02_Praproses_Fitur.ipynb

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      "cell_type": "markdown",
      "source": [
        "<h2><b>Input : </b></h2>\n",
        "\n",
        "*   train_microarray.csv\n",
        "\n",
        "<h2><b>Output : </b></h2>\n",
        "\n",
        "* rf1_features.pkl\n",
        "* rf2_features.pkl\n",
        "* rf3_features.pkl\n",
        "* ab1_features.pkl\n",
        "* ab2_features.pkl\n",
        "* ab3_features.pkl\n"
      ],
      "metadata": {
        "id": "s6ZttnxlOpR4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run All"
      ],
      "metadata": {
        "id": "U9LP0lohRHga"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import dan Load"
      ],
      "metadata": {
        "id": "uPe4IfRqRJeK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Library yang Digunakan"
      ],
      "metadata": {
        "id": "05XDKPTIFa67"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ic-zYJf7RFsB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import rcParams\n",
        "import numpy as np\n",
        "from sklearn.feature_selection import SelectKBest, chi2\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengatur Style"
      ],
      "metadata": {
        "id": "ItYr_lddv45P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rcParams['figure.figsize'] = 15, 10\n",
        "mpl.style.use(['ggplot'])"
      ],
      "metadata": {
        "id": "PIOI-wdJv4SU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Dataset berupa Data Train"
      ],
      "metadata": {
        "id": "zS7HP8TmFf81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('https://drive.google.com/uc?id=1MiU_0V6-LvZ_CyCuWf2HoW-WXNKSIFJQ')"
      ],
      "metadata": {
        "id": "QJ0800CtRXHE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat Dataset"
      ],
      "metadata": {
        "id": "9b1c3s50Fi9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head()"
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        "id": "zmay0uyBRo0K",
        "outputId": "c9137d17-0780-41aa-93c9-25da39e06375"
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      "execution_count": null,
      "outputs": [
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              "   211612_s_at  206628_at  222339_x_at  219678_x_at  217653_x_at  211996_s_at  \\\n",
              "0     2.535743   2.509137     2.477981     2.457776     2.052387     2.873506   \n",
              "1     2.587822   2.751712     2.437972     2.408562     1.957657     2.779933   \n",
              "2     2.529666   2.787718     2.558234     2.447706     2.059190     2.805088   \n",
              "3     2.274739   2.487072     2.602525     2.537130     2.273920     2.952965   \n",
              "4     2.551101   2.501445     2.675678     2.692597     2.321532     2.887101   \n",
              "\n",
              "   214594_x_at  207283_at  219233_s_at  213326_at  ...  208938_at  208566_at  \\\n",
              "0     2.285882   2.257423     2.828750   2.432269  ...   2.000653   1.908920   \n",
              "1     2.248177   2.158378     2.655283   2.439049  ...   2.000948   1.931161   \n",
              "2     2.255567   2.223798     2.724081   2.459016  ...   1.705573   1.917374   \n",
              "3     2.554384   2.275782     2.876030   2.520870  ...   1.936551   1.923020   \n",
              "4     2.752609   2.239391     2.718403   2.503886  ...   1.859982   1.990585   \n",
              "\n",
              "   207723_s_at  219777_at  222150_s_at  209100_at  205699_at  201547_at  \\\n",
              "0     1.670529   1.976986     2.538606   2.852094   1.812555   1.926493   \n",
              "1     1.588826   1.657637     2.380881   2.798405   1.852201   1.901823   \n",
              "2     1.609536   1.585730     2.390935   2.809363   2.005320   2.009139   \n",
              "3     1.610778   1.627205     2.278927   2.790865   1.939110   1.977171   \n",
              "4     1.636428   1.860937     2.351503   2.799492   1.846797   2.093768   \n",
              "\n",
              "   211069_s_at  Label  \n",
              "0     2.900288      1  \n",
              "1     2.934622      1  \n",
              "2     2.858905      0  \n",
              "3     2.952043      0  \n",
              "4     2.894002      0  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>211612_s_at</th>\n",
              "      <th>206628_at</th>\n",
              "      <th>222339_x_at</th>\n",
              "      <th>219678_x_at</th>\n",
              "      <th>217653_x_at</th>\n",
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              "      <th>214594_x_at</th>\n",
              "      <th>207283_at</th>\n",
              "      <th>219233_s_at</th>\n",
              "      <th>213326_at</th>\n",
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              "      <th>208938_at</th>\n",
              "      <th>208566_at</th>\n",
              "      <th>207723_s_at</th>\n",
              "      <th>219777_at</th>\n",
              "      <th>222150_s_at</th>\n",
              "      <th>209100_at</th>\n",
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              "      <th>0</th>\n",
              "      <td>2.535743</td>\n",
              "      <td>2.509137</td>\n",
              "      <td>2.477981</td>\n",
              "      <td>2.457776</td>\n",
              "      <td>2.052387</td>\n",
              "      <td>2.873506</td>\n",
