3 Classification/Classification-K-Nearest-neighbors-CustCat.ipynb

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    "\n",
    "\n",
    "# K-Nearest Neighbors\n",
    "\n",
    "Estimated time needed: **25** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Use K Nearest neighbors to classify data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "In this Lab you will load a customer dataset, fit the data, and use K-Nearest Neighbors to predict a data point. But what is **K-Nearest Neighbors**?\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "**K-Nearest Neighbors** is a supervised learning algorithm. Where the data is 'trained' with data points corresponding to their classification. To predict the class of a given data point, it takes into account the classes of the 'K' nearest data points and chooses the class in which the majority of the 'K' nearest data points belong to as the predicted class.\n"
   ]
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   "source": [
    "### Here's an visualization of the K-Nearest Neighbors algorithm.\n",
    "\n",
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/images/KNN_Diagram.png\">\n"
   ]
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  {
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   "source": [
    "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points), we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "In this sense, it is important to consider the value of k. Hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (data points) when it predicts the classification of the test point.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"https://#about_dataset\">About the dataset</a></li>\n",
    "        <li><a href=\"https://#visualization_analysis\">Data Visualization and Analysis</a></li>\n",
    "        <li><a href=\"https://#classification\">Classification</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn==0.23.1 in c:\\users\\cgata\\anaconda3\\lib\\site-packages (0.23.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\cgata\\anaconda3\\lib\\site-packages (from scikit-learn==0.23.1) (1.0.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\cgata\\anaconda3\\lib\\site-packages (from scikit-learn==0.23.1) (2.1.0)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\cgata\\anaconda3\\lib\\site-packages (from scikit-learn==0.23.1) (1.6.2)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\cgata\\anaconda3\\lib\\site-packages (from scikit-learn==0.23.1) (1.20.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn==0.23.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "Let's load required libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<div id=\"about_dataset\">\n",
    "    <h2>About the dataset</h2>\n",
    "</div>\n"
   ]
  },
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   "source": [
    "Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset,  with predefined labels, we need to build a model to be used to predict class of a new or unknown case.\n",
    "\n",
    "The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.\n",
    "\n",
    "The target field, called **custcat**, has four possible values that correspond to the four customer groups, as follows:\n",
    "1- Basic Service\n",
    "2- E-Service\n",
    "3- Plus Service\n",
    "4- Total Service\n",
    "\n",
    "Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.\n"
   ]
  },
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   },
   "source": [
    "### Load Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read the data using pandas library and print the first five rows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
