car_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "278c6b7c",
   "metadata": {},
   "source": [
    "## Classificationnmodels using car evaluation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7888869a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # import libaries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e69028",
   "metadata": {},
   "source": [
    "# import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d81dab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['buying','maintainance','doors','persons','lug_boot','safety','condition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1af44a5",
   "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>buying</th>\n",
       "      <th>maintainance</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>high</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buying maintainance doors persons lug_boot safety condition\n",
       "0  vhigh        vhigh     2       2    small    low     unacc\n",
       "1  vhigh        vhigh     2       2    small    med     unacc\n",
       "2  vhigh        vhigh     2       2    small   high     unacc\n",
       "3  vhigh        vhigh     2       2      med    low     unacc\n",
       "4  vhigh        vhigh     2       2      med    med     unacc"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"car_data_ml.data\",names=columns)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc79aee",
   "metadata": {},
   "source": [
    "# perform an EDA on the dataset\n",
    "\n",
    "* shape\n",
    "* dtype\n",
    "* desribe\n",
    "* info \n",
    "* corr\n",
    " \n",
    "\n",
    "* Count of cars by buying price (column chart)\n",
    "* Count of cars by maintainance\n",
    "* count of cars by number of doors\n",
    "* count of cars by number of persons\n",
    "* count of cars by lug_boot\n",
    "* count of cars by safety\n",
    "* Distribution of condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a61ecf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "339a407e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying</th>\n",
       "      <th>maintainance</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "      <td>1728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>576</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buying maintainance doors persons lug_boot safety condition\n",
       "count    1728         1728  1728    1728     1728   1728      1728\n",
       "unique      4            4     4       3        3      3         4\n",
       "top     vhigh        vhigh     2       2    small    low     unacc\n",
       "freq      432          432   432     576      576    576      1210"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "252a4e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1728 entries, 0 to 1727\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   buying        1728 non-null   object\n",
      " 1   maintainance  1728 non-null   object\n",
      " 2   doors         1728 non-null   object\n",
      " 3   persons       1728 non-null   object\n",
      " 4   lug_boot      1728 non-null   object\n",
      " 5   safety        1728 non-null   object\n",
      " 6   condition     1728 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc10e2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.corr of      buying maintainance  doors persons lug_boot safety condition\n",
       "0     vhigh        vhigh      2       2    small    low     unacc\n",
       "1     vhigh        vhigh      2       2    small    med     unacc\n",
       "2     vhigh        vhigh      2       2    small   high     unacc\n",
       "3     vhigh        vhigh      2       2      med    low     unacc\n",
       "4     vhigh        vhigh      2       2      med    med     unacc\n",
       "...     ...          ...    ...     ...      ...    ...       ...\n",
       "1723    low          low  5more    more      med    med      good\n",
       "1724    low          low  5more    more      med   high     vgood\n",
       "1725    low          low  5more    more      big    low     unacc\n",
       "1726    low          low  5more    more      big    med      good\n",
       "1727    low          low  5more    more      big   high     vgood\n",
       "\n",
       "[1728 rows x 7 columns]>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ed7e92d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['buying', 'maintainance', 'doors', 'persons', 'lug_boot', 'safety',\n",
       "       'condition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2397b359",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  buying  count\n",
       "0  vhigh    432\n",
       "1   high    432\n",
       "2    med    432\n",
       "3    low    432"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "df.buying.value_counts().reset_index().rename(columns={'index':'buying','buying':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6f5afc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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VK1eaZcuWmc6dO5uyZcuaFStWuC1z6tQp06lTJ2eZli1bmk8++cTEx8ebiIgI59ozf/nLX/LdX1ZWlts1UWrWrGmWLl1q5s2bZxo3bmwqVKhQ6IXHCrqoYIcOHUxiYqIznvfCaS+88IIzbsy5a6oEBQU547feeqv59NNPneu45N3WwhTlMbjURQWv5AJrR48eNTExMc46KlWqZP7xj3+YNWvWmKlTp5qQkBDTunVr5+J5xhjzyiuvuN3vgAEDzMqVK02PHj2cms/fZsyYYZKSki75uBd0IcawsDAjyTRv3tzMnz/ffP7552b+/Plu+/3OO+80p06duuR2nr/YZN6LUYaEhJhnn33WfPbZZ2bIkCFOv7e3t/nggw8KXd+UKVMuev2by1HQPqxYsaLp3Lmz+eSTT8zUqVPdrl914UUFz9uzZ4/b3939999vVq5caSZMmGDuuOMOt3U3atSo0AtOpqenu13AsEePHvnmnH/eXexvo6CLCp6/39OnT5tt27YV+rw9/xxLS0szUVFRznj9+vXN8uXLzYIFC0xkZKTx8/PjOjsWYk/iurFv3z4zevRo06hRIxMUFGR8fX3NLbfcYh566CGTkpJS6HLLly833bp1M9WqVTM+Pj4mICDA1KlTxwwaNMhs2bKl0OV+/fVX07dvXxMaGmq8vb1NtWrVTP/+/U1qamq+i61J/3cV4ILGLnzhvNT4ef/+979Nu3btTGhoqClbtqzx8/MzDRo0MDNnzrzix+5KHoOCLtpW0HZerpycHLNkyRLTuXNnU7VqVePj42NCQ0NN06ZNzbRp08zJkyfzzZ8+fbq57bbbjK+vr6lUqZLp2rWr2bhxY76L9kkyN998szHm4o9r3gveGWPM7NmzTf/+/U2jRo1MWFiY8fHxMd7e3qZKlSomJibGTJ06NV9thSnoYoaSzNKlS82TTz5pqlWrZry8vEylSpVM9+7dTXJy8kXXd+jQIePv728kmapVq5qzZ89eVh0XKqiel156yUyePNnUq1fP+Pj4mKCgINOmTRvz3nvvFbqer776yrRt29YEBgYaX19fU7duXfPSSy+ZnTt3FngfeS8CeF6vXr2cOXkvmGjMpZ93xhgTHR1d6Pi+ffsuOX7e4cOHzZgxY0zt2rWdxyE6OtqsXbvW7Xnkcrmu+HFH6eQypoDT8QEAHvX4449r/vz5GjNmjCZPnuzpcootISFBMTExioiI0C+//FJqL94XERHhXPwwNDSUc3gswTk7AFAKnT8x35avZ7dq1Ur+/v7q37+/R4POv/71Lz388MMFjp0+fVoHDhxw2i1btrxWZeEqI+wAgIf98ssvKleunL777junb+7cuYqOjladOnU8WFnRdejQQWPGjHHaS5YsUVZWlsfD2y+//KL33nuvwAtG/vOf/3R+MsXlcum555671uXhKin6ddABACUiNzdXJ0+e1Isvvqhhw4Zp1apVWrdunb744gtPl1ZkmZmZWrBggZo1a6bTp0/rmWeeUb9+/fL9dpWn9OjRQ6NGjVKTJk3kcrm0YcMGTZkyRdK56w299tprat26tYerREnhnB0A8LCMjAz1799fX3/9tQ4fPqwaNWro5ZdfVp8+fTxdWpFNnjxZ77zzjvbt26fAwED94Q9/0MyZMz3+cwz/+c9/FB8fr02bNmn37t06dOiQTpw4oaCgIEVFRalt27YaMmRIiV5gE55H2AEAAFbjnB0AAGA1ztnRuc/Lf/31VwUFBVn/Y5AAANjCGKPjx48rIiLioleAJ+xI+vXXXxUZGenpMgAAQBGkpqaqevXqhY4TdnTuN2qkcw9W+fLlPVwNAAC4HMeOHVNkZKTzPl4Ywo7+75eWy5cvT9gBAOA6c6lTUDhBGQAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1L08XcL1qXX2Qp0vABRL3z76q6x9/1x+v6vpxZcZ9Pfmqrj/50dFXdf24Mk3f/dtVXf+h+Lirun5cmdDecSW+To7sAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFqpCTuTJk2Sy+VSbGys02eMUVxcnCIiIuTv76+YmBjt2LHDbbmsrCwNHz5clSpVUmBgoLp06aL9+/df4+oBAEBpVSrCTlJSkt566y01bNjQrf/VV1/V1KlTNWvWLCUlJSk8PFz33Xefjh8/7syJjY3VsmXLtGTJEm3YsEEnTpxQp06dlJOTc603AwAAlEIeDzsnTpxQnz59NHv2bFWsWNHpN8Zo+vTpGjt2rLp376769etrwYIFOnXqlOLj4yVJR48e1Zw5czRlyhS1a9dOd955pxYuXKjvvvtOa9eu9dQmAQCAUsTjYWfYsGF64IEH1K5dO7f+ffv2KT09Xe3bt3f6fH19FR0drU2bNkmSUlJSdObMGbc5ERERql+/vjOnIFlZWTp27JjbDQAA2MnLk3e+ZMkSffPNN0pKSso3lp6eLkkKCwtz6w8LC9PPP//szPHx8XE7InR+zvnlCzJp0iSNHz++uOUDAIDrgMeO7KSmpmrEiBFauHCh/Pz8Cp3ncrnc2saYfH15XWrO888/r6NHjzq31NTUKyseAABcNzwWdlJSUnTgwAE1adJEXl5e8vLyUkJCgmbMmCEvLy/niE7eIzQHDhxwxsLDw5Wdna2MjIxC5xTE19dX5cuXd7sBAAA7eSzstG3bVt999522bt3q3Jo2bao+ffpo69atqlWrlsLDw7VmzRpnmezsbCUkJKhVq1aSpCZNmsjb29ttTlpamrZv3+7MAQAANzaPnbMTFBSk+vXru/UFBgYqNDTU6Y+NjdXEiRMVFRWlqKgoTZw4UQEBAerdu7ckKTg4WAMHDtSoUaMUGhqqkJAQjR49Wg0aNMh3wjMAALgxefQE5Ut57rnnlJmZqaFDhyojI0PNmzfX6tWrFRQU5MyZNm2avLy81KtXL2VmZqpt27aaP3++ypYt68HKAQBAaVGqws769evd2i6XS3FxcYqLiyt0GT8/P82cOVMzZ868usUBAIDrksevswMAAHA1EXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYzaNh5/XXX1fDhg1Vvnx5lS9fXi1bttSnn37qjBtjFBcXp4iICPn7+ysmJkY7duxwW0dWVpaGDx+uSpUqKTAwUF26dNH+/fuv9aYAAIBSyqNhp3r16po8ebKSk5OVnJyse++9V127dnUCzauvvqqpU6dq1qxZSkpKUnh4uO677z4dP37cWUdsbKyWLVumJUuWaMOGDTpx4oQ6deqknJwcT20WAAAoRTwadjp37qyOHTuqTp06qlOnjiZMmKBy5cpp8+bNMsZo+vTpGjt2rLp376769etrwYIFOnXqlOLj4yVJR48e1Zw5czRlyhS1a9dOd955pxYuXKjvvvtOa9eu9eSmAQCAUqLUnLOTk5OjJUuW6OTJk2rZsqX27dun9PR0tW/f3pnj6+ur6Ohobdq0SZKUkpKiM2fOuM2JiIhQ/fr1nTkFycrK0rFjx9xuAADATh4PO999953KlSsnX19fPfnkk1q2bJluu+02paenS5LCwsLc5oeFhTlj6enp8vHxUcWKFQudU5BJkyYpODjYuUVGRpbwVgEAgNLC42Gnbt262rp1qzZv3qynnnpK/fr1086dO51xl8vlNt8Yk68vr0vNef7553X06FHnlpqaWryNAAAApZbHw46Pj49q166tpk2batKkSbrjjjv097//XeHh4ZKU7wjNgQMHnKM94eHhys7OVkZGRqFzCuLr6+t8A+z8DQAA2MnjYScvY4yysrJUs2ZNhYeHa82aNc5Ydna2EhIS1KpVK0lSkyZN5O3t7TYnLS1N27dvd+YAAIAbm5cn7/yFF15Qhw4dFBkZqePHj2vJkiVav369Vq1aJZfLpdjYWE2cOFFRUVGKiorSxIkTFRAQoN69e0uSgoODNXDgQI0aNUqhoaEKCQnR6NGj1aBBA7Vr186TmwYAAEoJj4ad3377TY8++qjS0tIUHByshg0batWqVbrvvvskSc8995wyMzM1dOhQZWRkqHnz5lq9erWCgoKcdUybNk1eXl7q1auXMjMz1bZtW82fP19ly5b11GYBAIBSxKNhZ86cORcdd7lciouLU1xcXKFz/Pz8NHPmTM2cObOEqwMAADYodefsAAAAlCTCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsVqSwc++99+rIkSP5+o8dO6Z77723uDUBAACUmCKFnfXr1ys7Oztf/+nTp5WYmFjsogAAAEqK15VM3rZtm/PvnTt3Kj093Wnn5ORo1apVqlatWslVBwAAUExXFHYaNWokl8sll8tV4MdV/v7+mjlzZokVBwAAUFxXFHb27dsnY4xq1aqlr7/+WpUrV3bGfHx8VKVKFZUtW7bEiwQAACiqKwo7N998syQpNzf3qhQDAABQ0q4o7Fxoz549Wr9+vQ4cOJAv/Lz88svFLgwAAKAkFCnszJ49W0899ZQqVaqk8PBwuVwuZ8zlchF2AABAqVGksPOXv/xFEyZM0JgxY0q6HgAAgBJVpOvsZGRkqGfPniVdCwAAQIkrUtjp2bOnVq9eXdK1AAAAlLgifYxVu3ZtvfTSS9q8ebMaNGggb29vt/H//d//LZHiAAAAiqtIYeett95SuXLllJCQoISEBLcxl8tF2AEAAKVGkcLOvn37SroOAACAq6JI5+wAAABcL4p0ZGfAgAEXHZ87d26RigEAAChpRQo7GRkZbu0zZ85o+/btOnLkSIE/EAoAAOApRQo7y5Yty9eXm5uroUOHqlatWsUuCgAAoKSU2Dk7ZcqU0TPPPKNp06aV1CoBAACKrURPUP7xxx919uzZklwlAABAsRTpY6yRI0e6tY0xSktL08qVK9WvX78SKQwAAKAkFCnsbNmyxa1dpkwZVa5cWVOmTLnkN7UAAACupSKFnXXr1pV0HQAAAFdFkcLOeb///rt2794tl8ulOnXqqHLlyiVVFwAAQIko0gnKJ0+e1IABA1S1alW1adNGrVu3VkREhAYOHKhTp06VdI0AAABFVqSwM3LkSCUkJOjjjz/WkSNHdOTIEX300UdKSEjQqFGjSrpGAACAIivSx1hLly7VBx98oJiYGKevY8eO8vf3V69evfT666+XVH0AAADFUqQjO6dOnVJYWFi+/ipVqvAxFgAAKFWKFHZatmypcePG6fTp005fZmamxo8fr5YtW5ZYcQAAAMVVpI+xpk+frg4dOqh69eq644475HK5tHXrVvn6+mr16tUlXSMAAECRFSnsNGjQQHv37tXChQv1/fffyxijhx9+WH369JG/v39J1wgAAFBkRQo7kyZNUlhYmAYNGuTWP3fuXP3+++8aM2ZMiRQHAABQXEU6Z+fNN99UvXr18vXffvvteuONN4pdFAAAQEkpUthJT09X1apV8/VXrlxZaWlpxS4KAACgpBQp7ERGRmrjxo35+jdu3KiIiIjLXs+kSZPUrFkzBQUFqUqVKvrDH/6g3bt3u80xxiguLk4RERHy9/dXTEyMduzY4TYnKytLw4cPV6VKlRQYGKguXbpo//79Rdk0AABgmSKFnSeeeEKxsbGaN2+efv75Z/3888+aO3eunnnmmXzn8VxMQkKChg0bps2bN2vNmjU6e/as2rdvr5MnTzpzXn31VU2dOlWzZs1SUlKSwsPDdd999+n48ePOnNjYWC1btkxLlizRhg0bdOLECXXq1Ek5OTlF2TwAAGCRIp2g/Nxzz+nw4cMaOnSosrOzJUl+fn4aM2aMnn/++ctez6pVq9za8+bNU5UqVZSSkqI2bdrIGKPp06dr7Nix6t69uyRpwYIFCgsLU3x8vIYMGaKjR49qzpw5evfdd9WuXTtJ0sKFCxUZGam1a9fq/vvvL8omAgAASxTpyI7L5dIrr7yi33//XZs3b9a3336rw4cP6+WXXy5WMUePHpUkhYSESJL27dun9PR0tW/f3pnj6+ur6Ohobdq0SZKUkpKiM2fOuM2JiIhQ/fr1nTkAAODGVaQjO+eVK1dOzZo1K5FCjDEaOXKk7r77btWvX1/SuROhJeX7aYqwsDD9/PPPzhwfHx9VrFgx35zzy+eVlZWlrKwsp33s2LES2QYAAFD6FOnIztXw9NNPa9u2bVq8eHG+MZfL5dY2xuTry+ticyZNmqTg4GDnFhkZWfTCAQBAqVYqws7w4cO1YsUKrVu3TtWrV3f6w8PDJSnfEZoDBw44R3vCw8OVnZ2tjIyMQufk9fzzz+vo0aPOLTU1tSQ3BwAAlCIeDTvGGD399NP68MMP9cUXX6hmzZpu4zVr1lR4eLjWrFnj9GVnZyshIUGtWrWSJDVp0kTe3t5uc9LS0rR9+3ZnTl6+vr4qX7682w0AANipWOfsFNewYcMUHx+vjz76SEFBQc4RnODgYPn7+8vlcik2NlYTJ05UVFSUoqKiNHHiRAUEBKh3797O3IEDB2rUqFEKDQ1VSEiIRo8erQYNGjjfzgIAADcuj4ad119/XZIUExPj1j9v3jz1799f0rmvuWdmZmro0KHKyMhQ8+bNtXr1agUFBTnzp02bJi8vL/Xq1UuZmZlq27at5s+fr7Jly16rTQEAAKWUR8OOMeaSc1wul+Li4hQXF1foHD8/P82cOVMzZ84sweoAAIANSsUJygAAAFcLYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqHg07//73v9W5c2dFRETI5XJp+fLlbuPGGMXFxSkiIkL+/v6KiYnRjh073OZkZWVp+PDhqlSpkgIDA9WlSxft37//Gm4FAAAozTwadk6ePKk77rhDs2bNKnD81Vdf1dSpUzVr1iwlJSUpPDxc9913n44fP+7MiY2N1bJly7RkyRJt2LBBJ06cUKdOnZSTk3OtNgMAAJRiXp688w4dOqhDhw4FjhljNH36dI0dO1bdu3eXJC1YsEBhYWGKj4/XkCFDdPToUc2ZM0fvvvuu2rVrJ0lauHChIiMjtXbtWt1///3XbFsAAEDpVGrP2dm3b5/S09PVvn17p8/X11fR0dHatGmTJCklJUVnzpxxmxMREaH69es7cwqSlZWlY8eOud0AAICdSm3YSU9PlySFhYW59YeFhTlj6enp8vHxUcWKFQudU5BJkyYpODjYuUVGRpZw9QAAoLQotWHnPJfL5dY2xuTry+tSc55//nkdPXrUuaWmppZIrQAAoPQptWEnPDxckvIdoTlw4IBztCc8PFzZ2dnKyMgodE5BfH19Vb58ebcbAACwU6kNOzVr1lR4eLjWrFnj9GVnZyshIUGtWrWSJDVp0kTe3t5uc9LS0rR9+3ZnDgAAuLF59NtYJ06c0A8//OC09+3bp61btyokJEQ33XSTYmNjNXHiREVFRSkqKkoTJ05UQECAevfuLUkKDg7WwIEDNWrUKIWGhiokJESjR49WgwYNnG9nAQCAG5tHw05ycrLuuecepz1y5EhJUr9+/TR//nw999xzyszM1NChQ5WRkaHmzZtr9erVCgoKcpaZNm2avLy81KtXL2VmZqpt27aaP3++ypYte823BwAAlD4eDTsxMTEyxhQ67nK5FBcXp7i4uELn+Pn5aebMmZo5c+ZVqBAAAFzvSu05OwAAACWBsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBq1oSd1157TTVr1pSfn5+aNGmixMRET5cEAABKASvCznvvvafY2FiNHTtWW7ZsUevWrdWhQwf98ssvni4NAAB4mBVhZ+rUqRo4cKCeeOIJ3XrrrZo+fboiIyP1+uuve7o0AADgYdd92MnOzlZKSorat2/v1t++fXtt2rTJQ1UBAIDSwsvTBRTXwYMHlZOTo7CwMLf+sLAwpaenF7hMVlaWsrKynPbRo0clSceOHbvs+z2bm12EanG1XMm+K4rTOVmXnoRr5mrv7xPZ7O/S5Grv7+On2N+lifcV7O/zzw1jzEXnXfdh5zyXy+XWNsbk6ztv0qRJGj9+fL7+yMjIq1Ibrr7g4Hc8XQKuocnB0z1dAq6lf87ydAW4lgZNvuJFjh8/ruDg4ELHr/uwU6lSJZUtWzbfUZwDBw7kO9pz3vPPP6+RI0c67dzcXB0+fFihoaGFBiQbHTt2TJGRkUpNTVX58uU9XQ6uMvb3jYX9fWO5Ufe3MUbHjx9XRETERedd92HHx8dHTZo00Zo1a9StWzenf82aNeratWuBy/j6+srX19etr0KFClezzFKtfPnyN9Qfx42O/X1jYX/fWG7E/X2xIzrnXfdhR5JGjhypRx99VE2bNlXLli311ltv6ZdfftGTTz7p6dIAAICHWRF2HnroIR06dEh/+tOflJaWpvr16+tf//qXbr75Zk+XBgAAPMyKsCNJQ4cO1dChQz1dxnXF19dX48aNy/eRHuzE/r6xsL9vLOzvi3OZS31fCwAA4Dp23V9UEAAA4GIIOwAAwGqEHQAAYDXCjoXi4uLUqFGji86JiYlRbGzsFa3X5XJp+fLlRa4LJedS++9K99X69evlcrl05MiRYteG61dRXhdwdbFPSoY138bClfnwww/l7e3t6TJwlaSlpalixYqeLgMASgXCzg0qJCTE0yXgKgoPD/d0CQBQavAx1nXozTffVLVq1ZSbm+vW36VLF/Xr189pv/vuu6pRo4aCg4P18MMP6/jx485Y3kOjaWlpeuCBB+Tv76+aNWsqPj5eNWrU0PTp093u4+DBg+rWrZsCAgIUFRWlFStWXJVtxKXl5ubqueeeU0hIiMLDwxUXF+eM5f0Ya9OmTWrUqJH8/PzUtGlTLV++XC6XS1u3bnVbZ0pKipo2baqAgAC1atVKu3fvvjYbg4uKiYnR8OHDFRsbq4oVKyosLExvvfWWTp48qccff1xBQUG65ZZb9OmnnzrL7Ny5Ux07dlS5cuUUFhamRx99VAcPHnTGT548qccee0zlypVT1apVNWXKFE9sGq5ARkaGHnvsMVWsWFEBAQHq0KGD9u7dK+ncb0RVrlxZS5cudeY3atRIVapUcdpffvmlvL29deLEiWteu6cRdq5DPXv21MGDB7Vu3TqnLyMjQ5999pn69OkjSfrxxx+1fPlyffLJJ/rkk0+UkJCgyZML/yXZxx57TL/++qvWr1+vpUuX6q233tKBAwfyzRs/frx69eqlbdu2qWPHjurTp48OHz5c8huJS1qwYIECAwP11Vdf6dVXX9Wf/vQnrVmzJt+848ePq3PnzmrQoIG++eYb/fnPf9aYMWMKXOfYsWM1ZcoUJScny8vLSwMGDLjam4HLtGDBAlWqVElff/21hg8frqeeeko9e/ZUq1at9M033+j+++/Xo48+qlOnTiktLU3R0dFq1KiRkpOTtWrVKv3222/q1auXs75nn31W69at07Jly7R69WqtX79eKSkpHtxCXEr//v2VnJysFStW6Msvv5QxRh07dtSZM2fkcrnUpk0brV+/XtK594SdO3fqzJkz2rlzp6Rz5+Y1adJE5cqV8+BWeIjBdalLly5mwIABTvvNN9804eHh5uzZs2bcuHEmICDAHDt2zBl/9tlnTfPmzZ12dHS0GTFihDHGmF27dhlJJikpyRnfu3evkWSmTZvm9EkyL774otM+ceKEcblc5tNPP70KW4iLiY6ONnfffbdbX7NmzcyYMWOMMef21bJly4wxxrz++usmNDTUZGZmOnNnz55tJJktW7YYY4xZt26dkWTWrl3rzFm5cqWR5LYcPCPv/j579qwJDAw0jz76qNOXlpZmJJkvv/zSvPTSS6Z9+/Zu60hNTTWSzO7du83x48eNj4+PWbJkiTN+6NAh4+/v77wuoHQ4/1q9Z88eI8ls3LjRGTt48KDx9/c3//znP40xxsyYMcPUr1/fGGPM8uXLTdOmTU337t3NP/7xD2OMMe3bt3deI240HNm5TvXp00dLly5VVlaWJGnRokV6+OGHVbZsWUlSjRo1FBQU5MyvWrVqgUdqJGn37t3y8vJS48aNnb7atWsXeIJrw4YNnX8HBgYqKCio0PXi6rpwX0iF7+Pdu3erYcOG8vPzc/ruuuuuS66zatWqksT+LSUu3Ddly5ZVaGioGjRo4PSFhYVJOre/UlJStG7dOpUrV8651atXT9K5o74//vijsrOz1bJlS2f5kJAQ1a1b9xptDa7Url275OXlpebNmzt9oaGhqlu3rnbt2iXp3MedO3bs0MGDB5WQkKCYmBjFxMQoISFBZ8+e1aZNmxQdHe2pTfAows51qnPnzsrNzdXKlSuVmpqqxMRE9e3b1xnP+00rl8uV7xyf80whvxhSUP+VrBdX1+XuC2OMXC5Xvr5LrfP8Muzf0qGg/V3Y/srNzVXnzp21detWt9vevXvVpk2bQvc/Sq+LvU6f3/f169dXaGioEhISnLATHR2thIQEJSUlKTMzU3ffffe1LLvUIOxcp/z9/dW9e3ctWrRIixcvVp06ddSkSZMiratevXo6e/astmzZ4vT98MMPXHPFEvXq1dO2bduco4CSlJyc7MGKcLU1btxYO3bsUI0aNVS7dm23W2BgoGrXri1vb29t3rzZWSYjI0N79uzxYNW4mNtuu01nz57VV1995fQdOnRIe/bs0a233ipJznk7H330kbZv367WrVurQYMGOnPmjN544w01btzY7Yj/jYSwcx3r06ePVq5cqblz57od1blS9erVU7t27TR48GB9/fXX2rJliwYPHix/f/98RwRw/endu7dyc3M1ePBg7dq1S5999pn+9re/SRL711LDhg3T4cOH9cgjj+jrr7/Wf/7zH61evVoDBgxQTk6OypUrp4EDB+rZZ5/V559/ru3bt6t///4qU4a3hNIqKipKXbt21aBBg7RhwwZ9++236tu3r6pVq6auXbs682JiYhQfH6+GDRuqfPnyTgBatGiRYmJiPLcBHsYz+zp27733KiQkRLt371bv3r2Lta533nlHYWFhatOmjbp166ZBgwYpKCjI7TwPXJ/Kly+vjz/+WFu3blWjRo00duxYvfzyy5LE/rVURESENm7cqJycHN1///2qX7++RowYoeDgYCfQ/PWvf1WbNm3UpUsXtWvXTnfffXeRjw7j2pg3b56aNGmiTp06qWXLljLG6F//+pfbx5n33HOPcnJy3IJNdHS0cnJybtjzdSTJZfjwFgXYv3+/IiMjtXbtWrVt29bT5aCELVq0SI8//riOHj0qf39/T5cDAFcVV1CGJOmLL77QiRMn1KBBA6Wlpem5555TjRo11KZNG0+XhhLwzjvvqFatWqpWrZq+/fZbjRkzRr169SLoALghEHYgSTpz5oxeeOEF/ec//1FQUJBatWqlRYsW8ftZlkhPT9fLL7+s9PR0Va1aVT179tSECRM8XRYAXBN8jAUAAKzGCcoAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAOgVIiJiVFsbOxVvY8aNWpo+vTpV/U+AJQ+fPUcwA0jKSlJgYGBni4DwDVG2AFww6hcubKnSwDgAXyMBaDUOHv2rJ5++mlVqFBBoaGhevHFF3X+UmAul0vLly93m1+hQgXNnz9f0rnfinv66afdxg8dOiRfX1998cUXkvJ/jOVyufT222+rW7duCggIUFRUlFasWOG2jhUrVigqKkr+/v665557tGDBArlcLh05cqREtx3A1UPYAVBqLFiwQF5eXvrqq680Y8YMTZs2TW+//fZlLfvEE08oPj5eWVlZTt+iRYsUERGhe+65p9Dlxo8fr169emnbtm3q2LGj+vTpo8OHD0uSfvrpJz344IP6wx/+oK1bt2rIkCEaO3Zs8TYSwDVH2AFQakRGRmratGmqW7eu+vTpo+HDh2vatGmXtWyPHj3kcrn00UcfOX3z5s1T//795XK5Cl2uf//+euSRR1S7dm1NnDhRJ0+e1Ndffy1JeuONN1S3bl399a9/Vd26dfXwww+rf//+xdpGANceYQdAqdGiRQu3YNKyZUvt3btXOTk5l1zW19dXffv21dy5cyVJW7du1bfffnvJcNKwYUPn34GBgQoKCtKBAwckSbt371azZs3c5t91112XuzkASglOUAZwXXC5XMr7U35nzpxxaz/xxBNq1KiR9u/fr7lz56pt27a6+eabL7revD9263K5lJubK0kyxuQ7KsTPCQLXH47sACg1Nm/enK8dFRWlsmXLqnLlykpLS3PG9u7dq1OnTrnNb9CggZo2barZs2crPj5eAwYMKFY99erVU1JSkltfcnJysdYJ4Noj7AAoNVJTUzVy5Ejt3r1bixcv1syZMzVixAhJ575tNWvWLH3zzTdKTk7Wk08+me+ojHTu6M7kyZOVk5Ojbt26FaueIUOG6Pvvv9eYMWO0Z88e/fOf/3S+/XWx84AAlC6EHQClxmOPPabMzEzdddddGjZsmIYPH67BgwdLkqZMmaLIyEi1adNGvXv31ujRoxUQEJBvHY888oi8vLzUu3dv+fn5FauemjVr6oMPPtCHH36ohg0b6vXXX3e+jeXr61usdQO4dlyGD6ABWCQ1NVU1atRQUlKSGjduXOLrnzBhgt544w2lpqaW+LoBXB2coAzACmfOnFFaWpr++Mc/qkWLFiUWdF577TU1a9ZMoaGh2rhxo/7617/mu3ghgNKNsAPAChs3btQ999yjOnXq6IMPPiix9e7du1d/+ctfdPjwYd10000aNWqUnn/++RJbP4Crj4+xAACA1ThBGQAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABY7f8DmLKUy4pzY1MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='buying',palette='magma')\n",
    "\n",
    "title_font={\n",
    "    'family':'serif',\n",
    "    'size':'16',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by buying\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6655d1f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying          1728\n",
       "maintainance    1728\n",
       "doors           1728\n",
       "persons         1728\n",
       "lug_boot        1728\n",
       "safety          1728\n",
       "condition       1728\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "953958ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       unacc\n",
       "1       unacc\n",
       "2       unacc\n",
       "3       unacc\n",
       "4       unacc\n",
       "        ...  \n",
       "1723     good\n",
       "1724    vgood\n",
       "1725    unacc\n",
       "1726     good\n",
       "1727    vgood\n",
       "Name: condition, Length: 1728, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9640cc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unacc', 'acc', 'vgood', 'good'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.condition.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c97b57a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond=df['condition'].groupby(by=df['buying']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "daac79f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buying\n",
       "high     432\n",
       "low      432\n",
       "med      432\n",
