Random_forest_model_Data_set2_(2).ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ]
    },
    {
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          "text": [
            "(40964, 30)\n"
          ]
        }
      ],
      "source": [
        "dataset=pd.read_csv(\"Dataset 2.csv\")\n",
        "# print shape of dataset with rows and columns\n",
        "print(dataset.shape)"
      ]
    },
    {
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              "    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": [
        "# print the top5 records \n",
        "dataset.head()"
      ]
    },
    {
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>40960</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>40961</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <th>40963</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "<p>40964 rows × 30 columns</p>\n",
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              "  "
            ],
            "text/plain": [
              "       COIL_ID  GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
              "0        False  False          False           False         False   \n",
              "1        False  False          False           False         False   \n",
              "2        False  False          False           False         False   \n",
              "3        False  False          False           False         False   \n",
              "4        False  False          False           False         False   \n",
              "...        ...    ...            ...             ...           ...   \n",
              "40959    False  False          False           False         False   \n",
              "40960    False  False          False           False         False   \n",
              "40961    False  False          False           False         False   \n",
              "40962    False  False          False           False         False   \n",
              "40963    False  False          False           False         False   \n",
              "\n",
              "       COILING_TEMP  SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  ...  \\\n",
              "0             False     False     False           False            False  ...   \n",
              "1             False     False     False           False            False  ...   \n",
              "2             False     False     False           False            False  ...   \n",
              "3             False     False     False           False            False  ...   \n",
              "4             False     False     False           False            False  ...   \n",
              "...             ...       ...       ...             ...              ...  ...   \n",
              "40959         False     False     False           False            False  ...   \n",
              "40960         False     False     False           False            False  ...   \n",
              "40961         False     False     False           False            False  ...   \n",
              "40962         False     False     False           False            False  ...   \n",
              "40963         False     False     False           False            False  ...   \n",
              "\n",
              "           N     NI      P      S     SI     TI      V    UTS     YS  \\\n",
              "0      False  False  False  False  False  False  False  False  False   \n",
              "1      False  False  False  False  False  False  False  False  False   \n",
              "2      False  False  False  False  False  False  False  False  False   \n",
              "3      False  False  False  False  False  False  False  False  False   \n",
              "4      False  False  False  False  False  False  False  False  False   \n",
              "...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "40959  False  False  False  False  False  False  False  False  False   \n",
              "40960  False  False  False  False  False  False  False  False  False   \n",
              "40961  False  False  False  False  False  False  False  False  False   \n",
              "40962  False  False  False  False  False  False  False  False  False   \n",
              "40963  False  False  False  False  False  False  False  False  False   \n",
              "\n",
              "       P_ELONGATION  \n",
              "0             False  \n",
              "1             False  \n",
              "2             False  \n",
              "3             False  \n",
              "4             False  \n",
              "...             ...  \n",
              "40959         False  \n",
              "40960         False  \n",
              "40961         False  \n",
              "40962         False  \n",
              "40963         False  \n",
              "\n",
              "[40964 rows x 30 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.isna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "g4YMlzxJ5sQg",
        "outputId": "b74ff9a3-d5b0-49e1-d806-adb1741ebf2a"
      },
      "outputs": [
        {
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              "                   0\n",
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              "CU               0.0\n",
              "CR               0.0\n",
              "NB               0.0\n",
              "CA               0.0\n",
              "C                0.0\n",
              "B                0.0\n",
              "AL               0.0\n",
              "FM_EXIT_THIK     0.0\n",
              "STRIP_EXIT_THIK  0.0\n",
              "STRIP_EXIT_LEN   0.0\n",
              "SLAB_LEN         0.0\n",
              "SLAB_WID         0.0\n",
              "COILING_TEMP     0.0\n",
              "FM_DOUT_TEMP     0.0\n",
              "FURN_EXIT_TEMP   0.0\n",
              "COIL_GEN_TIME    0.0\n",
              "P_ELONGATION     0.0"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame((dataset.isna().sum()/dataset.isna().count()).sort_values(ascending=False)).head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "-DkjJjfq9Lmf",
        "outputId": "05a7e46e-21f8-4bd2-af51-77c93b9055eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of numerical variables:  28\n"
          ]
        },
        {
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              "      <td>1125</td>\n",
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              "      <td>42</td>\n",
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              "      <td>20097626</td>\n",
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              "      <td>1245</td>\n",
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              "      <td>0.014</td>\n",
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              "      <td>1075</td>\n",
              "      <td>882</td>\n",
              "      <td>611</td>\n",
              "      <td>1040</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>66.08</td>\n",
              "      <td>36</td>\n",
              "      <td>2.49</td>\n",
              "      <td>0.032</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0070</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>0.164</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.001</td>\n",
              "      <td>730</td>\n",
              "      <td>575</td>\n",
              "      <td>21</td>\n",
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              "      <th>3</th>\n",
              "      <td>20098318</td>\n",
              "      <td>1088</td>\n",
              "      <td>864</td>\n",
              "      <td>603</td>\n",
              "      <td>1260</td>\n",
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              "      <td>51.94</td>\n",
              "      <td>38</td>\n",
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              "      <td>0.053</td>\n",
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              "      <td>30</td>\n",
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              "      <td>848</td>\n",
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              "      <td>35</td>\n",
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            "text/plain": [
              "    COIL_ID  FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  SLAB_WID  SLAB_LEN  \\\n",
              "0  20081459            1081           869           680      1125   10750.0   \n",
              "1  20097626            1070           838           662      1245   10640.0   \n",
              "2  20097722            1075           882           611      1040   10750.0   \n",
              "3  20098318            1088           864           603      1260    9000.0   \n",
              "4  20098589            1061           848           650      1265    9811.0   \n",
              "\n",
              "   STRIP_EXIT_LEN  STRIP_EXIT_THIK  FM_EXIT_THIK     AL  ...       N     NI  \\\n",
              "0           69.81               34          1.99  0.048  ...  0.0025  0.007   \n",
              "1           68.63               34          2.05  0.051  ...  0.0022  0.007   \n",
              "2           66.08               36          2.49  0.032  ...  0.0070  0.008   \n",
              "3           51.94               38          4.99  0.053  ...  0.0035  0.008   \n",
              "4           61.23               35          2.31  0.051  ...  0.0032  0.007   \n",
              "\n",
              "       P       S     SI     TI      V  UTS   YS  P_ELONGATION  \n",
              "0  0.018  0.0100  0.004  0.001  0.001  346  294            42  \n",
              "1  0.014  0.0100  0.007  0.001  0.001  365  299            42  \n",
              "2  0.015  0.0030  0.164  0.002  0.001  730  575            21  \n",
              "3  0.021  0.0131  0.099  0.002  0.001  495  397            30  \n",
