Exo_planets.ipynb

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      "name": "Exo_planets.ipynb",
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      "authorship_tag": "ABX9TyP65eN9VzYgn+QhuLXDrGqS",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/CRDK1009/Exploring-Planets/blob/main/Exo_planets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWorTGOdf0Rh"
      },
      "source": [
        "#Our first glimpse at planets outside of the solar system we call home came in 1992 when several terrestrial-mass planets were detected orbiting the pulsar PSR B1257+12. In this dataset, you can become a space explorer too by analyzing the characteristics of all discovered exoplanets (plus some familiar faces like Mars, Saturn, and even Earth). Data fields include planet and host star attributes, discovery methods, and (of course) date of discovery.\n",
        "\n",
        "#Data was originally collected and continues to be updated by Hanno Rein at the Open Exoplanet Catalogue Github repository. If you discover any new exoplanets, please submit a pull request there.\n",
        "\n",
        "#Constants\n",
        "Jupiter mass: 1.8991766e+27 kg\n",
        "\n",
        "Solar mass: 1.9891e+30 kg\n",
        "\n",
        "Jupiter radius: 69911000 m\n",
        "\n",
        "Solar radius: 6.96e+08 m"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRnwzTIv-8Dd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfKuWYJsO_Ju"
      },
      "source": [
        "#READ THE DATA\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQcljhOqgAHN"
      },
      "source": [
        "##PlanetIdentifier: Primary identifier of planet\n",
        "\n",
        "##TypeFlag: [0=no known stellar binary companion; 1=P-type binary (circumbinary); 2=S-type binary; 3=orphan planet (no star)]\n",
        "\n",
        "##ListsPlanetIsOn: A list of lists the planet is on including \"Confirmed planets\", \"Controversial\", \"Kepler Objects of Interest\", etc.\n",
        "\n",
        "##Remaining field names are auto-descriptive and include units of measurement where available. Additional column descriptions can be found here.\n",
        "\n",
        "https://github.com/OpenExoplanetCatalogue/oec_tables/#column-description"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgXqNcfMAOaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "8736aef3-f70d-45ac-903e-b05f55a2096f"
      },
      "source": [
        "df=pd.read_csv('oec.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PlanetIdentifier</th>\n",
              "      <th>TypeFlag</th>\n",
              "      <th>PlanetaryMassJpt</th>\n",
              "      <th>RadiusJpt</th>\n",
              "      <th>PeriodDays</th>\n",
              "      <th>SemiMajorAxisAU</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>PeriastronDeg</th>\n",
              "      <th>LongitudeDeg</th>\n",
              "      <th>AscendingNodeDeg</th>\n",
              "      <th>InclinationDeg</th>\n",
              "      <th>SurfaceTempK</th>\n",
              "      <th>DiscoveryMethod</th>\n",
              "      <th>DiscoveryYear</th>\n",
              "      <th>LastUpdated</th>\n",
              "      <th>RightAscension</th>\n",
              "      <th>Declination</th>\n",
              "      <th>DistFromSunParsec</th>\n",
              "      <th>HostStarMassSlrMass</th>\n",
              "      <th>HostStarRadiusSlrRad</th>\n",
              "      <th>HostStarMetallicity</th>\n",
              "      <th>HostStarTempK</th>\n",
              "      <th>HostStarAgeGyr</th>\n",
              "      <th>ListsPlanetIsOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>16/07/11</td>\n",
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              "      <td>1.362</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>5627.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Confirmed planets</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HD 143761 c</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0790</td>\n",
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              "      <td>102.540000</td>\n",
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              "      <td>-0.31</td>\n",
              "      <td>5627.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Confirmed planets</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KOI-1843.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.054</td>\n",
              "      <td>0.176891</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>transit</td>\n",
              "      <td>2012.0</td>\n",
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              "      <td>0.00</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Controversial</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>Controversial</td>\n",
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              "      <td>19 00 03.14</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3584.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Controversial</td>\n",
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            "text/plain": [
              "  PlanetIdentifier  TypeFlag  ...  HostStarAgeGyr    ListsPlanetIsOn\n",
              "0      HD 143761 b         0  ...             NaN  Confirmed planets\n",
              "1      HD 143761 c         0  ...             NaN  Confirmed planets\n",
              "2      KOI-1843.03         0  ...             NaN      Controversial\n",
              "3      KOI-1843.01         0  ...             NaN      Controversial\n",
              "4      KOI-1843.02         0  ...             NaN      Controversial\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmxnKKdhAT9N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8fcab42c-f3d8-4b98-ae2a-56f85f8c86c7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3584, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pq0uw1qnAdER",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "59c86d3c-f7d6-4709-d891-55bc983da0d3"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier           0\n",
              "TypeFlag                   0\n",