       "vhigh    432\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60f1ddfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='buying'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cond.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ef11d1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "com=df.maintainance.value_counts().reset_index().rename(columns={'index':'maintainance','maintainance':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "81091593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=com,x='maintainance',palette='deep')\n",
    "\n",
    "title_font={\n",
    "    'family':'Calibri',\n",
    "    'size':'14',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by maintainance\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01b36a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "dom=df.doors.value_counts().reset_index().rename(columns={'index':'doors','doors':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dc936488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=dom,x='doors',palette='mako')\n",
    "\n",
    "title_font={\n",
    "    'family':'Calibri',\n",
    "    'size':'14',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by doors\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "504bcddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pom=df.persons.value_counts().reset_index().rename(columns={'index':'persons','persons':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1925af78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=pom,x='persons',palette='husl')\n",
    "\n",
    "title_font={\n",
    "    'family':'Calibri',\n",
    "    'size':'14',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by persons\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bbb1a221",
   "metadata": {},
   "outputs": [],
   "source": [
    "lom=df.lug_boot.value_counts().reset_index().rename(columns={'index':'lug_boot','lug_boot':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "36b6dc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=lom,x='lug_boot',palette='rocket')\n",
    "\n",
    "title_font={\n",
    "    'family':'Calibri',\n",
    "    'size':'14',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by lug_boot\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8c1b76db",
   "metadata": {},
   "outputs": [],
   "source": [
    "som=df.safety.value_counts().reset_index().rename(columns={'index':'safety','safety':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58b7315c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=som,x='safety',palette='inferno')\n",
    "\n",
    "title_font={\n",
    "    'family':'Calibri',\n",
    "    'size':'14',\n",
    "    'weight': 'bold'\n",
    "}\n",
    "plt.title(\"count of cars by safety\",fontdict=title_font)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee99d116",
   "metadata": {},
   "outputs": [],
   "source": [
    "dis=df.condition.value_counts().reset_index().rename(columns={'index':'condition','condition':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8637e263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(dis['count'],labels=dis['condition'],explode=(0,0,0.1,0),autopct='%1.2f%%')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f94b0e",
   "metadata": {},
   "source": [
    "# data pre processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6259bff",
   "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>buying</th>\n",
       "      <th>maintainance</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>small</td>\n",
       "      <td>high</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>low</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vhigh</td>\n",
       "      <td>vhigh</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>med</td>\n",
       "      <td>med</td>\n",
       "      <td>unacc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buying maintainance doors persons lug_boot safety condition\n",
       "0  vhigh        vhigh     2       2    small    low     unacc\n",
       "1  vhigh        vhigh     2       2    small    med     unacc\n",
       "2  vhigh        vhigh     2       2    small   high     unacc\n",
       "3  vhigh        vhigh     2       2      med    low     unacc\n",
       "4  vhigh        vhigh     2       2      med    med     unacc"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023a2828",
   "metadata": {},
   "source": [
    "# label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "558da431",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder # import libary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9dbc5e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "de2a296e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['buying',\n",
       " 'maintainance',\n",
       " 'doors',\n",
       " 'persons',\n",
       " 'lug_boot',\n",
       " 'safety',\n",
       " 'condition']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=list(df.columns)\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "774d634b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in cols:\n",
    "    df[i]= le.fit_transform(df[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "90b2868a",
   "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>buying</th>\n",
       "      <th>maintainance</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1724</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1728 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      buying  maintainance  doors  persons  lug_boot  safety  condition\n",
       "0          3             3      0        0         2       1          2\n",
       "1          3             3      0        0         2       2          2\n",
       "2          3             3      0        0         2       0          2\n",
       "3          3             3      0        0         1       1          2\n",
       "4          3             3      0        0         1       2          2\n",
       "...      ...           ...    ...      ...       ...     ...        ...\n",
       "1723       1             1      3        2         1       2          1\n",
       "1724       1             1      3        2         1       0          3\n",
       "1725       1             1      3        2         0       1          2\n",
       "1726       1             1      3        2         0       2          1\n",
       "1727       1             1      3        2         0       0          3\n",
       "\n",
       "[1728 rows x 7 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45bd38cb",
   "metadata": {},
   "source": [