              "4  0.015  0.0101  0.006  0.001  0.001  379  326            41  \n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# list of numerical variables\n",
        "numerical_features = [feature for feature in dataset.columns if dataset[feature].dtypes != 'O']\n",
        "\n",
        "print('Number of numerical variables: ', len(numerical_features))\n",
        "\n",
        "# visualise the numerical variables\n",
        "dataset[numerical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9wfFQ819vAc",
        "outputId": "b1ed366f-997b-417f-d49e-10476aa7a6ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Discrete Variables Count: 0\n"
          ]
        }
      ],
      "source": [
        "## Numerical variables are usually of 2 type\n",
        "## 1. Continous variable and Discrete Variables\n",
        "\n",
        "discrete_feature=[feature for feature in numerical_features if len(dataset[feature].unique())<10 ]\n",
        "print(\"Discrete Variables Count: {}\".format(len(discrete_feature)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GomefRyT9_uN",
        "outputId": "1f1d5571-3c42-440f-d460-507ce0d4e9c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Continuous feature Count 28\n"
          ]
        }
      ],
      "source": [
        "continuous_feature=[feature for feature in numerical_features if feature not in discrete_feature]\n",
        "print(\"Continuous feature Count {}\".format(len(continuous_feature)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yzYnTGgj-Go2",
        "outputId": "4dba34a5-a101-4255-9cfb-d2293b61f30a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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rsBVgdna25ubmBo6dn59n0Pp3brn2ae33/nMG72eaLdcj9dij4ezRcNPQo1GPRP5T3/N9wFeras+gwfslOZhegPx+Vf1xK389yZFV9WD7uOqhVt8LHNO3+dGttpcnP/7aX59v9aOXGC9JmpCRTqy3GzH+Jb07+R4GfGfYNu1KqSuAu6vqN/pW7eDJ751sAq7pq5/brtI6BXi8fey1CzgtyWHthPppwK627ptJTmn7OrfvtSRJEzDqJb5vB24B3ga8Hbg5ybBbwb8O+Cng1CR3tMcbgQ8BP57kXuDH2jLATuA+YAH4HeA9AO2E+iXAre3xgf0n2duY323bfBlPqkvSRI36cdYvAT9UVQ8BJHkZ8D+BTw7aoKo+Bwz63sYblhhfwPkDXmsbsG2J+m3AK4dNXpI0HqN+T+Q5+wOkefhpbCtJ+jtq1CORzybZBfxBW/7n9D5+kiStYcN+Y/04et/r+MUk/wz4kbbqz4HfH/fkJEmr27Ajkd8ELgJol+j+MUCSE9q6fzrW2UmSVrVh5zVmquqLBxZbbf1YZiRJmhrDQuTQZdY9/9mciCRp+gwLkduSvPvAYpJ/Cdw+nilJkqbFsHMivwB8Ksk5PBkas8AhwFvGOTFJ0uq3bIhU1deBH07yep78Ut+1VXXD2GcmSVr1Rv09kRuBG8c8F0nSlPFb55KkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ2NLUSSbEvyUJIv9dXen2TvAb+5vn/dRUkWktyT5PS++sZWW0iypa9+bJKbW/0TSQ4Z13uRJC1tnEciHwc2LlH/SFWd2B47AZIcD5wNvKJt87Ek65KsAy4DzgCOB97RxgJ8uL3WccCjwHljfC+SpCWMLUSq6s+AR0YcfiawvaqeqKqvAAvASe2xUFX3VdV3gO3AmUkCnAp8sm1/JXDWs/oGJElDjfob68+mC5KcC9wGXFhVjwJHATf1jdnTagAPHFA/GTgceKyq9i0x/imSbAY2A8zMzDA/Pz9wcouLiwPXX3jCviXrgyy3n2m2XI/UY4+Gs0fDTUOPJh0ilwOXANX+/jrw0+PeaVVtBbYCzM7O1tzc3MCx8/PzDFr/zi3XPq393n/O4P1Ms+V6pB57NJw9Gm4aejTREGm3lgcgye8An26Le4Fj+oYe3WoMqD8MHJrkoHY00j9ekjQhE73EN8mRfYtvAfZfubUDODvJc5McC2wAbgFuBTa0K7EOoXfyfUdVFb1b07+1bb8JuGYS70GS9KSxHYkk+QNgDjgiyR7gYmAuyYn0Ps66H/gZgKraneRq4C5gH3B+VX23vc4FwC5gHbCtqna3XbwP2J7kg8DngSvG9V4kSUsbW4hU1TuWKA/8H/qquhS4dIn6TmDnEvX76F29JUlaIX5jXZLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTNDRJLU2dhCJMm2JA8l+VJf7aVJrktyb/t7WKsnyUeTLCS5M8mr+7bZ1Mbfm2RTX/01Sb7YtvlokozrvUiSljbOI5GPAxsPqG0Brq+qDcD1bRngDGBDe2wGLode6ND7bfaT6f0U7sX7g6eNeXffdgfuS5I0ZmMLkar6M+CRA8pnAle251cCZ/XVr6qem4BDkxwJnA5cV1WPVNWjwHXAxrbuxVV1U1UVcFXfa0mSJmTS50RmqurB9vxrwEx7fhTwQN+4Pa22XH3PEnVJ0gQdtFI7rqpKUpPYV5LN9D4mY2Zmhvn5+YFjFxcXB66/8IR9T2u/y+1nmi3XI/XYo+Hs0XDT0KNJh8jXkxxZVQ+2j6QeavW9wDF9445utb3A3AH1+VY/eonxS6qqrcBWgNnZ2Zqbmxs0lPn5eQatf+eWawdut5T7zxm8n2m2XI/UY4+Gs0fDTUOPJv1x1g5g/xVWm4Br+urntqu0TgEebx977QJOS3JYO6F+GrCrrftmklPaVVnn9r2WJGlCxnYkkuQP6B1FHJFkD72rrD4EXJ3kPOCrwNvb8J3AG4EF4NvAuwCq6pEklwC3tnEfqKr9J+vfQ+8KsOcDn2kPSdIEjS1EquodA1a9YYmxBZw/4HW2AduWqN8GvPKZzFGS9Mz4jXVJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTODBFJUmeGiCSpsxUJkST3J/likjuS3NZqL01yXZJ729/DWj1JPppkIcmdSV7d9zqb2vh7k2xaifciSWvZSh6JvL6qTqyq2ba8Bbi+qjYA17dlgDOADe2xGbgceqEDXAycDJwEXLw/eCRJk7GaPs46E7iyPb8SOKuvflX13AQcmuRI4HTguqp6pKoeBa4DNk560pK0lh20Qvst4H8kKeC3q2orMFNVD7b1XwNm2vOjgAf6tt3TaoPqT5FkM72jGGZmZpifnx84scXFxYHrLzxh33Lv6SmW2880W65H6rFHw9mj4aahRysVIj9SVXuTfC9wXZK/7F9ZVdUC5lnRQmorwOzsbM3NzQ0cOz8/z6D179xy7dPa7/3nDN7PNFuuR+qxR8PZo+GmoUcr8nFWVe1tfx8CPkXvnMbX28dUtL8PteF7gWP6Nj+61QbVJUkTMvEQSfKCJC/a/xw4DfgSsAPYf4XVJuCa9nwHcG67SusU4PH2sdcu4LQkh7UT6qe1miRpQlbi46wZ4FNJ9u//v1XVZ5PcClyd5Dzgq8Db2/idwBuBBeDbwLsAquqRJJcAt7ZxH6iqRyb3NiRJEw+RqroP+MEl6g8Db1iiXsD5A15rG7Dt2Z6jJGk0q+kSX0nSlDFEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdrdSt4NeE9U/31vEfetOYZiJJ4+GRiCSpM0NEktSZISJJ6swQkSR1ZohIkjozRCRJnRkikqTOpj5EkmxMck+ShSRbVno+krSWTHWIJFkHXAacARwPvCPJ8Ss7K0laO6Y6RICTgIWquq+qvgNsB85c4TlJ0pox7bc9OQp4oG95D3DygYOSbAY2t8XFJPcs85pHAN941mb4NOTDK7HXTlasR1PEHg1nj4ZbLT0aOIdpD5GRVNVWYOsoY5PcVlWzY57SVLNHw9mj4ezRcNPQo2n/OGsvcEzf8tGtJkmagGkPkVuBDUmOTXIIcDawY4XnJElrxlR/nFVV+5JcAOwC1gHbqmr3M3zZkT72WuPs0XD2aDh7NNyq71GqaqXnIEmaUtP+cZYkaQUZIpKkztZsiAy7XUqS5yb5RFt/c5L1k5/lyhqhR/84yV8k2ZfkrSsxx5U2Qo/em+SuJHcmuT7JP1iJea6kEXr0s0m+mOSOJJ9bi3edGPX2TUl+IkklWT2X/VbVmnvQOwn/ZeD7gEOALwDHHzDmPcBvtednA59Y6Xmvwh6tB34AuAp460rPeZX26PXA97TnP+e/oyV79OK+528GPrvS815tPWrjXgT8GXATMLvS897/WKtHIqPcLuVM4Mr2/JPAG5JkgnNcaUN7VFX3V9WdwP9diQmuAqP06Maq+nZbvIned5nWklF69M2+xRcAa+1qn1Fv33QJ8GHgryc5uWHWaogsdbuUowaNqap9wOPA4ROZ3eowSo/Wuqfbo/OAz4x1RqvPSD1Kcn6SLwO/CvzrCc1ttRjaoySvBo6pqmsnObFRrNUQkSYqyU8Cs8CvrfRcVqOquqyqXg68D/j3Kz2f1STJc4DfAC5c6bksZa2GyCi3S/n/Y5IcBLwEeHgis1sdvKXMcCP1KMmPAb8EvLmqnpjQ3FaLp/vvaDtw1lhntPoM69GLgFcC80nuB04BdqyWk+trNURGuV3KDmBTe/5W4IZqZ7fWCG8pM9zQHiV5FfDb9ALkoRWY40obpUcb+hbfBNw7wfmtBsv2qKoer6ojqmp9Va2nd27tzVV128pM929bkyHSznHsv13K3cDVVbU7yQeSvLkNuwI4PMkC8F5gTf1q4ig9SvJDSfYAbwN+O8kzveXMVBnx39GvAS8E/rBdwrqmgnjEHl2QZHeSO+j9t7ZpwMv9nTRij1Ytb3siSepsTR6JSJKeHYaIJKkzQ0SS1JkhIknqzBCRJHVmiEgrIMnfS7I9yZeT3J5kZ5J/mOT/tEuB70pyVZKDV3qu0nIMEWnC2o08PwXMV9XLq+o1wEXADPDlqjoROIHeN5ffvnIzlYYzRKTJez3wN1X1W/sLVfUF+m7CV1XfBW7Bm15qlTNEpMl7JXD7cgOSPA84GfjsRGYkdWSISKvLy9vtP74OPNh+r0VatQwRafJ2A68ZsG7/OZGXA6+ZhnsnaW0zRKTJuwF4bpLN+wtJfoC+24FX1Tfo3fTzoslPTxqdISJNWPtJgbcAP9Yu8d0N/EfgawcM/e/A9yT50UnPURqVd/GVJHXmkYgkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzv4f5ZJtCWZ0/2AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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hCzBN0snA+cDGiNgTEXuBjUB3WnZcRGxJRyW3lLZlZmY1UyNniCT9CHh3RDyb5k8C/jEi3tzQTqTZwPeB04B/johpqS5gb0RMk3QncF16Fj2SNgGXAV3AMRFxTap/Cfgl0JfGvyvV3wlcFhHvHWH/KyiOeujo6Jjf29vYDWqDg4NMnTr14Pz2gX0NrTfk9BnHj2t8DsN7bgfuuR7uuR6v1J4XLFiwLSI6xxrT6DWUVw2FSbKbxo9upgLfAj4TEc8XGVKIiJBU+TWPiOgBegA6Ozujq6urofX6+vooj1226q5x7XfXksb2k9PwntuBe66He67HRO650duGvytpg6RlkpYBd1FcBB+TpKMowuRvI+LbqfxMOl1F+jkUVAPArNLqM1NtrPrMEepmZtYEYwaKpFMkvSMiPgf8BfCm9Pon0v/xj7GugJuARyPiT0uL1vGbL5tcCtxRql+iwtnAvoh4GtgAnCdperoYfx6wIS17XtLZaV+XlLZlZmY1O9Qpr68ClwOkI4xvA0g6PS173xjrvgP4MLBd0oOp9nngOmCtpOXATyku8kNxxHMB0E9x2/BH0n73SLoauD+Nuyoi9qTpT/Kb24bvTi8zM2uCQwVKR0RsH16MiO3pQvuo0sV1jbL43BHGB7BylG2tBlaPUN9KcaHfzMya7FDXUKaNsezYnI2YmVl7O1SgbJX08eFFSR8DtlXTkpmZtaNDnfL6DHC7pCX8JkA6gSnAB6pszMzM2suYgRIRzwBvl7SA31yruCsi7qm8MzMzayuNPg9lM7C54l7MzKyNHe7zUMzMzF7GgWJmZlk4UMzMLAsHipmZZeFAMTOzLBwoZmaWhQPFzMyycKCYmVkWDhQzM8vCgWJmZlk4UMzMLAsHipmZZVFZoEhaLelZSQ+XaldKGpD0YHpdUFp2uaR+SY9JOr9U7061fkmrSvU5ku5N9VslTanqvZiZ2aFVeYRyM9A9Qv36iDgjvdYDSJoHXAScmtb5hqRJkiYBNwALgXnAxWkswJfTtk4B9gLLK3wvZmZ2CJUFSkR8H9jT4PBFQG9E7I+IJ4B+4Mz06o+InRHxItALLJIk4BzgtrT+GmBx1jdgZmbjooiobuPSbODOiDgtzV8JLAOeB7YCl0bEXkl/DmyJiL9J424C7k6b6Y6Ij6X6h4GzgCvT+FNSfRZw99B+RuhjBbACoKOjY35vb29D/Q8ODjJ16tSD89sH9jX2xpPTZxw/rvE5DO+5HbjnerjnerxSe16wYMG2iOgca0xDD9jK6EbgaiDSz68AH616pxHRA/QAdHZ2RldXV0Pr9fX1UR67bNVd49rvriWN7Sen4T23A/dcD/dcj4ncc62Bkh4pDICkvwTuTLMDwKzS0Jmpxij13cA0SZMj4sCw8WZm1gS13jYs6eTS7AeAoTvA1gEXSTpa0hxgLnAfcD8wN93RNYXiwv26KM7TbQYuTOsvBe6o4z2YmdnIKjtCkfT3QBdwoqSngCuALklnUJzy2gX8AUBE7JC0FngEOACsjIiX0nY+BWwAJgGrI2JH2sVlQK+ka4AHgJuqei9mZnZolQVKRFw8QnnUX/oRcS1w7Qj19cD6Eeo7Ke4CMzOzFuC/lDczsywcKGZmloUDxczMsnCgmJlZFg4UMzPLwoFiZmZZOFDMzCwLB4qZmWXhQDEzsywcKGZmloUDxczMsnCgmJlZFg4UMzPLwoFiZmZZOFDMzCwLB4qZmWVRWaBIWi3pWUkPl2onSNoo6fH0c3qqS9LXJfVLekjSW0vrLE3jH5e0tFSfL2l7WufrklTVezEzs0Or8gjlZqB7WG0VsCki5gKb0jzAQornyM8FVgA3QhFAFI8OPovi6YxXDIVQGvPx0nrD92VmZjWqLFAi4vvAnmHlRcCaNL0GWFyq3xKFLcA0SScD5wMbI2JPROwFNgLdadlxEbElIgK4pbQtMzNrgsqeKT+Kjoh4Ok3/C9CRpmcAT5bGPZVqY9WfGqE+IkkrKI586OjooK+vr6FmBwcHXzb20tMPNLTekEb3k9PwntuBe66He67HRO657kA5KCJCUtS0rx6gB6CzszO6uroaWq+vr4/y2GWr7hrXfnctaWw/OQ3vuR2453q453pM5J7rvsvrmXS6ivTz2VQfAGaVxs1MtbHqM0eom5lZk9QdKOuAoTu1lgJ3lOqXpLu9zgb2pVNjG4DzJE1PF+PPAzakZc9LOjvd3XVJaVtmZtYElZ3ykvT3QBdwoqSnKO7Wug5YK2k58FPgQ2n4euACoB/4BfARgIjYI+lq4P407qqIGLrQ/0mKO8mOBe5OLzMza5LKAiUiLh5l0bkjjA1g5SjbWQ2sHqG+FTjtSHo0M7N8/JfyZmaWhQPFzMyycKCYmVkWDhQzM8vCgWJmZlk4UMzMLAsHipmZZeFAMTOzLBwoZmaWhQPFzMyycKCYmVkWDhQzM8vCgWJmZlk4UMzMLAsHipmZZeFAMTOzLJoSKJJ2Sdou6UFJW1PtBEkbJT2efk5PdUn6uqR+SQ9JemtpO0vT+MclLR1tf2ZmVr1mHqEsiIgzIqIzza8CNkXEXGBTmgdYCMxNrxXAjVAEEMVjhc8CzgSuGAohMzOrXyud8loErEnTa4DFpfotUdgCTJN0MnA+sDEi9kTEXmAj0F1302ZmVlDxOPeadyo9AewFAviLiOiR9FxETEvLBeyNiGmS7gSui4gfpGWbgMuALuCYiLgm1b8E/DIi/mSE/a2gOLqho6Njfm9vb0N9Dg4OMnXq1IPz2wf2jet9nj7j+HGNz2F4z+3APdfDPdfjldrzggULtpXOKI1octauGvd7ETEg6beBjZJ+XF4YESEpW9JFRA/QA9DZ2RldXV0NrdfX10d57LJVd41rv7uWNLafnIb33A7ccz3ccz0mcs9NOeUVEQPp57PA7RTXQJ5Jp7JIP59NwweAWaXVZ6baaHUzM2uC2gNF0qslvWZoGjgPeBhYBwzdqbUUuCNNrwMuSXd7nQ3si4ingQ3AeZKmp4vx56WamZk1QTNOeXUAtxeXSZgM/F1EfFfS/cBaScuBnwIfSuPXAxcA/cAvgI8ARMQeSVcD96dxV0XEnvrehpmZldUeKBGxE3jzCPXdwLkj1ANYOcq2VgOrc/doZmbj10q3DZuZWRtzoJiZWRYOFDMzy8KBYmZmWThQzMwsCweKmZll4UAxM7MsHChmZpaFA8XMzLJwoJiZWRYOFDMzy8KBYmZmWThQzMwsCweKmZll4UAxM7MsHChmZpaFA8XMzLJo+0CR1C3pMUn9klY1ux8zs4mqrQNF0iTgBmAhMA+4WNK85nZlZjYxtXWgAGcC/RGxMyJeBHqBRU3uycxsQprc7AaO0AzgydL8U8BZwwdJWgGsSLODkh5rcPsnAj873Ob05cNd84gcUc9N4p7r4Z7r8Urt+fWH2ki7B0pDIqIH6BnvepK2RkRnBS1Vxj3Xwz3Xwz3XI1fP7X7KawCYVZqfmWpmZlazdg+U+4G5kuZImgJcBKxrck9mZhNSW5/yiogDkj4FbAAmAasjYkfGXYz7NFkLcM/1cM/1cM/1yNKzIiLHdszMbIJr91NeZmbWIhwoZmaWxYQNlEN9ZYukoyXdmpbfK2l2adnlqf6YpPNbvWdJsyX9UtKD6fXNFur5P0j6oaQDki4ctmyppMfTa2mb9PxS6XOu7QaRBnr