              "      <td>2.285882</td>\n",
              "      <td>2.257423</td>\n",
              "      <td>2.828750</td>\n",
              "      <td>2.432269</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000653</td>\n",
              "      <td>1.908920</td>\n",
              "      <td>1.670529</td>\n",
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              "      <th>1</th>\n",
              "      <td>2.587822</td>\n",
              "      <td>2.751712</td>\n",
              "      <td>2.437972</td>\n",
              "      <td>2.408562</td>\n",
              "      <td>1.957657</td>\n",
              "      <td>2.779933</td>\n",
              "      <td>2.248177</td>\n",
              "      <td>2.158378</td>\n",
              "      <td>2.655283</td>\n",
              "      <td>2.439049</td>\n",
              "      <td>...</td>\n",
              "      <td>2.000948</td>\n",
              "      <td>1.931161</td>\n",
              "      <td>1.588826</td>\n",
              "      <td>1.657637</td>\n",
              "      <td>2.380881</td>\n",
              "      <td>2.798405</td>\n",
              "      <td>1.852201</td>\n",
              "      <td>1.901823</td>\n",
              "      <td>2.934622</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>2.529666</td>\n",
              "      <td>2.787718</td>\n",
              "      <td>2.558234</td>\n",
              "      <td>2.447706</td>\n",
              "      <td>2.059190</td>\n",
              "      <td>2.805088</td>\n",
              "      <td>2.255567</td>\n",
              "      <td>2.223798</td>\n",
              "      <td>2.724081</td>\n",
              "      <td>2.459016</td>\n",
              "      <td>...</td>\n",
              "      <td>1.705573</td>\n",
              "      <td>1.917374</td>\n",
              "      <td>1.609536</td>\n",
              "      <td>1.585730</td>\n",
              "      <td>2.390935</td>\n",
              "      <td>2.809363</td>\n",
              "      <td>2.005320</td>\n",
              "      <td>2.009139</td>\n",
              "      <td>2.858905</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>2.274739</td>\n",
              "      <td>2.487072</td>\n",
              "      <td>2.602525</td>\n",
              "      <td>2.537130</td>\n",
              "      <td>2.273920</td>\n",
              "      <td>2.952965</td>\n",
              "      <td>2.554384</td>\n",
              "      <td>2.275782</td>\n",
              "      <td>2.876030</td>\n",
              "      <td>2.520870</td>\n",
              "      <td>...</td>\n",
              "      <td>1.936551</td>\n",
              "      <td>1.923020</td>\n",
              "      <td>1.610778</td>\n",
              "      <td>1.627205</td>\n",
              "      <td>2.278927</td>\n",
              "      <td>2.790865</td>\n",
              "      <td>1.939110</td>\n",
              "      <td>1.977171</td>\n",
              "      <td>2.952043</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>2.551101</td>\n",
              "      <td>2.501445</td>\n",
              "      <td>2.675678</td>\n",
              "      <td>2.692597</td>\n",
              "      <td>2.321532</td>\n",
              "      <td>2.887101</td>\n",
              "      <td>2.752609</td>\n",
              "      <td>2.239391</td>\n",
              "      <td>2.718403</td>\n",
              "      <td>2.503886</td>\n",
              "      <td>...</td>\n",
              "      <td>1.859982</td>\n",
              "      <td>1.990585</td>\n",
              "      <td>1.636428</td>\n",
              "      <td>1.860937</td>\n",
              "      <td>2.351503</td>\n",
              "      <td>2.799492</td>\n",
              "      <td>1.846797</td>\n",
              "      <td>2.093768</td>\n",
              "      <td>2.894002</td>\n",
              "      <td>0</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat Ukuran Dataset"
      ],
      "metadata": {
        "id": "E9gkuRI_FlGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
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        "colab": {
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        },
        "id": "HYFQq5NjTepj",
        "outputId": "3f17b5b9-9e3b-4779-d489-27ea636ffc40"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(130, 22216)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reduksi Fitur Standard Deviation"
      ],
      "metadata": {
        "id": "41LZy3JFlluz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi Simpangan Baku setiap Kolom/Fitur"
      ],
      "metadata": {
        "id": "-n2H6h8dFuGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.iloc[:,:-1].std().plot(style='.', color='forestgreen')\n",
        "plt.xlabel('Feature Names', fontweight='bold')\n",
        "plt.ylabel('Standard Deviation Value', fontweight='bold')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftML8KHQlqzv",
        "outputId": "c403cabf-50a7-4009-d452-8a4dafd71dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memfilter Kolom dengan Simpangan Baku < 0.1 dan menghasilkan list berupa index-index dari kolom yang akan didrop"
      ],
      "metadata": {
        "id": "1lX8gfh_FzCV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "simpangan_baku = df_train.iloc[:,:-1].std()\n",
        "drop_kolom_index = []\n",
        "\n",
        "for i in range(0,len(simpangan_baku)):\n",
        "  if (simpangan_baku[i] < 0.1):\n",
