Python_Machine_Learning_For_Regresi.ipynb

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      "name": "Python Machine Learning For Regresi.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/Python_Machine_Learning_For_Regresi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwM0YTs_kxa4"
      },
      "source": [
        "# Implementasi Machine Learning dengan Regresi Linear \n",
        "Berikut adalah tahapan dlam membuat *machine learning* sederhana.\n",
        "\n",
        "* Memahami permasalahan yang akan diselesaikan.\n",
        "* Memilih metode yang akan digunakan.\n",
        "* Tahapan mempersiapkan data.\n",
        "* Tahapan implementasi."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHc2m1Qyk_v7"
      },
      "source": [
        "# Contoh Studi Kasus dan Dataset\n",
        "Pada bagian ini akan dibahas studi kasus sederhana untuk membuat machine learning dengan menerapkan metode *Simple Regresi Linear.* Sebuah penelitian dilakukan untuk mengetahui pengaruh lama bekerja terhadap pendapatan gaji sebuah perusahaan.\n",
        "\n",
        "Simple regresi linear akan digunakan untuk memprediksi nilai gaji yang akan diperoleh berdasarkan lama bekerja. Disamping itu hasil machine learning juga akan bisa melihat korelasi antara lama bekerja dengan nilai pendapatan yang diperoleh. Output lain yang bisa diperoleh adalah grafik korelasinya antara kedua variabel tersebut."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOpV8ytZlCGG"
      },
      "source": [
        "# Memahami Algoritma Simple Regresi Linear\n",
        "*Simple Regresi Linear* adalah salah satu metode yang biasa digunakan untuk menguji sejauh mana hubungan sebab akibat antara dua buah variabel. Kedua variabel tersebut sering disebut sebagai variabel faktor penyebab (X) terhadap variabel akibat (Y). Variabel X seringkali disebut sebagai *Predictor,* sedangkan variabel Y seringkali disebut sebagai *Response.*\n",
        "\n",
        "*Simple Regresi Linear* seringkali dipergunakan untuk melakukan prediksi atau peramalan baik tentang karakteristik baik yang bersifat kualitas maupun kuantitas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQ8Qr1VAlF9o"
      },
      "source": [
        "Model persamaan *Simple Regresi Linear*\n",
        "\n",
        "**Y = a + bX**\n",
        "\n",
        "dimana:\n",
        "* Y = Variabel Akibat *(Response)* yang juga disebut sebagai *dependent variable.*\n",
        "* X = Variabel Faktor Penyebab atau *predictor* yang juga merupakan *independent variable.*\n",
        "* a = konstanta.\n",
        "* b = koefisien regresi, yaitu besaran *Response* yang ditimbulkan oleh *Predictor.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "higrTZ0ilLRC"
      },
      "source": [
        "Penjelasan dari grafik diatas:\n",
