Random Forest SKSI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stasiun</th>\n",
       "      <th>Tahun</th>\n",
       "      <th>Bulan</th>\n",
       "      <th>Jumlah Ch</th>\n",
       "      <th>Keterangan Ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>315.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1980</td>\n",
       "      <td>2</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1980</td>\n",
       "      <td>3</td>\n",
       "      <td>334.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1980</td>\n",
       "      <td>4</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1980</td>\n",
       "      <td>5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Stasiun  Tahun  Bulan  Jumlah Ch  Keterangan Ch\n",
       "0  BINANGUN   1980      1      315.0              2\n",
       "1  BINANGUN   1980      2      123.0              1\n",
       "2  BINANGUN   1980      3      334.0              2\n",
       "3  BINANGUN   1980      4      246.0              1\n",
       "4  BINANGUN   1980      5       65.0              0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"data-curah-hujan.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stasiun</th>\n",
       "      <th>Tahun</th>\n",
       "      <th>Bulan</th>\n",
       "      <th>Jumlah Ch</th>\n",
       "      <th>Keterangan Ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5791</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5792</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5793</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>5794</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5795</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                 Stasiun  Tahun  Bulan  Jumlah Ch  Keterangan Ch\n",
       "5791  POS HUJAN WANAREJA   2019      8        NaN              3\n",
       "5792  POS HUJAN WANAREJA   2019      9        NaN              3\n",
       "5793  POS HUJAN WANAREJA   2019     10        NaN              3\n",
       "5794  POS HUJAN WANAREJA   2019     11        NaN              3\n",
       "5795  POS HUJAN WANAREJA   2019     12        NaN              3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5796 entries, 0 to 5795\n",
      "Data columns (total 5 columns):\n",
      "Stasiun          5796 non-null object\n",
      "Tahun            5796 non-null int64\n",
      "Bulan            5796 non-null int64\n",
      "Jumlah Ch        3786 non-null float64\n",
      "Keterangan Ch    5796 non-null int64\n",
      "dtypes: float64(1), int64(3), object(1)\n",
      "memory usage: 226.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stasiun</th>\n",
       "      <th>Tahun</th>\n",
       "      <th>Bulan</th>\n",
       "      <th>Jumlah Ch</th>\n",
       "      <th>Keterangan Ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1982</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1982</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1982</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1982</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>BINANGUN</td>\n",
       "      <td>1982</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>5791</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5792</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5793</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5794</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5795</td>\n",
       "      <td>POS HUJAN WANAREJA</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2010 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Stasiun  Tahun  Bulan  Jumlah Ch  Keterangan Ch\n",
       "26              BINANGUN   1982      3        NaN              3\n",
       "29              BINANGUN   1982      6        NaN              3\n",
       "31              BINANGUN   1982      8        NaN              3\n",
       "32              BINANGUN   1982      9        NaN              3\n",
       "33              BINANGUN   1982     10        NaN              3\n",
       "...                  ...    ...    ...        ...            ...\n",
       "5791  POS HUJAN WANAREJA   2019      8        NaN              3\n",
       "5792  POS HUJAN WANAREJA   2019      9        NaN              3\n",
       "5793  POS HUJAN WANAREJA   2019     10        NaN              3\n",
       "5794  POS HUJAN WANAREJA   2019     11        NaN              3\n",
       "5795  POS HUJAN WANAREJA   2019     12        NaN              3\n",
       "\n",
       "[2010 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for column in dataset.columns:\n",
    "    if dataset[column].dtype == type(object):\n",
    "        le = LabelEncoder()\n",
    "        dataset[column] = le.fit_transform(dataset[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       315\n",
      "1       123\n",
      "2       334\n",
      "3       246\n",
      "4        65\n",
      "       ... \n",
      "5784    202\n",
      "5785    193\n",
      "5787    104\n",
      "5788     16\n",
      "5789      0\n",
