svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f3b5805",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d01cc924",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod = pd.read_csv(\"ml.csv\", index_col = None,delimiter =\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf4a68c6",
   "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>_product_category_name_</th>\n",
       "      <th>_product_name_lenght_</th>\n",
       "      <th>_product_weight_g_</th>\n",
       "      <th>_product_length_cm_</th>\n",
       "      <th>_product_height_cm_</th>\n",
       "      <th>_product_width_cm_</th>\n",
       "      <th>_seller_city_</th>\n",
       "      <th>_seller_state_</th>\n",
       "      <th>_payment_type_</th>\n",
       "      <th>_payment_installments_</th>\n",
       "      <th>_payment_value_</th>\n",
       "      <th>_price_</th>\n",
       "      <th>_freight_value_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cool_stuff</td>\n",
       "      <td>58.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>volta redonda</td>\n",
       "      <td>SP</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>72.19</td>\n",
       "      <td>58.90</td>\n",
       "      <td>13.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pet_shop</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>3</td>\n",
       "      <td>259.83</td>\n",
       "      <td>239.90</td>\n",
       "      <td>19.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>moveis_decoracao</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>borda da mata</td>\n",
       "      <td>MG</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>5</td>\n",
       "      <td>216.87</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>perfumaria</td>\n",
       "      <td>42.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>2</td>\n",
       "      <td>25.78</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ferramentas_jardim</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>loanda</td>\n",
       "      <td>PR</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>3</td>\n",
       "      <td>218.04</td>\n",
       "      <td>199.90</td>\n",
       "      <td>18.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  _product_category_name_  _product_name_lenght_  _product_weight_g_  \\\n",
       "0              cool_stuff                   58.0               650.0   \n",
       "1                pet_shop                   56.0             30000.0   \n",
       "2        moveis_decoracao                   59.0              3050.0   \n",
       "3              perfumaria                   42.0               200.0   \n",
       "4      ferramentas_jardim                   59.0              3750.0   \n",
       "\n",
       "   _product_length_cm_  _product_height_cm_  _product_width_cm_  \\\n",
       "0                 28.0                  9.0                14.0   \n",
       "1                 50.0                 30.0                40.0   \n",
       "2                 33.0                 13.0                33.0   \n",
       "3                 16.0                 10.0                15.0   \n",
       "4                 35.0                 40.0                30.0   \n",
       "\n",
       "   _seller_city_ _seller_state_ _payment_type_  _payment_installments_  \\\n",
       "0  volta redonda             SP    credit_card                       2   \n",
       "1      sao paulo             SP    credit_card                       3   \n",
       "2  borda da mata             MG    credit_card                       5   \n",
       "3         franca             SP    credit_card                       2   \n",
       "4         loanda             PR    credit_card                       3   \n",
       "\n",
       "   _payment_value_  _price_  _freight_value_  \n",
       "0            72.19    58.90            13.29  \n",
       "1           259.83   239.90            19.93  \n",
       "2           216.87   199.00            17.87  \n",
       "3            25.78    12.99            12.79  \n",
       "4           218.04   199.90            18.14  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e4a1f060",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(71423, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d789f954",
   "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>_product_name_lenght_</th>\n",
       "      <th>_product_weight_g_</th>\n",
       "      <th>_product_length_cm_</th>\n",
       "      <th>_product_height_cm_</th>\n",
       "      <th>_product_width_cm_</th>\n",
       "      <th>_payment_installments_</th>\n",
       "      <th>_payment_value_</th>\n",
       "      <th>_price_</th>\n",
       "      <th>_freight_value_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>70413.000000</td>\n",
       "      <td>71413.000000</td>\n",
       "      <td>71413.000000</td>\n",
       "      <td>71413.000000</td>\n",
       "      <td>71413.000000</td>\n",
       "      <td>71423.000000</td>\n",
       "      <td>71423.000000</td>\n",
       "      <td>71423.000000</td>\n",
       "      <td>71423.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>48.757090</td>\n",
       "      <td>2090.455673</td>\n",
       "      <td>30.148082</td>\n",
       "      <td>16.554717</td>\n",
       "      <td>22.993755</td>\n",
       "      <td>2.999230</td>\n",
       "      <td>176.874182</td>\n",
       "      <td>120.627134</td>\n",
       "      <td>19.970961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.040944</td>\n",
       "      <td>3745.263810</td>\n",
       "      <td>16.146934</td>\n",
       "      <td>13.444142</td>\n",
       "      <td>11.721034</td>\n",
       "      <td>2.803115</td>\n",
       "      <td>283.638050</td>\n",
       "      <td>184.469311</td>\n",
       "      <td>15.592737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>39.900000</td>\n",
       "      <td>13.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>112.140000</td>\n",
       "      <td>74.900000</td>\n",
       "      <td>16.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>193.015000</td>\n",
