Classification WineQuality-white Dataset.ipynb

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      "name": "assignment8_RickyGunawan_Wine_with_Classification.ipynb",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score,f1_score, precision_score, recall_score, accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler,PowerTransformer,QuantileTransformer,MinMaxScaler,MaxAbsScaler,RobustScaler\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "import xgboost as xgb\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "# Klasifikasi\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.naive_bayes import GaussianNB,BernoulliNB,CategoricalNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import xgboost as xgb\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOPvdPQEefXX",
        "colab_type": "text"
      },
      "source": [
        "# **Dataset Wine Quality White**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ormrK9-VCEC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('/content/drive/My Drive/BCML/datasets/winequality-white.csv',delimiter=';')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P39HC0reZ5tc",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "5aaf2545-60c3-4297-9ad5-9bd91c6efc16"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "      <td>4898.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.854788</td>\n",
              "      <td>0.278241</td>\n",
              "      <td>0.334192</td>\n",
              "      <td>6.391415</td>\n",
              "      <td>0.045772</td>\n",
              "      <td>35.308085</td>\n",
              "      <td>138.360657</td>\n",
              "      <td>0.994027</td>\n",
              "      <td>3.188267</td>\n",
              "      <td>0.489847</td>\n",
              "      <td>10.514267</td>\n",
              "      <td>5.877909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.843868</td>\n",
              "      <td>0.100795</td>\n",
              "      <td>0.121020</td>\n",
              "      <td>5.072058</td>\n",
              "      <td>0.021848</td>\n",
              "      <td>17.007137</td>\n",
              "      <td>42.498065</td>\n",
              "      <td>0.002991</td>\n",
              "      <td>0.151001</td>\n",
              "      <td>0.114126</td>\n",
              "      <td>1.230621</td>\n",
              "      <td>0.885639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.987110</td>\n",
              "      <td>2.720000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.300000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>0.036000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.991723</td>\n",
              "      <td>3.090000</td>\n",
              "      <td>0.410000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.800000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>0.043000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>134.000000</td>\n",
              "      <td>0.993740</td>\n",
              "      <td>3.180000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>10.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.300000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>0.996100</td>\n",
              "      <td>3.280000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>11.400000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.200000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>65.800000</td>\n",
              "      <td>0.346000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>1.038980</td>\n",
              "      <td>3.820000</td>\n",
              "      <td>1.080000</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed_acidity  volatile_acidity  ...      alcohol      quality\n",
              "count    4898.000000       4898.000000  ...  4898.000000  4898.000000\n",
              "mean        6.854788          0.278241  ...    10.514267     5.877909\n",
              "std         0.843868          0.100795  ...     1.230621     0.885639\n",
              "min         3.800000          0.080000  ...     8.000000     3.000000\n",
              "25%         6.300000          0.210000  ...     9.500000     5.000000\n",
              "50%         6.800000          0.260000  ...    10.400000     6.000000\n",
              "75%         7.300000          0.320000  ...    11.400000     6.000000\n",
              "max        14.200000          1.100000  ...    14.200000     9.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "ijpgxkYXV1F_",
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          "height": 224