      "Name: Jumlah Ch, Length: 3786, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "dataset['Jumlah Ch'] = dataset['Jumlah Ch'].astype('int')\n",
    "print(dataset['Jumlah Ch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28e9ae27408>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sb.heatmap(dataset.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Stasiun', 'Tahun', 'Bulan', 'Jumlah Ch', 'Keterangan Ch'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([315, 123, 334, 246,  65,  18,  41, 107,  52, 347])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data = dataset[['Stasiun', 'Tahun', 'Bulan', 'Keterangan Ch']]\n",
    "X = np.asarray(feature_data)\n",
    "y = np.asarray(dataset['Jumlah Ch'])\n",
    "# X[0:10]\n",
    "y[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Stasiun     Tahun     Bulan  Jumlah Ch  Keterangan Ch\n",
      "0     0.000000  0.987580  0.000499   0.157115       0.000998\n",
      "1     0.000000  0.998075  0.001008   0.062002       0.000504\n",
      "2     0.000000  0.986067  0.001494   0.166337       0.000996\n",
      "3     0.000000  0.992368  0.002005   0.123294       0.000501\n",
      "4     0.000000  0.999458  0.002524   0.032811       0.000000\n",
      "...        ...       ...       ...        ...            ...\n",
      "5784  0.010349  0.994979  0.000493   0.099547       0.000493\n",
      "5785  0.010353  0.995408  0.000986   0.095153       0.000493\n",
      "5787  0.010387  0.998620  0.001978   0.051440       0.000495\n",
      "5788  0.010400  0.999911  0.002476   0.007924       0.000000\n",
      "5789  0.010401  0.999941  0.002972   0.000000       0.000000\n",
      "\n",
      "[3786 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "data_scaled = Normalizer().fit(dataset)\n",
    "normalized_data = data_scaled.transform(dataset)\n",
    "normalized_data = pd.DataFrame(normalized_data, \n",
    "                               index=dataset.index, \n",
    "                               columns=dataset.columns)\n",
    "print(normalized_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2650, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2650,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 82, 207, 280, ..., 481, 153,  80])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train,y_train)\n",
    "rfc_predictions = rfc.predict(X_test)\n",
    "rfc_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254.39700704225353"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_predictions.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  1.584507042253521 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy score: ', accuracy_score(\n",
    "    y_test, rfc_predictions)*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         1\n",
      "           1       0.00      0.00      0.00         4\n",
      "           2       0.25      0.14      0.18         7\n",
      "           3       0.00      0.00      0.00         4\n",
      "           4       0.00      0.00      0.00         3\n",
      "           5       0.00      0.00      0.00         8\n",
      "           6       0.12      0.10      0.11        10\n",
      "           7       0.00      0.00      0.00         8\n",
      "           8       0.00      0.00      0.00         7\n",
      "           9       0.00      0.00      0.00         2\n",
      "          10       0.00      0.00      0.00         2\n",
      "          11       0.50      0.25      0.33         4\n",
      "          12       0.00      0.00      0.00         3\n",
      "          13       0.00      0.00      0.00         4\n",
      "          14       0.00      0.00      0.00         2\n",
      "          15       0.00      0.00      0.00         3\n",
      "          16       0.00      0.00      0.00         6\n",
      "          17       0.00      0.00      0.00         3\n",
      "          18       0.00      0.00      0.00         1\n",
      "          19       0.00      0.00      0.00         5\n",
      "          20       0.00      0.00      0.00         8\n",
      "          21       0.00      0.00      0.00         2\n",
      "          22       0.00      0.00      0.00         2\n",
      "          23       0.00      0.00      0.00         2\n",
      "          24       0.00      0.00      0.00         4\n",
      "          25       0.00      0.00      0.00         2\n",
      "          26       0.00      0.00      0.00         3\n",
      "          27       0.00      0.00      0.00         3\n",
      "          28       0.20      1.00      0.33         1\n",
      "          29       0.00      0.00      0.00         2\n",
      "          30       0.00      0.00      0.00         3\n",
      "          31       0.00      0.00      0.00         1\n",
      "          32       0.00      0.00      0.00         3\n",
      "          33       0.00      0.00      0.00         3\n",
      "          34       0.00      0.00      0.00         1\n",
      "          35       0.00      0.00      0.00         1\n",