+rKRHJD0kaZOk15eWternPFbPrfo5f0LS9tTXD8rf3NHCvzdG7Pmwfm9ExIR7UVzA/wnwBmAK8CNg3rAxnwS+maYvAm5N0/PS+KOBOWk7k1q859nAwy36Oc8G3gTcAlxYqp8A7Ew/p6fp6a3cc1o22KKf8wLgt9L0fyn922jlz3nEnlv8cz6uNP1+4LtpupV/b4zW87h/b0zUI5RGvrJlEbAmTd8GnCtJqd4bEfsj4gmgP22vlXtulkP2HBG7IuIh4F+HrXs+sDEi9kTEXmAj0N3iPTdLIz1vjohfpNktFH+zBa39OY/Wc7M00vPzpdlXA0N3PbXs740xeh63iRooI31ly4zRxkTEAWAf8NoG163CkfQMMEfSA5K+J+mdVTc7vJ9kPJ9VK3/OYzlG0lZJWyQtztvaqMbb83Lg7sNcN5cj6Rla+HOWtFLST4A/Bv7beNatwJH0DOP8vdHWf4diDXsa+LcRsVvSfOB/STp12P+ZWB6vj4gBSW8A7pG0PSJ+0uymhkj6z0An8B+b3UujRum5ZT/niLgBuEHS7wNfBGq7LnW4Rul53L83JuoRSiNf2XJwjKTJwPHA7gbXrcJh95wOs3cDRMQ2inOqv1N5x0f2WbXy5zyqiBhIP3cCfcBbcjY3ioZ6lvQu4AvA+yNi/3jWrcCR9NzSn3NJLzB09NTSn3PJwZ4P6/dG1ReFWvFFcWS2k+Li2NCFqlOHjVnJyy9wr03Tp/Lyi2s7qefi2pH0fNJQjxQX5waAE1qh59LYm/n/L8o/QXGheHqabvWepwNHp+kTgccZdgG0if823pJ+IcwdVm/Zz3mMnlv5c55bmn4fsDVNt/LvjdF6HvfvjUrfTCu/gAuA/5v+wX4h1a6i+D8hgGOAf6C4eHYf8IbSul9I6z0GLGz1noH/BOwAHgR+CLyvhXr+XYrzui9QHAHuKK370fRe+oGPtHrPwNuB7ek/2u3A8hbq+R+BZ9K/gQeBdW3wOY/Yc4t/zl8r/be2mdIv7xb+vTFiz4fze8NfvWJmZllM1GsoZmaWmQPFzMyycKCYmVkWDhQzM8vCgWJmZlk4UMxqJunfSOqV9BNJ2yStl7RC0p3Dxt08/NuMzVqZA8WsRunLOm8H+iLi30XEfOByoKO5nZkdOQeKWb0WAL+OiIPPloiIHwH/u3ktmeXhQDGr12nAtmY3YVYFB4pZaxjtKyv8VRbWNhwoZvXaAcwfob6b4ksPy04AflZ5R2aZOFDM6nUPcLSkFUMFSW+ieBDa6yS9MdVeD7yZ4ov5zNqCvxzSrGaSXgd8leJI5VfALuAzwG8DX6H41uhfA5+PiI1NatNs3BwoZmaWhU95mZlZFg4UMzPLwoFiZmZZOFDMzCwLB4qZmWXhQDEzsywcKGZmlsX/A02FXJvQb6ppAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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iSerMEJEkdTaxEElyUJJPJvlImz8myW1Jtib5YJJDWvtj2/zWtnz10DYuaO2fSfKiyRyJJB24Jnkm8jrg00PzbwfeUVXPAB4Czmnt5wAPtfZ3tH4kOQ44E3g2sBZ4V5KDeqpdksSEQiTJUcDpwHvbfIAXANe2LlcCL23TZ7R52vJTWv8zgE1V9Y2q+hywFTihnyOQJMHkzkR+F/h14Ntt/inAl6tqd5vfDhzZpo8EPg/Qlj/c+j/aPs86kqQeHNz3DpP8LPBgVd2ZZKanfW4ANgBMTU0xOzvbx26ZOhTOW7N78Y7NuHWNs+1xDNfd12u1v+zatWvF1byHtU+Gte+b3kMEeB7wkiSnAY8Dngi8E1iV5OB2tnEUsKP13wEcDWxPcjDwJOBLQ+17DK/zr1TVRmAjwPT0dM3MzOzvY5rXZVdfxyVbRn+Jt501M9b2159//ZgVjea8NbsfrXvcmiZtdnaWvv599zdrnwxr3ze9D2dV1QVVdVRVrWZwYfxjVXUWcAvwstZtHXBdm97c5mnLP1ZV1drPbHdvHQMcC/xNT4chSWIyZyILeSOwKcnbgE8Cl7f2y4H3J9kK7GQQPFTVPUmuAe4FdgPnVtW3+i9bkg5cEw2RqpoFZtv0fcxzd1VVfR34hQXWvwi4aOkqlCTtje9YlyR1ZohIkjozRCRJnRkikqTODBFJUmfL6RbfA97qJXrzoCQtFc9EJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmdGSKSpM4MEUlSZ4aIJKkzQ0SS1JkhIknqzBCRJHVmiEiSOjNEJEmd+cmGmqhxP81x28WnL1ElkrrwTESS1FnvIZLk6CS3JLk3yT1JXtfan5zkpiSfbd8Pb+1JcmmSrUnuSvKcoW2ta/0/m2Rd38ciSQe6SZyJ7AbOq6rjgJOAc5McB5wP3FxVxwI3t3mAFwPHtq8NwLthEDrAhcCJwAnAhXuCR5LUj95DpKoeqKq/bdP/D/g0cCRwBnBl63Yl8NI2fQZwVQ3cCqxK8jTgRcBNVbWzqh4CbgLW9ngoknTAm+g1kSSrgR8DbgOmquqBtugLwFSbPhL4/NBq21vbQu2SpJ5M7O6sJIcBfwz8alV9Jcmjy6qqktR+3NcGBkNhTE1NMTs7u782vVdTh8J5a3b3sq/9abjupX6txn19Fqtn165dvf377m/WPhnWvm8mEiJJvo9BgFxdVR9qzV9M8rSqeqANVz3Y2ncARw+tflRr2wHMzGmfnW9/VbUR2AgwPT1dMzMz83Xb7y67+jou2bLy7qI+b83uR+vedtbMku5r/bi3+C5Sz+zsLH39++5v1j4Z1r5vJnF3VoDLgU9X1e8MLdoM7LnDah1w3VD72e0urZOAh9uw143AqUkObxfUT21tkqSeTOLP5OcBrwS2JPlUa3sTcDFwTZJzgPuBl7dlNwCnAVuBR4BXAVTVziRvBW5v/d5SVTv7OQRJEkwgRKrqr4AssPiUefoXcO4C27oCuGL/VSdJGsfKG7DXsjbuY0wkrWw+9kSS1JkhIknqzOEs7ZXDU5L2xjMRSVJnhogkqTNDRJLUmSEiSerMEJEkdWaISJI6M0QkSZ0ZIpKkzgwRSVJnhogkqTMfezKGcR8Bct6aJSpEkpYJz0QkSZ0ZIpKkzhzOkpa5cYdRt118+hJVIn03z0QkSZ15JiLtI88UdCAzRLSiLPYf9nlrdrN+qI//YUtLy+EsSVJnhogkqTOHs6Q5/Fx5aXSGiNSzhUJq7vWcPnlzgLoyRKTvMZ5JrUwrNchXfIgkWQu8EzgIeG9VXTzhkrSM+B+qtLRWdIgkOQj4PeCFwHbg9iSbq+reyVYmfW+bG86LDcUtl7+a9xiuf9RhxOV2DMvFig4R4ARga1XdB5BkE3AGYIhIy8hSD9X0cca53M5qV59//VjX0ZYqBFNVS7LhPiR5GbC2qn6pzb8SOLGqXjOn3wZgQ5t9FvCZnko8Avjnnva1P63UusHaJ8XaJ6PP2n+wqp46t3Gln4mMpKo2Ahv73m+SO6pquu/97quVWjdY+6RY+2Qsh9pX+psNdwBHD80f1dokST1Y6SFyO3BskmOSHAKcCWyecE2SdMBY0cNZVbU7yWuAGxnc4ntFVd0z4bKG9T6Etp+s1LrB2ifF2idj4rWv6AvrkqTJWunDWZKkCTJEJEmdGSL7KMnaJJ9JsjXJ+fMsf32Se5PcleTmJD84iTrns1jtQ/3+Y5JKsmxugxyl9iQvb6/9PUn+sO8aFzLCz8zTk9yS5JPt5+a0SdQ5V5IrkjyY5O4FlifJpe247krynL5rXMgItZ/Vat6S5K+T/GjfNS5ksdqH+j03ye72/rn+VJVfHb8YXMz/B+CHgEOA/wMcN6fPycDj2/QvAx+cdN2j1t76PQH4OHArMD3pusd43Y8FPgkc3uZ/YNJ1j1H7RuCX2/RxwLZJ191q+WngOcDdCyw/DfgoEOAk4LZJ1zxG7T859LPy4pVU+9DP1ceAG4CX9VmfZyL75tHHrlTVN4E9j115VFXdUlWPtNlbGbyXZTlYtPbmrcDbga/3WdwiRqn91cDvVdVDAFX1YM81LmSU2gt4Ypt+EvB/e6xvQVX1cWDnXrqcAVxVA7cCq5I8rZ/q9m6x2qvqr/f8rLC8fk9Hed0BXgv8MdD7z7khsm+OBD4/NL+9tS3kHAZ/qS0Hi9behiOOrqrl9dCg0V73ZwLPTPK/k9zanva8HIxS+28Av5hkO4O/LF/bT2n7bNzfh+VqOf2eLirJkcDPAe+exP5X9PtEVpIkvwhMA8+fdC2jSPIY4HeA9RMupauDGQxpzTD4q/LjSdZU1ZcnWtVoXgG8r6ouSfITwPuTHF9V3550Yd/rkpzMIER+atK1jOF3gTdW1beT9L5zQ2TfjPTYlSQ/A7wZeH5VfaOn2hazWO1PAI4HZtsP5r8BNid5SVXd0VuV8xvldd/OYFz7X4DPJfl7BqFyez8lLmiU2s8B1gJU1SeSPI7Bg/aWy5DcQlb0Y4iS/AjwXuDFVfWlSdczhmlgU/s9PQI4LcnuqvqTPnbucNa+WfSxK0l+DPh94CXLaFweFqm9qh6uqiOqanVVrWYwTrwcAgRGe9zNnzA4CyHJEQyGt+7rs8gFjFL7PwKnACT5YeBxwD/1WmU3m4Gz211aJwEPV9UDky5qFEmeDnwIeGVV/f2k6xlHVR0z9Ht6LfArfQUIeCayT2qBx64keQtwR1VtBn4LOAz4o/aXwj9W1UsmVnQzYu3L0oi13wicmuRe4FvAG5bDX5cj1n4e8D+T/BqDi+zrq92CM0lJPsAgmI9o12suBL4PoKrew+D6zWnAVuAR4FWTqfS7jVD7fwOeAryr/Z7urmXyZN8Rap8oH3siSerM4SxJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIPWpPQ/6DofmDk/xTko+0+fVJvt3e+Lanz91JVvdfrbQ4Q0Tq11eB45Mc2uZfyPzvtn9zr1VJHRkiUv9uAE5v068APjBn+UeAZyd5Vq9VSR0YIlL/NgFntmdi/Qhw25zl3wZ+E3hT34VJ4zJEpJ5V1V3AagZnITcs0O0PgZOSHNNXXVIXPjtLmozNwG8zeCbSU+YubM/YugR4Y891SWMxRKTJuAL4clVtSTKzQJ/3Ab/O4LH80rLkcJY0AVW1vaouXaTPN4FLgR/opyppfD7FV5LUmWcikqTODBFJUmeGiCSpM0NEktSZISJJ6swQkSR1ZohIkjr7/9PJU/WPI7STAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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upHbM3Y6ZwbnL1MjMVV06SzfXfIDiDs7jgV+NYJ/vBh6NiJ9FxK+BbwNvB8alS2kAk4DtaXo7MBkgLT8aeKayfYh1zMysRVT7hM3zgNuB9wHnARsk1fuYgMeBGZKOTJ+1nA7cB9zGvkcPzAduSNNr2PcHo+cCt0ZxvW8NMDeNSpsCTE0ZzcyshVR76exTwFsjYgeApNcAP2Dfh/dVi4gNklYDP6G4DHcXxWWtm4AeSZemtuVpleXANyT1ATspRpoREVvSiLX70nYu9IgzM7PWU22hecXeIpM8Q/0j1oiIpcDSQc2PMMSosYj4JcWZ1FDbuYxiUIGZmbWoagvN9yStBa5L838I3JwnkpmZHUoOWGgkHQ90RMSfSfoD4B1p0Y+Bb+UOZ2Zm7e9gZzRfBC4GiIhvU4wQQ9K0tOz3s6YzM7O2d7DPWToiYtPgxtTWmSWRmZkdUg5WaMYdYNkRjQxiZmaHpoMVmjslfWhwo6QPAhvzRDIzs0PJwT6j+RjwHUnz2FdYuoAxwH/JGczMzA4NByw0EfE08HuSZgInpeabIuLW7MnMzOyQUO3zaG6juEWMmZlZTer+634zM7NqVHtnAKtDZwNv+29m1q58RmNmZlm50JiZWVYuNGZmlpULjZmZZeVCY2ZmWbnQmJlZVk0pNJLGSVot6QFJ90t6m6QJktZJeih9HZ/6StKXJfVJulfSyRXbmZ/6PyRpfjOOxczMDqxZZzRfAr4XEW8E3gzcDywBbomIqcAtaR7gTGBqei0CrgaQNIHicdDTKR4BvXRvcTIzs9ZReqGRdDTwLmA5QET8KiKeBeYAK1O3lcA5aXoOcG0U1gPjJB0HnAGsi4idEbELWAfMLvFQzMysCoqIcncovQVYBtxHcTazEbgI2B4R41IfAbsiYpykG4HLI+JHadktwCeBbuDwiLg0tX8a2BMRVw6xz0UUZ0N0dHSc0tPTU3Xe/v5+xo4dW9exbtq+u671GqHjCHh6z/7t0yYeXX6YKo3kvW6mdszdjpnBucs0OPPMmTM3RkRXPdtqxi1oRgMnAx+NiA2SvsS+y2QARERIalgFjIhlFMWNrq6u6O7urnrd3t5eaulfaUETb0GzeNoAX9i0/7d367zu8sNUaSTvdTO1Y+52zAzOXaZGZm7GZzTbgG0RsSHNr6YoPE+nS2KkrzvS8u3A5Ir1J6W24drNzKyFlF5oIuIp4AlJJ6Sm0ykuo60B9o4cmw/ckKbXABek0WczgN0R8SSwFpglaXwaBDArtZmZWQtp1t2bPwp8S9IY4BHgAxRFb5WkhcBjwHmp783AWUAf8IvUl4jYKelzwB2p3yURsbO8QzAzs2o0pdBExN0Uj4Qe7PQh+gZw4TDbWQGsaGw6MzNrJN8ZwMzMsnKhMTOzrFxozMwsKxcaMzPLyoXGzMyycqExM7OsXGjMzCwrFxozM8vKhcbMzLJyoTEzs6xcaMzMLCsXGjMzy8qFxszMsnKhMTOzrFxozMwsKxcaMzPLyoXGzMyycqExM7OsmlZoJI2SdJekG9P8FEkbJPVJul7SmNR+WJrvS8s7K7ZxcWp/UNIZzTkSMzM7kGae0VwE3F8xfwVwVUQcD+wCFqb2hcCu1H5V6oekE4G5wJuA2cBXJY0qKbuZmVWpKYVG0iTgbODraV7AacDq1GUlcE6anpPmSctPT/3nAD0R8UJEPAr0AaeWcwRmZlYtRUT5O5VWA38JvAr4U2ABsD6dtSBpMvDdiDhJ0mZgdkRsS8seBqYDn0nrfDO1L0/rrB60OyQtAhYBdHR0nNLT01N11v7+fsaOHVvXcW7avruu9Rqh4wh4es/+7dMmHl1+mCqN5L1upnbM3Y6ZwbnLNDjzzJkzN0ZEVz3bGt2wVFWS9B5gR0RslNRdxj4jYhmwDKCrqyu6u6vfbW9vL7X0r7RgyU11rdcIi6cN8IVN+397t87rLj9MlUbyXjdTO+Zux8zg3GVqZObSCw3wduC9ks4CDgdeDXwJGCdpdEQMAJOA7an/dmAysE3SaOBo4JmK9r0q1zEzsxZR+mc0EXFxREyKiE6KD/NvjYh5wG3AuanbfOCGNL0mzZOW3xrF9b41wNw0Km0KMBW4vaTDMDOzKjXjjGY4nwR6JF0K3AUsT+3LgW9I6gN2UhQnImKLpFXAfcAAcGFEvFh+bDMzO5CmFpqI6AV60/QjDDFqLCJ+CbxvmPUvAy7Ll9DMzEbKdwYwM7OsXGjMzCwrFxozM8vKhcbMzLJyoTEzs6xcaMzMLCsXGjMzy8qFxszMsmqlOwPYIaCzxhuJbr387ExJzKxV+IzGzMyycqExM7OsXGjMzCwrFxozM8vKhcbMzLJyoTEzs6xcaMzMLCsXGjMzy6r0QiNpsqTbJN0naYuki1L7BEnrJD2Uvo5P7ZL0ZUl9ku6VdHLFtuan/g9Jml/2sZiZ2cE144xmAFgcEScCM4ALJZ0ILAFuiYipwC1pHuBMYGp6LQKuhqIwAUuB6RSPgF66tziZmVnrKP0WNBHxJPBkmv43SfcDE4E5QHfqthLoBT6Z2q+NiADWSxon6bjUd11E7ASQtA6YDVxX2sG0Id8ixszKpuL3d5N2LnUCPwROAh6PiHGpXcCuiBgn6Ubg8oj4UVp2C0UB6gYOj4hLU/ungT0RceUQ+1lEcTZER0fHKT09PVVn7O/vZ+zYsXUd36btu+tarxE6joCn94x8O9MmHl1T/1qPuXL7I3mvm6kdc7djZnDuMg3OPHPmzI0R0VXPtpp2U01JY4H/A3wsIp4rakshIkJSwypgRCwDlgF0dXVFd3d31ev29vZSS/9KC2o8e2ikxdMG+MKmBnx7Nz1f4wq17XPrvO6XpkfyXjdTO+Zux8zg3GVqZOamjDqT9EqKIvOtiPh2an46XRIjfd2R2rcDkytWn5Tahms3M7MW0oxRZwKWA/dHxF9VLFoD7B05Nh+4oaL9gjT6bAawO33OsxaYJWl8GgQwK7WZmVkLacals7cD7wc2Sbo7tf05cDmwStJC4DHgvLTsZuAsoA/4BfABgIjYKelzwB2p3yV7BwaYmVnraMaosx8BGmbx6UP0D+DCYba1AljRuHRmZtZovjOAmZll5UJjZmZZudCYmVlWLjRmZpaVC42ZmWXlQmNmZlm50JiZWVYuNGZmlpULjZmZZeVCY2ZmWbnQmJlZVi40ZmaWlQuNmZll1bQnbJoBdFY8hXTxtIGDPpV06+Vn545kZg3mMxozM8vKhcbMzLJyoTEzs6xcaMzMLKu2LzSSZkt6UFKfpCXNzmNmZr+trUedSRoFfAX4z8A24A5JayLivuYms1w6DzIqrRFabWRbrcfcavnN2rrQAKcCfRHxCICkHmAOkKXQlPFLzpqv1u/zNbOPyrr9WlWz/cFDyV2cLCdFRLMz1E3SucDsiPhgmn8/MD0iPjKo3yJgUZo9AXiwht0cC/y8AXHL1o652zEztGfudswMzl2mwZl/JyJeU8+G2v2MpioRsQxYVs+6ku6MiK4GR8quHXO3Y2Zoz9ztmBmcu0yNzNzugwG2A5Mr5ielNjMzaxHtXmjuAKZKmiJpDDAXWNPkTGZmVqGtL51FxICkjwBrgVHAiojY0uDd1HXJrQW0Y+52zAztmbsdM4Nzl6lhmdt6MICZmbW+dr90ZmZmLc6FxszMsnrZFxpJh0u6XdI9krZI+mxqnyJpQ7q1zfVpsAGSDkvzfWl5ZxOzj5J0l6Qb2yjzVkmbJN0t6c7UNkHSOkkPpa/jU7skfTnlvlfSyU3KPE7SakkPSLpf0tvaIPMJ6T3e+3pO0sfaIPefpH+HmyVdl/59tsPP9UUp8xZJH0ttLfdeS1ohaYekzRVtNeeUND/1f0jS/IPuOCJe1i9AwNg0/UpgAzADWAXMTe1fA/44TX8Y+Fqangtc38TsHwf+HrgxzbdD5q3AsYPaPg8sSdNLgCvS9FnAd9P3aAawoUmZVwIfTNNjgHGtnnlQ/lHAU8DvtHJuYCLwKHBEml8FLGj1n2vgJGAzcCTFAKsfAMe34nsNvAs4Gdhc0VZTTmAC8Ej6Oj5Njz/gfpvxjWnVV/pB+QkwneIvYken9rcBa9P0WuBtaXp06qcmZJ0E3AKcBtyYfhhaOnPa/1b2LzQPAsel6eOAB9P03wDnD9WvxLxHp19+GtTespmHOIZZwL+0em6KQvNE+gU2Ov1cn9HqP9fA+4DlFfOfBj7Rqu810MlvF5qacgLnA39T0f5b/YZ6vewvncFLl6DuBnYA64CHgWcjYiB12UbxjwD2/WMgLd8NHFNuYgC+SPHD/Js0fwytnxkggO9L2qji1kAAHRHxZJp+CuhI0y/lTiqPqSxTgJ8Bf5cuU35d0lG0dubB5gLXpemWzR0R24ErgceBJyl+TjfS+j/Xm4F3SjpG0pEUZwKTaeH3epBac9ac34UGiIgXI+ItFGcJpwJvbHKkA5L0HmBHRGxsdpY6vCMiTgbOBC6U9K7KhVH8F6mVxtyPprjUcHVE/CfgeYrLCy9pwcwvSZ9nvBf4h8HLWi13+mxgDkVxfx1wFDC7qaGqEBH3A1cA3we+B9wNvDioT0u918PJldOFpkJEPAvcRnF6Pk7S3j9orby1zUu3vUnLjwaeKTnq24H3StoK9FBcPvsSrZ0ZeOl/rUTEDuA7FIX9aUnHpXzHUZxZQmvcYmgbsC0iNqT51RSFp5UzVzoT+ElEPJ3mWzn3u4FHI+JnEfFr4NsUP+vt8HO9PCJOiYh3AbuAn9La73WlWnPWnP9lX2gkvUbSuDR9BMWzbe6nKDjnpm7zgRvS9Jo0T1p+a/pfQGki4uKImBQRnRSXRW6NiHm0cGYASUdJetXeaYrPDjYPyjc49wVp9MsMYHfFKX4pIuIp4AlJJ6Sm0ykeQ9GymQc5n32XzaC1cz8OzJB0pCSx771u6Z9rAEmvTV//PfAHFIN0Wvm9rlRrzrXALEnj01norNQ2vLI+gGrVF/C7wF3AvRS/9P5nan89cDvQR3HZ4bDUfnia70vLX9/k/N3sG3XW0plTvnvSawvwqdR+DMXAhocoRuxMSO2ieLDdw8AmoKtJud8C3Jl+Rv6RYqRNS2dOWY6i+B/+0RVtLZ0b+CzwQPq3+A3gsFb/uU5Z/i9FUbwHOL1V32uK/3Q8Cfya4mx9YT05gT9K73sf8IGD7de3oDEzs6xe9pfOzMwsLxcaMzPLyoXGzMyycqExM7OsXGjMzCwrFxqzEkjqrLxjbmr7jKTnVdxd+T5Je7TvbsvnSpqR7kp8t4q7Rn+mSfHNRqStH+VsdghYGhFXplvc3xjFrZAAkPQgcF5E3CNpFHDCMNswa2kuNGat67UUf1xHRLxI8QeBZm3Hl87MWtdVwIOSviPpv0s6vNmBzOrhQmNWjuFuwTHsrTki4hKgi+KuwP+V4s7AZm3HhcasHM9Q3COt0gSKh3UNKyIejoirKW4w+WZJzXqOkFndXGjMShAR/cCTkk6D4jntFM9a+dFw60g6O93FGGAqxTNOns2d1azRPBjArDwXAF+R9Fdp/rMR8fAB+r8fuErSL4ABYF4aFGDWVnz3ZjMzy8qXzszMLCsXGjMzy8qFxszMsnKhMTOzrFxozMwsKxcaMzPLyoXGzMyy+v80kYDKSp7cOwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#  Lets analyse the continuous values by creating histograms to understand the distribution\n",