              "PlanetaryMassJpt        2271\n",
              "RadiusJpt                810\n",
              "PeriodDays                99\n",
              "SemiMajorAxisAU         2178\n",
              "Eccentricity            2476\n",
              "PeriastronDeg           3256\n",
              "LongitudeDeg            3541\n",
              "AscendingNodeDeg        3538\n",
              "InclinationDeg          2919\n",
              "SurfaceTempK            2843\n",
              "DiscoveryMethod           63\n",
              "DiscoveryYear             10\n",
              "LastUpdated                8\n",
              "RightAscension            10\n",
              "Declination               10\n",
              "DistFromSunParsec       1451\n",
              "HostStarMassSlrMass      168\n",
              "HostStarRadiusSlrRad     321\n",
              "HostStarMetallicity     1075\n",
              "HostStarTempK            129\n",
              "HostStarAgeGyr          3067\n",
              "ListsPlanetIsOn            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhgGijvqPHTV"
      },
      "source": [
        "#VALUE COUNTS OF EACH TARGET CONSTANT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yvt5jY7WAgdM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "f44c0e14-2114-4089-fbcf-9394e13fede8"
      },
      "source": [
        "df['ListsPlanetIsOn'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Confirmed planets                                                                     3295\n",
              "Confirmed planets, Planets in binary systems, S-type                                   135\n",
              "Controversial                                                                           72\n",
              "Confirmed planets, Planets in binary systems, P-type                                    21\n",
              "Controversial, Planets in binary systems, S-type                                        20\n",
              "Confirmed planets, Planets in open clusters                                             13\n",
              "Solar System                                                                             9\n",
              "Controversial, Planets in binary systems, P-type                                         7\n",
              "Retracted planet candidate                                                               4\n",
              "Kepler Objects of Interest                                                               4\n",
              "Confirmed planets, Orphan planets                                                        2\n",
              "Confirmed planets, Planets in binary systems, P-type, Planets in globular clusters       1\n",
              "Planets in binary systems, S-type, Confirmed planets                                     1\n",
              "Name: ListsPlanetIsOn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0fJOEyQPVga"
      },
      "source": [
        "#REPLACE CATEGORICAL VALUES WITH NUMERICAL VALUES\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imKKSSAJBPZF"
      },
      "source": [
        "cleanup_nums = {\"ListsPlanetIsOn\": {\"Confirmed planets\": 1, \"Confirmed planets, Planets in binary systems, S-type\": 2, \"Controversial\": 3, \"Confirmed planets, Planets in binary systems, P-type\": 4,\n",
        "                                  \"Controversial, Planets in binary systems, S-type\": 5, \"Confirmed planets, Planets in open clusters\": 6, \"Solar System\":7,\n",
        "                                  'Controversial, Planets in binary systems, P-type':8,'Kepler Objects of Interest':9,'Retracted planet candidate':10,'Confirmed planets, Orphan planets':11,\n",
        "                                  'Confirmed planets, Planets in binary systems, P-type, Planets in globular clusters':12,'Planets in binary systems, S-type, Confirmed planets':13 }}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpZ86VRTELVM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "cc62f4a2-fbe7-47d3-e71e-4b3f1ed775fe"
      },
      "source": [
        "df.replace(cleanup_nums, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PlanetIdentifier</th>\n",
              "      <th>TypeFlag</th>\n",
              "      <th>PlanetaryMassJpt</th>\n",
              "      <th>RadiusJpt</th>\n",
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              "      <th>HostStarMetallicity</th>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KOI-1843.03</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.054</td>\n",
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              "      <td>0.114</td>\n",
              "      <td>4.194525</td>\n",
              "      <td>0.0390</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>89.38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>transit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19 00 03.14</td>\n",
              "      <td>+40 13 14.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3584.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KOI-1843.02</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.071</td>\n",
              "      <td>6.356006</td>\n",
              "      <td>0.0520</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>transit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19 00 03.14</td>\n",
              "      <td>+40 13 14.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.460</td>\n",
              "      <td>0.450</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3584.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  PlanetIdentifier  TypeFlag  ...  HostStarAgeGyr  ListsPlanetIsOn\n",
              "0      HD 143761 b         0  ...             NaN                1\n",
              "1      HD 143761 c         0  ...             NaN                1\n",
              "2      KOI-1843.03         0  ...             NaN                3\n",
              "3      KOI-1843.01         0  ...             NaN                3\n",
              "4      KOI-1843.02         0  ...             NaN                3\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6fplI3iPd7w"
