Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15f4d0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbe3b57d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be55016d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAECCAYAAADXWsr9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAL30lEQVR4nO3db6iW9R3H8c9nppQlWctVaGTFECJYmsiiiE0zbIV7skChaLGRD7YoNgjbk9EzH0V7MOKI1YLMSEsYsbU8ZMRgqx3Nlnls1KFIqTQ80T9Qsu8e3JfhTHauY9fvd+5zvu8X3Hif+9zn/n7Pkc99/bmv6/o6IgRgavvORDcAoDyCDiRA0IEECDqQAEEHEiDoQAJ9EXTbK2y/afst22sL13rE9gHbu0vWOa7eRba3295j+w3bdxeud7rtV2y/1tS7v2S9puY026/afrZ0rabeO7Zft73L9lDhWrNtb7G91/aw7asL1lrQ/E7Hbp/YvqeTF4+ICb1JmibpbUmXSpoh6TVJlxesd52kRZJ2V/r9LpS0qLk/S9J/Cv9+lnRWc3+6pJcl/bDw7/gbSU9IerbS3/QdSedVqvWYpF8292dIml2p7jRJH0i6uIvX64cl+hJJb0XESEQckfSkpJ+WKhYRL0k6VOr1T1Lv/YjY2dz/VNKwpLkF60VEfNZ8Ob25FTsqyvY8STdJ2lCqxkSxfbZ6C4aHJSkijkTEx5XKL5P0dkS828WL9UPQ50p677iv96lgECaS7fmSFqq3lC1ZZ5rtXZIOSNoWESXrPSjpXklfFaxxopD0vO0dtu8sWOcSSQclPdpsmmywfWbBesdbJWlTVy/WD0FPwfZZkp6WdE9EfFKyVkQcjYgrJc2TtMT2FSXq2L5Z0oGI2FHi9f+PayNikaQbJf3K9nWF6pym3mbeQxGxUNLnkoruQ5Ik2zMkrZS0uavX7Ieg75d00XFfz2semzJsT1cv5Bsj4pladZvVzO2SVhQqcY2klbbfUW+Ta6ntxwvV+lpE7G/+PSBpq3qbfyXsk7TvuDWiLeoFv7QbJe2MiA+7esF+CPq/JH3f9iXNO9kqSX+e4J46Y9vqbeMNR8QDFerNsT27uX+GpOWS9paoFRH3RcS8iJiv3v/bCxFxa4lax9g+0/asY/cl3SCpyCcoEfGBpPdsL2geWiZpT4laJ1itDlfbpd6qyYSKiC9t/1rS39Tb0/hIRLxRqp7tTZJ+JOk82/sk/T4iHi5VT72l3m2SXm+2myXpdxHxl0L1LpT0mO1p6r2RPxURVT72quR8SVt77586TdITEfFcwXp3SdrYLIRGJN1RsNaxN6/lktZ0+rrNrnwAU1g/rLoDKIygAwkQdCABgg4kQNCBBPoq6IUPZ5ywWtSj3kTX66ugS6r5x6z6H0c96k1kvX4LOoACihwwY3tKH4VzwQUXjPtnvvjiC82cOfOU6s2dO/6T+Q4ePKg5c+acUr3Dhw+P+2cOHTqkc88995TqDQ8Pj/tnIkLN0XHjdvTo0VP6uckiIr7xh5nwQ2Ano9tvv71qvXXr1lWtNzIyUrXe4sWLq9YbHR2tWq8fsOoOJEDQgQQIOpAAQQcSIOhAAgQdSICgAwkQdCCBVkGvOTIJQPfGDHpzkcE/qncJ2sslrbZ9eenGAHSnzRK96sgkAN1rE/Q0I5OAqaqzk1qaE+Vrn7MLoIU2QW81Miki1ktaL03901SByabNqvuUHpkEZDDmEr32yCQA3Wu1jd7MCSs1KwxAYRwZByRA0IEECDqQAEEHEiDoQAIEHUiAoAMJEHQggSkxqaX2JJNbbrmlar01a9ZUrTcwMFC13lVXXVW13uDgYNV6/YAlOpAAQQcSIOhAAgQdSICgAwkQdCABgg4kQNCBBAg6kABBBxJoM5LpEdsHbO+u0RCA7rVZov9J0orCfQAoaMygR8RLkg5V6AVAIWyjAwkwew1IoLOgM3sN6F+sugMJtPl4bZOkf0haYHuf7V+UbwtAl9oMWVxdoxEA5bDqDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJEHQgAUd0f1h67WPdL7300prlNDo6WrXe0NBQ1Xq1XXbZZRPdwpQSET7xMZboQAIEHUiAoAMJEHQgAYIOJEDQgQQIOpAAQQcSIOhAAgQdSKDNxSEvsr3d9h7bb9i+u0ZjALrT5rruX0r6bUTstD1L0g7b2yJiT+HeAHSkzey19yNiZ3P/U0nDkuaWbgxAd8a1jW57vqSFkl4u0g2AIlqPZLJ9lqSnJd0TEZ+c5PvMXgP6VKug256uXsg3RsQzJ3sOs9eA/tVmr7slPSxpOCIeKN8SgK612Ua/RtJtkpba3tXcflK4LwAdajN77e+SvnFpGgCTB0fGAQkQdCABgg4kQNCBBAg6kABBBxIg6EACBB1IoPVJLf1sZGSkar3as95q1xscHKxa75xzzqlar/bsvH7AEh1IgKADCRB0IAGCDiRA0IEECDqQAEEHEiDoQAIEHUiAoAMJtLkK7Om2X7H9WjN77f4ajQHoTptj3Q9LWhoRnzXXd/+77b9GxD8L9wagI22uAhuSPmu+nN7cGNAATCKtttFtT7O9S9IBSdsigtlrwCTSKugRcTQirpQ0T9IS21ec+Bzbd9oesj3UcY8AvqVx7XWPiI8lbZe04iTfWx8RiyNicUe9AehIm73uc2zPbu6fIWm5pL2F+wLQoTZ73S+U9Jjtaeq9MTwVEc+WbQtAl9rsdf+3pIUVegFQCEfGAQkQdCABgg4kQNCBBAg6kABBBxIg6EACBB1IwL2zUDt+UZvTWDtUezbZtm3bqtarbfny5VXr1Z71FhE+8TGW6EACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEiDoQAIEHUigddCbIQ6v2ubCkMAkM54l+t2Shks1AqCctiOZ5km6SdKGsu0AKKHtEv1BSfdK+qpcKwBKaTOp5WZJByJixxjPY/Ya0KfaLNGvkbTS9juSnpS01PbjJz6J2WtA/xoz6BFxX0TMi4j5klZJeiEibi3eGYDO8Dk6kECbIYtfi4gXJb1YpBMAxbBEBxIg6EACBB1IgKADCRB0IAGCDiRA0IEECDqQALPX8A21Z70NDAxUrTcyMlK13tq1a6vWY/YakBRBBxIg6EACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEmh1zbjmUs+fSjoq6Usu6QxMLuO5OOSPI+KjYp0AKIZVdyCBtkEPSc/b3mH7zpINAehe21X3ayNiv+3vSdpme29EvHT8E5o3AN4EgD7UaokeEfubfw9I2ippyUmew+w1oE+1maZ6pu1Zx+5LukHS7tKNAehOm1X38yVttX3s+U9ExHNFuwLQqTGDHhEjkn5QoRcAhfDxGpAAQQcSIOhAAgQdSICgAwkQdCABgg4kQNCBBMZzPjoa69atq1pvcHCwar3as9euv/76qvU2b95ctV4/YIkOJEDQgQQIOpAAQQcSIOhAAgQdSICgAwkQdCABgg4kQNCBBFoF3fZs21ts77U9bPvq0o0B6E7bY93/IOm5iPiZ7RmSZhbsCUDHxgy67bMlXSfp55IUEUckHSnbFoAutVl1v0TSQUmP2n7V9oZmkMP/sH2n7SHbQ513CeBbaRP00yQtkvRQRCyU9LmktSc+iZFMQP9qE/R9kvZFxMvN11vUCz6ASWLMoEfEB5Les72geWiZpD1FuwLQqbZ73e+StLHZ4z4i6Y5yLQHoWqugR8QuSWx7A5MUR8YBCRB0IAGCDiRA0IEECDqQAEEHEiDoQAIEHUiA2WunYHR0tGq9gYGBqvVqqz0Lbc2aNVXr9QOW6EACBB1IgKADCRB0IAGCDiRA0IEECDqQAEEHEiDoQAJjBt32Atu7jrt9YvueCr0B6MiYh8BGxJuSrpQk29Mk7Ze0tWxbALo03lX3ZZLejoh3SzQDoIzxBn2VpE0lGgFQTuugN9d0XynppKcaMXsN6F/jOU31Rkk7I+LDk30zItZLWi9JtqOD3gB0ZDyr7qvFajswKbUKejMmebmkZ8q2A6CEtiOZPpf03cK9ACiEI+OABAg6kABBBxIg6EACBB1IgKADCRB0IAGCDiRA0IEEHNH9+Se2D0o6lXPWz5P0Ucft9EMt6lGvVr2LI2LOiQ8WCfqpsj0UEYunWi3qUW+i67HqDiRA0IEE+i3o66doLepRb0Lr9dU2OoAy+m2JDqAAgg4kQNCBBAg6kABBBxL4L1UWlWrGdSlmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.gray()\n",
