Diabetes.ipynb

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    "colab": {
      "name": "Diabetes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "ssOQYq4XxreS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Regular EDA(Exploratory Data Analysis) & Plotting Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "uEeiA0ttebVi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EP8ikJiLSrmf"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Impoting Data"
      ],
      "metadata": {
        "id": "V2ku0faFyjxi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dia = pd.read_csv(\"https://raw.githubusercontent.com/kuchbhi-kunal/nidan/main/diabetes_012_health_indicators_BRFSS2015.csv\")"
      ],
      "metadata": {
        "id": "JoMf37slSyLs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia"
      ],
      "metadata": {
        "id": "3j4UDNv9S7pT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "9b8b9b38-f055-4692-a3c1-5ac6ce2c5bd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Diabetes_012  HighBP  HighChol  CholCheck   BMI  Smoker  Stroke  \\\n",
              "0                0.0     1.0       1.0        1.0  40.0     1.0     0.0   \n",
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              "...              ...     ...       ...        ...   ...     ...     ...   \n",
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              "\n",
              "        HeartDiseaseorAttack  PhysActivity  Fruits  ...  AnyHealthcare  \\\n",
              "0                        0.0           0.0     0.0  ...            1.0   \n",
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              "3                        0.0           1.0     1.0  ...            1.0   \n",
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              "\n",
              "        NoDocbcCost  GenHlth  MentHlth  PhysHlth  DiffWalk  Sex   Age  \\\n",
              "0               0.0      5.0      18.0      15.0       1.0  0.0   9.0   \n",
              "1               1.0      3.0       0.0       0.0       0.0  0.0   7.0   \n",
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              "\n",
              "        Education  Income  \n",
              "0             4.0     3.0  \n",
              "1             6.0     1.0  \n",
              "2             4.0     8.0  \n",
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              "          document.querySelector('#df-691bbf10-94ec-48f9-b582-1e7818684b45 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-691bbf10-94ec-48f9-b582-1e7818684b45');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "fD6vSQOIypIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dia.info()"
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        "colab": {
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        },
        "outputId": "3586bab6-7884-475a-8e8d-6e91dded355a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 253680 entries, 0 to 253679\n",
            "Data columns (total 22 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   Diabetes_012          253680 non-null  float64\n",
            " 1   HighBP                253680 non-null  float64\n",
            " 2   HighChol              253680 non-null  float64\n",
            " 3   CholCheck             253680 non-null  float64\n",
            " 4   BMI                   253680 non-null  float64\n",
            " 5   Smoker                253680 non-null  float64\n",
            " 6   Stroke                253680 non-null  float64\n",
            " 7   HeartDiseaseorAttack  253680 non-null  float64\n",
            " 8   PhysActivity          253680 non-null  float64\n",
            " 9   Fruits                253680 non-null  float64\n",
            " 10  Veggies               253680 non-null  float64\n",
            " 11  HvyAlcoholConsump     253680 non-null  float64\n",
            " 12  AnyHealthcare         253680 non-null  float64\n",
            " 13  NoDocbcCost           253680 non-null  float64\n",
            " 14  GenHlth               253680 non-null  float64\n",
            " 15  MentHlth              253680 non-null  float64\n",
            " 16  PhysHlth              253680 non-null  float64\n",
            " 17  DiffWalk              253680 non-null  float64\n",
            " 18  Sex                   253680 non-null  float64\n",
            " 19  Age                   253680 non-null  float64\n",
            " 20  Education             253680 non-null  float64\n",
            " 21  Income                253680 non-null  float64\n",
            "dtypes: float64(22)\n",
            "memory usage: 42.6 MB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "dia.isna().sum()"
      ],
      "metadata": {
        "id": "SVKO-BOhSw_o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cae4ce5c-750c-486a-95be-bd742a7ae826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diabetes_012            0\n",
              "HighBP                  0\n",
              "HighChol                0\n",
              "CholCheck               0\n",
              "BMI                     0\n",
              "Smoker                  0\n",
              "Stroke                  0\n",
              "HeartDiseaseorAttack    0\n",
              "PhysActivity            0\n",
              "Fruits                  0\n",
              "Veggies                 0\n",
              "HvyAlcoholConsump       0\n",
              "AnyHealthcare           0\n",
              "NoDocbcCost             0\n",
              "GenHlth                 0\n",
              "MentHlth                0\n",
              "PhysHlth                0\n",
              "DiffWalk                0\n",
              "Sex                     0\n",
              "Age                     0\n",
              "Education               0\n",
              "Income                  0\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 6
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        "id": "4j13akc5S9Kb",
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          "height": 739
        },
        "outputId": "f5b4671f-d1a8-4e8a-95e8-a381b227d5b6"
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      "execution_count": null,
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              "                         0     1     2     3     4\n",
              "Diabetes_012           0.0   0.0   0.0   0.0   0.0\n",
              "HighBP                 1.0   0.0   1.0   1.0   1.0\n",
              "HighChol               1.0   0.0   1.0   0.0   1.0\n",
              "CholCheck              1.0   0.0   1.0   1.0   1.0\n",
              "BMI                   40.0  25.0  28.0  27.0  24.0\n",
              "Smoker                 1.0   1.0   0.0   0.0   0.0\n",
              "Stroke                 0.0   0.0   0.0   0.0   0.0\n",
              "HeartDiseaseorAttack   0.0   0.0   0.0   0.0   0.0\n",
              "PhysActivity           0.0   1.0   0.0   1.0   1.0\n",
              "Fruits                 0.0   0.0   1.0   1.0   1.0\n",
              "Veggies                1.0   0.0   0.0   1.0   1.0\n",
              "HvyAlcoholConsump      0.0   0.0   0.0   0.0   0.0\n",
              "AnyHealthcare          1.0   0.0   1.0   1.0   1.0\n",
              "NoDocbcCost            0.0   1.0   1.0   0.0   0.0\n",
              "GenHlth                5.0   3.0   5.0   2.0   2.0\n",
              "MentHlth              18.0   0.0  30.0   0.0   3.0\n",
              "PhysHlth              15.0   0.0  30.0   0.0   0.0\n",
              "DiffWalk               1.0   0.0   1.0   0.0   0.0\n",
              "Sex                    0.0   0.0   0.0   0.0   0.0\n",
              "Age                    9.0   7.0   9.0  11.0  11.0\n",
              "Education              4.0   6.0   4.0   3.0   5.0\n",
              "Income                 3.0   1.0   8.0   6.0   4.0"
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              "\n",
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              "      <th>Diabetes_012</th>\n",
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              "      <th>HighBP</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>NoDocbcCost</th>\n",
              "      <td>0.0</td>\n",
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              "      <td>2.0</td>\n",
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              "      <th>MentHlth</th>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>PhysHlth</th>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>DiffWalk</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>Age</th>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>11.0</td>\n",
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              "    <tr>\n",
              "      <th>Education</th>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
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              "                                                     [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",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"Education\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "vRERf8XETHRk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia.head().T"
      ],
      "metadata": {
        "id": "QeAZvY_XT4ph",
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          "height": 708
        },
        "outputId": "573c707f-b656-4999-c109-afa8031a583f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         0     1     2     3     4\n",
              "Diabetes_012           0.0   0.0   0.0   0.0   0.0\n",
              "HighBP                 1.0   0.0   1.0   1.0   1.0\n",
              "HighChol               1.0   0.0   1.0   0.0   1.0\n",
              "CholCheck              1.0   0.0   1.0   1.0   1.0\n",
              "BMI                   40.0  25.0  28.0  27.0  24.0\n",
              "Smoker                 1.0   1.0   0.0   0.0   0.0\n",
              "Stroke                 0.0   0.0   0.0   0.0   0.0\n",
              "HeartDiseaseorAttack   0.0   0.0   0.0   0.0   0.0\n",
              "PhysActivity           0.0   1.0   0.0   1.0   1.0\n",
              "Fruits                 0.0   0.0   1.0   1.0   1.0\n",
              "Veggies                1.0   0.0   0.0   1.0   1.0\n",
              "HvyAlcoholConsump      0.0   0.0   0.0   0.0   0.0\n",
              "AnyHealthcare          1.0   0.0   1.0   1.0   1.0\n",
              "NoDocbcCost            0.0   1.0   1.0   0.0   0.0\n",
              "GenHlth                5.0   3.0   5.0   2.0   2.0\n",
              "MentHlth              18.0   0.0  30.0   0.0   3.0\n",
              "PhysHlth              15.0   0.0  30.0   0.0   0.0\n",
              "DiffWalk               1.0   0.0   1.0   0.0   0.0\n",
              "Sex                    0.0   0.0   0.0   0.0   0.0\n",
              "Age                    9.0   7.0   9.0  11.0  11.0\n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Checking the Value Counts Graphically*"
      ],
      "metadata": {
        "id": "P7FHwEHGyvlr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dia.hist(figsize=(50,50), bins=50, color=\"red\");"
      ],
      "metadata": {
        "id": "hxLRw3YeUKBA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "outputId": "3ed85be3-c2a5-4bce-fc99-507904c5d4fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x3600 with 25 Axes>"
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Correlation***"
      ],
      "metadata": {
        "id": "3uIZu1hGy5i6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = dia.corr()\n",
        "fig , ax = plt.subplots(figsize=(10,8))\n",
        "ax = sns.heatmap(corr_matrix,\n",
        "                annot=True,\n",
        "                linewidths=0.5,\n",
        "                fmt='.2f')"
      ],
      "metadata": {
        "id": "vkCrbxSkU3Jm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "outputId": "ec8f86c5-a44a-4de6-e0a4-7e02fca7401b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['BMI'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "lL-uAzCfucOy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "adb3f056-9b90-48cf-bcbd-06c67b4c05ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3634630150>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['Income'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "r6Qs-FKBvD6P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "61e0e243-ffd5-4245-b1b0-e11fac107b60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3638f66390>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['MentHlth'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "Vouqk3o7vSEd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "bf0b6979-fca2-4590-af5a-094632cfbbe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3639ad5a50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['PhysHlth'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "egNuzWDbvmdL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d1b3a302-142e-4497-c434-516d96e46ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3638c83190>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['Age'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "H_knDt5lwCgV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "0ded7dbd-b307-4706-83b3-9f9f7b68e3aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f363908bbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['GenHlth'].plot(kind='hist')"