      },
      "source": [
        "#SPLIT THE DATA ACCORDING TO THE TARGET DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwJdQKjOERX-"
      },
      "source": [
        "df_1=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 1)]) \n",
        "df_2=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 2)]) \n",
        "df_3=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 3)]) \n",
        "df_4=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 4)]) \n",
        "df_5=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 5)])\n",
        "df_6=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 6)]) \n",
        "df_7=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 7)]) \n",
        "df_8=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 8)]) \n",
        "df_9=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 9)]) \n",
        "df_10=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 10)])\n",
        "df_11=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 11)]) \n",
        "df_12=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 12)]) \n",
        "df_13=pd.DataFrame(df[(df['ListsPlanetIsOn'] == 13)]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N61kQw8jYBTv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "c14192c8-5d8f-4a00-e2bc-9520594fcc12"
      },
      "source": [
        "df['Declination'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-60 30 42       9\n",
              "-34 59 23       8\n",
              "+49 18 19       7\n",
              "+38 20 37       7\n",
              "-05 02 36.44    7\n",
              "               ..\n",
              "+18 46 12       1\n",
              "-56 02 24       1\n",
              "+42 44 35       1\n",
              "+35 39 42       1\n",
              "+45 02 04       1\n",
              "Name: Declination, Length: 2592, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOSt23-4FhIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "21137c09-01b1-4914-a71c-f51b3d9cc130"
      },
      "source": [
        "df_1.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier           0\n",
              "TypeFlag                   0\n",
              "PlanetaryMassJpt        2208\n",
              "RadiusJpt                630\n",
              "PeriodDays                75\n",
              "SemiMajorAxisAU         2142\n",
              "Eccentricity            2401\n",
              "PeriastronDeg           3058\n",
              "LongitudeDeg            3276\n",
              "AscendingNodeDeg        3272\n",
              "InclinationDeg          2730\n",
              "SurfaceTempK            2632\n",
              "DiscoveryMethod           53\n",
              "DiscoveryYear              0\n",
              "LastUpdated                0\n",
              "RightAscension             1\n",
              "Declination                1\n",
              "DistFromSunParsec       1407\n",
              "HostStarMassSlrMass      134\n",
              "HostStarRadiusSlrRad     244\n",
              "HostStarMetallicity      989\n",
              "HostStarTempK             59\n",
              "HostStarAgeGyr          2879\n",
              "ListsPlanetIsOn            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYiKBXDwWpq9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "343a985a-0bfd-4450-b26d-54cfdca1d1a0"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier         object\n",
              "TypeFlag                  int64\n",
              "PlanetaryMassJpt        float64\n",
              "RadiusJpt               float64\n",
              "PeriodDays              float64\n",
              "SemiMajorAxisAU         float64\n",
              "Eccentricity            float64\n",
              "PeriastronDeg           float64\n",
              "LongitudeDeg            float64\n",
              "AscendingNodeDeg        float64\n",
              "InclinationDeg          float64\n",
              "SurfaceTempK            float64\n",
              "DiscoveryMethod          object\n",
              "DiscoveryYear           float64\n",
              "LastUpdated              object\n",
              "RightAscension           object\n",
              "Declination              object\n",
              "DistFromSunParsec       float64\n",
              "HostStarMassSlrMass     float64\n",
              "HostStarRadiusSlrRad    float64\n",
              "HostStarMetallicity     float64\n",
              "HostStarTempK           float64\n",
              "HostStarAgeGyr          float64\n",
              "ListsPlanetIsOn           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_CnIiCLPo-v"
      },
      "source": [
        "#FILL ALL MISSING VALUES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8encRmqKdWZR"
      },
      "source": [
        "df_1=df_1.fillna(df_1.mean())\n",
        "df_2=df_2.fillna(df_2.mean())\n",
        "df_3=df_3.fillna(df_3.mean())\n",
        "df_4=df_4.fillna(df_4.mean())\n",
        "df_5=df_5.fillna(df_5.mean())\n",
        "df_6=df_6.fillna(df_6.mean())\n",
        "df_7=df_7.fillna(df_7.mean())\n",
        "df_8=df_8.fillna(df_8.mean())\n",
        "df_9=df_9.fillna(df_9.mean())\n",
        "df_10=df_10.fillna(df_10.mean())\n",
        "df_12=df_12.fillna(df_12.mean())\n",
        "df_13=df_13.fillna(df_13.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYfZ6OXHmT8F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "46542501-8c6b-4fa4-c3fd-75dd72831a6e"
      },
      "source": [
        "df_5.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier        0\n",
              "TypeFlag                0\n",
              "PlanetaryMassJpt        0\n",
              "RadiusJpt               0\n",
              "PeriodDays              0\n",
              "SemiMajorAxisAU         0\n",
              "Eccentricity            0\n",
              "PeriastronDeg           0\n",
              "LongitudeDeg            0\n",
              "AscendingNodeDeg        0\n",
              "InclinationDeg          0\n",
              "SurfaceTempK            0\n",
              "DiscoveryMethod         0\n",