        },
        "outputId": "9c81d1e8-372e-4d9f-9a5f-5e3691bf7118"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.045</td>\n",
              "      <td>45.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1.0010</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>8.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed_acidity  volatile_acidity  citric_acid  ...  sulphates  alcohol  quality\n",
              "0            7.0              0.27         0.36  ...       0.45      8.8        6\n",
              "1            6.3              0.30         0.34  ...       0.49      9.5        6\n",
              "2            8.1              0.28         0.40  ...       0.44     10.1        6\n",
              "3            7.2              0.23         0.32  ...       0.40      9.9        6\n",
              "4            7.2              0.23         0.32  ...       0.40      9.9        6\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
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    {
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        "id": "9UYAgySIV0-J",
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          "height": 238
        },
        "outputId": "54abd6d6-2512-4f89-9f96-47bfc2b113f4"
      },
      "source": [
        "dataset.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed_acidity           0\n",
              "volatile_acidity        0\n",
              "citric_acid             0\n",
              "residual_sugar          0\n",
              "chlorides               0\n",
              "free_sulfur_dioxide     0\n",
              "total_sulfur_dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEJIM87AWC6O",
        "colab_type": "code",
        "colab": {
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          "height": 221
        },
        "outputId": "c8232073-5b0a-44ec-e3a6-72cc076fac80"
      },
      "source": [
        "dataset.corr()['quality'][:-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fixed_acidity          -0.113663\n",
              "volatile_acidity       -0.194723\n",
              "citric_acid            -0.009209\n",
              "residual_sugar         -0.097577\n",
              "chlorides              -0.209934\n",
              "free_sulfur_dioxide     0.008158\n",
              "total_sulfur_dioxide   -0.174737\n",
              "density                -0.307123\n",
              "pH                      0.099427\n",
              "sulphates               0.053678\n",
              "alcohol                 0.435575\n",
              "Name: quality, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEYhfMumWoGo",
        "colab_type": "code",
        "colab": {
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          "height": 944
        },
        "outputId": "ba19a2a5-effc-4dc9-d854-80e606fc8bd0"
      },
      "source": [
        "corr = dataset.corr()\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(corr, cbar=True, square= True, fmt='.1f', annot=True, annot_kws={'size':15}, cmap='Greens')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c0a7334e0>"
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        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hE0M42LVYEPS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for index in range(len(dataset)):\n",
        "  if dataset.loc[index,'citric_acid'] <= 0:\n",
        "    dataset.loc[index,'citric_acid'] = 0.33\n",
        "    # print('ada')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMv4jFBLeoed",
        "colab_type": "text"
      },
      "source": [
        "## **No.1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1WMEmskW_Tm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "outputId": "33731aaf-f3d5-4c96-8c40-ca030ee5aaf7"
      },
      "source": [
        "scale=[StandardScaler(),PowerTransformer(method='yeo-johnson'),PowerTransformer(method='box-cox'),\n",
        "       QuantileTransformer(n_quantiles=100, random_state=10),MinMaxScaler(),MaxAbsScaler(),RobustScaler()]\n",
        "\n",
        "for index,value in enumerate(scale):\n",
        "  scaler = value\n",
        "  feature_used= dataset.drop(['quality'],axis=1)\n",
        "  label = dataset['quality']\n",
        "  feature_scaled =scaler.fit_transform(feature_used.values)\n",
        "  feature_train, feature_test,label_train, label_test = train_test_split(feature_scaled, label, test_size=0.25,random_state=10)\n",
        "\n",
        "  Linear_reg = LinearRegression()\n",
        "  Linear_reg.fit(feature_train, label_train)\n",