        "\n",
        "for feature in continuous_feature:\n",
        "    data=dataset.copy()\n",
        "    data[feature].hist(bins=25)\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel(\"Count\")\n",
        "    plt.title(feature)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oQquxc9e-Q9X",
        "outputId": "028ad4f6-f1b3-4374-9054-6a50fb3e0176"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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tI4EjSCbRA3gIODXd/hzgJ1Wv2qzKDrzo3rJDY/TQIQ4Nq6isT8edQ3K21EhJq4EZwGCAiLgOuAyYnQ5DCbgwIl6V9E/Af0p6lyTcZkbEM+luLwRul/QtYAnw/Sy/BrO8eS7Dak2mwRERZ/SzfA1wbJH2XwFF73UQEauAwypSoFkN+8jlC3j59fIuUTpgj51ZcMGRlS3ILJX3HIeZFeG5DKtlJQeHpOEkp8T6TCWzCtueM6YcGFYtfQaHpG8AP4yI59LJ6vuADwLvSPpMRPyiGkWaNbrtGZYCh4ZVV39nVU0Clqevz0k/jwI+DvxbVkWZNZO2afO2ay7DoWHV1t9Q1d8KhqQmALdHxGbgWUmeHzHbTuXOZQh4wYFhOenvl/9bkg4GXgY+AfxrwbL3ZlaVWYPz5LfVs/6Gqr5CckPB50juSPsCgKQTSK6hMLMS+UI+q3f99TjGRMSBPRsj4l7g3mxKMmtM+5V5F1twL8NqS3/BcTFwVzUKMWtk5fYyHBhWizzBbZYhz2VYI+ovOA6U9GSRdgEREYdmUJNZQyg3NHzrc6t1/QXHC8BJ1SjErFG4l2GNrt/TcSPi91WpxKzObU9guJdh9aS/4Hi/pAsK3gfwKvDL7lNzzcy3Prfm0l9wzCJ5FnihNuAiSZdExO2ZVGVWJ9zLsGbUZ3BExKXF2iWNAH4BODisaXkuw5pVWafjRsR6Sap0MWb1YHsCAxwaVv/KCg5JnwBe62edG4ETgXURcXCR5bsBtwKtaR3fjYibJLUD1wK7ApuByyPijnSb2SR35v1zupvPRcTScr4Gs1JtT2B4LsMaSX/P41gG29wlYQSwBji7n33PBq4Bbull+fnAMxFxkqRRwHJJtwFvAmdHxPOS9gIWS5ofERvS7aZGxJ39/NtmFeVhKbO/66/HcWKP9wH8KSLe6G/HEbFQUltfqwBD0yGvXYD1wDsR8buCfayRtI7kGSAbiu/GLDseljLbVn+T41lew3ENcA9J72UoMCki3i1cQdJhwBBgZUHz5emTCR8ApkXEWxnWaE3MvQyz4vK8V9UEYClwFDAWWCDpkYjYCCBpT+AHwDkFgTId+CNJmFwPXAh8s9jOJU0BpgC0trZm+GVYo3Evw6xv/T2PI0uTgbsisYLk9iYHAkjaFZgHXBQRj3VvEBFr0/XfAm4CDutt5xFxfUR0RETHqFGjMv1CrHFsby/DoWHNIM8ex0vA0cAjkkYD44BVkoYAdwO39JwEl7RnRKxN50UmAk9Vu2hrTO5lmA1cZsEhaQ5wJDBS0mpgBjAYICKuAy4DZqdnbgm4MCJelXQW8M/A7pI+l+6u+7Tb29IzsEQyzPXFrOq35uDAMCudIsp9Jln96OjoiM7OzrzLsBrjyW+zvklaHBEdPdvznOMwy41Dw6x8fgKgNRUPTZltPweHNQ33Mswqw8FhDc+BYVZZDg5rWB6WMsuGg8MaknsZZtlxcFhDcS/DLHsODmsY7mWYVYeDw+qeA8OsunwBoNU1h4ZZ9bnHYXXJgWGWH/c4rO44NMzy5R6H1Q0HhlltcI/D6oJDw6x2uMdhNc2BYVZ73OOwmuXQMKtN7nFYzXFgmNU29zispjg0zGpfpj0OSTcCJwLrIuLgIst3A24FWtNavhsRN6XLzgEuTlf9VkTcnLb/IzAbaAHuBb4SzfD82wbnwDCrH1kPVc0GrgFu6WX5+cAzEXGSpFHAckm3AbsAM4AOIIDFku6JiNeAa4HzgEUkwXEc8PNMvwrLjG9KaFZ/Mg2OiFgoqa2vVYChkkQSFuuBd4AJwIKIWA8gaQFwnKSHgV0j4rG0/RZgIg6OuuRehll9ynty/BrgHmANMBSYFBHvShoD/KFgvdXAmPRjdZH2bUiaAkwBaG1trXzlVjYHhll9y3tyfAKwFNgLaAeukbRrJXYcEddHREdEdIwaNaoSu7QKcGiY1b+8exyTgZnp5PYKSS8ABwJdwJEF6+0NPJy2792jvasqldp2Kzc0HBhmtSXv4HgJOBp4RNJoYBywClgB/Juk4el6xwLTI2K9pI2SDieZHD8b+Pcc6rYSuJdh1liyPh13DknPYaSk1SRnSg0GiIjrgMuA2ZKWAQIujIhX020vA36T7uqb3RPlwP/m76fj/hxPjNcsB4ZZY1IzXALR0dERnZ2deZfRVBwaZvVP0uKI6OjZnvdQlTUYB4ZZ43NwWEX4Qj6z5uHgsO3mXoZZc8n7Og6rcw4Ns+bjHoeVxYFh1rwcHFYSz2WYmYPDBsy9DDMDB4cNgHsZZlbIwWF9ci/DzHpycFhRDgwz641Px7VtODTMrC8ODtuKQ8PM+uOhKnNYmFlJ3ONocg4NMyuVexxNyqfYmlm5HBxNyL0MM9seDo4m4l6GmVWCg6NJuJdhZpWSWXBIuhE4EVgXEQcXWT4VOLOgjg8Ao9KPOwpW3R/4RkRcLekS4DzglXTZ1yPi3my+gsbgXoaZVVqWPY7ZwDXALcUWRsQsYBaApJOA/xMR64H1QHvaPgjoAu4u2PSqiPhudmU3DvcyzCwLmQVHRCyU1DbA1c8A5hRpPxpYGRG/r1RdzcCBYWZZyv06DknvBY4Dflxk8elsGyhfkvSkpBslDe9jv1MkdUrqfOWVV3pbreE4NMwsa4qI7Hae9Dh+VmyOo2CdScBZEXFSj/YhwBrgHyLi5bRtNPAqEMBlwJ4R8S/91dHR0RGdnZ3lfhl1wYFhZpUmaXFEdPRsr4Wzqor1KgCOB37bHRoAha8l3QD8LPvyapsnv82s2nINDkm7AR8HziqyeJt5D0l7RsTa9O3JwFPZVljb3MswszxkeTruHOBIYKSk1cAMYDBARFyXrnYycH9EvNFj252BY4Av9NjtdyS1kwxVvVhkeVNwL8PM8pTlWVVnDGCd2SSn7fZsfwPYvUj7ZytRWz1zL8PM8lYLcxw2QOWGhgPDzCrJwVEH3Msws1ri4Khx7mWYWa1xcNQo9zLMrFY5OGqMA8PMal3utxyxv3NomFk9cHDUCIeGmdULD1XlzIFhZvXGwZETB4aZ1SsPVeXAoWFm9cw9jip63/R5vFPmXewdGGZWKxwcVeIL+cysUTg4MnbMlQ/z/Lo3+l+xCIeGmdUiB0eGPJdhZo3IwZGBuUu6+OodS8va1oFhZrXOwVFh5Q5NjR46hEUXHZNBRWZmleXgqJC5S7q44I6lvFvidjsKVlzhXoaZ1Q8HRwUcOuM+Nr61ueTtPCxlZvUoy2eO3wicCKyLiIOLLJ8KnFlQxweAURGxXtKLwOvAZuCdiOhItxkB3AG0kTxz/LSIeC2rr6E/Z97wax5dub7k7Q7YY2cWXHBk5QsyM6uCLK8cnw0c19vCiJgVEe0R0Q5MB/47Igp/C38iXd5R0DYNeCAiDgAeSN/nopzQeM8g8eLMTzo0zKyuZRYcEbEQGOhv1jOAOQNY79PAzenrm4GJZZS23eYu6SopNAYJrp7UznOXn5BhVWZm1ZH7HIek95L0TL5U0BzA/ZIC+M+IuD5tHx0Ra9PXfwRG97HfKcAUgNbW1orVO3dJF9PvWjbg9T0sZWaNJvfgAE4CHu0xTPWxiOiStAewQNJzaQ9mi4iINFiKSsPmeoCOjo4y7xC1rVnzl7Pp7f4nwgVcNamdiePHVOqfNjOrCbUQHKfTY5gqIrrSz+sk3Q0cBiwEXpa0Z0SslbQnsK7axa7ZsKnfdXxNhpk1slyDQ9JuwMeBswradgZ2iIjX09fHAt9MF98DnAPMTD//JKva5i7pYtb85azZsIm9hrUwdcI4Jo4fw17DWujqJTyOGDuC2877aFYlmZnVhCxPx50DHAmMlLQamAEMBoiI69LVTgbuj4jCS61HA3dL6q7vvyLivnTZTOCHks4Ffg+clkXt3fMY3UNSXRs2bZnXmDph3FbLAFoGD+KKUw7xsJSZNQVFVGz4v2Z1dHREZ2fngNc/YuaDRXsVY4a18Oi0o3rtjZiZNRJJi3tcEgHUxhxHzeltHqO7feL4MQ4KM2tafnRsEXsNaymp3cysmTg4ipg6YRwtgwdt1dYyeBBTJ4zLqSIzs9rhoaoiuoehPI9hZrYtB0cvPI9hZlach6rMzKwkDg4zMyuJg8PMzEri4DAzs5I4OMzMrCRNccsRSa+Q3NsqKyOBVzPcfyXUQ41QH3W6xsqphzqbucZ9I2JUz8amCI6sSeosdj+XWlIPNUJ91OkaK6ce6nSN2/JQlZmZlcTBYWZmJXFwVMb1/a+Su3qoEeqjTtdYOfVQp2vswXMcZmZWEvc4zMysJA4OMzMriYOjB0k3Slon6alelk+VtDT9eErSZkkj0mUvSlqWLuss2GaEpAWSnk8/D8+jRknjCtqXStoo6avpNpdI6ipYdsL21DjAOneT9FNJT0h6WtLkgmXnpMfreUnnFLT/Y3qMV0j6f0ofTl/tGiW1S/p12vakpEkF28yW9ELBsWzPo8Z02eaCOu4paN9P0qL0ON4haUgeNUr6RI/vyb9Kmpguq+hxHGCdwyXdnf6fPi7p4IJlx0lanh6zaQXt1T6WRWuUtI+khyQ9kx7jrxRsU9mf74jwR8EH8M/Ah4CnBrDuScCDBe9fBEYWWe87wLT09TTg23nVWNA+CPgjyQU+AJcA/1rNYwl8vftYAKOA9cAQYASwKv08PH09PF3vceBwQMDPgeNzqvH9wAFp+17AWmBY+n42cGrexzF9/5detvkhcHr6+jrgf+VVY8E6I9L292ZxHAdY5yxgRvr6QOCB9PUgYCWwf/r//wRwUE7Hsrca9wQ+lL4eCvyuoMaK/ny7x9FDRCwk+eYdiDOAOQNY79PAzenrm4GJZZS2RYVqPBpYGRGZXVE/gDoDGJr2GnZJ130HmAAsiIj1EfEasAA4TtKewK4R8VgkPw23kP2xLFpjRPwuIp5P97EGWEfyC7HituM4FpWudxRwZ9pUje/JgdR4KvDziHhze2rpywDqPAh4MF33OaBN0mjgMGBFRKyKiL8BtwOfzulYFq0xItZGxG/T9teBZ4FMHirk4CiTpPcCxwE/LmgO4H5JiyVNKWgfHRFr09d/BEbnWGO309k2UL6Udn9v1HYOpw3QNcAHgDXAMuArEfEuyTf7HwrWW522jUlf92zPo8YtJB1G8lfoyoLmy9NjeZWknXKs8T2SOiU91j0EBOwObIiI7l/cNXEcKf49Wc3jCElP4hTY8v+6L7A3vX9P5nEse6txC0ltwHhgUUFzxX6+HRzlOwl4NCIK/zL4WER8CDgeOF/SP/fcKP1LuVrnQBerkXQM9lPAjwqarwXGAu0kwy7fq0J9E4ClJEM97cA1knatwr9bij5rTHtBPwAmF/winE4yhPBhkuGXC3Oscd9IbkXxGeBqSWMzrqWcGruP4yHA/IJtqn0cAWYCwyQtBb4MLAE2V+HfLUWfNUraheSPxa9GxMa0uaI/3w6O8m3z11FEdKWf1wF3k3RvAV5OfzC6f0DW5VVj6njgtxHxcndDRLwcEZvTX3438PfaszQZuCsSK4AXSH5RdAH7FKy3d9rWxdZ/WXW351Ej6S++ecBFEfFY9wbpkEFExFvATWR/LHutseB7chXwMMlfoX8i+cXT/ejoXI9j6jTg7oh4u7shh+NIRGyMiMkR0Q6cTTL8uIrevyerfiz7qBFJg0lC47aIuKtgm4r+fDs4yiBpN+DjwE8K2naWNLT7NXAs0H1WxD1A95lB5xRuV80aC2wz79EdbKmT+XvtWXqJZK6FdBx5HMkPwHzg2PTskeEkx3J+Oty3UdLh6djy2WR/LIvWmPba7gZuiYg7Czco+CNBJOPdWR/L3moc3j28I2kkcATwTNrrfYhkTgGq8z3Z2/91t16/J6t4HJE0rOCsqM8DC9O/2n8DHJCeQTWE5I+ye/I4lr3VmB6n7wPPRsSVPbap7M93pWbZG+WD5Jt3LfA2yXjlucAXgS8WrPM54PYe2+1PMvb4BPA0yV+h3ct2Bx4Angd+AYzIo8a0fWeSv5J269H+A5Kx5ydJgm7PrI8lybDF/em/+xRwVsG2/wKsSD8mF7R3pOuuJBk3Vx41Amel2ywt+GhPlz1YsP6twC451fhPadsT6edze3y/Pp4e3x8BO+X4f91G8lf6Dj32WdHjOMA6P5dhEL4AAAF6SURBVEpyNtJy4C7Ss/nSZSeky1ay9c93tY9l0RqBj5EMgz9Z8D15QhY/377liJmZlcRDVWZmVhIHh5mZlcTBYWZmJXFwmJlZSRwcZmZWEgeHWUYktfW8w2l6l9I30juUPiNpU8EdS09Nr1FZlL5/VtIlOZVv1qsd+1/FzCpsRkR8N72f0M8iuQIYAEnLgdMi4glJg0gulDOrKQ4Os9qyB8nFX0TEZuCZfMsx25aHqsxqy1XA8vRBPV+Q9J68CzLrycFhlp3ebsvQ6+0aIuKbJLdVuZ/kjrb3ZVCX2XZxcJhl508kTzAsNAJ4ta+NImJlRFxLclPAD0raPaP6zMri4DDLSET8BVgr6ShInj1P8mCtX/a2jaRPpnc5BTiA5DkLG7Ku1awUnhw3y9bZwH9I6r7N9aURsbKP9T8LXCXpTZJHq56ZTpKb1QzfHdfMzErioSozMyuJg8PMzEri4DAzs5I4OMzMrCQODjMzK4mDw8zMSuLgMDOzkvx/+DSwYmXj8mkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# We will be using logarithmic transformation\n",
        "\n",
        "\n",
        "for feature in continuous_feature:\n",
        "    data=dataset.copy()\n",
        "    if 0 in data[feature].unique():\n",
        "        pass\n",
        "    else:\n",
        "        data[feature]=np.log(data[feature])\n",
        "        data['UTS']=np.log(data['UTS'])\n",
        "        plt.scatter(data[feature],data['UTS'])\n",
        "        plt.xlabel(feature)\n",
        "        plt.ylabel('UTS')\n",
        "        plt.title(feature)\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4ROgoqf-aZl",
        "outputId": "f1de9b21-4ed5-4970-9443-eddbc9ca62fb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['GRADE', 'COIL_GEN_TIME']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "categorical_features=[feature for feature in dataset.columns if data[feature].dtypes=='O']\n",
        "categorical_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ENFpOvSq-hbl",
        "outputId": "be271cbe-78f2-4ce5-ed80-90920c462eb5"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GRADE</th>\n",
              "      <th>COIL_GEN_TIME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>8/10/2020 2:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>9/14/2020 2:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>JVHCM30A00</td>\n",
              "      <td>9/14/2020 6:47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>JVHST01C00</td>\n",
              "      <td>9/15/2020 11:55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>JVHWT02A00</td>\n",
              "      <td>9/16/2020 1:14</td>\n",
