KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the Nearest Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age      Income  Marital      Risk\n",
      "0   22  $46,156.98   Single  Bad loss\n",
      "1   33  $24,188.10  Married  Bad loss\n",
      "2   28  $28,787.34    Other  Bad loss\n",
      "3   51  $23,886.72    Other  Bad loss\n",
      "4   25  $47,281.44   Single  Bad loss\n",
      "['Bad loss' 'Good risk']\n"
     ]
    }
   ],
   "source": [
    "data = {\"Age\":[22,33,28,51,25,39,54,55,50,60],\"Marital\":['Single','Married','Other','Other','Single','Single','Signle','Married','Married','Married'],\"Income\":['$46,156.98','$24,188.10','$28,787.34','$23,886.72','$47,281.44','$33,994.90','$28,716.50','$49,186.75','$46,726.50','$36,120.34'],'Risk':['Bad loss','Bad loss','Bad loss','Bad loss','Bad loss','Good risk','Good risk','Good risk','Good risk','Good risk']}\n",
    "df = pd.DataFrame(data)\n",
    "print df.head()\n",
    "print df['Risk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the categorical target variable from the Risk column into numerical\n",
    "labels = df['Risk'].astype('category').cat.categories.tolist()\n",
    "replace_map = {'Risk':{ k: v for k,v in zip(labels,list(range(1,len(labels)+1)))}}\n",
    "df.replace(replace_map, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Marital</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>$46,156.98</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>$24,188.10</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>$28,787.34</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>$23,886.72</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>$47,281.44</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39</td>\n",
       "      <td>$33,994.90</td>\n",
       "      <td>Single</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>$28,716.50</td>\n",
       "      <td>Signle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55</td>\n",
       "      <td>$49,186.75</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>$46,726.50</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>$36,120.34</td>\n",
       "      <td>Married</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age      Income  Marital  Risk\n",
       "0   22  $46,156.98   Single     1\n",
       "1   33  $24,188.10  Married     1\n",
       "2   28  $28,787.34    Other     1\n",
       "3   51  $23,886.72    Other     1\n",
       "4   25  $47,281.44   Single     1\n",
       "5   39  $33,994.90   Single     2\n",
       "6   54  $28,716.50   Signle     2\n",
       "7   55  $49,186.75  Married     2\n",
       "8   50  $46,726.50  Married     2\n",
       "9   60  $36,120.34  Married     2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the Marital with one-hot-encoding\n",
    "df = pd.get_dummies(df,columns=['Marital'],prefix=['Marital'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Marital_Married</th>\n",
       "      <th>Marital_Other</th>\n",
       "      <th>Marital_Signle</th>\n",
       "      <th>Marital_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>$46,156.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>$24,188.10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>$28,787.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>$23,886.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>$47,281.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39</td>\n",
       "      <td>$33,994.90</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54</td>\n",
       "      <td>$28,716.50</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55</td>\n",
       "      <td>$49,186.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>$46,726.50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>$36,120.34</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age      Income  Risk  Marital_Married  Marital_Other  Marital_Signle  \\\n",
       "0   22  $46,156.98     1                0              0               0   \n",
       "1   33  $24,188.10     1                1              0               0   \n",
       "2   28  $28,787.34     1                0              1               0   \n",
       "3   51  $23,886.72     1                0              1               0   \n",
       "4   25  $47,281.44     1                0              0               0   \n",
       "5   39  $33,994.90     2                0              0               0   \n",
       "6   54  $28,716.50     2                0              0               1   \n",
       "7   55  $49,186.75     2                1              0               0   \n",
       "8   50  $46,726.50     2                1              0               0   \n",
       "9   60  $36,120.34     2                1              0               0   \n",
       "\n",
       "   Marital_Single  \n",
       "0               1  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  \n",
       "5               1  \n",
       "6               0  \n",
       "7               0  \n",
       "8               0  \n",