    "for i in range(5) : \n",
    "    plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "11b78e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
       "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
       "       [ 0.,  0., 10., ..., 12.,  1.,  0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(digits.data)\n",
    "digits.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "494e1b12",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1    2     3     4     5    6    7    8    9   ...   54   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  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  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  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  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  0.0   \n",
       "\n",
       "    57   58    59    60    61   62   63  \n",
       "0  0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
       "1  0.0  0.0  11.0  16.0  10.0  0.0  0.0  \n",
       "2  0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
       "3  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f61b83e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f89f2a71",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2     3     4     5    6    7    8    9  ...   55   56   57  \\\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  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  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  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  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  0.0   \n",
       "\n",
       "    58    59    60    61   62   63  target  \n",
       "0  6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
       "1  0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
       "2  0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
       "3  7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
       "4  0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f0ac456",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df.drop(['target'], axis = 'columns'), digits.target, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0dff5ccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1437"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43254927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb25ce9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(n_estimators = 50)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f609f37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694444444444444"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd846259",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "deb865fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 37,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0, 29,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 41,  0,  0,  0,  1,  1,  1],\n",
       "       [ 0,  0,  0,  0, 33,  0,  0,  0,  0,  1],\n",
       "       [ 0,  0,  0,  1,  1, 34,  1,  0,  0,  1],\n",
       "       [ 1,  0,  0,  0,  0,  0, 36,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 34,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0, 30,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0, 36]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fd8f19fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize=(10, 8))\n",
    "sn.heatmap(cm, annot = True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf9f3bc",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}