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              "          document.querySelector('#df-e9cf4a7e-a2e5-4663-b100-c9e2027f4bfc button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-e9cf4a7e-a2e5-4663-b100-c9e2027f4bfc');\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",
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            "text/plain": [
              "        GRADE    COIL_GEN_TIME\n",
              "0  JVHGLD4AS0   8/10/2020 2:25\n",
              "1  JVHGLD4AS0   9/14/2020 2:30\n",
              "2  JVHCM30A00   9/14/2020 6:47\n",
              "3  JVHST01C00  9/15/2020 11:55\n",
              "4  JVHWT02A00   9/16/2020 1:14"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset[categorical_features].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDUrFAwd-mcK",
        "outputId": "0abe599e-7e12-4add-85b0-e8aa687b09f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The feature is GRADE and number of categories are 110\n",
            "The feature is COIL_GEN_TIME and number of categories are 40959\n"
          ]
        }
      ],
      "source": [
        "for feature in categorical_features:\n",
        "    print('The feature is {} and number of categories are {}'.format(feature,len(dataset[feature].unique())))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iJry3f_m-uI8"
      },
      "outputs": [],
      "source": [
        "for feature in categorical_features:\n",
        "    temp=dataset.groupby(feature)['UTS'].count()/len(dataset)\n",
        "    temp_df=temp[temp>0.01].index\n",
        "    dataset[feature]=np.where(dataset[feature].isin(temp_df),dataset[feature],'Rare_var')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "aDM62AeL-zud",
        "outputId": "5793c66d-f5f3-4233-9d61-75ce526aaf1d"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>COIL_ID</th>\n",
              "      <th>GRADE</th>\n",
              "      <th>COIL_GEN_TIME</th>\n",
              "      <th>FURN_EXIT_TEMP</th>\n",
              "      <th>FM_DOUT_TEMP</th>\n",
              "      <th>COILING_TEMP</th>\n",
              "      <th>SLAB_WID</th>\n",
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              "      <th>STRIP_EXIT_LEN</th>\n",
              "      <th>STRIP_EXIT_THIK</th>\n",
              "      <th>...</th>\n",
              "      <th>N</th>\n",
              "      <th>NI</th>\n",
              "      <th>P</th>\n",
              "      <th>S</th>\n",
              "      <th>SI</th>\n",
              "      <th>TI</th>\n",
              "      <th>V</th>\n",
              "      <th>UTS</th>\n",
              "      <th>YS</th>\n",
              "      <th>P_ELONGATION</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20081459</td>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1081</td>\n",
              "      <td>869</td>\n",
              "      <td>680</td>\n",
              "      <td>1125</td>\n",
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              "      <td>294</td>\n",
              "      <td>42</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20097626</td>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1070</td>\n",
              "      <td>838</td>\n",
              "      <td>662</td>\n",
              "      <td>1245</td>\n",
              "      <td>10640.0</td>\n",
              "      <td>68.63</td>\n",
              "      <td>34</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0022</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>365</td>\n",
              "      <td>299</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20097722</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1075</td>\n",
              "      <td>882</td>\n",
              "      <td>611</td>\n",
              "      <td>1040</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>66.08</td>\n",
              "      <td>36</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0070</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>0.164</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.001</td>\n",
              "      <td>730</td>\n",
              "      <td>575</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20098318</td>\n",
              "      <td>JVHST01C00</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1088</td>\n",
              "      <td>864</td>\n",
              "      <td>603</td>\n",
              "      <td>1260</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>51.94</td>\n",
              "      <td>38</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0035</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.001</td>\n",
              "      <td>495</td>\n",
              "      <td>397</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20098589</td>\n",
              "      <td>JVHWT02A00</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1061</td>\n",
              "      <td>848</td>\n",
              "      <td>650</td>\n",
              "      <td>1265</td>\n",
              "      <td>9811.0</td>\n",
              "      <td>61.23</td>\n",
              "      <td>35</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0032</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0101</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>379</td>\n",
              "      <td>326</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>20099556</td>\n",
              "      <td>JVHGLC4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1017</td>\n",
              "      <td>839</td>\n",
              "      <td>641</td>\n",
              "      <td>1203</td>\n",
              "      <td>8750.0</td>\n",
              "      <td>57.28</td>\n",
              "      <td>34</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>366</td>\n",
              "      <td>327</td>\n",
              "      <td>41</td>\n",
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              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>20099558</td>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1025</td>\n",
              "      <td>858</td>\n",
              "      <td>678</td>\n",
              "      <td>940</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>65.42</td>\n",
              "      <td>36</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>355</td>\n",
              "      <td>308</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>20099565</td>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1061</td>\n",
              "      <td>872</td>\n",
              "      <td>689</td>\n",
              "      <td>940</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>67.29</td>\n",
              "      <td>35</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0031</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>352</td>\n",
              "      <td>303</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>20099570</td>\n",
              "      <td>JVHGLD4AS0</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1054</td>\n",
              "      <td>871</td>\n",
              "      <td>686</td>\n",
              "      <td>940</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>67.29</td>\n",
              "      <td>35</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0100</td>\n",
              "      <td>0.009</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>350</td>\n",
              "      <td>302</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>20099573</td>\n",
              "      <td>JVHWT01A00</td>\n",
              "      <td>Rare_var</td>\n",
              "      <td>1058</td>\n",
              "      <td>859</td>\n",
              "      <td>613</td>\n",
              "      <td>920</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>69.63</td>\n",
              "      <td>34</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0025</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.0090</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "      <td>384</td>\n",
              "      <td>336</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 30 columns</p>\n",
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            "text/plain": [
              "     COIL_ID       GRADE COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  \\\n",
              "0   20081459  JVHGLD4AS0      Rare_var            1081           869   \n",
              "1   20097626  JVHGLD4AS0      Rare_var            1070           838   \n",
              "2   20097722    Rare_var      Rare_var            1075           882   \n",
              "3   20098318  JVHST01C00      Rare_var            1088           864   \n",
              "4   20098589  JVHWT02A00      Rare_var            1061           848   \n",
              "..       ...         ...           ...             ...           ...   \n",
              "95  20099556  JVHGLC4AS0      Rare_var            1017           839   \n",
              "96  20099558  JVHGLD4AS0      Rare_var            1025           858   \n",
              "97  20099565  JVHGLD4AS0      Rare_var            1061           872   \n",
              "98  20099570  JVHGLD4AS0      Rare_var            1054           871   \n",
              "99  20099573  JVHWT01A00      Rare_var            1058           859   \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",
              "95           641      1203    8750.0           57.28               34  ...   \n",
              "96           678       940   10750.0           65.42               36  ...   \n",
              "97           689       940   10750.0           67.29               35  ...   \n",
              "98           686       940   10750.0           67.29               35  ...   \n",
              "99           613       920   10750.0           69.63               34  ...   \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",
              "95  0.0023  0.008  0.010  0.0064  0.008  0.001  0.001  366  327            41  \n",
              "96  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  355  308            43  \n",
              "97  0.0031  0.008  0.016  0.0100  0.006  0.001  0.001  352  303            44  \n",
              "98  0.0036  0.008  0.015  0.0100  0.009  0.001  0.001  350  302            44  \n",
              "99  0.0025  0.007  0.020  0.0090  0.012  0.001  0.001  384  336            36  \n",
              "\n",
              "[100 rows x 30 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l3EUebih_Blg"
      },
      "outputs": [],
      "source": [
        "for feature in categorical_features:\n",
        "    labels_ordered=dataset.groupby([feature])['UTS'].mean().sort_values().index\n",
        "    labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}\n",
        "    dataset[feature]=dataset[feature].map(labels_ordered)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CuqOVr06_seE"
      },
      "outputs": [],
      "source": [
        "scaling_feature=[feature for feature in dataset.columns if feature not in ['UTS','YS','P_ELONGATION']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jazp-KOt_wso",
        "outputId": "d5a9368c-c758-4c3d-f27e-b273dc6f0b39"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['COIL_ID',\n",
              " 'GRADE',\n",
              " 'COIL_GEN_TIME',\n",
              " 'FURN_EXIT_TEMP',\n",
              " 'FM_DOUT_TEMP',\n",
              " 'COILING_TEMP',\n",
              " 'SLAB_WID',\n",
              " 'SLAB_LEN',\n",
              " 'STRIP_EXIT_LEN',\n",
              " 'STRIP_EXIT_THIK',\n",
              " 'FM_EXIT_THIK',\n",
              " 'AL',\n",
              " 'B',\n",
              " 'C',\n",
              " 'CA',\n",
              " 'NB',\n",
              " 'CR',\n",
              " 'CU',\n",
              " 'MN',\n",
              " 'MO',\n",
              " 'N',\n",
              " 'NI',\n",
              " 'P',\n",
              " 'S',\n",
              " 'SI',\n",
              " 'TI',\n",
              " 'V']"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scaling_feature\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o2-FiEVV_z4b"
      },
      "outputs": [],
      "source": [
        "# scaling is not required in case of random forest model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "vZKuYQ6QAKWU",
        "outputId": "bd76f723-c793-495e-e58b-e1fb9ec23b2d"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GRADE</th>\n",
              "      <th>COIL_GEN_TIME</th>\n",
              "      <th>FURN_EXIT_TEMP</th>\n",
              "      <th>FM_DOUT_TEMP</th>\n",
              "      <th>COILING_TEMP</th>\n",
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              "      <th>STRIP_EXIT_THIK</th>\n",
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              "      <td>680</td>\n",
              "      <td>1125</td>\n",
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              "      <td>1.99</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1070</td>\n",
              "      <td>838</td>\n",
              "      <td>662</td>\n",
              "      <td>1245</td>\n",
              "      <td>10640.0</td>\n",
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              "      <td>34</td>\n",
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              "      <td>0.23</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0022</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.0100</td>\n",
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              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1075</td>\n",
              "      <td>882</td>\n",
              "      <td>611</td>\n",
              "      <td>1040</td>\n",
              "      <td>10750.0</td>\n",
              "      <td>66.08</td>\n",
              "      <td>36</td>\n",
              "      <td>2.49</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006</td>\n",
              "      <td>1.35</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0070</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0030</td>\n",
              "      <td>0.164</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1088</td>\n",
              "      <td>864</td>\n",
              "      <td>603</td>\n",
              "      <td>1260</td>\n",
              "      <td>9000.0</td>\n",
              "      <td>51.94</td>\n",
              "      <td>38</td>\n",
              "      <td>4.99</td>\n",
              "      <td>...</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0035</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.0131</td>\n",
              "      <td>0.099</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.001</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1061</td>\n",
              "      <td>848</td>\n",
              "      <td>650</td>\n",
              "      <td>1265</td>\n",
              "      <td>9811.0</td>\n",
              "      <td>61.23</td>\n",
              "      <td>35</td>\n",
              "      <td>2.31</td>\n",
              "      <td>...</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0032</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0101</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>40959</th>\n",
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              "      <td>0.006</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0027</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.012</td>\n",
              "      <td>0.0086</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40960</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1066</td>\n",
              "      <td>878</td>\n",
              "      <td>645</td>\n",
              "      <td>1270</td>\n",
              "      <td>10700.0</td>\n",
              "      <td>69.56</td>\n",
              "      <td>34</td>\n",
              "      <td>1.99</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.011</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40961</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1081</td>\n",
              "      <td>873</td>\n",
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              "      <td>69.56</td>\n",
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              "      <td>0.001</td>\n",
              "      <td>0.0032</td>\n",
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              "      <td>0.011</td>\n",
              "      <td>0.0045</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
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              "    <tr>\n",
              "      <th>40962</th>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1075</td>\n",
              "      <td>894</td>\n",
              "      <td>652</td>\n",
              "      <td>1270</td>\n",
              "      <td>10700.0</td>\n",
              "      <td>62.24</td>\n",
              "      <td>38</td>\n",
              "      <td>5.78</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.0064</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40963</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1070</td>\n",
              "      <td>870</td>\n",
              "      <td>627</td>\n",
              "      <td>1265</td>\n",
              "      <td>10700.0</td>\n",
              "      <td>54.79</td>\n",
              "      <td>43</td>\n",
              "      <td>7.78</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.0033</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.0041</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>40964 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b50aea4b-d04e-4a49-a759-6cc8b4cf78e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b50aea4b-d04e-4a49-a759-6cc8b4cf78e4 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-b50aea4b-d04e-4a49-a759-6cc8b4cf78e4');\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",
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              "  "
            ],
            "text/plain": [
              "       GRADE  COIL_GEN_TIME  FURN_EXIT_TEMP  FM_DOUT_TEMP  COILING_TEMP  \\\n",
              "0          0              0            1081           869           680   \n",
              "1          0              0            1070           838           662   \n",
              "2         12              0            1075           882           611   \n",
              "3         14              0            1088           864           603   \n",
              "4          7              0            1061           848           650   \n",
              "...      ...            ...             ...           ...           ...   \n",
              "40959      7              0            1075           870           635   \n",
              "40960      7              0            1066           878           645   \n",
              "40961      7              0            1081           873           635   \n",
              "40962     10              0            1075           894           652   \n",
              "40963     14              0            1070           870           627   \n",
              "\n",
              "       SLAB_WID  SLAB_LEN  STRIP_EXIT_LEN  STRIP_EXIT_THIK  FM_EXIT_THIK  ...  \\\n",
              "0          1125   10750.0           69.81               34          1.99  ...   \n",
              "1          1245   10640.0           68.63               34          2.05  ...   \n",
              "2          1040   10750.0           66.08               36          2.49  ...   \n",
              "3          1260    9000.0           51.94               38          4.99  ...   \n",
              "4          1265    9811.0           61.23               35          2.31  ...   \n",
              "...         ...       ...             ...              ...           ...  ...   \n",