        "  predictions = Linear_reg.predict(feature_test)\n",
        "\n",
        "  mse = mean_squared_error(label_test, predictions)\n",
        "  mae = mean_absolute_error(label_test, predictions)\n",
        "  r2 = r2_score(label_test, predictions)\n",
        "  print(\"\\n{}\".format(value))\n",
        "  print(\"MSE (Mean Squared Error)\", mse)\n",
        "  print(\"MAE (Mean Absolute Error)\", mae)\n",
        "  print(\"r^2 score\", r2)\n",
        "  print('RMSE (Root Mean Squared Error', np.sqrt(mean_squared_error(label_test, predictions)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "StandardScaler(copy=True, with_mean=True, with_std=True)\n",
            "MSE (Mean Squared Error) 0.5548082996403448\n",
            "MAE (Mean Absolute Error) 0.5804277307089775\n",
            "r^2 score 0.28955008495114465\n",
            "RMSE (Root Mean Squared Error 0.7448545493184189\n",
            "\n",
            "PowerTransformer(copy=True, method='yeo-johnson', standardize=True)\n",
            "MSE (Mean Squared Error) 0.553078299702105\n",
            "MAE (Mean Absolute Error) 0.5801977819625543\n",
            "r^2 score 0.29176540564831843\n",
            "RMSE (Root Mean Squared Error 0.7436923421026366\n",
            "\n",
            "PowerTransformer(copy=True, method='box-cox', standardize=True)\n",
            "MSE (Mean Squared Error) 0.5495736675844238\n",
            "MAE (Mean Absolute Error) 0.5799931896296917\n",
            "r^2 score 0.29625320006649514\n",
            "RMSE (Root Mean Squared Error 0.7413323597310614\n",
            "\n",
            "QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=100,\n",
            "                    output_distribution='uniform', random_state=10,\n",
            "                    subsample=100000)\n",
            "MSE (Mean Squared Error) 0.549487944578978\n",
            "MAE (Mean Absolute Error) 0.5854684653018515\n",
            "r^2 score 0.2963629711387308\n",
            "RMSE (Root Mean Squared Error 0.7412745406251169\n",
            "\n",
            "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
            "MSE (Mean Squared Error) 0.5548082996403455\n",
            "MAE (Mean Absolute Error) 0.5804277307089775\n",
            "r^2 score 0.28955008495114376\n",
            "RMSE (Root Mean Squared Error 0.7448545493184193\n",
            "\n",
            "MaxAbsScaler(copy=True)\n",
            "MSE (Mean Squared Error) 0.5548082996403396\n",
            "MAE (Mean Absolute Error) 0.5804277307089789\n",
            "r^2 score 0.2895500849511512\n",
            "RMSE (Root Mean Squared Error 0.7448545493184153\n",
            "\n",
            "RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,\n",
            "             with_scaling=True)\n",
            "MSE (Mean Squared Error) 0.554808299640345\n",
            "MAE (Mean Absolute Error) 0.5804277307089775\n",
            "r^2 score 0.2895500849511444\n",
            "RMSE (Root Mean Squared Error 0.7448545493184189\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MXY5hfXoiz7",
        "colab_type": "text"
      },
      "source": [
        "berdasarkan hasil di atas yang menunjukan kinerja terbaik dalam dataset ini adalah quantile transformer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvJ4hlyxeyni",
        "colab_type": "text"
      },
      "source": [
        "## **No.2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8wTPSoZe4LF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = preprocessing.QuantileTransformer(n_quantiles=100, random_state=10)\n",
        "feature_used= dataset.drop(['quality'],axis=1)\n",
        "label = dataset['quality']\n",
        "feature_scaled =scaler.fit_transform(feature_used.values)\n",
        "feature_train, feature_test,label_train, label_test = train_test_split(feature_scaled, label, test_size=0.25,random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_G0pn1IZgIyX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "86f461d0-a836-45d2-b955-2d32ce10f30e"
      },
      "source": [
        "algorithm =[RandomForestRegressor,xgb.XGBRegressor,SVR,KNeighborsRegressor]\n",
        "n=[1,3,5,7,9,11,13,15]\n",
        "kernel=['linear', 'poly', 'rbf']\n",
        "for index, value in enumerate(algorithm):\n",
        "  if value == RandomForestRegressor:\n",
        "    reg = RandomForestRegressor(max_depth=100, random_state=10)\n",
        "    reg.fit(feature_train, label_train)\n",
        "    predictions = reg.predict(feature_test)\n",
        "  if value == xgb.XGBRegressor:\n",