              "DiscoveryYear           0\n",
              "LastUpdated             1\n",
              "RightAscension          0\n",
              "Declination             0\n",
              "DistFromSunParsec       0\n",
              "HostStarMassSlrMass     0\n",
              "HostStarRadiusSlrRad    0\n",
              "HostStarMetallicity     0\n",
              "HostStarTempK           0\n",
              "HostStarAgeGyr          0\n",
              "ListsPlanetIsOn         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0JBID9CQA68"
      },
      "source": [
        "#VALUES TAKEN FOR ANALYSIS (LISTS OF PLANETS)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8elFPTuhrnw"
      },
      "source": [
        "##I have chosen these values as my target constant because they had previous values to fill for their missing values while the others did not therefore I chose 1,2,3,5."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTH9tjHDmrtC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "40afe606-4e20-41de-d78f-20c8fb1af516"
      },
      "source": [
        "1,2,3,5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 2, 3, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v80weJuvcAVG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "7d6e43bb-ca32-4991-93f1-a088cf55fadc"
      },
      "source": [
        "df_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PlanetIdentifier</th>\n",
              "      <th>TypeFlag</th>\n",
              "      <th>PlanetaryMassJpt</th>\n",
              "      <th>RadiusJpt</th>\n",
              "      <th>PeriodDays</th>\n",
              "      <th>SemiMajorAxisAU</th>\n",
              "      <th>Eccentricity</th>\n",
              "      <th>PeriastronDeg</th>\n",
              "      <th>LongitudeDeg</th>\n",
              "      <th>AscendingNodeDeg</th>\n",
              "      <th>InclinationDeg</th>\n",
              "      <th>SurfaceTempK</th>\n",
              "      <th>DiscoveryMethod</th>\n",
              "      <th>DiscoveryYear</th>\n",
              "      <th>LastUpdated</th>\n",
              "      <th>RightAscension</th>\n",
              "      <th>Declination</th>\n",
              "      <th>DistFromSunParsec</th>\n",
              "      <th>HostStarMassSlrMass</th>\n",
              "      <th>HostStarRadiusSlrRad</th>\n",
              "      <th>HostStarMetallicity</th>\n",
              "      <th>HostStarTempK</th>\n",
              "      <th>HostStarAgeGyr</th>\n",
              "      <th>ListsPlanetIsOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HD 143761 b</td>\n",
              "      <td>0</td>\n",
              "      <td>1.045</td>\n",
              "      <td>0.360402</td>\n",
              "      <td>39.845800</td>\n",
              "      <td>0.2196</td>\n",
              "      <td>0.037000</td>\n",
              "      <td>270.600000</td>\n",
              "      <td>113.476092</td>\n",
              "      <td>80.80613</td>\n",
              "      <td>83.84673</td>\n",
              "      <td>843.945249</td>\n",
              "      <td>RV</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>16/07/11</td>\n",
              "      <td>16 01 03</td>\n",
              "      <td>+33 18 13</td>\n",
              "      <td>17.236</td>\n",
              "      <td>0.889</td>\n",
              "      <td>1.362</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>5627.0</td>\n",
              "      <td>4.634975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HD 143761 c</td>\n",
              "      <td>0</td>\n",
              "      <td>0.079</td>\n",
              "      <td>0.360402</td>\n",
              "      <td>102.540000</td>\n",
              "      <td>0.4123</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>113.476092</td>\n",
              "      <td>80.80613</td>\n",
              "      <td>83.84673</td>\n",
              "      <td>843.945249</td>\n",
              "      <td>RV</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>16/07/11</td>\n",
              "      <td>16 01 03</td>\n",
              "      <td>+33 18 13</td>\n",
              "      <td>17.236</td>\n",
              "      <td>0.889</td>\n",
              "      <td>1.362</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>5627.0</td>\n",
              "      <td>4.634975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Kepler-9 b</td>\n",
              "      <td>0</td>\n",
              "      <td>0.250</td>\n",
              "      <td>0.840000</td>\n",
              "      <td>19.224180</td>\n",
              "      <td>0.1430</td>\n",
              "      <td>0.062600</td>\n",
              "      <td>153.940443</td>\n",
              "      <td>113.476092</td>\n",
              "      <td>80.80613</td>\n",
              "      <td>87.10000</td>\n",
              "      <td>707.200000</td>\n",
              "      <td>transit</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>15/12/03</td>\n",
              "      <td>19 02 17</td>\n",
              "      <td>+38 24 03</td>\n",
              "      <td>650.000</td>\n",
              "      <td>1.070</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.12</td>\n",
              "      <td>5777.0</td>\n",
              "      <td>4.634975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kepler-9 c</td>\n",
              "      <td>0</td>\n",
              "      <td>0.170</td>\n",
              "      <td>0.820000</td>\n",
              "      <td>39.031060</td>\n",
              "      <td>0.2290</td>\n",
              "      <td>0.068400</td>\n",
              "      <td>153.940443</td>\n",
              "      <td>113.476092</td>\n",
              "      <td>80.80613</td>\n",
              "      <td>87.20000</td>\n",
              "      <td>557.900000</td>\n",
              "      <td>transit</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>15/12/03</td>\n",
              "      <td>19 02 17</td>\n",
              "      <td>+38 24 03</td>\n",
              "      <td>650.000</td>\n",
              "      <td>1.070</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.12</td>\n",