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       "  <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",
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       "      <td>24</td>\n",
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       "      <td>2</td>\n",
       "      <td>33</td>\n",
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       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>3</td>\n",
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      "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 12)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 12 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   region   1000 non-null   int64  \n",
      " 1   tenure   1000 non-null   int64  \n",
      " 2   age      1000 non-null   int64  \n",
      " 3   marital  1000 non-null   int64  \n",
      " 4   address  1000 non-null   int64  \n",
      " 5   income   1000 non-null   float64\n",
      " 6   ed       1000 non-null   int64  \n",
      " 7   employ   1000 non-null   int64  \n",
      " 8   retire   1000 non-null   float64\n",
      " 9   gender   1000 non-null   int64  \n",
      " 10  reside   1000 non-null   int64  \n",
      " 11  custcat  1000 non-null   int64  \n",
      "dtypes: float64(2), int64(10)\n",
      "memory usage: 93.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <td>1000.0000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0220</td>\n",
       "      <td>35.526000</td>\n",
       "      <td>41.684000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>11.551000</td>\n",
       "      <td>77.535000</td>\n",
       "      <td>2.671000</td>\n",
       "      <td>10.987000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>2.331000</td>\n",
       "      <td>2.487000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.8162</td>\n",
       "      <td>21.359812</td>\n",
       "      <td>12.558816</td>\n",
       "      <td>0.500225</td>\n",
       "      <td>10.086681</td>\n",
       "      <td>107.044165</td>\n",
       "      <td>1.222397</td>\n",
       "      <td>10.082087</td>\n",
       "      <td>0.211745</td>\n",
       "      <td>0.499961</td>\n",
       "      <td>1.435793</td>\n",
       "      <td>1.120306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1668.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          region       tenure          age      marital      address  \\\n",
       "count  1000.0000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean      2.0220    35.526000    41.684000     0.495000    11.551000   \n",
       "std       0.8162    21.359812    12.558816     0.500225    10.086681   \n",
       "min       1.0000     1.000000    18.000000     0.000000     0.000000   \n",
       "25%       1.0000    17.000000    32.000000     0.000000     3.000000   \n",
       "50%       2.0000    34.000000    40.000000     0.000000     9.000000   \n",
       "75%       3.0000    54.000000    51.000000     1.000000    18.000000   \n",
       "max       3.0000    72.000000    77.000000     1.000000    55.000000   \n",
       "\n",
       "            income           ed       employ       retire       gender  \\\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000   \n",
       "mean     77.535000     2.671000    10.987000     0.047000     0.517000   \n",
       "std     107.044165     1.222397    10.082087     0.211745     0.499961   \n",
       "min       9.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%      29.000000     2.000000     3.000000     0.000000     0.000000   \n",
       "50%      47.000000     3.000000     8.000000     0.000000     1.000000   \n",
