#014 Latihan SKLearn With Grid Search.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Latihan SKLearn With Grid Search.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymCOXMGHGNoB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "77c3aa88-60fd-44b2-abe0-a8af36f38766"
      },
      "source": [
        "#Mengimport library dasar dan mengubah dataset menjadi dataframe\n",
        "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.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>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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufBv1t9hLcP2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7c5980f0-4f4f-4f1b-9353-826bc0f858d0"
      },
      "source": [
        "#Pisahkan atribut dan label pada dataset, jika terdapat 1 atribut pada dataset maka perlu di ubah bentuknya agar bisa dipakai pada pelatihan model\n",
        "import numpy as np\n",
        "x = data['YearsExperience']\n",
        "y = data['Salary']\n",
        "x = x[:,np.newaxis]\n",
        "#Menggunakan grid search, import library GridSearchCV dari sklearn.model_selection\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "#Buat model uji dengan grid search dalam hal ini model SVR\n",
        "from sklearn.svm import SVR\n",
        "model = SVR()\n",
        "#Buat sebuah python dictionary yang berisi nama parameter yang akan diuji, serta nilai-nilainya\n",
        "parameters = {\n",
        "    'kernel': ['rbf'],\n",
        "    'C':     [1000, 10000, 100000],\n",
        "    'gamma': [0.5, 0.05,0.005]\n",
        "}\n",
        "grid_search = GridSearchCV(model, parameters)\n",
        "#Panggil fungsi fit() pada objek grid search yang telah kita buat\n",
        "grid_search.fit(x,y)\n",
        "#Setelah grid search mencari parameter terbaik pada model\n",
        "#Menampilkan parameter terbaik dengan memanggil atribut best_params_ dari objek grid search\n",
        "print(grid_search.best_params_)\n",
        "#Membuat model SVM baru dengan parameter hasil grid search dan melatihnya pada data\n",
        "model_baru  = SVR(C=100000, gamma=0.005, kernel='rbf')\n",
        "model_baru.fit(x,y)\n",
        "#Memvisualisasikan SVR dengan parameter hasil grid search\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, model_baru.predict(x))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 100000, 'gamma': 0.005, 'kernel': 'rbf'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f217d94fc50>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}