income</th>\n","      <th>ed</th>\n","      <th>employ</th>\n","      <th>retire</th>\n","      <th>gender</th>\n","      <th>reside</th>\n","      <th>custcat</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2</td>\n","      <td>13</td>\n","      <td>44</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>64.0</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>3</td>\n","      <td>11</td>\n","      <td>33</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>136.0</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>68</td>\n","      <td>52</td>\n","      <td>1</td>\n","      <td>24</td>\n","      <td>116.0</td>\n","      <td>1</td>\n","      <td>29</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2</td>\n","      <td>33</td>\n","      <td>33</td>\n","      <td>0</td>\n","      <td>12</td>\n","      <td>33.0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2</td>\n","      <td>23</td>\n","      <td>30</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>30.0</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   region  tenure  age  marital  ...  retire  gender  reside  custcat\n","0       2      13   44        1  ...     0.0       0       2        1\n","1       3      11   33        1  ...     0.0       0       6        4\n","2       3      68   52        1  ...     0.0       1       2        3\n","3       2      33   33        0  ...     0.0       1       1        1\n","4       2      23   30        1  ...     0.0       0       4        3\n","\n","[5 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"ryzcdcqThBzg","colab_type":"code","colab":{}},"source":["X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] \n","y = df['custcat']"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"arokE3A7hZuO","colab_type":"code","outputId":"7eb52fb6-f226-4759-e8de-3e4d7a68120f","executionInfo":{"status":"ok","timestamp":1589884091306,"user_tz":-360,"elapsed":757,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=4)\n","print ('Train set:', X_train.shape,  y_train.shape)\n","print ('Test set:', X_test.shape,  y_test.shape)"],"execution_count":29,"outputs":[{"output_type":"stream","text":["Train set: (750, 11) (750,)\n","Test set: (250, 11) (250,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TX4MhfjBjHh3","colab_type":"code","outputId":"87415b34-96a3-41db-b406-5234ad9d745c","executionInfo":{"status":"ok","timestamp":1589884092980,"user_tz":-360,"elapsed":1054,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["k_range = range(1, 26)\n","\n","# We can create Python dictionary using [] or dict()\n","scores = []\n","\n","# We use a loop through the range 1 to 26\n","# We append the scores in the dictionary\n","for k in k_range:\n","    knn = KNeighborsClassifier(n_neighbors=k)\n","    knn.fit(X_train, y_train)\n","    y_pred = knn.predict(X_test)\n","    scores.append(metrics.accuracy_score(y_test, y_pred))\n","\n","print(scores)"],"execution_count":30,"outputs":[{"output_type":"stream","text":["[0.272, 0.292, 0.272, 0.26, 0.304, 0.296, 0.34, 0.34, 0.332, 0.304, 0.3, 0.288, 0.292, 0.288, 0.3, 0.296, 0.296, 0.312, 0.324, 0.328, 0.32, 0.328, 0.332, 0.336, 0.34]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"GGxcOuaejUxq","colab_type":"code","outputId":"1aaf2228-a01f-4a5e-e44f-489ca6739c77","executionInfo":{"status":"ok","timestamp":1589884095406,"user_tz":-360,"elapsed":1696,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":297}},"source":["import