        "* Jumlah pembelian adalah Y, atau variabel akibat yang juga disebut *dependent variable.*\n",
        "* Biaya Promosi adalah X, atau predictor, variabel faktor penyebab, yang juga disebut sebagai variabel *independent.*\n",
        "* Garis lurus (warna merah) disebut sebagai *predicted value* dari Y untuk X.\n",
        "* Titik-titik yang berada di seputar garis lurus tersebut adalah *observed* value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mAcm4ooWlVWU"
      },
      "source": [
        "# Implementasi Program dengan Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uz9_pzxZlYjX"
      },
      "source": [
        "## Mempersiapkan Library\n",
        "Berikut Library yang akan digunakan:\n",
        "* NumPy, yaitu library yang akan digunakan untuk kebutuhan *scientific* dan matematika.\n",
        "* Pandas, yaitu library yang digunakan untuk manipulasi data seperti membuat tabel, mengubah dimensi data, mengecek data dan sebagainya.\n",
        "* Matplotlib, yaitu library yang digunakan untuk membuat grafik plot sesuai kebutuhan.\n",
        "* Sklearn, adala library untuk berbagai metode dan algoritma yang digunakan dalam *machine learning.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aw4psq35l0cO"
      },
      "source": [
        "1. Import Library yang dibutuhkan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsOEolSncfYU"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0thPEqFkmZZl"
      },
      "source": [
        "2. Memanggil Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjnRzEbJmfX1",
        "outputId": "9ddd68a9-8d80-4c15-80bf-547f259fe585"
      },
      "source": [
        "!wget -O datagaji1.csv https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/datagaji1.csv\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-03 18:47:54--  https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/datagaji1.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 456 [text/plain]\n",
            "Saving to: ‘datagaji1.csv’\n",
            "\n",
            "datagaji1.csv       100%[===================>]     456  --.-KB/s    in 0s      \n",
            "\n",
            "2021-10-03 18:47:54 (18.9 MB/s) - ‘datagaji1.csv’ saved [456/456]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3XKkQAVmgi3"
      },
      "source": [
        "## Melihat isi dataset.\n",
        "Pandas bisa digunakan untuk menampilkan sebagian isi dari dataset dengan menggunakan perintah sebagai berikut."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HhhQG6OikGlB",
        "outputId": "e5028423-9b5c-4d2e-d5a0-99a4b00483cc"
      },
      "source": [
        "data = pd.read_csv('datagaji1.csv', sep=',')\n",
        "data.head()"
      ],
      "execution_count": 3,
      "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>Tahun</th>\n",