        "    drop_kolom_index.append(i)\n",
        "\n",
        "print(len(drop_kolom_index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqd9Lo33mPSw",
        "outputId": "9c3d7bb0-d4ef-46cf-d00d-44df4d965770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengubah index yang akan didrop tersebut menjadi nama kolom"
      ],
      "metadata": {
        "id": "tt4YxHAhGCzI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drop_kolom = []\n",
        "\n",
        "for i in range(0,len(drop_kolom_index)):\n",
        "  drop_kolom.append(df_train.columns[drop_kolom_index[i]])\n",
        "\n",
        "print(len(drop_kolom))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ql2QrF1lo6sM",
        "outputId": "c63f245d-3cc9-4be6-aad4-fba90bee19b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ukuran setelah melakukan Drop"
      ],
      "metadata": {
        "id": "Nwdm0R-vGHpA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.drop(columns=drop_kolom, inplace=True)\n",
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgtVjMQwr2_M",
        "outputId": "15cb5f0a-a17d-4da7-d7ca-c22252c6b974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(130, 5707)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi setelah dilakukan drop"
      ],
      "metadata": {
        "id": "NQoDqrJLGKdV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.iloc[:,:-1].std().plot(style='.', color='forestgreen')\n",
        "plt.xlabel('Feature Names', fontweight='bold')\n",
        "plt.ylabel('Standard Deviation Value', fontweight='bold')\n",
        "plt.ylim(0,0.6)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHUfgAHEqjqH",
        "outputId": "6a0cb587-5cd8-4789-f4ba-96c05e51de10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split X_train dan y_train"
      ],
      "metadata": {
        "id": "SimEz56450hf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan Split untuk X_train dan y_train"
      ],
      "metadata": {
        "id": "gZz2J7GsGtME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_train.iloc[:,:-1]\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evF6xgkCwRer",
        "outputId": "8f7e2ce8-0d4d-48cb-9397-a7845d36e590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   211612_s_at  219678_x_at  217653_x_at  211996_s_at  214594_x_at  \\\n",
              "0     2.535743     2.457776     2.052387     2.873506     2.285882   \n",
              "1     2.587822     2.408562     1.957657     2.779933     2.248177   \n",
              "2     2.529666     2.447706     2.059190     2.805088     2.255567   \n",
              "3     2.274739     2.537130     2.273920     2.952965     2.554384   \n",
              "4     2.551101     2.692597     2.321532     2.887101     2.752609   \n",
              "\n",
              "   218155_x_at  202936_s_at  207730_x_at  209703_x_at  216609_at  ...  \\\n",
              "0     2.518572     2.656989     2.833421     2.448979   1.982433  ...   \n",
              "1     2.508590     2.366057     2.871436     2.460372   2.139794  ...   \n",
              "2     2.658405     2.542071     2.871085     2.435954   2.113104  ...   \n",
              "3     2.736684     2.465318     2.988069     2.551221   2.204011  ...   \n",
              "4     2.752873     2.608392     3.098642     2.643402   2.483343  ...   \n",
              "\n",
              "   205978_at  220394_at  212055_at  203460_s_at  217783_s_at  204971_at  \\\n",
              "0   1.635546   1.696228   2.724899     2.789691     3.078939   3.367350   \n",
              "1   1.619527   1.667691   2.772350     2.775073     3.123781   3.267103   \n",
              "2   1.785995   1.766007   2.499228     2.743806     3.017190   3.297073   \n",
              "3   1.732963   1.806890   2.705551     2.538612     3.057966   3.011900   \n",
              "4   1.712801   1.753526   2.646272     2.565008     3.049509   3.370768   \n",
              "\n",
              "   209395_at  208938_at  219777_at  222150_s_at  \n",
              "0   1.766702   2.000653   1.976986     2.538606  \n",
              "1   1.813445   2.000948   1.657637     2.380881  \n",
              "2   1.861616   1.705573   1.585730     2.390935  \n",
              "3   1.817295   1.936551   1.627205     2.278927  \n",
              "4   2.066245   1.859982   1.860937     2.351503  \n",
              "\n",
              "[5 rows x 5706 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>211612_s_at</th>\n",
              "      <th>219678_x_at</th>\n",
              "      <th>217653_x_at</th>\n",
              "      <th>211996_s_at</th>\n",
              "      <th>214594_x_at</th>\n",
              "      <th>218155_x_at</th>\n",
              "      <th>202936_s_at</th>\n",
              "      <th>207730_x_at</th>\n",
              "      <th>209703_x_at</th>\n",
              "      <th>216609_at</th>\n",
              "      <th>...</th>\n",
              "      <th>205978_at</th>\n",
              "      <th>220394_at</th>\n",
              "      <th>212055_at</th>\n",
              "      <th>203460_s_at</th>\n",
              "      <th>217783_s_at</th>\n",
              "      <th>204971_at</th>\n",
              "      <th>209395_at</th>\n",
              "      <th>208938_at</th>\n",
              "      <th>219777_at</th>\n",
              "      <th>222150_s_at</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.535743</td>\n",
              "      <td>2.457776</td>\n",