       "      <td>134.900000</td>\n",
       "      <td>21.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>40425.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>13664.080000</td>\n",
       "      <td>6735.000000</td>\n",
       "      <td>375.280000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       _product_name_lenght_  _product_weight_g_  _product_length_cm_  \\\n",
       "count           70413.000000        71413.000000         71413.000000   \n",
       "mean               48.757090         2090.455673            30.148082   \n",
       "std                10.040944         3745.263810            16.146934   \n",
       "min                 5.000000            0.000000             7.000000   \n",
       "25%                42.000000          300.000000            18.000000   \n",
       "50%                52.000000          700.000000            25.000000   \n",
       "75%                57.000000         1800.000000            38.000000   \n",
       "max                72.000000        40425.000000           105.000000   \n",
       "\n",
       "       _product_height_cm_  _product_width_cm_  _payment_installments_  \\\n",
       "count         71413.000000        71413.000000            71423.000000   \n",
       "mean             16.554717           22.993755                2.999230   \n",
       "std              13.444142           11.721034                2.803115   \n",
       "min               2.000000            6.000000                0.000000   \n",
       "25%               8.000000           15.000000                1.000000   \n",
       "50%              13.000000           20.000000                2.000000   \n",
       "75%              20.000000           30.000000                4.000000   \n",
       "max             105.000000          118.000000               24.000000   \n",
       "\n",
       "       _payment_value_       _price_  _freight_value_  \n",
       "count     71423.000000  71423.000000     71423.000000  \n",
       "mean        176.874182    120.627134        19.970961  \n",
       "std         283.638050    184.469311        15.592737  \n",
       "min           0.000000      0.850000         0.000000  \n",
       "25%          64.000000     39.900000        13.080000  \n",
       "50%         112.140000     74.900000        16.280000  \n",
       "75%         193.015000    134.900000        21.160000  \n",
       "max       13664.080000   6735.000000       375.280000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2099267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_product_category_name_    1010\n",
       "_product_name_lenght_      1010\n",
       "_product_weight_g_           10\n",
       "_product_length_cm_          10\n",
       "_product_height_cm_          10\n",
       "_product_width_cm_           10\n",
       "_seller_city_                 0\n",
       "_seller_state_                0\n",
       "_payment_type_                0\n",
       "_payment_installments_        0\n",
       "_payment_value_               0\n",
       "_price_                       0\n",
       "_freight_value_               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c64e8e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59bfbb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_product_category_name_    0\n",
       "_product_name_lenght_      0\n",
       "_product_weight_g_         0\n",
       "_product_length_cm_        0\n",
       "_product_height_cm_        0\n",
       "_product_width_cm_         0\n",
       "_seller_city_              0\n",
       "_seller_state_             0\n",
       "_payment_type_             0\n",
       "_payment_installments_     0\n",
       "_payment_value_            0\n",
       "_price_                    0\n",
       "_freight_value_            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45be6f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Others                    37103\n",
       "cama_mesa_banho            7095\n",
       "beleza_saude               6027\n",
       "esporte_lazer              5482\n",
       "moveis_decoracao           5255\n",
       "informatica_acessorios     4973\n",
       "utilidades_domesticas      4477\n",
       "Name: _product_category_name_, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod[\"_product_category_name_\"].replace(['perfumaria', 'artes', 'bebes',\n",
    "       'instrumentos_musicais', 'cool_stuff','utilidades_domesticas'\n",
    "       'eletrodomesticos', 'brinquedos',\n",
    "       'construcao_ferramentas_seguranca',\n",
    "       'malas_acessorios',\n",
    "       'ferramentas_jardim', 'moveis_escritorio', 'automotivo',\n",
    "       'eletronicos', 'fashion_calcados', 'telefonia', 'papelaria',\n",
    "       'fashion_bolsas_e_acessorios', 'pcs', 'casa_construcao',\n",
    "       'relogios_presentes', 'construcao_ferramentas_construcao',\n",
    "       'pet_shop', 'eletroportateis', 'agro_industria_e_comercio',\n",
    "       'moveis_sala', 'sinalizacao_e_seguranca', 'climatizacao',\n",
    "       'consoles_games', 'livros_interesse_geral',\n",
    "       'construcao_ferramentas_ferramentas',\n",
    "       'fashion_underwear_e_moda_praia', 'fashion_roupa_masculina',\n",
    "       'moveis_cozinha_area_de_servico_jantar_e_jardim','eletrodomesticos',\n",
    "       'industria_comercio_e_negocios', 'telefonia_fixa',\n",
    "       'construcao_ferramentas_iluminacao', 'livros_tecnicos',\n",
    "       'eletrodomesticos_2', 'artigos_de_festas', 'bebidas',\n",
    "       'market_place', 'la_cuisine', 'construcao_ferramentas_jardim',\n",
    "       'fashion_roupa_feminina', 'casa_conforto', 'audio',\n",
    "       'alimentos_bebidas', 'musica', 'alimentos',\n",
    "       'tablets_impressao_imagem', 'livros_importados',\n",
    "       'portateis_casa_forno_e_cafe', 'fashion_esporte',\n",
    "       'artigos_de_natal', 'fashion_roupa_infanto_juvenil',\n",
    "       'dvds_blu_ray', 'artes_e_artesanato', 'pc_gamer', 'moveis_quarto',\n",
    "       'cine_foto', 'fraldas_higiene', 'flores', 'casa_conforto_2',\n",
    "       'portateis_cozinha_e_preparadores_de_alimentos',\n",