              "40959      1270   10700.0           69.56               34          1.99  ...   \n",
              "40960      1270   10700.0           69.56               34          1.99  ...   \n",
              "40961      1270   10700.0           69.56               34          1.99  ...   \n",
              "40962      1270   10700.0           62.24               38          5.78  ...   \n",
              "40963      1265   10700.0           54.79               43          7.78  ...   \n",
              "\n",
              "          CU    MN     MO       N     NI      P       S     SI     TI      V  \n",
              "0      0.007  0.20  0.001  0.0025  0.007  0.018  0.0100  0.004  0.001  0.001  \n",
              "1      0.006  0.23  0.001  0.0022  0.007  0.014  0.0100  0.007  0.001  0.001  \n",
              "2      0.006  1.35  0.001  0.0070  0.008  0.015  0.0030  0.164  0.002  0.001  \n",
              "3      0.010  0.75  0.001  0.0035  0.008  0.021  0.0131  0.099  0.002  0.001  \n",
              "4      0.005  0.32  0.001  0.0032  0.007  0.015  0.0101  0.006  0.001  0.001  \n",
              "...      ...   ...    ...     ...    ...    ...     ...    ...    ...    ...  \n",
              "40959  0.006  0.33  0.001  0.0027  0.007  0.012  0.0086  0.007  0.001  0.001  \n",
              "40960  0.006  0.33  0.001  0.0034  0.008  0.011  0.0063  0.015  0.008  0.001  \n",
              "40961  0.006  0.34  0.001  0.0032  0.007  0.011  0.0045  0.017  0.001  0.001  \n",
              "40962  0.006  0.47  0.001  0.0034  0.007  0.015  0.0064  0.018  0.001  0.001  \n",
              "40963  0.006  0.74  0.001  0.0033  0.008  0.025  0.0041  0.139  0.003  0.001  \n",
              "\n",
              "[40964 rows x 26 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X=dataset.drop(['COIL_ID','UTS','YS','P_ELONGATION'],axis=1)\n",
        "\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZsNPp2DAQm3",
        "outputId": "4a1316ad-1a69-47e3-d32b-5f3ed5c27922"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[346],\n",
              "       [365],\n",
              "       [730],\n",
              "       ...,\n",
              "       [389],\n",
              "       [405],\n",
              "       [486]])"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y=dataset[['UTS'\n",
        "       ]].values\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2MsJrrTRAX87"
      },
      "outputs": [],
      "source": [
        "# Splitting the data into a train set and a test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "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": "SPvV-wgSAoTw"
      },
      "outputs": [],
      "source": [
        "# Fitting the model to the training dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Gf6mmsnAuLn"
      },
      "outputs": [],
      "source": [
        "# Initializing the Random Forest Regression model with 100 decision trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wzUBAZIGA4ms"
      },
      "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": "m5N64IGZAxuS"
      },
      "outputs": [],
      "source": [
        "from pandas.core.common import random_state\n",
        "model=RandomForestRegressor(n_estimators=10,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZVdZ1DTA1-X",
        "outputId": "c2bfe4d8-1023-40e8-f331-c4cd56ce7ecf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=10, random_state=0)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Fitting the Random Forest Regression model to the data\n",
        "model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GM3vilidBDe8"
      },
      "outputs": [],
      "source": [
        "# Calculating the loss after training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jr1D9sLeBcl6"
      },
      "outputs": [],
      "source": [
        "#  predicting the target values of the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zgzxFW40BfuV"
      },
      "outputs": [],
      "source": [
        "y_pred=model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScdSl-ufBiO3",
        "outputId": "3a62db76-c2e7-4972-fc2a-7ce46b1db5e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([364.1, 361.9, 466.6, ..., 382.5, 382.1, 359.4])"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eh7egTNRlnrr",
        "outputId": "5227388f-0fc6-4027-df71-0904cb471177"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9413196567795603"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ic_-sknLln1w",
        "outputId": "019898d0-6018-4631-b5ef-3ceb92e8d99e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[DecisionTreeRegressor(max_features='auto', random_state=209652396),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=398764591),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=924231285),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=1478610112),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=441365315),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=1537364731),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=192771779),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=1491434855),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=1819583497),\n",
              " DecisionTreeRegressor(max_features='auto', random_state=530702035)]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.estimators_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rccPu4O261O3",
        "outputId": "d12dca21-de47-44b8-c9e6-c463c8ec9a95"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean_random_forest_Acc :  0.9403298099843092\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "model_accuracies = cross_val_score(estimator =model , X = X_train, y = y_train, cv = 8)\n",
        "print(\"Mean_random_forest_Acc : \", model_accuracies.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaeIE-L_gbgJ",
        "outputId": "d051afad-9c03-4444-fb32-61be217999d7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:926: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best hyper parameter: {'bootstrap': True, 'max_features': 'sqrt', 'n_estimators': 100}\n"
          ]
        }
      ],
      "source": [
        "# Run GridSearch to tune the hyper-parameter\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "rfr=RandomForestRegressor()\n",
        "k_fold_cv = 5 \n",
        "grid_params = {\n",
        "\"n_estimators\"  : [10,50,100],\n",
        "\"max_features\" : [\"auto\", \"log2\", \"sqrt\"],\n",
        "\"bootstrap\" : [True, False]\n",
        " }\n",
        "grid = GridSearchCV(rfr, param_grid=grid_params, cv=k_fold_cv, \n",
        " n_jobs = 1, verbose = 0, return_train_score=True)\n",
        "grid.fit(X_train, y_train)\n",
        "print('Best hyper parameter:', grid.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8iklnZcL8pUZ",
        "outputId": "93b1a2f6-04a6-40df-d415-3dc4e78f29ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Results from Grid Search \n",
            "\n",
            " The best estimator across ALL searched params:\n",
            " RandomForestRegressor(max_features='sqrt')\n",
            "\n",
            " The best score across ALL searched params:\n",
            " 0.946349803308169\n",
            "\n",
            " The best parameters across ALL searched params:\n",
            " {'bootstrap': True, 'max_features': 'sqrt', 'n_estimators': 100}\n"
          ]
        }
      ],
      "source": [
        "print(\" Results from Grid Search \" )\n",
        "print(\"\\n The best estimator across ALL searched params:\\n\",grid.best_estimator_)\n",
        "print(\"\\n The best score across ALL searched params:\\n\",grid.best_score_)\n",
        "print(\"\\n The best parameters across ALL searched params:\\n\",grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "MLcTqqh-Blyt"
      },
      "outputs": [],
      "source": [
        "# RMSE (Root Mean Square Error)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "I3FbETnhBtYp"
      },
      "outputs": [],
      "source": [
        "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": "lXO8nT7XBwGM",
        "outputId": "2c19a340-1e4c-4e4c-b053-1db99f122863"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "RMSE: 11.346\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "iwGnsD1RBy5v"
      },
      "outputs": [],
      "source": [
        "X=dataset[['FM_DOUT_TEMP', 'STRIP_EXIT_LEN', 'C', 'NB', 'COILING_TEMP', 'MN', 'P', 'TI', 'CR', 'SI', 'FM_EXIT_THIK', 'GRADE', 'N']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "UxVw7Qu6D2-5",
        "outputId": "2bffbe19-7637-40cc-e88c-b57575c03cb0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d477ed05-2463-407d-ac43-8d8f74516a64\">\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",
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              "\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>FM_DOUT_TEMP</th>\n",
              "      <th>STRIP_EXIT_LEN</th>\n",
              "      <th>C</th>\n",
              "      <th>NB</th>\n",
              "      <th>COILING_TEMP</th>\n",
              "      <th>MN</th>\n",
              "      <th>P</th>\n",
              "      <th>TI</th>\n",
              "      <th>CR</th>\n",
              "      <th>SI</th>\n",
              "      <th>FM_EXIT_THIK</th>\n",
              "      <th>GRADE</th>\n",
              "      <th>N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>869</td>\n",
              "      <td>69.81</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>0.001</td>\n",
              "      <td>680</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.004</td>\n",
              "      <td>1.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>838</td>\n",
              "      <td>68.63</td>\n",
              "      <td>0.0385</td>\n",
              "      <td>0.001</td>\n",
              "      <td>662</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.007</td>\n",
              "      <td>2.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>882</td>\n",
