#013 Latihan SKLearn SVR.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Latihan SKLearn SVR.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-Sm36TgsD9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2eb125da-f3dd-477a-8d89-edbc5e4febda"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "data = pd.read_csv('Salary_Data.csv')\n",
        "data.info() #Mengcek Nilai yang Hilang pada DataSet & Serta Atribut yang bukan Numerik"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   YearsExperience  30 non-null     float64\n",
            " 1   Salary           30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 608.0 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CeKxHyPlVmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "1eb5a641-abc2-4e06-d23e-95f80ab69187"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>YearsExperience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.1</td>\n",
              "      <td>39343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.3</td>\n",
              "      <td>46205.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.5</td>\n",
              "      <td>37731.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.0</td>\n",
              "      <td>43525.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.2</td>\n",
              "      <td>39891.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   YearsExperience   Salary\n",
              "0              1.1  39343.0\n",
              "1              1.3  46205.0\n",
              "2              1.5  37731.0\n",
              "3              2.0  43525.0\n",
              "4              2.2  39891.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Jav_R__lY26",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "396aba0c-4111-41ef-912f-bd9445c3cdd6"
      },
      "source": [
        "#Pisahkan antara atribut dan label yang ingin diprediksi\n",
        "#Ketika hanya terdapat satu atribut pada dataframe, maka atribut tersebut perlu diubah bentuknya agar bisa diterima oleh model dari library SKLearn\n",
        "#Untuk mengubah bentuk atribut kita membutuhkan library numpy\n",
        "import numpy as np\n",