matplotlib.pyplot as plt\n","\n","# allow plots to appear within the notebook\n","%matplotlib inline\n","\n","# plot the relationship between K and testing accuracy\n","# plt.plot(x_axis, y_axis)\n","plt.plot(k_range, scores)\n","plt.xlabel('Value of K for KNN')\n","plt.ylabel('Testing Accuracy')"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Testing Accuracy')"]},"metadata":{"tags":[]},"execution_count":31},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deXxU9dX48c/JDiEJZAFCWAJhMSCIGkAi1rpV3HfF1oq/to+1Vau1i7a1trW7VruobfVprVoXautSanGrj21FFgHZQfY1C/skQPbM+f0xd3QMkzAJc2c979drXpm5c++dcx3Jyf0u5yuqijHGGNNRSrQDMMYYE5ssQRhjjAnKEoQxxpigLEEYY4wJyhKEMcaYoNKiHUC4FBYWamlpabTDMMaYuLJkyZK9qloU7L2ESRClpaUsXrw42mEYY0xcEZFtnb1nTUzGGGOCsgRhjDEmKEsQxhhjgrIEYYwxJihLEMYYY4JyNUGIyHQRWSciG0XkriDv3yQiK0VkmYjMFZGxHd4fKiKHROTrbsZpjDHmSK4lCBFJBR4BzgPGAtd2TADAs6o6XlUnAvcBD3Z4/0HgVbdiNMYY0zk350FMBjaq6mYAEZkFXAKs8e+gqvUB+2cDH9YeF5FLgS3AYRdjND3w8tIqNu851K1jCvpkcu3koWSkWaumMeH0rzW72N/QwtUVQ8J+bjcTRAmwI+D1TmBKx51E5GbgDiADONPZ1ge4EzgH6LR5SURuBG4EGDp0aLjiNl1oam3nq88vQxVEQj9OFTwNrdx29ij3gjMmibR7lV++uZ6H397ISUP7cuVJg0lJ6cY/yhBEfSa1qj4CPCIinwbuBmYC3wd+qaqHpIvfQqr6GPAYQEVFha18FAE1dU2owoNXn8DlJw0O+bjbZi3l4bc3cP74gYwakONihMYkvv2HW7ht1lLe2bCXayqG8INLxoU9OYC7ndRVQOA9z2BnW2dmAZc6z6cA94nIVuB24NsicosbQZruqfY0AlCc16tbx91z4Vj6ZKZx14sr8XotlxvTU8t3eLjoobks3LKfn10+np9fOYGs9FRXPsvNBLEIGCUiw0UkA5gBzA7cQUQC2xsuADYAqOppqlqqqqXAr4CfqOrDLsZqQuRPECV9u5cgCvpk8t0Lx7Jk2wGeXthp6RdjTCdUlWcXbueq388H4IWbKpkx2d2mddeamFS1zfmr/3UgFXhcVVeLyL3AYlWdDdwiImcDrcABfM1LJoZVe5oQgQF5md0+9rITS3hpaRU/f/UDzi4fwKBuJhljklVTazvffXkVf12yk0+MLuLX10ykX3aG65/rah+Eqs4B5nTYdk/A89tCOMf3wx+Z6alqTyNFfTLJTOv+La2I8JPLxvOpX/6X7768ij/MrKCrPiZjDGzf18CXnlnC6up6vnLWKG47axSpLvQ3BGNjDk23VNc1HtNf/kPye/P1c8fw1ge7+ceKmjBGZkziefuD3Vz08Fx27G/g8RsquOOc0RFLDmAJwnRTlaex2/0PHd1QWcoJg/P4wezVHDjcEqbIjEkc7V7lwTfX87knFzGoby9eufU0zjxuQMTjsARhQqaqVHsaGdQ365jOk5oi/OyKCdQ1tvKjf64NU3TGJAZPQwuff3IRv3lrA5efOJiXvlzJ0ILeUYnFEoQJ2YGGVppavWHpXC4vzuWm08t44f2d/Hf9njBEZ0z8W1VVx4UPzWXexn38+LLj+cVV7g1hDYUlCBOyns6B6MwtZ45kRFE2335pJQ0tbWE5pzHx6vlFO7j8d/PwepXnb5rKZ6YMi/ogDksQJmQ9nQPRmaz0VH52+QR2HmjkwTfWh+WcxsSbptZ27nphBd98YQWTSvvxj1unMXFI32iHBViCMN3gTxDH2gcRaPLwfD4zZSiPv7uF5Ts8YTuvMfFgx/4Grvr9fGYt2sHNZ5Tx1OemUNCn+3OM3GIJwoSsuq6JzLQU8sM8QefO846jKCeTO19YQWu7N6znNiZW/Wf9Hi56eC5b9x7mf6+v4BvnHhfRIayhsARhQuYf4hrudtHcrHR+eMnxfFB7kMf+uzms5zYm1ni9ym/e2sANf3qPgblZ/OPWaZwzNvJDWENhCcKEzDfE1Z3yGJ8aN5Dzxw/k129tYFM315owJl7UNbTyhacW8+Cb67l0YgkvfflUSguzox1WpyxBmJCFYw5EV75/8Tiy0lL4llV8NQlodXUdFz08l3c27OGHl4zjwatPoFdG9IawhsIShAlJS5uX3QebwzbENZj+OVncfcFY3tuyn+cWbXftc4yJtL8u3sHlv51HS5uXv3xxKp+dWhr1IayhsARhQrKr3rdQULiGuHbmqorBVJYV8LM5H1Bb1+TqZxnjtua2dr790kq+8bcVnDS0H698ZRonDe0X7bBCZgnChOSjIa7uJgh/xdeWdi/3vrLa1c8yxk1VnkaufnQBzy7czk2nl/Hnz0+mMIaGsIYi6kuOmvhQXRf+ORCdKS3MZsakIfxl8Q7avRpzQ/+MOZq5G/Zy63Pv09qu/P66k5l+/MBoh9QjdgdhQlLt8TX3RGqRn/GD+9LU6mXL3sMR+TxjwsHrVR55eyPXP76QopxMZt9yatwmB3A5QYjIdBFZJyIbReSuIO/fJCIrRWSZiMwVkbHO9snOtmUislxELnMzTnN0VZ5GCrIzIlY4rLw4B4C1NfUR+TxjjlVdYys3/nkJ97++jgsnDOLlm09lRFGfaId1TFxLECKSCjwCnAeMBa71J4AAz6rqeFWdCNwHPOhsXwVUONunA4+KiDWHRZGbcyCCGdm/D2kpYgnCxIW1NfVc/PBc/r1uN9+7aCy/njGR3hnx/yvLzSuYDGxU1c0AIjILuARY499BVQP/9WcD6mxvCNie5d9uoqfa00hpQeQm9GSmpVJW1McShIl5Ly3dybdeXEluVjqzbjyFitL8aIcUNm4miBJgR8DrncCUjjuJyM3AHUAGcGbA9inA48Aw4LOqekQ9aBG5EbgRYOjQoeGM3XRQ7Wmisqwwop9ZXpzDgs37I/qZxoSqpc3Lj/65hqfmb2PK8Hwe+vSJ9M9xfxBHJEW9k1pVH1HVMuBO4O6A7QtVdRwwCfiWiBzxX15VH1PVClWtKCoqilzQSaa+qZVDzW2uz4HoqLw4l9r6JluW1MScmrpGrnlsPk/N