              "      <td>5777.0</td>\n",
              "      <td>4.634975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kepler-9 d</td>\n",
              "      <td>0</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.147000</td>\n",
              "      <td>1.592851</td>\n",
              "      <td>0.0271</td>\n",
              "      <td>0.160493</td>\n",
              "      <td>153.940443</td>\n",
              "      <td>113.476092</td>\n",
              "      <td>80.80613</td>\n",
              "      <td>83.84673</td>\n",
              "      <td>1601.500000</td>\n",
              "      <td>transit</td>\n",
              "      <td>2010.0</td>\n",
              "      <td>14/03/10</td>\n",
              "      <td>19 02 17</td>\n",
              "      <td>+38 24 03</td>\n",
              "      <td>650.000</td>\n",
              "      <td>1.070</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.12</td>\n",
              "      <td>5777.0</td>\n",
              "      <td>4.634975</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  PlanetIdentifier  TypeFlag  ...  HostStarAgeGyr  ListsPlanetIsOn\n",
              "0      HD 143761 b         0  ...        4.634975                1\n",
              "1      HD 143761 c         0  ...        4.634975                1\n",
              "5       Kepler-9 b         0  ...        4.634975                1\n",
              "6       Kepler-9 c         0  ...        4.634975                1\n",
              "7       Kepler-9 d         0  ...        4.634975                1\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LPlFpryZjcX"
      },
      "source": [
        "a=df_1['RightAscension'].mode()\n",
        "b=df_1['Declination'].mode()\n",
        "c=df_1['DiscoveryMethod'].mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdaCMosQgnFZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "03997cb6-6b19-4040-be7f-515c8ef95d47"
      },
      "source": [
        "a,b,c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    01 37 54\n",
              " 1    18 57 44\n",
              " 2    19 19 26\n",
              " dtype: object, 0    +38 20 37\n",
              " 1    +49 18 19\n",
              " 2    -60 30 42\n",
              " dtype: object, 0    transit\n",
              " dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FH22p-QbpI1"
      },
      "source": [
        "d=df_2['RightAscension'].mode()\n",
        "e=df_2['Declination'].mode()\n",
        "f=df_2['DiscoveryMethod'].mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3A-kZ6mgr4n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "e131fbc4-0723-4176-a8f5-262bfc15e0cf"
      },
      "source": [
        "d,e,f"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    08 52 35.81093\n",
              " 1    19 19 00.54910\n",
              " dtype: object, 0    +28 19 50.9511\n",
              " 1    +41 38 04.5682\n",
              " dtype: object, 0    RV\n",
              " dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgeChxwBbxQ1"
      },
      "source": [
        "g=df_3['RightAscension'].mode()\n",
        "h=df_3['Declination'].mode()\n",
        "i=df_3['DiscoveryMethod'].mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BKxOUF5guqv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "10d5cb71-d7a0-469c-b54a-f637ff72bbe1"
      },
      "source": [
        "g,h,i"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    01 44 04\n",
              " dtype: object, 0    -15 56 15\n",
              " dtype: object, 0    RV\n",
              " dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MvLh_KjbyV1"
      },
      "source": [
        "j=df_5['RightAscension'].mode()\n",
        "k=df_5['Declination'].mode()\n",
        "l=df_5['DiscoveryMethod'].mode()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBaEA7p1gzE_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "9ffdfc7f-d5cf-46dd-e5ca-d0ca80c7717d"
      },
      "source": [
        "j,k,l"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    17 18 57\n",
              " dtype: object, 0    -34 59 23\n",
              " dtype: object, 0    RV\n",
              " dtype: object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HUrKilkb79V"
      },
      "source": [
        "df_1['RightAscension']=df_1['RightAscension'].fillna('01 37 54')\n",
        "df_1['Declination']=df_1['Declination'].fillna('+38 20 37')\n",
        "df_1['DiscoveryMethod']=df_1['DiscoveryMethod'].fillna('transit')\n",
        "df_2['RightAscension']=df_2['RightAscension'].fillna('08 52 35.81093')\n",
        "df_2['Declination']=df_2['Declination'].fillna('+28 19 50.9511')\n",
        "df_2['DiscoveryMethod']=df_2['DiscoveryMethod'].fillna('RV')\n",
        "df_3['RightAscension']=df_3['RightAscension'].fillna('01 44 04')\n",
        "df_3['Declination']=df_3['Declination'].fillna('-15 56 15')\n",
        "df_3['DiscoveryMethod']=df_3['DiscoveryMethod'].fillna('RV')\n",
        "df_5['RightAscension']=df_5['RightAscension'].fillna('17 18 57')\n",
        "df_5['Declination']=df_5['Declination'].fillna('-34 59 23')\n",
        "df_5['DiscoveryMethod']=df_5['DiscoveryMethod'].fillna('RV')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GZViLXnifN8Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "fadf16e2-acde-4e53-9887-85c270e51822"
      },
      "source": [
        "df_1.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier        0\n",
              "TypeFlag                0\n",
              "PlanetaryMassJpt        0\n",
              "RadiusJpt               0\n",
              "PeriodDays              0\n",
              "SemiMajorAxisAU         0\n",
              "Eccentricity            0\n",
              "PeriastronDeg           0\n",
              "LongitudeDeg            0\n",
              "AscendingNodeDeg        0\n",
              "InclinationDeg          0\n",
              "SurfaceTempK            0\n",
              "DiscoveryMethod         0\n",
              "DiscoveryYear           0\n",
              "LastUpdated             0\n",
              "RightAscension          0\n",
