Day4/Day4 - KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_nam = ['id', 'Clump Thickness', 'Uniformity of Cell Size', \n",
    "       'Uniformity of Cell Shape', 'Marginal Adhesion', \n",
    "       'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',\n",
    "       'Normal Nucleoli', 'Mitoses', 'Class']\n",
    "path = r'/home/unknown/Downloads/100daysofmlcode-farhankarim/Data/Day 4 - KNN - Breast Cancer Wisconsin/breast-cancer-wisconsinclean.csv'\n",
    "df = pd.read_csv(path,names=col_nam,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000025</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1002945</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1015425</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        id  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0  1000025                5                        1   \n",
       "1  1002945                5                        4   \n",
       "2  1015425                3                        1   \n",
       "3  1016277                6                        8   \n",
       "4  1017023                4                        1   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                         1                  1                            2   \n",
       "1                         4                  5                            7   \n",
       "2                         1                  1                            2   \n",
       "3                         8                  1                            3   \n",
       "4                         1                  3                            2   \n",
       "\n",
       "  Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0           1                3                1        1      2  \n",
       "1          10                3                2        1      2  \n",
       "2           2                3                1        1      2  \n",
       "3           4                3                7        1      2  \n",
       "4           1                3                1        1      2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>id</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.830000e+02</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.076720e+06</td>\n",
       "      <td>4.442167</td>\n",
       "      <td>3.150805</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>2.830161</td>\n",
       "      <td>3.234261</td>\n",
       "      <td>3.445095</td>\n",
       "      <td>2.869693</td>\n",
       "      <td>1.603221</td>\n",
       "      <td>2.699854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.206440e+05</td>\n",
       "      <td>2.820761</td>\n",
       "      <td>3.065145</td>\n",
       "      <td>2.988581</td>\n",
       "      <td>2.864562</td>\n",
       "      <td>2.223085</td>\n",
       "      <td>2.449697</td>\n",
       "      <td>3.052666</td>\n",
       "      <td>1.732674</td>\n",
       "      <td>0.954592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.337500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.776170e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.171795e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.238705e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.345435e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  Clump Thickness  Uniformity of Cell Size  \\\n",
       "count  6.830000e+02       683.000000               683.000000   \n",
       "mean   1.076720e+06         4.442167                 3.150805   \n",
       "std    6.206440e+05         2.820761                 3.065145   \n",
       "min    6.337500e+04         1.000000                 1.000000   \n",
       "25%    8.776170e+05         2.000000                 1.000000   \n",
       "50%    1.171795e+06         4.000000                 1.000000   \n",
       "75%    1.238705e+06         6.000000                 5.000000   \n",
       "max    1.345435e+07        10.000000                10.000000   \n",
       "\n",
       "       Uniformity of Cell Shape  Marginal Adhesion  \\\n",
       "count                683.000000         683.000000   \n",
       "mean                   3.215227           2.830161   \n",
       "std                    2.988581           2.864562   \n",
       "min                    1.000000           1.000000   \n",
       "25%                    1.000000           1.000000   \n",
       "50%                    1.000000           1.000000   \n",
       "75%                    5.000000           4.000000   \n",
       "max                   10.000000          10.000000   \n",
       "\n",
       "       Single Epithelial Cell Size  Bland Chromatin  Normal Nucleoli  \\\n",
       "count                   683.000000       683.000000       683.000000   \n",
       "mean                      3.234261         3.445095         2.869693   \n",
       "std                       2.223085         2.449697         3.052666   \n",
       "min                       1.000000         1.000000         1.000000   \n",
       "25%                       2.000000         2.000000         1.000000   \n",
       "50%                       2.000000         3.000000         1.000000   \n",
       "75%                       4.000000         5.000000         4.000000   \n",
       "max                      10.000000        10.000000        10.000000   \n",
       "\n",
       "          Mitoses       Class  \n",
       "count  683.000000  683.000000  \n",
       "mean     1.603221    2.699854  \n",
       "std      1.732674    0.954592  \n",
       "min      1.000000    2.000000  \n",
       "25%      1.000000    2.000000  \n",
       "50%      1.000000    2.000000  \n",
       "75%      1.000000    4.000000  \n",
       "max     10.000000    4.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 683 entries, 0 to 698\n",
      "Data columns (total 11 columns):\n",
      "id                             683 non-null int64\n",
      "Clump Thickness                683 non-null int64\n",
      "Uniformity of Cell Size        683 non-null int64\n",
      "Uniformity of Cell Shape       683 non-null int64\n",
      "Marginal Adhesion              683 non-null int64\n",
      "Single Epithelial Cell Size    683 non-null int64\n",
      "Bare Nuclei                    683 non-null object\n",
      "Bland Chromatin                683 non-null int64\n",
      "Normal Nucleoli                683 non-null int64\n",
      "Mitoses                        683 non-null int64\n",
      "Class                          683 non-null int64\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 84.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bare Nuclei</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "       Bare Nuclei\n",
       "count          683\n",
       "unique          10\n",
       "top              1\n",
       "freq           402"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "5      30\n",
       "2      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "?      16\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: Bare Nuclei, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bare Nuclei'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bare Nuclei'].replace(\"?\", np.NAN, inplace=True)\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f7b6198bcc0>"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "<Figure size 2022.38x1980 with 132 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THIS IS GOING TO BE A VERY LARGE PLOT\n",
    "sns.pairplot(df,hue='Class',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df.drop('Class',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_features = scaler.transform(df.drop('Class',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>id</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.123664</td>\n",
       "      <td>0.197905</td>\n",
       "      <td>-0.702212</td>\n",
       "      <td>-0.741774</td>\n",
       "      <td>-0.639366</td>\n",
       "      <td>-0.555608</td>\n",
       "      <td>-0.698853</td>\n",
       "      <td>-0.181827</td>\n",
       "      <td>-0.612927</td>\n",
       "      <td>-0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.118956</td>\n",
       "      <td>0.197905</td>\n",
       "      <td>0.277252</td>\n",
       "      <td>0.262783</td>\n",
       "      <td>0.758032</td>\n",
       "      <td>1.695166</td>\n",
       "      <td>1.772867</td>\n",
       "      <td>-0.181827</td>\n",
       "      <td>-0.285105</td>\n",
       "      <td>-0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.098833</td>\n",
       "      <td>-0.511643</td>\n",
       "      <td>-0.702212</td>\n",
       "      <td>-0.741774</td>\n",
       "      <td>-0.639366</td>\n",
       "      <td>-0.555608</td>\n",
       "      <td>-0.424217</td>\n",
       "      <td>-0.181827</td>\n",
       "      <td>-0.612927</td>\n",
       "      <td>-0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.097459</td>\n",
       "      <td>0.552679</td>\n",
       "      <td>1.583204</td>\n",
       "      <td>1.602192</td>\n",
       "      <td>-0.639366</td>\n",
       "      <td>-0.105454</td>\n",
       "      <td>0.125054</td>\n",
       "      <td>-0.181827</td>\n",
       "      <td>1.354008</td>\n",
       "      <td>-0.3484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.096256</td>\n",
       "      <td>-0.156869</td>\n",
       "      <td>-0.702212</td>\n",
       "      <td>-0.741774</td>\n",
       "      <td>0.059333</td>\n",
       "      <td>-0.555608</td>\n",
       "      <td>-0.698853</td>\n",
       "      <td>-0.181827</td>\n",
       "      <td>-0.612927</td>\n",
       "      <td>-0.3484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0 -0.123664         0.197905                -0.702212   \n",
       "1 -0.118956         0.197905                 0.277252   \n",
       "2 -0.098833        -0.511643                -0.702212   \n",
       "3 -0.097459         0.552679                 1.583204   \n",
       "4 -0.096256        -0.156869                -0.702212   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                 -0.741774          -0.639366                    -0.555608   \n",
       "1                  0.262783           0.758032                     1.695166   \n",
       "2                 -0.741774          -0.639366                    -0.555608   \n",
       "3                  1.602192          -0.639366                    -0.105454   \n",
       "4                 -0.741774           0.059333                    -0.555608   \n",
       "\n",
       "   Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  \n",
       "0    -0.698853        -0.181827        -0.612927  -0.3484  \n",
       "1     1.772867        -0.181827        -0.285105  -0.3484  \n",
       "2    -0.424217        -0.181827        -0.612927  -0.3484  \n",
       "3     0.125054        -0.181827         1.354008  -0.3484  \n",
       "4    -0.698853        -0.181827        -0.612927  -0.3484  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat = pd.DataFrame(scaled_features,columns=df.columns[:-1])\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,df['Class'],\n",
    "                                                    test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[125   6]\n",
      " [  6  68]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          2       0.95      0.95      0.95       131\n",
      "          4       0.92      0.92      0.92        74\n",
      "\n",
      "avg / total       0.94      0.94      0.94       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,100):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Error Rate')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,100),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WITH K=20\n",
      "\n",
      "\n",
      "[[125   6]\n",
      " [  3  71]]\n",
      "\n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          2       0.98      0.95      0.97       131\n",
      "          4       0.92      0.96      0.94        74\n",
      "\n",
      "avg / total       0.96      0.96      0.96       205\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# NOW WITH K=30\n",
    "knn = KNeighborsClassifier(n_neighbors=20)\n",
    "\n",
    "knn.fit(X_train,y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('WITH K=20')\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test,pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pred))"
   ]
  }
 ],
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