38aNnxjBM1+YknDJAdxNEFXAkIDXg51tnZkFXNpxo6quBQ4Bx4c1OhOySM2B6Ki8OBewjmoTW+Zt2suFv5nL+tqD/PYzJ/Ht88tJS43639qucPOqFgGjRGS4iGQAM4DZgTuIyKiAlxcAG5ztw/2d0iIyDDgO2OpirKYLbqwDEQp/glhjCcLEAFXl9//ZxHV/WEi/7Az+fss0zh9fHO2wXOVaH4SqtonILcDrQCrwuKquFpF7gcWqOhu4RUTOBlqBA8BM5/BpwF0i0gp4gS+r6l63YjVdq3LmQES6iakoJ5PCPpmsrTkY0c818WXZDg8/e3Utd5wzhsnD3esg/taLK5m1aAcXTCjmvismkJ0Z/6OUjsbVK1TVOcCcDtvuCXh+WyfH/Rn4s5uxmdBVexpJT5WolAkoL86xJiYTlKryzMLt3PuPNbS0e/naX5fxxu2nu1Ih9V9rdjFr0Q6++IkR3HXecXFRaC8cErPhzIRVtaeRgXlZpESh5MXY4lw27j5kK82Zj2lsaefrf13B3S+vonJkAb+/7mR27G/kl/8K/9rmB5ta+e7fVzFmQA5f+9SYpEkOYLWYTAiqPY0McrHMd1fKi3Npafeyac8hjhuYG5UYTGzZtu8wNz39Ph/U1nP72aP4ypmjSEkRrp08hD+8s5mLJgxi/OC8sH3e/a+vo7a+id9+5iQy0pLrb+rkulrTI9Wepoj3P/j5O6o/sH4IA7y1dhcXPTSXak8jj98widvPHv3hne1d55VT2Ce8a5sv3rqfPy/Yxg2VpZwYR2W6w8UShOlSu1eprW+K+BBXvxFF2WSkplg/RJJr9yoPvrGOzz+5mCH5vXnl1mmcMab/x/bJ65XOvZccz5qaev73nWNf27y5rZ27XlzJoLxefP1TY475fPHIEoTp0u6DTbR7NWoJIj01hVED+thQ1yR24HALN/zpPX7zfxu56uTBvPClSobk9w667/TjBzJ93EB+9a8Nx1wJ+JG3N7Fx9yF+fNnxSTFiKRhLEKZL0ZoDEai8ONeGuiapFTs9XPjQXBZu3s9PLx/PfVdOOGpF4R9cMo7MtBTuemFFj9c2X1d7kN/9eyOXnVjCJzvcqSQTSxCmS9GaAxGovDiXvYea2XOwOWoxmMib9d52rvzdfAD+etNUrp08NKQRRANys/j2+eUs3LKf5xfvOOr+HbV7lTtfWEFOVjrfvbBjAerkYgnCdMl/B1EczQQx0NaGSCZNre1882/LuevFlUwZkc8/bp3GCUP6duscMyYN4ZQR+fx4zlp21XdvbfOn5m9l2Q4P91w4lvzsjG4dm2gsQZguVXsayc1Ko08U22CtJlNyuenpJTy/eCe3njmSJ/7f5B79khYRfnr5BJrbvHzv76Gvbb7zQAP3v76OT44p4pKJg7r9uYnGEoTpUqQXCgqmX3YGA3OzLEEkgdXVdfx73R6+ce4YvvapMce0HvnwwmxuP3sUr62u5bVVNUfdX1W5++VVAPzo0uOTakJcZyxBmC5Fcw5EIF/JDeuoTnRPzttKr/RUrjtlWFjO9z+njWBscS73/H01dY2tXe47e3n1h8lpcL/go6SSjSUI06XquujfQYCvmWnTnkM0t7VHOxTjkv2HW3h5WTWXn1RCXq/0sJwzPTWFn18xgb2HmvnZqx90+dk/+McaJg7py/VTS8Py2YnAEoTp1OHmNjwNrTGTINq8yoZdh6IdinHJrEXbaWnzMrOyNKznHT84jy+cNoLn3tvOgs37gu7zw1fWUN/Yys+vmHBMzVqJxhKE6VRNXfTnQPhZR3Via2v38vT8bZw6soDRA3LCfv6vnj2aofm9+daLK2lq/fhd6L/X7ealpVV8+ZNljBkY/s+OZ5YgTKdiYQ6E3/DCbLLSU6wfIkG9uWYX1XVNzHSpeadXRio/uWw8W/Ye5jdvbfhw++HmNr7z0irKirK5+cyRrnx2PLMEYToVC3Mg/FJThDEDbG2IRPWneVsZ3K8XZ5UPcO0zpo0q5KqTB/Pofzezptr3/9EDb6ynytPIz6+YQGZa+NeRiHeuJggRmS4i60Rko4jcFeT9m0RkpYgsE5G5IjLW2X6OiCxx3lsiIme6GacJrtrTSIrAgJzILxQUTHlxLmtr61HtWfkEE5vWVNfz3pb9XD91mOvt/9+5oJx+vdO584UVLNm2nz/N28JnTxlGRal7K9HFM9cShIikAo8A5wFjgWv9CSDAs6o6XlUnAvcBDzrb9wIXqep4fMuQ2upyUVDtaWJgblbMLMheXpyLp6GVXfVWciOR+Ie2XlMx1PXP6ts7g+9fPI6VVXV89o/vMSAni29OT85KraFw81/+ZGCjqm5W1RZgFnBJ4A6qGthekA2os32pqlY721cDvUQkNv6MTSKxMEkukHVUJ54Dh1t4eVkVl55YQl7v8AxtPZoLxhdzdnl/Glra+dGlx5OTFZnPjUdu1k8oAQIrZe0EpnTcSURuBu4AMoBgTUlXAO+r6hF/NorIjcCNAEOHuv/XR7KprmvkhMHdq4HjpuOKfSNM1tTUc8ZxyVthM5HMWrSD5jYvN4R5aGtXRIRfXjORlVV1VJYVRuxz41HU2w5U9RFVLQPuBO4OfE9ExgE/B77YybGPqWqFqlYUFRW5H2wS8XqVGk/0FgoKJjcrncH9etkdRIJoa/fy9IJtTB1REPHhpTlZ6ZYcQuBmgqgChgS8Huxs68ws4FL/CxEZDLwEXK+qm1yJ0HRq7+FmWtq9lMTAHIhAvrUhLEEkgn+t3UWVp5EbTi2NdiimE24miEXAKBEZLiIZwAxgduAOIjIq4OUFwAZne1/gn8BdqvquizGaTlQ7cyCK82LnDgJ8pb+37D18xGQnE3+emLeVkr69ONvFoa3m2LiWIFS1DbgFeB1YCzyvqqtF5F4RudjZ7RYRWS0iy/D1Q8z0bwdGAvc4Q2CXiYg1OkfQRyvJxViCKM7Fq74Vv0z8WltTz4LNkRnaanrO1SL/qjoHmNNh2z0Bz2/r5LgfAT9yMzbTNX+CiIVZ1IECRzJ1dxEZEzuemr+VrPQUrpk05Kj7muiJeie1iU3VniayM1LJ7RVbi7UPze9Ndkaq9UPEMU9DCy8treKyE0vo2zu5V2yLdZYgTFD+ORCxtmhKSoowZqCtDRHP/rJoB02t4a/aasLPEoQJKlbWgQjGSm7Er3av8tT8bZwyIp/jBuZGOxxzFEdNECLygDMfwSSRWJtFHai8OJeDTW3sPNAY7VBMN304tNXuHuJCKHcQa4HHRGShU1wvz+2gTHQ1tbaz91BLzM2B8LOSG/HriXdtaGs8OWqCUNU/qOqpwPVAKbBCRJ4VkTPcDs5ER01dbM6B8DtuYA4iWD9EnFlXe5D5m/dx3SnDYqYApOlaSN+SU5n1OOexF1gO3CEis1yMzURJrM6B8MvOTGNYfm+7g4gzT8zbSmZaCjNsaGvcOOoYRhH5JXAh8H/AT