              "Declination             0\n",
              "DistFromSunParsec       0\n",
              "HostStarMassSlrMass     0\n",
              "HostStarRadiusSlrRad    0\n",
              "HostStarMetallicity     0\n",
              "HostStarTempK           0\n",
              "HostStarAgeGyr          0\n",
              "ListsPlanetIsOn         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aILoNEoGQIKU"
      },
      "source": [
        "#JOINING THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3zlQGOafSJ3"
      },
      "source": [
        "DF=df_1.append(df_2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lJkJzvziYpr"
      },
      "source": [
        "Df_1=DF.append(df_3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIhXTI1Hitsv"
      },
      "source": [
        "df_final=Df_1.append(df_5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRpWy92Wiyv0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "10eac840-b52f-48ac-b191-2ebf983b5de7"
      },
      "source": [
        "df_final.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3522, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-h2DKOgjI5B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "9b8e7591-eee4-433e-b463-bce33fe7b764"
      },
      "source": [
        "df_final.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier        0\n",
              "TypeFlag                0\n",
              "PlanetaryMassJpt        0\n",
              "RadiusJpt               0\n",
              "PeriodDays              0\n",
              "SemiMajorAxisAU         0\n",
              "Eccentricity            0\n",
              "PeriastronDeg           0\n",
              "LongitudeDeg            0\n",
              "AscendingNodeDeg        0\n",
              "InclinationDeg          0\n",
              "SurfaceTempK            0\n",
              "DiscoveryMethod         0\n",
              "DiscoveryYear           0\n",
              "LastUpdated             8\n",
              "RightAscension          0\n",
              "Declination             0\n",
              "DistFromSunParsec       0\n",
              "HostStarMassSlrMass     0\n",
              "HostStarRadiusSlrRad    0\n",
              "HostStarMetallicity     0\n",
              "HostStarTempK           0\n",
              "HostStarAgeGyr          0\n",
              "ListsPlanetIsOn         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kd9wn6VJjQFz"
      },
      "source": [
        "df_final=df_final.drop(['LastUpdated','DiscoveryYear'],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqnDNmS7jf2Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "ae741b5a-321b-4157-e629-cfb96fb40660"
      },
      "source": [
        "df_final.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier         object\n",
              "TypeFlag                  int64\n",
              "PlanetaryMassJpt        float64\n",
              "RadiusJpt               float64\n",
              "PeriodDays              float64\n",
              "SemiMajorAxisAU         float64\n",
              "Eccentricity            float64\n",
              "PeriastronDeg           float64\n",
              "LongitudeDeg            float64\n",
              "AscendingNodeDeg        float64\n",
              "InclinationDeg          float64\n",
              "SurfaceTempK            float64\n",
              "DiscoveryMethod          object\n",
              "RightAscension           object\n",
              "Declination              object\n",
              "DistFromSunParsec       float64\n",
              "HostStarMassSlrMass     float64\n",
              "HostStarRadiusSlrRad    float64\n",
              "HostStarMetallicity     float64\n",
              "HostStarTempK           float64\n",
              "HostStarAgeGyr          float64\n",
              "ListsPlanetIsOn           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJrUNGD_QS-T"
      },
      "source": [
        "#ENCODING OBJECT VARIABLES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqDO9ySvjm7u"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "df_final['PlanetIdentifier']=le.fit_transform(df_final['PlanetIdentifier'])\n",
        "df_final['DiscoveryMethod']=le.fit_transform(df_final['DiscoveryMethod'])\n",
        "df_final['RightAscension']=le.fit_transform(df_final['RightAscension'])\n",
        "df_final['Declination']=le.fit_transform(df_final['Declination'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7451SNamkl5B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "e9ac1336-9860-4731-dab4-953264ca3dce"
      },
      "source": [
        "df_final.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PlanetIdentifier          int64\n",
              "TypeFlag                  int64\n",
              "PlanetaryMassJpt        float64\n",
              "RadiusJpt               float64\n",
              "PeriodDays              float64\n",
              "SemiMajorAxisAU         float64\n",
              "Eccentricity            float64\n",
              "PeriastronDeg           float64\n",
              "LongitudeDeg            float64\n",
              "AscendingNodeDeg        float64\n",
              "InclinationDeg          float64\n",
              "SurfaceTempK            float64\n",
              "DiscoveryMethod           int64\n",
              "RightAscension            int64\n",
              "Declination               int64\n",
              "DistFromSunParsec       float64\n",
              "HostStarMassSlrMass     float64\n",
              "HostStarRadiusSlrRad    float64\n",
              "HostStarMetallicity     float64\n",
              "HostStarTempK           float64\n",
              "HostStarAgeGyr          float64\n",
              "ListsPlanetIsOn           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ed3qGc5ykqlB"
      },
      "source": [
        "X=df_final.drop('ListsPlanetIsOn',axis=1)\n",
        "y=df_final['ListsPlanetIsOn']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LS2kYGhBlNLt"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X=scaler.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHs0Da3eleYM"