        "    xgb_reg = xgb.XGBRegressor(max_depth=3,n_estimators=125,colsample_bytree=1,learning_rate=0.27,objective='reg:squarederror')\n",
        "    xgb_reg.fit(feature_train, label_train)\n",
        "    predictions = xgb_reg.predict(feature_test)\n",
        "  if value == SVR:\n",
        "    for i,v in enumerate(kernel):\n",
        "      reggression = SVR(kernel=v)\n",
        "      reggression.fit(feature_train, label_train)\n",
        "      predictions = reggression.predict(feature_test)\n",
        "\n",
        "      mse = mean_squared_error(label_test, predictions)\n",
        "      mae = mean_absolute_error(label_test, predictions)\n",
        "      r2 = r2_score(label_test, predictions)\n",
        "      print('\\n{} kernel={}'.format(value,v))\n",
        "      print(\"MSE (Mean Squared Error) = {} \".format(mse))\n",
        "      print(\"MAE (Mean Absolute Error) = {} \".format(mae))\n",
        "      print(\"r^2 score  = {} \".format(r2))\n",
        "      print('RMSE (Root Mean Squared Error = {}  \\n'.format(np.sqrt(mean_squared_error(label_test, predictions))))\n",
        "      plt.scatter(label_test, predictions)\n",
        "    continue\n",
        "  if value == KNeighborsRegressor:\n",
        "    for i,v in enumerate(n):\n",
        "      reggression = KNeighborsRegressor(n_neighbors=v)\n",
        "      reggression.fit(feature_train, label_train)\n",
        "      predictions = reggression.predict(feature_test)\n",
        "\n",
        "      mse = mean_squared_error(label_test, predictions)\n",
        "      mae = mean_absolute_error(label_test, predictions)\n",
        "      r2 = r2_score(label_test, predictions)\n",
        "      print('\\n{} n_neighbors={}'.format(value,v))\n",
        "      print(\"MSE (Mean Squared Error) = {} \".format(mse))\n",
        "      print(\"MAE (Mean Absolute Error) = {} \".format(mae))\n",
        "      print(\"r^2 score  = {}\".format(r2))\n",
        "      print('RMSE (Root Mean Squared Error = {}\\n'.format(np.sqrt(mean_squared_error(label_test, predictions))))\n",
        "      plt.scatter(label_test, predictions)\n",
        "    continue\n",
        "\n",
        "  mse = mean_squared_error(label_test, predictions)\n",
        "  mae = mean_absolute_error(label_test, predictions)\n",
        "  r2 = r2_score(label_test, predictions)\n",
        "  print('\\n',value)\n",
        "  print(\"\\nMSE (Mean Squared Error)\", mse)\n",
        "  print(\"MAE (Mean Absolute Error)\", mae)\n",
        "  print(\"r^2 score\", r2)\n",
        "  print('RMSE (Root Mean Squared Error', np.sqrt(mean_squared_error(label_test, predictions)))\n",
        "  plt.scatter(label_test, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " <class 'sklearn.ensemble._forest.RandomForestRegressor'>\n",
            "\n",
            "MSE (Mean Squared Error) 0.34928089795918366\n",
            "MAE (Mean Absolute Error) 0.4317306122448979\n",
            "r^2 score 0.5527345491331848\n",
            "RMSE (Root Mean Squared Error 0.5909999136710459\n",
            "\n",
            " <class 'xgboost.sklearn.XGBRegressor'>\n",
            "\n",
            "MSE (Mean Squared Error) 0.44778562744355904\n",
            "MAE (Mean Absolute Error) 0.5183854050538977\n",
            "r^2 score 0.4265960671756306\n",
            "RMSE (Root Mean Squared Error 0.6691678619326835\n",
            "\n",
            "<class 'sklearn.svm._classes.SVR'> kernel=linear\n",
            "MSE (Mean Squared Error) = 0.5552507394870209 \n",
            "MAE (Mean Absolute Error) = 0.5807488748463339 \n",
            "r^2 score  = 0.28898352646294434 \n",
            "RMSE (Root Mean Squared Error = 0.7451514876097483  \n",
            "\n",
            "\n",
            "<class 'sklearn.svm._classes.SVR'> kernel=poly\n",
            "MSE (Mean Squared Error) = 0.5215825947013364 \n",
            "MAE (Mean Absolute Error) = 0.5553819953185789 \n",
            "r^2 score  = 0.33209667134262255 \n",
            "RMSE (Root Mean Squared Error = 0.7222067534309938  \n",
            "\n",
            "\n",
            "<class 'sklearn.svm._classes.SVR'> kernel=rbf\n",
            "MSE (Mean Squared Error) = 0.4825505392278933 \n",
            "MAE (Mean Absolute Error) = 0.5232802722867175 \n",
            "r^2 score  = 0.3820784767937414 \n",
            "RMSE (Root Mean Squared Error = 0.694658577452185  \n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=1\n",
            "MSE (Mean Squared Error) = 0.6285714285714286 \n",
            "MAE (Mean Absolute Error) = 0.45714285714285713 \n",