              "      <th>Gaji</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>66029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.3</td>\n",
              "      <td>83088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.9</td>\n",
              "      <td>81363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.0</td>\n",
              "      <td>93940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.8</td>\n",
              "      <td>91738</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Tahun   Gaji\n",
              "0    5.1  66029\n",
              "1    5.3  83088\n",
              "2    5.9  81363\n",
              "3    6.0  93940\n",
              "4    6.8  91738"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8_RufdX6sXgn"
      },
      "source": [
        "Kode tersebut jika dieksekusi akan menghasilkan 3 variabel baru yang dapat diproses yaitu:\n",
        "* Variabel dataset: berisi keseluruhan data,\n",
        "* Variabel X: berisi data sebagai independent variable.\n",
        "* Variabel y: berisi data sebagai dependent variable.\n",
        "\n",
        "Jika ingin melihat variabel-variabel dalam dataset, bisa menggunakan perintah sebagai berikut:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNBdK61rskiU"
      },
      "source": [
        "Atau jika ingin melihat *shape* dari dataset bisa dilakukan perintah sebagai berikut."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_i158vgjmsPT",
        "outputId": "f2a04976-d1c8-4a03-e93d-0da6e500f396"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmW4B5IFiBTS",
        "outputId": "588ddb7a-5bbe-46ca-e28b-b759dc0305e4"
      },
      "source": [
        "# Memilih variable X\n",
        "X = data.iloc[:,:-1].values\n",
        "X"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5.1],\n",
              "       [ 5.3],\n",
              "       [ 5.9],\n",
              "       [ 6. ],\n",
              "       [ 6.8],\n",
              "       [ 7.1],\n",
              "       [ 4.1],\n",
              "       [ 4.5],\n",
              "       [ 4.9],\n",
              "       [ 3.7],\n",
              "       [ 3.9],\n",
              "       [ 4. ],\n",
              "       [ 4. ],\n",
              "       [ 1.1],\n",
              "       [ 1.3],\n",
              "       [ 1.5],\n",
              "       [ 2. ],\n",
              "       [ 2.2],\n",
              "       [ 2.9],\n",
              "       [ 3. ],\n",
              "       [ 3.2],\n",
              "       [ 3.2],\n",
              "       [ 9. ],\n",
              "       [ 9.5],\n",
              "       [ 9.6],\n",
              "       [10.3],\n",
              "       [10.5],\n",
              "       [ 7.9],\n",
              "       [ 8.2],\n",
              "       [ 8.7],\n",
