XGBOOST_dataset2_(1).ipynb

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      "execution_count": null,
      "metadata": {
        "id": "AUoN-dXSQDci"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import explained_variance_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RE8wyslRQWiA"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('Dataset 2.csv')"
      ]
    },
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            "text/plain": [
              "    COIL_ID       GRADE    COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
              "0  20081459  JVHGLD4AS0   8/10/2020 2:25            1081           869   \n",
              "1  20097626  JVHGLD4AS0   9/14/2020 2:30            1070           838   \n",
              "2  20097722  JVHCM30A00   9/14/2020 6:47            1075           882   \n",
              "3  20098318  JVHST01C00  9/15/2020 11:55            1088           864   \n",
              "4  20098589  JVHWT02A00   9/16/2020 1:14            1061           848   \n",
              "\n",
              "   COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
              "0           680      1125   10750.0           69.81               34  ...   \n",
              "1           662      1245   10640.0           68.63               34  ...   \n",
              "2           611      1040   10750.0           66.08               36  ...   \n",
              "3           603      1260    9000.0           51.94               38  ...   \n",
              "4           650      1265    9811.0           61.23               35  ...   \n",
              "\n",
              "        N     NI      P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
              "0  0.0025  0.007  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
              "1  0.0022  0.007  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
              "2  0.0070  0.008  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
              "3  0.0035  0.008  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
              "4  0.0032  0.007  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
              "\n",
              "[5 rows x 30 columns]"
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          "execution_count": 3,
          "metadata": {},
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        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDEat7m1Q65Q",
        "outputId": "71c0866f-f399-4179-b3ac-1344c0508b61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "  Using cached https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "Requirement already satisfied: joblib~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.1.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.7.3)\n",
            "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (3.2.2)\n",
            "Requirement already satisfied: pydantic>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.8.2)\n",
            "Requirement already satisfied: PyYAML>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (6.0)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (2.11.3)\n",
            "Requirement already satisfied: visions[type_image_path]==0.7.5 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.7.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.21.6)\n",
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            "Requirement already satisfied: phik>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.12.2)\n",
            "Requirement already satisfied: tangled-up-in-unicode==0.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.2.0)\n",
            "Requirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (2.28.1)\n",
            "Requirement already satisfied: tqdm>=4.48.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (4.64.0)\n",
            "Requirement already satisfied: seaborn>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.11.2)\n",
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            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (2.6.3)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (21.4.0)\n",
            "Requirement already satisfied: imagehash in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (4.2.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (7.1.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling==3.2.0) (2.0.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling==3.2.0) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2022.6.15)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (1.24.3)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash->visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "! pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gVak0S3-RWAy"
      },
      "outputs": [],
      "source": [
        "from pandas_profiling import ProfileReport"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0vrGe9GYSFpR"
      },
      "outputs": [],
      "source": [
        "profile = ProfileReport(df, title=\"property_prediction\", html={'style':{'full_width':True}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "63ef8f3b6f35460eb635105220dee34e",
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        },
        "id": "Hp8X2IbrSlSb",
        "outputId": "3bc58c7d-4960-4ca2-f2ff-ce660ac31115"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7680d20ae19b4961a530cc3c11f0e690",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "691524a117df457ca0b4dabe12807fb8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "059eaecf0f1646f8a9a30aa9669c933f",
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              "version_minor": 0