      ],
      "metadata": {
        "id": "5v6A1WXKwRCw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "75a8fabc-72e9-4f0b-fa41-13834dcf6326"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36391d2850>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the frequency of the Diabetes_012\n",
        "\n",
        "dia['Diabetes_012'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "id": "rxnz5-qyeCiL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "c975ecd7-acb6-48a6-d91c-3a3d70421fda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3638fa6850>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop('Diabetes_012', axis=1).corrwith(dia.Diabetes_012).plot(kind='bar', grid=True, figsize=(20, 8), title=\"Correlation with Diabetes Prediction\",color=\"Black\")"
      ],
      "metadata": {
        "id": "XQe7Kfuk0-g1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "outputId": "31127845-cb18-4430-cb49-96695c0ad464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f363936b650>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": 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luXhPjwcAAADA/mejcxKdleQh4/cPSfJPG3w8AAAAAGZgoyHR7yf5gar6YJJ7jcupqqOr6i+XN6qqf0/ysiT3rKqdVfVDGzwuAAAAAFO0x+Fmu9Pdn0xyz1XWn5/k4RPL37uR4wAAAACwb220JxEAAAAAC0BIBAAAAICQCAAAAAAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAASQ6edQMAYF/r7nXtt2PHjnXvCwAA80ZPIgAAAACERAAAAAAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAAiJAIAAAAgAiJAAAAAIiQCAAAAIAIiQAAAACIkAgAAACACIkAAAAASHLwrBsAAAAAsAi6e1377dixY937TpOeRAAAAAAIiQAAAAAQEgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAABAhEQAAAAAREgEAAAAQIREAAAAAERIBAAAAECERAAAAABESAQAAAJCkunvWbVhVVX0iyYc38ZCHJrlkE4+32dQ339Q3vxa5tkR9805982uRa0vUN+/UN78WubZEffNOfdNzeHffdLU79tuQaLNV1fndffSs27GvqG++qW9+LXJtifrmnfrm1yLXlqhv3qlvfi1ybYn65p36NofhZgAAAAAIiQAAAAAQEk36i1k3YB9T33xT3/xa5NoS9c079c2vRa4tUd+8U9/8WuTaEvXNO/VtAnMSAQAAAKAnEQAAAABCIgAAAAAiJAJgL1VVrbLu2rNoCwAAMD0Hz7oBs1BV10nyyIakz40AACAASURBVCTfmuTdSZ7b3VfMtlXTU1U/trv7u/vvN6st+0pVbevupRXr7t3dr5xVm6alqk7f3f3d/fTNasu+UFUHJfnF7v7DWbeFdXtukoctL1TVliT/lOSeM2vRFFXV05I8r7vfO+u2sHeq6gFJXtXdl1fV45J8Z5IndffbZ9w0rqaqum53f27W7dgXqup7uvuNe1oHs1BVt0hyeCbeH3b362fXoumpqhO6+19WrHtkd//5rNo0TVX1M9393Inlg5I8rrufOMNmTUVVHZbkyUlu3t0nVNWRSY6ZrJfpO1B7Er0wydEZAqITkjxtts2Zuvvs5nbvGbZrmp5TVXdYXqiqByb5rRm2Z5quv4fbXOvuryR54KzbsS9V1W2r6rVV9Z5x+Y7jG9ZFsbOq/jRJqupGSV6T5EWzbdJUvT/JX1TVW6vqkVV1g1k3aJqq6qlVdUhVXXN8nn6iqn5y1u2akt8aA6K7J7lXhkDzz2bcpqmpqltX1Suq6pKquriq/qmqbj3rdk1DVd2tqt6X5D/H5aOW/84skD+5muvmTlX9WFV9sKo+XVWfqarLq+ozs27XNI3/259TVa+pqtct32bdrmmoqqckeWOSxyX51fH26Jk2arp+q6q+f3mhqn4tyYkzbM+03bOqzq6qm1XV7ZO8JQvwnmH0giSvTnLzcfm/kvzSzFqzD+yP7xsOyKubVdW7u/s7xu8PTvK27v7OGTeLvTC+KH55kgcl+d4kP5Xk3t396Zk2jKulqv4wyTWTvCTJZ5fXL8qn/VV1boYXWM/u7juP697T3XfY/Z7zo6qemuSQJNuS/H53/92MmzR1VXW7JD+dIdR8Y5LndPc5s23VxlXVO7v7TlV1vwwfHJye5PXdfdSMm7ZhVfWO7r5zVf1eknd394uX1826bdNQVW9J8qwkfzuuOjnJL3T3XWfXqumoqrcmOSnJWYv2d7OqjklytwxvbCZ70R6S5H4L8rt3QZL7dPf7Z92WfaWq3pXkz5MsJfnK8vqVPdvnUVV9IMkdu/uLs27LvlBVhyZ5ZYbXZscn+bYkD+zuL820YVNUVT+R4f/DZ5M8aFF6KFbVed39XZP/y5dfx8y6bdOyP75vOCCHmyX58vI33X3FKtNrLITx0+8nJLnHuOrcJGcsQpDS3RdW1clJ/jHJ/yb5we7+/IybNRVV9ce7u7+7f3Gz2rIPLf9hP2NiXSf5/lW2nUfX7e63rfjbMvdDWlcMZX1rht57b0vSVfVjizCUddnYVfvbxtslSd6V5PSqekR3nzzTxm3cNcevP5LkZd396QX6P/iRqnp2kh9I8pRxrqxF6jV93e7+64nlF1XVr86sNVPW3ReteC5+Za1t58y1kmzJ8Lp78tP9z2QIxhbBxxc5IBpd0d0L0zNxhQsz/G9YyJCouy+pqvsm+bcMId9JvUA9JarqNkkeleTvknx7klPGUGURhu5+tqpukuF9Qqrqu5PM/XvZFfa79w0Hakh01EQX2EryDeNyJenuPmR2TZuq5yV5T5IfH5dPSfL8JLuds2h/VlXvzvhHYnTjJAcleWtVpbvvOJuWTdUjM5y3lyb5vwzPy4XS3cfNug372CVV9S35+j+0k5J8dLZNmor7rFh+R4YXlffJUOtChERjT7d7J3ldkid399vGu54yfto6715RVf+Z5PNJfraqbprkCzNu07T8eIZPic/s7suq6mYZPp1bFP9SVY9Jsj3D79xPJDm7qm6cJN39qVk2boMuqqq7ZQidr5nhDc9ChA7dfW6Sc6vqBd394SSpqmsk2dLdcz0ka+LDg/Or6iUZPrz7WtCwCB8eLP9+Zfjb+XNJ/iFXrnFuf++q6k8y/C35XJJ3VtVrc+Xa5vqDyaq6PEN9NX69VpJbJzmpqhbpPd8rkpzW3f9WQ9JwepLzktx+ts2aitOTnJXkW6rqjUlumsUJ15ftd+8bDsjhZgeK1brizXv3vKo6fHf3L7/4mmdjWv6ADC/+r8gwJOvl3X3ZTBs2RbXgk9CNwyH/IsPwgkuT/E+SBy/C8/NAUFU/neSl3f3ZVe67wbz3xhx711wvyae7+ytVdb0Mb1Y/PuOmTcU4H9Ftuvv5YwC2pbv/Z9btmoaq2l0d3d1zOz/ROBzkGRnmkqoMc509qrs/OdOGTVFVvTjDB0FfyfAG7pAkz+juP5hpwzagqp6/m7u7ux+2m/vnwvh7txw0rDTvv3cP2c3d3d1/tWmNYd2q6pCVgXNV3ba7/2tWbZqmcXqY22X4HfxAd395D7vMlTXeN/xkd39oZm0SEg1X0khyZJIPdfcls27PtFTVm5P8ane/YVz+ngyfrh4z25Zt3NjV8L3dffm4fEiSb+/ut862ZdNVVVszzDlxepJfXzHMYG5V1b9k6NX2m9191PjH/x3Lc4XNuxqvvje++b7GOJHuQlx9L0mq6oUZ3rxdNi7fKMnTFuHNQJJU1Wu7+557WjevqurtK+fhW23dPKqqJ2S4MMXtuvu2VXXzDEPqvmfGTWM3xuGdf9XdD551W/alifnAHpzhynuPSbK0CL2g6wC4cltVXae7v7CndfOoqh7V3c/Y07p5U1W7/b+2QHNhLn/4eovuPn6RPnyt1a/a/ekM8w5evNnt2Zcm3zfMui0H5HCzcUzqHyf5VIZZ/J+V5ONJjqiqX+/uF86yfVP0yCR/Nc5NVBnqfehMWzQ9f5bhBdayXausm2vjP7YHZphb418yjKFeFId290ur6rHJ1+YGW5S5J5Lh6ns/1d3LVyk4OckvZ5g0cRHccbJnW3dfWlVzPzFwVV0nyXWTHDoGX8ufGh+S5BYza9iUVNU3ZajjG8bzNVnfdWfWsOm6X5I7J3l7knT3/1XVolzhZTlM+ZEkR+TKl6l++qzaNA1jj7bDq+paizSR7CquOQ6l+9Ekz+zuL1fVonxa+ye56muw1dbNszflqvWstm4ePSRDT75JD11l3bzZ3RWsF2kuzBdk/PB1XP6vDCMR5j4kSvIzSY5JsnzhkGMzvCe6VVWdsQgfoFfVDTNchOmIJAcvz000y+GeB2RIlOR3kvxgkhtkeMLdcZwI+RuTvDbJQoRE3f2uDPMvHTIuz/W49xVqcsK57v7q2Btl7lXVGRneBLw/w7wTj+3uuZ/0eIVFn4TupCQvr6rJq+/94GybNFXXqKobdfelydfma1iE379HZLj60M0zhgyjzyR55kxaNF0/lOFF/9Ykk6HC5Ul+YxYN2ge+1N29/MZ7/FRukbwiw/xR707y1Rm3ZdouTPLGqjorV77q5VwHYCs8O8mHMkyE//pxCP1cvzarr1+57aZVdfrEXYdkmDNy7i1ywF5VD8xwpeBbjb97y66f4cPluXYAzIG5bJE/fD04w2iRjydf6zX1V0numuT1SeY+JEpydpK3ZD/6374IL+rX46vLYzSr6n+6+8Ik6e6Lq2ph3oyP807cP1dNJc/YzW7z4sKq+sUMvYeS5OcyvMBcBI/LMBb1qPH25PHcLU+sPvfd0rPgk9D1Al99b/S0JG+uqpdleF6elOR3Z9ukjRu71T+jqn6hu/9k1u2ZtrGX7Aur6v7d/Xezbs8+8tIarm52w6r6f0keluQ5M27TNG1dkP8Bq/nv8XaNXPkKYAuju/84Q0/2ZR+uqnl/E3sgXLltkQP2N2WYIPfQXLnXzeVJ/mMmLdpHquoOGaYXuc7yugWac2mRP3y95Yo5Ey8e132qqhZlbqLrdPfpe95s8xyQcxJV1bsydFW7Roar1xybr38qcE53HzWblk1XVb0qwx+IpUxcRra7d9f1ci6Mvb7+OEM30c7QA+yXFmFs6gEyOfetklyUiUnoktypu8+bacM2qK569b1vzPA7+MUki3L1vSRJVd0+yfKbm9d19/tm2Z5pqKrv7+7XrTH+fe6v0lNVP9ndL6qqX8mVn6dJFqfHRlX9QIaee5Xk1d39rzNu0tRU1VOSvLa7XzPrtrD3xuH/T0hyj3HVuUnOmPfJ8JPhtcsivD7ZnQUP2BfaOF/dsRlCorOTnJDkDd29EEHmOEXFnyS5Q4YrJN80yUndPfdBX1X9aZJvTvKycdX9k+zMcOXSVy5Cb7Gq+uUMU6e8MvvJlRMP1JDoQxm6ci3cVQomVdV7uvsOs24HG1fDVV8+2QvyC1tVS0nu290fGZfvkeRZ8z5x9YEQ8E0aw9rJT+T+d4bN2bCqemJ3P6FWv1rP3F+lp6oe0d3PHl8sX0V3P3Gz28Teqar7JXlRhg+5vpyv9zCd+8s413Alul/LcMnmyb8rizJnSKrq7zK8gVue1uCUJEd196rB9DyoqldkldB5WXffdxObs0+sGEZ3FfMcsNfXLxF/lbuyIH9bkq99iHdUhoukHDUOWXpRd//AjJu2IVX1XUku6u6PjdNuPCJDiPK+JI+fZcgwLTUMp/ixJHcfV12a5LDu/vnZtWq6qurnM/TIvyxf/32caSZxQA436+4jZt2GTfKmqvqO7n73rBsybVV12wxDzQ7r7jtU1R0zhA5PmnHTNmzsIvr7GcaC/06GsbaHZpgH5qe6+1WzbN+UPDLJP1bVfTJM+Ph7SX54tk3auMkQqKqOyjAfUZL8+zhH2EIYJ/9/Woa5ey5OcniGObRuP8t2bVR3L4cnD+/uRRnL/zXd/ezx68KFQVX1hu6++ypveBbqjU6GoS7HZLiqy0J8aDDhbzJMtHrvDP8jHpLkEzNt0fR9S3fff2L5iVX1zpm1ZjrOnHUDNsFCDn9Mku5e2NpW+Pw4f+kV41ytFye55awbNQXPTnKv8fu7ZZi4+heS3CnDJdXnvqfUOM/ghUm+O8kDMkzJsWg9+n4lybf2fnSV9QMyJJpUVbfI8AZn8iohr59dizZuYsjLwUl+evzF+mIWa06b52ToZrj8puc/qurFSeY+JMowQe5vZJhY/XVJTujut1T9//buPFqyujz3+PfpBmRWNKKoUZlEUWYxCKhxvA6oyAwicZ4FxGgkmlxBjXEgVwTjiK2AgCCgDBGRUSIqdEszqUQEFQ2goAxCFCHP/eO3q7v6cHo4dJ3z6/2r57PWWX1q7z5rvb1OddWud7+DnggcD/Q+SWT70m6m1NmUIazPs93MhwFJBwBvAAbtScdK+nxDc24+SHmzPsf2Vt1MjX0rxzRK13ftul+jtNI19WG8q5SarN2st5VStnfs/mz9A88NwFWtPSc7D7N9lMra7QuBCyX1ugV5Ev8jaUfb/wllRTzQ63l13e+qaS0m1ge6xROL1UIlSmdut0HqC5QxHH8Evl83pJGYPfQ72hP4fNcSeXLfE9BdQcDe3dctlGsytdBeNolrgbtrBzFsrJNEXW//npSSvMFdY1MmpffZTrUDmAGr275kMIy708rQ8ZUG8yZUVjv+AMD2Tyf8e3tnkrL01Skze46S1ERZeud1wN/YvgsWvNZ8n9Iv3oK/2L5V0ixJs2yfL+mTtYMaoSdSXkffRnlungGcMPhg14Azhr5flbI2/r8rxTJSi/nAc6ftVoZbXgdcIOlbLDq3oLftLkMGv6MbJb2E8pxc4gfYHnozcHQ3mwhK28Sr64Wz/CaZxbeIFm5MSvrUks674prqEZhH+f0JWI/y/25wsWmg1yM4JO0MXGz7rd2hz3Y3gdZuYV4PMFvSSi5bkJ8LvHHoXN8/5/8UuAjYyfa1sGB2T4vuAuZLOp9F39urvbb0/cmzvHYGNrH956X+zX5Zl7IK8VvDByW9iFJe2cJclFskbcjCKf67UbYztGB49eHEO4x9v3s8DmXpUC6whtuV7mPyGWh9dZukNSkJ9a9K+i1DK6v7zvbdwImUTVnrAIdTBsw2sc554uBVSccDrSTAfkRpIfgD5f/cQ4CbJN0MvMH2vJrBjcD13dcq3VdLPtQlT95FSaivDTT1gaBrO96ia3fB9h2VQxqFwY1JAWfSQOv4JIZfNw6hDB9vgu31B99Lusz2VjXjmQb7Ap+WdDdlk9v3KEmjFhJEUDoMLpR0C+Uzw0UAkjai/9vNdgH2As7vEnsn0Na19LBvdF8rjLEcXD3Q3Ynb3fYfa8cySpLOA14zcUhuN1R3TgtDICVtQOm13Z7yYeB6YF/bv6gZ1yhIuo/ygVvAaiwsPxRlReLKtWIbpW5o4Lbdw0vcwGa6gW7I5d8Bp3aHdga+bLuJahtJa1AuRmYBr6S0Rh7bUFk6kp5FqTR9ITAX+FqrW20kbQKcaXuj2rEsL0lfAL5u+9vd4xdQhnjOAQ63/Tc144vx1L0n3G77qAnHXwes1dB7w49sb107junUaCIFaPv3p7JV9+mUzw1Pp2zLutR275Oa3SzT9YCzhyrYnwCsaftHVYMbge6a8+WUtrPnAEcDp7qxLZ+SVgGe0D28pnYF9FgmiSQdQanIeDRl0v25rCClXaMg6VLb2y7m3BUtlP4OdC8cs2zfWTuWWHaS9gA+DlxASX49A3i37a/XjGsUJM2izOv5Ews3MVxk+7J6UY2WpI/a/oelHeurbgPmZZRqotMGF12tmGS4803AwS0kwSRd6QlbEgfve5Lm296yVmyj0JWiTzZPqoWbP18BDrB9W/d4HeCwPs/KGlDZ6LndxIv+7kPB3Fauy1pOMgy0/G9s+d8G0M323IGSKNoO+G2j822a1b0v7A7safu5teMZFUl/S9l6+QvK56K/Bv6u5pzkcW03m9v9OQ84rWYg02SdJZxbfcaimEaSHkS5O/x4YKXBrB7bh1YMK5bd+4BtB9VDKquPzwF6nyTqtmd8urvT2Ps7OIvxfGBiQuhFkxzrq80baQO5n26V7JNt/6p2LNPkRkn/QClLh1INdrOk2SzayttXfz/0/aqU98FW5vFtPkgQAdj+g6RWKjZWmuyusO171PNhg5KGkwqrdb+zBf+mFioZWtZVuQ2sO+Fx7+edSfpHSuXQw4FrgB9QFsS80Q1uMW2d7T9QOkk+XzuWETsMeIHta2BBJdjxwDa1AhrLJJHtr9SOYZqdI+nDwPsHG1C6i5BDKNuyWvBNSq/tPIaqwKI3Zk1oL7uV0rrUinMl7Qqc0tIWIklvAd4KbChpuJ9/LUqvf69Jeo/tjwEfljRZtUavq0xhwSrZM4HNlvqX+2kfyryQQW//97pjs4E9agU1KpPMVPqepEuqBDN6sySt030IGAwhb+U6dZakR9i+efhg13bdd4cNfX8TMJxUMKU9pNeGqi9FSYQNbiIMtgavXS245Te8EfILEx63YD/KCIfTKdcpP7Td91k90Z6VBwkiANv/JanqeJGxbDcbWMxGhtsplUYfsn3rzEe1/LoWrC8CTwMG6w+3oPy7Xt/CDCZJV9l+Su044oGR9HFgc0qWHMrd/ittv6deVKPTXVCuQbnD/yfauJCkGyq7DvAR4L1Dp+5sYR6RpJfaPl3S3012vpUbDF1bz5G2W1sv3rwJ29tmUe4yfsr2JpVCGhlJ+wH/CJxEec3cDfiw7WOqBjYC3b9tf8pQ7kFlzTaUtusjW3ltiVgRda+b27OwzWxN4HLKAOs5NWOLAJD0JUq187HdoVcCs2u2W497kuhjlK1Dx3WH9qK0Y90E7Gj7pbViG4VuuPOTu4dX276uZjyjJOnzwBG2r6wdSzwwXaXNDt3Di2yfuqS/HysOScfYftXSjvWVpN1tn7S0Y30l6afAxpTe98GQfLcwF0XS6Sz+5s/nbP9p5qMaHUnXs7Ci4V7K0oZDbTexnU7Sk4HBjJDzbP+4Zjyj1G2YfS/wFMrv8GrgXyduou0zSdvTjQEYHLN9dLWARqzl977uM9GHKEspzqLcyHun7WOX+IM9ImklSnL2mcCbgPVtN7G1NPqtG6PyNoZmmQL/7oob2Mc9SXS/AW2DY5MNv+wjSY8GHseib9jVhmAtr6Hqr5UoH3Kuo7SbNfMhZ5x0a4CHn5u9r0YBkHTuxIF6kx3rq4mvnd2F1xW2N60Y1sgs6b2hVkyjIOmxtn/Vbbq8H0/YiNlHkg6nzJ4YrlK8g/K+sXYLH+Za1s2OegSLvi+0Oj+rKZKOATakVLAPZr24hTbdgZbf+waD/SW9AtgJOAj4ru0tKoe2XCS9jFJBtAPlxvnVlDbk71MqiX5XMbwIYEEX0J8Gc7K698IH2b57yT85fVrp9X6gZkt6mu1LACRtS5lbAA0MgpT0UcoF8tUsHNhpoLdJIsobV/ScpDdRZmT9ifLcFOW5uUHNuJaXpFUp1Yh/1W1gGAzvXJuyTbHXJB1MaQeZOJPhHsosg17r7vS/GHi0pE8NnVqbBt4TKHN6trb9S0kn2961dkDTYPsJ2z1PH2z8lHR1tahGRNLuwFm275T0fmBrSnt874cDS3oHZZ7UzZQkw+B9oZmbPy1vcAOeCmza0hy+gaW897UyQHfwmfAlwEm2b+/5TPWBV1OSQu8B5tm+p244EZM6F3geMBgJsxpwNiXBWcW4J4leD3xJ0pqUF/s7gNd32byPVI1sNHYGNqlZqjYN1gX+amJ5dvfh7rdA7++Ej4m/B55i+5bagYzYm4ADgUdRhqoP3EnZptFrtj8CfETSR2wfPHyu9oC9EflvSlvSy7j/7++dVSIareEr/l4nZJdgzUHFFJTqKcr8CSgf6Prun2yfJGlHygXlx4HPAH9TN6yROIByzdLLeZDLqOUNblcBjwRurB3IqC3pva8hZ3StyP8DvKXbOtvr9tzO423v0kpbYDRr1eGZwbb/KKnqRvKxThJ1Qzs364axMmHa/Yl1ohqp64CVaWv710eB10xy/MfAHBrYojEmfg5UK6GcRhdTXjt2s31ENwB5V8rsl+OW9IN9MrhI7rYmPoeyPWonSptIb9m+HLhc0qnAXRPLfqsGNxpezPcteRfwn5J+TkmKrQ+8tbv508Jw4EEbz0uAz9s+U9KHagY0QjdQ5ke1rOUNbn8F/LjbtrfgutP2y+qFNFq2D25tjMOA7fd2c4lut32fpLuBl9eOawRWkbQPsL2kXSaetH1KhZgiJrpL0taDqmBJ21ASttWM5UwiSfvaPlbSQZOdt/1vkx3vC0lHUD4APJqy1excFn3D7m1/+KBtYDHnrshMon7o7pzOAX5II89NKPMKgOfZ/r2kZwInAO8AtgSeZHu3qgGOiKTtKImhnYGHUobtnTb44NN3kn5A+T3+sXu8JnC27Wplv6Mg6T4WDqpejYWJ2ia27w10AyCf2D28pu/DqodJOgP4DfB8SqvZ/wCX9HluyNC12JOBTYAzWfR9odfXZMMa3+D2rMmO275wpmOZLpL+lbLk5scsOnep94kwSfOAo4DjW3kvB+iqLl8J7AGcNuG0G2n1jJ7rRt6cQKloF6Uqc0/b85b4g9OolbsXU7VG9+daVaOYPnO7P+dx/xfEvltnCeeqluXFlHwOOA+4koXzslowe2j49p6UO/0nAydLml8xrpGQ9C/A7sCvKIOBDwHmNri+eYUr+x2Fcdji0v2eDgIeZ/sNkjaWtIntM2rHNiJ7AC8EPmH7NknrAe+uHNPyGlyL/ar7WqX7gsYq3mwfLWkupQLTwC6tbHCzfWE3FH9j2+d0/xdbe815Be2NcRjYk1Kpf2n3HJ1DuTnS9/+D69l+i6TLbLcyPyoaY/tSSU+k3CiBcoPrLzVjGstKougvSZ8FbgXeP3jj6lpeDgEeafuNNeOLZdO9Wbcyh2EBSVcBW9q+t+vtf+OgDF3SVbafUjfC5SPpt8B/AZ8ETrf9Z0nX2W5qvo2k7wHvmFD2e6Ttp9eNLJZG0tcoN0j2s/2U7oPqxba3rBzayLS6AUzS7rZPWtqxvpO0NfAMyg2S77UwdBxA0huANwIPtb2hpI2Bz7ay1RNA0reA3YdvIrRG0ixK+/hnKNVSc4DD3dPts1q4tbr3G0qjbZK2Bx7Pou/tR9eKZywriSZsrbmfvre8DGjhuvhht1MqjT7U0+GQ7wK+CFw7VJmxBeXf9PpqUcVUfUvSG4HTWbStoJcXIUOOBy6UdAulDeQiAEkb0casjfUobS57A5+UdD5l28tKtlvY/jVwIHCSpEXKfuuGFMtoQ9t7StobwPbdamRFD9xvA9jw1tIWWq0PprRhLe1Yb0n6Z0o15smU15Y5kk6y3cJcqbcBT6O0kWP7Z5LWrRvSyN0NzJfUzBiHYZI2p1QTvZjyHP0qsCOl8ruvifZbJZ0NrC/pft0VLbQKRv9JOgbYEJjPUCsrkCTRDBvu7zuEcsHVom9RnmiDgbl7UVqybgK+DLy0TlgPnO27gL0lbUCZXwBwte3rKoYVU7d39+d7JxzvdUWK7Q93F4/rsWiZ9izKbKJe6wY5nwWc1c192Yky2+Y3ks61vU/VAEdksrJfyuylWPHdI2k1uhskkjakreUNzW0A67aTvhh49ISbeGsDLSWfocxG2WIwJ6ubcTMfaCFJ9Gfb9wxyspJWorF2QcoIh9bGOAALZhLdRplL9N6hlrofStqhXmTL7SWU+W3HAIdVjiVicZ4KbLoitXeOfbtZq20vsLDEcrJjkq60vVmt2B6orkx7sVop225VN5jtBts3dY+Ht399oIFKouZ1pei72T5x6NjawM41y2Kng6SHUJ6f+1AGjz+qckixGJI+TankWx14H7ApcDawA/Bq2xfUi250uuq957dUuSdpC2Aryk27fx46dSdwfmNDdM8HXmH7tu7xQ4BTbPd+M2u3Ges2YD/KTZG3Aj+2/b6qgY1Yl4R+rO1rascySpI2aPmGq6SH2/5d7TgiJiPpJGB/2zfWjmUgSaKGe1QlXQ68wfYl3eNtgS/a3qKvybHuAmtx3MKFVsvGZftX6yTNtf3U2nFMh+4DwMspiaGtKEN1dwa+a7ulIetNkXQApVp2PeA7lAHIPwJ+aPuWmrGNkqSjaHQDWINtqwsMbZ19LLAt5TlqSvvuJbbvt5q7b7obCK8DXtAd+rbtL1YMaeQkvRT4BLCK7fUlbQkc2kLLUlcdvCv3n4lyaK2YRkHSJ20fKOl0Jqlsa+F3F/3XOGF2kQAAE/dJREFUfb7dEriERd/bqz0/kyRqO0m0LfAlYE1K7/sdlLk9VwMvGa4EiJgJki4frGru7vz/zvYHusfzWxou27KuReIW4GuUlepA/2dKSTqOMlD2bEoC8zzgWtvrVw0sllm3XWmv7ms1Srv1Cbb/q2pgIyJp0vZ424fMdCyjspj5iQvY7v28pa5qdrH6vCFS0suBx9j+dPf4EuDhlN/pe2x/vWZ8o9S1ZD0HuGBwo7WFpRQAks6izE6cx8KZKNjudYuWpG1sz5P0rMnO275wpmOKmGhFfH6OZZJI0p0svCBZnTKIDkoixbbXrhLYNJH0YADbLQzOBUDSysBbgGd2hy4APld7XWAsWevbv8aFpOsnOey+bznrhuHPogwKPMH2r1vc3jYuJG1FuVGyue2mVnFLWhOghS1LXWJvsWz/cqZimW6S1gD+1M13G2yqe5Dtu5f8kyuubhvkXrZv6B7PpyRS1gTmNLbd7Ae2txuuxpd0RSOJzOavwSQ9HCBtZxFLN5aDq22vVTuG6SRpX9vHSjpownGgjbJ0ymrOlYF/7x6/qjuWDWcrtta3f42FVitrbG/ZDazeGzine56uJekRtm+uHF4sg25Y7osolUTPpdxA+EDFkEZK0lMoA1gf2j2+BdjP9tVVA1sOLSWBlsG5wPOAQXJvNUrl4vbVIlp+qwwSRJ3/7KpKf98lxVpytaR9gNmSNgb2By6uHNOoXCxpM9tX1g5k1CR9AHg75SaQJN0LHNH3VrrovwmFK4uconLhylhWErVO0ptsf67FsvSB4balJR2LFY+k7Vi4/euu7tgTgDUzeLwfxqWST9I2lNlEuwO/tt3nD3JNk/R8SnLvxZSe/hOAbw5eY1oh6WLgfbbP7x7/LfAvLTw3u/eGI4AnAasAs4G7Wqrunqytuu+t1pKutb3RYs793PaGMx3TdJE0GIz/AsqHuG8DHxxsq+ujoXbPlYCNgesoM1EGH1J7XSXV3TB/EaVy/fru2AaUG8tn2f5/NeOLWFElSRS91A1A3t32z7vHGwBfb3W+VMSKRNIXKZV8gzkarwLus91kJZ9KGeYzBq2RseKRdB5l/tDJLW3DmqjlGySS5lIqwE6irAPeD3iC7YOrBjZCXWvWOwY3RLpE9JG2n143sgdO0lcpM3q+MOH4m4C/tb13nchiWbTe7inpMspGyFsmHH845WZl75b4RMyEsWw3a52kTy3pvO39ZyqWafRu4HxJ11HudjwOeE3dkCLGxrYTPpSe121TbEK3yvlDlJbIs4DNgXdWDSqWaIw2W14n6Z8oLWcA+1Lu/DfB9rWSZncze+Z0H/CaSRIBBwInSfpvyrXLI4E964a03N4JfKNrwxpUA28DPIiyGbL3JJ22pPM935B1M/BmYCPgSuCoxrYMrjzZhkvbv+uqoiNiEkkStWne0PeHAJO2nfWZ7XO7fvBNukPX2P7zkn4mIkbmPkkbTqjku28pP9MnL7D9HkmvAH4B7AJ8Fzi2alQR8FrK+/oplBaRi7pjLbhb0irA/C5ReyNlhkgzbF/azT0bvnbpdZuu7d8C20t6DvDk7vCZts+rGNaoPR24gTJX8YeUBF8rvgL8hfJa8iJgU+CAqhGN1j0P8FzEWEu7WeOGNzC0RtL2wOMZSnbaPrpaQBFjQtJzgTmUCoYFlXyDOSl9N9jy0rXVfd32Wa209EQ/SVoVWGviVh5J6wJ39HkmykDX9nIzZR7RO4EHA/9u+9qqgY3QuMxza023hW4w92xz4Ezg+D4PjB+QdKXtzbrvVwIuaWl0g6T7gMlm0wlY1XaqiSImkUqi9jWZBZR0DLAhMJ+FFQymrK6OiGk0BpV8Z0j6KaXd7C3d7ILefwiPXvsUpfXxlAnHd6AM0X3LjEc0YrZ/KWk1YL0WFmwsRjaz9lDX/ngWcJakB1GSRRdIOsT2kXWjW24LEpS27x1sQm6F7dm1Y4joo1QSNU7Sj1q6IzAg6SfAps4TOGLGdRteDgIeZ/sNg4SR7TMqhzYykh4K3G77vm6N81q2b6odV4wnSfNsb7OYc1fbfvJk5/pE0kuBT1BWqq8vaUvg0J7Pe1lEy4PHW9clh15CSRA9HjgN+JLt39SMa3lNqLQRsBpwNyvACu6IqCeVRA2SdCcLK4hWl3TH4BTtvOBfRRn4eGPtQCLG0BzK7LPBRp7fUDYSNZEkkjQPOIoyf+IP3Rr1plapR++svoRzrczt+QDwNEoLFrbnS1q/ZkDToPV5bk2SdDTwFOA/gENsX1U5pJFJpU1ETCZJogbZXqt2DNNF0umUBNhawI8lXQIsaHNp6Y5jxApsQ9t7StobwPbdaqtGfU/KtsRLu7XccyirclO5GLX8VtLTbF8yfFDStsDvFvMzffMX27dPeClp7f9cNrP2076UGwUHAPsPPUdbuvkaEbFAkkTRN6cBj6BsYRj2DFJVFDFT7ulmhxhA0oYMJWv7rhuU+75u1fhOwJcoFQBzgMNt/75qgDGO3g2cKOnLLNxg+lRgP2CvWkGN2NXdGvXZXQvr/sDFlWMaqTGY59Yk261U60VELJPMJIpekXQGcLDtKycc3wz4F9svrRNZxPiQ9Hzg/ZRVuWdThue+2vYFNeMaJUmbU+7wvxj4NvBVYEfgVba3rBlbjCdJjwDeSml7AbgaOLJbQd573ayz91EGcYvy/+6DLWxuA5D0MGAf4IndoZ8AxyXpHBERK5okiaJXJF1qe9vFnFuwxjMiplf3gWc7yoe5H9i+pXJII9PNJLqNMpfo5OE7/ZJOsb1LteAionckPQk4j5L4uozyurkVZa36c2z/tGJ4ERERi0iSKHpF0s9sb7yYc9fa3mimY4oYN5J2AObbvkvSvsDWlDasX1YObSQkbWD7utpxRAxIupLJ5/MMZqJsPsMhjczQrMFJtTBrUNLXgRNtnzjh+K7APrZ3rRNZRETE/SVJFL0i6XjgPNtfmHD89cDzbe9ZJ7KI8SHpCmALYHPKUOejgD1sP6tqYCPSrTrelbLmeMHsPtuH1oopxpukxy3pfJ8TtJIGrxsCvgC8fvi87QtnPKgRk3SN7U2mei4iIqKGDK6OvjkQOFXSK1l0eOcqwCuqRRUxXu61bUkvBz5t+yhJr6sd1Ah9E7id8hqTobJR3XASqJtNNGi7vqTvM4mGk0CS/thCUmgSdz3AcxERETMuSaLoFds3A9tLejYLh3eeafu8imFFjJs7JR1MWQv8TEmzgJUrxzRKj7H9wtpBREwkaQ/g48AFlMqbIyS92/bXqwY2Oq2Wt68r6aBJjgt4+EwHExERsSRJEkUv2T4fOL92HBFjak/Klp7X2b5J0mMpH1xbcbGkzSZuUYxYAbwP2HZQPSTp4cA5QG+TRJIeOvRwtqR1KMkTABrZ/vUFYK3FnPviTAYSERGxNJlJFBERwSLDgVcCNgauo7Sb9X44cLRh4hbProrv8j5v9pR0PeX/nSY5bdsbzHBIERERYy2VRBERMSWStgOOAJ5EmQc2G/ij7QdXDWz57VQ7gIilOEvSt4Hju8d7Av9RMZ7lZnv92jHMFEmPobx27tAdugg4wPav60UVERGxqFQSRUTElEiaC+wFnEQZHL8f8ATbB1cNbDlJWhV4M7ARcCVwlO1760YVsahubfqCJIPtU2vGMyqSTqZsSjzL9v/Wjmc6SPoOcBxwTHdoX+CVtp9fL6qIiIhFJUkUERFTImmu7adKumLQgiXpMttb1Y5teUj6GvAXyt39FwG/tH1A3agixoOk5wGvAbajJKDn2L6mblSjJWm+7S2XdiwiIqKmtJtFRMRU3S1pFWC+pI8BNwKzKsc0CpsOZrtIOgq4pHI8EYuQtAvwUWBdygyfwbystasGNgK2zwHOkfRgYO/u+xsoQ5+Ptf2XqgGOxq2S9mVhu+DewK0V44mIiLifFi7qIyJiZr2K8v7xduAu4K+BXatGNBoLPoSmzSxWUB8DXmb7wbbXtr1WCwmiAUkPA14NvB64DDgc2Br4TsWwRum1wB7ATZTk+m6U6qmIiIgVRtrNIiJiyiStBjy2pXYQSfdRkl5QKjRWA+6moWqN6DdJ37O9w9L/Zv9IOhXYhDKv58u2bxw6N9f2U6sFFxERMUaSJIqIiCmR9FLgE8AqtteXtCVwqO2XVQ4tokldmxnAs4BHAt8A/jw4b/uUGnGNkqRn2z6/dhzTQdI/L+G0bX9wxoKJiIhYiiSJIiJiSiTNA54DXDAYVi3pysE8n4gYLUlzlnDatl87Y8FMI0nbA49naGam7aOrBTQikt41yeE1gNcBD7O95gyHFBERsVgZXB0REVP1F9u3Sxo+ljsOEdPEdvNzayQdA2wIzAfu6w4b6H2SyPZhg+8lrQUcQJlFdAJw2OJ+LiIiooYkiSIiYqqulrQPMFvSxsD+wMWVY4ponqTHAEcAg7lEFwEH2P51vahG5qmUDYNNJpwlPRQ4CHgl8BVga9t/qBtVRETE/WW7WURETNU7gCdTZqIcD9wBHFg1oojxMAc4DXhU93V6d6wFV1HmLTVH0seBS4E7gc1sfyAJooiIWFFlJlFERDxgkmYDa9i+o3YsEa2TNN/2lks71keSzge2BC5h4VBu2355vahGQ9L/Uv5N97Joa242J0ZExAon7WYRETElko4D3kyZG3IpsLakw21/vG5kEc27VdK+lAo+gL2BWyvGM0ofGPpewDOAveqEMlq2U7kfERG9kTetiIiYqk27yqGdgW8B6wOvqhtSxFh4LbAHcBNwI7AbZQBy79m+kNK6uhPwZcoGxc/WjCkiImIcpZIoIiKmamVJK1OSREfa/ouk9C5HTDPbvwReVjuOUZL0BEpF1N7ALcDXKOMQnl01sIiIiDGVSqKIiJiqzwG/ANYAvivpcZQKgIiYRpK+IukhQ4/XkfSlmjGNwE8pVUM72d7R9hGUVtaIiIioIIOrIyJiuUlayfa9teOIaJmky2xvtbRjfSJpZ8rsoR2As4ATgC/aXr9qYBEREWMq7WYREbFMJO1r+1hJBy3mr/zbjAYUMX5mSVpnsD5d0kPp+bWc7W8A35C0BvBy4EBgXUmfAU61fXbVACMiIsZMry8sIiJiRq3R/blW1SgixtdhwPclnUTZALYb8OG6IY2G7buA44DjJK0D7A78A5AkUURExAxKu1lERERET0jalDLDB+A82z+uGU9ERES0JUmiiIhYZpKeDbwdeGJ36CeUDWcXVAsqonFdW9li2f79TMUSERERbUuSKCIilomklwBHAocCP6K0u2wNvB94u+3/qBheRLMkXQ+Y8n+O7nu6x7a9QZXAIiIiojlJEkVExDKRdAFwgO3LJxzfHDjC9rOqBBYRERERESMxq3YAERHRG4+cmCACsH0F8IgK8USMLUkbSvonSVfXjiUiIiLakSRRREQsq7se4LmIGAFJj5L0TkmXAldTruP2qhxWRERENCTtZhERsUwk3QZ8d7JTwI6215nhkCLGgqQ3AnsDjwZO7L6+aXv9qoFFREREc5IkioiIZSJpiTOHbF84U7FEjBNJ9wDfB95le2537LoMrI6IiIhRW6l2ABER0Q+DJJCkA2wfPnxO0gFAkkQR02M9YHfgMEmPpFQSrVw3pIiIiGhRKokiImJKJP3I9tYTjl1me6taMUWMC0mPAfaktJ+tAZxq+x/rRhURERGtSJIoIiKWiaS9gX2AZ7DobKK1gP+1/dwqgUWMKUlPAPayfWjtWCIiIqINSRJFRMQykfQ4YH3gI8B7h07dCVxh+94qgUWMCUnzgC8Bx9n+Q+14IiIioj1JEkVExDKTNBs4x/aza8cSMW4kbQS8htJuNheYA5ztXMxFRETEiCRJFBERUyLpXGAX27fXjiViHEmaBewEfAa4j5IsOtz276sGFhEREb2X7WYRETFVfwSulPQd4K7BQdv71wspYjxI2pxSTfRi4GTgq8COwHnAlhVDi4iIiAYkSRQREVN1SvcVETOom0l0G3AU8F7bf+5O/VDSDvUii4iIiFak3SwiIiKiByRtbPtnteOIiIiIdiVJFBERUyJpY8qGs02BVQfHbW9QLaiIMSDpV8BZwNeA8zKwOiIiIkZtVu0AIiKid+ZQBubeCzwbOBo4tmpEEePhicA5wNuA6yUdKWnHyjFFREREQ1JJFBERUyJpnu1tJF1pe7PhY7VjixgXktYBDgdeaXt27XgiIiKiDRlcHRERU/XnbgX3zyS9HfgNsGblmCLGgqRnAXsCLwTmAnvUjSgiIiJakkqiiIiYEknbAj8BHgJ8EFgb+LjtH1QNLKJxkn4BXAacCJxm+666EUVERERrkiSKiIgHRNLqtu+uHUfEuJC0tu07ascRERER7Uq7WURETImkpwNHUVrMHitpC+BNtt9aN7KINkk6AnD3/f3O295/pmOKiIiINiVJFBERU/VJ4P8ApwHYvlzSM+uGFNG0uUPfHwL831qBRERERNuSJIqIiCmzfcOEiob7asUS0TrbXxl8L+nA4ccRERERo5QkUURETNUNkrYHLGll4ADKIOuImH4ZJhkRERHTZlbtACIionfeDLwNeDTwG2DL7nFERERERPRYtptFRERErMAk3cnCCqLVgcFWQQG2vXaVwCIiIqI5SRJFRMQyGd6wNJlsWIqIiIiI6LfMJIqIiGWVDUsREREREQ1LJVFEREyZpMtsb1U7joiIiIiIGJ0Mro6IiAcidxgiIiIiIhqTJFFERERERERERKTdLCIilk02LEVEREREtC1JooiIiIiIiIiISLtZREREREREREQkSRQRERERERERESRJFBERERERERERJEkUEREREREREREkSRQREREREREREcD/B5R81MT7KuHQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train Test Split"
      ],
      "metadata": {
        "id": "uuGUt2W5C7Bj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"Fruits\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "j4UMzIzMJR3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"AnyHealthcare\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "XC0yWwSzJjjj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"NoDocbcCost\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "lotW0fPUJwIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"MentHlth\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "kiBJV8oBKFdt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dia.drop(\"CholCheck\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "qOvctD44KTyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = dia.drop(\"Diabetes_012\",axis=1)\n",
        "Y = dia[\"Diabetes_012\"]"
      ],
      "metadata": {
        "id": "AEiSB8RM7PLX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42,stratify=Y)"
      ],
      "metadata": {
        "id": "QEQv5Wvv8jVP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.hist(figsize=(50,50), bins=50, color=\"red\");"
      ],
      "metadata": {
        "id": "GkEwF3rd-1hN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "outputId": "b9e4a20b-7077-43d5-809c-2feeccbdf8ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x3600 with 16 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.info()"
      ],
      "metadata": {
        "id": "EkWPSTcgIbgw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfee4f9b-4764-473e-b5ed-c3833fe40717"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 202944 entries, 185494 to 110153\n",
            "Data columns (total 15 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   HighBP                202944 non-null  float64\n",
            " 1   HighChol              202944 non-null  float64\n",
            " 2   BMI                   202944 non-null  float64\n",
            " 3   Smoker                202944 non-null  float64\n",
            " 4   Stroke                202944 non-null  float64\n",
            " 5   HeartDiseaseorAttack  202944 non-null  float64\n",
            " 6   PhysActivity          202944 non-null  float64\n",
            " 7   Veggies               202944 non-null  float64\n",
            " 8   HvyAlcoholConsump     202944 non-null  float64\n",
            " 9   GenHlth               202944 non-null  float64\n",
            " 10  PhysHlth              202944 non-null  float64\n",
            " 11  DiffWalk              202944 non-null  float64\n",
            " 12  Sex                   202944 non-null  float64\n",
            " 13  Age                   202944 non-null  float64\n",
            " 14  Income                202944 non-null  float64\n",
            "dtypes: float64(15)\n",
            "memory usage: 24.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Converting Columns\n",
        "\n",
        "\n",
        "\n",
        "*   Applying Standadrdisation to the float columns\n",
        "*   Converting many necessary columns to category\n",
        "\n"
      ],
      "metadata": {
        "id": "WvDS8J1ZJGxn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dia['PhysActivity'].value_counts()"
      ],
      "metadata": {
        "id": "kEajdgbsosL5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9648b399-f8bf-4431-87a8-5cf2ca9a6fe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    191920\n",
              "0.0     61760\n",
              "Name: PhysActivity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['GenHlth'].value_counts()"
      ],
      "metadata": {
        "id": "cxU7ZNsxrjMP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98511d29-d094-4ad2-e2d8-17bfa00547d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    89084\n",
              "3.0    75646\n",
              "1.0    45299\n",
              "4.0    31570\n",
              "5.0    12081\n",
              "Name: GenHlth, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia['PhysHlth'].value_counts().max"
      ],
      "metadata": {
        "id": "JUDQ49jWrzT-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e05ded3-d2bb-41c1-c28e-c0255eba6ae6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame._add_numeric_operations.<locals>.max of 0.0     160052\n",
              "30.0     19400\n",
              "2.0      14764\n",
              "1.0      11388\n",
              "3.0       8495\n",
              "5.0       7622\n",
              "10.0      5595\n",
              "15.0      4916\n",
              "4.0       4542\n",
              "7.0       4538\n",
              "20.0      3273\n",
              "14.0      2587\n",
              "25.0      1336\n",
              "6.0       1330\n",
              "8.0        809\n",
              "21.0       663\n",
              "12.0       578\n",
              "28.0       522\n",
              "29.0       215\n",
              "9.0        179\n",
              "18.0       152\n",
              "16.0       112\n",
              "27.0        99\n",
              "17.0        96\n",
              "24.0        72\n",
              "22.0        70\n",
              "26.0        69\n",
              "13.0        68\n",
              "11.0        60\n",
              "23.0        56\n",
              "19.0        22\n",
              "Name: PhysHlth, dtype: int64>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dia[\"Veggies\"].value_counts()"
      ],
      "metadata": {
        "id": "8Kz4ES1bMTZq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f8580a8-86f0-4975-f60b-e5b03d6d3f42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    205841\n",
              "0.0     47839\n",
              "Name: Veggies, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a Column Trnasformer\n",
        "ct = make_column_transformer(\n",
        "    (MinMaxScaler(), ['BMI', 'Income','PhysHlth','Age','GenHlth']),\n",
        "    (OneHotEncoder(handle_unknown='ignore'),['HighBP','HighChol','Smoker','Stroke','HeartDiseaseorAttack','PhysActivity','Veggies','HvyAlcoholConsump','DiffWalk','Sex']))"