              "      <td>2.052387</td>\n",
              "      <td>2.873506</td>\n",
              "      <td>2.285882</td>\n",
              "      <td>2.518572</td>\n",
              "      <td>2.656989</td>\n",
              "      <td>2.833421</td>\n",
              "      <td>2.448979</td>\n",
              "      <td>1.982433</td>\n",
              "      <td>...</td>\n",
              "      <td>1.635546</td>\n",
              "      <td>1.696228</td>\n",
              "      <td>2.724899</td>\n",
              "      <td>2.789691</td>\n",
              "      <td>3.078939</td>\n",
              "      <td>3.367350</td>\n",
              "      <td>1.766702</td>\n",
              "      <td>2.000653</td>\n",
              "      <td>1.976986</td>\n",
              "      <td>2.538606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.587822</td>\n",
              "      <td>2.408562</td>\n",
              "      <td>1.957657</td>\n",
              "      <td>2.779933</td>\n",
              "      <td>2.248177</td>\n",
              "      <td>2.508590</td>\n",
              "      <td>2.366057</td>\n",
              "      <td>2.871436</td>\n",
              "      <td>2.460372</td>\n",
              "      <td>2.139794</td>\n",
              "      <td>...</td>\n",
              "      <td>1.619527</td>\n",
              "      <td>1.667691</td>\n",
              "      <td>2.772350</td>\n",
              "      <td>2.775073</td>\n",
              "      <td>3.123781</td>\n",
              "      <td>3.267103</td>\n",
              "      <td>1.813445</td>\n",
              "      <td>2.000948</td>\n",
              "      <td>1.657637</td>\n",
              "      <td>2.380881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.529666</td>\n",
              "      <td>2.447706</td>\n",
              "      <td>2.059190</td>\n",
              "      <td>2.805088</td>\n",
              "      <td>2.255567</td>\n",
              "      <td>2.658405</td>\n",
              "      <td>2.542071</td>\n",
              "      <td>2.871085</td>\n",
              "      <td>2.435954</td>\n",
              "      <td>2.113104</td>\n",
              "      <td>...</td>\n",
              "      <td>1.785995</td>\n",
              "      <td>1.766007</td>\n",
              "      <td>2.499228</td>\n",
              "      <td>2.743806</td>\n",
              "      <td>3.017190</td>\n",
              "      <td>3.297073</td>\n",
              "      <td>1.861616</td>\n",
              "      <td>1.705573</td>\n",
              "      <td>1.585730</td>\n",
              "      <td>2.390935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.274739</td>\n",
              "      <td>2.537130</td>\n",
              "      <td>2.273920</td>\n",
              "      <td>2.952965</td>\n",
              "      <td>2.554384</td>\n",
              "      <td>2.736684</td>\n",
              "      <td>2.465318</td>\n",
              "      <td>2.988069</td>\n",
              "      <td>2.551221</td>\n",
              "      <td>2.204011</td>\n",
              "      <td>...</td>\n",
              "      <td>1.732963</td>\n",
              "      <td>1.806890</td>\n",
              "      <td>2.705551</td>\n",
              "      <td>2.538612</td>\n",
              "      <td>3.057966</td>\n",
              "      <td>3.011900</td>\n",
              "      <td>1.817295</td>\n",
              "      <td>1.936551</td>\n",
              "      <td>1.627205</td>\n",
              "      <td>2.278927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.551101</td>\n",
              "      <td>2.692597</td>\n",
              "      <td>2.321532</td>\n",
              "      <td>2.887101</td>\n",
              "      <td>2.752609</td>\n",
              "      <td>2.752873</td>\n",
              "      <td>2.608392</td>\n",
              "      <td>3.098642</td>\n",
              "      <td>2.643402</td>\n",
              "      <td>2.483343</td>\n",
              "      <td>...</td>\n",
              "      <td>1.712801</td>\n",
              "      <td>1.753526</td>\n",
              "      <td>2.646272</td>\n",
              "      <td>2.565008</td>\n",
              "      <td>3.049509</td>\n",
              "      <td>3.370768</td>\n",
              "      <td>2.066245</td>\n",
              "      <td>1.859982</td>\n",
              "      <td>1.860937</td>\n",
              "      <td>2.351503</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 5706 columns</p>\n",
              "</div>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7d2ac2b-e020-4cc9-8120-9568ee7c5858 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7d2ac2b-e020-4cc9-8120-9568ee7c5858');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_train.iloc[:,-1]\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEO_Pnc7wio5",
        "outputId": "87c0c7cd-c76e-4c4d-89dd-eba9557fa5d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Seleksi Fitur"
      ],
      "metadata": {
        "id": "BkIAGvXSWsPt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Select KBest "
      ],
      "metadata": {
        "id": "bmWRV_B7OZxl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menentukan nilai K"
      ],
      "metadata": {
        "id": "qyXXQWq5ja53"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nilai_k = [100,200,300,400,500,600,700,800,900,1000]"
      ],
      "metadata": {
        "id": "bcBRLR9xiNAb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chi2"