1T1Peetn4vIOjeDM9FRFaNzIAJZyY34UtfQyktLd3LpxBL6ZdvQ1ngRyh3ECmCiqn4xIDn4TXYhJhNlNZ4mRGBAXuxWWC8vzmXb/gYON7dFOxQTgr8s3m5DW+NQKAnCQ8Cdhoj0FZFLAVS1zq3ATPRUexop6pMZ00swlhfnogofWMmNmOcf2jp5eD5jB9nQ1ngSSoL4XmAiUFUP8D33QjLRFstzIPzKnbUhrJkp9r21dhc7DzTy/+zuIe6EkiCC7RNb9RdMWFV5GmO6/wF8/SM5WWmWIOLAk/O3Migvi3PG2tDWeBNKglgsIg+KSJnzeBBY4nZgJjpUlWpPI8V5sTkHwk9EKB9oHdWxbsOug7y7cR/XTbWhrfEolG/sVqAF+IvzaAZudjMoEz0HGlppavXGfBMT+JqZPqg9iNdrJTdi1V8W7SAjNYUZk2xJ4Hh01KYiVT0M3BWBWEwMiPU5EIHKi3NpaGln+/4GSguzox2O6UBVeW11LaeNKiTfhrbGpVBqMRWJyP0iMkdE/s//COXkIjJdRNaJyEYROSLJOKU7VjoLAs0VkbHO9gIReVtEDonIw92/LNNTsboORDBWciO2ra6uZ+eBRs49fmC0QzE9FEoT0zPAB8Bw4AfAVnzLiXbJmX39CHAeMBa41p8AAjyrquNVdSJwH/Cgs70J+C7w9RDiM2H00R1EbPdBAIwZmEOKWIKIVa+tqiU1RazuUhwLJUEUqOofgVZV/Y+qfg44M4TjJgMbVXWzqrYAs4BLAndQ1cB/2dmAOtsPq+pcfInCRFB1XROZaSlx0SSQlZ7K8MJs1lhNppj02upapgzPj4v/l0xwoQxXbXV+1ojIBUA1kB/CcSXAjoDXO4EpHXcSkZvxrUedQWiJJ/DYG4EbAYYOtU6wcPAPcY21hYI6U16cy9LtnmiHYTrYuPsgG3cf4vqpw6IdijkGodxB/Mgp8f01fE0+fwC+Gq4AVPURVS0D7gTu7uaxj6lqhapWFBUVhSukpFbtaaQ4DpqX/MqLc6nyNFLX2Hr0nU3EvL56FwCfGmv9D/GsywTh9COMUtU6VV2lqmeo6smqOjuEc1cBgWUbBzvbOjMLuDSE8xoXVXsaGRSjZb6DGet0VH9g/RAx5bVVtZw4tC8DY3w+jelalwlCVduBa3t47kXAKBEZLiIZwAzgY4lFREYFvLwA2NDDzzJh0NLmZffB5rgY4upnI5liz84DDaysquM8G70U90Lpg3jXGWr6F+Cwf6Oqvt/VQaraJiK3AK8DqcDjqrpaRO4FFjt3IbeIyNn4+jkOADP9x4vIViAXyHCKA35KVdd06+pMt+yqb0I1Poa4+g3IzaRf73Qr2hdD/M1L546zBBHvQkkQE52f9wZsU0LoUFbVOcCcDtvuCXh+WxfHloYQmwmjeJok5ycitjZEjHl9VS3lxbkMK7DJi/EulJnUtrRokqiui585EIHKi3N5ZuE22r1Kakp8jL5KVLsPNrFo235uP2t0tEMxYRDKinL3BNuuqvcG227iV7XHN+0knu4gwLdGdVOrly17DzOyf59oh5PU3lyzC1WYbv0PCSGUYa6HAx7t+GZGl7oYk4mSKk8jBdkZZKXH7kJBwVhHdex4bVUtwwuzGT3AEnUiCKWJ6YHA1yLyC3wdzybBxNscCL9RA/qQliKsrannohMGRTucpFXX0Mr8Tfv4wmkj4maipelaTwq098Y3p8EkmHibA+GXmZZKWVEfu4OIsrc+2EWbV615KYGE0gexEqdGEr7hqkV8fESTSRDVniYqywqjHUaPlBfnsGDz/miHkdReW1VLcV4WE0ryoh2KCZNQhrleGPC8Ddilqm0uxWOipL6plUPNbXE1ByJQeXEuLy+r5sDhFvpZcbiIa2hp4z/r93Dt5KGk2EiyhBFKE1MxsF9Vt6lqFdBLRI4oumfiWzzOgQhkHdXR9Z91e2hu81rzUoIJJUH8DjgU8Pqws83EGE9DC3+cu4XWdm+3j42ndSCC8SeIZTutsms0vLqqloLsDCaVhlLo2cSLUBKEqOqHi/6qqpfQmqZMhL26qpYfvrKGR/+zqdvHVjlzIOK1iakoJ5PxJXk8+MZ6Hp+7hYD/ZY3Lmtva+b8PdnPO2AE2UTHBhJIgNovIV0Qk3XncBmx2OzDTff67gN+8tZGNuw8dZe8jj01PFQr7ZLoRWkQ88z9TOOO4/tz7yhpufW4ph5utqywS5m3cx6HmNltaNAGFkiBuAirxler2L/pzo5tBmZ6p8jTSr3c6vTJS+faLK/F6Q/8rutrTyMC8rLjuYMzNSufR607mG+eOYc7KGi595F027eleojTd99qqWnIy06gsK4h2KCbMjpogVHW3qs5Q1f6qOkBVP62quyMRnOmeak8jZUV9+M4F5by3dT/PLdrerWPjcQ5ERykpws1njOTPn5/CvsMtXPLwu7y2qibaYSWstnYvb67dxZnl/clMi68Z+ObojpogRORJEekb8LqfiDzublimJ6o9TQzq24urTh7MqSML+NmcD6itC21Z72pPU9z2PwRz6shCXrl1GmX9+3DT0+/z0zlraetB573p2qKtB9h/uIXpVto7IYXSxDRBVT8cGqKqB4AT3QvJ9ITXq9TUNTLIWU/6J5eNp9Xr5e6XVx21w7bdq9TWN8XtENfODOrbi+e/eArXnTKUR/+7mev+uJA9B5ujHVZCeX11LZlpKZw+xpb8TUShJIgUEennfyEi+dgoppiz93Azre1KiTNMdVhBNnecM5p/rd3Fq6tquzx298Em2r2acAkCfGU4fnTpeB646gSWbvdw4UPvsGTbgWiHlRC8XuW1VbWcPrqI3hn2KyERhZIgHgDmi8gPReRHwDzg/lBOLiLTRWSdiGwUkbuCvH+TiKwUkWUiMldExga89y3nuHUicm6oF5SsgpXq/typwxlfksc9f19NXUNrF8fG9xyIUFxx8mBe/HIlmWmpzHhsPk/O22pDYY/R8p0eauubbHJcAgulk/op4HJgF1ALXO5s65KIpAKP4CsPPha4NjABOJ5V1fGqOhG4D3jQOXYsvjWsxwHTgd865zOdCDYTOi01hZ9dMZ4DDS38ZM7aTo+titN1ILpr3KA8/nHLND4xqojvzV7NV/+yjIYWGwrbU6+triUtRTirfEC0QzEuCamaq6quUdWHgVeBK0RkdQiHTQY2qupmVW0BZgGXdDhvYF2EbD4qCngJMEtVm1V1C7DROZ/pxIcJosNIpHGD8rjxEyP4y+IdzNu4t8tji/MS9w7CL693Ov97fQVfO2c0f19ezeW/nYenoSXaYR2zZTs83P/6BxGb+6GqvL6qlsqRheT1So/IZ5rIC2UU0yAR+aqILAJWO8fMCOHcJcCOgNc7nW0dz3+ziGzCdwfxlW4ee6OILBaRxXv27AkhpMRV5WkkOyOV3F5HtgXfdtYoSgt6862XVtLY0n7E+9WeRnKz0sjJSo5/6Ckpwq1njeLxGybxQe1Bnl6wLdohHbPvzV7NI29v4rLfvsvmCMz9WLfrIFv3NdjopQTXaYJwfvm+DfwbKAA+D9So6g9UdWW4AlDVR1S1DLgTuLubxz6mqhWqWlFUlNyjKKo9H41g6igrPZWfXD6ebfsa+NVb6zs9NtmcMaY/p40q5M8LtvWoflWsWLr9AMt3eLjipMHsOdjMxQ+/y2tHGZhwrF5bVYsInDPWmpcSWVd3EA87739aVe9W1RV81AQUiipgSMDrwc62zswCLu3hsUnPPweiM5VlhcyYNIQ/vLOFVVV1RxybSHMguuOGylJ21Tfz+mp3f6G66cl5W+mTmcYPLhnHK185jbKibG56egk/e/UD1+Z+vLaqlknD8inKid/SLOboukoQxcBzwAPOSKIfAt1pg1gEjBKR4SKSga9ZanbgDiIyKuDlBcAG5/lsYIaIZIrIcGAU8F43Pjvp+OdAdOVb55eTn53BnS+s+NgvjuoQjk1UnxzTn6H5vXni3a3RDqVHdh9s4p8ra7jy5MH0yUyjpG8vnr9pKp+eMpTf/2cTn/3je+w9FN65H1v3HuaD2oNWeykJdJogVHWfqv5eVU8HzgI8wC4RWSsiPznaiZ1FhW7Bt371WuB5VV0tIveKyMXObreIyGoRWQbcAcx0jl0NPA+sAV4DblbVIxvPDQBNre3sPdTy4RyIzuT1SueHl4xjdXU9f5i7BYDDzW14GlqTNkGkpgjXTx3G4m0HjrizigfPLtxOa7sys7L0w22Zaan85LLx3H/lBN7ffoALfzOX97eHb+6H/27r3HHWvJToQh3FtFNVH1DVCnwjjEKq36Cqc1R1tKqWqeqPnW33qOps5/ltqjpOVSeq6hlOYvAf+2PnuDGq+mr3Ly151NSFPkx1+vHFnDtuAL98cz1b9x6mpi7x50AczVUVQ+iVnsoT87ZGO5RuaWnz8szC7XxyTBHDC7OPeP+qiiG8+OVK0tOEax6dz5/nh2fux2uraxlfksfgfr2P+TiWvxUAAB2+SURBVFwmtoWUIAKp6npVtTWpY0h3V4O795LjyUhL4VsvrmTngfheSS4c8nqlc8XJJcxeXs2+MDfHuOnVVTXsOdjMDQF3Dx2NG5THK7ecxrSRhXz376v52vPLg45kC1VNXSNLt3tsclyS6HaCMLGnqpM5EJ0ZkJvFt88vZ/7mfTzy9kbfsUmcIABmTi2lpc3LrEU7jr5zjHhi3laGF2bziVFdj+DL653OH2dO4o5zRvPSsiou++27bN17uEef+cbqXQCca8Nbk4IliARQ7WlEBAbkhT6i5JqKIUwZns+irQdIERiQ5KNRRg3IYdrIQp6OkyGvy3d4WLrdw8ypw0JawyMlRfjKWaP40w2TqK1v4qKH5/KvNbu6/bmvraplVP8+jOzfpydhmzhz1ApbInJSkM11wDanI9pEWbWnkaI+md2qx5+SIvz08vFM//U7FGZnkJZqfyvMrCzlf55azBurd3HBhOJoh9OlJ+dtJTsjlStOHtyt4z45pj//uGUaX3pmCV94ajHjS/K6tUjUyp0ebj5jZHfDNXEqlBKMvwVOAlYAAhyPb0Z1noh8SVXfcDE+E4Kaup6V6h5R1If7r5yAp4tCfsnkzOP6MyS/F0/O2xrTCWLPwWb+saKaz0wZ1qPZ70Pye/O3myr55b/W80HNwW4de1b5AK6ZNOToO5qEEEqCqAY+7x9h5BTSuxf4JvAiYAkiyqo8jZQPzO3RsZdMPKKCSdJKTRGuP6WUH89Zy+rqOsYNyot2SEE9955vaOv1U4f1+BxZ6al867zyMEZlElEo7QqjOww/XQMcp6qb3QvLhEpVnVIZyTtMNZyudoa8PhmjQ15b2rw8vWAbp48uYkSR9QMYd4WSIFaLyO9E5HTn8VtgjYhkAtY2EWUHGlppavUm/SikcMnrnc5lJ5Xw8rJq9h+OvSqvr62uZfdRhrYaEy6hJIgb8JXbvt15bHa2tQJnuBWYCc1HpbotQYTLDZX+Ia/box3KEZ54dwulBb05fXRyF6c0kRHKgkGNzizqy5zHL1S1QVW9qup+XWHTJf8ciGQttueG0QNyqCwr4On521wrdtcTK3Z6eH+7h+unlnZr5JExPRXKehCnisibIrJeRDb7H5EIzhxdMiwXGg03VJZSXdfEmz2YK+CWJ+ZtpXdGKldWdG9oqzE9Fcoopj8CXwWWAFYwL8bU1DWRmZZCfnZGtENJKGeVD2Bwv178ad5Wzhsf/SGvew8188ryGmZMHkJukizsZKIvlD6IOlV9VVV3OxVe96nqPtcjMyGp8jRS0slCQabn/FVe39uynzXV9Uc/wGXPLdxOS7uX66eWRjsUk0RCSRBvi8j9IjJVRE7yP1yPLIYt3rqfK383L2Lr/3YlWVeDi4SrK4aQlZ5yTENed+xvYMZj8/nuy6toau3ZDXhru5enF27jtFGFVuLCRFQoCWIKUAH8BHjAefzCzaBi3YtLq1i87QALt0T/RsrmQLinb+8MLjtxMC8vq+JAD4a8vr1uNxc+NJcVO+v484JtXPX7+ew80NDt87y2qpZd9Ta01UReKKOYzgjyODMSwcWq+Zv2fexntLS0edl9sNnuIFw0s3IYzd2s8ur1Kr98cz2fe2IRg/r24tXbTuOxz57M1r2HufChufx3/Z5uxfDkvK0MK+jNGWP6dzd8Y45JpwlCRK5zft4R7BHKyUVkurNc6UYRuSvI+3eIyBoRWSEib4nIsID3fi4iq5zHNT25ODfU1DWyxSmVPC/KCWJXfROqoZf5Nt133MBcpo4o4OkFoQ159TS08LknF/HrtzZw2YklvPilSoYVZPOpcQOZfes0BuRkMfNP7/HQWxvweo++eM+qqjoWbzvAZ08JrWqrMeHU1R2Ef4mqnCCPozaEikgq8AhwHjAWuNap4xRoKVChqhOAvwH3OcdegK9A4ER8TVxfF5GeFRsKM/9dw/RxA1lTU9+jpodwqermQkGmZ2ZWllLlaeRfa7se8rqqqo4LH5rLuxv38qNLj+eBq06gV8ZHFXaHF2bz0s2VXHzCIB54cz3/89Ri6o5SKNE/tPWqCiuQZyKvqzWpH3We/ktVfxD4AN4K4dyTgY2qullVW4BZ+JYrDfyMt1XV3yi7APAP8B4L/FdV21T1ML5KstNDvyz3zNu0j7690/nctOGoEtV+CJsDERlnl/enpG+vLpckfX7xDi7/3TzavcrzX5zKdacMCzqyrHdGGr+6ZiI/uHgc/1m/h4sentvpKKl9h5qZvbyay08qIa+XDW01kRdKJ/VDIW7rqAQIbLjd6WzrzOcB/9rTy4HpItJbRArxlfQ44k8oEblRRBaLyOI9e7rXrtsTqsr8TfuYOqKAiUP60jsjNar9EN1Zi9r0XFpqCp+dOowFm/eztubjv8ybWtv51osr+ObfVlAxrB+v3DqNE4