K Nearest neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "composite-image",
   "metadata": {},
   "source": [
    "# **K Nearest Neighbors Classification**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "functioning-statistics",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "other-albany",
   "metadata": {},
   "source": [
    "## Visualization for K nearest neighbors\n",
    "\n",
    "<img src = \"https://ibm.box.com/shared/static/mgkn92xck0z05v7yjq8pqziukxvc2461.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dominican-performer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import sklearn \n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.ticker as ticker\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import wget\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-uniform",
   "metadata": {},
   "source": [
    "## ***Get the data***\n",
    "\n",
    " ### 1- Basic Service    2- E-Service    3- Plus Service    4- Total Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "vital-lawyer",
   "metadata": {},
   "outputs": [],
   "source": [
    "# wget.download(\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv\", \"Telecust.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "miniature-cisco",
   "metadata": {},
   "outputs": [
    {
     "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  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tele_data = pd.read_csv(\"Telecust.csv\")\n",
    "tele_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "noticed-insurance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
      "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
      "      dtype='object')\n",
      "\n",
      "\n",
      "Size:  (1000, 12)\n"
     ]
    }
   ],
   "source": [
    "print(tele_data.columns)\n",
    "print(\"\\n\\nSize: \",tele_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "express-probe",
   "metadata": {},
   "source": [
    "## ***Data Visualization and Analysis***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "nasty-hardware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See how many of each category are present in dataset\n",
    "\n",
    "tele_data[\"custcat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "increased-aruba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "sensitive-optimum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='income', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "sns.histplot(data=tele_data.income, color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "excellent-landing",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "affecting-model",
   "metadata": {},
   "source": [
    "### Extract features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "heard-kitty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
       "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tele_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "environmental-williams",
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  \n",
       "0       2  \n",
       "1       6  \n",
       "2       2  \n",
       "3       1  \n",
       "4       4  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inputs\n",
    "\n",
    "x = tele_data[['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
    "       'employ', 'retire', 'gender', 'reside']]\n",
    "\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "twenty-coverage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    3\n",
       "3    1\n",
       "4    3\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = tele_data.custcat\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acting-baltimore",
   "metadata": {},
   "source": [