              "       [ 1.5],\n",
              "       [ 1.7],\n",
              "       [ 1.9],\n",
              "       [ 2.4],\n",
              "       [ 2.6],\n",
              "       [ 3.3],\n",
              "       [10.1],\n",
              "       [10.3],\n",
              "       [ 8.1],\n",
              "       [ 8.3]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFhll-YQiHaO",
        "outputId": "8f207263-d51c-47d0-dfaf-f618e7fa8d32"
      },
      "source": [
        "# Memilih Variable Y\n",
        "y = data.iloc[:,1].values\n",
        "y"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 66029,  83088,  81363,  93940,  91738,  98273,  57081,  61111,\n",
              "        67938,  57189,  63218,  55794,  56957,  39343,  46205,  37731,\n",
              "        43525,  39891,  56642,  60150,  54445,  64445, 105582, 116969,\n",
              "       112635, 122391, 121872, 101302, 113812, 109431,  39593,  46455,\n",
              "        37981,  43775,  40141,  56892, 122000, 121000, 101000, 101800])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYVKH_mhs22D"
      },
      "source": [
        "## Split Dataset\n",
        "Pada bagian ini digunakan untuk membagi dataset manjadi dua bagian yaitu Training Set dan Testing Set. Training Set akan digunakan sebagai data latih, yaitu menggunakan 80% dari data. Sementara Testing Set digunakan untuk data uji dan dalam kasus ini menggunakan 20% dari data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fznue4qiOtZ"
      },
      "source": [
        "# Split DataSet\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 0)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jY4znJyRiSmb",
        "outputId": "6b976611-85fd-49c9-cc03-a4d7581794ce"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.9],\n",
              "       [ 1.5],\n",
              "       [ 8.2],\n",
              "       [ 4. ],\n",
              "       [ 2.9],\n",
              "       [ 8.7],\n",
              "       [ 7.9],\n",
              "       [ 3.3],\n",
              "       [10.3],\n",
              "       [ 5.9],\n",
              "       [ 8.3],\n",
              "       [ 1.5],\n",
              "       [ 2.6],\n",
              "       [ 2. ],\n",
              "       [10.1],\n",
              "       [ 4.9],\n",
              "       [ 1.1],\n",
              "       [ 7.1],\n",
              "       [ 2.2],\n",
              "       [ 1.3],\n",
              "       [ 2.4],\n",
              "       [ 4.5],\n",
              "       [ 1.9],\n",
              "       [ 5.3],\n",
              "       [10.5],\n",
              "       [ 4. ],\n",
              "       [ 1.7],\n",
              "       [ 9.6],\n",
              "       [ 4.1],\n",
              "       [ 9.5],\n",
