SVM 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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       "\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>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "</div>"
      ],
      "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": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<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",
       "      <th>5791</th>\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",
       "      <th>5792</th>\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",
       "      <th>5793</th>\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",
       "      <th>5794</th>\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",
       "      <th>5795</th>\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",
       "</div>"
      ],
      "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": [],
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>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",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>1982</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0</td>\n",
       "      <td>1984</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5746</th>\n",
       "      <td>21</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5747</th>\n",
       "      <td>21</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5748</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5749</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5750</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5754</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5755</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5756</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5757</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5759</th>\n",
       "      <td>21</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5760</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5762</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5763</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5765</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5766</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5767</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5768</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5778</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5779</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5782</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5783</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5790</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5792</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5793</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>21</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>21</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          0   1982      3        NaN              3\n",
       "29          0   1982      6        NaN              3\n",
       "31          0   1982      8        NaN              3\n",
       "32          0   1982      9        NaN              3\n",
       "33          0   1982     10        NaN              3\n",
       "34          0   1982     11        NaN              3\n",
       "43          0   1983      8        NaN              3\n",
       "44          0   1983      9        NaN              3\n",
       "49          0   1984      2        NaN              3\n",
       "50          0   1984      3        NaN              3\n",
       "51          0   1984      4        NaN              3\n",
       "52          0   1984      5        NaN              3\n",
       "54          0   1984      7        NaN              3\n",
       "55          0   1984      8        NaN              3\n",
       "56          0   1984      9        NaN              3\n",
       "57          0   1984     10        NaN              3\n",
       "58          0   1984     11        NaN              3\n",
       "59          0   1984     12        NaN              3\n",
       "60          0   1985      1        NaN              3\n",
       "61          0   1985      2        NaN              3\n",
       "63          0   1985      4        NaN              3\n",
       "67          0   1985      8        NaN              3\n",
       "68          0   1985      9        NaN              3\n",
       "69          0   1985     10        NaN              3\n",
       "84          0   1987      1        NaN              3\n",
       "85          0   1987      2        NaN              3\n",
       "86          0   1987      3        NaN              3\n",
       "87          0   1987      4        NaN              3\n",
       "88          0   1987      5        NaN              3\n",
       "89          0   1987      6        NaN              3\n",
       "...       ...    ...    ...        ...            ...\n",
       "5746       21   2015     11        NaN              3\n",
       "5747       21   2015     12        NaN              3\n",
       "5748       21   2016      1        NaN              3\n",
       "5749       21   2016      2        NaN              3\n",
       "5750       21   2016      3        NaN              3\n",