       "75%      83.000000     4.000000    17.000000     0.000000     1.000000   \n",
       "max    1668.000000     5.000000    47.000000     1.000000     1.000000   \n",
       "\n",
       "            reside      custcat  \n",
       "count  1000.000000  1000.000000  \n",
       "mean      2.331000     2.487000  \n",
       "std       1.435793     1.120306  \n",
       "min       1.000000     1.000000  \n",
       "25%       1.000000     1.000000  \n",
       "50%       2.000000     3.000000  \n",
       "75%       3.000000     3.000000  \n",
       "max       8.000000     4.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"visualization_analysis\">\n",
    "    <h2>Data Visualization and Analysis</h2> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Let’s see how many of each class is in our data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['custcat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### 281 Plus Service, 266 Basic-service, 236 Total Service, and 217 E-Service customers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['custcat'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can easily explore your data using visualization techniques:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Income column Data Type:  float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean       77.535000\n",
       "std       107.044165\n",
       "min         9.000000\n",
       "25%        29.000000\n",
       "50%        47.000000\n",
       "75%        83.000000\n",
       "max      1668.000000\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Income column Data Type: ', df['income'].dtypes)\n",
    "df['income'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'income'}>]], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEICAYAAAC9E5gJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAASIUlEQVR4nO3dfbBcdX3H8feHYMES5aFgJk2iN9SUEWTqwy3qoPam0IKihNpi41AbKk7qFDva2qlBOq39g05sp452kDqpMEaxXFKQISPDKJN6q51qkSBPAVOiBAjEpCIIUUsb/PaPPddu0nuTu7m7e3fh/Zq5s2d/+ztnP3tY7ueesw9JVSFJem47bK4DSJLmnmUgSbIMJEmWgSQJy0CShGUgScIy0LNMki1JxuY6hzRs4ucMJEkeGUiSLAM9uyTZnuTMJB9OsiHJZ5I81Zw+Gm2btyTJ55P8Z5LHklzejB+W5M+SPJhkd7P+0c1tI0kqye8leTjJ40nek+SXk9yV5InJ7bTdz7uS3NfM/WKSl/R3j0gzYxno2excYBw4BtgITP7Cnwd8AXgQGAEWNfMALmx+lgMnAvMn12vzGmAZ8NvAx4BLgTOBU4C3J/mV5n7OAz4EvA04AfgqcE03H6DULb5moGeVJNuBdwOvB15fVWc24ycDm6vq+UleR6scFlbV3v3W3wRcX1VXNNdPAu4Bng8sBh4AFlfVI83tjwF/UFXXNtevB75aVR9LcjNwXVVd2dx2GLAHeFlVPdjL/SB1yiMDPZt9t235R8CRSQ4HlgAP7l8EjZ+ndcQw6UHgcGBB29iutuUfT3F9frP8EuDjzemjJ4DvA6F1JCINFMtAz0UPAy9uimF/j9L6JT7pxcBe9v2F38n9/H5VHdP28/yq+rdD2JbUU5aBnotuBXYCa5McleTIJKc3t10D/FGSpUnmA38FXDvNUcTBfBK4JMkpAEmOTnJ+Nx6A1G2WgZ5zquoZ4K3AS4GHgB20XgwGuAr4LPAVWq8P/Bfwh4d4PzcAHwHGkzxJ67WHN80qvNQjvoAsSfLIQJJkGUiSsAwkSVgGkiRaH6aZc8cff3yNjIx0vN4Pf/hDjjrqqO4H6jFz988wZgZz99uw5t68efP3quqEbmxrIMpgZGSE2267reP1JiYmGBsb636gHjN3/wxjZjB3vw1r7iRd+1oTTxNJkiwDSZJlIEnCMpAkYRlIkrAMJElYBpIkLANJEpaBJIkB+QTybI2suWnK8e1rz+lzEkkaTh4ZSJIsA0mSZSBJwjKQJGEZSJKwDCRJWAaSJCwDSRKWgSQJy0CShGUgScIykCRhGUiSsAwkSVgGkiQsA0kSHZRBknlJvpnkC83145LckuT+5vLYtrmXJNmWZGuSs3oRXJLUPZ0cGbwPuK/t+hpgU1UtAzY110lyMrASOAU4G7giybzuxJUk9cKMyiDJYuAc4FNtwyuA9c3yeuC8tvHxqnq6qh4AtgGndSWtJKknZnpk8DHgT4GftI0tqKqdAM3li5rxRcDDbfN2NGOSpAF1+MEmJHkLsLuqNicZm8E2M8VYTbHd1cBqgAULFjAxMTGDTe9rz549TExM8IFT9055+6Fssx8mcw+bYcw9jJnB3P02rLm76aBlAJwOnJvkzcCRwAuTXA3sSrKwqnYmWQjsbubvAJa0rb8YeHT/jVbVOmAdwOjoaI2NjXUcfmJigrGxMS5cc9OUt2+/oPNt9sNk7mEzjLmHMTOYu9+GNXc3HfQ0UVVdUlWLq2qE1gvD/1xVvwNsBFY101YBNzbLG4GVSY5IshRYBtza9eSSpK6ZyZHBdNYCG5JcBDwEnA9QVVuSbADuBfYCF1fVM7NOKknqmY7KoKomgIlm+THgjGnmXQZcNstskqQ+8RPIkiTLQJJkGUiSsAwkSVgGkiQsA0kSloEkCctAkoRlIEnCMpAkYRlIkrAMJElYBpIkLANJEpaBJAnLQJKEZSBJwjKQJGEZSJKwDCRJWAaSJCwDSRKWgSQJy0CShGUgScIykCRhGUiSsAwkSVgGkiQsA0kSloEkCctAkoRlIEnCMpAkYRlIkrAMJElYBpIkLANJEjMogyRHJrk1yZ1JtiT5y2b8uCS3JLm/uTy2bZ1LkmxLsjXJWb18AJKk2ZvJkcHTwK9W1S8BrwDOTvJaYA2wqaqWAZua6yQ5GVgJnAKcDVyRZF4PskuSuuSgZVAte5qrz2t+ClgBrG/G1wPnNcsrgPGqerqqHgC2Aad1M7QkqbtSVQef1PrLfjPwUuATVfXBJE9U1TFtcx6vqmOTXA58vaqubsavBG6uquv22+ZqYDXAggULXj0+Pt5x+D179jB//nzufuQHU95+6qKjO95mP0zmHjbDmHsYM4O5+21Ycy9fvnxzVY12Y1uHz2RSVT0DvCLJMcANSV5+gOmZahNTbHMdsA5gdHS0xsbGZhJlHxMTE4yNjXHhmpumvH37BZ1vsx8mcw+bYcw9jJnB3P02rLm7qaN3E1XVE8AErdcCdiVZCNBc7m6m7QCWtK22GHh0tkElSb0zk3cTndAcEZDk+cCZwLeAjcCqZtoq4MZmeSOwMskRSZYCy4Bbu5xbktRFMzlNtBBY37xucBiwoaq+kORrwIYkFwEPAecDVNWWJBuAe4G9wMXNaSZJ0oA6aBlU1V3AK6cYfww4Y5p1LgMum3U6SVJf+AlkSZJlIEmyDCRJWAaSJCwDSRKWgSQJy0CShGUgScIykCRhGUiSmOFXWA+rkem+2nrtOX1OIkmDzSMDSZJlIEmyDCRJWAaSJCwDSRKWgSQJy0CShGUgScIykCRhGUiSsAwkSVgGkiQsA0kSloEkCctAkoRlIEnCMpAkYRlIkrAMJElYBpIkLANJEpaBJAnLQJKEZSBJwjKQJGEZSJKwDCRJzKAMkixJ8uUk9yXZkuR9zfhxSW5Jcn9zeWzbOpck2ZZka5KzevkAJEmzN5Mjg73AB6rqZcBrgYuTnAysATZV1TJgU3Od5raVwCnA2cAVSeb1IrwkqTsOWgZVtbOqbm+WnwLuAxYBK4D1zbT1wHnN8gpgvKqerqoHgG3AaV3OLUnqolTVzCcnI8BXgJcDD1XVMW23PV5Vxya5HPh6VV3djF8J3FxV1+23rdXAaoAFCxa8enx8vOPwe/bsYf78+dz9yA86Wu/URUd3fF/dNJl72Axj7mHMDObut2HNvXz58s1VNdqNbR0+04lJ5gPXA++vqieTTDt1irH/1zhVtQ5YBzA6OlpjY2MzjfJTExMTjI2NceGamzpab/sFnd9XN03mHjbDmHsYM4O5+21Yc3fTjN5NlOR5tIrgc1X1+WZ4V5KFze0Lgd3N+A5gSdvqi4FHuxNXktQLM3k3UYArgfuq6qNtN20EVjXLq4Ab28ZXJjkiyVJgGXBr9yJLkrptJqeJTgfeCdyd5I5m7EPAWmBDkouAh4DzAapqS5INwL203ol0cVU90+3gkqTuOWgZVNW/MvXrAABnTLPOZcBls8glSeojP4EsSbIMJEmWgSQJy0CShGUgScIykCRhGUiSsAwkSVgGkiQsA0kSloEkCctAkoRlIEnCMpAkYRlIkrAMJElYBpIkLANJEpaBJAnLQJKEZSBJwjKQJGEZSJKwDCRJWAaSJCwDSRKWgSQJy0CShGUgScIykCRhGUiSsAwkSVgGkiTg8LkOMBdG1tw05fj2tef0OYkkDQaPDCRJloEkyTKQJDGDMkhyVZLdSe5pGzsuyS1J7m8uj2277ZIk25JsTXJWr4JLkrpnJkcGnwbO3m9sDbCpqpYBm5rrJDkZWAmc0qxzRZJ5XUsrSeqJg5ZBVX0F+P5+wyuA9c3yeuC8tvHxqnq6qh4AtgGndSeqJKlXUlUHn5SMAF+oqpc315+oqmPabn+8qo5Ncjnw9aq6uhm/Eri5qq6bYpurgdUACxYsePX4+HjH4ffs2cP8+fO5+5EfdLzuVE5ddHRXtnMwk7mHzTDmHsbMYO5+G9bcy5cv31xVo93YVrc/Z5ApxqZsm6paB6wDGB0drbGxsY7vbGJigrGxMS6c5nMDndp+QecZDsVk7mEzjLmHMTOYu9+GNXc3Heq7iXYlWQjQXO5uxncAS9rmLQYePfR4kqR+ONQy2AisapZXATe2ja9MckSSpcAy4NbZRZQk9dpBTxMluQYYA45PsgP4C2AtsCHJRcBDwPkAVbUlyQbgXmAvcHFVPdOj7JKkLjloGVTVO6a56Yxp5l8GXDabUJKk/vITyJIky0CSZBlIkrAMJElYBpIkLANJEpaBJAnLQJKEZSBJwjKQJGEZSJKwDCRJWAaSJCwDSRKWgSQJy0CShGUgSWIG/9LZc8nImpumHN++9pw+J5Gk/vLIQJJkGUiSLANJEpaBJAnLQJKE7yaaEd9lJOnZziMDSZJlIEmyDCRJWAaSJCwDSRKWgSQJy0CShJ8zmBU/fyDp2cIjA0mSZSBJ8jRRX02eVvrAqXu5sO0Uk6eVJM01y6AHpnstQZIGlaeJJEmWgSSph6eJkpwNfByYB3yqqtb26r6GXTdPK/n6g6RD0ZMySDIP+ATwa8AO4BtJNlbVvb24Px2cn4mQdCC9OjI4DdhWVd8BSDIOrAAsgx7r9Cij05Lo1vzp9Ho7/TBoWQ+Uxz8G+mfQ/yBLVXV/o8lvAWdX1bub6+8EXlNV722bsxpY3Vw9Cdh6CHd1PPC9WcadC+bun2HMDObut2HNfVJVvaAbG+rVkUGmGNundapqHbBuVneS3FZVo7PZxlwwd/8MY2Ywd78Nc+5ubatX7ybaASxpu74YeLRH9yVJmqVelcE3gGVJlib5GWAlsLFH9yVJmqWenCaqqr1J3gt8kdZbS6+qqi09uKtZnWaaQ+bun2HMDObut+d87p68gCxJGi5+AlmSZBlIkoa0DJKcnWRrkm1J1sx1nnZJliT5cpL7kmxJ8r5m/MNJHklyR/Pz5rZ1Lmkey9YkZ81h9u1J7m7y3daMHZfkliT3N5fHDlLuJCe17dM7kjyZ5P2DuL+TXJVkd5J72sY63r9JXt38d9qW5O+STPVW7l7n/psk30pyV5IbkhzTjI8k+XHbfv/kXOSeJnPHz4kB2dfXtmXenuSOZry7+7qqhuqH1gvS3wZOBH4GuBM4ea5zteVbCLyqWX4B8B/AycCHgT+ZYv7JzWM4AljaPLZ5c5R9O3D8fmN/DaxpltcAHxm03Ps9N74LvGQQ9zfwRuBVwD2z2b/ArcDraH2e52bgTXOQ+9eBw5vlj7TlHmmft992+pZ7mswdPycGYV/vd/vfAn/ei309jEcGP/2qi6r6b2Dyqy4GQlXtrKrbm+WngPuARQdYZQUwXlVPV9UDwDZaj3FQrADWN8vrgfPaxgct9xnAt6vqwQPMmbPcVfUV4PtT5Jnx/k2yEHhhVX2tWv/Xf6Ztnb7lrqovVdXe5urXaX2WaFr9zj3Nvp7OQO/rSc1f928HrjnQNg419zCWwSLg4bbrOzjwL9s5k2QEeCXw783Qe5vD6qvaTgcM0uMp4EtJNqf1dSEAC6pqJ7SKDnhRMz5IuSetZN//UQZ9f0Pn+3dRs7z/+Fx6F62/PictTfLNJP+S5A3N2KDk7uQ5MSiZJ70B2FVV97eNdW1fD2MZHPSrLgZBkvnA9cD7q+pJ4O+BXwBeAeykdbgHg/V4Tq+qVwFvAi5O8sYDzB2k3KT14cZzgX9qhoZhfx/IdDkHKn+SS4G9wOeaoZ3Ai6vqlcAfA/+Y5IUMRu5OnxODkLndO9j3j52u7uthLIOB/6qLJM+jVQSfq6rPA1TVrqp6pqp+AvwD/3dqYmAeT1U92lzuBm6glXFXc9g5efi5u5k+MLkbbwJur6pdMBz7u9Hp/t3Bvqdk5ix/klXAW4ALmtMRNKdaHmuWN9M6//6LDEDuQ3hOzHnmSUkOB94GXDs51u19PYxlMNBfddGc17sSuK+qPto2vrBt2m8Ak+8W2AisTHJEkqXAMlov/vRVkqOSvGBymdYLhPc0+VY101YBNzbLA5G7zT5/NQ36/m7T0f5tTiU9leS1zXPtd9vW6Zu0/vGqDwLnVtWP2sZPSOvfMyHJiU3u7wxC7k6fE4OQuc2ZwLeq6qenf7q+r3v5ynivfoA303qXzreBS+c6z37ZXk/rkOwu4I7m583AZ4G7m/GNwMK2dS5tHstWevxuhQPkPpHWOyruBLZM7lfg54BNwP3N5XGDlLvJ8bPAY8DRbWMDt79pldVO4H9o/fV20aHsX2CU1i+ybwOX03yTQJ9zb6N1nn3yOf7JZu5vNs+fO4HbgbfORe5pMnf8nBiEfd2Mfxp4z35zu7qv/ToKSdJQniaSJHWZZSBJsgwkSZaBJAnLQJKEZSBJwjKQJAH/C+ch7ZFQjClTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='income', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's define feature sets, X:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
       "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.,  13.,  44.,   1.,   9.,  64.,   4.,   5.,   0.,   0.,   2.],\n",
       "       [  3.,  11.,  33.,   1.,   7., 136.,   5.,   5.,   0.,   0.,   6.],\n",
       "       [  3.,  68.,  52.,   1.,  24., 116.,   1.,  29.,   0.,   1.,   2.],\n",
       "       [  2.,  33.,  33.,   0.,  12.,  33.,   2.,   0.,   0.,   1.,   1.],\n",
       "       [  2.,  23.,  30.,   1.,   9.,  30.,   1.,   2.,   0.,   0.,   4.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']].values  #.astype(float)\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our labels?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3, 1, 3], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['custcat'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization gives the data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on the distance of data points:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02696767, -1.055125  ,  0.18450456,  1.0100505 , -0.25303431,\n",
       "        -0.12650641,  1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "        -0.23065004],\n",
       "       [ 1.19883553, -1.14880563, -0.69181243,  1.0100505 , -0.4514148 ,\n",
       "         0.54644972,  1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "         2.55666158],\n",
       "       [ 1.19883553,  1.52109247,  0.82182601,  1.0100505 ,  1.23481934,\n",
       "         0.35951747, -1.36767088,  1.78752803, -0.22207644,  0.96655883,\n",
       "        -0.23065004],\n",
       "       [-0.02696767, -0.11831864, -0.69181243, -0.9900495 ,  0.04453642,\n",
       "        -0.41625141, -0.54919639, -1.09029981, -0.22207644,  0.96655883,\n",
       "        -0.92747794],\n",
       "       [-0.02696767, -0.58672182, -0.93080797,  1.0100505 , -0.25303431,\n",
       "        -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n",
       "         1.16300577]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Train Test Split\n",
    "\n",
    "Out of Sample Accuracy is the percentage of correct predictions that the model makes on data that the model has NOT been trained on. Doing a train and test on the same dataset will most likely have low out-of-sample accuracy, due to the likelihood of our model overfitting.\n",
    "\n",
    "It is important that our models have a high, out-of-sample accuracy, because the purpose of any model, of course, is to make correct predictions on unknown data. So how can we improve out-of-sample accuracy? One way is to use an evaluation approach called Train/Test Split.\n",
    "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n",
    "\n",
