RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits=load_digits()\n",
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gray()\n",
    "plt.subplots(1, 5, figsize=(10, 10))\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i+1)\n",
    "    plt.imshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1     2     3     4     5    6    7    8    9   ...   54   55  \\\n",
       "0     0.0  0.0   5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1     0.0  0.0   0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2     0.0  0.0   0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  5.0  0.0   \n",
       "3     0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  9.0  0.0   \n",
       "4     0.0  0.0   0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "...   ...  ...   ...   ...   ...   ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1792  0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  4.0  0.0   \n",
       "1793  0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  1.0  0.0   \n",
       "1794  0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1795  0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  2.0  0.0   \n",
       "1796  0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  8.0  0.0   \n",
       "\n",
       "       56   57   58    59    60    61   62   63  \n",
       "0     0.0  0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
       "1     0.0  0.0  0.0  11.0  16.0  10.0  0.0  0.0  \n",
       "2     0.0  0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
       "3     0.0  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4     0.0  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
       "...   ...  ...  ...   ...   ...   ...  ...  ...  \n",
       "1792  0.0  0.0  2.0  14.0  15.0   9.0  0.0  0.0  \n",
       "1793  0.0  0.0  6.0  16.0  14.0   6.0  0.0  0.0  \n",
       "1794  0.0  0.0  2.0   9.0  13.0   6.0  0.0  0.0  \n",
       "1795  0.0  0.0  5.0  12.0  16.0  12.0  0.0  0.0  \n",
       "1796  0.0  1.0  8.0  12.0  14.0  12.0  1.0  0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(digits.data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0     0\n",
       "1     1\n",
       "2     2\n",
       "3     3\n",
       "4     4\n",
       "...  ..\n",
       "1792  9\n",
       "1793  0\n",
       "1794  8\n",
       "1795  9\n",
       "1796  8\n",
       "\n",
       "[1797 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target=pd.DataFrame(digits.target)\n",
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1     2     3     4     5    6    7    8    9  ...   55   56  \\\n",
       "0     0.0  0.0   5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1     0.0  0.0   0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2     0.0  0.0   0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "3     0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0   \n",
       "4     0.0  0.0   0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "...   ...  ...   ...   ...   ...   ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "1792  0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  0.0  0.0   \n",
       "1793  0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1794  0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1795  0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "1796  0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  0.0  0.0   \n",
       "\n",
       "       57   58    59    60    61   62   63  target  \n",
       "0     0.0  6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
       "1     0.0  0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
       "2     0.0  0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
       "3     0.0  7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
       "4     0.0  0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
       "...   ...  ...   ...   ...   ...  ...  ...     ...  \n",
       "1792  0.0  2.0  14.0  15.0   9.0  0.0  0.0       9  \n",
       "1793  0.0  6.0  16.0  14.0   6.0  0.0  0.0       0  \n",
       "1794  0.0  2.0   9.0  13.0   6.0  0.0  0.0       8  \n",
       "1795  0.0  5.0  12.0  16.0  12.0  0.0  0.0       9  \n",
       "1796  1.0  8.0  12.0  14.0  12.0  1.0  0.0       8  \n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target']=target\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=df.drop('target', axis='columns')\n",
    "y=df.target\n",
    "\n",
    "X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1437\n",
      "1797\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model=RandomForestClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 5, 4, 8, 2, 4, 3, 3, 0, 8, 7, 0, 1, 8, 6, 9, 7, 9, 7, 1, 2,\n",
       "       6, 7, 8, 8, 5, 8, 5, 9, 3, 3, 7, 3, 4, 1, 9, 2, 5, 4, 2, 1, 0, 9,\n",
       "       2, 3, 6, 1, 9, 4, 4, 5, 8, 4, 8, 5, 9, 7, 1, 0, 4, 5, 8, 4, 7, 9,\n",
       "       0, 7, 1, 3, 9, 3, 3, 8, 0, 7, 3, 6, 5, 2, 0, 8, 8, 0, 1, 1, 2, 8,\n",
       "       8, 8, 2, 6, 3, 4, 7, 9, 2, 2, 9, 2, 5, 0, 8, 0, 4, 8, 8, 0, 6, 7,\n",
       "       3, 3, 9, 1, 5, 4, 6, 0, 8, 8, 1, 1, 7, 9, 9, 5, 2, 3, 3, 8, 7, 6,\n",
       "       2, 5, 4, 3, 3, 7, 6, 7, 2, 7, 4, 9, 5, 1, 9, 4, 6, 1, 1, 1, 4, 0,\n",
       "       8, 9, 1, 2, 3, 5, 0, 3, 4, 1, 5, 4, 9, 3, 5, 6, 4, 0, 8, 6, 7, 0,\n",
       "       9, 9, 4, 7, 3, 5, 2, 0, 6, 7, 5, 3, 9, 7, 1, 3, 2, 8, 3, 3, 1, 7,\n",
       "       1, 1, 1, 7, 1, 6, 7, 6, 9, 5, 2, 3, 5, 2, 9, 5, 4, 8, 2, 9, 1, 5,\n",
       "       4, 2, 3, 9, 0, 2, 0, 2, 1, 0, 5, 0, 6, 4, 2, 1, 9, 0, 9, 0, 6, 9,\n",
       "       4, 4, 9, 7, 5, 6, 1, 3, 7, 0, 8, 6, 2, 0, 1, 2, 3, 8, 4, 4, 3, 5,\n",
       "       7, 3, 7, 2, 0, 2, 0, 9, 2, 8, 6, 3, 6, 0, 6, 6, 6, 7, 1, 6, 1, 7,\n",
       "       6, 0, 6, 3, 7, 4, 6, 2, 8, 0, 8, 4, 7, 3, 3, 0, 0, 2, 3, 9, 7, 4,\n",
       "       6, 7, 9, 7, 6, 0, 5, 6, 2, 7, 1, 0, 5, 5, 6, 4, 7, 2, 5, 1, 4, 6,\n",
       "       6, 5, 0, 2, 9, 8, 7, 9, 6, 7, 0, 8, 3, 5, 9, 4, 1, 5, 5, 4, 7, 3,\n",
       "       9, 2, 7, 3, 3, 6, 6, 3])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694444444444444"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 0, 33,  0,  0,  0,  1,  0,  0,  0,  0],\n",
       "       [ 0,  0, 34,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 39,  0,  0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0, 33,  0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0,  0, 32,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 37,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 40,  0,  0],\n",
       "       [ 0,  1,  2,  1,  0,  0,  0,  0, 29,  0],\n",
       "       [ 0,  0,  0,  1,  0,  1,  0,  0,  1, 36]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted=model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm=confusion_matrix(y_test, predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.722222222222214, 0.5, 'Truth')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}