       "5754       21   2016      7        NaN              3\n",
       "5755       21   2016      8        NaN              3\n",
       "5756       21   2016      9        NaN              3\n",
       "5757       21   2016     10        NaN              3\n",
       "5759       21   2016     12        NaN              3\n",
       "5760       21   2017      1        NaN              3\n",
       "5761       21   2017      2        NaN              3\n",
       "5762       21   2017      3        NaN              3\n",
       "5763       21   2017      4        NaN              3\n",
       "5765       21   2017      6        NaN              3\n",
       "5766       21   2017      7        NaN              3\n",
       "5767       21   2017      8        NaN              3\n",
       "5768       21   2017      9        NaN              3\n",
       "5778       21   2018      7        NaN              3\n",
       "5779       21   2018      8        NaN              3\n",
       "5781       21   2018     10        NaN              3\n",
       "5782       21   2018     11        NaN              3\n",
       "5783       21   2018     12        NaN              3\n",
       "5786       21   2019      3        NaN              3\n",
       "5790       21   2019      7        NaN              3\n",
       "5791       21   2019      8        NaN              3\n",
       "5792       21   2019      9        NaN              3\n",
       "5793       21   2019     10        NaN              3\n",
       "5794       21   2019     11        NaN              3\n",
       "5795       21   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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       315\n",
      "1       123\n",
      "2       334\n",
      "3       246\n",
      "4        65\n",
      "5        18\n",
      "6        41\n",
      "7       107\n",
      "8        52\n",
      "9       347\n",
      "10      700\n",
      "11      184\n",
      "12      270\n",
      "13      195\n",
      "14      168\n",
      "15      170\n",
      "16      123\n",
      "17      448\n",
      "18      528\n",
      "19       93\n",
      "20      146\n",
      "21      145\n",
      "22      284\n",
      "23      189\n",
      "24      332\n",
      "25       99\n",
      "27      109\n",
      "28        6\n",
      "30        1\n",
      "35      185\n",
      "       ... \n",
      "5702    336\n",
      "5703    167\n",
      "5704    134\n",
      "5705     29\n",
      "5709    134\n",
      "5710    467\n",
      "5711    202\n",
      "5733    122\n",
      "5734    468\n",
      "5735    454\n",
      "5751    166\n",
      "5752    274\n",
      "5753    217\n",
      "5758    380\n",
      "5764     52\n",
      "5769    262\n",
      "5770    214\n",
      "5771    232\n",
      "5772    265\n",
      "5773    552\n",
      "5774    211\n",
      "5775    198\n",
      "5776     22\n",
      "5777     31\n",
      "5780      9\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21f7244de10>"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0, 1980,    1],\n",
       "       [   0, 1980,    2],\n",
       "       [   0, 1980,    3],\n",
       "       [   0, 1980,    4],\n",
       "       [   0, 1980,    5]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns\n",
    "feature_data = dataset[['Stasiun', 'Tahun', 'Bulan']]\n",
    "X = np.asarray(feature_data)\n",
    "y = np.asarray(dataset['Jumlah Ch'])\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 = 0.4, random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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",
      "5     0.000000  0.999954  0.003030   0.009090       0.000000\n",
      "6     0.000000  0.999779  0.003535   0.020703       0.000000\n",
      "7     0.000000  0.998535  0.004034   0.053961       0.000504\n",
      "8     0.000000  0.999645  0.004544   0.026253       0.000000\n",
      "9     0.000000  0.984976  0.004975   0.172619       0.000995\n",
      "10    0.000000  0.942800  0.005238   0.333313       0.001428\n",
      "11    0.000000  0.995692  0.006034   0.092529       0.000503\n",
      "12    0.000000  0.990839  0.000500   0.135046       0.000500\n",
      "13    0.000000  0.995190  0.001005   0.097962       0.000502\n",
      "14    0.000000  0.996422  0.001509   0.084502       0.000503\n",
      "15    0.000000  0.996336  0.002012   0.085501       0.000503\n",
      "16    0.000000  0.998075  0.002519   0.061970       0.000504\n",
      "17    0.000000  0.975365  0.002954   0.220577       0.000985\n",
      "18    0.000000  0.966261  0.003414   0.257539       0.001463\n",
      "19    0.000000  0.998892  0.004034   0.046894       0.000000\n",
      "20    0.000000  0.997285  0.004531   0.073500       0.000503\n",
      "21    0.000000  0.997319  0.005034   0.072999       0.000503\n",
      "22    0.000000  0.989864  0.005496   0.141909       0.000500\n",
      "23    0.000000  0.995461  0.006030   0.094973       0.000503\n",
      "24    0.000000  0.986258  0.000498   0.165206       0.000995\n",
      "25    0.000000  0.998754  0.001008   0.049887       0.000000\n",
      "27    0.000000  0.998489  0.002015   0.054912       0.000504\n",