      "          36       0.00      0.00      0.00         2\n",
      "          37       0.00      0.00      0.00         2\n",
      "          38       0.00      0.00      0.00         3\n",
      "          39       0.00      0.00      0.00         3\n",
      "          40       0.00      0.00      0.00         4\n",
      "          41       0.00      0.00      0.00         4\n",
      "          42       0.00      0.00      0.00         3\n",
      "          43       0.00      0.00      0.00         0\n",
      "          44       0.00      0.00      0.00         3\n",
      "          45       0.00      0.00      0.00         1\n",
      "          46       0.00      0.00      0.00         0\n",
      "          47       0.00      0.00      0.00         2\n",
      "          48       0.00      0.00      0.00         3\n",
      "          49       0.00      0.00      0.00         2\n",
      "          50       0.00      0.00      0.00         0\n",
      "          51       0.00      0.00      0.00         2\n",
      "          52       0.00      0.00      0.00         2\n",
      "          53       0.00      0.00      0.00         3\n",
      "          54       0.00      0.00      0.00         0\n",
      "          55       0.20      0.33      0.25         3\n",
      "          56       0.00      0.00      0.00         5\n",
      "          57       0.00      0.00      0.00         2\n",
      "          58       0.00      0.00      0.00         2\n",
      "          59       0.00      0.00      0.00         1\n",
      "          60       0.00      0.00      0.00         1\n",
      "          61       0.00      0.00      0.00         0\n",
      "          62       0.00      0.00      0.00         1\n",
      "          63       0.00      0.00      0.00         3\n",
      "          64       0.00      0.00      0.00         1\n",
      "          65       0.00      0.00      0.00         1\n",
      "          66       0.00      0.00      0.00         3\n",
      "          67       0.00      0.00      0.00         2\n",
      "          68       0.00      0.00      0.00         3\n",
      "          69       0.33      0.33      0.33         3\n",
      "          70       0.00      0.00      0.00         3\n",
      "          71       0.00      0.00      0.00         2\n",
      "          72       0.00      0.00      0.00         1\n",
      "          73       0.00      0.00      0.00         1\n",
      "          74       0.00      0.00      0.00         2\n",
      "          75       0.00      0.00      0.00         5\n",
      "          76       0.00      0.00      0.00         1\n",
      "          77       0.00      0.00      0.00         0\n",
      "          78       0.00      0.00      0.00         1\n",
      "          79       0.00      0.00      0.00         2\n",
      "          80       0.00      0.00      0.00         4\n",
      "          81       0.00      0.00      0.00         1\n",
      "          82       0.00      0.00      0.00         2\n",
      "          83       0.00      0.00      0.00         3\n",
      "          84       0.00      0.00      0.00         2\n",
      "          85       0.00      0.00      0.00         5\n",
      "          86       0.00      0.00      0.00         2\n",
      "          87       0.00      0.00      0.00         2\n",
      "          88       0.00      0.00      0.00         3\n",
      "          89       0.00      0.00      0.00         2\n",
      "          90       0.00      0.00      0.00         1\n",
      "          91       0.00      0.00      0.00         5\n",
      "          92       0.00      0.00      0.00         3\n",
      "          93       0.00      0.00      0.00         1\n",
      "          94       0.00      0.00      0.00         4\n",
      "          95       0.00      0.00      0.00         1\n",
      "          96       0.00      0.00      0.00         1\n",
      "          97       0.00      0.00      0.00         1\n",
      "          98       0.00      0.00      0.00         5\n",
      "          99       0.00      0.00      0.00         1\n",
      "         100       0.00      0.00      0.00         1\n",
      "         101       0.00      0.00      0.00         1\n",
      "         102       0.00      0.00      0.00         1\n",
      "         103       0.00      0.00      0.00         3\n",
      "         104       0.00      0.00      0.00         1\n",
      "         105       0.00      0.00      0.00         2\n",
      "         106       0.00      0.00      0.00         1\n",
      "         107       0.00      0.00      0.00         2\n",
      "         108       0.00      0.00      0.00         1\n",
      "         109       0.00      0.00      0.00         2\n",
      "         110       0.00      0.00      0.00         5\n",
      "         111       0.00      0.00      0.00         3\n",
      "         112       0.00      0.00      0.00         0\n",
      "         113       0.00      0.00      0.00         2\n",