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            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "profile.to_notebook_iframe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w0mFQyJcm7ug"
      },
      "outputs": [],
      "source": [
        "## Correlation\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "#get correlations of each features in dataset\n",
        "corrmat = df.corr()\n",
        "top_corr_features = corrmat.index\n",
        "plt.figure(figsize=(20,20))\n",
        "#plot heat map\n",
        "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")## Correlation\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "#get correlations of each features in dataset\n",
        "corrmat = df.corr()\n",
        "top_corr_features = corrmat.index\n",
        "plt.figure(figsize=(20,20))\n",
        "#plot heat map\n",
        "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g1iz2Js0VCQt"
      },
      "outputs": [],
      "source": [
        "X=df.drop(['COIL_ID','UTS','YS','P_ELONGATION','GRADE','COIL_GEN_TIME'],axis=1)\n",
        "\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5DbWFTYQVCY9"
      },
      "outputs": [],
      "source": [
        "y=df[['UTS'\n",
        "       ]].values\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bNfu14_-St-_"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q3WFfrXZU1Ol"
      },
      "outputs": [],
      "source": [
        "# Fitting XGB regressor model and default base learner is Decision Tree\n",
        "xgb_reg = xgb.XGBRegressor(objective=\"reg:linear\", n_estimators=75, subsample=0.75, max_depth=7)\n",
        "xgb_reg.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ERy5Sl9mTvsB"
      },
      "outputs": [],
      "source": [
        "# Making Predictions\n",
        "y_pred = xgb_reg.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qISaPCxgVgd4"
      },
      "outputs": [],
      "source": [
        "# Variance_score\n",
        "print((explained_variance_score(y_pred, y_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X-gWAnrQVkLD"
      },
      "outputs": [],
      "source": [
        "#  To convert data table into a matrix\n",
        "kc_dmatrix = xgb.DMatrix(data=X, label=y, feature_names=X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfPvLPmfVonb"
      },
      "outputs": [],
      "source": [
        "#  Create the parameter dictionary: params\n",
        "params = {\"objective\": \"reg:linear\", \"max_depth\": 2}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KsryBRY4VvaQ"
      },
      "outputs": [],
      "source": [
        "# Train the model: xg_reg\n",
        "xg_reg = xgb.train(params=params, dtrain=kc_dmatrix, num_boost_round=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "emLY9QtrVyc9"
      },
      "outputs": [],
      "source": [
        "# Plot the first tree as num_trees = 0 and features importance\n",
        "xgb.plot_tree(xg_reg, num_trees=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sqaIbbalV1xM"
      },
      "outputs": [],
      "source": [
        "xgb.plot_importance(xg_reg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M4-s2PvmV8Zo"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error # for calculating the cost function\n",
        "from sklearn.ensemble import RandomForestRegressor # for building the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TdniOFQqWIP2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "rmse=float(format(np.sqrt(mean_squared_error(y_pred,y_test)),'.3f'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "5OJ_3vRGWKsd",
        "outputId": "9dd8f194-4d84-46b0-a92e-de9ffcef0770"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "RMSE: 11.411\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ifXpkAlNm7x6"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "GB_accuracies = cross_val_score(estimator =xgb_reg , X = X_train, y = y_train, cv = 8)\n",
        "print(\"Mean_GB_Acc : \", GB_accuracies.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Feor41jA9Jv_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from xgboost import XGBRegressor\n",
        "xgb1 = XGBRegressor()\n",
        "parameters = {'nthread':[4], #when use hyperthread, xgboost may become slower\n",
        "              'objective':['reg:linear'],\n",
        "              'learning_rate': [.03, 0.05, .07], #so called `eta` value\n",
        "              'max_depth': [5, 6, 7],\n",
        "              'min_child_weight': [4],\n",
        "              'silent': [1],\n",
        "              'subsample': [0.7],\n",
        "              'colsample_bytree': [0.7],\n",
        "              'n_estimators': [500]}\n",
        "\n",
        "xgb_grid = GridSearchCV(xgb1,\n",
        "                        parameters,\n",
        "                        cv = 2,\n",
        "                        n_jobs = 5,\n",
        "                        verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GY_AZajt9NPW"
      },
      "outputs": [],
      "source": [
        "xgb_grid.fit(X_train,\n",
        "         y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y_C65lIHv4y2"
      },
      "outputs": [],
      "source": [
        "print(\" Results from Grid Search \" )\n",
        "print(\"\\n The best estimator across ALL searched params:\\n\",xgb_grid.best_estimator_)\n",
        "print(\"\\n The best score across ALL searched params:\\n\",xgb_grid.best_score_)\n",
        "print(\"\\n The best parameters across ALL searched params:\\n\",xgb_grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ggJ9E5HlWaCO"
      },
      "outputs": [],
      "source": [