      ],
      "metadata": {
        "id": "qF3Nv0DljdLC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_fitur = []\n",
        "\n",
        "for k in nilai_k:\n",
        "  seleksi_chi2 = SelectKBest(score_func=chi2, k=k)\n",
        "  hasil_chi2 = seleksi_chi2.fit(X,y)\n",
        "  list_fitur.append(list(hasil_chi2.get_feature_names_out()))\n",
        "\n",
        "for i in range(0,len(list_fitur)):\n",
        "  for j in range(0,len(list_fitur[i])):\n",
        "    if (list_fitur[i][j] == 'Label'):\n",
        "      print(i,j)"
      ],
      "metadata": {
        "id": "NcLAApAlmXBh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pembuatan Model"
      ],
      "metadata": {
        "id": "DWMne8_WuI7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_seleksi = RandomForestClassifier()\n",
        "ab_seleksi = AdaBoostClassifier()\n",
        "\n",
        "rf_akurasi = []\n",
        "ab_akurasi = []"
      ],
      "metadata": {
        "id": "3S2xM8GHEgjz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "LuagEPXFuSYg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nilai Akurasi Cross Val pada masing-masing K"
      ],
      "metadata": {
        "id": "hZV3dD6KuYcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"====================RANDOM FOREST====================\")\n",
        "for i in range(0,len(nilai_k)):\n",
        "  score = cross_val_score(rf_seleksi, X[list_fitur[i]], y, scoring=\"accuracy\", cv=10)\n",
        "  print(\"\\nNilai K=\" + str(nilai_k[i]))\n",
        "  print(\"Akurasi masing-masing = \")\n",
        "  print(score)\n",
        "  print(\"Rata-rata = \", score.mean())\n",
        "  rf_akurasi.append(score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuVIUq72D9KX",
        "outputId": "bd16247d-27f6-4466-e395-050364f1fd49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "====================RANDOM FOREST====================\n",
            "\n",
            "Nilai K=100\n",
            "Akurasi masing-masing = \n",
            "[0.46153846 0.84615385 0.84615385 0.61538462 0.61538462 0.84615385\n",
            " 0.92307692 0.76923077 0.84615385 0.53846154]\n",
            "Rata-rata =  0.7307692307692307\n",
            "\n",
            "Nilai K=200\n",
            "Akurasi masing-masing = \n",
            "[0.53846154 0.76923077 0.76923077 0.53846154 0.46153846 0.69230769\n",
            " 0.92307692 0.92307692 0.76923077 0.53846154]\n",
            "Rata-rata =  0.6923076923076923\n",
            "\n",
            "Nilai K=300\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.76923077 0.84615385 0.61538462 0.46153846 0.84615385\n",
            " 0.92307692 0.84615385 0.92307692 0.61538462]\n",
            "Rata-rata =  0.7461538461538462\n",
            "\n",
            "Nilai K=400\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.84615385 0.84615385 0.53846154 0.46153846 0.69230769\n",
            " 0.92307692 0.84615385 0.84615385 0.46153846]\n",
            "Rata-rata =  0.7076923076923076\n",
            "\n",
            "Nilai K=500\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.84615385 0.76923077 0.53846154 0.69230769 0.76923077\n",
            " 0.84615385 0.92307692 0.84615385 0.46153846]\n",
            "Rata-rata =  0.7307692307692307\n",
            "\n",
            "Nilai K=600\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.84615385 0.84615385 0.53846154 0.61538462 0.76923077\n",
            " 0.92307692 0.92307692 0.84615385 0.53846154]\n",
            "Rata-rata =  0.7461538461538461\n",
            "\n",
            "Nilai K=700\n",
            "Akurasi masing-masing = \n",
            "[0.69230769 0.84615385 0.76923077 0.53846154 0.76923077 0.76923077\n",
            " 0.84615385 0.76923077 0.84615385 0.38461538]\n",
            "Rata-rata =  0.7230769230769231\n",
            "\n",
            "Nilai K=800\n",
            "Akurasi masing-masing = \n",
            "[0.46153846 0.84615385 0.69230769 0.61538462 0.38461538 0.69230769\n",
            " 0.92307692 0.76923077 0.84615385 0.53846154]\n",
            "Rata-rata =  0.676923076923077\n",
            "\n",
            "Nilai K=900\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.84615385 0.84615385 0.53846154 0.76923077 0.76923077\n",
            " 0.84615385 0.92307692 0.84615385 0.46153846]\n",
            "Rata-rata =  0.7461538461538461\n",
            "\n",
            "Nilai K=1000\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.69230769 0.84615385 0.53846154 0.69230769 0.76923077\n",
            " 0.92307692 0.76923077 0.76923077 0.46153846]\n",
            "Rata-rata =  0.7076923076923077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menyimpan Top 3 Features"
      ],
      "metadata": {
        "id": "5r40Asg-uhdt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top3_rf_index = sorted(range(len(rf_akurasi)), key=lambda i: rf_akurasi[i])[-3:]\n",
        "top3_rf_index.sort()\n",
        "\n",
        "rf1_features = list_fitur[top3_rf_index[0]]\n",
        "rf2_features = list_fitur[top3_rf_index[1]]\n",
        "rf3_features = list_fitur[top3_rf_index[2]]\n",
        "\n",
        "print(\"Banyak fitur terpilih pada Random Forest: \")\n",
        "for i in range(0, len(top3_rf_index)):\n",
        "  print(\"RF\" + str(i+1) + \" =\", nilai_k[top3_rf_index[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poBSWJ3Yn7zV",