       "9               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Income'] = df.Income.str[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    46156.98\n",
       "1    24188.10\n",
       "2    28787.34\n",
       "3    23886.72\n",
       "4    47281.44\n",
       "5    33994.90\n",
       "6    28716.50\n",
       "7    49186.75\n",
       "8    46726.50\n",
       "9    36120.34\n",
       "Name: Income, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Income'] = df['Income'].str.replace(',','')\n",
    "df['Income'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "df[['Age','Income']] = scaler.fit_transform(df[['Age','Income']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Marital_Married</th>\n",
       "      <th>Marital_Other</th>\n",
       "      <th>Marital_Signle</th>\n",
       "      <th>Marital_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.880246</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.289474</td>\n",
       "      <td>0.011912</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.193700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.763158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.924691</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.399532</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.190900</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.868421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.902757</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.483542</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age    Income  Risk  Marital_Married  Marital_Other  Marital_Signle  \\\n",
       "0  0.000000  0.880246     1                0              0               0   \n",
       "1  0.289474  0.011912     1                1              0               0   \n",
       "2  0.157895  0.193700     1                0              1               0   \n",
       "3  0.763158  0.000000     1                0              1               0   \n",
       "4  0.078947  0.924691     1                0              0               0   \n",
       "5  0.447368  0.399532     2                0              0               0   \n",
       "6  0.842105  0.190900     2                0              0               1   \n",
       "7  0.868421  1.000000     2                1              0               0   \n",
       "8  0.736842  0.902757     2                1              0               0   \n",
       "9  1.000000  0.483542     2                1              0               0   \n",
       "\n",
       "   Marital_Single  \n",
       "0               1  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  \n",
       "5               1  \n",
       "6               0  \n",
       "7               0  \n",
       "8               0  \n",
       "9               0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "         metric_params=None, n_jobs=None, n_neighbors=3, p=2, radius=1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = NearestNeighbors(3)\n",
    "neigh.fit(df[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 7, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbors = neigh.kneighbors(df[-1:],3,return_distance=False)[0]\n",
    "neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Marital_Married</th>\n",
       "      <th>Marital_Other</th>\n",
       "      <th>Marital_Signle</th>\n",
       "      <th>Marital_Single</th>\n",
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       "  </thead>\n",
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       "      <th>8</th>\n",
       "      <td>0.736842</td>\n",
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       "      <td>0.868421</td>\n",
       "      <td>1.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Age    Income  Risk  Marital_Married  Marital_Other  Marital_Signle  \\\n",
       "8  0.736842  0.902757     2                1              0               0   \n",
       "7  0.868421  1.000000     2                1              0               0   \n",
       "1  0.289474  0.011912     1                1              0               0   \n",
       "\n",
       "   Marital_Single  \n",
       "8               0  \n",
       "7               0  \n",
       "1               0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[neighbors, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the Risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>income</th>\n",
       "      <th>risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>other</td>\n",
       "      <td>28060.70</td>\n",
       "      <td>bad loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>other</td>\n",
       "      <td>28009.34</td>\n",
       "      <td>bad loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>other</td>\n",
       "      <td>27614.60</td>\n",
       "      <td>bad loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>other</td>\n",
       "      <td>27287.18</td>\n",
       "      <td>bad loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>other</td>\n",
       "      <td>26954.06</td>\n",
       "      <td>bad loss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age marital_status    income      risk\n",