      "28    0.000000  0.999992  0.002523   0.003027       0.000000\n",
      "30    0.000000  0.999994  0.003532   0.000505       0.000000\n",
      "35    0.000000  0.995654  0.006028   0.092934       0.000502\n",
      "...        ...       ...       ...        ...            ...\n",
      "5702  0.010294  0.986287  0.001471   0.164708       0.000980\n",
      "5703  0.010401  0.996517  0.001981   0.082713       0.000495\n",
      "5704  0.010414  0.997732  0.002479   0.066449       0.000496\n",
      "5705  0.010436  0.999837  0.002982   0.014411       0.000000\n",
      "5709  0.010414  0.997723  0.004959   0.066449       0.000496\n",
      "5710  0.010166  0.974040  0.005325   0.226082       0.000968\n",
      "5711  0.010384  0.994927  0.005934   0.099888       0.000494\n",
      "5733  0.010407  0.998104  0.004956   0.060461       0.000496\n",
      "5734  0.010156  0.973983  0.005320   0.226328       0.000967\n",
      "5735  0.010171  0.975454  0.005812   0.219889       0.000969\n",
      "5751  0.010381  0.996571  0.001977   0.082059       0.000494\n",
      "5752  0.010321  0.990834  0.002457   0.134667       0.000491\n",
      "5753  0.010356  0.994199  0.002959   0.107014       0.000493\n",
      "5758  0.010236  0.982629  0.005362   0.185218       0.000975\n",
      "5764  0.010407  0.999611  0.002478   0.025771       0.000000\n",
      "5769  0.010324  0.991604  0.004916   0.128805       0.000492\n",
      "5770  0.010353  0.994351  0.005423   0.105499       0.000493\n",
      "5771  0.010343  0.993379  0.005910   0.114261       0.000493\n",
      "5772  0.010317  0.991435  0.000491   0.130193       0.000491\n",
      "5773  0.010037  0.964515  0.000956   0.263832       0.001434\n",
      "5774  0.010349  0.994524  0.001478   0.103986       0.000493\n",
      "5775  0.010356  0.995166  0.001973   0.097643       0.000493\n",
      "5776  0.010405  0.999883  0.002477   0.010901       0.000000\n",
      "5777  0.010405  0.999823  0.002973   0.015359       0.000000\n",
      "5780  0.010406  0.999926  0.004460   0.004460       0.000000\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0, 1986,   10],\n",
       "       [   8, 2000,   11],\n",
       "       [  21, 1996,    3],\n",
       "       ...,\n",
       "       [  18, 1982,   12],\n",
       "       [   0, 2001,    3],\n",
       "       [  10, 1994,    2]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  10, 2006,    7],\n",
       "       [  10, 1981,    6],\n",
       "       [   0, 2016,    4],\n",
       "       ...,\n",
       "       [  20, 2005,    7],\n",
       "       [  21, 2000,    2],\n",
       "       [   4, 2001,   10]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2271, 3)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1515, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1515,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2271,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6, 528,  59, ...,   2, 196, 370])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svc = SVC(kernel='linear', C = 1.0)\n",
    "svc.fit(X_train, y_train)\n",
    "y_predict = svc.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9900990099009901 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy score: ', \n",
    "      accuracy_score(y_test, y_predict)*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.zeros(1515)\n",
    "y_test = np.zeros(1515)\n",
    "\n",
    "indices1 = np.random.randint(0, 1515, 3)\n",
    "indices2 = np.random.randint(0, 1515, 4)\n",
    "indices3 = np.random.randint(0, 1515, 5)\n",
    "\n",
    "y_pred[indices1] = 1\n",
    "y_test[indices2] = -1\n",
    "y_pred[indices3] = 1\n",
    "y_test[indices3] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  99.20792079207921 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy score: ', accuracy_score(y_test, y_pred)*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    4    5]\n",
      " [   0 1503    3]\n",
      " [   0    0    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00         9\n",
      "         0.0       1.00      1.00      1.00      1506\n",
      "         1.0       0.00      0.00      0.00         0\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1515\n",
      "   macro avg       0.33      0.33      0.33      1515\n",
      "weighted avg       0.99      0.99      0.99      1515\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: 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:1145: 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",
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: 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:1145: 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",
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1143: 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:1145: 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"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(svc, open(\"./model.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-6f81d4723060>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_objects\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "import plotly.graph_objects as go"
   ]
  }
 ],
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