    "# SPLIT DATA INTO TRAIN AND TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0699c402",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0c065b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "97b348aa",
   "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>buying</th>\n",
       "      <th>maintainance</th>\n",
       "      <th>doors</th>\n",
       "      <th>persons</th>\n",
       "      <th>lug_boot</th>\n",
       "      <th>safety</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   buying  maintainance  doors  persons  lug_boot  safety\n",
       "0       3             3      0        0         2       1\n",
       "1       3             3      0        0         2       2\n",
       "2       3             3      0        0         2       0\n",
       "3       3             3      0        0         1       1\n",
       "4       3             3      0        0         1       2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7a58c631",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "009f1b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "Name: condition, dtype: int32"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f57c2df8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "45b13ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.array(y).reshape(1728,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34ea30d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train,y_test = test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ea335cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1382, 6)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a835d873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 6)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "acd66831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1382, 1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "152ad58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05311b67",
   "metadata": {},
   "source": [
    "# machine learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bab515b",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "030f131a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ffff20c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K nearest neighbor - k is the unit , it is used for classification for categorical data\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix # measuring accuracy of data\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "256bd48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "26435cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "knn.fit(X_train,y_train) #training the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d634e2da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [0]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [1]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [0]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
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      " [2]\n",
      " [2]\n",
      " [3]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [2]\n",
      " [1]\n",
      " [2]\n",
      " [3]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [1]\n",
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      " [2]\n",
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      " [2]\n",
      " [2]\n",
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      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [3]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [1]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [3]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [3]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [0]\n",
      " [0]\n",
      " [3]\n",
      " [2]\n",
      " [0]\n",
      " [2]\n",
      " [0]\n",
      " [1]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]\n",
      " [2]]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "eefa2647",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d06adac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 0, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 1, 2,\n",
       "       2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 1, 3, 2, 2, 0, 2, 2, 0, 2, 0, 2, 2, 0, 0, 2, 0,\n",
       "       2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 1, 0, 2,\n",
       "       2, 3, 2, 2, 0, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 0, 2, 0, 2, 2, 2, 0,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0,\n",
       "       0, 2, 2, 0, 2, 0, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0,\n",
       "       2, 3, 2, 2, 0, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2,\n",
       "       3, 0, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 3, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 2, 0, 3, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5ffb44d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "g=[0,2,2,2,2,2]\n",
    "test_data=np.array(g).reshape((1,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aa8f5a1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\base.py:420: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "caa71959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7687861271676301"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9eb78e66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy is 76.88%\n"
     ]
    }
   ],
   "source": [
    "print(f'KNN accuracy is {round(accuracy_score(y_test,prediction)*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8eb1e9a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zTH9b+oZaxjdzOXfUy8O14Yev9Fj/h29ylJD4u/B2qampatq0qYKDg1WlShV1795de/bscZhjGIaSk5MVFRWlgIAAxcXFadeuXQ5zCgsLNWTIEIWFhSkoKEjdunXTsWPH3IqFBMFN2Zkn9Pqkt9S7c3/17txfm/69VdPmpOp3d0ZLkgICA7Ttm52aMektiyP1bU2aNFT/fr20Y8duq0PxCdmZJ/RGytvqm/iM+iY+oy3/3qopcyYp+s6aDvPadm6t+o3rKTvzhDWB+jj+LtxQVOS5hxvWrFmjQYMGacOGDUpPT9fFixeVkJCg/Px8+5wpU6botdde08yZM7Vp0yZFRESoU6dOOnPmjH3O0KFDtXjxYi1atEgZGRnKy8tTUlKSLl0qfmWEBMFNa9P/rX+v3qAjB47qyIGjemPy2zqbX6DYJvUkScv+sVJ/mzZXG9dutjhS3xUUFKh5c1/XgIGjlPNTrtXh+ISM9PVav3qjjh44pqMHjumtl2frbH6BYn75u5CkyhFhGjHx/zR+0ERdukj59mbj78JNRpHnHm5YsWKF+vbtq/r166thw4aaM2eOjhw5oi1btvwclmEoLS1NY8aMUY8ePRQTE6N58+bp7NmzWrhwoSQpNzdXs2fP1quvvqqOHTuqUaNGWrBggXbu3KlVq1YVOxa3E4Rjx45pzJgxio+PV926dVWvXj3Fx8drzJgxOnr0qLvLlWilSpVSwgMdFBBYTju27Lr6C3BTTJ8+UcuXr9bq1RlWh+KTSpUqpY4PtFdAYDnt3Pzz34XNZtP411/QglmLdHDvIWsD9FH8XbjJgxWEwsJCnT592uFRWFhYrDByc39O5kJDQyVJBw8eVFZWlhISEuxz/P391a5dO61bt06StGXLFl24cMFhTlRUlGJiYuxzisOtTYoZGRlKTExUtWrVlJCQoISEBBmGoezsbC1ZskQzZszQ8uXLde+9915xncLCQtOXU2QUqZStZBQ0at31O8399C2V9S+rgvwCPffUC/xLz0s88kg3Nbo7Vq3uTbI6FJ9zx13R+tu/3rT/XYzqN06H9h2WJD0xqKcuXbqkD2d/bHGUvom/C2ulpqZqwoQJDmPjx49XcnLyFV9nGIaGDx+u1q1bKyYmRpKUlZUlSQoPD3eYGx4ersOHD9vnlC1bVrfddptpzq+vLw63EoRhw4apf//+mjZt2mWPDx06VJs2bbriOq6+rIigaooMru5OOJY59P0R9ez4B5UPKa8OXeL00utj1L/HEJIEi1WtGqlXpyarS9Lvi52dw3MOf39UT3bqr/IVyiu+S1u9OH20BvT4P/mX89dj/R9Wn/uetjpEn8TfxTXy4J0UR48ereHDhzuM+fv7X/V1gwcP1o4dO5SRYa762Gw2h+eGYZjGnBVnzm+5lSB8++23WrBgwWWPP/PMM3rrratvznP1ZbW9s7M7oVjq4oWLOnrouCTpu+17VL9hXfXq/4gmjXzF4sh8W+NGDRQeXlkb1n9mH/Pz81Ob1s01YEBfBVe4Q0Vc23zDXLxwUcd++bv47449qnf3XXqs/0M6tO+IbgurqCWbPrTP9fMrrT+NH6DHn35YDzZ/3KqQfQJ/F9fIg9+Jv79/sRKC3xoyZIiWLl2qtWvXqmrVqvbxiIgIST9XCSIjI+3j2dnZ9qpCRESEzp8/r5ycHIcqQnZ2tlq1alXsGNxKECIjI7Vu3TrVqVPH5fH169c7BHw5rr6sktJecMVmk8qULWN1GD5v9ZcZatS4o8PY395+VXv27tfUqbP4l6AFypYtq+Uff65NX29xGE9bOEUrPk7Xpx8stygy38HfRcliGIaGDBmixYsX66uvvlJ0dLTD8ejoaEVERCg9PV2NGjWSJJ0/f15r1qzRyy+/LElq0qSJypQpo/T0dD366KOSpMzMTH377beaMmVKsWNxK0EYMWKEnn32WW3ZskWdOnVSeHi4bDabsrKylJ6ernfeeUdpaWnuLFniDB79R/179QZlHc9WUPlA3de9o5q0aqTBvZ6TJFWoGKyI28NVOTxMklTzjp/bJj9mn9KPJ05ZFrcvyMvL1+7djtcL5589q1M/5pjG4VnPPt9f61dvVPYPJxRYPkCdHmivxq3u1rDfj9TpnNM6nXPaYf6li5f0Y/YpHfnetzY2W4G/i2tk0Y81DRo0SAsXLtQ///lPBQcH2/cMhISEKCAgQDabTUOHDlVKSopq166t2rVrKyUlRYGBgerVq5d9br9+/fTcc8+pUqVKCg0N1YgRIxQbG6uOHTte6e0duJUgDBw4UJUqVdK0adP017/+1X49ZenSpdWkSRPNnz/fnq3cqkLDQvWXGeMUVqWS8s7ka9/u7zW413P2yxrbJbTWhOlj7PMn//UlSdJfp76rv776riUxAzdaaOXblDxjjCpVCVXemXx9/90BDfv9SH2zdsvVXwx4I4sqK7NmzZIkxcXFOYzPmTNHffv2lSSNHDlSBQUFGjhwoHJyctS8eXN9/vnnCg4Ots+fNm2a/Pz89Oijj6qgoEAdOnTQ3LlzVbp06WLHYjMMw7iWD3HhwgWdPHlSkhQWFqYyZa6vxN44svV1vR6esyvnsNUh4BeNQu+wOgT84j+nvrc6BPxG4bkbW30qWP66x9YKSPyTx9a6ma75txjKlClTrP0GAACUOOzN4MeaAAAwsWgPgjcpuZcOAACAG4YKAgAAzmgxkCAAAGBCi4EEAQAAEyoI7EEAAABmVBAAAHBGi4EEAQAAE1oMtBgAAIAZFQQAAJxRQSBBAADA5Np+puiWQosBAACYUEEAAMAZLQYSBAAATEgQaDEAAAAzKggAADjjRkkkCAAAmNBiIEEAAMCEyxzZgwAAAMyoIAAA4IwWAwkCAAAmJAi0GAAAgBkVBAAAnHGZIwkCAADOjCKuYqDFAAAATKggAADgjE2KJAgAAJiwB4EWAwAAMKOCAACAMzYpkiAAAGDCHgQSBAAATEgQ2IMAAADMqCAAAOCMn3smQQAAwIQWAy0GAABgRgUBAABnXOZIggAAgAl3UqTFAAAAzKggAADgjBaD9yQI3546ZHUI+EUpG4Ulb7Ht1AGrQ8AvitjV7lMMzjctBgAAYOY1FQQAALwGLQYSBAAATLiKgQQBAAATKgjsQQAAAGZUEAAAcMZVDCQIAACY0GKgxQAAAMyoIAAA4IyrGEgQAAAwocVAiwEAAJhRQQAAwAm/xUCCAACAGS0GWgwAAMCMCgIAAM6oIJAgAABgwmWOJAgAAJhQQWAPAgAAMKOCAACAE4MKAgkCAAAmJAi0GAAAgBkVBAAAnHEnRRIEAABMaDHQYgAAAGZUEAAAcEYFgQQBAABnhkGCQIsBAACYUEEAAMAZLQYSBAAATEgQSBAAAHDGrZbZgwAAgNdYu3atunbtqqioKNlsNi1ZssTheN++fWWz2RweLVq0cJhTWFioIUOGKCwsTEFBQerWrZuOHTvmdiwkCAAAOCsyPPdwQ35+vho2bKiZM2dedk7nzp2VmZlpf3z22WcOx4cOHarFixdr0aJFysjIUF5enpKSknTp0iW3YqHFAACAM4vutJyYmKjExMQrzvH391dERITLY7m5uZo9e7b+/ve/q2PHjpKkBQsWqFq1alq1apXuu+++YsdCBQEAgBuosLBQp0+fdngUFhZe83pfffWVqlSpojvvvFNPP/20srOz7ce2bNmiCxcuKCEhwT4WFRWlmJgYrVu3zq33IUEAAMCJUWR47JGamqqQkBCHR2pq6jXFlZiYqPfee0+rV6/Wq6++qk2bNql9+/b2hCMrK0tly5bVbbfd5vC68PBwZWVlufVetBgAAHDmwasYRo8ereHDhzuM+fv7X9Najz32mP2fY2JidM8996hGjRpatmyZevTocdnXGYYhm83m1nuRIAAAcAP5+/tfc0JwNZGRkapRo4b27dsnSYqIiND58+eVk5PjUEXIzs5Wq1at3FqbFgMAAM6KPPi4gX788UcdPXpUkZGRkqQmTZqoTJkySk9Pt8/JzMzUt99+63aCQAUBAAAnVt0oKS8vT/v377c/P3jwoLZt26bQ0FCFhoYqOTlZDz30kCIjI3Xo0CG98MILCgsL04MPPihJCgkJUb9+/fTcc8+pUqVKCg0N1YgRIxQbG2u/qqG4SBAAAPASmzdvVnx8vP35r3sX+vTpo1mzZmnnzp2aP3++fvrpJ0VGRio+Pl4ffPCBgoOD7a+ZNm2a/Pz89Oijj6qgoEAdOnTQ3LlzVbp0abdisRle8puWZf2rWh3CNfnjH5/QM398UjVq/Bz/7t17NSklTStXfmlxZNeulK1kdp7Gjh2msWOHOYxlZWWrZs17LIrId92K5+JSkXs3mfEWI0cO1oPdE1WnTi0VFJzT+g2b9cILKdq793urQ7suF84fv6Hr5zwU57G1bvv4K4+tdTNRQbhOx49naszYVH3//UFJ0hO9H9HH/5itZs06a/d3ey2Ozvfs2rVH99/fy/7c3TuHwXM4F96hbZsWmjVrnjZv2SY/Pz+9NGGUPlu2UA0axuns2QKrw/Na/BYDCcJ1W7ZslcPzF8dP0R//+KSaNW9MgmCBixcv6n//O2F1GBDnwlskde3t8Lz/08OU+cNONW7cQBkZGy2KqgSw6E6K3qRk1pK9VKlSpfToI90UFBSgjRu2WB2OT6pVK1oHDmzSf/+bofnzZyo6urrVIfkszoV3CgmpIEnKyfnJ2kDg9Ty+B+Ho0aMaP3683n333cvOKSwsNN1mslJYXbdv4uAtYurfpbVr/6ly5fyVl5evJ/sM0YoVq60O65qV1D0ICQlxCgwM0L59BxQeXlnPPz9Ed955hxo37qhTp36yOjyfciuei5K6B8HZJ5/M0W0VQxTf/vI31SkJbvQehB+7tvPYWpX+tcZja91MHk8Qtm/frsaNG1+x35icnKwJEyY4jJUqFazSfhU8GcpNU6ZMGVWvfrtCQiqox4P36w9/6KmOHR/Wd//dZ3Vo16SkJgjOAgMDtHv313rttbf0+uvvWB2OT7sVzsWtkCC8Pn2SEhM7KC7+QR0/nml1ONflhicIXTyYICwrmQmC23sQli5desXjBw4cuOoarm47WSmsrruheI0LFy7o++8PSZK2bt2hJvc01OAh/TRo0PPWBubjzp4t0K5de1SrVrTVofg8zoX10qb9RUlJCWrfoUeJTw5wc7idIHTv3l02m01XKjxcrVXg6raTJbW94IrNZpN/2bJWh+HzypYtqzp1aunf//7G6lB8HufCWtPTJuqBBzqrY6dHdOjQUavDKREMNim6v0kxMjJSH3/8sYqKilw+tm7deiPi9Fp/eWmU7r23mWrUqKqY+nfppQkj1a5tS72/aLHVofmc1NQxatOmuWrWrKamTe/W+++/pQoVymvBgn9YHZrP4Vx4jxmvp6hXrx564snBOnMmT+HhlRUeXlnlypWzOjTvVkJutXwjuV1BaNKkibZu3aru3bu7PH616sKtpkqVyprz7nRFRlZRbu4Z7fz2OyV17a0vvvja6tB8zu23R2revJkKC7tNJ06c0jffbFXbtt115MiN7VXCjHPhPZ59to8kafUXHzuM9+s3TPP//qEVIaGEcHuT4tdff638/Hx17tzZ5fH8/Hxt3rxZ7dq5t8GjpN5J8VZ0q2xSBDzpVtikeCu50ZsUT3Ty3CbFyuklc5Mit1qGCQkCYEaC4F1udIKQ3cFzCUKVL0pmgsCdFAEAcMImRe6kCAAAXKCCAACAM+PWufT+WpEgAADghBYDLQYAAOACFQQAAJwYRbQYSBAAAHBCi4EWAwAAcIEKAgAATgyuYiBBAADAGS0GWgwAAMAFKggAADjhKgYSBAAATLzjZwytRYIAAIATKgjsQQAAAC5QQQAAwAkVBBIEAABM2INAiwEAALhABQEAACe0GEgQAAAw4VbLtBgAAIALVBAAAHDCbzGQIAAAYFJEi4EWAwAAMKOCAACAEzYpkiAAAGDCZY4kCAAAmHAnRfYgAAAAF6ggAADghBYDCQIAACZc5kiLAQAAuEAFAQAAJ1zmSIIAAIAJVzHQYgAAAC5QQQAAwAmbFEkQAAAwYQ8CLQYAAOACFQQAAJywSZEEAQAAE/YgeFGCUCWootUh4BdZeTlWh4BfFPzwtdUh4BeBUW2sDgE3EXsQ2IMAAABc8JoKAgAA3oIWAwkCAAAm7FGkxQAAAFygggAAgBNaDCQIAACYcBUDLQYAAOACFQQAAJwUWR2AFyBBAADAiSFaDLQYAACACRUEAACcFHEjBBIEAACcFdFiIEEAAMAZexDYgwAAAFygggAAgBMucyRBAADAhBYDLQYAAOACFQQAAJzQYiBBAADAhASBFgMAAF5j7dq16tq1q6KiomSz2bRkyRKH44ZhKDk5WVFRUQoICFBcXJx27drlMKewsFBDhgxRWFiYgoKC1K1bNx07dsztWEgQAABwYsjmsYc78vPz1bBhQ82cOdPl8SlTpui1117TzJkztWnTJkVERKhTp046c+aMfc7QoUO1ePFiLVq0SBkZGcrLy1NSUpIuXbrkViy0GAAAcFJk0UUMiYmJSkxMdHnMMAylpaVpzJgx6tGjhyRp3rx5Cg8P18KFC/XMM88oNzdXs2fP1t///nd17NhRkrRgwQJVq1ZNq1at0n333VfsWKggAABwAxUWFur06dMOj8LCQrfXOXjwoLKyspSQkGAf8/f3V7t27bRu3TpJ0pYtW3ThwgWHOVFRUYqJibHPKS4SBAAAnBTJ5rFHamqqQkJCHB6pqalux5SVlSVJCg8PdxgPDw+3H8vKylLZsmV12223XXZOcdFiAADAiSd/zHH06NEaPny4w5i/v/81r2ezOfY/DMMwjTkrzhxnVBAAAHBS5MGHv7+/KlSo4PC4lgQhIiJCkkyVgOzsbHtVISIiQufPn1dOTs5l5xQXCQIAACVAdHS0IiIilJ6ebh87f/681qxZo1atWkmSmjRpojJlyjjMyczM1LfffmufU1y0GAAAcFLkZjneU/Ly8rR//37784MHD2rbtm0KDQ1V9erVNXToUKWkpKh27dqqXbu2UlJSFBgYqF69ekmSQkJC1K9fPz333HOqVKmSQkNDNWLECMXGxtqvaiguEgQAAJx4cg+COzZv3qz4+Hj781/3LvTp00dz587VyJEjVVBQoIEDByonJ0fNmzfX559/ruDgYPtrpk2bJj8/Pz366KMqKChQhw4dNHfuXJUuXdqtWGyGYVj1PTioGhpjdQj4RVZeztUn4aYo+OFrq0PALwKj2lgdAn7jwvnjN3T9jyJ/77G1Hsl8z2Nr3UxUEAAAcMJvMZAgAABgYtWdFL0JVzEAAAATKggAADgpcvNHlm5FJAgAADjxit37FqPFAAAATKggAADghE2KJAgAAJhwmSMJAgAAJuxBYA8CAABwgQqCm5q3bKJnh/xBsQ3rKSKyivr1/pNWfrbafvzYqW9dvm7i+Ff11ow5NytMn9WmdXM999wANW4Uq6ioCPV4+CktXbrS6rBuKX+b/4FWrfm3Dh4+pnL+ZXV3bD0NG/CUomtUlSRduHhRM96ep6/Xb9axHzJVPihILZo20rBn/6AqlSvZ1zn54ylNfWO21m/6j86