        "X=df[['FM_DOUT_TEMP', 'STRIP_EXIT_LEN', 'C', 'NB', 'COILING_TEMP', 'MN', 'P', 'TI', 'CR', 'SI', 'FM_EXIT_THIK', 'N']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D0M8SZCbXhL8"
      },
      "outputs": [],
      "source": [
        "y=df[['UTS'\n",
        "       ]].values\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_d9V9kxJXkIi"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z8ClQV5HXqGN"
      },
      "outputs": [],
      "source": [
        "# Fitting XGB regressor model and default base learner is Decision Tree\n",
        "xgb_reg = xgb.XGBRegressor(objective=\"reg:linear\", n_estimators=75, subsample=0.75, max_depth=7)\n",
        "xgb_reg.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H1WUR-VCXtJ9"
      },
      "outputs": [],
      "source": [
        "# Making Predictions\n",
        "y_pred = xgb_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hzasp7JhX5t0"
      },
      "outputs": [],
      "source": [
        "# Variance_score\n",
        "print((explained_variance_score(y_pred, y_test)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-eR6WjaSX87c"
      },
      "outputs": [],
      "source": [
        "#  To convert data table into a matrix\n",
        "kc_dmatrix = xgb.DMatrix(data=X, label=y, feature_names=X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z61lkF0bYGDB"
      },
      "outputs": [],
      "source": [
        "#  Create the parameter dictionary: params\n",
        "params = {\"objective\": \"reg:linear\", \"max_depth\": 2}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DJ8lDzHKYJK_"
      },
      "outputs": [],
      "source": [
        "# Train the model: xg_reg\n",
        "xg_reg = xgb.train(params=params, dtrain=kc_dmatrix, num_boost_round=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "npW0a0i1YOG2"
      },
      "outputs": [],
      "source": [
        "# Plot the first tree as num_trees = 0 and features importance\n",
        "xgb.plot_tree(xg_reg, num_trees=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHww9HUoYSUy"
      },
      "outputs": [],
      "source": [
        "xgb.plot_importance(xg_reg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0X9Zhln3YWXQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "rmse=float(format(np.sqrt(mean_squared_error(y_pred,y_test)),'.3f'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TQ-b4X98YdEr"
      },
      "outputs": [],
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OfkA275XDK1-"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "GB_accuracies = cross_val_score(estimator =xgb_reg , X = X_train, y = y_train, cv =8)\n",
        "print(\"Mean_GB_Acc : \", GB_accuracies.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jRxRW0uWDL6w"
      },
      "outputs": [],
      "source": [
        "\n",
        "xgb1 = XGBRegressor()\n",
        "parameters = {'nthread':[4], #when use hyperthread, xgboost may become slower\n",
        "              'objective':['reg:linear'],\n",
        "              'learning_rate': [.03, 0.05, .07], #so called `eta` value\n",
        "              'max_depth': [5, 6, 7],\n",
        "              'min_child_weight': [4],\n",
        "              'silent': [1],\n",
        "              'subsample': [0.7],\n",
        "              'colsample_bytree': [0.7],\n",
        "              'n_estimators': [500]}\n",
        "\n",
        "xgb_grid = GridSearchCV(xgb1,\n",
        "                        parameters,\n",
        "                        cv = 2,\n",
        "                        n_jobs = 5,\n",
        "                        verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LX0Yo9b8p6yX"
      },
      "outputs": [],
      "source": [
        "xgb_grid.fit(X_train,\n",
        "         y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sqY4La5TzPXr"
      },
      "outputs": [],
      "source": [
        "print(\" Results from Grid Search \" )\n",
        "print(\"\\n The best estimator across ALL searched params:\\n\",xgb_grid.best_estimator_)\n",
        "print(\"\\n The best score across ALL searched params:\\n\",xgb_grid.best_score_)\n",
        "print(\"\\n The best parameters across ALL searched params:\\n\",xgb_grid.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ryEGt3E3z7VK"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYrIOvC1NSGj",
        "outputId": "f207e9a7-d8a9-4c6c-e35b-f8be4a2ea583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.87065855138223"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "RnXPy_BiNXpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bny9zzr0Nh8q",
        "outputId": "da842dbf-9889-444e-bd29-325ef9797e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "134.71428519917328"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse=float(format(np.sqrt(mean_squared_error(y_pred,y_test)),'.3f'))"
      ],
      "metadata": {
        "id": "-EEG6pzANoa8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCsKP0epOVGz",
        "outputId": "df390cf7-c818-4f9e-ecd0-3f3c8fd22b93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "RMSE: 11.607\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(y_test, y_pred)\n",
        "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
        "ax.set_xlabel('Measured')\n",
        "ax.set_ylabel('Predicted')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jnVPlDTXOXFn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "e81d89ee-df27-4173-87f5-72d209962dd3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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        ""
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": []
    }
  ],
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