      ],
      "metadata": {
        "id": "7RQ-j1K3IoV1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting column transfrmer\n",
        "ct.fit(X_train)"
      ],
      "metadata": {
        "id": "9gmXCOEEp6e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84cfe52d-7923-4cc0-d0d0-3c5834d19760"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('minmaxscaler', MinMaxScaler(),\n",
              "                                 ['BMI', 'Income', 'PhysHlth', 'Age',\n",
              "                                  'GenHlth']),\n",
              "                                ('onehotencoder',\n",
              "                                 OneHotEncoder(handle_unknown='ignore'),\n",
              "                                 ['HighBP', 'HighChol', 'Smoker', 'Stroke',\n",
              "                                  'HeartDiseaseorAttack', 'PhysActivity',\n",
              "                                  'Veggies', 'HvyAlcoholConsump', 'GenHlth',\n",
              "                                  'DiffWalk', 'Sex'])])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transforming Data\n",
        "X_train_ct = ct.transform(X_train)\n",
        "X_test_ct = ct.transform(X_test)"
      ],
      "metadata": {
        "id": "mQWOhi23soK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_ct"
      ],
      "metadata": {
        "id": "IPvscZdrVzUb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31fd5dea-86d8-4bb0-c3fb-ec04b0a7a051"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.15116279, 0.42857143, 0.        , ..., 0.        , 0.        ,\n",
              "        1.        ],\n",
              "       [0.29069767, 0.57142857, 0.        , ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       [0.26744186, 0.28571429, 0.        , ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.13953488, 0.28571429, 0.4       , ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       [0.11627907, 1.        , 0.        , ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       [0.19767442, 0.71428571, 0.06666667, ..., 0.        , 0.        ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling"
      ],
      "metadata": {
        "id": "SM6nn8cpIMpB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "mUkAcyOGPwjg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_ct.shape"
      ],
      "metadata": {
        "id": "EyxxNIq0c_Mm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3353ad74-7edf-4c76-b5b0-8bccfb532b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(202944, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train"
      ],
      "metadata": {
        "id": "GuQZl4_gdCvj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0331aaa-52bc-4591-d7f2-630cf7452846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "185494    0.0\n",
              "217030    0.0\n",
              "641       0.0\n",
              "217863    0.0\n",
              "53677     0.0\n",
              "         ... \n",
              "230663    0.0\n",
              "202400    0.0\n",
              "176569    0.0\n",
              "248159    0.0\n",
              "110153    0.0\n",
              "Name: Diabetes_012, Length: 202944, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting Random Seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the modele\n",
        "model_1 = tf.keras.Sequential([\n",
        "  tf.keras.layers.Dense(30), # hidden layer 1, using \"relu\" for activation (same as tf.keras.activations.relu)\n",
        "  tf.keras.layers.Dense(100, activation=\"relu\"),\n",
        "  tf.keras.layers.Dense(100, activation='relu'),\n",
        "  tf.keras.layers.Dense(3, activation=\"Softmax\")\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_1.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(lr=0.01), metrics=['accuracy'])\n",
        "\n",
        "#Creating a CallBack\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20))\n",
        "\n",
        "# Fit the model\n",
        "history = model_1.fit(X_train_ct, Y_train, epochs=100,callbacks=[lr_scheduler])"
      ],
      "metadata": {
        "id": "HYWblOv4svRn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76fd490e-ec70-4c25-97ce-b6f9c05edf2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6342/6342 [==============================] - 21s 3ms/step - loss: 0.4174 - accuracy: 0.8414 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "6342/6342 [==============================] - 21s 3ms/step - loss: 0.4020 - accuracy: 0.8463 - lr: 1.1220e-04\n",
            "Epoch 3/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3994 - accuracy: 0.8470 - lr: 1.2589e-04\n",
            "Epoch 4/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3981 - accuracy: 0.8482 - lr: 1.4125e-04\n",
            "Epoch 5/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3971 - accuracy: 0.8484 - lr: 1.5849e-04\n",
            "Epoch 6/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3960 - accuracy: 0.8490 - lr: 1.7783e-04\n",
            "Epoch 7/100\n",
            "6342/6342 [==============================] - 15s 2ms/step - loss: 0.3956 - accuracy: 0.8494 - lr: 1.9953e-04\n",
            "Epoch 8/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3949 - accuracy: 0.8498 - lr: 2.2387e-04\n",
            "Epoch 9/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3950 - accuracy: 0.8498 - lr: 2.5119e-04\n",
            "Epoch 10/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3949 - accuracy: 0.8495 - lr: 2.8184e-04\n",
            "Epoch 11/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8498 - lr: 3.1623e-04\n",
            "Epoch 12/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3948 - accuracy: 0.8498 - lr: 3.5481e-04\n",
            "Epoch 13/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8496 - lr: 3.9811e-04\n",
            "Epoch 14/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8498 - lr: 4.4668e-04\n",
            "Epoch 15/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8499 - lr: 5.0119e-04\n",
            "Epoch 16/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3947 - accuracy: 0.8497 - lr: 5.6234e-04\n",
            "Epoch 17/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3949 - accuracy: 0.8495 - lr: 6.3096e-04\n",
            "Epoch 18/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3950 - accuracy: 0.8498 - lr: 7.0795e-04\n",
            "Epoch 19/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3952 - accuracy: 0.8497 - lr: 7.9433e-04\n",
            "Epoch 20/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3952 - accuracy: 0.8497 - lr: 8.9125e-04\n",
            "Epoch 21/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3955 - accuracy: 0.8491 - lr: 0.0010\n",
            "Epoch 22/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3957 - accuracy: 0.8494 - lr: 0.0011\n",
            "Epoch 23/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3962 - accuracy: 0.8493 - lr: 0.0013\n",
            "Epoch 24/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3965 - accuracy: 0.8488 - lr: 0.0014\n",
            "Epoch 25/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3967 - accuracy: 0.8488 - lr: 0.0016\n",
            "Epoch 26/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3966 - accuracy: 0.8492 - lr: 0.0018\n",
            "Epoch 27/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3967 - accuracy: 0.8492 - lr: 0.0020\n",
            "Epoch 28/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3972 - accuracy: 0.8484 - lr: 0.0022\n",
            "Epoch 29/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3977 - accuracy: 0.8488 - lr: 0.0025\n",
            "Epoch 30/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3982 - accuracy: 0.8486 - lr: 0.0028\n",
            "Epoch 31/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3981 - accuracy: 0.8483 - lr: 0.0032\n",
            "Epoch 32/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3984 - accuracy: 0.8487 - lr: 0.0035\n",
            "Epoch 33/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3991 - accuracy: 0.8483 - lr: 0.0040\n",
            "Epoch 34/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3993 - accuracy: 0.8479 - lr: 0.0045\n",
            "Epoch 35/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4003 - accuracy: 0.8474 - lr: 0.0050\n",
            "Epoch 36/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4003 - accuracy: 0.8476 - lr: 0.0056\n",
            "Epoch 37/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4015 - accuracy: 0.8471 - lr: 0.0063\n",
            "Epoch 38/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4026 - accuracy: 0.8469 - lr: 0.0071\n",
            "Epoch 39/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4034 - accuracy: 0.8466 - lr: 0.0079\n",
            "Epoch 40/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4032 - accuracy: 0.8466 - lr: 0.0089\n",
            "Epoch 41/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4046 - accuracy: 0.8456 - lr: 0.0100\n",
            "Epoch 42/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4060 - accuracy: 0.8455 - lr: 0.0112\n",
            "Epoch 43/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4081 - accuracy: 0.8431 - lr: 0.0126\n",
            "Epoch 44/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4083 - accuracy: 0.8425 - lr: 0.0141\n",
            "Epoch 45/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4090 - accuracy: 0.8423 - lr: 0.0158\n",
            "Epoch 46/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4109 - accuracy: 0.8424 - lr: 0.0178\n",
            "Epoch 47/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4116 - accuracy: 0.8424 - lr: 0.0200\n",
            "Epoch 48/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4123 - accuracy: 0.8424 - lr: 0.0224\n",
            "Epoch 49/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4164 - accuracy: 0.8424 - lr: 0.0251\n",
            "Epoch 50/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4205 - accuracy: 0.8423 - lr: 0.0282\n",
            "Epoch 51/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4543 - accuracy: 0.8423 - lr: 0.0316\n",
            "Epoch 52/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4935 - accuracy: 0.8424 - lr: 0.0355\n",
            "Epoch 53/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4936 - accuracy: 0.8424 - lr: 0.0398\n",
            "Epoch 54/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4939 - accuracy: 0.8424 - lr: 0.0447\n",
            "Epoch 55/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4939 - accuracy: 0.8424 - lr: 0.0501\n",
            "Epoch 56/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4941 - accuracy: 0.8424 - lr: 0.0562\n",
            "Epoch 57/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4945 - accuracy: 0.8424 - lr: 0.0631\n",
            "Epoch 58/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4947 - accuracy: 0.8424 - lr: 0.0708\n",
            "Epoch 59/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4951 - accuracy: 0.8424 - lr: 0.0794\n",
            "Epoch 60/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4955 - accuracy: 0.8424 - lr: 0.0891\n",
            "Epoch 61/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4958 - accuracy: 0.8424 - lr: 0.1000\n",
            "Epoch 62/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4964 - accuracy: 0.8424 - lr: 0.1122\n",
            "Epoch 63/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4964 - accuracy: 0.8424 - lr: 0.1259\n",
            "Epoch 64/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4970 - accuracy: 0.8424 - lr: 0.1413\n",
            "Epoch 65/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4974 - accuracy: 0.8424 - lr: 0.1585\n",
            "Epoch 66/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4985 - accuracy: 0.8424 - lr: 0.1778\n",
            "Epoch 67/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4987 - accuracy: 0.8424 - lr: 0.1995\n",
            "Epoch 68/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.4999 - accuracy: 0.8424 - lr: 0.2239\n",
            "Epoch 69/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5005 - accuracy: 0.8424 - lr: 0.2512\n",
            "Epoch 70/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5016 - accuracy: 0.8424 - lr: 0.2818\n",
            "Epoch 71/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5025 - accuracy: 0.8424 - lr: 0.3162\n",
            "Epoch 72/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.5039 - accuracy: 0.8424 - lr: 0.3548\n",
            "Epoch 73/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5050 - accuracy: 0.8424 - lr: 0.3981\n",
            "Epoch 74/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5058 - accuracy: 0.8424 - lr: 0.4467\n",
            "Epoch 75/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.5072 - accuracy: 0.8424 - lr: 0.5012\n",