ePaua1avq6ScfU0I8tzT2pJEjB+vB7omqU6eWCgrOaf2GzXrhhRTt3fu91aF5NfYgUEFwW2BQgHZ/u0fjRqW4PN7ornYOj+GDx6qoqEifLU13OR+eFRQUqB07dutPQ8daHcota/O2nerZo6sWvj1Nb6el6OKlS/rjsDE6W3BOknTuXKF27/lez/TtqQ/fnam0lLE6fOSYBo+a4LDO8y9N1aEjxzTz5fH6ZP4sdWx3r0a8OFnf7d3v6m1xjdq2aaFZs+apdZuuSry/p/xK++mzZQsVGBhgdWherciDj5KKCoKbvlyVoS9XZVz2+InsHx2eJyTGa93X3+jI4WM3OjRIWrHyS61Y+aXVYdzS/vraRIfnE18YprZJPbV7zz7dc3esgssH6Z3pjgn06OED1LP/UGVmZSsyoookafuu7zRuxGDF1qsjSXqmb0/N/2Cxdu/5XnXvrHVzPowPSOra2+F5/6eHKfOHnWrcuIEyMjZaFBVKAioIN1BY5UrqkNBWixZ8YnUowA2Tl39WkhRSIfjyc/LOymazKTg4yD7WuEF9rfhirXJPn/m5yrbqK52/cEFNG8Xe8Jh9WUhIBUlSTs5P1gbi5aggUEG4oR55vJvy885q+aerrA4FuCEMw9CU199W4wb1Vft3NV3OKSw8r2mz5uj+TnEqH/T/E4SpL43WiBdTdW/io/IrXVrlyvlreso4Va8adZOi902vvDJeGRkbtWvXHqtD8WoGexDcTxAKCgq0ZcsWhYaGql69eg7Hzp07pw8//FBPPvnkFdcoLCxUYWGhw5hhFMlmu7UKGo/9/kEt/uhTFRaetzoU4IaY9Nqb2vv9Qc2fNdXl8QsXL+rP4yfLMIo0bsQgh2Mz3p6n02fy9M70FFUMCdHqr9fruXEpmvfmK7rzjuibEb7PeX36JMXG1FVc/INWh4ISwK3/R967d6/q1q2rtm3bKjY2VnFxccrMzLQfz83N1R/+8IerrpOamqqQkBCHx5lzJ92P3os1a9FYte78nRb+nfYCbk0pr72pLzM26N0ZLyuiSmXT8QsXL+q5cSk6lpmlv6WlOFQPjhz7QQs//pf+MnqYWtzTSHfV/p0GPvV71b+rtt7/+NOb+TF8Rtq0vygpKUGdEh7R8eOZV3+Bj6PF4GaCMGrUKMXGxio7O1t79uxRhQoVdO+99+rIkSNuveno0aOVm5vr8AguF+bWGt7u8d49tP0/u/QdZTzcYgzD0KRX39SqNev07uuTVTUqwjTn1+TgyNEf9E5aiir+0vf+1blfKoi2Uo513FKlSskwSvK/Ur3T9LSJ6t49UQn3PapDh45aHU6JQILgZoth3bp1WrVqlcLCwhQWFqalS5dq0KBBatOmjb788ksF/ea/EK7E399f/v7+DmMlpb0QGBSgmtHV7c+r1bhd9WLq6KecXP1wPEuSVD44SEkPJOilca7LrrhxgoICVavW/y9PR9esroYN6+vUqRwdPfqDhZHdOia++oY+S/9Kr09+UUGBATr54ylJUvnyQSrn76+LFy9p+JhJ2r13v96YMkFFRUX2OSEVglWmTBlF16im6lWj9NKUGRoxuL9CKgRr9dfrtX7Tf/TGlGQLP92tZ8brKXr88e7q8dBTOnMmT+HhP1d7cnPP6Ny5cxZHB29mMwyj2HeUrFChgjZu3Ki6des6jA8ZMkRLlizRwoULFRcXp0uXLrkdSNXQGLdfY4WW9zbVR/8y3/Dow4VLNHzwz9fe/77Pw0qeNEqN68brzJm8mx3idcvKy7E6hGvWrm1LfbHqH6bxefM/VL/+wyyI6PoU/PC11SGYxNyb6HJ84gvD1b1LJx3P/J/ue7ivyznvznhZzRo3kCQdPnpc02bN0dYdu1RQUKBqVaPUt+dD6ta5w40K/boERpXMGzhdOH/c5Xi/fsM0/+8f3uRoPOdyn8tTZlTrffVJxTTk6AKPrXUzuZUgNGvWTEOGDNETTzxhOjZ48GC99957On369C2dIPiCkpwg3Gq8MUHwVSU1QbhV3egEYXp1zyUI/3ekZCYIbtX1H3zwQb3//vsuj82cOVM9e/aUG/kGAABeiT0IblYQbiQqCN6DCoL3oILgPaggeJcbXUGY5sEKwrASWkHgRkkAADgpyf/l7ykkCAAAOPGK0rrFSsa1hQAA4KaiggAAgJMifouBBAEAAGfsQaDFAAAAXKCCAACAEzYpkiAAAGBSRIpAiwEAAJhRQQAAwAmbFEkQAAAwocFAggAAgAkVBPYgAAAAF6ggAADghDspkiAAAGDCZY60GAAAgAtUEAAAcEL9gAQBAAATrmKgxQAAAFygggAAgBM2KZIgAABgQnpAiwEAALhABQEAACdsUiRBAADAhD0IJAgAAJiQHrAHAQAAuEAFAQAAJ+xBIEEAAMDEoMlAiwEAAJhRQQAAwAktBhIEAABMuMyRFgMAAHCBCgIAAE6oH5AgAABgQouBFgMAAHCBCgIAAE64ioEEAQAAE26URIIAAIAJFQT2IAAAABe8poJw4myu1SHgFzarA4BdYFQbq0MAfBItBi9KEAAA8Ba0GGgxAAAAF6ggAADgpMigxUCCAACAE9IDWgwAAMAFKggAADjhtxhIEAAAMOEyR1oMAADABRIEAACcFHnw4Y7k5GTZbDaHR0REhP24YRhKTk5WVFSUAgICFBcXp127dl3PR70sEgQAAJwUyfDYw13169dXZmam/bFz5077sSlTpui1117TzJkztWnTJkVERKhTp046c+aMJz++JPYgAABg4sk9CIWFhSosLHQY8/f3l7+/v8v5fn5+DlUDe0yGobS0NI0ZM0Y9evSQJM2bN0/h4eFauHChnnnmGY/FLFFBAADghkpNTVVISIjDIzU19bLz9+3bp6ioKEVHR+vxxx/XgQMHJEkHDx5UVlaWEhIS7HP9/f3Vrl07rVu3zuNxU0EAAMCJJ3+LYfTo0Ro+fLjD2OWqB82bN9f8+fN155136n//+58mTpyoVq1aadeuXcrKypIkhYeHO7wmPDxchw8f9mDEPyNBAADAieHBWy1fqZ3gLDEx0f7PsbGxatmype644w7NmzdPLVq0kCTZbI6/uWsYhmnME2gxAADgpYKCghQbG6t9+/bZ9yX8Wkn4VXZ2tqmq4AkkCAAAOLHyKobfKiws1HfffafIyEhFR0crIiJC6enp9uPnz5/XmjVr1KpVq+v9yCa0GAAAcOLJPQjuGDFihLp27arq1asrOztbEydO1OnTp9WnTx/ZbDYNHTpUKSkpql27tmrXrq2UlBQFBgaqV69eHo+FBAEAAC9x7Ngx9ezZUydPnlTlypXVokULbdiwQTVq1JAkjRw5UgUFBRo4cKBycnLUvHlzff755woODvZ4LDbDkzsxroN/uWpWh4BfFBVZlTsDQPFcOH/8hq6fVL2Lx9b69Mgyj611M1FBAADACb/myCZFAADgAhUEAACceEn33VIkCAAAOGEnFgkCAAAmnvyxppKKPQgAAMCECgIAAE64ioEEAQAAEzYp0mIAAAAuUEEAAMAJLQYSBAAATLiKgRYDAABwgQoCAABOitikSIIAAIAz0gNaDAAAwAUqCAAAOOEqBhIEAABMSBBIEAAAMOFOiuxBAAAALlBBAADACS0GEgQAAEy4kyItBo/7858HqfDcUU19ZbzVofickSMHa/26ZTr14x4dP7Zd//jHbN155x1Wh+WTOBfeg3OBa0WC4EFNmjRU/369tGPHbqtD8Ult27TQrFnz1LpNVyXe31N+pf302bKFCgwMsDo0n8O58B6ci2tjGIbHHiWVzfCS6P3LVbM6hOsSFBSojRuW60//N0bPP/8n7di+SyP+PMHqsK5JUVGR1SF4RFhYqDJ/2Kn49j2UkbHR6nB8GufCe9wq5+LC+eM3dP3Gka09ttbWzAyPrXUzUUHwkOnTJ2r58tVavbpk/g/hVhQSUkGSlJPzk7WBgHPhRTgXKC63Nyl+99132rBhg1q2bKm77rpL//3vfzV9+nQVFhaqd+/eat++/VXXKCwsVGFhocOYYRiy2WzuhuMVHnmkmxrdHatW9yZZHQp+45VXxisjY6N27dpjdSg+j3PhPTgXxeMlxXVLuZUgrFixQg888IDKly+vs2fPavHixXryySfVsGFDGYah++67TytXrrxqkpCamqoJExzL76VKB8vPL8T9T2CxqlUj9erUZHVJ+r0p6YF1Xp8+SbExdRUX/6DVofg8zoX34FwUH5c5urkHoVWrVmrfvr0mTpyoRYsWaeDAgRowYIAmTZokSRozZow2bdqkzz///IrruKoghFWuVyIrCN263qePPnpHFy9etI/5+fmpqKhIRUVFCq5wR4nr6Ze0eJ2lTfuLunXrrPYdeujQoaNWh+PTOBfe41Y7Fzd6D0LDiFYeW2t71jqPrXUzuZUghISEaMuWLapVq5aKiork7++vjRs3qnHjxpKkb7/9Vh07dlRWVpbbgZTUTYrlywepevWqDmN/e/tV7dm7X1OnztLu3SWvjFeSE4TpaRP1wAOd1bHTI9q//6DV4fg0zoX3uBXPxY1OEBpEtPTYWjuy1ntsrZvpmm+UVKpUKZUrV04VK1a0jwUHBys3N9cTcZUYeXn5piQg/+xZnfoxp0QmByXZjNdT9Pjj3dXjoad05kyewsMrS5Jyc8/o3LlzFkfnWzgX3oNzcW2K2IPgXoJQs2ZN7d+/X7Vq1ZIkrV+/XtWrV7cfP3r0qCIjIz0bIVBMzz7bR5K0+ouPHcb79Rum+X//0IqQfBbnwntwLq4Nd1J0M0EYMGCALl26ZH8eExPjcHz58uXFuorhVpeQ8KjVIfikMmVvtzoE/IJz4T04F7hW3CgJJiV5DwIA33Cj9yDUrdLMY2t9l/2Nx9a6mfixJgAAnNBi4E6KAADABSoIAAA44SoGEgQAAExoMdBiAAAALlBBAADACS0GEgQAAExoMdBiAAAALlBBAADAiWFwwzgSBAAAnBTRYiBBAADAmZf8CoGl2IMAAABMqCAAAOCEFgMJAgAAJrQYaDEAAAAXqCAAAOCEOymSIAAAYMKdFGkxAAAAF6ggAADghE2KJAgAAJhwmSMtBgAA4AIVBAAAnNBiIEEAAMCEyxxJEAAAMKGCwB4EAADgAhUEAACccBUDCQIAACa0GGgxAAAAF6ggAADghKsYSBAAADDhx5poMQAAABeoIAAA4IQWAwkCAAAmXMVAiwEAALhABQEAACdsUiRBAADAhBYDCQIAACYkCOxBAAAALlBBAADACfUDyWZQR/GIwsJCpaamavTo0fL397c6HJ/H+fAenAvvwbmAO0gQPOT06dMKCQlRbm6uKlSoYHU4Po/z4T04F96DcwF3sAcBAACYkCAAAAATEgQAAGBCguAh/v7+Gj9+PBt/vATnw3twLrwH5wLuYJMiAAAwoYIAAABMSBAAAIAJCQIAADAhQQAAACYkCAAAwIQEwUPefPNNRUdHq1y5cmrSpIm+/vprq0PySWvXrlXXrl0VFRUlm82mJUuWWB2ST0pNTVXTpk0VHBysKlWqqHv37tqzZ4/VYfmsWbNmqUGDBqpQoYIqVKigli1bavny5VaHBS9HguABH3zwgYYOHaoxY8boP//5j9q0aaPExEQdOXLE6tB8Tn5+vho2bKiZM2daHYpPW7NmjQYNGqQNGzYoPT1dFy9eVEJCgvLz860OzSdVrVpVkydP1ubNm7V582a1b99eDzzwgHbt2mV1aPBi3AfBA5o3b67GjRtr1qxZ9rG6deuqe/fuSk1NtTAy32az2bR48WJ1797d6lB83okTJ1SlShWtWbNGbdu2tTocSAoNDdUrr7yifv36WR0KvBQVhOt0/vx5bdmyRQkJCQ7jCQkJWrdunUVRAd4lNzdX0s//pwRrXbp0SYsWLVJ+fr5atmxpdTjwYn5WB1DSnTx5UpcuXVJ4eLjDeHh4uLKysiyKCvAehmFo+PDhat26tWJiYqwOx2ft3LlTLVu21Llz51S+fHktXrxY9erVszoseDESBA+x2WwOzw3DMI0Bvmjw4MHasWOHMjIyrA7Fp9WpU0fbtm3TTz/9pI8//lh9+vTRmjVrSBJwWSQI1yksLEylS5c2VQuys7NNVQXA1wwZMkRLly7V2rVrVbVqVavD8Wlly5ZVrVq1JEn33HOPNm3apOnTp+uvf/2rxZHBW7EH4TqVLVtWTZo0UXp6usN4enq6WrVqZVFUgLUMw9DgwYP1ySefaPXq1YqOjrY6JDgxDEOFhYVWhwEvRgXBA4YPH64nnnhC99xzj1q2bKm3335bR44c0bPPPmt1aD4nLy9P+/fvtz8/ePCgtm3bptDQUFWvXt3CyHzLoEGDtHDhQv3zn/9UcHCwvcIWEhKigIAAi6PzPS+88IISExNVrVo1nTlzRosWLdJXX32lFStWWB0avBiXOXrIm2++qSlTpigzM1MxMTGaNm0al3NZ4KuvvlJ8fLxpvE+fPpo7d+7ND8hHXW7/zZw5c9S3b9+bGwzUr18/ffHFF8rMzFRISIgaNGigUaNGqVOnTlaHBi9GggAAAEzYgwAAAExIEAAAgAkJAgAAMCFBAAAAJiQIAADAhAQBAACYkCAAAAATEgQAAGBCggAAAExIEAAAgAkJAgAAMPl/+/Fuvrr6GbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('KNN confusion matrix')\n",