       "0   34          other  28060.70  bad loss\n",
       "1   37          other  28009.34  bad loss\n",
       "2   29          other  27614.60  bad loss\n",
       "3   33          other  27287.18  bad loss\n",
       "4   39          other  26954.06  bad loss"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_risk = pd.read_csv('ClassifyRisk', usecols=['age','marital_status','income','risk'])\n",
    "df_risk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the categorical target variable from the Risk column into numerical\n",
    "labels = df_risk['risk'].astype('category').cat.categories.tolist()\n",
    "replace_map = {'risk':{ k: v for k,v in zip(labels,list(range(1,len(labels)+1)))}}\n",
    "df_risk.replace(replace_map, inplace=True)\n",
    "\n",
    "# Convert the Marital with one-hot-encoding\n",
    "df_risk = pd.get_dummies(df_risk,columns=['marital_status'],prefix=['marital_status'])\n",
    "\n",
    "#Scale the age and income variable\n",
    "scaler = MinMaxScaler()\n",
    "df_risk[['age','income']] = scaler.fit_transform(df_risk[['age','income']])\n",
    "\n",
    "print df_risk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        age    income  risk  marital_status_married  marital_status_other  \\\n",
      "0  0.346939  0.202218     1                       0                     1   \n",
      "\n",
      "   marital_status_single  \n",
      "0                      0  \n",
      "\n",
      "\n",
      "        age    income  risk  marital_status_married  marital_status_other  \\\n",
      "1  0.408163  0.201404     1                       0                     1   \n",
      "2  0.244898  0.195148     1                       0                     1   \n",
      "3  0.326531  0.189959     1                       0                     1   \n",
      "4  0.448980  0.184680     1                       0                     1   \n",
      "5  0.224490  0.173868     1                       0                     1   \n",
      "\n",
      "   marital_status_single  \n",
      "1                      0  \n",
      "2                      0  \n",
      "3                      0  \n",
      "4                      0  \n",
      "5                      0  \n"
     ]
    }
   ],
   "source": [
    "to_be_predicted = df_risk[:1]\n",
    "df_risk.drop(df_risk.index[0], inplace=True)\n",
    "print to_be_predicted\n",
    "print \"\\n\"\n",
    "print df_risk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset\n",
    "X = df_risk[['age','income','marital_status_married','marital_status_other','marital_status_single']]\n",
    "y = df_risk[['risk']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0\n"
     ]
    }
   ],
   "source": [
    "# k - odd number, which is a square root of the number of instances of the test set\n",
    "import math\n",
    "print math.sqrt(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors = 7)\n",
    "neigh.fit(X_train,y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1,\n",
       "       2, 1, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2,\n",
       "       1, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = neigh.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19  4]\n",
      " [ 2 24]]\n"
     ]
    }
   ],
   "source": [
    "#Evaluate model\n",
    "#cols - predicted, rows - actual\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: [ 0.86363636  0.88888889]\n",
      "Accuracy: 0.877551020408\n"
     ]
    }
   ],
   "source": [
    "# Accuracy = TP+TN/TP+FP+FN+TN - the ratio of correctly predicted observation to the total observations\n",
    "# Precision = TP/TP+FP -he ratio of correctly predicted positive observations to the total predicted positive observations\n",
    "# Recall = TP/TP+FN - the ratio of correctly predicted positive observations to the all observations in actual class\n",
    "# F1 Score = 2*(Recall * Precision) / (Recall + Precision)\n",
    "print('F1 score: {}'.format(f1_score(y_test, y_pred,average=None)))\n",
    "print('Accuracy: {}'.format(accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted [1]\n",
      "Actual [1]\n"
     ]
    }
   ],
   "source": [
    "predicted = neigh.predict(to_be_predicted[['age','income','marital_status_married','marital_status_other','marital_status_single']])\n",
    "print 'Predicted {}'.format(predicted)\n",
    "print 'Actual {}'.format(to_be_predicted[['risk']].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted [2]\n"
     ]
    }
   ],
   "source": [
    "predicted = neigh.predict([[20,2000,0,0,1]])\n",
    "print 'Predicted {}'.format(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.90      0.83      0.86        23\n",
      "           2       0.86      0.92      0.89        26\n",
      "\n",
      "   micro avg       0.88      0.88      0.88        49\n",
      "   macro avg       0.88      0.87      0.88        49\n",
      "weighted avg       0.88      0.88      0.88        49\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print classification_report(y_test,neigh.predict(X_test))"
   ]
  }
 ],
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