            "Epoch 76/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5100 - accuracy: 0.8424 - lr: 0.5623\n",
            "Epoch 77/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5114 - accuracy: 0.8424 - lr: 0.6310\n",
            "Epoch 78/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5141 - accuracy: 0.8424 - lr: 0.7079\n",
            "Epoch 79/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5154 - accuracy: 0.8424 - lr: 0.7943\n",
            "Epoch 80/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5177 - accuracy: 0.8424 - lr: 0.8913\n",
            "Epoch 81/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5219 - accuracy: 0.8419 - lr: 1.0000\n",
            "Epoch 82/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5238 - accuracy: 0.8420 - lr: 1.1220\n",
            "Epoch 83/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.5296 - accuracy: 0.8419 - lr: 1.2589\n",
            "Epoch 84/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5321 - accuracy: 0.8411 - lr: 1.4125\n",
            "Epoch 85/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5362 - accuracy: 0.8412 - lr: 1.5849\n",
            "Epoch 86/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.5407 - accuracy: 0.8413 - lr: 1.7783\n",
            "Epoch 87/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5466 - accuracy: 0.8410 - lr: 1.9953\n",
            "Epoch 88/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5468 - accuracy: 0.8407 - lr: 2.2387\n",
            "Epoch 89/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5605 - accuracy: 0.8394 - lr: 2.5119\n",
            "Epoch 90/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5657 - accuracy: 0.8372 - lr: 2.8184\n",
            "Epoch 91/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5754 - accuracy: 0.8354 - lr: 3.1623\n",
            "Epoch 92/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5813 - accuracy: 0.8331 - lr: 3.5481\n",
            "Epoch 93/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.5866 - accuracy: 0.8301 - lr: 3.9811\n",
            "Epoch 94/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.6081 - accuracy: 0.8278 - lr: 4.4668\n",
            "Epoch 95/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.6199 - accuracy: 0.8280 - lr: 5.0119\n",
            "Epoch 96/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.6334 - accuracy: 0.8231 - lr: 5.6234\n",
            "Epoch 97/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.6627 - accuracy: 0.8179 - lr: 6.3096\n",
            "Epoch 98/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.6864 - accuracy: 0.8123 - lr: 7.0795\n",
            "Epoch 99/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.7125 - accuracy: 0.8062 - lr: 7.9433\n",
            "Epoch 100/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.7466 - accuracy: 0.8046 - lr: 8.9125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model_1.predict(X_test_ct)"
      ],
      "metadata": {
        "id": "8yHPZczaQiki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OZGjJZA7W8r",
        "outputId": "a82ef341-f0cd-4291-b440-7421fcb25c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.9878162e-01, 1.9751664e-05, 1.1986481e-03],\n",
              "       [9.9878162e-01, 1.9751664e-05, 1.1986481e-03],\n",
              "       [9.9878162e-01, 1.9751664e-05, 1.1986481e-03],\n",
              "       ...,\n",
              "       [9.9878162e-01, 1.9751664e-05, 1.1986481e-03],\n",
              "       [9.9878162e-01, 1.9751664e-05, 1.1986481e-03],\n",
              "       [9.9878162e-01, 1.9751664e-05, 1.1986481e-03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = Y_pred.argmax(axis=1)"
      ],
      "metadata": {
        "id": "3vbsoWUtQb8t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.math.confusion_matrix(y_test,y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpo19HCn63Ei",
        "outputId": "9095e664-ff44-44a8-9117-17154116015d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 3), dtype=int32, numpy=\n",
              "array([[42741,     0,     0],\n",
              "       [  926,     0,     0],\n",
              "       [ 7069,     0,     0]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.evaluate(X_test_ct,y_test)"
      ],
      "metadata": {
        "id": "9E1n4fFtj5ui",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84851a99-5740-4528-a47e-ba18d1236fe3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1586/1586 [==============================] - 3s 2ms/step - loss: 1.1359 - accuracy: 0.8424\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1359317302703857, 0.8424195647239685]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(history.history).plot()"
      ],
      "metadata": {
        "id": "Gos4NvzVR4r_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1e5285a0-4ffb-4bc8-f656-43b902d92892"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3558056590>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the Learning rate versus the loss\n",
        "lrs = 1e-4 * (10**(tf.range(100)/20))\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.semilogx(lrs,history.history['loss'])\n",
        "plt.xlabel(\"Learning Rate\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Learning Rate vs. Loss\")"
      ],
      "metadata": {
        "id": "u7tVE7kET2Lp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "902a0c5b-3056-4117-f389-17719d5d3815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Learning Rate vs. Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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7jk7pU5pgOJMkSdNE/+AQO5s7OGcKn9IEw5kkSZomGpo76R9MnLfUnjNJkqTMDY/UPMfTmpIkSdnbtr+d8tLgzEWzsy7ltBQ0nEXEdRGxPSJ2RMR7jrPOayNia0Q8ERGfHdE+GBGP5B8bC1mnJEma+rbvb2dd3WwqyqZ231NZoXYcEaXAHcDLgUZgU0RsTCltHbHOeuC9wDUppdaIWDxiF90ppUsLVZ8kSZpetu07ypVrF2RdxmkrZLS8CtiRUmpIKfUBdwM3jlrnrcAdKaVWgJTSwQLWI0mSpqkj3f08e6RnSk8+O6yQ4Ww5sHfE68Z820hnA2dHxI8i4sGIuG7EsqqIqM+331TAOiVJ0hS3/didAab2YAAo4GnNk3j/9cC1wArgBxFxUUqpDVidUmqKiDOB70TEYymlnSM3johbgVsBVq1aNbmVS5KkorF9+J6aU3waDShsz1kTsHLE6xX5tpEagY0ppf6U0i7gKXJhjZRSU/65AfgecNnoN0gp3ZlS2pBS2lBXVzfxn0CSJE0JT+5vZ05VGUvmVGVdymkrZDjbBKyPiLURUQHcDIwedfkVcr1mRMQicqc5GyJifkRUjmi/BtiKJEnSGLbvb+fcpXOIiKxLOW0FC2cppQHgNuA+4EngCymlJyLi9oi4Ib/afcDhiNgKfBd4d0rpMHAeUB8RP823f2DkKE9JkqRhKaVcOJsG15tBga85SyndC9w7qu0vRvycgD/KP0au8wBwUSFrkyRJ00NjazcdvQPTYqQmeIcASZI0xW3Lj9Sc6rdtGmY4kyRJU9r2aXJPzWGGM0mSNKU9ub+dlQuqmV2Z9QxhE8NwJkmSprTcYIDpcb0ZGM4kSdIU1tM/yK5DndNmpCYYziRJ0hS242AHg0PJnjNJkqRiMN1GaoLhTJIkTWHb9x+lsqyENQtnZV3KhDGcSZKkKWvb/nbWnzGbstLpE2mmzyeRJEkzzrZpNlITDGeSJGmKOtTRS3N777QaqQmGM0mSNEV984kDAFy5ZkHGlUwsw5kkSZqSPl+/l3POqOXiFXOzLmVCGc4kSdKUs31/Oz/d28Zrr1xJRGRdzoQynEmSpCnn85v2Ul4a/Pply7MuZcIZziRJ0pTSOzDIl7c08vLzz2BBTUXW5Uw4w5kkSZpSvr31IK1d/bx2w8qsSykIw5kkSZpSPl+/l2Vzq3jR+rqsSykIw5kkSZoymtq6uf/pZl5zxQpKS6bXQIBhhjNJkjRl3FPfSErwm9P0lCYYziRJ0hQxNJT44ua9XHPWQlYumD43Oh/NcCZJkqaEB3YeprG1e9oOBBhmOJMkSVPC5+v3Mre6nFdesCTrUgrKcCZJkopeW1cf9z2xn5suXUZVeWnW5RSU4UySJBW9L9Y30jcwxH+5clXWpRSc4UySJBW1gcEhPvXAM1y9dgHnL5uTdTkFZziTJElF7RtP7KeprZu3/OLarEuZFIYzSZJU1D7xw12sXjiLl513RtalTArDmSRJKloP72lly5423vzCNdP2jgCjGc4kSVLR+sQPd1FbVTat7wgwmuFMkiQVpcbWLr7+2D5ef9UqairLsi5n0hjOJElSUfr0A88QEdzywjVZlzKpDGeSJKnodPQOcPdDe3nVhUtYNq8663ImleFMkiQVnS/W76W9d2DGTJ8xkuFMkiQVlcGhxCd/9AxXrJ7PZavmZ13OpDOcSZKkovLdbQfZ09I1I3vNwHAmSZKKzPefaqamopSXnz8zJp0dzXAmSZKKykO7WrhizQLKS2dmTJmZn1qSJBWlls4+th9o5+q1C7IuJTOGM0mSVDQ2PdMCYDiTJEkqBg/taqGyrISLVszNupTMFDScRcR1EbE9InZExHuOs85rI2JrRDwREZ8d0X5LRDydf9xSyDolSVJxeGhXC5etmkdlWWnWpWSmYDeqiohS4A7g5UAjsCkiNqaUto5YZz3wXuCalFJrRCzOty8A/hLYACRgc37b1kLVK0mSsnW0p58nnj3CbS9dn3UpmSpkz9lVwI6UUkNKqQ+4G7hx1DpvBe4YDl0ppYP59lcC30opteSXfQu4roC1SpKkjG3e3cpQmtnXm0Fhw9lyYO+I1435tpHOBs6OiB9FxIMRcd1JbEtE3BoR9RFR39zcPIGlS5KkyfbQrhbKSoLLZ+BdAUbKekBAGbAeuBZ4HfCxiJg33o1TSnemlDaklDbU1dUVqERJkjQZHtrVwsUr5lJdMXOvN4PChrMmYOWI1yvybSM1AhtTSv0ppV3AU+TC2ni2lSRJ00R33yCPNrZx1dqFWZeSuUKGs03A+ohYGxEVwM3AxlHrfIVcrxkRsYjcac4G4D7gFRExPyLmA6/It0mSpGloy55W+gfTjL/eDAo4WjOlNBARt5ELVaXAXSmlJyLidqA+pbSR50LYVmAQeHdK6TBARLyPXMADuD2l1FKoWiVJUrZ+squFkoAr1szs682ggOEMIKV0L3DvqLa/GPFzAv4o/xi97V3AXYWsT5IkFYeHdrVw/rI5zKkqz7qUzGU9IECSJM1wvQODPLynlavWeL0ZGM4kSVLGHms8Qu/AEFd5vRlgOJMkSRn7ya7cZeWGsxzDmSRJytRDu1o4+4zZLKipyLqUomA4kyRJmRkYHKL+mRZ7zUYwnEmSpMxs3XeUzr5BJ58dwXAmSZIy862tBwC4ao09Z8MMZ5IkKRNfrN/L339nB6+84AyWzK3KupyiYTiTJEmT7itbmviTLz3Ki9Yv4sM3X5Z1OUXFcCZJkibV1x7dxx994RF+Ye1C7nzDBqrKS7MuqagYziRJ0qS574n9vOvuLVyxej6feNMGqisMZqMZziRJ0qT47raD3PbZh7lw+VzuetOVzKoo6C2+pyzDmSRJKrgte1r53X/dzDlLavn0b19FrTc4Py7DmSRJKqhdhzp5y6frWVxbxSffdBVzqw1mJzKucBYRNRFRkv/57Ii4ISL8zUqSpBNqbu/llrseAuDTv30VdbWVGVdU/Mbbc/YDoCoilgPfBN4AfKpQRUmSpKmvs3eAt3x6Ewfbe/jELRtYu6gm65KmhPGGs0gpdQG/Afy/lNJvAhcUrixJkjSVDQwOcdtnH+bxpiPc8frLuWzV/KxLmjLGHc4i4gXAbwFfy7c59lWSJI3pff+xle9ub+Z/3nQRLzvvjKzLmVLGG87+AHgv8OWU0hMRcSbw3cKVJUmSpqpt+4/yzw/u5pYXrOb1V6/KupwpZ1wTjKSUvg98HyA/MOBQSumdhSxMkiRNTX/zje3MrizjD19+dtalTEnjHa352YiYExE1wOPA1oh4d2FLkyRJU81Du1r4zraD/O6165g3qyLrcqak8Z7WPD+ldBS4Cfg6sJbciE1JkiQAUkp84OtPcsacSt78wrVZlzNljTeclefnNbsJ2JhS6gdS4cqSJElTzbe2HuDhPW2862Vne8/M0zDecPZPwDNADfCDiFgNHC1UUZIkaWoZHEp88L7tnLmohtduWJF1OVPauMJZSukjKaXlKaVfSTm7gZcUuDZJkjRFfOnhRp4+2MEfv/Icykq9O+TpGO+AgLkR8bcRUZ9//F9yvWiSJGmG6+kf5EPfeopLVs7jVRcuybqcKW+80fYuoB14bf5xFPhkoYqSJElTx2d+vJtnj/Twp9edQ0RkXc6UN655zoB1KaVXj3j9PyLikUIUJEmSpo7Nu1v40Lef4sVn1/HCdYuyLmdaGG/PWXdE/OLwi4i4BuguTEmSJGkq2Ly7hTd+4iEWz6nig6+5OOtypo3x9py9HfjniJibf90K3FKYkiRJUrHbvLuVW+7axOI5Vdx96y9wxpyqrEuaNsZ7+6afApdExJz866MR8QfAo4UsTpIkFZ9cMHuIutpKPvdWg9lEO6mxrimlo/k7BQD8UQHqkSRJRezhPblgtmh2BZ976y+wZK7BbKKdzkQkDseQJGkGae/p582f3MTC2RV87laDWaGM95qzsXj7JkmSZpCvPbqPI939fPLNV7J0bnXW5UxbJwxnEdHO2CEsAI+KJEkzyBfq97J+8WwuWzkv61KmtROGs5RS7WQVIkmSiteOg+08vKeNP/uV85xotsC8+ZUkSXpeX6xvpLQkuOmy5VmXMu0ZziRJ0gn1Dw7xpYebeOm5i6mrrcy6nGnPcCZJkk7o+9ubOdTRy2s3rMy6lBnBcCZJkk7oC/V7WTS7gmvPqcu6lBmhoOEsIq6LiO0RsSMi3jPG8jdFRHNEPJJ//M6IZYMj2jcWsk5JkjS2Qx29fGfbQX7j8hWUl9qnMxlOZ56zE4qIUuAO4OVAI7ApIjamlLaOWvXzKaXbxthFd0rp0kLVJ0mSnt9XtjQxMJT4zStWZF3KjFHICHwVsCOl1JBS6gPuBm4s4PtJkqQJlFLi85v2cunKeaw/w9m1Jkshw9lyYO+I1435ttFeHRGPRsQ9ETHySsOqiKiPiAcj4qax3iAibs2vU9/c3DyBpUuSpJ82HuHpgx0OBJhkWZ88/iqwJqV0MfAt4NMjlq1OKW0AXg98KCLWjd44pXRnSmlDSmlDXZ0XKUqSNJG+UL+XqvISrr9kadalzCgFu+YMaAJGRu0V+bZjUkqHR7z8OPA3I5Y15Z8bIuJ7wGXAzkIVK0nSTNXdN8g9m/dyuLOPnv4hevoH6ekf5D8e3cerLlzKnKryrEucUQoZzjYB6yNiLblQdjO5XrBjImJpSmlf/uUNwJP59vlAV0qpNyIWAdcwIrhJkqSJMTA4xDs++zDf2XYQgIrSEirLS6guL2XxnEp++5q1GVc48xQsnKWUBiLiNuA+oBS4K6X0RETcDtSnlDYC74yIG4ABoAV4U37z84B/ioghcqdePzDGKE9JknQaUkr8+b8/zne2HeT2Gy/gt65eTWmJ983MWqSUsq5hQmzYsCHV19dnXYYkSVPGh7/9NH/37ad4x0vW8e5Xnpt1OTNKRGzOX1v/c7IeECBJkjLwhU17+btvP8VvXL6cP37FOVmXoxEMZ5IkzTDf3XaQ9375MV58dh1//eqLifBUZjExnEmSNINseqaF3/vXhzlvaS3/77cu95ZMRcgjIknSDPG1R/fxWx//CUvnVnHXm65kdmUhJ23QqfKoSJI0zaWU+Nj9Dbz/3m1sWD2fj71xA/NrKrIuS8dhOJMkaRobGBzif3x1K595cDe/etFS/u9rL6GqvDTrsnQChjNJkqaprr4B3vm5LXz7yYO87cVn8qfXnUuJ85gVPcOZJEnT0DOHOnn7v2zmqQPtvO/GC3jDC9ZkXZLGyXAmSdI0851tB3jX3Y9QWhJ88s1X8Utn12Vdkk6C4UySpGliaCjx4f98mg//59Ocv3QO//SGK1i5YFbWZekkGc4kSZoGjnT18wef38J3tzfz6stX8L9+/UIv/J+iDGeSJE1hbV19fPJHz/DJH+2iu3+Q9910If/16lXO+j+FGc4kSZqCDnX08vH7d/GZHz9DZ98grzj/DN71y+u5YNncrEvTaTKcSZI0xdzx3R38/XeepndgiOsvXsY7XrKOc5fMybosTRDDmSRJU8idP9jJB+/bznUXLOHd153DurrZWZekCWY4kyRpivjS5kbef+82fvXipXzk5ssodULZackbn0uSNAV8Z9sB/uRLj3LNWQv529deYjCbxgxnkiQVuc27W/m9f304P3fZBirLnCJjOjOcSZJUxJ4+0M5vf2oTS+dW88k3X8nsSq9Imu48wpIkFZnBocQDOw/x5Yeb+MYT+6mpLOOff/sqFs2uzLo0TQLDmSRJReLpA+18cXMj//5IEweO9lJbVcaNly7j1hev8zZMM4jhTJKkjKWUuOtHz/D+e58kgGvPWcxf/tpyXnruYm/BNAMZziRJylBP/yDv/bfH+PKWJl55wRm8/9cvYqGnL2c0w5kkSRlpauvmbZ+p5/Gmo/y3l5/NO15yFiVOkTHjGc4kScrAgw2Hece/PkzfwBAff+MGfvn8M7IuSUXCcCZJ0iRJKbF5dysfv38X39y6nzWLavjYGzd4Cyb9DMOZJEkFNjA4xDee2M/H79/FI3vbmFtdzu9eu463/dI65lSVZ12eiozhTJKkAjjU0cuDDYd5sOEw393WTFNbN2sWzuJ9N17Aq69YwawK/wnW2PyTIUnSBPIKwvMAAB2wSURBVHly31HufmgPP244zFMHOgCoqSjl6jMX8lc3XMDLzl3sBf96XoYzSZJO046DHXzo20/xH4/uo7q8lKvWLuDXL1vBC9Yt5MJlcygr9W6JGj/DmSRJp2jP4S4+/J9P8+UtjVSVl3LbS87irS86k7mzvI5Mp85wJknSOKWU2Nncyf1PN3P/04f4wVPNlJYEb/nFtbz9l9Y5eawmhOFMkqTn8ZOGw3x5SxP3P32IprZuANYuquFNL1zD77zoTJbMrcq4Qk0nhjNJko7jscYjfPCb2/nBU83UVpVxzbpF/N5L1vHi9XXeiFwFYziTJGmUHQc7+Ntvbefex/Yzf1Y5f/Yr5/GGF6z2JuSaFIYzSZKAZ9u6+cFTzXxvezPf3Lqf6vJS3vWy9fzOi9ZS60SxmkSGM0nSjNM/OMTeli52HOzgwYYWfvB0MzsO5uYlWzKnygv8lSnDmSRpWjtwtIdH9rbxaGMbTx/oYGdzB3tauugfTABUlJVw9doF3HzlSl58dh3rF88mwolilR3DmSRp2hgcSjzedIQfNxzmkT1t/LSxjX1HegAoKwnWLKrhrMWzeeUFS1hXN5sz62o4d8kcqiu8lkzFw3AmSZqyBgaH2NncyQM7D/HAztx9LNt7BgBYvXAWV65ZwKUr53HJynlcsGyOF/RrSihoOIuI64APA6XAx1NKHxi1/E3AB4GmfNM/pJQ+nl92C/Df8+3/M6X06ULWKkkqbke6+nm0qY1t+9rZtr+d7QeO8vSBDnoHhgBYuaCaX71oKS9Yt5AXrltEXa3Xi2lqKlg4i4hS4A7g5UAjsCkiNqaUto5a9fMppdtGbbsA+EtgA5CAzfltWwtVrySpePT0D7LrUCcP72lly542tuxpZWdz57HldbWVnLukljf8wmrOXTqHq9cucN4xTRuF7Dm7CtiRUmoAiIi7gRuB0eFsLK8EvpVSaslv+y3gOuBzBapVkjTJBocSTx1oZ/PuVp4+0M6zR3rYd6SbfW09HO7sO7begpoKLl81j9+4fAWXrpzHeUvnsKCmIsPKpcIqZDhbDuwd8boRuHqM9V4dES8GngL+MKW09zjbLh+9YUTcCtwKsGrVqgkqW5I0EQ6293CovY+egUF6+gbpGRiku2+IhuYONu1uZcvuVtp7c9eH1VaWsWxeNUvnVXHR8nksm1vFqoWzuHTlPFYtmOXoSc0oWQ8I+CrwuZRSb0S8Dfg08NLxbpxSuhO4E2DDhg2pMCVKkk6ku2+QQx29bN/fzmNNR3i86QiPNR3hYHvvcbc5+4zZXH/JMjasns+GNfMNYNIIhQxnTcDKEa9X