      },
      "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=500)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CdVB6boQdiP"
      },
      "source": [
        "#COMPENSIATE FOR THE IMBALANCE PRESENT IN THE DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNdq49XVl5R7"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "model = XGBClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLn7xCctQtxY"
      },
      "source": [
        "#XGB HYPERPARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yTS6oABnCSn"
      },
      "source": [
        "gbm_param_grid = {\n",
        "    'colsample_bytree': range(0, 1),\n",
        "    'n_estimators': range(50,100),\n",
        "    'max_depth': range(1, 10)\n",
        "}\n",
        "    \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDMPajDXn5CQ"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WB43UUkiQz20"
      },
      "source": [
        "#RANDOM FOREST HYPERPARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6MwvMeWqm9c"
      },
      "source": [
        "random_grid = {'n_estimators': range(100,2000),\n",
        "               'max_depth': range(1,100),\n",
        "               'min_samples_split': range(1,10),\n",
        "               'min_samples_leaf': range(1,10)}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZO5hdO5Q7--"
      },
      "source": [
        "#RANDOM SEARCH for XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-T3lEbKpnPLb"
      },
      "source": [
        "randomized_mse = RandomizedSearchCV(estimator=model,param_distributions=gbm_param_grid,n_iter=50, scoring='balanced_accuracy', cv=4, verbose=1,n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwf8kQoInzkz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "outputId": "09b363fb-350f-4cd2-ca20-f087980090f5"
      },
      "source": [
        "randomized_mse.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 50 candidates, totalling 200 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    9.3s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:   35.2s\n",
            "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   35.5s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=4, error_score=nan,\n",
              "                   estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                           colsample_bylevel=1,\n",
              "                                           colsample_bynode=1,\n",
              "                                           colsample_bytree=1, gamma=0,\n",
              "                                           learning_rate=0.1, max_delta_step=0,\n",
              "                                           max_depth={'scale_pos_weight': [1,\n",
              "                                                                           10,\n",
              "                                                                           25,\n",
              "                                                                           50,\n",
              "                                                                           75,\n",
              "                                                                           99,\n",
              "                                                                           100,\n",
              "                                                                           1000]},\n",
              "                                           min_child_weight=1, missing=None,\n",
              "                                           n_estimators=100, n_jobs=1,\n",
              "                                           nthread=None,\n",
              "                                           objective='binary:logis...\n",
              "                                           random_state=0, reg_alpha=0,\n",
              "                                           reg_lambda=1, scale_pos_weight=1,\n",
              "                                           seed=None, silent=None, subsample=1,\n",
              "                                           verbosity=1),\n",
              "                   iid='deprecated', n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={'colsample_bytree': range(0, 1),\n",
              "                                        'max_depth': range(1, 10),\n",
              "                                        'n_estimators': range(50, 100)},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='balanced_accuracy',\n",
              "                   verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2PD4p2-n_Dr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "378b1bbb-4ee1-4631-c9a6-9facf93eda8a"
      },
      "source": [
        "randomized_mse.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=2,\n",
              "              min_child_weight=1, missing=None, n_estimators=94, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZq8SB0LprlU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3711cc44-de85-472c-e2b2-178114ddc8c7"
      },
      "source": [
        "randomized_mse.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9391964285714286"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poIiujitRER8"
      },
      "source": [
        "#VISUALISING HYPERPARAMETERS (XGB)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3_e0KzhwzDW"
      },
      "source": [
        "xgb_df=pd.DataFrame(randomized_mse.cv_results_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8r14hQECx1_7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "b749cbdf-0808-44e7-988f-ceb00735700f"
      },
      "source": [
        "xgb_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n",
              "       'param_n_estimators', 'param_max_depth', 'param_colsample_bytree',\n",
              "       'params', 'split0_test_score', 'split1_test_score', 'split2_test_score',\n",
              "       'split3_test_score', 'mean_test_score', 'std_test_score',\n",
              "       'rank_test_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnVaMDATyCg7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "00cc00aa-25d6-4ac9-f969-277f6aeea096"