        "x = data['YearsExperience']\n",
        "y = data['Salary']\n",
        "x = x[:,np.newaxis]\n",
        "#Buat objek support vector regression, menggunakan parameter C = 1000, gamma = 0.05, dan kernel ‘rbf’\n",
        "from sklearn.svm import SVR\n",
        "model  = SVR(C=1000, gamma=0.05, kernel='rbf')\n",
        "model.fit(x,y)  #Latih model menggunakan fungis fit()\n",
        "#Memvisualisasikan model SVR menyesuaikan terhadap pola yang terdapat pada data \n",
        "#Menggunakan library matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, model.predict(X))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2e9beccef0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j14E2dh/soCMMNZ1dWsQ7Fldy9weu4p1LqrlhcRXV5SUR11gyjUJCJIcMhcLO/W3s3N/GrgPtw6OMls8t54NXz+edS6tZs6SKlVdUaHSRTEghIZLFEgnn9eFQaOfFA22cGREKH3v7QtZeWcvaK2uZP2dWxLWVbKSQEMkiiYTzxvEz7Nzfzs79bbx4oJ3TPcnmo2W1ZXzk+vOhsKBSoSBTp5AQyWCJhLPn+NkLmo+GQmFpbRkbrl3A2qtqWHtlLQsrdQlUST+FhEgGSSScvScuDIWhNY2W1JSx/tr5w2cKui62zASFhEiE3J3fnOjihTdPsXN/O7sOtA2PPlpcE+O2q0MoXFVLnUJBIqCQEJlhJ8708rN9p/j5vlZ+3tzGqa7kxLX66hi3DoXClTXUV5dFXFMRhYTItOvuH2TX/nZ+tu8UP9vXyr6TXUByJvN7r5rLzSvm8p6rallco1CQzKOQEEmzeMJ5teU0P28+xU9/08pLhzoYiDulRQW8e3kNv91Qz80r5rF6QQUFmqcgGU4hIZIGh9u7+Xlz8kzh/73ZNtzZfO2iOXz+5uW8f+U8blxarfWOJOsoJEQm4UzvAC+82cbPQxPSW23dACysnMVtV8/n5pVzWbdiLnNna90jyW4KCckr25pauH/7Xo529rCoKsY961exaU3dhM8biCf49eHO4X6FXx85TTzhlJcUsvbKWja/dxnvWzmPq+aVa6lsySkKCckb25pauPepV+gZiAPQ0tnDvU+9AnBRULg7b7aeGx6BtHN/G119gxQYvL2+irs/cBU3r5jLmiXVlBQVzPhnEZkpCgnJG/dv3zscEEN6BuLcv30vm9bUcfJsLy+82cbP9p3iF82nOHa6F0jObP74DYt434q5vPequVSW6Sprkj8UEpI3jnb2jFne0tnD+m//lL0nzgJQVVbMuquSfQrvWzlXQ1MlrykkJG8srJzF0XB2MNoVc0rZtKaOdStquXZRpZbQFgkUEpKzegfiNB3q5IWwDtKJEZfkHFJSWMB/+a3r+O2GxWxrauHf/cNLl92pLZLLFBKSM/oG4/zqUCc797fzwv5TvHSok/7BBAUG19VVcufNy8Hh/7x8lOOney8Igsvp1BbJJwoJyVq9A3F+dbiTXx5o54X9yUtz9g0mMINrFs7hM2uX8p6ramlYVkNl7Hxn8x9/9OqLXmuiTm2RfKWQkKzReraP3QfbaXyrg8aDHbx29DQDcQfg6oVz+NRNS1l7ZQ03La+97BFIqTq1U5WL5AuFhFxkshPO0imRcJpbu0IgtLP7YAcHw6zmkqICbqiv4vffdyUNS6u5cWk1VWUlU3q/RVUxWsYIBF2zQfKdQkIuEFXbfFffIC8f6aTpUCe7D3aw+2DH8BXYastLuHFpNZ+6aQk3Lq3huro5lBaldw2ke9avuuBzA8SKC7ln/aq0vo9ItlFIyAWmu21+W1ML/+3ZPRw93UtVrJhVCyro7B5g38mzJJItR1w1r5wN1y7gxmXVvGtZDctqy6Z9qYuhzxb1GZRIplFIyAXS3TbfOxDnjWNnePXoGX748lF27W8nZAGdPQPsOtDO6gUVfOmWldywpIob6quoLp9a09FkbVpTp1AQGUUhIReYbNv8QDzBwbZz7DvRxb6Tydve42d4s/Uc8XCKUGAMB8RIZ3sH+eptb0tH9UUkzRQScoGJ2ub7BxMcOHWOfSfPsu9EF80nu9h38iwHTp0bHmkEyeszr7yigg3XLuDaukquq6tk3dYdY76nRhCJZC6FhFxgqLnlW8/u4djpXqrLinnXshqeffU4/33HPt5q6x4+MzCDpTVlrLiigluvns/KK2bztvkVXDmvnLKSi/9p1WkEkUjWUUjkqf7BBCfO9HKko4fDHd0cbk/eDrV3c7ijh9awhEVH9wDP7znJ0toy3nZFBR+5fiErrpjNyiuSYXA5V1pL9wiiTBiqK5LrFBI5oqc/Tnt3Px3n+mk/10/H0Hb3QPjbT2d3P+3nBmjr6qO1qw8f0UFQYMlf9Iury7hl1RUsqS1jaW0ZK6+oYNncsrQMOU3nCCItoyEyM8x9rK7E7NXQ0OCNjY2X/bwXD7Rz4FQXsZIiyksKiZUUUl5SRHlpIbGSImYVFVBaXEhJYQHFhZb2IZnuTn88Qd9ggr6BBL0DcU73DNDZPXDxwX/owB/ut5/rp28wMebrmkFVrJjq8hKqy5K3mvJiFlbGqKuKsbBqFktqylhUFaO4MHsunrNu644xm67qqmL8YsstEdRIJLuZ2W53bxhdPqUzCTOrAh4GriM5cOXzwF7g+8Ay4C3gd9y9w5JH1QeAjwDdwGfd/aXwOpuB/xRe9pvu/lgovxH4DhADfgR8xacp1Z7+VQvf3XXokvY1g9KighAY4x9YJ6psPOH0DcbpG0xwKZ+sqqw4HOyLWVQ1i2sXzaGmvISqcPBPhkDJcChUxopTLnu9ramFLf/0SlY212gZDZGZMdXmpgeAZ939E2ZWApQBfww87+5bzWwLsAX4OvBhYGW43QQ8CNxkZjXAN4AGksfU3Wb2jLt3hH3+ANhFMiQ2AD+eYp3HdO9Hrubuf7OCnv5BzvXF6e6P090/yLn+OD39g8O/8IcO6P2DyV/9A/HkgnLjMVLvUGBQWlw4fKZSOuJvZax4+Jf/0AG/KE2/9rO9uUbLaIjMjEmHhJlVAu8HPgvg7v1Av5ltBD4QdnsM+FeSIbEReDycCew0syozWxj2fc7d28PrPgdsMLN/Bea4+85Q/jiwiWkKidmlRcwuzZ8ummxf9VTLaIjMjKn8LF0OtAJ/Z2ZNZvawmZUD8939WNjnODA/bNcBh0c8/0goG6/8yBjlFzGzu8ys0cwaW1tbp/CR8ke2N9dsWlPHfbdfT11VDCPZF3Hf7ddnRcCJZJOp/HQuAt4JfMndd5nZAySbloa5u5vZtPeMu/tDwEOQ7Lie7vfLBbnQXKNlNESm31TOJI4AR9x9V7j/JMnQOBGakQh/T4bHW4DFI55fH8rGK68fo1zS4J71q4iNmuOg5hoRGW3SIeHux4HDZjZ0VLkVeB14BtgcyjYDT4ftZ4DPWNJa4HRoltoOfMjMqs2sGvgQsD08dsbM1oaRUZ8Z8VoyRdPZXLOtqYV1W3ewfMsPWbd1B9ualO0i2WqqPbVfAr4bRjbtBz5HMnh+YGZ3AgeB3wn7/ojk8NdmkkNgPwfg7u1m9mfAL8N+fzrUiQ3czfkhsD9mmjqt89V0NNdczqipkTOmK2PFmEFn90DWDccVyWWaTJdHZmIZi0ud5DY6TEaLFReqI1pkBqWaTJc9U2xlSoYOyi2dPTjnf+GnuynoUkdNjTUEd6Sh4bgiEi2FRJ4Yb15EOqUaHTW6/FKG2mbLcFyRXKaQyBMzNS/iUkdNVZUVT/ha2TQcVyRXKSTyxKX+wp+qSx01NVFXmIbjimSG/FmHIs9dzjIWU+3gvpRRU6d7BlI+VqfRTSIZQyGRJy71Wg4ztfBfqhnfWupbJLMoJPLIpfzCn6mF/7RAn0h2UEjIBWaqgzudV6kTkemjkJALzOTCf1qgTyTzaXSTXEAL/4nISDqTkAuoGUhERlJIyEXUDCQiQ9TcJCIiKSkkREQkJYWEiIikpJAQEZGUFBIiIpKSQkJERFJSSIiISEoKCRERSUkhISIiKSkkREQkJYWEiIikpLWbJjDVS3mKiGQzhcQ4ZupSniIimUrNTeMY71KeIiL5QCExjpm6lKeISKZSSIwj1SU7p+NSniIimUghMQ5dylNE8p06rscxnZfy1KgpEckGCokJTMelPDVqSkSyhZqbIqBRUyKSLRQSEdCoKRHJFgqJCGjUlIhkC4VEBDRqSkSyhTquIzCdo6ZERNJJIRGR6Rg1JSKSblNubjKzQjNrMrN/DveXm9kuM2s2s++bWUkoLw33m8Pjy0a8xr2hfK+ZrR9RviGUNZvZlqnWVURELk86+iS+Arwx4v63gG+7+wqgA7gzlN8JdITyb4f9MLNrgDuAa4ENwN+E4CkE/hr4MHAN8Mmwb87b1tTCuq07WL7lh6zbuoNtTS1RV0lE8tSUQsLM6oGPAg+H+wbcAjwZdnkM2BS2N4b7hMdvDftvBJ5w9z53PwA0A+8Ot2Z33+/u/cATYd+0y6SD8tBEu5bOHpzzE+0UFCIShameSfwl8DUgEe7XAp3uPhjuHwGGGt7rgMMA4fHTYf/h8lHPSVWeVpl2UNZEOxHJJJMOCTP7GHDS3XensT6TrctdZtZoZo2tra2X9dxMOyhrop2IZJKpnEmsAz5uZm+RbAq6BXgAqDKzoVFT9cDQT/IWYDFAeLwSaBtZPuo5qcov4u4PuXuDuzfMmzfvsj5Eph2UNdFORDLJpEPC3e9193p3X0ay43mHu38K+AnwibDbZuDpsP1MuE94fIe7eyi/I4x+Wg6sBF4EfgmsDKOlSsJ7PDPZ+qYy2YPydPVjaKKdiGSS6Zhx/XXgj8ysmWSfwyOh/BGgNpT/EbAFwN1fA34AvA48C3zB3eOh3+KLwHaSo6d+EPZNq8kclKezH2PTmjruu/166qpiGFBXFeO+26/XnAoRiYQlf8znjoaGBm9sbLys51zutR3Wbd1ByxjNUXVVMX6x5ZbLrrOISNTMbLe7N4wu14xrLn/2c6b1Y4iITBct8DcJ6lwWkXyhkJgEdS6LSL5Qc9MkaBVXEckXColJ0iquIpIP1NwkIiIpKSRERCQlhYSIiKSkkBARkZQUEiIiklLOLcthZq3AwajrcRnmAqeirkTE9B3oOwB9B1F//qXuftEy2jkXEtnGzBrHWi8ln+g70HcA+g4y9fOruUlERFJSSIiISEoKieg9FHUFMoC+A30HoO8gIz+/+iRERCQlnUmIiEhKCgkREUlJIRERM1tsZj8xs9fN7DUz+0rUdYqCmRWaWZOZ/XPUdYmCmVWZ2ZNmtsfM3jCz90Rdp5lmZl8N/wdeNbPvmdmsqOs03czsUTM7aWavjiirMbPnzGxf+FsdZR2HKCSiMwj8B3e/BlgLfMHMrom4TlH4CvBG1JWI0APAs+6+GngHefZdmFkd8GWgwd2vAwqBO6Kt1Yz4DrBhVNkW4Hl3Xwk8H+5HTiEREXc/5u4vhe2zJA8OeXWBCjOrBz4KPBx1XaJgZpXA+4FHANy93907o61VJIqAmJkVAWXA0YjrM+3c/adA+6jijcBjYfsxYNOMVioFhUQGMLNlwBpgV7Q1mXF/CXwNSERdkYgsB1qBvwtNbg+bWXnUlZpJ7t4C/DlwCDgGnHb3f4m2VpGZ7+7HwvZxYH6UlRmikIiYmc0G/gn49+5+Jur6zBQz+xhw0t13R12XCBUB7wQedPc1wDkypIlhpoR2940kA3MRUG5m/zbaWkXPk3MTMmJ+gkIiQmZWTDIgvuvuT0Vdnxm2Dvi4mb0FPAHcYmb/EG2VZtwR4Ii7D51BPkkyNPLJB4ED7t7q7gPAU8B7I65TVE6Y2UKA8PdkxPUBFBKRMTMj2Rb9hrv/RdT1mWnufq+717v7MpIdlTvcPa9+Qbr7ceCwma0KRbcCr0dYpSgcAtaaWVn4P3EredZ5P8IzwOawvRl4OsK6DFNIRGcd8GmSv6B/FW4fibpSMuO+BHzXzF4GbgD+a8T1mVHhLOpJ4CXgFZLHpIxcniKdzOx7wAvAKjM7YmZ3AluB28xsH8kzrK1R1nGIluUQEZGUdCYhIiIpKSRERCQlhYSIiKSkkBARkZQUEiIikpJCQkREUlJIiIhISv8fEFARIJcpjkgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}