8NyF/wCklA6PePlx4G9GbHvtqG2/N+EVSpIA6BsYorWrj5SgJICAIBgcSjx7pJvG1m72tnTR2NpFY2s3LZ19tHb20dLVR0//0LH9lASsq5vNL561iAuXz2XZvCqqykupKi+lOv+8ZE4Vc2c54750PIUMZ5uA9RGxllzYuhl4/cgVImJpSmlf/uUNwJP5n+8D3h8R8/OvXwG8t4C1StK0MjA4RFt3P21d/Rzp7qe9p5/2ngGO5p9bO/toauvm2bZumtq6OdjeSxrH+YeFNRWsmF/NkjlVx679mj+rgvmzylm3eDbnL51DTWXWJ2Wkqa1g36CU0kBE3EYuaJUCd6WUnoiI24H6lNJG4J0RcQMwALQAb8pv2xIR7yMX8ABuHx4cIEkzQU//IK1dfbR29tPa1UdX3yB9A0P0DeafB4Y42jPwMz1YLZ19tHb10dbZf+xaruOpKC1h6bwqls+r5kXr61g2r5rFtZWURDCUEgkgJSKCpXOrWLlgFsvnVRu8pEkQaTz/VZoCNmzYkOrr67MuQ9IM0t7TzyN72xgYTAwMJQYGhxgYSvQODNGR76Fq7x2gvWeArr4BhhK54JMSKeVGK/YMDNHTN0h3/yBdfQN09w3S1t1PV9/guGqYVVHKgpqKn+nBmjcr9/O8WeXMm1XO3OpyaqvKmVNVRm1VObVVZcyqKPUaLylDEbE5pbRhrGX+F0iSTtH7793G5x7ac8J1KstKqK0qp6aylNIIIiAiKAkoiaCyvJRZ5aXU1VZSXT6L6opS5lWXMz8fthbU5MLWrIpSKspKqCzLPVeUllBbVeaM99I0ZDiTpFO082AHFy6fw/tuvJDy0hJKS4Ly0qC8NBfIZleWUVFWknWZkqYYw5kknaI9LV384vpFXLZq/vOvLEnj5H/pJOkU9PQPsv9oD6u8ZZCkCWY4k6RT0NjaDWA4kzThDGeSdAr2tnQBeLNtSRPOcCZJp2BPPpzZcyZpohnOJOkU7Gnporq8lEWzK7IuRdI0YziTpFOw+3CXN+uWVBCGM0k6BXtburzeTFJBGM4k6SSllNjT0uX1ZpIKwnAmSSfpUEcf3f2DrFpQnXUpkqYhw5kknaRjIzUX2nMmaeIZziTpJO11Gg1JBWQ4k6STNNxztmK+4UzSxDOcSdJJ2tPSxRlzKqkqL826FEnTkOFMkk6SIzUlFZLhTJJOknOcSSokw5kknYSe/kH2H+2x50xSwRjOJOkkNLV1kxKsdhoNSQViOJOkk7DHaTQkFZjhTJJOwvAcZ15zJqlQDGeSdBL2HO6iqryEutmVWZciaZoynEnSSRieRiMisi5F0jRlOJOkk+AcZ5IKzXAmSeOUUnKOM0kFZziTpHFq6eyjs2/QnjNJBWU4k6RxchoNSZPBcCZJ42Q4kzQZDGeSNE7Dc5ytmG84k1Q4hjNJGqc9LV0srq2kuqI061IkTWOGM0kaJ6fRkDQZDGeSNE57W7oNZ5IKznAmSePQNzDEs0e6neNMUsEZziRpHJrauknJkZqSCs9wJknjcGwajYWGM0mFZTiTpHFwjjNJk8VwJknjsOdwJ5VlJdTNrsy6FEnTnOFMksZhT/6G5yUlkXUpkqY5w5kkjcMzh7pY4/VmkiZBQcNZRFwXEdsjYkdEvOcE6706IlJEbMi/XhMR3RHxSP7x0ULWKUknMjiU2HW4k3V1s7MuRdIMUFaoHUdEKXAH8HKgEdgUERtTSltHrVcLvAv4yahd7EwpXVqo+iRpvJpau+kbGOLMupqsS5E0AxSy5+wqYEdKqSGl1AfcDdw4xnrvA/4a6ClgLZJ0ynY2dwDYcyZpUhQynC0H9o543ZhvOyYiLgdWppS+Nsb2ayNiS0R8PyJeNNYbRMStEVEfEfXNzc0TVrgkjTQczs40nEmaBJkNCIiIEuBvgf82xuJ9wKqU0mXAHwGfjYg5o1dKKd2ZUtqQUtpQV1dX2IIlzVg7mzuZP6ucBTUVWZciaQYoZDhrAlaOeL0i3zasFrgQ+F5EPAP8ArAxIjaklHpTSocBUkqbgZ3A2QWsVZKOq6G5w1OakiZNIcPZJmB9RKyNiArgZmDj8MKU0pGU0qKU0pqU0hrgQeCGlFJ9RNTlBxQQEWcC64GGAtYqSce1s7nTwQCSJk3BRmumlAYi4jbgPqAUuCul9ERE3A7Up5Q2nmDzFwO3R0Q/MAS8PaXUUqhaJel4jnT3c6ij154zSZOmYOEMIKV0L3DvqLa/OM661474+UvAlwpZmySNR4ODASRNMu8QIEknsLO5E4B1ntaUNEkMZ5J0Ag3NHZSVBCsXeOsmSZPDcCZJJ7CzuYPVC2dRXupfl5Imh3/bSNIJNDR3er2ZpEllOJOk4xgYHOIZb3guaZIZziTpOBpbu+kfTA4GkDSpDGeSdBzeU1NSFgxnknQcw+HMnjNJk8lwJknH0dDcycKaCubN8obnkiaP4UySjmOnNzyXlAHDmSQdR4M3PJeUAcOZJI2hrauPw5199pxJmnSGM0kaw/A9Ne05kzTZDGeSNIbnRmracyZpchnOJGkMO5s7KC8NVsyvzroUSTOM4UySxtDQ3MmahTWUecNzSZPMv3UkaQxOoyEpK4YzSRqlf3CIPYe7HAwgKROGM0kaZU9LFwNDyZ4zSZkwnEnSKA1OoyEpQ4YzSRpleBqNM+05k5QBw5kkjdLQ3MGi2ZXMrS7PuhRJM5DhTJJGGBpKPLmvnXWe0pSUEcOZJOUd7ennbf+ymceajvDis+uyLkfSDFWWdQGSVAyePtDO2z6zmd0tXfzF9efz5mvWZF2SpBnKcDZOfQNDfOWRJlYvmMXVZy7MuhxJE+hrj+7j3ff8lFkVZXz2d672Oy4pU4azcSotCf7mG9u5cs18/+KWpomBwSH+5r7t3PmDBi5fNY9//K9XcMacqqzLkjTDGc7GqbQk+NWLlnD3pr109A4wu9JfnTSVHe7o5fc/t4UHdh7mDb+wmj+//nwqyrwMV1L2/JvoJFx/yTJ6B4b4zycPZF2KpNPwWOMRbviHH1G/u5X/85uX8L6bLjSYSSoa/m10Eq5YNZ8lc6r46k/3ZV2KpFP0xfq9vPqjDwDwpbe/kNdcsSLjiiTpZ3lu7iSUlAS/evFSPvPj3Rzp7neCSqnIpZRo7epnT0sXe1u6+P5TzdyzuZEXrlvI37/uMhbOrsy6REn6OYazk3T9xUv5xA938a2tB/wft1QEUko0tnaz61Anu/MhbPfhTva0dLO3pYuO3oFj65YE3PriM/mTV55DWaknDiQVJ8PZSbp05TxWzK/mPx591nAmZejA0R6+vKWJezY3suNgx7H2irISVi2YxaoFs7h67QJW5n9etWAWK+ZXU+NgHklFzr+lTlJE7tTmJ+7fRWtnH/NrKrIuSZoRBocSB472UL+7lS9tbuT+p5sZSrBh9Xxuv/ECzjmjltULa1hcW0lJSWRdriSdMsPZKfi1i5fxT99v4BtP7Od1V63Kuhxpynu86QhP7jtKz8AQPX2D9PQP0t0/SGtXH42tudOTTW3d9A8mAJbNreL3rj2LV1+xgrWLvAempOnFcHYKLlg2h7WLaviPR581nEmnqKN3gH9/pInPPbSHx5uO/tzy8tJgTlU5K+ZXc8HyuVx34VJWLqhm/eJaNqyeb++YpGnLcHYKIoLrL17KHd/dQXN7L3W1jviSxmNgcIifNrZxz+ZG/v2RZ+nqG+TcJbW878YLePHZdVRXlFJdXkpVeSnlXrAvaYYynJ2i6y9ext9/ZwffeHwfb3jBmqzLkYrW3pYu7n/6EPc/3cyPdhziaM8AVeUl/NrFy3j91au4dOU8IuwFk6RhhrNTdM6SWtYvns1XHzWcSSMd6ernxw2H+OGOQ/zw6UM8c7gLgKVzq3jVhUt50dmLePHZdcypcp5ASRqL4ew0XH/xMj70n09x4GiPN0vWtJVS4mB7L7sOdfLMoU52Heqksa2bkggqSkuoKCuhMn/roy17Wnm06QgpQU1FKVefuZBbXriGF62vY11djT1kkjQOBQ1nEXEd8GGgFPh4SukDx1nv1cA9wJUppfp823uBtwCDwDtTSvcVstZTcf0lS/m7bz/Fl7c08fZfWpd1OdJpGxxKNDR38FjTER5tPHJsFGVn3+CxdSpKS1g+vxqAvoEhegeG6BsYZHAocd7SObzrZev5xbMWccnKeV43JkmnoGDhLCJKgTuAlwONwKaI2JhS2jpqvVrgXcBPRrSdD9wMXAAsA74dEWenlAYpIuvqZnPF6vl84OvbqH+mhXe97GwuWjE367KkcWvv6WfLnjbqn2lh0zOt/LSxja58EKsuL+X8ZXN4zRUrWLd4NmsW1rB2UQ3L5lVT6khJSSqYQvacXQXsSCk1AETE3cCNwNZR670P+Gvg3SPabgTuTin1ArsiYkd+fz8uYL2n5FNvvpJPP/AMH7t/F7/2Dz/kZecu5l2/vJ6LV8zLujTNYL0Dg2zf385jTUd4vOkoze29QG6OsJR7Yv/RHp7cd5ShlLut0XlLc0Hs4hXzuHjFXNbVzTaESVIGChnOlgN7R7xuBK4euUJEXA6sTCl9LSLePWrbB0dtu3z0G0TErcCtAKtWZTPfWG1VObe9dD23vHAN//zj3Xzs/gZu+IcfcdXaBfzS2XVcc9YiLlo+13/kNCGOdPfT2NrF3pZuGlu7ONozkJuwtS83aWt3/yC7mjt56kA7A0O5FDa3upxl86oJYPiSrwiYNyv3Z/fKNfO5bNV8ZntbI0kqCpn9bRwRJcDfAm861X2klO4E7gTYsGFDmpjKTk1tVTnveMlZvPEFq/nnH+/ma4/u44P3beeD922ntqqMF5y5kKvPXMj5S+dw7pJab/s0g6SUaOvq50B7Dy0dfRzu7KOlM/d8tLt/zG26+gbo7B2ks2+Azt4B2nsGeLatm6M9Az+3blV5CdXlz80Ptnx+NW8950wuWj6Xi5bPZcX8ai/El6QppJDhrAlYOeL1inzbsFrgQuB7+X84lgAbI+KGcWxbtIZD2jtechaHO3p5YOdhfrQjN63AN7ceOLbekjlVnLu0lvOXzuHSlfO4fPV8Fs12Mtss9Q8O0dX3XC9USomykhLKSoOykqC0JEjAwGBiYGjo2HNH7yCHO3o53NHHoc7cc3N7L/uP9rD/SA8HjvbQOzA05nvWVpVREkFKP/t/i1kVZdRUllJTWUZNRRkr5s/iyjULWDG/mpULZrFyfu4m3nOry50pX5KmmRj9j8KE7TiiDHgKeBm5YLUJeH1K6YnjrP894I9TSvURcQHwWXLXmS0D/hNYf6IBARs2bEj19fUT+yEm2MH2Hrbta2fb/qM8ua+dJ/cdZcfBjmOnn1YtmMXlq+Zx8Yp5LJ5TyYJZFcybVcGCmgrm15RTWVaa8ScoXiklegeG6O4bpCt/mq+zd4DDnb00t+cehzr6aO7opaNngI7egeeeewfo6hs4dt/G0zWropRFsytZMreKJXOqjj0vnlPJwppKFs7OHdN51eWUOZpRkmakiNicUtow1rKC9ZyllAYi4jbgPnJTadyVUnoiIm4H6lNKG0+w7RMR8QVygwcGgHcU20jNU7G4torFtVW8+Oy6Y209/YM81nSEh3e38vCeVn608zBfeeTZMbdfWFPBsnnVLJtXxbJ51SydW0V5aQmDQyn3SInBwURJyXM9PeWlJZSWBJVlJdRUllFdUcqs8lyPTGlJ0D84RP/gEH0Dif7BIRK5+almVZQxu7KMWZWlVJaVMDCY6B8aon8wMTCYez72vsPvPZSIgJIISvLPAANDKT/lwuCxqRd6+gfp6R86doPr3v5BegeH6B/I9Ub1D+bqGRpKJHLhK/ecm77hSHc/R7r7OdqTe+7oHeD5/p9RW1VG3exKaqvLmV1ZyqLZs6ipzH/OijJmVZTmH2VUV5RQEpH/nEMMDCUGBhMlAWX532l5aVBWUnIsjC2cXcHCmkqqKwzRkqRTV7Ces8k2FXrOxiOlxKGO3DVJrV19tHb20drVz+GOXvYd7eHZtm6ebeumqbX7Z+aemg4qykqoKM2dRiwvLaG8JCgpCSIgGH6G8tIS5laXM7e6nDn559qq54JndUXu2quaijIW1VayaHYFi2ZXUlVuaJIkFYdMes50aiKCutrK572ZekqJ9t4BhoZyPWWlkespKy0JhvK9WMO9WwODQ/T0D9HVP3DsmqrO3gEGhxIVZSW5IFRaQkVZkBJ09Q0euyC9q2+A3oGhY71ww71FZfnn4fcsy4eoXG0wlBJD+eeRs8hX5B9VZfkQVVZKZXlumRetS5JkOJuyIsJ7E0qSNA15NbIkSVIRMZxJkiQVEcOZJElSETGcSZIkFRHDmSRJUhExnEmSJBURw5kkSVIRMZxJkiQVEcOZJElSETGcSZIkFRHDmSRJUhExnEmSJBURw5kkSVIRMZxJkiQVEcOZJElSEYmUUtY1TIiIaAZ251/OBY4cZ9XjLRurfay2RcChUyzzdJ3ocxV6P+PZ5nTWOd12j8vpred3ZmL3M1HH5UTr+J3xO1MIU/07M1HHZaz2iT4uq1NKdWMuSSlNuwdw58kuG6v9OG31xfi5Cr2f8WxzOuucbrvH5fTW8zszsfuZqONysr//k2mficdlIo+N35mJ3U+xHZex2ifzuEzX05pfPYVlY7WfaD9ZmKh6TmU/49nmdNaZqPYsFPtxGc96fmcmdj8TdVxOtI7fGb8zhTDVvzMTdVzG814FM21Oa06WiKhPKW3Iug79LI9L8fLYFCePS/Hy2BSnyTwu07XnrJDuzLoAjcnjUrw8NsXJ41K8PDbFadKOiz1nkiRJRcSeM0mSpCJiOJMkSSoihjNJkqQiYjiTJEkqIoazCRQRNRFRHxHXZ12LnhMR50XERyPinoj43azr0XMi4qaI+FhEfD4iXpF1PcqJiDMj4hMRcU/Wtcx0+X9XPp3/nvxW1vXoOYX8nhjOgIi4KyIORsTjo9qvi4jtEbEjIt4zjl39KfCFwlQ5M03EsUkpPZlSejvwWuCaQtY7k0zQsflKSumtwNuB/1LIemeKCTouDSmltxS20pnrJI/RbwD35L8nN0x6sTPMyRybQn5PDGc5nwKuG9kQEaXAHcCrgPOB10XE+RFxUUT8x6jH4oh4ObAVODjZxU9zn+I0j01+mxuArwH3Tm7509qnmIBjk/ff89vp9H2KiTsuKoxPMc5jBKwA9uZXG5zEGmeqTzH+Y1MwZYXc+VSRUvpBRKwZ1XwVsCOl1AAQEXcDN6aU/jfwc6ctI+JaoIbcgeuOiHtTSkOFrHsmmIhjk9/PRmBjRHwN+GzhKp45Juh7E8AHgK+nlB4ubMUzw0R9Z1Q4J3OMgEZyAe0R7FApuJM8NlsLVYcH+viW89z/ViD3BVl+vJVTSn+WUvoDcv/wf8xgVlAndWwi4tqI+EhE/BP2nBXaSR0b4PeBXwZeExFvL2RhM9zJfmcWRsRHgcsi4r2FLk7A8Y/RvwGvjoh/pPjuwzlTjHlsCvk9sedsgqWUPpV1DfpZKaXvAd/LuAyNIaX0EeAjWdehn5VSOkzuOkBlLKXUCbw56zr08wr5PbHn7PiagJUjXq/Ityl7Hpvi5bEpTh6X4ucxKl6TfmwMZ8e3CVgfEWsjogK4GdiYcU3K8dgUL49NcfK4FD+PUfGa9GNjOAMi4nPAj4FzIqIxIt6SUhoAbgPuA54EvpBSeiLLOmcij03x8tgUJ49L8fMYFa9iOTaRUirk/iVJknQS7DmTJEkqIoYzSZKkImI4kyRJKiKGM0mSpCJiOJMkSSoihjNJkqQiYjiTVLQiomOS3++BCdrPtRFxJCIeiYhtEfF/xrHNTRFx/kS8v6SpzXAmacaIiBPeTzil9MIJfLv7U0qXApcB10fENc+z/k2A4UyS4UzS1BIR6yLiGxGxOSLuj4hz8+2/FhE/iYgtEfHtiDgj3/5XEfGZiPgR8Jn867si4nsR8f/bu5eQKMMojOP/J+iCGNUiImphShARmEQRRuEiiCKqTUS0KBBbBEVEl1XkokUQVBRFtmoT2XUhBa270QVBF0IR6cYookWlES7itJgjDKKjLsRxeH4wzDfvZd7zbobDeefj65V0rOi7B/O9KfsfZuXrjiRl345s65R0VdKTUvFGxF+gC1iW81skvZfULemRpCpJjcAu4GJW2+rG2qeZVT4nZ2Y209wCjkbEOuAkcCPbXwIbI6IBaAdOF81ZDWyNiP35eRWwDdgAnJM0e5R1GoDjObcW2CRpHtAGbM/1F48XrKRFwErgeTY9joj1EVFP4VEwzRHxmsKz+k5FxNqI+Fxin2ZW4UqW+M3MyomkaqAReJCFLIC5+b4cuCdpKTAH6Cua2pEVrGFPI2IIGJL0HVgC9I9Y7l1E9Oe6XUANMAj0RsTwd98FDo8R7mZJ3RQSsysR8S3b10g6DywEqik8r28y+zSzCufkzMxmklnAz/wv10jXgEsR0SGpCWgt6vszYuxQ0fU/Rv8tnMiYUl5ExE5JK4A3ku5HRBdwG9gTEd2SDgFNo8wttU8zq3A+1jSzGSMifgN9kvYCqKA+uxcAX/L64BSF8BGolVSTn/eNNyGrbBeAM9k0H/iaR6kHioYOZN94+zSzCufkzMzKWZWk/qLXCQoJTXMeGfYAu3NsK4VjwE7gx1QEk0ejR4Bnuc4A8GsCU28CWzKpOwu8BV4BH4rGtAOn8oaGOsbep5lVOEXEdMdgZjZjSKqOiMG8e/M68CkiLk93XGZWOVw5MzObnJa8QaCHwlFq2zTHY2YVxpUzMzMzszLiypmZmZlZGXFyZmZmZlZGnJyZmZmZlREnZ2ZmZmZlxMmZmZmZWRn5D+FEY4dGaMF2AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Setting random seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "#Create the model\n",
        "model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(100),\n",
        "    tf.keras.layers.Dense(100,activation='relu'),\n",
        "    tf.keras.layers.Dense(100,activation='relu'),\n",
        "    tf.keras.layers.Dense(3,activation='softmax')\n",
        "])\n",
        "\n",
        "#Compile the Model\n",
        "model_2.compile(\n",
        "    loss='sparse_categorical_crossentropy', \n",
        "    optimizer=tf.keras.optimizers.Adam(lr=0.001), \n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "#Fit the Model\n",
        "history_2=model_2.fit(X_train_ct,Y_train,epochs=100)"
      ],
      "metadata": {
        "id": "W-Jx__AUIWLJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0ea5dd8-a859-4260-bfdf-b4c2a5baca29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.4068 - accuracy: 0.8452\n",
            "Epoch 2/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3997 - accuracy: 0.8481\n",
            "Epoch 3/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3984 - accuracy: 0.8484\n",
            "Epoch 4/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3973 - accuracy: 0.8491\n",
            "Epoch 5/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3968 - accuracy: 0.8495\n",
            "Epoch 6/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3961 - accuracy: 0.8494\n",
            "Epoch 7/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3960 - accuracy: 0.8491\n",
            "Epoch 8/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3955 - accuracy: 0.8495\n",
            "Epoch 9/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3956 - accuracy: 0.8492\n",
            "Epoch 10/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3955 - accuracy: 0.8492\n",
            "Epoch 11/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3952 - accuracy: 0.8496\n",
            "Epoch 12/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3954 - accuracy: 0.8494\n",
            "Epoch 13/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3952 - accuracy: 0.8497\n",
            "Epoch 14/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3951 - accuracy: 0.8494\n",
            "Epoch 15/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3949 - accuracy: 0.8499\n",
            "Epoch 16/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3948 - accuracy: 0.8498\n",
            "Epoch 17/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3949 - accuracy: 0.8497\n",
            "Epoch 18/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3948 - accuracy: 0.8497\n",
            "Epoch 19/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3949 - accuracy: 0.8498\n",
            "Epoch 20/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8499\n",
            "Epoch 21/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8499\n",
            "Epoch 22/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3945 - accuracy: 0.8497\n",
            "Epoch 23/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8502\n",
            "Epoch 24/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3947 - accuracy: 0.8496\n",
            "Epoch 25/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8499\n",
            "Epoch 26/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3946 - accuracy: 0.8496\n",
            "Epoch 27/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3945 - accuracy: 0.8502\n",
            "Epoch 28/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3948 - accuracy: 0.8499\n",
            "Epoch 29/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3943 - accuracy: 0.8502\n",
            "Epoch 30/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3949 - accuracy: 0.8499\n",
            "Epoch 31/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8500\n",
            "Epoch 32/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8500\n",
            "Epoch 33/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8501\n",
            "Epoch 34/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3943 - accuracy: 0.8504\n",