f26/J8IsLMylL+8sVTaG5r57LfvssLS3Yesd+sRTtoafMy04a2mijpqg9iqoh8DSjq0P/wfSD0lWlC4PR3VAD3AzhrTMwB5gHPAfMJMklPVR9T1QpVrSgqcr82zY79jVR5GplaVkBGWgoVpflR7Yeo8jRSkJ1BVnpYvw4TxIxJviGvT83f+uG2nQcauPrR+Tz33g6+9MkynvrcZAr6hL4y38nD8nnl1tOYOKQvX/vrcu5+eSXNbb7/zVvbvfx5/jamjSxk1ICcMF+NMaHp6g4iA19fQxof73+oB64M4dxVfPyv/sHOto8RkbOB7wAXq+qHK8ar6o9VdaKqnoNvoaL1IXymq+Zt2gtAZVnBhz837D7E7oNNUYnH5kBETt/eGVw6sYSXllbhaWjhv+v3cOFDc9my5zCPffZk7px+XI9W5SvKyeSZL0zhxk+M4OkF27n60QVUexp5Y/UuauubbGiriapOS22o6n+A/4jIE6q6DUBEUoA+qhrK1NJFwCgRGY4vMcwAPh24g4icCDwKTFfV3QHbU4G+qrpPRCYAE4iBhYnmbdpHUU4mZU4dfn+iWLB5PxefMCji8VR7GhlemH30HU1YzKwsZdaiHXzhycUs2X6A0f1z+P1nTz7m7yAtNYVvn1/OxCF9+cZfl3PhQ3PJz85gSH4vzjjOhraa6AnlT56fikiuiGQDq/CtBfGNox3krFd9C/A6sBZ4XlVXi8i9InKxs9v9+O5S/ioiy0RktrM9HXhHRNYAjwHXRXv9a1Vl/mZf/4O/83HcoDxystKY79xZRDqeqgN2BxFJ5cW5nDIin8XbDnDxCYN46ebKsCbo88cX8/dbppGfncHG3YeYObWUVBvaaqIolGJ9Y1W1XkQ+g68T+S58hfvuP9qBqjoHX19C4LZ7Ap6f3clxTfhGMsWMTXsOsedg84d3DeCr1zNleEFU+iHqm9o43NJucyAi7L4rTmBtbT2fGjvAlfpXI/v34e83n8qrq2q56IToFwk0yS2UO4h0EUkHLgVmq2orcPQZPgnGnwQqywo/tr2yrIBt+xo+nJMQKdU2ByIqhhb05txxA10tjpidmcaVJw8mM80GH5joCiVBPApsxTdx7r/ObOfol7eMsPmb9lHStxdD8j/+C3mqc0cR6eGuNXU2B8IY465QajH9RlVLVPV89dlGki016vU6/Q9lBUf85ThmQA752RkfjnCKlCqPb+SUrSRnjHFLKCvKDRCRP4rIq87rscBM1yOLIWtr6/E0tH6s/8EvJUWYOqKABZv2oRq5lrdqTyPpqUJhN8bdG2NMd4TSxPQEvpFI/nGc64Hb3QooFvmbj6YGSRAAp5QVUF3XxLZ93S/l3FPVnkaK83pZATdjjGu6mkntH+FUqKrPA174cPhqUi09On/TPoYXZlPcyYgh/51FJEcz2ToQxhi3dXUH8Z7z87CIFOCMXBKRU4A6twOLFW3tXhZu2d/p3QPAiMJsBuRmMn9zJBNEkw1xNca4qqt5EP62izuA2UCZiLwLFBFaqY2EsLKqjkPNbUH7H/xEfP0QczfuRVVdXx+63avU1jfZEFdjjKu6ShBFAQsDvYRvwpsAzcDZ+EpwJzz/XcEpIzpPEOCbH/Hysmo27D7EaJeLq+0+2ES7Vy1BGGNc1VUTUyq+Mhg5+OZApDnbejvbksL8TfsYMyDnqKOFIjkfwtaBMMZEQld3EDWqem/EIolBzW3tLNq6nxmThh513yH5vRncrxfzNu1lpssVOG0OhDEmErq6g0j68ZPLd9TR1Ortsv8hUGVZAQs27w9preFj4b+DKLYEYYxxUVcJ4qyIRRGj5m3aiwhMGR5qgiikrrGVNTXuViKp9jSS1yudPpmh1Fo0xpie6WpN6v2RDCQWzdu0j+MH5ZHXO7T1gCPVD2ELBRljIqH7S2AlicaWdpZuPxBy8xLAgNwsRhRlu16XqcrTxKA866A2xrjL1QQhItNFZJ2IbBSRu4K8f4eIrBGRFSLyllMp1v/efSKyWkTWishvxO3JBR0s2XaA1nbllG4kCPD1Q7y3ZT+t7V6XIvNVcrU7CGOM21xLEM6yoY8A5+Fb/Odap9BfoKVAhapOAP4G3OccWwmcim+p0eOBScDpbsUazLxNe0lLESaV5nfruKkjCjnc0s7KKncmmx9ubsPT0GoJwhjjOjfvICYDG1V1s6q2ALOASwJ3UNW3VdVf4W4BMNj/FpAFZACZ+JYg3eVirEeYt2kfJwzp2+2O4FNG+BKKW/0Qtg6EMSZS3EwQJcCOgNc7nW2d+Ty+JU1R1fnA20CN83hdVdd2PEBEbhSRxSKyeM+ePWEL/GBTKyur6rrV/+BX0CeT4wbmuJYgbA6EMSZSYqKTWkSuAypw1rkWkZFAOb47ihLgTBE5reNxqvqYqlaoakVRUVHY4lm0dT/tXmXqUcprdKayrJBFW/fT3Bb+ore21KgxJlLcTBBVwJCA14OdbR8jImcD3wEuVtVmZ/NlwAJVPaSqh/DdWUx1MdaPmbdxHxlpKZw0rF+Pjp9aVkBzm5el2z1hjsyXIFJThP45tlCQMcZdbiaIRcAoERkuIhnADHxVYT8kIifiW/P6YlXdHfDWduB0EUkTkXR8HdRHNDG5Zd6mfZw8tB9Z6T1bNH7y8HxSxJ1+iCpPIwNyMklLjYmbP2NMAnPtt4yzsNAt+FajWws8r6qrReReEbnY2e1+fAUB/yoiy0TEn0D+BmwCVgLLgeWq+g+3Yg104HALa2vru1z/4WjyeqUzviTPlQRhk+SMMZHiaq0GVZ2Dr0x44LZ7Ap6f3clx7cAX3YytMwu37EOVHnVQBzqlrIDH526hsaWdXhk9uxMJpqauiRMG9w3b+YwxpjPWTtHBvE376J2RyoRj/CVcWVZIa7uyeFv4KpZ4vUqNxxYKMsZEhiWIDuZv2sek0nwy0o7tP82k0n6kpUhY16nee7iZlnYvJTYHwhgTAZYgAuw+2MSG3YeOqf/Br3dGGhOH9A1rgqh25kDYHYQxJhIsQQTwdyofa/+DX2VZASt3eqhvag3L+WwOhDEmkixBBJi/aR85WWmMG5QXlvNNLSvEq7BoS3j6ISxBGGMiyRJEgPmb9zFleAGpKeEpHHvi0L5kpKWErZmpytNIdkYquVm2UJAxxn2WIBw7DzSwbV9D2JqXALLSU6kY1i9sCcI/ginClc+NMUnKEoTjw/6HkeFLEODrh1hbU8+Bwy3HfK5qWwfCGBNBliAc8zfvIz87g9H9c8J6Xv+IqAWbj/0uwmZRG2MiyRIEoKrM37SPqSMKSAlT/4PfhMF96Z2ReszNTE2t7ew91GJzIIwxEWMJAti6r4GauqawzH/oKD01hcnD85l/jHcQNXU2B8IYE1mWIAj//IeOpo4oYOPuQ+yub+rxOWyIqzEm0ixB4Ft/ekBuJsMLs105f2VZIcAx3UVU+RNEniUIY0xkJH2C8Pc