              "       [ 3.2],\n",
              "       [ 3. ],\n",
              "       [ 3.7],\n",
              "       [ 8.1],\n",
              "       [ 6. ],\n",
              "       [ 5.1]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxfn-cLYiXYq",
        "outputId": "7b9ced4a-35b9-449a-e4ac-6fa5ae6f5dd6"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 9. ],\n",
              "       [ 3.2],\n",
              "       [10.3],\n",
              "       [ 6.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrzuFalLs_1Q"
      },
      "source": [
        "## Visualisasi Data\n",
        "Visualisasi data antara Nilai Gaji berdasarkan Lama Bekerja"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yjwozx-xiZbc",
        "outputId": "f85612e4-8dfc-4c96-ffa7-da9e700eb7f0"
      },
      "source": [
        "plt.scatter(data['Tahun'], data['Gaji'])\n",
        "plt.xlabel('Tahun')\n",
        "plt.ylabel('Gaji')\n",
        "plt.title('Grafik Nilai Gaji berdasarkan Lama Bekerja')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mNkxeDzM0Bxczs2HyepihObiYmQ2T18MMzcHFzGyYvB5maB7QNzMbJq+HGZqDi5nZCHg9TG3uFjMzs8K55WJmY5YXOpbHwcXM2sJwA4UXOpbLwcXMWt5QgaJS4Km10NHBpfEcXMys5Q21Ir5S4Bmcf4AXOjZHwwb0JV0oaZ2kW3JpO0u6TtKd6fdOKV2Szpa0StLNkvbNlVmQ8t8paUEu/ZWSVqYyZ0tSrWOYWfuqtSK+WuCZkH0kPIsXOjZHI2eLXQTMHZR2MnB9RMwErk/PAQ4DZqaf44BzIQsUwOeAVwP7AZ/LBYtzgffmys0d4hhm1qZqrYivFng2R3ihY4kaFlwi4pfAhkHJ84CL0+OLgfm59EsicwPQKWl3YA5wXURsiIhHgOuAuWnbDhFxQ0QEcMmgfVU6hpm1qVor4qsFnq7ODk4/ch+6OjtQ7rnHW5qj2WMuu0XE2vT4AWC39LgLWJ3Ltyal1UpfUyG91jHMrE0NtSJ+8BjLQODxQsfylDagHxEhKco8hqTjyLrheMELXtDIqpjZKFULFL4US2tqdnB5UNLuEbE2dW2tS+m9wB65fNNSWi9w4KD0X6T0aRXy1zrGs0TEecB5AN3d3Q0NdGZjVSssVHQLpfU0+/IvS4CBGV8LgKtz6e9Ks8b2Bx5NXVtLgUMl7ZQG8g8FlqZtj0naP80Se9egfVU6hpkVbGD9SW9fP8GWacCLV/QOWdbGtkZORb4c+C0wS9IaSccCC4E3SLoTOCQ9B7gWuAtYBXwb+CBARGwAvgDclH5OS2mkPOenMn8GfpLSqx3DzArmOzJaNQ3rFouIt1XZdHCFvAEcX2U/FwIXVkjvAfaukP5wpWOYWfF8R0arxldFNrMR8x0ZrRoHFzMbMd+R0arxtcXMbMQ8DdiqcXAxs1HxNGCrxN1iZmZWOAcXMzMrnLvFzNpQK6yKN6vFwcWszfj2vdYO3C1m1ma8Kt7agYOLWZvxqnhrBw4uZm3Gq+KtHTi4mLWZZq6KX7yilwMWLmPGyddwwMJlvtqx1c0D+mZtplmr4j1xwEbDwcWsDTVjVXytiQMOLjYUd4uZWUWeOGCj4eBiZhV54oCNhoOLmVXky+nbaHjMxcwq8uX0bTQcXMysKl9O30bK3WJmZlY4BxczMyucg4uZmRXOwcXMzArn4GJmZoVzcDEzs8I5uJiZWeEcXMzMrHAOLmZmVjgHFzMzK5yDi5mZFc7BxczMCucLV5o1yOIVvb6isI1bDi5mDeD7z9t4524xswaodf95s/HAwcWsAXz/eRvvanaLSfp6RHxU0o+AGLw9Io5oWM3M2tjUzg56KwSS0dx/3mM41k6GGnP5bvr91UZXxGwsOXHOrK3GXGB095/3GI61m5rBJSKWp9//05zqmI0NRd9/vtYYjoOLtaKhusWujIi3SFrJs7vFAtgAfD0irh7OQSV9DPiXtI+VwHuA3YErgF2A5cA7I+JpSZOBS4BXAg8Db42Ie9J+TgGOBTYDH46IpSl9LnAWMAE4PyIWDqd+ZkUo8v7zHsOxdjNUt9hH0u/Dq2yfAlwG1B1cJHUBHwb2ioh+SVcCRwNvBM6MiCsk/SdZ0Dg3/X4kIl4s6WjgK8BbJe2Vyr0UmAr8t6SXpMOcA7wBWAPcJGlJRNxWbx3NWk0jxnDMGqnmbLGIWJt+31vlZzlwzAiOOxHokDQR2A5YCxwELErbLwbmp8fz0nPS9oMlKaVfERFPRcTdwCpgv/SzKiLuioinyVpD80ZQR7OWceKcWXRMmrBV2mjGcMwara6pyJL2l3STpMclPS1ps6THYMu4TL0iopdsgsB9ZEHlUbJusL6I2JSyrQEG+hO6gNWp7KaUf5d8+qAy1dIrva7jJPVI6lm/fv1wXoZZU82f3cXpR+5DV2cHAro6Ozj9yH083mItq94V+t8g64L6AdANvAt4Sc0SVUjaiawlMQPoS/ucO5J9jVZEnAecB9Dd3f2sqdZmraTIMRyzRqt7EWVErAImRMTmiPgOIw8IhwB3R8T6iNgIXAUcAHSmbjKAaUBvetwL7AGQtu9INrD/TPqgMtXSzcysSeoNLk9K2hb4o6R/T7O9Rrq6/z5gf0nbpbGTg4HbgJ8DR6U8C9gySWBJek7aviwiIqUfLWmypBnATOB3wE3ATEkzUp2PTnnNzKxJ6g0Q70x5jweeIGsNvHkkB4yIG8kG5n9PNg15G7KuqU8BH5e0imxM5YJU5AJgl5T+ceDktJ9bgSvJAtNPgeNTq2oTcAKwFLgduDLlNTOzJlHWCKiyUZoHTIuIc9LzG4G/I1ufclJELKpauM10d3dHT09P2dUwM2srkpZHRPfg9KFaLiexdZfSZLLFjAcCHyisdmZmNqYMNVts24jIT+v9VURsADZIem4D62VmZm1sqJbLTvknEXFC7umuxVfHzMzGgqGCy42S3js4UdL7yGZmmZmZPctQ3WIfAxZLejvZ7C7Ixlwms+XyLGZmZlsZ6pL764DXSjqI7AKRANdExLKG18zMzNpWXZd/ScHEAcXMzOoy0lX2ZmZmVTm4mJlZ4RxczMyscA4uZmZWOAcXMzMrnIOLmZkVzsHFzMwK5+BiZmaFc3AxM7PCObiYmVnhHFzMzKxwDi5mZla4ui5caTbeLV7RyxlL7+D+vn6mdnZw4pxZzJ/dVXa1zFqWg4vZEBav6OWUq1bSv3EzAL19/Zxy1UoABxizKtwtZjaEM5be8UxgGdC/cTNnLL2jpBqZtT4HF7Mh3N/XP6x0M3NwMRvS1M6OYaWbmYOL2ZBOnDOLjkkTtkrrmDSBE+fMArIxmQMWLmPGyddwwMJlLF7RW0Y1zVqKB/TNhjAwaF9ptpgH+80qc3Axq8P82V0Vg0WtwX4HFxvP3C1mNgoe7DerzC0XK8x4XGg4tbOD3gqBxIP9Nt655WKFGBh76O3rJ9gy9jDWB7eHGuw3G6/ccrFCNHrsoVVbRbUG+83GMwcXK0Qjxx5afUZWtcF+s/HM3WJWiEYuNPTlV8zaj4OLFaKRYw+ekWXWfhxcrBDzZ3dx+pH70NXZgYCuzg5OP3KfQrqLfPkVs/bjMRcrTKPGHk6cM2urMRcYXauoVScHmI0lDi7W8oqckdXqkwPMxgoHF2sLRbWKfLkWs+YoZcxFUqekRZL+JOl2Sa+RtLOk6yTdmX7vlPJK0tmSVkm6WdK+uf0sSPnvlLQgl/5KSStTmbMlqYzXOVa181WAPTnArDnKGtA/C/hpROwJvBy4HTgZuD4iZgLXp+cAhwEz089xwLkAknYGPge8GtgP+NxAQEp53psrN7cJr2lcaPeV+J4cYNYcTQ8uknYEXgdcABART0dEHzAPuDhluxiYnx7PAy6JzA1Ap6TdgTnAdRGxISIeAa4D5qZtO0TEDRERwCW5fdkotfuaE1+uxaw5ymi5zADWA9+RtELS+ZKeC+wWEWtTngeA3dLjLmB1rvyalFYrfU2F9GeRdJykHkk969evH+XLGh/avVupkVOmzWyLMgb0JwL7Ah+KiBslncWWLjAAIiIkRaMrEhHnAecBdHd3N/x4Y8FYuAqwL9di1nhltFzWAGsi4sb0fBFZsHkwdWmRfq9L23uBPXLlp6W0WunTKqRbAdytZGb1aHpwiYgHgNWSBj6NDgZuA5YAAzO+FgBXp8dLgHelWWP7A4+m7rOlwKGSdkoD+YcCS9O2xyTtn2aJvSu3LxulRnYrtfMsNDPbWlnrXD4EXCZpW+Au4D1kge5KSccC9wJvSXmvBd4IrAKeTHmJiA2SvgDclPKdFhEb0uMPAhcBHcBP0o8VpJ5upeGugh/O4sb8vju3m0QEPNq/0avtzVqIsglV1t3dHT09PWVXoyWNNlBA1nVWq4VzwMJlFcdyujo7+PXJB9Xcd95QxzGzYklaHhHdg9N94UqraSTrWkYyXbneWWiV9j2c45hZczi4WE2NDBR59S5urGfKc7tMizYbyxxcrKZGBoq8emehdW43qeo+6jmOmTWHg4vV1MhAkVfvLLShhgg9LdqsNfiqyFbTcO6lkh/437FjEs+ZtA19T9Y/i6ueWWiP9m+suq3Ls8XMWoaDi9VU771UBs/i6uvfSMekCZz51lcU+mFf7QoBg2eVmVm5HFxsSPW0KJp1n5Si70ppZo3h4GKFaNYFLYu8K6WZNY6DixWimRe09IUnzVqfZ4tZIXxBSzPLc8vFCuHuKjPLc3Cxwri7yswGuFvMzMwK5+BiZmaFc3AxM7PCObiYmVnhHFzMzKxwDi5mZlY4BxczMyucg4uZmRXOwcXMzArn4GJmZoVzcDEzs8L52mINkr/lb70XcRxJGTOzVuTg0gCDb/nb29fPKVetBKgaLEZSxsysVblbrAFq3fK3yDJmZq3KwaUBRnLL32bdJtjMrBkcXBqg2q19a93ydyRlzMxalYNLA4zklr++TbCZjSUe0G+Akdzyt94ynoVmZu1AEVF2HVpCd3d39PT0lF2NmgbPKIOsdXP6kfvUPQutnjJmZvWStDwiugenu1usjXgWmpm1CweXNuJZaGbWLhxc2ohnoZlZu3BwaSOehWZm7cKzxdpII2ehmZkVybPFknaYLWZm1mpabraYpAmSVkj6cXo+Q9KNklZJ+r6kbVP65PR8Vdo+PbePU1L6HZLm5NLnprRVkk5u9mtrJ4tX9HLAwmXMOPkaDli4jMUresuukpmNAWWOuXwEuD33/CvAmRHxYuAR4NiUfizwSEo/M+VD0l7A0cBLgbnAN1PAmgCcAxwG7AW8LeUd84YbKAbWwPT29RNsuRKzA4yZjVYpwUXSNOBNwPnpuYCDgEUpy8XA/PR4XnpO2n5wyj8PuCIinoqIu4FVwH7pZ1VE3BURTwNXpLyFG8m3/ka1FEYSKLwGxswapayWy9eBk4C/pee7AH0RsSk9XwMMjDh3AasB0vZHU/5n0geVqZZeqJF8mDeypTCSQOE1MGbWKE0PLpIOB9ZFxPJmH7tCXY6T1COpZ/369cMq22qr5UcSKLwGxswapYyWywHAEZLuIeuyOgg4C+iUNDA1ehow8HW+F9gDIG3fEXg4nz6oTLX0Z4mI8yKiOyK6d91112G9iGaulq+nK20kgcJrYMysUZoeXCLilIiYFhHTyQbkl0XEMcDPgaNStgXA1enxkvSctH1ZZPOnlwBHp9lkM4CZwO+Am4CZafbZtukYS4p+Hc1aLV9vV9pIAsX82V2cfuQ+dHV2IKCrs8MXtDSzQrTSIspPAVdI+iKwArggpV8AfFfSKmADWbAgIm6VdCVwG7AJOD4iNgNIOgFYCkwALoyIW4uu7IlzZlW82nCtD/PX77krl95wX8X0amp1peWDwEgXS86f3eVgYmaFKzW4RMQvgF+kx3eRzfQanOevwP+tUv5LwJcqpF8LXFtgVZ9lJB/mP/9T5XGdaukwvK40BwozaxWt1HJpO8P9MB/poHtvhe0edDezVuYLVzaRB93NbLxwcGkiD7qb2XjhbrEm8qC7mY0XDi5N5kBhZuOBu8XMzKxwDi5mZlY4BxczMyucg4uZmRXOwcXMzAqn7BqQJmk9cG/Z9RiGKcBDZVeiZD4HPgfgc1D2639hRDzrAokOLm1KUk9EdJddjzL5HPgcgM9Bq75+d4uZmVnhHFzMzKxwDi7t67yyK9ACfA58DsDnoCVfv8dczMyscG65mJlZ4RxczMyscA4ubUbSHpJ+Luk2SbdK+kjZdSqDpAmSVkj6cdl1KYOkTkmLJP1J0u2SXlN2nZpN0sfS/8Atki6X9Jyy69Roki6UtE7SLbm0nSVdJ+nO9HunMus4wMGl/WwCPhERewH7A8dL2qvkOpXhI8DtZVeiRGcBP42IPYGXM87OhaQu4MNAd0TsDUwAji63Vk1xETB3UNrJwPURMRO4Pj0vnYNLm4mItRHx+/T4L2QfKuPqBjGSpgFvAs4vuy5lkLQj8DrgAoCIeDoi+sqtVSkmAh2SJgLbAfeXXJ+Gi4hfAhsGJc8DLk6PLwbmN7VSVTi4tDFJ04HZwI3l1qTpvg6cBPyt7IqUZAawHvhO6ho8X9Jzy65UM0VEL/BV4D5gLfBoRPys3FqVZreIWJsePwDsVmZlBji4tClJzwN+CHw0Ih4ruz7NIulwYF1ELIJXlX8AAAKKSURBVC+7LiWaCOwLnBsRs4EnaJGukGZJ4wrzyALtVOC5kt5Rbq3KF9nakpZYX+Lg0oYkTSILLJdFxFVl16fJDgCOkHQPcAVwkKRLy61S060B1kTEQIt1EVmwGU8OAe6OiPURsRG4CnhtyXUqy4OSdgdIv9eVXB/AwaXtSBJZX/vtEfG1suvTbBFxSkRMi4jpZAO4yyJiXH1jjYgHgNWSZqWkg4HbSqxSGe4D9pe0XfqfOJhxNqkhZwmwID1eAFxdYl2e4eDSfg4A3kn2jf0P6eeNZVfKmu5DwGWSbgZeAXy55Po0VWq1LQJ+D6wk+yxrycugFEnS5cBvgVmS1kg6FlgIvEHSnWQtuoVl1nGAL/9iZmaFc8vFzMwK5+BiZmaFc3AxM7PCObiYmVnhHFzMzKxwE8uugNl4IGkXsosKAjwf2Ex2CReA/SLi6Vzei4AfR8SiplbSrEAOLmZNEBEPk61HQdKpwOMR8dVSK2XWQO4WMyuJpPdKuknSHyX9UNJ2uc2vk/QbSXdJOirlPzB//xpJ35D07vT4Hkmfl/R7SSsl7dncV2O2NQcXs/JcFRGvioiB+7Ecm9u2O/APwOHUv+L6oYjYFzgX+GShNTUbJgcXs/LsLen/SVoJHAO8NLdtcUT8LSJuo/5LqA9cxHQ5ML24apoNn4OLWXkuAk6IiH2AzwP52/Q+lXus9HsTW//PDr6t70CZzXg81Urm4GJWnu2BtekWCsfUkf9eYC9JkyV1kl0J2Kwl+duNWXk+S3YX0fXp9/a1MkfEaklXArcAdwMrGl5DsxHyVZHNzKxw7hYzM7PCObiYmVnhHFzMzKxwDi5mZlY4BxczMyucg4uZmRXOwcXMzAr3/wEhcFagPofxqgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFeuagvFtUug"
      },
      "source": [
        "## *Fitting* metode Simple Linear Regression\n",
        "Bagian ini digunakan untuk melakukan fitting metode Simple Linear Regression pada training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be0bNW6Fi_tS",