            "Epoch 35/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3943 - accuracy: 0.8502\n",
            "Epoch 36/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8501\n",
            "Epoch 37/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3942 - accuracy: 0.8501\n",
            "Epoch 38/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3943 - accuracy: 0.8501\n",
            "Epoch 39/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8500\n",
            "Epoch 40/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8503\n",
            "Epoch 41/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3940 - accuracy: 0.8502\n",
            "Epoch 42/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8503\n",
            "Epoch 43/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8504\n",
            "Epoch 44/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8501\n",
            "Epoch 45/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8502\n",
            "Epoch 46/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3945 - accuracy: 0.8501\n",
            "Epoch 47/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8498\n",
            "Epoch 48/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3944 - accuracy: 0.8499\n",
            "Epoch 49/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3941 - accuracy: 0.8504\n",
            "Epoch 50/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8499\n",
            "Epoch 51/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3943 - accuracy: 0.8505\n",
            "Epoch 52/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3945 - accuracy: 0.8498\n",
            "Epoch 53/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8502\n",
            "Epoch 54/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3943 - accuracy: 0.8501\n",
            "Epoch 55/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8502\n",
            "Epoch 56/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8506\n",
            "Epoch 57/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3942 - accuracy: 0.8499\n",
            "Epoch 58/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8501\n",
            "Epoch 59/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3940 - accuracy: 0.8503\n",
            "Epoch 60/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3946 - accuracy: 0.8504\n",
            "Epoch 61/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3943 - accuracy: 0.8501\n",
            "Epoch 62/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8500\n",
            "Epoch 63/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8501\n",
            "Epoch 64/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3946 - accuracy: 0.8500\n",
            "Epoch 65/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8500\n",
            "Epoch 66/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8500\n",
            "Epoch 67/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3949 - accuracy: 0.8497\n",
            "Epoch 68/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3946 - accuracy: 0.8501\n",
            "Epoch 69/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3945 - accuracy: 0.8501\n",
            "Epoch 70/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3946 - accuracy: 0.8500\n",
            "Epoch 71/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3945 - accuracy: 0.8498\n",
            "Epoch 72/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8502\n",
            "Epoch 73/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8502\n",
            "Epoch 74/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8500\n",
            "Epoch 75/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8499\n",
            "Epoch 76/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3951 - accuracy: 0.8500\n",
            "Epoch 77/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8503\n",
            "Epoch 78/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3947 - accuracy: 0.8497\n",
            "Epoch 79/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3941 - accuracy: 0.8501\n",
            "Epoch 80/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3944 - accuracy: 0.8497\n",
            "Epoch 81/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3951 - accuracy: 0.8496\n",
            "Epoch 82/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3943 - accuracy: 0.8499\n",
            "Epoch 83/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8502\n",
            "Epoch 84/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8501\n",
            "Epoch 85/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8502\n",
            "Epoch 86/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3942 - accuracy: 0.8504\n",
            "Epoch 87/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3945 - accuracy: 0.8499\n",
            "Epoch 88/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8502\n",
            "Epoch 89/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3938 - accuracy: 0.8501\n",
            "Epoch 90/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8503\n",
            "Epoch 91/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3939 - accuracy: 0.8502\n",
            "Epoch 92/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3948 - accuracy: 0.8503\n",
            "Epoch 93/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3940 - accuracy: 0.8501\n",
            "Epoch 94/100\n",
            "6342/6342 [==============================] - 14s 2ms/step - loss: 0.3940 - accuracy: 0.8501\n",
            "Epoch 95/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8500\n",
            "Epoch 96/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3944 - accuracy: 0.8497\n",
            "Epoch 97/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3940 - accuracy: 0.8501\n",
            "Epoch 98/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3941 - accuracy: 0.8498\n",
            "Epoch 99/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3938 - accuracy: 0.8499\n",
            "Epoch 100/100\n",
            "6342/6342 [==============================] - 13s 2ms/step - loss: 0.3940 - accuracy: 0.8500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred1=model_2.predict(X_test_ct)\n",
        "y_preds1 = Y_pred1.argmax(axis=1)\n",
        "tf.math.confusion_matrix(y_test,y_preds1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YMQceY1DSZZ",
        "outputId": "e7a208d8-6ecc-40af-81d6-1cb87a0238a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 3), dtype=int32, numpy=\n",
              "array([[42348,     0,   393],\n",
              "       [  885,     0,    41],\n",
              "       [ 6410,     0,   659]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "KXL54YCVJHmT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {\n",
        "    \"max_depth\": [3, 4, 5, 7],\n",
        "    \"learning_rate\": [0.1, 0.01, 0.05],\n",
        "    \"gamma\": [0, 0.25, 1],\n",
        "    \"reg_lambda\": [0, 1, 10],\n",
        "    \"scale_pos_weight\": [1, 3, 5],\n",
        "    \"subsample\": [0.8],\n",
        "    \"colsample_bytree\": [0.5],\n",
        "}"
      ],
      "metadata": {
        "id": "3vtLswgBzK_8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "# Init classifier\n",
        "xgb_cl = xgb.XGBClassifier(subsample= 0.8,\n",
        " scale_pos_weight= 5,\n",
        " reg_lambda= 1,\n",
        " max_depth= 5,\n",
        " learning_rate= 0.01,\n",
        " gamma= 1,\n",
        " colsample_bytree= 0.5)\n",
        "\n",
        "# Init Grid Search\n",
        "grid_cv = RandomizedSearchCV(xgb_cl, param_grid, n_jobs=-1, cv=3, scoring=\"roc_auc\",verbose=2,n_iter=20)\n",
        "\n",
        "# Fit\n",
        "grid_cv.fit(X_train_ct, Y_train)"
      ],
      "metadata": {
        "id": "7E7rs8d1yWMx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4c8687c-9468-484d-9212-7aa40c0f85a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan\n",
            " nan nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3,\n",
              "                   estimator=XGBClassifier(colsample_bytree=0.5, gamma=1,\n",
              "                                           learning_rate=0.01, max_depth=5,\n",
              "                                           scale_pos_weight=5, subsample=0.8),\n",
              "                   n_iter=20, n_jobs=-1,\n",
              "                   param_distributions={'colsample_bytree': [0.5],\n",
              "                                        'gamma': [0, 0.25, 1],\n",
              "                                        'learning_rate': [0.1, 0.01, 0.05],\n",
              "                                        'max_depth': [3, 4, 5, 7],\n",
              "                                        'reg_lambda': [0, 1, 10],\n",
              "                                        'scale_pos_weight': [1, 3, 5],\n",
              "                                        'subsample': [0.8]},\n",
              "                   scoring='roc_auc', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_cv.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3Pyhr1-TC9D",
        "outputId": "9d32b54c-19d2-4663-e1fa-ba7ea65cf475"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'subsample': 0.8,\n",
              " 'scale_pos_weight': 3,\n",
              " 'reg_lambda': 1,\n",
              " 'max_depth': 3,\n",
              " 'learning_rate': 0.1,\n",
              " 'gamma': 0,\n",
              " 'colsample_bytree': 0.5}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "lCXVL6FHydCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl = xgb.XGBClassifier(subsample= 0.8,\n",
        " scale_pos_weight= 5,\n",
        " reg_lambda= 1,\n",
        " max_depth= 5,\n",
        " learning_rate= 0.01,\n",
        " gamma= 1,\n",
        " colsample_bytree= 0.5)"
      ],
      "metadata": {
        "id": "9t5MBI50Tv38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl.fit(X_train_ct,Y_train)\n",
        "Y_preds = xgb_cl.predict(X_test_ct)"
      ],
      "metadata": {
        "id": "ualIGsT2I-ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0EnOPU8JHpv",
        "outputId": "cb8359c2-be94-494f-852b-3b101114421a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,Y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8mJOQcEJMRc",
        "outputId": "e010a2ab-84e3-4db2-d150-7e8dc642e6df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8466966256701356"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_test,Y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1M5DUzjUFLl",
        "outputId": "bb024edf-4b1a-47a0-fd80-83dd465e9ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[42422,     0,   319],\n",
              "       [  892,     0,    34],\n",
              "       [ 6533,     0,   536]])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#Different HyperParameters\n",
        "rf_grid = {\"n_estimators\": np.arange(10,100,10),\n",
        "          \"max_depth\": [None,3,5,10],\n",
        "          \"min_samples_split\":np.arange(2,10,2),\n",
        "          \"min_samples_leaf\":np.arange(1,10,2),\n",
        "          \"max_features\":[0.5,1,\"sqrt\",'log2'],\n",
        "          \"max_samples\":[2000,5000,10000]}\n",
        "\n",
        "#Instatantiate Randomised Search CV\n",
        "rs_model = RandomizedSearchCV(RandomForestClassifier(n_jobs=-1,random_state=42),\n",
        "                             param_distributions=rf_grid,\n",
        "                             n_iter=50,\n",
        "                             cv=5,\n",
        "                             verbose=2)\n",
        "rs_model.fit(X_train_ct,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4xVAzY4JVfg",