        "outputId": "441cd1d5-3659-4176-9ffb-0e5d99309ef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Banyak fitur terpilih pada Random Forest: \n",
            "RF1 = 300\n",
            "RF2 = 600\n",
            "RF3 = 900\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### AdaBoost"
      ],
      "metadata": {
        "id": "GYAOIGl6ugB6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nilai Akurasi Cross val pada masing-masing K"
      ],
      "metadata": {
        "id": "KB3zjNCoum3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"====================ADABOOST====================\")\n",
        "for i in range(0,len(nilai_k)):\n",
        "  score = cross_val_score(ab_seleksi, X[list_fitur[i]], y, scoring=\"accuracy\", cv=10)\n",
        "  print(\"\\nNilai K=\" + str(nilai_k[i]))\n",
        "  print(\"Akurasi masing-masing = \")\n",
        "  print(score)\n",
        "  print(\"Rata-rata = \", score.mean())\n",
        "  ab_akurasi.append(score.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wBXbsqxOcMkN",
        "outputId": "6e22ec1c-cac2-4c66-bb9d-bd4ebcea2448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "====================ADABOOST====================\n",
            "\n",
            "Nilai K=100\n",
            "Akurasi masing-masing = \n",
            "[0.69230769 0.76923077 0.69230769 0.61538462 0.30769231 0.76923077\n",
            " 0.76923077 0.92307692 0.84615385 0.61538462]\n",
            "Rata-rata =  0.7\n",
            "\n",
            "Nilai K=200\n",
            "Akurasi masing-masing = \n",
            "[0.84615385 0.76923077 0.53846154 0.46153846 0.61538462 0.69230769\n",
            " 0.92307692 0.84615385 0.84615385 0.69230769]\n",
            "Rata-rata =  0.723076923076923\n",
            "\n",
            "Nilai K=300\n",
            "Akurasi masing-masing = \n",
            "[0.69230769 0.61538462 0.61538462 0.46153846 0.61538462 0.76923077\n",
            " 0.92307692 0.84615385 0.69230769 0.46153846]\n",
            "Rata-rata =  0.6692307692307693\n",
            "\n",
            "Nilai K=400\n",
            "Akurasi masing-masing = \n",
            "[0.76923077 0.76923077 0.61538462 0.53846154 0.61538462 0.69230769\n",
            " 0.84615385 0.76923077 0.76923077 0.30769231]\n",
            "Rata-rata =  0.6692307692307693\n",
            "\n",
            "Nilai K=500\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.84615385 0.46153846 0.38461538 0.61538462 0.61538462\n",
            " 0.84615385 0.84615385 0.84615385 0.46153846]\n",
            "Rata-rata =  0.653846153846154\n",
            "\n",
            "Nilai K=600\n",
            "Akurasi masing-masing = \n",
            "[0.53846154 0.76923077 0.53846154 0.53846154 0.46153846 0.69230769\n",
            " 0.76923077 0.92307692 0.76923077 0.46153846]\n",
            "Rata-rata =  0.6461538461538462\n",
            "\n",
            "Nilai K=700\n",
            "Akurasi masing-masing = \n",
            "[0.76923077 0.69230769 0.61538462 0.38461538 0.53846154 0.61538462\n",
            " 0.76923077 0.92307692 0.84615385 0.61538462]\n",
            "Rata-rata =  0.676923076923077\n",
            "\n",
            "Nilai K=800\n",
            "Akurasi masing-masing = \n",
            "[0.61538462 0.61538462 0.46153846 0.53846154 0.38461538 0.69230769\n",
            " 0.92307692 0.84615385 0.84615385 0.61538462]\n",
            "Rata-rata =  0.6538461538461539\n",
            "\n",
            "Nilai K=900\n",
            "Akurasi masing-masing = \n",
            "[0.76923077 0.61538462 0.53846154 0.46153846 0.53846154 0.61538462\n",
            " 0.61538462 0.76923077 0.92307692 0.61538462]\n",
            "Rata-rata =  0.6461538461538462\n",
            "\n",
            "Nilai K=1000\n",
            "Akurasi masing-masing = \n",
            "[0.84615385 0.69230769 0.69230769 0.46153846 0.53846154 0.61538462\n",
            " 0.69230769 0.76923077 0.76923077 0.61538462]\n",
            "Rata-rata =  0.6692307692307692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top3_ab_index = sorted(range(len(ab_akurasi)), key=lambda i: ab_akurasi[i])[-3:]\n",
        "top3_ab_index.sort()\n",
        "\n",
        "ab1_features = list_fitur[top3_ab_index[0]]\n",
        "ab2_features = list_fitur[top3_ab_index[1]]\n",
        "ab3_features = list_fitur[top3_ab_index[2]]\n",
        "\n",
        "print(\"Banyak fitur terpilih pada AdaBoost: \")\n",
        "for i in range(0, len(top3_ab_index)):\n",
        "  print(\"AB\" + str(i+1) + \" =\", nilai_k[top3_ab_index[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xa3A4wzFu0ck",
        "outputId": "52dc6ada-7789-43d9-c82b-ab740d0957c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Banyak fitur terpilih pada AdaBoost: \n",
            "AB1 = 100\n",
            "AB2 = 200\n",
            "AB3 = 700\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualisasi Akurasi Cross Val Model terhadap Banyak Fitur"
      ],
      "metadata": {
        "id": "hhzyvNaivgg-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(nilai_k, rf_akurasi, linewidth=4, marker='o', color='forestgreen')\n",
        "plt.plot(nilai_k, ab_akurasi, linewidth=4, marker='o', color='goldenrod')\n",
        "plt.xlabel('Number of Features', fontweight='bold')\n",
        "plt.ylabel('Accuracy', fontweight='bold')\n",
        "plt.legend(['Random Forest', 'AdaBoost'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "BiN65_cOIqnm",