/VJYVujZ8dOygXHKz0o6p/HeNpwkRGJBnCwUZYyIj6RPEzgONHGxuc6X/wS81RThtVBFvrNlFU2vPliGt9jRS1CeTzLTwlQ43xpiuJH2CGJLfmxXf+xQXTRjk6ud8ZspQ9h9u4ZUVNT063uZAGGMizdUEISLTRWSdiGwUkbuCvH+HiKwRkRUi8paIDHO2n+GsMOd/NInIpW7FmZWeGtZFfYKZWlbA6AF9+NO7W1DVbh9f5WmkxBKEMSaCXEsQIpIKPAKcB4wFrhWRsR12WwpUqOoEfMuM3gegqm+r6kRVnQicCTQAb7gVaySICDMrS1ldXc+SbQe6dayqOpPkbA6EMSZy3LyDmAxsVNXNqtoCzAIuCdzBSQQNzssFwOAg57kSeDVgv7h12Ykl5Gal8cS8rd067kBDK02tXmtiMsZElJsJogTYEfB6p7OtM58HXg2yfQbwXLADRORGEVksIov37NnT40AjpXdGGtdMGsKrq2qprQt9ToTNgTDGRENMdFKLyHVABXB/h+3FwHjg9WDHqepjqlqhqhVFRUXuBxoG108txavKMwu3hXxMtc2BMMZEgZsJogoYEvB6sLPtY0TkbOA7wMWq2tzh7auBl1Q1PEuyxYAh+b0567gBPLtwe8hDXj+6g7A+CGNM5LiZIBYBo0RkuIhk4Gsqmh24g4icCDyKLznsDnKOa+mkeSme/b9TS9l3uIV/hjjktbquicy0FPKzM1yOzBhjPuJaglDVNuAWfM1Da4HnVXW1iNwrIhc7u90P9AH+6gxn/TCBiEgpvjuQ/7gVY7RUlhUwqn8fnpi3NaQhr/4hrrZQkDEmklxdu1JV5wBzOmy7J+D52V0cu5WuO7Xjln/I690vr+L97R5OHtavy/1tHQhjTDTERCd1MrrsxBJyQhzyanMgjDHRYAkiSrIz07i6YgivrqxhVxdlwFvavOw+2Gx3EMaYiLMEEUXXTx1GuyrPLOh8yOuu+iZUbYirMSbyLEFE0bCCbM4c059n39tOc1vwIa82Sc4YEy2WIKLshlNL2Xuo8yGv1XU2B8IYEx2WIKJs2shCyoqyOx3yWu2xtaiNMdFhCSLKRIQbKktZsbOOpTs8R7xf5WmkIDuDrHRbKMgYE1mWIGLA5ScNJiczjSeDDHm1ORDGmGixBBEDsjPTuKpiCP9cUcPuDkNebQ6EMSZaLEHEiA+HvC7c/rHt1Z4mu4MwxkSFJYgYUVqYzRlj+vPMwu20tHkBqG9q5VBzm82BMMZEhSWIGDKzspS9h5qZs9I35NXmQBhjoskSRAw5bWQhI4qy+ZPTWW3rQBhjoskSRAxJSRFmTi1l+Q4PS7cfoMqZA1FidxDGmCiwBBFjrjh5MH2cIa/VnkbSU4XCPpnRDssYk4QsQcSYPplpXHnyYP65soblOzwU5/UiJcUWCjLGRJ6rCUJEpovIOhHZKCJ3BXn/DhFZIyIrROQtERkW8N5QEXlDRNY6+5S6GWssuX7qMFrblXmb9ln/gzEmalxLECKSCjwCnAeMBa4VkbEddlsKVKjqBOBvwH0B7z0F3K+q5cBkINia1QlpRFEfPjmmCLARTMaY6HHzDmIysFFVN6tqCzALuCRwB1V9W1UbnJcLgMEATiJJU9U3nf0OBeyXFGZWlgK2DoQxJnrcTBAlwI6A1zvpeo3pzwOvOs9HAx4ReVFElorI/c4dyceIyI0islhEFu/ZsydsgceC00cV8ZWzRnHpiYOiHYoxJknFRCe1iFwHVAD3O5vSgNOArwOTgBHADR2PU9XHVLVCVSuKiooiFG1kpKQId5wzmpH9c6IdijEmSbmZIKqAIQGvBzvbPkZEzga+A1ysqs3O5p3AMqd5qg14GTjJxViNMcZ04GaCWASMEpHhIpIBzABmB+4gIicCj+JLDrs7HNtXRPy3BWcCa1yM1RhjTAeuJQjnL/9bgNeBtcDzqrpaRO4VkYud3e4H+gB/FZFlIjLbObYdX/PSWyKyEhDgf92K1RhjzJEk2DKX8aiiokIXL14c7TCMMSauiMgSVa0I9l5MdFIbY4yJPZYgjDHGBGUJwhhjTFCWIIwxxgSVMJ3UIrIH2Oa8LAT2RjGcaErma4fkvv5kvnZI7us/lmsfpqpBZxonTIIIJCKLO+uVT3TJfO2Q3NefzNcOyX39bl27NTEZY4wJyhKEMcaYoBI1QTwW7QCiKJmvHZL7+pP52iG5r9+Va0/IPghjjDHHLlHvIIwxxhwjSxDGGGOCSqgEISLTRWSdiGwUkbuiHU+kichWEVnpVMZN+MqFIvK4iOwWkVUB2/JF5E0R2eD87BfNGN3SybV/X0SqnO9/mYicH80Y3SIiQ0TkbRFZIyKrReQ2Z3vCf/ddXLsr333C9EE4S5KuB87Bt+DQIuBaVU2adSREZCtQoapJMVlIRD4BHAKeUtXjnW33AftV9WfOHwn9VPXOaMbphk6u/fvAIVX9RTRjc5uIFAPFqvq+iOQAS4BL8a06mdDffRfXfjUufPeJdAcxGdjorELXAswCLolyTMZFqvpfYH+HzZcATzrPn8T3jyfhdHLtSUFVa1T1fef5QXzrzZSQBN99F9fuikRKECXAjoDXO3HxP1yMUuANEVkiIjdGO5goGaCqNc7zWmBANIOJgltEZIXTBJVwTSwdiUgpcCKwkCT77jtcO7jw3SdSgjAwTVVPAs4DbnaaIZKW+tpPE6MNNTS/A8qAiUAN8EB0w3GXiPQBXgBuV9X6wPcS/bsPcu2ufPeJlCCqgCEBrwc725KGqlY5P3cDL+Frdks2u5x2Wn977e6j7J8wVHWXqrarqhffEr0J+/2LSDq+X5DPqOqLzuak+O6DXbtb330iJYhFwCgRGS4iGcAMYHaUY4oYEcl2Oq0QkWzgU8Cqro9KSLOBmc7zmcDfoxhLRPl/OTouI0G/fxER4I/AWlV9MOCthP/uO7t2t777hBnFBOAM7foVkAo8rqo/jnJIESMiI/DdNQCkAc8m+vWLyHPAJ/GVOt4FfA94GXgeGIqv/PvVqppwnbmdXPsn8TUxKLAV+GJAm3zCEJFpwDvASsDrbP42vrb4hP7uu7j2a3Hhu0+oBGGMMSZ8EqmJyRhjTBhZgjDGGBOUJQhjjDFBWYIwxhgTlCUIY4wxQVmCMHHFqWR5bodtt4vI77o45t8i4upi9iLynFPm4Ksdtn9fRL7uPM9yqox+P8jxV4nIWhF5+xhiOBTw/HwRWS8iw5wYGkSkfyf7qog8EPD668FiNMnHEoSJN8/hmwQZaIazPSpEZCAwSVUnqOovO9knA9/s1yWq+v0gu3we+B9VPSPEz0zr4r2zgN8A56nqNmfzXuBrnRzSDFwuIoWhfLZJHpYgTLz5G3CB8wvXX7BsEPCOiPxORBY7dfJ/EOzgDn85XykiTzjPi0TkBRFZ5DxODXJsloj8SXxrbiwVEf8v8zeAEqcO/2lBPjYN+AuwQVWPWKdERO4BpgF/FJH7O/scEblBRGaLyP8Bb3VyfZ/AV2rhQlXdFPDW48A1IpIf5LA2fGsafzXIeyaJWYIwccWZGfsevoKE4Lt7eN4pzvYdVa0AJgCni8iEbpz618AvVXUScAXwhyD73OwLQcfjm7n6pIhkARcDm1R1oqq+E+S4bwItqnp7J9d0L7AY+IyqfqOLzwE4CbhSVU8PcqpMfDPJL1XVDzq8dwhfkritk+t/BPiMiOR18r5JQpYgTDwKbGYKbF66WkTeB5YC44Cx3Tjn2cDDIrIMX02fXKdiZqBpwNMAzi/gbcDoEM49F6gUkVD2PdrnvNlF+YhWYB6+5qpgfgPM9NfsCuRUBH0K+EqIMZokYAnCxKO/A2eJyElAb1VdIiLDga8DZ6nqBOCfQFaQYwNrywS+nwKc4twFTFTVElU9RHj8F7gdeLVDUbWeONzFe158K4tNFpFvd3xTVT3As/juUIL5Fb7kkn2MMZoEYQnCxB3nF/fb+JpM/HcPufh+edaJyAA+aoLqaJeIlItICr6ql35vALf6X4jIxCDHvgN8xnl/NL6icOtCjPkF4BfAayLS9yi7H8vnNAAX4GsuCnYn8SDwRXz9Ih2P3Y+v2F1ndyAmyViCMPHqOeAE5yequhxf09IH+P5KfreT4+4CXsHXFBNY7fIrQIUzVHUNcFOQY38LpIjISnydzjeoanOoAavq7/BV3J0d0KcQzLF+zn5gOnC3iFzc4b29TgyZnRz+AL4KscZYNVdjjDHB2R2EMcaYoCxBGGOMCcoShDHGmKAsQRhjjAnKEoQxxpigLEEYY4wJyhKEMcaYoP4/1y2OMlQgQkcAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"DFr1w0agi4Aq","colab_type":"code","outputId":"d801d0ec-da3d-46d1-92bc-fb61d81be6bd","executionInfo":{"status":"ok","timestamp":1589884125469,"user_tz":-360,"elapsed":1144,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["from sklearn.neighbors import KNeighborsClassifier\n","\n","k = 7\n","#Train Model and Predict  \n","neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n","neigh"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n","                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n","                     weights='uniform')"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"code","metadata":{"id":"RXNUHRf1bUci","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":337},"outputId":"857a8742-cada-4288-f2e3-21d1b8c39483","executionInfo":{"status":"ok","timestamp":1589884127298,"user_tz":-360,"elapsed":1095,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["predictions = neigh.predict(X_test)\n","print('Classification Report:\\n',classification_report(y_test, predictions))\n","\n","print('Confusion Matrix:\\n',confusion_matrix(y_test, predictions))\n","print('Accuracy Score:',accuracy_score(y_test, predictions))"],"execution_count":35,"outputs":[{"output_type":"stream","text":["Classification Report:\n","               precision    recall  f1-score   support\n","\n","           1       0.34      0.53      0.41        64\n","           2       0.36      0.29      0.32        51\n","           3       0.34      0.36      0.35        70\n","           4       0.31      0.17      0.22        65\n","\n","    accuracy                           0.34       250\n","   macro avg       0.34      0.34      0.33       250\n","weighted avg       0.34      0.34      0.33       250\n","\n","Confusion Matrix:\n"," [[34 10 14  6]\n"," [10 15 17  9]\n"," [27  9 25  9]\n"," [29  8 17 11]]\n","Accuracy Score: 0.34\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9cNiQ26HjZzO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"eede69cd-6c0e-46c8-ad20-2f265767d0d7","executionInfo":{"status":"ok","timestamp":1589884262932,"user_tz":-360,"elapsed":1289,"user":{"displayName":"Fazly Rabbi Khan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMXAHw3161kgjaCR7P1g9H9jC98PuMWQQDNeNmMQ=s64","userId":"01152345321765059291"}}},"source":["new_observation = [[2,11,0,22,0,1,40,3,5,0,1]]\n","neigh.predict(new_observation)"],"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1])"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"Ikf7as8se9On","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}