    "## Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "balanced-firmware",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x= preprocessing.StandardScaler().fit(x).transform(x.astype(float))\n",
    "# x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "tribal-county",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  \n",
       "0       2  \n",
       "1       6  \n",
       "2       2  \n",
       "3       1  \n",
       "4       4  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interior-editor",
   "metadata": {},
   "source": [
    "## Split the data into train & test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sorted-shelf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set:  (800, 11)\n",
      "Test set:  (200, 11)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.2,random_state=42)\n",
    "print(\"Training set: \",train_x.shape)\n",
    "print(\"Test set: \",test_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "lonely-hands",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
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       "      <td>37</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     region  tenure  age  marital  address  income  ed  employ  retire  \\\n",
       "29        2      13   33        1        9    19.0   4       0     0.0   \n",
       "535       3      31   34        0        9   105.0   4       7     0.0   \n",
       "695       1      72   75        0       37    33.0   1      44     1.0   \n",
       "557       1      10   23        0        4    36.0   2       1     0.0   \n",
       "836       1      72   59        1       12    65.0   3      21     0.0   \n",
       "\n",
       "     gender  reside  \n",
       "29        1       2  \n",
       "535       1       4  \n",
       "695       1       1  \n",
       "557       0       3  \n",
       "836       1       6  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "conceptual-substance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>171.0</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     region  tenure  age  marital  address  income  ed  employ  retire  \\\n",
       "521       1      48   41        0       21    43.0   2       7     0.0   \n",
       "737       1      15   38        1       11    46.0   5      11     0.0   \n",
       "740       3      34   29        0        5    26.0   3       4     0.0   \n",
       "660       2      45   54        0       25   171.0   3      33     0.0   \n",
       "411       3       5   31        0        1    21.0   3       1     0.0   \n",
       "\n",
       "     gender  reside  \n",
       "521       0       1  \n",
       "737       0       3  \n",
       "740       1       2  \n",
       "660       1       1  \n",
       "411       1       2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "important-peninsula",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "521    2\n",
       "737    1\n",
       "740    2\n",
       "660    3\n",
       "411    1\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strange-cancellation",
   "metadata": {},
   "source": [
    "# Classification: K nearest neighbors: 4 neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "small-sheffield",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "developmental-florida",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=4).fit(train_x, train_y)\n",
    "knn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broken-proof",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "uniform-excess",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 1, 3, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = knn.predict(test_x)\n",
    "yhat[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moderate-dining",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "horizontal-healing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  55.75\n",