              "      <td>66.08</td>\n",
              "      <td>0.2809</td>\n",
              "      <td>0.001</td>\n",
              "      <td>611</td>\n",
              "      <td>1.35</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.020</td>\n",
              "      <td>0.164</td>\n",
              "      <td>2.49</td>\n",
              "      <td>12</td>\n",
              "      <td>0.0070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>864</td>\n",
              "      <td>51.94</td>\n",
              "      <td>0.1554</td>\n",
              "      <td>0.001</td>\n",
              "      <td>603</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.021</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.099</td>\n",
              "      <td>4.99</td>\n",
              "      <td>14</td>\n",
              "      <td>0.0035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>848</td>\n",
              "      <td>61.23</td>\n",
              "      <td>0.0610</td>\n",
              "      <td>0.001</td>\n",
              "      <td>650</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.001</td>\n",
              "      <td>0.015</td>\n",
              "      <td>0.006</td>\n",
              "      <td>2.31</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d477ed05-2463-407d-ac43-8d8f74516a64')\"\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",
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              "  </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-d477ed05-2463-407d-ac43-8d8f74516a64 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-d477ed05-2463-407d-ac43-8d8f74516a64');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   FM_DOUT_TEMP  STRIP_EXIT_LEN       C     NB  COILING_TEMP    MN      P  \\\n",
              "0           869           69.81  0.0335  0.001           680  0.20  0.018   \n",
              "1           838           68.63  0.0385  0.001           662  0.23  0.014   \n",
              "2           882           66.08  0.2809  0.001           611  1.35  0.015   \n",
              "3           864           51.94  0.1554  0.001           603  0.75  0.021   \n",
              "4           848           61.23  0.0610  0.001           650  0.32  0.015   \n",
              "\n",
              "      TI     CR     SI  FM_EXIT_THIK  GRADE       N  \n",
              "0  0.001  0.018  0.004          1.99      0  0.0025  \n",
              "1  0.001  0.016  0.007          2.05      0  0.0022  \n",
              "2  0.002  0.020  0.164          2.49     12  0.0070  \n",
              "3  0.002  0.022  0.099          4.99     14  0.0035  \n",
              "4  0.001  0.015  0.006          2.31      7  0.0032  "
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "s82Fs80kD_EB",
        "outputId": "ab7cb36d-1ebc-4e86-8bf7-119a53c8a9b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[346],\n",
              "       [365],\n",
              "       [730],\n",
              "       ...,\n",
              "       [389],\n",
              "       [405],\n",
              "       [486]])"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y=dataset[['UTS'\n",
        "       ]].values\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qlW-_5ziEEV_"
      },
      "outputs": [],
      "source": [
        "# splitting the dataset into the training and testing set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ru6vpgLgEY6k"
      },
      "outputs": [],
      "source": [
        "# Splitting the data into a train set and a test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "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": {
        "colab": {
          "background_save": true
        },
        "id": "POAk3OLREjyK"
      },
      "outputs": [],
      "source": [
        "# Fitting the model to the training dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "B_Mn4fzuEpAW"
      },
      "outputs": [],
      "source": [
        "# Initializing the Random Forest Regression model with 10 decision trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zJ4d3skME2Jk"
      },
      "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": {
        "colab": {
          "background_save": true
        },
        "id": "Ml0fRzrfE7pS"
      },
      "outputs": [],
      "source": [
        "from pandas.core.common import random_state\n",
        "model=RandomForestRegressor(n_estimators=10,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "NoDTPsb3FADR",
        "outputId": "33e9fbeb-86a2-4850-a6fd-1bfcf607a95b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=10, random_state=0)"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Fitting the Random Forest Regression model to the data\n",
        "model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ixPa_4T9FGfB"
      },
      "outputs": [],
      "source": [
        "# Calculating the loss after training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QL3iXYwmFMYT"
      },
      "outputs": [],
      "source": [
        "#  predicting the target values of the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "pTbMn9pjFP6T"
      },
      "outputs": [],
      "source": [
        "y_pred=model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "dcKQLjcKFT6S",
        "outputId": "f1f23022-c786-489d-e26b-1f65de0e29c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([361.7, 355.4, 472.8, ..., 379.5, 383.3, 363. ])"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Pofp1elrpTEp",
        "outputId": "61152460-bbec-47be-95e0-ffe47f7a43e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9396851735327411"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3Wxti9yyFZnc"
      },
      "outputs": [],
      "source": [
        "# RMSE (Root Mean Square Error)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KAzpFu4xFh2_"
      },
      "outputs": [],
      "source": [
        "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": "4tiQfYG0FmnP",
        "outputId": "6dae0e68-8b38-487b-8cb0-807f02204ff8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "RMSE: 11.503\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "0DZDjNTNX9Jr",
        "outputId": "8bd3d68b-2c6f-4f86-bb3e-601db8212e22"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean_random_forest_Acc :  0.9393457275224014\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "model_accuracies = cross_val_score(estimator =model , X = X_train, y = y_train, cv = 8)\n",
        "print(\"Mean_random_forest_Acc : \", model_accuracies.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "PysyaFybFqt2",
        "outputId": "57ee31ab-4ccb-43db-9691-12af4fdce7e0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:680: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:926: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best hyper parameter: {'bootstrap': True, 'max_features': 'sqrt', 'n_estimators': 100}\n"
          ]
        }
      ],
      "source": [
        "# Run GridSearch to tune the hyper-parameter\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "rfr=RandomForestRegressor()\n",
        "k_fold_cv = 5\n",
        "grid_params = {\n",
        "\"n_estimators\"  : [10,50,100],\n",
        "\"max_features\" : [\"auto\", \"log2\", \"sqrt\"],\n",
        "\"bootstrap\" : [True, False]\n",
        " }\n",
        "grid = GridSearchCV(rfr, param_grid=grid_params, cv=k_fold_cv, \n",
        " n_jobs = 1, verbose = 0, return_train_score=True)\n",
        "grid.fit(X_train, y_train)\n",
        "print('Best hyper parameter:', grid.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "09hwL5rFfZPv",
        "outputId": "684455eb-aad5-4c53-f77d-36cb60814771"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Results from Grid Search \n",
            "\n",
            " The best estimator across ALL searched params:\n",
            " RandomForestRegressor(max_features='sqrt')\n",
            "\n",
            " The best score across ALL searched params:\n",
            " 0.9452849018300213\n",
            "\n",
            " The best parameters across ALL searched params:\n",
            " {'bootstrap': True, 'max_features': 'sqrt', 'n_estimators': 100}\n"
          ]
        }
      ],
      "source": [
        "print(\" Results from Grid Search \" )\n",
        "print(\"\\n The best estimator across ALL searched params:\\n\",grid.best_estimator_)\n",
        "print(\"\\n The best score across ALL searched params:\\n\",grid.best_score_)\n",
        "print(\"\\n The best parameters across ALL searched params:\\n\",grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "metadata": {
        "id": "l5SfOuULrK0e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_absolute_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yW945f_rPT0",
        "outputId": "c7478051-1a1a-4872-f91e-8d405ad3ddee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.638859595589731"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "pbbSW12nrRUs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ph3P4644rWgV",
        "outputId": "1eff87e0-224e-4d04-c702-f22104a11c13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132.32037414188264"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b0jde1-zER4P"
      },
      "outputs": [],
      "source": [
        "rmse=float(format(np.sqrt(mean_squared_error(y_pred,y_test)),'.3f'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-k1eDTfEXvx",
        "outputId": "aa26bd33-aee1-4229-8815-449dd9d49346"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "RMSE: 11.503\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nRMSE:\",rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "jF6b2GIejKUw",
        "outputId": "9e56cd11-93ba-4806-acb1-ce6cb1d415af"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "kohO_X4_rr_w"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Random_forest_model_Data_set2_(2).ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}