        "outputId": "5b6760f7-5ec5-42b5-c518-b2723a6cc81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Append dengan Label"
      ],
      "metadata": {
        "id": "8gnFRHtBHNkZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_features.append('Label')\n",
        "rf2_features.append('Label')\n",
        "rf3_features.append('Label')\n",
        "ab1_features.append('Label')\n",
        "ab2_features.append('Label')\n",
        "ab3_features.append('Label')"
      ],
      "metadata": {
        "id": "deDs9FDi3pie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menjamin agar tidak ada nama yang sama"
      ],
      "metadata": {
        "id": "wfgP5VwR8MTy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf1_features = list(dict.fromkeys(rf1_features))\n",
        "rf2_features = list(dict.fromkeys(rf2_features))\n",
        "rf3_features = list(dict.fromkeys(rf3_features))\n",
        "ab1_features = list(dict.fromkeys(ab1_features))\n",
        "ab2_features = list(dict.fromkeys(ab2_features))\n",
        "ab3_features = list(dict.fromkeys(ab3_features))"
      ],
      "metadata": {
        "id": "VUqEmgRS7H2F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Banyak kolom (termasuk Label)"
      ],
      "metadata": {
        "id": "1b4JTvTk8WNE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(rf1_features))\n",
        "print(len(rf2_features))\n",
        "print(len(rf3_features))\n",
        "print(len(ab1_features))\n",
        "print(len(ab2_features))\n",
        "print(len(ab3_features))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AleTVrKF7YKC",
        "outputId": "efdbc0e6-2e34-4e37-f44d-34ecb9390566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "301\n",
            "601\n",
            "901\n",
            "101\n",
            "201\n",
            "701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melihat contoh Dataframe yang sudah diseleksi fitur"
      ],
      "metadata": {
        "id": "uUSj_e-58aOD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_feature = df_train[rf3_features]\n",
        "df_feature.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mKdcTcvJ3296",
        "outputId": "a570af83-93b8-40eb-8498-816336cc2b1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   211612_s_at  219678_x_at  217653_x_at  211996_s_at  214594_x_at  \\\n",
              "0     2.535743     2.457776     2.052387     2.873506     2.285882   \n",
              "1     2.587822     2.408562     1.957657     2.779933     2.248177   \n",
              "2     2.529666     2.447706     2.059190     2.805088     2.255567   \n",
              "3     2.274739     2.537130     2.273920     2.952965     2.554384   \n",
              "4     2.551101     2.692597     2.321532     2.887101     2.752609   \n",
              "\n",
              "   218155_x_at  202936_s_at  207730_x_at  209703_x_at  216609_at  ...  \\\n",
              "0     2.518572     2.656989     2.833421     2.448979   1.982433  ...   \n",
              "1     2.508590     2.366057     2.871436     2.460372   2.139794  ...   \n",
              "2     2.658405     2.542071     2.871085     2.435954   2.113104  ...   \n",
              "3     2.736684     2.465318     2.988069     2.551221   2.204011  ...   \n",
              "4     2.752873     2.608392     3.098642     2.643402   2.483343  ...   \n",
              "\n",
              "   203290_at  210064_s_at  213668_s_at  202173_s_at  43544_at  201324_at  \\\n",
              "0   2.234970     3.122554     2.321798     2.154980  2.740056   1.712145   \n",
              "1   2.157700     3.221756     2.254661     2.231532  2.753174   1.503306   \n",
              "2   2.291107     3.190819     2.133749     2.242538  2.636099   1.633511   \n",
              "3   2.367685     2.812985     2.272442     2.062988  2.846455   1.436609   \n",
              "4   2.486182     3.232683     2.223057     2.249837  2.681201   1.847065   \n",
              "\n",
              "   216841_s_at  206149_at  205681_at  Label  \n",
              "0     2.341958   3.059717   2.352974      1  \n",
              "1     2.303909   2.702865   2.804416      1  \n",
              "2     2.400200   2.927471   2.320344      0  \n",
              "3     1.886354   2.842158   1.897309      0  \n",
              "4     2.190492   2.959261   2.686662      0  \n",
              "\n",
              "[5 rows x 901 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11d8bc40-3b99-4b6a-ada8-114770d8d539\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>211612_s_at</th>\n",
              "      <th>219678_x_at</th>\n",
              "      <th>217653_x_at</th>\n",
              "      <th>211996_s_at</th>\n",
              "      <th>214594_x_at</th>\n",
              "      <th>218155_x_at</th>\n",
              "      <th>202936_s_at</th>\n",
              "      <th>207730_x_at</th>\n",
              "      <th>209703_x_at</th>\n",
              "      <th>216609_at</th>\n",
              "      <th>...</th>\n",
              "      <th>203290_at</th>\n",
              "      <th>210064_s_at</th>\n",
              "      <th>213668_s_at</th>\n",
              "      <th>202173_s_at</th>\n",
              "      <th>43544_at</th>\n",
              "      <th>201324_at</th>\n",
              "      <th>216841_s_at</th>\n",
              "      <th>206149_at</th>\n",
              "      <th>205681_at</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.535743</td>\n",
              "      <td>2.457776</td>\n",