            "r^2 score  = 0.19509402018643607\n",
            "RMSE (Root Mean Squared Error = 0.7928249671720918\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=3\n",
            "MSE (Mean Squared Error) = 0.5387755102040817 \n",
            "MAE (Mean Absolute Error) = 0.524625850340136 \n",
            "r^2 score  = 0.3100805887312309\n",
            "RMSE (Root Mean Squared Error = 0.734013290209436\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=5\n",
            "MSE (Mean Squared Error) = 0.529795918367347 \n",
            "MAE (Mean Absolute Error) = 0.5448163265306123 \n",
            "r^2 score  = 0.3215792455857104\n",
            "RMSE (Root Mean Squared Error = 0.7278708115918284\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=7\n",
            "MSE (Mean Squared Error) = 0.5088713036234901 \n",
            "MAE (Mean Absolute Error) = 0.5428571428571428 \n",
            "r^2 score  = 0.3483738893876145\n",
            "RMSE (Root Mean Squared Error = 0.7133521596122704\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=9\n",
            "MSE (Mean Squared Error) = 0.5033308138070043 \n",
            "MAE (Mean Absolute Error) = 0.5401360544217686 \n",
            "r^2 score  = 0.35546866522640974\n",
            "RMSE (Root Mean Squared Error = 0.7094581127924356\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=11\n",
            "MSE (Mean Squared Error) = 0.49841119919042 \n",
            "MAE (Mean Absolute Error) = 0.5421150278293134 \n",
            "r^2 score  = 0.3617683903543344\n",
            "RMSE (Root Mean Squared Error = 0.7059824354687728\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=13\n",
            "MSE (Mean Squared Error) = 0.5025190194420964 \n",
            "MAE (Mean Absolute Error) = 0.5481946624803767 \n",
            "r^2 score  = 0.35650819408342194\n",
            "RMSE (Root Mean Squared Error = 0.7088857590910516\n",
            "\n",
            "\n",
            "<class 'sklearn.neighbors._regression.KNeighborsRegressor'> n_neighbors=15\n",
            "MSE (Mean Squared Error) = 0.5015473922902495 \n",
            "MAE (Mean Absolute Error) = 0.5471020408163265 \n",
            "r^2 score  = 0.35775239397721637\n",
            "RMSE (Root Mean Squared Error = 0.7082001075192303\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FH8mbPwbo0rp",
        "colab_type": "text"
      },
      "source": [
        "menurut hasil di atas yang terbaik adalah random forest. jika di lihat mengapa bisa jelek semua ? ini di karenakan data yang kita olah merupakan data yang target nya itu bisa merupakan klasifikasi. maka dari itu jika di lihat pada gambar sebaran datanya tidak menunjukan diagonal. maka dari itu akurasi yang di dapat kan sangat kurang."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7SWpyrs3zOt",
        "colab_type": "text"
      },
      "source": [
        "## **Test Klasifikasi**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcef0zb7Estr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "5d99ecf9-b7b7-4bb0-c7db-11771453d4c3"
      },
      "source": [
        "classifier = LogisticRegression(max_iter=500)\n",
        "classifier.fit(feature_train, label_train)\n",
        "predictions = classifier.predict(feature_test)\n",
        "\n",
        "print('Accuracy of {} on test set: {:.2f}'.format('Logistik Regression',classifier.score(feature_test, label_test))) # .score() -> Accuracy\n",
        "print(f1_score(label_test, predictions, average='macro'))\n",
        "print(precision_score(label_test, predictions, average='macro'))\n",
        "print(recall_score(label_test, predictions, average='macro'),'\\n')\n",
        "plt.scatter(label_test, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of Logistik Regression on test set: 0.53\n",
            "0.2072257182863228\n",
            "0.22586632679446134\n",
            "0.21242179243137474 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2c04c7f080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhNmlXsX4Pcq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f6bc93ff-73f8-469b-9001-f270156d7442"
      },
      "source": [
        "algoritma=[RandomForestClassifier,KNeighborsClassifier,SVC,LinearSVC,GaussianNB,BernoulliNB,LogisticRegression,LinearDiscriminantAnalysis,DecisionTreeClassifier,xgb.XGBClassifier]\n",
        "\n",
        "for index,value in enumerate(algoritma):\n",
        "  if value == RandomForestClassifier:\n",
        "    classifier = value(n_estimators=5, bootstrap=True, max_features = \"sqrt\")\n",
        "    classifier.fit(feature_train, label_train)\n",