        "outputId": "18d6e040-7a70-4bfc-de00-cec692c65114"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Afk3SXMtbYI"
      },
      "source": [
        "Dengan demikian, *fitting* Simple Linear Regression pada training set (X_train dan y_train) sudah dilakukan dan machine learning dengan model *Simple Regresi Linear* sudah siap dijalankan dengan kondisi seperti di atas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBLK9Md1jDwF",
        "outputId": "12a3ba16-afeb-46bc-c29e-5b4b74c3f4a2"
      },
      "source": [
        "regressor.score(X_train, y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.963085677471505"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QTFmYUXtho0"
      },
      "source": [
        "## Melakukan prediksi untuk hasil Test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhTpdIWPjJPK"
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTvpcjPljL_M",
        "outputId": "1b1ca750-1ffb-4ba8-da2b-01929910a7f2"
      },
      "source": [
        "y_pred.round(2)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([110927.92,  55356.33, 123383.63,  89849.04])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEXsBNNejNZm",
        "outputId": "d9ed4f55-9046-4ba3-fa8b-9d923cf7ce92"
      },
      "source": [
        "y_test"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([105582,  54445, 122391,  91738])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFciFEVItqSb"
      },
      "source": [
        "Sampai pada tahap ini, machine learning sudah menghasilkan variabel y_test dan y_pred sebagai berikut.\n",
        "* Variabel y_test adalah data sesungguhnya (observasi) dari nilai penjualan\n",
        "* Variabel y_pred adalah data hasil prediksi dari nilai penjualan *machine learning.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJy_ZBvotta_"
      },
      "source": [
        "## Visualisasi hasil *Machine Learning.*\n",
        "Visualisasi menggunakan grafik plot sehingga gambaran hasil dari model secara keseluruhan dapat kita lihat."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "A_hxYLLCjP6d",
        "outputId": "20625a56-d30c-431f-c8b9-1e22b3055a2b"
      },
      "source": [
        "plt.scatter(X_test, y_test)\n",
        "plt.plot(X_test, y_pred, color = 'red')\n",
        "plt.xlabel('Tahun')\n",
        "plt.ylabel('Gaji')\n",
        "plt.title('Grafik Nilai Gaji berdasarkan Lama Bekerja (Predict)')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3aYGhhbkjlaG",
        "outputId": "4830d20d-ed5d-487e-b2e6-eacbb7d97dc3"
      },
      "source": [
        "plt.scatter(X_train, y_train)\n",
        "plt.plot(X_train, regressor.predict(X_train), color = 'red')\n",
        "plt.xlabel('Tahun')\n",
        "plt.ylabel('Gaji')\n",
        "plt.title('Grafik Nilai Gaji berdasarkan Lama Bekerja (Train)')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}