    "sns.heatmap(confusion_matrix(y_test,prediction),annot=True,fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c7fa58",
   "metadata": {},
   "source": [
    "# Naives bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "23ae8b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bf767d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb = GaussianNB()\n",
    "nb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6fef0ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_pred=nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e9830069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 3, 3, 0, 3, 3, 2, 2, 3, 2, 3, 3, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2,\n",
       "       0, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3,\n",
       "       3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2,\n",
       "       3, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 0, 0, 2, 2, 3, 2, 2, 3, 3,\n",
       "       2, 2, 3, 3, 2, 2, 2, 2, 0, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 0, 3, 2, 2, 2, 2, 3, 2, 2,\n",
       "       3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 0, 2, 2, 3, 3, 3, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 3, 3, 2, 2, 0, 2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 2, 3,\n",
       "       2, 2, 3, 3, 2, 3, 0, 2, 3, 3, 0, 2, 2, 3, 2, 3, 2, 2, 3, 2, 2, 3,\n",
       "       3, 2, 2, 2, 3, 3, 0, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 3, 3, 3, 0, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2,\n",
       "       3, 3, 3, 2, 2, 3, 2, 0, 3, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 3,\n",
       "       2, 2, 3, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c7e1be29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy is 64.74%\n"
     ]
    }
   ],
   "source": [
    "print(f'KNN accuracy is {round(accuracy_score(y_test,nb_pred)*100,2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d05098e",
   "metadata": {},
   "source": [
    "# Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c33a258b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "36a6bd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1f807123",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf= RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "501b007b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_16120\\1593328843.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(X_train,y_train)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b963ca65",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_predict=rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7b3622b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 3, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       0, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2,\n",
       "       1, 2, 2, 3, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 3, 0,\n",
       "       0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 3, 2, 2, 2, 1, 2, 2, 2, 2, 2,\n",
       "       2, 0, 2, 2, 2, 2, 0, 0, 2, 2, 0, 2, 2, 0, 0, 0, 2, 2, 2, 0, 0, 0,\n",
       "       2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 0, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0,\n",
       "       0, 2, 2, 3, 2, 0, 1, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       0, 2, 2, 2, 0, 3, 2, 1, 2, 3, 2, 2, 2, 2, 0, 2, 0, 0, 2, 2, 0, 2,\n",
       "       2, 2, 2, 2, 2, 3, 0, 2, 0, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 0, 1, 2, 2, 0, 2, 0, 3, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 0, 2, 3, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,\n",
       "       2, 2, 2, 0, 0, 3, 2, 0, 2, 0, 1, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bba2a529",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy is 98.27%\n"
     ]
    }
   ],
   "source": [
    "print(f'KNN accuracy is {round(accuracy_score(y_test,rf_predict)*100,2)}%') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "211667d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['buying', 'maintainance', 'doors', 'persons', 'lug_boot', 'safety',\n",
       "       'condition'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "760d72dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot qty against price\n",
    "\n",
    "plt.scatter(\"condition\",\"buying\", data=df)\n",
    "sns.regplot(x=\"condition\",y=\"buying\", data=df)\n",
    "plt.xlabel(\"condition\")\n",
    "plt.ylabel(\"buying\")\n",
    "plt.title(\"Relationship between buying and condition\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "23120cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"buying\",axis=1)\n",
    "y=df[\"buying\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7e7da21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9c9ffa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "de9eb2c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "37323c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d9678e47",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "16087f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "6503b2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "67c4bb4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.22      0.15      0.18        78\\n           1       0.42      0.05      0.10        91\\n           2       0.27      0.84      0.41        86\\n           3       0.40      0.04      0.08        91\\n\\n    accuracy                           0.27       346\\n   macro avg       0.33      0.27      0.19       346\\nweighted avg       0.33      0.27      0.19       346\\n'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report (y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c1356d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7a265441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  1, 62,  3],\n",
       "       [15,  5, 69,  2],\n",
       "       [11,  2, 72,  1],\n",
       "       [17,  4, 66,  4]], dtype=int64)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "04d43ec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2cbc8e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26878612716763006"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef661ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6363fab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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