    "       'seguros_e_servicos', 'moveis_colchao_e_estofado',\n",
    "       'cds_dvds_musicais'], 'Others', inplace=True)\n",
    "\n",
    "prod[\"_product_category_name_\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9b93ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition = prod['_product_category_name_'] == 'Others'\n",
    "index_to_drop = prod[condition].index\n",
    "prod.drop(index_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0c7df986",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod[\"_product_category_name_\"] = prod[\"_product_category_name_\"].map({'cama_mesa_banho': 0, 'esporte_lazer': 1, 'moveis_decoracao': 2\n",
    "                                                                  ,'beleza_saude': 3, 'utilidades_domesticas': 4, 'informatica_acessorios': 5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb135593",
   "metadata": {},
   "outputs": [],
   "source": [
    "prod[\"_payment_type_\"] = prod[\"_payment_type_\"].map({'credit_card': 0, 'boleto': 1, 'voucher': 2, 'debit_card': 3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6820f5a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8ae9f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = prod._product_category_name_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5763ab6a",
   "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>_product_weight_g_</th>\n",
       "      <th>_product_length_cm_</th>\n",
       "      <th>_product_height_cm_</th>\n",
       "      <th>_product_width_cm_</th>\n",
       "      <th>_payment_installments_</th>\n",
       "      <th>_payment_value_</th>\n",
       "      <th>_price_</th>\n",
       "      <th>_freight_value_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3050.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>5</td>\n",
       "      <td>216.87</td>\n",
       "      <td>199.00</td>\n",
       "      <td>17.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>450.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
       "      <td>34.59</td>\n",
       "      <td>21.90</td>\n",
       "      <td>12.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3</td>\n",
       "      <td>157.60</td>\n",
       "      <td>145.95</td>\n",
       "      <td>11.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>950.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>68.87</td>\n",
       "      <td>59.99</td>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1383.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.32</td>\n",
       "      <td>74.00</td>\n",
       "      <td>23.32</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71418</th>\n",
       "      <td>10150.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>343.40</td>\n",
       "      <td>299.99</td>\n",
       "      <td>43.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71419</th>\n",
       "      <td>8950.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>386.53</td>\n",
       "      <td>350.00</td>\n",
       "      <td>36.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71420</th>\n",
       "      <td>967.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>116.85</td>\n",
       "      <td>99.90</td>\n",
       "      <td>16.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71421</th>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>64.71</td>\n",
       "      <td>55.99</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71422</th>\n",
       "      <td>600.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55.79</td>\n",
       "      <td>43.00</td>\n",
       "      <td>12.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33309 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       _product_weight_g_  _product_length_cm_  _product_height_cm_  \\\n",
       "2                  3050.0                 33.0                 13.0   \n",
       "5                   450.0                 24.0                  8.0   \n",
       "8                  2000.0                 30.0                 12.0   \n",
       "10                  950.0                 35.0                 25.0   \n",
       "12                 1383.0                 50.0                 10.0   \n",
       "...                   ...                  ...                  ...   \n",
       "71418             10150.0                 89.0                 15.0   \n",
       "71419              8950.0                 45.0                 26.0   \n",
       "71420               967.0                 21.0                 24.0   \n",
       "71421               100.0                 20.0                 20.0   \n",
       "71422               600.0                 30.0                  3.0   \n",
       "\n",
       "       _product_width_cm_  _payment_installments_  _payment_value_  _price_  \\\n",
       "2                    33.0                       5           216.87   199.00   \n",
       "5                    15.0                       1            34.59    21.90   \n",
       "8                    16.0                       3           157.60   145.95   \n",
       "10                   20.0                       3            68.87    59.99   \n",
       "12                   40.0                       4            97.32    74.00   \n",
       "...                   ...                     ...              ...      ...   \n",
       "71418                40.0                       1           343.40   299.99   \n",
       "71419                38.0                       1           386.53   350.00   \n",
       "71420                19.0                       3           116.85    99.90   \n",
       "71421                20.0                       3            64.71    55.99   \n",
       "71422                19.0                       1            55.79    43.00   \n",
       "\n",
       "       _freight_value_  \n",
       "2                17.87  \n",
       "5                12.69  \n",
       "8                11.65  \n",
       "10                8.88  \n",
       "12               23.32  \n",
       "...                ...  \n",
       "71418            43.41  \n",