        "outputId": "9b0f70a6-a780-4c2a-d654-71a76dc83f96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=30; total time=   1.0s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=30; total time=   0.9s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=30; total time=   0.9s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=30; total time=   0.8s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=30; total time=   0.8s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=3, min_samples_split=6, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=3, min_samples_split=6, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=3, min_samples_split=6, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=3, min_samples_split=6, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=3, min_samples_split=6, n_estimators=40; total time=   1.2s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=6, n_estimators=20; total time=   0.7s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=6, n_estimators=80; total time=   1.7s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=6, n_estimators=80; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=6, n_estimators=80; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=6, n_estimators=80; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=6, n_estimators=80; total time=   1.6s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=60; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=60; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=60; total time=   1.7s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=60; total time=   1.7s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=60; total time=   1.7s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=sqrt, max_samples=2000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=40; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=40; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=40; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=40; total time=   1.0s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=40; total time=   1.0s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=None, max_features=sqrt, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=None, max_features=sqrt, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=None, max_features=sqrt, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=None, max_features=sqrt, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=sqrt, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.4s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.8s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.8s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.8s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.8s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=70; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=80; total time=   3.4s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=80; total time=   2.6s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=80; total time=   2.6s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=80; total time=   2.6s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=80; total time=   2.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   2.3s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   2.2s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   2.2s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   2.2s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=90; total time=   2.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=7, min_samples_split=8, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=7, min_samples_split=8, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=7, min_samples_split=8, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=7, min_samples_split=8, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=7, min_samples_split=8, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=5, min_samples_split=6, n_estimators=70; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=20; total time=   0.7s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=20; total time=   0.8s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=4, n_estimators=90; total time=   1.9s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=80; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=80; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=80; total time=   1.4s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=80; total time=   1.4s\n",
            "[CV] END max_depth=10, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=80; total time=   1.4s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=80; total time=   1.4s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=30; total time=   0.6s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=30; total time=   0.6s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=3, min_samples_split=4, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=50; total time=   1.7s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=10, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   0.7s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=5000, min_samples_leaf=7, min_samples_split=2, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=10000, min_samples_leaf=7, min_samples_split=2, n_estimators=90; total time=   3.0s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=10000, min_samples_leaf=7, min_samples_split=2, n_estimators=90; total time=   3.1s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=10000, min_samples_leaf=7, min_samples_split=2, n_estimators=90; total time=   3.1s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=10000, min_samples_leaf=7, min_samples_split=2, n_estimators=90; total time=   3.1s\n",
            "[CV] END max_depth=None, max_features=0.5, max_samples=10000, min_samples_leaf=7, min_samples_split=2, n_estimators=90; total time=   3.1s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=None, max_features=1, max_samples=2000, min_samples_leaf=5, min_samples_split=4, n_estimators=10; total time=   0.4s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=4, n_estimators=70; total time=   1.6s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=4, n_estimators=70; total time=   1.6s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=4, n_estimators=70; total time=   1.6s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=4, n_estimators=70; total time=   2.0s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=4, n_estimators=70; total time=   2.2s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=1, min_samples_split=8, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=90; total time=   1.7s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=90; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=5000, min_samples_leaf=5, min_samples_split=4, n_estimators=90; total time=   1.7s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=2, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=2, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=2, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=2, n_estimators=60; total time=   1.2s\n",
            "[CV] END max_depth=3, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=2, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=90; total time=   2.5s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=90; total time=   2.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, max_samples=10000, min_samples_leaf=9, min_samples_split=4, n_estimators=90; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=6, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=6, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=6, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=6, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=2000, min_samples_leaf=1, min_samples_split=6, n_estimators=80; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=9, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=9, min_samples_split=6, n_estimators=70; total time=   1.6s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=9, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=9, min_samples_split=6, n_estimators=70; total time=   1.5s\n",
            "[CV] END max_depth=5, max_features=0.5, max_samples=5000, min_samples_leaf=9, min_samples_split=6, n_estimators=70; total time=   2.0s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.5s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.1s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=7, min_samples_split=8, n_estimators=60; total time=   1.0s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.0s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.0s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=10000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=40; total time=   0.8s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=40; total time=   0.8s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=40; total time=   0.8s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=40; total time=   0.9s\n",
            "[CV] END max_depth=5, max_features=log2, max_samples=5000, min_samples_leaf=5, min_samples_split=8, n_estimators=40; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=80; total time=   1.8s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=80; total time=   1.8s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=80; total time=   1.8s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=80; total time=   1.8s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=10000, min_samples_leaf=5, min_samples_split=8, n_estimators=80; total time=   1.8s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=log2, max_samples=10000, min_samples_leaf=9, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.3s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.4s\n",
            "[CV] END max_depth=10, max_features=0.5, max_samples=5000, min_samples_leaf=3, min_samples_split=8, n_estimators=50; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=1, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=1, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=1, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=1, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=1, max_samples=2000, min_samples_leaf=1, min_samples_split=8, n_estimators=50; total time=   0.9s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=3, max_features=0.5, max_samples=2000, min_samples_leaf=1, min_samples_split=4, n_estimators=70; total time=   1.3s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=2000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=2000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=2000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.6s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=2000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.5s\n",
            "[CV] END max_depth=None, max_features=log2, max_samples=2000, min_samples_leaf=7, min_samples_split=6, n_estimators=20; total time=   0.5s\n",
            "CPU times: user 1min 52s, sys: 5.76 s, total: 1min 58s\n",
            "Wall time: 5min 29s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=RandomForestClassifier(n_jobs=-1, random_state=42),\n",
              "                   n_iter=50,\n",
              "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
              "                                        'max_features': [0.5, 1, 'sqrt',\n",
              "                                                         'log2'],\n",
              "                                        'max_samples': [2000, 5000, 10000],\n",
              "                                        'min_samples_leaf': array([1, 3, 5, 7, 9]),\n",
              "                                        'min_samples_split': array([2, 4, 6, 8]),\n",
              "                                        'n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
              "                   verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_model.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xu8ROahoGlJe",
        "outputId": "9e3da0eb-3ab3-42b2-92c2-b1ea0cb116d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 80,\n",
              " 'min_samples_split': 8,\n",
              " 'min_samples_leaf': 1,\n",
              " 'max_samples': 10000,\n",
              " 'max_features': 0.5,\n",
              " 'max_depth': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs = RandomForestClassifier(n_jobs=-1,random_state=42,n_estimators= 70,\n",
        " min_samples_split= 6,\n",
        " min_samples_leaf= 9,\n",
        " max_samples= 10000,\n",
        " max_features= 0.5,\n",
        " max_depth= None)"
      ],
      "metadata": {
        "id": "n0EJLz1NV0Yf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rs.fit(X_train_ct,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XY3EsD27WKZP",
        "outputId": "5b1fa37a-09a9-4f66-c28e-1ef36bcf8789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_features=0.5, max_samples=10000, min_samples_leaf=9,\n",
              "                       min_samples_split=6, n_estimators=70, n_jobs=-1,\n",
              "                       random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_preds_rs = rs.predict(X_test_ct)"
      ],
      "metadata": {
        "id": "ykX2Q44TWN-a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,Y_preds_rs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_FfctBwWbfT",
        "outputId": "5161321a-3bc2-49c3-842f-da0c0ed6a6b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.848076316619363"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,Y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onPMzqz2WiCx",
        "outputId": "c794ac0d-e132-4c21-adc5-6e89c000bd9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[42422,     0,   319],\n",
              "       [  892,     0,    34],\n",
              "       [ 6533,     0,   536]])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Svaing The Model"
      ],
      "metadata": {
        "id": "kV5uVHfcE4Jw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#HDF5 format\n",
        "model_2.save(\"Diabetes_model_hdf5.h5\")"
      ],
      "metadata": {
        "id": "oohScoryWuHr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}