        "  elif value == KNeighborsClassifier:\n",
        "    classifier = value(13)\n",
        "    classifier.fit(feature_train, label_train)\n",
        "  elif value == SVC:\n",
        "    kernels=[\"linear\",\"poly\",\"rbf\"]\n",
        "    for index,value_ker in enumerate(kernels):\n",
        "      svc_classifier = SVC(kernel = value_ker)\n",
        "      svc_classifier.fit(feature_train, label_train)\n",
        "      # prediction\n",
        "      prediction = svc_classifier.predict(feature_test)\n",
        "      #hasil\n",
        "      print('Accuracy of SVC with Kernel {} on test set: {:.2f}'.format(value_ker,svc_classifier.score(feature_test, label_test))) # .score() -> Accuracy\n",
        "      print(f1_score(label_test, prediction, average='macro'))\n",
        "      print(precision_score(label_test, prediction, average='macro'))\n",
        "      print(recall_score(label_test, prediction, average='macro'),'\\n')\n",
        "    continue\n",
        "  elif value == xgb.XGBClassifier:\n",
        "    classifier = value(n_estimators=5,learning_rate=0.1,colsample_bytree=0.3,max_depth=5)\n",
        "    classifier.fit(feature_train, label_train)\n",
        "  else:\n",
        "    classifier = value()\n",
        "    classifier.fit(feature_train, label_train)\n",
        "  \n",
        "  # prediction\n",
        "  prediction = classifier.predict(feature_test)\n",
        "  #hasil\n",
        "  print('Accuracy of {} on test set: {:.2f}'.format(value,classifier.score(feature_test, label_test))) # .score() -> Accuracy\n",
        "  print(f1_score(label_test, prediction, average='macro'))\n",
        "  print(precision_score(label_test, prediction, average='macro'))\n",
        "  print(recall_score(label_test, prediction, average='macro'),'\\n')\n",
        "  plt.scatter(label_test, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of <class 'sklearn.ensemble._forest.RandomForestClassifier'> on test set: 0.64\n",
            "0.3862170497379208\n",
            "0.4413724405862771\n",
            "0.3599773589208825 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of <class 'sklearn.neighbors._classification.KNeighborsClassifier'> on test set: 0.53\n",
            "0.2263268619088706\n",
            "0.2230001613629222\n",
            "0.2316116476291757 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of SVC with Kernel linear on test set: 0.52\n",
            "0.167099173911163\n",
            "0.15219974309569687\n",
            "0.19200667279898972 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of SVC with Kernel poly on test set: 0.56\n",
            "0.2658887409869393\n",
            "0.37897982229578886\n",
            "0.25141838496238555 \n",
            "\n",
            "Accuracy of SVC with Kernel rbf on test set: 0.56\n",
            "0.2265208740276688\n",
            "0.23927703085244983\n",
            "0.22974339115414383 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of <class 'sklearn.svm._classes.LinearSVC'> on test set: 0.53\n",
            "0.19930199123583617\n",
            "0.23908720616381215\n",
            "0.2081367751806331 \n",
            "\n",
            "Accuracy of <class 'sklearn.naive_bayes.GaussianNB'> on test set: 0.48\n",
            "0.221219739286561\n",
            "0.23309386211766073\n",
            "0.23401557932590075 \n",
            "\n",
            "Accuracy of <class 'sklearn.naive_bayes.BernoulliNB'> on test set: 0.44\n",
            "0.08892043788317365\n",
            "0.2058170774442238\n",
            "0.1436210847975554 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of <class 'sklearn.linear_model._logistic.LogisticRegression'> on test set: 0.53\n",
            "0.20861805969292355\n",
            "0.2280282190202049\n",
            "0.21332744850313312 \n",
            "\n",
            "Accuracy of <class 'sklearn.discriminant_analysis.LinearDiscriminantAnalysis'> on test set: 0.53\n",
            "0.24977557013566234\n",
            "0.2754936930457542\n",
            "0.24318872036613842 \n",
            "\n",
            "Accuracy of <class 'sklearn.tree._classes.DecisionTreeClassifier'> on test set: 0.61\n",
            "0.3918436717911759\n",
            "0.3919594937204752\n",
            "0.39225949627329904 \n",
            "\n",
            "Accuracy of <class 'xgboost.sklearn.XGBClassifier'> on test set: 0.53\n",
            "0.1768661440258576\n",
            "0.24788210290459167\n",
            "0.1951203986340911 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}