       "71419            36.53  \n",
       "71420            16.95  \n",
       "71421             8.72  \n",
       "71422            12.79  \n",
       "\n",
       "[33309 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = [2,3,4,5,9,10,11,12]\n",
    "X = prod.iloc[:,col]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d356be5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49723567",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "sc = RobustScaler()\n",
    "X_train= sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b9b4255",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c25f81f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b97806cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, degree=2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='rbf', C=100\n",
    "             ,degree = 2)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7149d540",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9be8d373",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "accuracy = clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a5f76137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.28"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(accuracy*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9d5d8a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Définir les paramètres à tester\n",
    "#params = {\n",
    "##   'C': [0.1, 1, 10, 100],\n",
    "#    'kernel': ['linear', 'poly', 'rbf'],\n",
    " #   'gamma': ['scale', 'auto'] + [0.1, 1, 10],\n",
    "#    'degree': [2, 3, 4]\n",
    "#}\n",
    "\n",
    "# Créer le modèle SVM\n",
    "#model = svm.SVC()\n",
    "#\n",
    "# Effectuer la recherche de grille\n",
    "#grid = GridSearchCV(model, params, cv=3)\n",
    "#grid.fit(X_train, y_train)\n",
    "\n",
    "# Afficher les meilleurs paramètres trouvés\n",
    "#print('Best Parameters:', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbdc48a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chleg\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The number of FixedLocator locations (6), usually from a call to set_ticks, does not match the number of ticklabels (33309).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [32]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_confusion_matrix\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_confusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mclf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdisplay_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBlues\u001b[49m\u001b[43m)\u001b[49m  \n\u001b[0;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:88\u001b[0m, in \u001b[0;36mdeprecated._decorate_fun.<locals>.wrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fun)\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     87\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg, category\u001b[38;5;241m=\u001b[39m\u001b[38;5;167;01mFutureWarning\u001b[39;00m)\n\u001b[1;32m---> 88\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fun(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_plot\\confusion_matrix.py:577\u001b[0m, in \u001b[0;36mplot_confusion_matrix\u001b[1;34m(estimator, X, y_true, labels, sample_weight, normalize, display_labels, include_values, xticks_rotation, values_format, cmap, ax, colorbar)\u001b[0m\n\u001b[0;32m    574\u001b[0m         display_labels \u001b[38;5;241m=\u001b[39m labels\n\u001b[0;32m    576\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm, display_labels\u001b[38;5;241m=\u001b[39mdisplay_labels)\n\u001b[1;32m--> 577\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdisp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    578\u001b[0m \u001b[43m    \u001b[49m\u001b[43minclude_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    579\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    580\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    581\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxticks_rotation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxticks_rotation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    582\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalues_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalues_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    583\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolorbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolorbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    584\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_plot\\confusion_matrix.py:163\u001b[0m, in \u001b[0;36mConfusionMatrixDisplay.plot\u001b[1;34m(self, include_values, cmap, xticks_rotation, values_format, ax, colorbar)\u001b[0m\n\u001b[0;32m    161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m colorbar:\n\u001b[0;32m    162\u001b[0m     fig\u001b[38;5;241m.\u001b[39mcolorbar(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mim_, ax\u001b[38;5;241m=\u001b[39max)\n\u001b[1;32m--> 163\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    164\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxticks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_classes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    165\u001b[0m \u001b[43m    \u001b[49m\u001b[43myticks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_classes\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    166\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxticklabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisplay_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    167\u001b[0m \u001b[43m    \u001b[49m\u001b[43myticklabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisplay_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    168\u001b[0m \u001b[43m    \u001b[49m\u001b[43mylabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTrue label\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    169\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPredicted label\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    170\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    172\u001b[0m ax\u001b[38;5;241m.