      "Test set Accuracy:  30.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(train_y, knn.predict(train_x))*100)\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(test_y, yhat)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "square-saying",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "severe-program",
   "metadata": {},
   "source": [
    "# Classification K nearest neighbors: 6 neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "covered-exposure",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuarcy:  51.625\n",
      "Test accuracy:  33.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "knn2 = KNeighborsClassifier(n_neighbors=6)\n",
    "knn2.fit(train_x, train_y)\n",
    "\n",
    "# Get predictions\n",
    "yhat1 = knn2.predict(test_x)\n",
    "\n",
    "# Accuracy\n",
    "print(\"Train accuarcy: \", accuracy_score(train_y, knn2.predict(train_x))*100)\n",
    "print(\"Test accuracy: \", accuracy_score(test_y, yhat1)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "negative-sheep",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "complex-record",
   "metadata": {},
   "source": [
    "## K: The hyperparameter: My Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "positive-scanner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracies are:  [0.305, 0.32, 0.33, 0.3, 0.33, 0.33, 0.335, 0.325, 0.31] \n",
      "\n",
      "\n",
      "The maximum accuracy is  0.335  for K: 7\n"
     ]
    }
   ],
   "source": [
    "m = 10\n",
    "acc = []\n",
    "for i in range(1,m):\n",
    "    tempknn = KNeighborsClassifier(n_neighbors=i)\n",
    "    tempknn.fit(train_x, train_y)\n",
    "    tempyhat = tempknn.predict(test_x)\n",
    "    acc.append(accuracy_score(test_y, tempyhat))\n",
    "    \n",
    "print(\"The accuracies are: \",acc,\"\\n\\n\")\n",
    "\n",
    "maxacc = max(acc)\n",
    "maxindex = acc.index(maxacc)+1\n",
    "print(\"The maximum accuracy is \",maxacc,\" for K:\", maxindex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "sensitive-timer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x232506d9220>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(range(1,10), acc, c=\"blue\")\n",
    "plt.legend(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incoming-coordinator",
   "metadata": {},
   "source": [
    "## K value: IBM method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fourth-charger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.305, 0.32 , 0.33 , 0.3  , 0.33 , 0.33 , 0.335, 0.325, 0.31 ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(train_x,train_y)\n",
    "    yhat=neigh.predict(test_x)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(test_y, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==test_y)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "vital-statistics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u4z7BkLIx358QB0OWoOJAXnGALYUlSVMaKgoEE+6ir7UzMtVJgS2FJeTG+fVOS1AuFgopmwv8CXnxc3e2FgZccfd+NJQEQxRYOyPjgAJ/kE35/rgtm1uCcqmSYIiNBf6kHY6cSG2QrLRnnOQPhq9L+ePws8QSlAsVlYSPepK9JFTgD8Z9KyBrZ2TcIKThakyBP74OlixBuYhq+B6ZLYXWZaBUPN8bZe2MjJso4bP58PXs+PiEsQTlEsGQsqmgxKZe2EFpG6R4ZO2MjBsVlQTZGCcVGktQLuAPhNiQX2yloArkFwfi7r4Oa2dk3CxeWiRZgnJYgT/A5gJ/0gwh3xOlpYl4Ye2MTDwobZHk5sFIlqAcoqpsKSixCesqyR9HM89aOyMTT/KLwwfJbhyKbgnKAaUti+K5+7gT4qENkrUzMvGoOBC+rcVtlxksQVWzZGlZFAtub4Nk7YxMPAuGlE0ua5FkCaoaJVvLolhwcxska2dk4l1piyS3XEO1BFUNkrllUSy4sQ2StTMyiaTQHx6K7nRJ3RJUjJVE2ozYkOPocWMbpHi+odiY8pS4oEWSJagYKm1ZFHLZ0X4icFMbpAJ/wK4pmoTkdIskS1AxYC2LqocbzlqsnZFJdNtaJBVUf4skS1BRZi2Lqo8b2iBttYMQkySKAtXfIskSVBRZy6Lq52QbpKKSIH77vzZJpLRFUnUNRbcEFSUF/gCbrGVRtXOqDZK1MzLJSjU8FL06BipZgtpLZVsWGWc40QbJ2hmZZFfaIimWfDHdeoILBENsKSyxEVwukFtcQprPg8cjMd+XtTMyJizWJW47g9pD1rLIXaqrDZK1MzKm+sQ0QYlIXxH5WUSWicit5bzeT0QWiEiOiMwVke47vO4Vke9F5NNYxllV1rLInaqjDZK1MzKm+sQsQYmIFxgBnAS0AgaKSKsdFvsSaKuq2cBFwCs7vD4UWBKrGKsqFGmm6PTQZlOxWLZBsnZGxlSvWJ5BdQKWqeovquoH3gX6lV1AVfP070+TLPj7lhIRaQycws5JyxHb2n7YsGJXC6mSG6MDCDfcGGxMMollgjoAWFHm8crIc9sRkTNF5CfgM8JnUaWeBoYBu8wIInJppDw4d926dXsddHmsZVF8KYxBG6T8YmtnZEx1i2WCKm841U7vcFUdp6otgDOA+wFE5FRgrarO291OVPUlVe2gqh0aNmy4lyHvtG1rWRSnonm2E3RBxwpjklEsE9RKoEmZx42BVRUtrKrTgUNEpAHQDThdRH4jXBo8XkTeiWGsO7GWRfEtmm2QrJ2RMc6IZYKaAxwmIs1EJBUYAIwvu4CIHCoiEvm+PZAKbFDVf6lqY1VtGllviqqeF8NYt2MtixJDNNogWTsjY5wTsxt1VTUgIlcDEwAv8JqqLhaRyyOvjwT6A+eLSAlQCJyrDs9EV+AP2H0uCaK0DVLdrNQ9Wt/aGRnjLHHbzKR7o0OHDjp37tw9Xj+3qMSGESegWukpZKR6q7zelsIS6xhhzC6IwD4106OwHZmnqh12fN46SZQRCCZOsjZ/yy0uqfLU1dbOyBjnWYIyCa+qbZCsnZEx7mAJyiSFoiqcEVk7I2PcwRKUSRq5Rbtvg2TtjIxxD5tuwySN0jZItdJTKlzG2hklpq3FW/h25QxmrpzG/zb+zJmHn8M/WpyLz2MfgW5m/zsmqRT6g6T7vKT6di4eWDujxJHvz+O7Vd8wc8U0Zq6YxoK13xPSEGneNPbJasS1Ey7m2dmPcePRt3F687Pweqo+ytPEniUok3Ryi0qol5VK5B5xwNoZxbvCQCFzV33LzBXTmLHiK3LWzCMQCpDiSaF9o44M7XQL3Zr0pMN+nUn1pvLfZeN5fNYDXPHfCxj+3aPc1OV2Tj3sTDxiVz3cxO6DKmOTdStPGjXSfGSl/X18Zv/38aU4UMy8v2ZvO0Oa/9ds/EE/XvHSdt/2dGvSk+5NjqXD/keTlZJV7jZCGuI///uIJ759kKUbf6JVg9bc3OUO+h5y2nYHL6Zisb4PyhJUGfYhlTwEqJeVis/roagkyBa79uRqJcESctbMZeaK6cxcMY05q2ZRFCxCEFrv045uTXrQrUlPOu/flZpptaq07WAoyMc/v8+T3z7EL5uX0Wafdtzc5U56NetriWo3LEFVgSUoUxWpXg+1M1JYn19ssyO7TCAUYOHanG1nSN+t+oaCknwAWjVovS0hHX1Ad+qk143aPscuGcNT3z7EH1t/o32jjgzrehc9DzzBElUFLEFVgSUoU1Vej9g9Ty4Q0hCL1y2IJKTpfPvnDHL9WwE4rF4LujXpSbfGPejS+BgaZEZ3Wp0dlQRLeP/Hd3jqu4f5M3cFnfbvwrAud9H9wGNjut94ZAmqCixBGRMfVJWfNvzIzBXT+GblNL5Z8TWbizcB0KzOIXRvcizdmvSga+Me7JPVyJEYiwPFjFn8Js/MfpTVeavo2rgHw7rcydGNuzsSjxtZgqoCS1DGuJOqsnzT0m2j7L5Z+TUbCsMzYDeudWA4ITXuSbcmPdi/ZmNng91BUaCIdxa+yrOzn2BtwV/0OPB4hnW5kw77H+10aI6zBFUFlqCMcQdV5fctv4ZLdivD15HW5P8FwH419qdb4550bdKTbk16clDtps4GW0kFJQW8ueAlnp/zFBsK13F80xO5ucsdtGu00+dq0rAEVQWWoNxjQ+F66qXXt4vLURAIBVi+aanTYexWMBRg4dofIgnpK/7MXQlAg8x96Na4x7ah383qHBLXfxf5/jxe+2EkL8wdzqaijZx48Knc3OUOjtynrdOhVTtLUFVgCcp5P29YwhOzHuA/Sz/ihs63MazrnU6HFNdCGmLAR6cx/Y8pTodSaXXT69G18TF0a3Is3Zr0pHm9FnGdkCqSW7yVV3JeYOS8Z9hSvJlTDu3HTV3upGWDI5wOrdpYgqoCS1DOWb5pKU9++yDjfnqfzJQsDql7GIvXLWDCoJlJeWQZLW/88BK3ThnK0E7DaNWgtdPh7Nah9ZrTssGRSdWRYUvRZl76/nlemv8cef5cTm/enxuPvp3m9Vs4HVrMWYKqAktQ1e+3zb/w1HcPM3bJaNK86VyUfTlXdrgej3jo8WZ7GtXYj88HTCfFW3GDVlO+lVv/oOdbR9Fhv868+4//JORZSCLZVLSRkfOe4eXvR1BYUsA/WpzLjUffzsF1D3U6tJixGXWNK63Y+js3TrqSbm+0YfzPY7mk3VXMvuhH7jzmQepnNKBuej0eOf5pFq7N4YV5w50ON+6oKjdPvhpFebzXCEtOcaBuej3+1e1eZl+0hCuOuo7Pl33CMW9mM3TCpfy++Venw4tLdgZVhp1B7d6q3JU8Pfsxxix6AxFhSOv/45qON9Goxv7lLn/Jp4OZ8MunTB78XVKUPKLlvcXvMHTiJTx03HAuyr7c6XDMHlib/xfPz3mSNxe8TFCDDGh1PkM7D6NJrYOcDi1qrMRXBZagYmdN3mqenfM4by98FVVl4JEXMLTTzRxQs8ku11uXv4Yeb7Xn4LqHMv6cKTatQSWsyVtNj7fa07LBEXx09sSkup6TiP7KW8Wzcx7nnYWvoaoMbn0RQzvdzH41DnA6tL3meIlPRE4VsXdIslpXsJa7pw2j82uteOOHlzi75WBmXrCQx054drfJCaBh1r48cOwTzFs9m1dyRlRDxPFNVbl1ylCKA0U82ftFS04JoFGN/XnouOF8c8FCBh7xT95Z+CpHv3YEd0y9kbWRe8NM+XZ7BiUi7wBdgA+B11V1SXUEtifsDCp6NhSu54W5w3ktZyTFwSLOajmIGzr/i6Z1Dq7ytlSVIZ/0Z+aKr5g6ZO4ebSNZfPLzWC77fAh3HvMgV3W4welwTAz8vuU3nv7uEd7/8R1Svan8s+2lXNXhBhpm7uN0aFXmihKfiNQCBgIXAgq8DoxR1dy9jiyKLEHtvU1FG/n3vGd5+fsRFJTkc2aLc7ih820cWq/5Xm13Ve5Ker51FK33yWbsWf+1M4NyrC9YR8+3jqJJrQP5dMBXNh15gvt183Ke+vZhPvxpDGnedP4v+wqu7HA99TLqOx1apTle4gNQ1a2Ez6DeBfYDzgTmi8g1ex2ZcYWtxVt4YtaDdHq1JU/PfpTjm/bhq/Pn8cJJb+x1cgLYv2Zj7unxCN+snM47C1+LQsSJ586vbmJr8WaG9/m3Jack0KzOITzX9xWmnT+fvoecyoi5T9Hx1RY8+s29bC7a5HR4rlCZEt9pwEXAIcDbwJuqulZEMoElquqaISl2BlV1ef5cXvn+BV6c9zRbijdz8qGnc9PRd9CqYfRvClVVzvnwFL5fM5evhsylca0Do76PeDVh+af8c/zZ3NzlTm48+janwzEOWLJ+MU9++yCfLh1HrbTaXN3hRq7qcIOrBxY5XuITkbeAV1R1ejmvnaCqX+51dFFiCary8kvyeT0n3E9sY9EGejc7mZu73EGbfdvFdL+/b/mNY986iqMbd2f0GR/b/T2EOxH0eKsd9TMa8sWgGaR6U50OyTho8boFPPbN/Uz45VO6NenJiye94diUI7vjhhLf3cDsMhvKEJGmAG5KTqZyCgOF/Hv+s3R+rRUPzLiDtvu25/OB03n7jA9jnpwADqrdlNu738/U3ybywZLRMd9fPLj363+xvmAdw/uMtORkOKJhG97s9wFP93mJ+atn0+udLsxaOcPpsBxRmQT1AVD2tCIYec7EkaJAEa98/wJHv9aKu6fdQsv6RzD+nC8Z84/xtG/UsVpjuSj7cjrt34W7vro56YfZTvv9S0YveoMrO1xP233bOx2OcZEBRwzh84HTqZFag7PG9uX5OU8S0uSo8JSqTILyqaq/9EHkezvMixP+oJ83f3iZrq8fyR1f3UizOofw0dkT+eCsz+l0QFdHYvKIh6d6j6QwUMCtU4aSSDeLV0W+P48bJ1/JoXWbc+PRtzsdjnGhlg2OZMKgmZx8aD8emHEHF4w/J6kGUFQmQa0TkdNLH4hIP2B97EIy0VASLGH0ojfo+kZrbplyLfvXbMwH/T9n3NmT6Nr4GKfD49B6zbmpyx18vmw8/1n6kdPhOOLBmXfy59YVDO8zknTf3tfxTWKqmVaLl055hweOfZKpv02k96gu/LBmvtNhVYvKJKjLgdtE5A8RWQHcAlwW27DMngqEAry3+B26v9mWGyZdQYOMhow+8xP+c+5UjjnwOFcNSrjiqOtos097/jXlejYUJtcxz7crZ/BazkgubnclHffv4nQ4xuVEhIvbXckn50wmpEFOe+843vjhpYSvPlS6F5+I1Igs76qbc8tK5lF8wVCQT/73AU9++xDLNy3lyIZtGdblTnoffLKrktKOlqxfRJ9RXTm9eX9GnPS60+FUi8JAISe83YlAKMDU8+eSlZLldEgmjmws3MBVX1zE1N8m8o8W5/L4Cc+TlVrDkVhiPYqvUncDisgpwBFAeumHnaret9dRmb0W0hCfLh3HE7Me5H8bl9Ci/hG8euoYTjr09Ljo1tCywZFc22kYT377IP0OP5s+B5/sdEgx9/g39/PL5mV80P9zS06myupl1GfUGeN4dvbjPDbrPhau/YFXTh3N4fVbOh1a1FWmWexI4FzgGkCAs4FK3ZwrIn1F5GcRWSYit5bzej8RWSAiOSIyV0S6R55vIiJTRWSJiCwWkaFV+qmSgKry+bJPOOGdzlz62Xkoyr9PfpspQ2ZzymFnxEVyKjW00zBa1D+CYZOvZkvRZqfDianv/5rLyPnPcN6RF3HMgcc5HY6JUx7xcF3nW3j/H5+xqWgjfUd356Of3nU6rKirzKdYV1U9H9ikqvcSbhy72zbWIuIFRgAnAa2AgSLSaofFvgTaqmo24W4Vr0SeDwA3qmpL4GjgqnLWTUqqysRfPqfP6K5c9J8BFAeKGNH3Nb4aMpd+h58VV4mpVKo3leF9RrK2YA33fZ24XRSKA8VcN/Ey9s1qxF09HnI6HJMAuh94LJMHz6Ltvu248r8XcsuXQykOFDsdVtRU5tOsKPJvgYjsD5QAzSqxXidgmar+Ehma/i7Qr+wCqpqnf18EyyLciBZVXa2q8yPf5wJLgPifPGUvrclbzVkfnsT5n/Rna/EWnunzMtP/+T39Ww50dTuUymjXqANXHHUdoxa9zvTfpzgdTkw8M/sxft7wI4+d8Dy10mo7HY5JEI1q7M/Ys77gqg438OaClzjtveP5fctvTocVFZVJUP8RkTrA48B84DdgTCXWOwBYUebxSspJMiJypoj8BHxG+Cxqx9ebAu2A78rbiYhcGikPzl23bl0lwopPM1ZMo9eoLsxfPYdHjn+GGf/8gXOPOC+hmore1OUODql7GDdOvpJ8f57T4UTV4nULeHbOY5zVYiC9Dz7J6XBMgvF5fNx5zIO8efoH/LZlOX1GdWHC8k+dDmuv7TJBRSYq/FJVN6vqh4SvPbVQ1bsqse3yho7tNGRQVcepagvgDOD+HfZfg3AX9esiHdV33qDqS6raQVU7NGzYsBJhxZeQhnhm9mOc8+HJ1Emvw38Hfs0FbS8lxZvidGhRl+HL4KneL7Jy6x88NLMyf2LxIRAKcP3Ey6mTXo/7jn3c6XBMAjvxkFOZNGgWB9Vuxj/Hn839X99OIBRwOqw9tssEpaoh4Mkyj4tVdUslt72S7a9VNQZW7WJf04FDRKQBgIikEE5Oo1Q1Ke/k3Fi4gSGf9OfhmXfTr/lZfDFwBi0aJPaluM4HdOOi7Mt5LWck3/050+lwouLFucNZsPZ7Hj5ueFzN9WPi00F1mjH+3Cmc3/piRsx9irPGnsSavNVOh7VHKlPimygi/aXqN9PMAQ4TkWYikgoMAMaXXUBEDi3droi0J9xCaUPkuVcJT+fxVBX3mxDm/zWH3qO68PUfU3jk+Gd44aQ3HLvXobrd1u0+Gtc6kBsmXUFhoNDpcPbK0o0/88S3D3LKoWdwWvN/OB2OSRLpvnQe6/UcI/q+xg9r5nPCqKOZ8cdXTodVZZVJUDcQbg5bLCJbRSRXRMott5WlqgHgamAC4UEO76vqYhG5XEQujyzWH1gkIjmER/ydGxk00Q0YAhwfGYKeIyKJf4MM4VF6r+a8SL/3TkBEGH/OFC5oe6mrb7aNtqzUGjzZ6wWWb1rKE7MecDqcPRYMBbl+4mVkpmTx8PHDnQ7HJKH+LQfyxaAZ1E2vyzkfncLT3z0aVw1nK91JIh7EeyeJPH8uN0y6gvH/+5DezU7m2b4vUze9nmPxOO3GSVcyZvGbfDZgGu0a7XSTueu9PP957px2M8+d+CpntxrkdDgmieX787hp8lWM+/l9jm96Is/3fTUq5WY3TFjYo7zny5vA0GnxnKCWrF/MJZ8O4pfNy/hX13u4quONcXlPUzRtLd5CjzfbUTejPhMGzYyruZJ+3/wrx77dga5NevBOv4+S6gzYuJOq8uaCl7lr2s00zNyXl095h/b7ddqrbbphwsKby3zdCfwHuGevIzLbvP/jKE4ecwxbi7cy9qwvuKbTzUmfnABqpdXmsV7Ps2T9Ip6dHT+j31SVGydfidfj5bETnrXkZFxBRLig7aWMP2cKHvHQ7/1evJrzoqsbzu72U1BVTyvz1Rs4ElgT+9ASX2GgkBsnXcm1Ey6mXaOOTD5vliumwnCTPgefTP8WA3h69iP8uG6h0+FUyqhFrzNjxVfcfczDHFBzt01XjKlW2Y2OYtLgWRx7UG9un3oDl30+hDy/O3uA78lh+krCScrshd82/8Jp7x7HqEWvM7TTMN7v/xn7ZDVyOixXuu/Yx6mdVpfrJl7u+ns6/sxdwT3Tb6V7k2M5r/VO950b4wp10uvyZr8PuL37/Xy6dBwnju7GkvWLnA5rJ5VpFvuciDwb+Xoe+Br4IfahJa7Pl31C71FdWLn1D97u9xH/6nZvQnWEiLb6GQ14+PjhLFg7n5HznnE6nAqpKsO+vJZgKMiTvV6w0p5xNY94uKbjTYw96wtyi3M5eUwP3lv8jtNhbacyZ1BzgXmRr1nALap6XkyjSlAlwRLunnYLF/1nAIfUPYyJg2dZ25tKOu2wf3Dyoafz+Kz7Wbbxf06HU64Pf3qXL3/9gtu63ctBdSrTrtIY53VtfAyTz5tFu0YdGTrxEm6cdKVr7j+szCi+LKBIVYORx14gTVULqiG+KnHzKL7VeX9y2WdDmL1qFhdlX87dxzxCmi8tJvtKVGvyVtPzraNoXr8FH58z2VUDSdblr6HHW+05pO5hfHLOl3HfvNckn0AowOOz7ueZ2Y9xZMO2vHLqaJrWOXiX67hhFN+XQEaZxxnA5L2OKIlM+/1LTnjnaBavW8jIk9/ioeOGW3LaA/vW2I/7jn2c2atm8VrOSKfD2c6/pl5PQUk+w/v825KTiUs+j49/dbuXt/t9xMqtf9B7VBc+W/qxozFVJkGlq+q21tKR7zNjF1LiCIaCPDHrQQZ8dBoNM/fhi0EzOOPws50OK66d3XIQxzXtw0Mz73LNlAL/+d9HfLp0HDcdfTuH1Tvc6XCM2Su9Dz6JSefN4pC6h/F/nw7k7mm3UBIscSSWyiSo/EifPABE5CjAHQVKF1tfsI7BH5/BE98+QP+WA/l84HT78IoCEeHxE57DIx5umnSl4/dwbCzcwL+mXk+bfdpxRYfrHY3FmGhpUusgPjnnSy7Kvpx/z3+Wf3zQh1W5K6s9jsokqOuAD0TkaxH5GniPcI89U4E5q2bRe9TRzFr5NU/0GsFzJ75CVkqW02EljMa1DuTO7g/y9YqpjF70hqOx3DVtGJuLNjK8z0gbiWkSSpovjYeOG87Ik9/ix/WL6DWqC9N+/7JaY6jMjbpzgBbAFcCVQEtVnRfrwOKRqvLS/Oc484M+pHrT+HTAV5zX+iIbbhwDQ9r8H10b9+Ce6beyOu9PR2KY9Mt/GbtkNNd0vJkjGrZxJAZjYu2Mw89mwqCZNMzchwEfncYTsx4kGApWy74rcx/UVUCWqi5S1YVADRG5MvahxZetxVu4+NNB3DVtGL2bncTEQd/Qep9sp8NKWB7x8FTvFwmEShg2+dpqL/VtLd7CsC+v5vD6rbiu0y3Vum9jqtuh9Zrz+cDp9G85kCe+fYDBH5/B+oLYz2BemRLfJaq6ufSBqm4CLolZRHFo0dofOHF0N75Y/h/u7vEwr532HrXT6zgdVsJrWudgbu12D5N+/ZxxP79Xrfu+/+vbWZP/F8P7jLQRmSYpZKVk8dyJr/BErxHMWvk1vUcdzZxVs2K6z8okKE/ZyQoj90HFT1vpGBu96E1OffdYCksK+OjsCVxx1HVW0qtGF2dfxVH7deL2qTeyLr96WkTO+OMr3l74Kpe1v5b2jTpWyz6NcQMR4bzWF/HpgK9I86Zzxvt9ePrbp2O2v8okqAnA+yJygogcD4wBvohZRHGioKSAoRMu5YZJl9PpgK5MOu9bOh/Qzemwko7X42V473+TX5LHbVNviPn+8kvyuWHSFRxc51CGdb0r5vszxo1a75PNhEEz6d3sJNbmr43Zfioz7OgW4FLCgyQEmAi8HLOI4sDyTUu5+NNB/LR+MTd0vo0bj77Nbs50UPP6Lbjx6Nt5eObdfLb0Y0457IyY7euRmXfzx9bfGHf2JDJ8GbtfwZgEVTu9Dq+f/h4NasSuoFaZUXwhVR2pqmepan9gMfBczCJyufH/+5ATR3djTd5qRp/5CcO63mnJyQWuPOp6Wu+Tza1TrmNT0caY7GP2n9/wyvcvcGHby+jSuHtM9mFMPBGRmH7+VaqZmYhki8ijIvIbcD/wU8wicil/0M8dU2/k0s/O4/D6LZl03iyOa9rb6bBMRIo3heG9R7KpaAN3T4v+qLqiQBE3TLqCA2o14fbu90d9+8aYnVWYoESkuYjcJSJLgOcJzwMlqnqcqibVGdTKrX9wxvu9eCXnBS5tdzXjzp5kE9G50JH7tOWajjfx/o/v8OWvE6K67Se/fZBlm/7HE71GUCO1ZlS3bYwp367OoH4CTgBOU9XukaRUPXdnuciU3ybSe1QX/rfxJ145dTT3Hfs4qV4bxOhW13W6leb1WnLz5KvILd4alW3+sGY+L8wdzsAj/smxB/WKyjaNMbu3qwTVH/gLmCoiL4vICYQHSSSFYCjIo9/cy+BxZ7BfjQOYOOgbTj3sTKfDMruR5kvj6T4j+St/NffPuGOvt+cP+rl+4uU0yGzIPT0eiUKExpjKqjBBqeo4VT2XcJujr4DrgX1F5EUR6VNN8TliXf4aBnx0GsO/e4QBR5zPZwOncXDdQ50Oy1RS+/06cWn7a3hrwcvMXDF9r7b13Jwn+HH9Qh474Tm7+dqYalaZUXz5qjpKVU8FGgM5wK2xDswp366cQa/IHdLD+/yb4X1G2nDiODSsy100q3MIN0y6nPyS/D3axpL1i3n6u0c48/BzOPGQU6McoTFmd6o0JamqblTVf6vq8bEKyCmqyrOzn6T/2L5kpdTg84HTGXjE+U6HZfZQZkomT/Z+kd+3/Mpj39xb5fUDoQDXT7yMmmm1uf/YJ2IQoTFmd9wzZ7bDbvvyNu6ZfhsnHXo6EwbNpFXD1k6HZPZS18bHcEHby3hp/vPMXfVtldZ9af5z5KyZx0PHPUWDzIYxitAYsyvi9IRv0dShQwedO3fuHq3766Zf+WDRJ5zf5jLrpZdA8vy59HzrKDJTspg0eBbpvvTdrrN801JOeLsTxzbtxeunvW9/D8ZUQAT2qbn799TutyPzVLXDjs/bGVREs7rNuKT9lfZhlGBqpNbkiV4jWLrxJ4Z/9/Bulw9piBsmXk6aL51Hjn/G/h6McZAlKJPwjmvam3NbDeH5OU+ycG3OLpd944eX+G7VN9zT81Ea1di/egI0xpTLEpRJCvf2fJT6GQ25buJllARLyl3mjy2/88CMOzj2oF4MaDWkmiM0xuzIEpRJCnXS6/JYr2dZvG4Bz899cqfXVZWbJl+JIDze63kr7RnjApagTNLoe8hp9Gt+FsO/e5if1v+43WtjFr/F9D+mcMcxD9Ck1kEORWiMKcsSlEkqDx73FDVSa3HDpMsJhsKtJf/KW8U902/h6AO68882lzgcoYk1OzmOHzFNUCLSV0R+FpFlIrJT9wkR6SciC0QkR0Tmikj3yq5rzJ5okNmQB499gvl/zeHl759HVRn25bX4A8U81ftFPGLHbInG6xEyUr3UzkihYY009qmZTu2MFLwey1RuV5kZdfeIiHiBEUBvwlN1zBGR8apatrbyJTBeVVVE2gDvAy0qua4xe+SMw8/h45/H8sjMeygMFDLxl8+4u8fD1m8xQXg9QqrPQ6o3/OUpJxGlp3hJT/FSVBIkvzhAIJQ494MmklgeLnYClqnqL6rqB94F+pVdQFXz9O87hbMArey6xuwpEeHRE54h1ZfGo9/cS7tGHbi03TVOh2X2kG+HM6QGNdKolZ5Ceoq33ORUVnqKl/o10qidkYLPzqhcJ5YJ6gBgRZnHKyPPbUdEzhSRn4DPgIuqsm5k/Usj5cG569ati0rgJvE1qrE/jxz/NPtmNWJ473/HdNpqE107JqT6VUhIFbFE5U4xK/FR/txRO51Hq+o4YJyI9CA8nXyvyq4bWf8l4CUItzra42hN0vlHiwGcefi5NqTc5XyRkl3KLkp20WKlP3eJZYJaCZSdF70xsKqihVV1uogcIiINqrquMXvKkpP7VGdCqoglKneIZYKaAxwmIs2AP4EBwKCyC4jIocDyyCCJ9kAqsAHYvLt1jTGJIcXrIcUrpHg9pPk8rjpoKE1UxYEg+cVBSoIhp0NKKjFLUKoaEJGrgQmAF3hNVReLyOWR10cSnlb+fBEpAQqBcyODJspdN1axGmOqT2lCKh1p56aEVJE0n5c0nyWq6mbTbZSxKd+P3/7wjIkaAXxxmJB2xxJVWKyn24hlic8Yk2RKE1L4GpIkTELakZ1RVQ9LUCbqUrweMlK85BaVlD/00lSJ1yPUzkhxOoxK8XkkIRNSRSxRxZYlKBM1KV4PWWnhNyxASJW84oDDUcW/WukppHitBZOblSYqfyBEfnHALhVEiSUos9d2TEylstJ8FAdCdlS5FzJTvaT6LDnFi1Sfh1RfqiWqKLEEZfZYqtdDVppvlx+gtdJ9bMz3W6lvD3g9Qo00e4vGI0tU0WF//abKKpOYSvm8Hmqk+8gtslJfVdXOSEmq6zmJyBLV3rEEZSqtKomprMxUH8UlIXtzVkFWms+uOyUQS1R7xhKU2a09TUxl1cpIYUNesZX6KsHnEbJSrXltIiqbqAr8AYoDlqh2xRKUqVCaz0Nm6t4lplJej1iprxKEcDK30l5iK01UJcHwGZUlqvJZgjI7SfN5YlJislLf7mVaaS+ppHg91Mm0RFURS1Bmm1glprKs1Fcxn43aS1qWqMpn7wZTLYmplNcj1ExPYWtRScz3FU8E4qZbhImdsokqr8gGU1iCSmLVmZjKykgNt4axo8S/ZaX58Flpz0SkeD3UzUolrzhAfhJ3Y7EElYTSfV4y07yOXuuomZ6CP7+YBGqmv8dSIqMkjdlRjTQfqV4PWwpLCCXhm8UO2ZJIus9L/axUamc639vN6xFqpVtJSwh32zCmIqk+D/WzUklNwjNse2ckgXSfl6w0r+tKSKVTaidzqc9Ke6YyPB6hblYq+cWBpGrAbAkqgbk1MZVVKz2F9Ula6rPSnqmq0mvGyVLyc+8nl9lj6Sl/l/LcnJwgfGSYjKU+K+2ZPVVa8ktLgi739g5JIOkpXrJS3X3GVJ70FC/FJSGKAkGnQ6k2NdKttGf2nMcj1MlMpcAfIK8okLD3FVqCSgDpKV5qpPnweuK3PU7NdB/F+cGkKPWlesMtpIzZW5mpf5f8gqHEe/PYIVycEsKJqUGNNGpnpMR1coLkKfWV9tozJlpSvOGSX7ov8RoM22FcHMpI9ZKVGt9nTOVJhlJfjfTE+38zzhMRamemkOr3kFtUkjAlP0tQccQjQu2MlISeArxmug9/fighRyhZac/EWkaqF59XEqbkl7ifdAkmrfRmvQROThAu9dVMwNFtIlbaM9VjW8kvJf5Lfon9aZcAhPBZRZ3MVDxJUhpKT/EmxJurrJpp8X+d0MQPiVRbaqWnEM9/dZagXMwbuXs8GctCNdN8eBJk0r40n4cMmyHXOCAj1Uu9rFR8cXpwZAnKpUr75jndM88pHo9QKyP+E7NIuDGuMU7xeT3Uy0qNy4Ok5Pz0c7Fwh4EUamfatN9pvvgv9dVKt9KecZ5I+DaO2hnxVfKzBOUipSW9eDzSiZVa6fFb6kvzeeI+wZrEkp4SXyU/S1AuUdo/L1lLehURic9SnwhJceOxiT/xVPKzT0OHbSvpZVhJryJpPm9cvJnKqpWekjSjLk382a7k5+I/U0tQDvJ5JG6OZJwWT6P60hPg2plJDuHKTZprKzfujCoJbBv+6dI/DLcpva/D7cKj9uKvJGmSlzdyoJzpwgNl+3SsZiL8fQNdnJwRuEVqHNxPZKU9E69qpqdQJ9NdJb+YJigR6SsiP4vIMhG5tZzXB4vIgsjXNyLStsxr14vIYhFZJCJjRCQ9lrFWB59HqJ+VZuWfvVDTxdOKWGnPxLs0n7tKfjGLQkS8wAjgJKAVMFBEWu2w2K9AT1VtA9wPvBRZ9wDgWqCDqh4JeIEBsYq1OpSW9Nz64RovSi/uuo1HErOHoEk+pSW/rDTn/55jmSY7ActU9RdV9QPvAv3KLqCq36jqpsjDb4HGZV72ARki4gMygVUxjDVmrKQXfak+j+vq5TXTfVbaMwmlRprP8ZJfLBPUAcCKMo9XRp6ryP8B/wVQ1T+BJ4A/gNXAFlWdWN5KInKpiMwVkbnr1q2LSuDREu4qbCW9WHDTDMKJ2NzWGAiX/BpkpZHqUMkvlnst79Oj3AlKROQ4wgnqlsjjuoTPtpoB+wNZInJeeeuq6kuq2kFVOzRs2DAqgUdDppX0YsotpT6PCDVdUAoxJlY8kQ43NRz4O49lgloJNCnzuDHllOlEpA3wCtBPVTdEnu4F/Kqq61S1BPgI6BrDWKNGBOpkpliD0GrghlJfrQwr7ZnkkJXmo25marXejxjLBDUHOExEmolIKuFBDuPLLiAiBxJOPkNU9X9lXvoDOFpEMiV84eYEYEkMY42K1EhJL81n5Z7qUiPN51hfsfQUr/1fm6SSGpk4Na2aJk6N2TmbqgZE5GpgAuFReK+p6mIRuTzy+kjgLqA+8EJkAEEgUq77TkTGAvOBAPA9kRF+bpWV5nPkFDjZhXv1pbAx31+t+/WIUMtG7Zkk5PEIdTJTyS8OUOAPxnRfohr/89aX6tChg86dO3eP19+U78cfDFVpHU+kw0GiT8XudnnFAfKLA9W2vzqZKXb2ZJJeIBiKSjccEZmnqh12fN4+VfdCuKSXasnJBbJSvdVW6stItdKeMUDMW7XZJ+seqpHmo25Wql0gd4nSUl+s/ze8Hhu1Z0x1sQRVRR4R6ma64y5rs70Ur4fMGP+/2A3XxlQf+5StgjSfx5qBulyNNB/FJUECoehfW81I9Vo515hqZO+2ShDCrWzqZFpJLx7UjkGpz0p7xlQ/S1C74Y3cRZ2Zah9O8cLn9US9BGulPWOqn33q7kK6z0utDJ99MMWhrDQfxYEQJVW8baA8mVbaM8YR9q4rhxA+Yq6daUfN8axWum+vS31ej9gN2MY4xBLUDkrnQnH7zK1m93xeDzX2sttD7Qw7SDHGKXZoWEZ42gSPfSAlkMxUH0Ule1bqy0z1umZmUWOSkb37yshI9VpySkB7MqrPZ6U9YxxnCcokPK9HqlTqEwh3pbCDFWMcZQnKJIXMVF+lZwXNTPNZac8YF7B3oUkalenVZ6U9Y9zDEpRJGl6P7HKmYyF8vcoY4w6WoExSyUj1Vljqy0rzxXz6AGNM5dm70SSd8ACI7Z9LiUF7JGPM3rEEZZJOuPHr36W8cOcQS07GuI0lKJOUwrPihv/8rbRnjDvZu9IkrZrpKaT5rLRnjFtZgjJJy+sR6mSmOh2GMaYClqCMMca4kiUoY4wxrmQJyhhjjCtZgjLGGONKlqCMMca4kiUoY4wxrmQJyhhjjCtZgjLGGONKlqCMMca4kiUoY4wxriSq6nQMUSMi64Df92ITDYD1UQonlizO6ImHGMHijLZ4iDMeYoToxHmQqjbc8cmESlB7S0TmqmoHp+PYHYszeuIhRrA4oy0e4oyHGCG2cVqJzxhjjCtZgjLGGONKlqC295LTAVSSxRk98RAjWJzRFg9xxkOMEMM47RqUMcYYV7IzKGOMMa5kCcoYY4wrWYICROQ1EVkrIoucjmVXRKSJiEwVkSUislhEhjod045EJF1EZovID5EY73U6pl0REa+IfC8inzodS0VE5DcRWSgiOSIy1+l4yiMidURkrIj8FPn77OJ0TDsSkcMjv8PSr60icp3TcZVHRK6PvH8WicgYEUl3OqYdicjQSHyLY/V7tGtQgIj0APKAt1T1SKfjqYiI7Afsp6rzRaQmMA84Q1V/dDi0bUREgCxVzRORFGAGMFRVv3U4tHKJyA1AB6CWqp7qdDzlEZHfgA6q6tqbNkXkTeBrVX1FRFKBTFXd7HBYFRIRL/An0FlV9+bm/qgTkQMIv29aqWqhiLwPfK6qbzgb2d9E5EjgXaAT4Ae+AK5Q1aXR3I+dQQGqOh3Y6HQcu6Oqq1V1fuT7XGAJcICzUW1Pw/IiD1MiX648ChKRxsApwCtOxxLPRKQW0AN4FUBV/W5OThEnAMvdlpzK8AEZIuIDMoFVDsezo5bAt6paoKoBYBpwZrR3YgkqTolIU6Ad8J3DoewkUjbLAdYCk1TVdTFGPA0MA0IOx7E7CkwUkXkicqnTwZTjYGAd8HqkXPqKiGQ5HdRuDADGOB1EeVT1T+AJ4A9gNbBFVSc6G9VOFgE9RKS+iGQCJwNNor0TS1BxSERqAB8C16nqVqfj2ZGqBlU1G2gMdIqUA1xFRE4F1qrqPKdjqYRuqtoeOAm4KlKSdhMf0B54UVXbAfnArc6GVLFICfJ04AOnYymPiNQF+gHNgP2BLBE5z9motqeqS4BHgUmEy3s/AIFo78cSVJyJXNf5EBilqh85Hc+uRMo8XwF9nY2kXN2A0yPXd94FjheRd5wNqXyquiry71pgHOG6v5usBFaWOVMeSzhhudVJwHxVXeN0IBXoBfyqqutUtQT4COjqcEw7UdVXVbW9qvYgfIkkqtefwBJUXIkMQHgVWKKqTzkdT3lEpKGI1Il8n0H4zfaTo0GVQ1X/paqNVbUp4XLPFFV11VEqgIhkRQbEECmb9SFcXnENVf0LWCEih0eeOgFwzcCdcgzEpeW9iD+Ao0UkM/KeP4Hw9WZXEZF9Iv8eCPyDGPxOfdHeYDwSkTHAsUADEVkJ3K2qrzobVbm6AUOAhZFrPAC3qernzoW0k/2ANyOjpDzA+6rq2iHccWBfYFz4cwofMFpVv3A2pHJdA4yKlM9+AS50OJ5yRa6X9AYuczqWiqjqdyIyFphPuGz2Pe5se/ShiNQHSoCrVHVTtHdgw8yNMca4kpX4jDHGuJIlKGOMMa5kCcoYY4wrWYIyxhjjSpagjDHGuJIlKJNwRERF5Mkyj28SkXuitO03ROSsaGxrN/s5O9IVfOoOzzeN/HzXlHnueRG5YDfbq664x4rIwZHvfxORBpHvjxKRX0WknYic6vYu98YdLEGZRFQM/KP0w9EtIveGVdb/AVeq6nHlvLYWGBq55yjmIg1LK7PcEYBXVX/Z4fk2hLtLnKuq3wOfEe7ikRn1YE1CsQRlElGA8I2N1+/4wo5nEiKSF/n3WBGZJiLvi8j/ROQRERks4bmtForIIWU200tEvo4sd2pkfa+IPC4ic0RkgYhcVma7U0VkNLCwnHgGRra/SEQejTx3F9AdGCkij5fz860DvgT+Wc72LonE8IOIfLhDEigv7nQReT0Sw/ciclzk+QtE5AMR+Q/hRrX7ich0Cc+jtEhEjiknrsHAJzs81xL4GBiiqrMh3PGecAssV05vYtzDEpRJVCOAwSJSuwrrtAWGAq0Jd+xorqqdCE/HcU2Z5ZoCPQlP1TFSwpPJ/R/hrtMdgY7AJSLSLLJ8J+B2VW1Vdmcisj/hhpvHA9lARxE5Q1XvA+YCg1X15gpifQS4sZyzso9UtaOqtiXcHuf/dhP3VQCq2ppwC6A35e/J8boA/1TV44FBwIRIE+C2QE45MXUjPEdZWZ8AV6vqjB2enwuUl+SM2cYSlElIkS7vbwHXVmG1OZE5t4qB5UDpFAcLCX+4l3pfVUORydl+AVoQ7pF3fqQF1XdAfeCwyPKzVfXXcvbXEfgq0hQ0AIwiPK9SZX6+X4HZhBNHWUdGzpIWEj6jOWI3cXcH3o5s8yfgd6B5ZPlJqlo6T9oc4MLItbzWkfnIdrQf4bO7siYDF5eTSNcS7tRtTIUsQZlE9jThM4iycxMFiPzdRxpxlr2OU1zm+1CZxyG271u5Y38wBQS4RlWzI1/Nyszhk19BfFLJn6MiDwG3sP37+A3CZyytgXuBslOFVxR3RbbFHZnUswfhWWjfFpHzy1m+cIf9AVwd+feFHZ5PjyxvTIUsQZmEFTn6f5/ty1y/AUdFvu9HeMbfqjpbRDyR61IHAz8DE4ArJDwdCiLSXHY/ad93QE8RaRA5wxhIeGbSSomc8fzI9tdyagKrI3EMrkTc00uXE5HmwIGR57cjIgcRnj/rZcId9cubTmMJcOgOz4UiP9fhInJfmeeb47Ku7MZ9LEGZRPckUHY038uEk8JsoDMVn93sys+EE8l/gctVtYjwdaofgfkisgj4N7uZLUBVVwP/AqYSnvBtvqruOMhgdx4kPDFkqTsJJ75J7DzNSXlxvwB4IyXB94ALIiXOHR0L5IjI90B/4Jlylvksstx2ItvrR3jk3lWRp4+LLG9MhaybuTEmKiQ8/9dUwjMAB3ex3L6Epw05odqCM3HJEpQxJmpE5ETCE2r+sYtlOgIlqppTbYGZuGQJyhhjjCvZNShjjDGuZAnKGGOMK1mCMsYY40qWoIwxxriSJShjjDGu9P89K4ooH7GWTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Nabors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "respiratory-suite",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "republican-wales",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "confidential-myanmar",
   "metadata": {},
   "source": [
    "# K nearest neighbors: 7 neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fossil-warning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuarcy:  49.75\n",
      "Test accuracy:  33.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "knn2 = KNeighborsClassifier(n_neighbors=7)\n",
    "knn2.fit(train_x, train_y)\n",
    "\n",
    "# Get predictions\n",
    "yhat1 = knn2.predict(test_x)\n",
    "\n",
    "# Accuracy\n",
    "print(\"Train accuarcy: \", accuracy_score(train_y, knn2.predict(train_x))*100)\n",
    "print(\"Test accuracy: \", accuracy_score(test_y, yhat1)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "subjective-occupation",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "pleasant-drilling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x23251fdd2e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "plot_confusion_matrix(knn2, test_x, test_y, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspected-copyright",
   "metadata": {},
   "source": [
    "### Very bad confusion matrix as all the values are spread everywhere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "complimentary-stream",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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