              "      <td>2.052387</td>\n",
              "      <td>2.873506</td>\n",
              "      <td>2.285882</td>\n",
              "      <td>2.518572</td>\n",
              "      <td>2.656989</td>\n",
              "      <td>2.833421</td>\n",
              "      <td>2.448979</td>\n",
              "      <td>1.982433</td>\n",
              "      <td>...</td>\n",
              "      <td>2.234970</td>\n",
              "      <td>3.122554</td>\n",
              "      <td>2.321798</td>\n",
              "      <td>2.154980</td>\n",
              "      <td>2.740056</td>\n",
              "      <td>1.712145</td>\n",
              "      <td>2.341958</td>\n",
              "      <td>3.059717</td>\n",
              "      <td>2.352974</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.587822</td>\n",
              "      <td>2.408562</td>\n",
              "      <td>1.957657</td>\n",
              "      <td>2.779933</td>\n",
              "      <td>2.248177</td>\n",
              "      <td>2.508590</td>\n",
              "      <td>2.366057</td>\n",
              "      <td>2.871436</td>\n",
              "      <td>2.460372</td>\n",
              "      <td>2.139794</td>\n",
              "      <td>...</td>\n",
              "      <td>2.157700</td>\n",
              "      <td>3.221756</td>\n",
              "      <td>2.254661</td>\n",
              "      <td>2.231532</td>\n",
              "      <td>2.753174</td>\n",
              "      <td>1.503306</td>\n",
              "      <td>2.303909</td>\n",
              "      <td>2.702865</td>\n",
              "      <td>2.804416</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.529666</td>\n",
              "      <td>2.447706</td>\n",
              "      <td>2.059190</td>\n",
              "      <td>2.805088</td>\n",
              "      <td>2.255567</td>\n",
              "      <td>2.658405</td>\n",
              "      <td>2.542071</td>\n",
              "      <td>2.871085</td>\n",
              "      <td>2.435954</td>\n",
              "      <td>2.113104</td>\n",
              "      <td>...</td>\n",
              "      <td>2.291107</td>\n",
              "      <td>3.190819</td>\n",
              "      <td>2.133749</td>\n",
              "      <td>2.242538</td>\n",
              "      <td>2.636099</td>\n",
              "      <td>1.633511</td>\n",
              "      <td>2.400200</td>\n",
              "      <td>2.927471</td>\n",
              "      <td>2.320344</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.274739</td>\n",
              "      <td>2.537130</td>\n",
              "      <td>2.273920</td>\n",
              "      <td>2.952965</td>\n",
              "      <td>2.554384</td>\n",
              "      <td>2.736684</td>\n",
              "      <td>2.465318</td>\n",
              "      <td>2.988069</td>\n",
              "      <td>2.551221</td>\n",
              "      <td>2.204011</td>\n",
              "      <td>...</td>\n",
              "      <td>2.367685</td>\n",
              "      <td>2.812985</td>\n",
              "      <td>2.272442</td>\n",
              "      <td>2.062988</td>\n",
              "      <td>2.846455</td>\n",
              "      <td>1.436609</td>\n",
              "      <td>1.886354</td>\n",
              "      <td>2.842158</td>\n",
              "      <td>1.897309</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.551101</td>\n",
              "      <td>2.692597</td>\n",
              "      <td>2.321532</td>\n",
              "      <td>2.887101</td>\n",
              "      <td>2.752609</td>\n",
              "      <td>2.752873</td>\n",
              "      <td>2.608392</td>\n",
              "      <td>3.098642</td>\n",
              "      <td>2.643402</td>\n",
              "      <td>2.483343</td>\n",
              "      <td>...</td>\n",
              "      <td>2.486182</td>\n",
              "      <td>3.232683</td>\n",
              "      <td>2.223057</td>\n",
              "      <td>2.249837</td>\n",
              "      <td>2.681201</td>\n",
              "      <td>1.847065</td>\n",
              "      <td>2.190492</td>\n",
              "      <td>2.959261</td>\n",
              "      <td>2.686662</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 901 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11d8bc40-3b99-4b6a-ada8-114770d8d539')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11d8bc40-3b99-4b6a-ada8-114770d8d539 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-11d8bc40-3b99-4b6a-ada8-114770d8d539');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Export"
      ],
      "metadata": {
        "id": "9a-gZLxmRMdV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan Export untuk best feature"
      ],
      "metadata": {
        "id": "L_i0yYAaRPUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rf1_features, 'rf1_features.pkl')\n",
        "joblib.dump(rf2_features, 'rf2_features.pkl')\n",
        "joblib.dump(rf3_features, 'rf3_features.pkl')\n",
        "joblib.dump(ab1_features, 'ab1_features.pkl')\n",
        "joblib.dump(ab2_features, 'ab2_features.pkl')\n",
        "joblib.dump(ab3_features, 'ab3_features.pkl')"
      ],
      "metadata": {
        "id": "ZX9Iiwi6RfJJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1a68819-cac1-42c5-a37d-f4b93fe8359a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ab3_features.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    }
  ]
}