\u001b[39mset_ylim((n_classes \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.5\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m))\n\u001b[0;32m    173\u001b[0m plt\u001b[38;5;241m.\u001b[39msetp(ax\u001b[38;5;241m.\u001b[39mget_xticklabels(), rotation\u001b[38;5;241m=\u001b[39mxticks_rotation)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py:116\u001b[0m, in \u001b[0;36mArtist.__init_subclass__.<locals>.<lambda>\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_autogenerated_signature\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m    109\u001b[0m     \u001b[38;5;66;03m# Don't overwrite cls.set if the subclass or one of its parents\u001b[39;00m\n\u001b[0;32m    110\u001b[0m     \u001b[38;5;66;03m# has defined a set method set itself.\u001b[39;00m\n\u001b[0;32m    111\u001b[0m     \u001b[38;5;66;03m# If there was no explicit definition, cls.set is inherited from\u001b[39;00m\n\u001b[0;32m    112\u001b[0m     \u001b[38;5;66;03m# the hierarchy of auto-generated set methods, which hold the\u001b[39;00m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;66;03m# flag _autogenerated_signature.\u001b[39;00m\n\u001b[0;32m    114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m--> 116\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Artist\u001b[38;5;241m.\u001b[39mset(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    117\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mset\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.set\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py:1164\u001b[0m, in \u001b[0;36mArtist.set\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m   1159\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1160\u001b[0m     \u001b[38;5;66;03m# docstring and signature are auto-generated via\u001b[39;00m\n\u001b[0;32m   1161\u001b[0m     \u001b[38;5;66;03m# Artist._update_set_signature_and_docstring() at the end of the\u001b[39;00m\n\u001b[0;32m   1162\u001b[0m     \u001b[38;5;66;03m# module.\u001b[39;00m\n\u001b[0;32m   1163\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m-> 1164\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py:1066\u001b[0m, in \u001b[0;36mArtist.update\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m   1063\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m callable(func):\n\u001b[0;32m   1064\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1065\u001b[0m                                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas no property \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1066\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n\u001b[0;32m   1068\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpchanged()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:75\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__.<locals>.wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 75\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m get_method(\u001b[38;5;28mself\u001b[39m)(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py:1798\u001b[0m, in \u001b[0;36mAxis._set_ticklabels\u001b[1;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[0;32m   1796\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fontdict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1797\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mupdate(fontdict)\n\u001b[1;32m-> 1798\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_ticklabels(labels, minor\u001b[38;5;241m=\u001b[39mminor, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py:1720\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[1;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[0;32m   1716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(locator, mticker\u001b[38;5;241m.\u001b[39mFixedLocator):\n\u001b[0;32m   1717\u001b[0m     \u001b[38;5;66;03m# Passing [] as a list of ticklabels is often used as a way to\u001b[39;00m\n\u001b[0;32m   1718\u001b[0m     \u001b[38;5;66;03m# remove all tick labels, so only error for > 0 ticklabels\u001b[39;00m\n\u001b[0;32m   1719\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m-> 1720\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1721\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of FixedLocator locations\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1722\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), usually from a call to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1723\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m set_ticks, does not match\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1724\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the number of ticklabels (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ticklabels)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1725\u001b[0m     tickd \u001b[38;5;241m=\u001b[39m {loc: lab \u001b[38;5;28;01mfor\u001b[39;00m loc, lab \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs, ticklabels)}\n\u001b[0;32m   1726\u001b[0m     func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_with_dict, tickd)\n",
      "\u001b[1;31mValueError\u001b[0m: The number of FixedLocator locations (6), usually from a call to set_ticks, does not match the number of ticklabels (33309)."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(clf, X_test, y_test,display_labels=y,cmap=plt.cm.Blues)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c32c09e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0   0.635743  0.796151  0.706962      1403\n",
      "           1   0.542973  0.486413  0.513139      1104\n",
      "           2   0.734332  0.513333  0.604260      1050\n",
      "           3   0.481074  0.652858  0.553954      1207\n",
      "           4   0.614601  0.409040  0.491180       885\n",
      "           5   0.634555  0.598223  0.615854      1013\n",
      "\n",
      "    accuracy                       0.592765      6662\n",
      "   macro avg   0.607213  0.576003  0.580892      6662\n",
      "weighted avg   0.604897  0.592765  0.588415      6662\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred,digits=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61569bf7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}