      "         115       0.33      0.25      0.29         4\n",
      "         116       0.00      0.00      0.00         2\n",
      "         117       0.00      0.00      0.00         4\n",
      "         118       0.00      0.00      0.00         2\n",
      "         120       0.00      0.00      0.00         1\n",
      "         121       0.00      0.00      0.00         5\n",
      "         122       0.00      0.00      0.00         2\n",
      "         123       0.00      0.00      0.00         1\n",
      "         124       0.00      0.00      0.00         2\n",
      "         125       0.00      0.00      0.00         4\n",
      "         126       0.00      0.00      0.00         3\n",
      "         127       0.00      0.00      0.00         1\n",
      "         128       0.00      0.00      0.00         0\n",
      "         129       0.00      0.00      0.00         2\n",
      "         130       0.33      0.33      0.33         3\n",
      "         131       0.00      0.00      0.00         2\n",
      "         132       0.00      0.00      0.00         2\n",
      "         133       0.00      0.00      0.00         1\n",
      "         134       0.00      0.00      0.00         4\n",
      "         135       0.00      0.00      0.00         3\n",
      "         136       0.00      0.00      0.00         1\n",
      "         137       0.25      0.20      0.22         5\n",
      "         138       0.00      0.00      0.00         6\n",
      "         139       0.33      0.25      0.29         4\n",
      "         140       0.00      0.00      0.00         2\n",
      "         141       0.00      0.00      0.00         5\n",
      "         142       0.00      0.00      0.00         1\n",
      "         143       0.00      0.00      0.00         3\n",
      "         144       0.00      0.00      0.00         3\n",
      "         146       0.00      0.00      0.00         2\n",
      "         147       0.33      0.33      0.33         3\n",
      "         148       0.00      0.00      0.00         2\n",
      "         149       0.00      0.00      0.00         3\n",
      "         150       0.00      0.00      0.00         2\n",
      "         151       0.00      0.00      0.00         3\n",
      "         152       0.00      0.00      0.00         2\n",
      "         153       0.00      0.00      0.00         0\n",
      "         154       0.00      0.00      0.00         4\n",
      "         155       0.00      0.00      0.00         2\n",
      "         156       0.00      0.00      0.00         4\n",
      "         157       0.00      0.00      0.00         2\n",
      "         158       0.00      0.00      0.00         1\n",
      "         159       0.00      0.00      0.00         3\n",
      "         160       0.00      0.00      0.00         1\n",
      "         161       0.00      0.00      0.00         1\n",
      "         162       0.00      0.00      0.00         0\n",
      "         163       0.00      0.00      0.00         3\n",
      "         164       0.00      0.00      0.00         5\n",
      "         165       0.00      0.00      0.00         3\n",
      "         166       0.00      0.00      0.00         3\n",
      "         167       0.00      0.00      0.00         2\n",
      "         168       0.00      0.00      0.00         2\n",
      "         169       0.00      0.00      0.00         1\n",
      "         170       0.00      0.00      0.00         2\n",
      "         171       0.00      0.00      0.00         1\n",
      "         172       0.00      0.00      0.00         1\n",
      "         173       0.00      0.00      0.00         5\n",
      "         174       0.00      0.00      0.00         0\n",
      "         175       0.00      0.00      0.00         2\n",
      "         176       0.00      0.00      0.00         1\n",
      "         177       0.00      0.00      0.00         0\n",
      "         178       0.00      0.00      0.00         2\n",
      "         179       0.00      0.00      0.00         4\n",
      "         180       0.00      0.00      0.00         5\n",
      "         181       0.00      0.00      0.00         2\n",
      "         182       0.00      0.00      0.00         5\n",
      "         183       0.00      0.00      0.00         1\n",
      "         184       0.00      0.00      0.00         2\n",
      "         185       0.00      0.00      0.00         6\n",
      "         186       0.00      0.00      0.00         4\n",
      "         187       0.00      0.00      0.00         1\n",
      "         188       0.00      0.00      0.00         5\n",
      "         189       0.00      0.00      0.00         1\n",
      "         190       0.00      0.00      0.00         2\n",
      "         191       0.00      0.00      0.00         4\n",
      "         192       0.00      0.00      0.00         2\n",
      "         193       0.00      0.00      0.00         5\n",
      "         194       0.00      0.00      0.00         1\n",