    "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that has been used to train the model. It is more realistic for the real world problems.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# y = df['custcat'].values\n",
    "# X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div id=\"classification\">\n",
    "    <h2>Classification</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h3>K nearest neighbor (KNN)</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Import library\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Classifier implementing the k-nearest neighbors vote.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Training\n",
    "\n",
    "Let's start the algorithm with k=4 for now:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = 4\n",
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Predicting\n",
    "\n",
    "We can use the model to make predictions on the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted values by the model\n",
    "yhat = neigh.predict(X_test)\n",
    "yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 1, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual values of the test set\n",
    "# how closely the actual labels and predicted labels are matched in the test set\n",
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Accuracy evaluation\n",
    "\n",
    "In multilabel classification, **accuracy classification score** is a function that computes subset accuracy. This function is equal to the jaccard_score function. Essentially, it calculates how closely the actual labels and predicted labels are matched in the test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.5475\n",
      "Test set Accuracy:  0.32\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "\n",
    "Can you build the model again, but this time with k=6?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Values [3 1 3 2 4]\n",
      "Actual Values [4 1 1 2 4]\n",
      "Train set Accuracy:  0.5025\n",
      "Test set Accuracy:  0.34\n"
     ]
    }
   ],
   "source": [
    "# write your code here\n",
    "k = 9\n",
    "# Train the model & Predict\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "yhat = neigh.predict(X_test)\n",
    "print('Predicted Values', yhat[0:5])\n",
    "print('Actual Values', y_test[0:5])\n",
    "\n",
    "# Accuracy evaluation\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "k = 6\n",
    "neigh6 = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "yhat6 = neigh6.predict(X_test)\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh6.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat6))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### What about other K?\n",
    "\n",
    "K in KNN, is the number of nearest neighbors to examine. It is supposed to be specified by the user. So, how can we choose right value for K?\n",
    "The general solution is to reserve a part of your data for testing the accuracy of the model. Then choose k =1, use the training part for modeling, and calculate the accuracy of prediction using all samples in your test set. Repeat this process, increasing the k, and see which k is the best for your model.\n",
    "\n",
    "We can calculate the accuracy of KNN for different values of k.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3  , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Plot the model accuracy for a different number of neighbors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\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.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.34 with k= 9\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "@Charles Gatama"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}