      "         195       0.00      0.00      0.00         4\n",
      "         196       0.00      0.00      0.00         4\n",
      "         197       0.00      0.00      0.00         1\n",
      "         198       0.00      0.00      0.00         9\n",
      "         199       0.00      0.00      0.00         4\n",
      "         200       0.00      0.00      0.00         0\n",
      "         201       0.00      0.00      0.00         0\n",
      "         202       0.00      0.00      0.00         7\n",
      "         203       0.00      0.00      0.00         2\n",
      "         204       0.00      0.00      0.00         0\n",
      "         205       0.00      0.00      0.00         3\n",
      "         206       0.00      0.00      0.00         4\n",
      "         207       0.00      0.00      0.00         1\n",
      "         208       0.00      0.00      0.00         5\n",
      "         209       0.00      0.00      0.00         3\n",
      "         210       0.00      0.00      0.00         2\n",
      "         211       0.00      0.00      0.00         3\n",
      "         212       0.00      0.00      0.00         2\n",
      "         213       0.00      0.00      0.00         0\n",
      "         214       0.00      0.00      0.00         1\n",
      "         215       0.00      0.00      0.00         3\n",
      "         216       0.00      0.00      0.00         0\n",
      "         217       0.00      0.00      0.00         1\n",
      "         218       0.00      0.00      0.00         2\n",
      "         219       0.00      0.00      0.00         2\n",
      "         220       0.00      0.00      0.00         4\n",
      "         221       0.00      0.00      0.00         4\n",
      "         222       0.00      0.00      0.00         4\n",
      "         223       0.00      0.00      0.00         2\n",
      "         224       0.00      0.00      0.00         1\n",
      "         225       0.00      0.00      0.00         2\n",
      "         226       0.00      0.00      0.00         2\n",
      "         227       0.00      0.00      0.00         4\n",
      "         228       0.00      0.00      0.00         3\n",
      "         229       0.00      0.00      0.00         2\n",
      "         230       0.00      0.00      0.00         0\n",
      "         231       0.00      0.00      0.00         2\n",
      "         232       0.00      0.00      0.00         2\n",
      "         233       0.00      0.00      0.00         2\n",
      "         234       0.00      0.00      0.00         5\n",
      "         235       0.25      0.50      0.33         2\n",
      "         236       0.00      0.00      0.00         2\n",
      "         237       0.00      0.00      0.00         4\n",
      "         238       0.00      0.00      0.00         2\n",
      "         239       0.00      0.00      0.00         3\n",
      "         240       0.00      0.00      0.00         2\n",
      "         241       0.00      0.00      0.00         1\n",
      "         242       0.00      0.00      0.00         1\n",
      "         243       0.00      0.00      0.00         4\n",
      "         244       0.00      0.00      0.00         5\n",
      "         245       0.00      0.00      0.00         0\n",
      "         246       0.00      0.00      0.00         7\n",
      "         247       0.00      0.00      0.00         1\n",
      "         248       0.00      0.00      0.00         1\n",
      "         249       0.00      0.00      0.00         3\n",
      "         250       0.00      0.00      0.00         1\n",
      "         251       0.00      0.00      0.00         1\n",
      "         253       0.00      0.00      0.00         2\n",
      "         254       0.00      0.00      0.00         1\n",
      "         255       0.00      0.00      0.00         3\n",
      "         256       0.00      0.00      0.00         4\n",
      "         257       0.00      0.00      0.00         1\n",
      "         258       0.00      0.00      0.00         2\n",
      "         259       0.00      0.00      0.00         5\n",
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      "        1043       0.00      0.00      0.00         0\n",
      "        1081       0.00      0.00      0.00         0\n",
      "        1121       0.00      0.00      0.00         1\n",
      "        1140       0.00      0.00      0.00         1\n",
      "        1164       0.00      0.00      0.00         0\n",
      "        1280       0.00      0.00      0.00         1\n",
      "        1388       0.00      0.00      0.00         0\n",
      "        1439       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.02      1136\n",
      "   macro avg       0.01      0.01      0.01      1136\n",
      "weighted avg       0.02      0.02      0.02      1136\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, rfc_predictions))"
   ]
  }
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