      },
      "source": [
        " plt.scatter('param_n_estimators', 'mean_test_score',data=xgb_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3916216c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arXqeLb50q3n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5b3c82d6-95e0-43c5-b08a-359dadfdff4e"
      },
      "source": [
        "plt.scatter('param_max_depth', 'mean_test_score',data=xgb_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f39161cfd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dofgcsir0rq_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ee4a4242-0189-42dc-c781-fc520d814a0b"
      },
      "source": [
        "plt.scatter('param_colsample_bytree', 'mean_test_score',data=xgb_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3915028be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_g0-pHivpu_A"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uckkNesKqF_l"
      },
      "source": [
        "randomized_mse_rf = RandomizedSearchCV(estimator=clf,param_distributions=random_grid,n_iter=50, scoring='balanced_accuracy', cv=4, verbose=1,n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa-yRyXhqOOC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "outputId": "a2df69bc-3358-4720-d581-a945ff7dc663"
      },
      "source": [
        "randomized_mse_rf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 50 candidates, totalling 200 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:  1.2min\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  5.6min\n",
            "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:  5.7min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=4, error_score=nan,\n",
              "                   estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                    ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features='auto',\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    max_samples=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    n_estimators=100,\n",
              "                                                    n_jobs...\n",
              "                                                    oob_score=False,\n",
              "                                                    random_state=None,\n",
              "                                                    verbose=0,\n",
              "                                                    warm_start=False),\n",
              "                   iid='deprecated', n_iter=50, n_jobs=-1,\n",
              "                   param_distributions={'max_depth': range(1, 100),\n",
              "                                        'min_samples_leaf': range(1, 10),\n",
              "                                        'min_samples_split': range(1, 10),\n",
              "                                        'n_estimators': range(100, 2000)},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring='balanced_accuracy',\n",
              "                   verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-X6AA-eDqSVL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ef70931a-f7ac-478c-ea58-c54ceaff0d4c"
      },
      "source": [
        "randomized_mse_rf.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQYc9eaBRU5s"
      },
      "source": [
        "#VISUALISING RANDOM FOREST HYPERPARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDyMj3iVrcLv"
      },
      "source": [
        "rf_df=pd.DataFrame(randomized_mse_rf.cv_results_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03eg3twPvnqd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "c774b443-daa9-4a0b-8b6f-f4279777fed2"
      },
      "source": [
        "rf_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n",
              "       'param_n_estimators', 'param_min_samples_split',\n",
              "       'param_min_samples_leaf', 'param_max_depth', 'params',\n",
              "       'split0_test_score', 'split1_test_score', 'split2_test_score',\n",
              "       'split3_test_score', 'mean_test_score', 'std_test_score',\n",
              "       'rank_test_score'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tcpc7qz-8Qmc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "ec2ce54b-a6ec-4533-8f12-45a0804106e9"
      },
      "source": [
        "plt.scatter('param_n_estimators', 'mean_test_score',data=rf_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3914fd9358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzNXL4Yp8Q_r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c1429419-faf7-42a4-ed54-a4e9dceebcc5"
      },
      "source": [
        "plt.scatter('param_max_depth', 'mean_test_score',data=rf_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f39114ef710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4r0lsvD8Q1s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9ab607d4-06b9-4ecd-f4fe-da3b68a8148a"
      },
      "source": [
        "plt.scatter('param_min_samples_split', 'mean_test_score',data=rf_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3911287cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYDhmwof8QZQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e6c82efd-ff84-49ac-84ac-ff11aa6aca78"
      },
      "source": [
        "plt.scatter('param_n_estimators', 'mean_test_score',data=rf_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f3914e66a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWr0lEQVR4nO3df5BdZ33f8fdHaxmUBJCNdhgsyZZgjINTUgt2bGYIP1qKJTwJNmaayoTGJMx4MoOZBoI7VvEU6oaY1ClJOvFAzdTlR4iNmziqOk1HccAk/2BHK2Rb2CCQFYK1dkDBCNphB2T52z/2rLha7d29uzr3x8rv18zO3vuc59z7Pc85ez57zzl7NlWFJOnZbdWwC5AkDZ9hIEkyDCRJhoEkCcNAkgScNewC5lq3bl1t2rRp2GVI0oqyd+/ef6yq8eXOP3JhsGnTJiYnJ4ddhiStKEn+/nTm9zCRJMkwkCQZBpIkDANJEoaBJIkeriZKcgfwi8B3quqfzDM9wB8CVwA/BN5ZVV9upl0L3NR0/e2q+lRbhY+CnfumuHX3AZ44Os15a9dww9aLuGrL+q7T/9nPjnPf14507X/Tzv3c+cDjHK9iLOGayzby21e9YhiLNhA37dzPZx/4FrP3Svyp1av4nat//qQxmbVz3xT/4X89wvd+eAyAtWtW86G3/FzXvgutl151e09g3tdv632XUl8/36/z9df+1Gqq4PvTx3ralkdJt3Garx3mX7fDqnGQsthdS5O8Dvh/wKe7hMEVwHuYCYPLgD+sqsuSnAtMAhNAAXuBV1XV9xZ6v4mJiVoJl5bu3DfFjnv2M33s+Im2NavHuOXqV5zY0OZOn6uz/0079/PH93/rlD7vePX5Z2QgdFveVYGP/vIlp4TqDX/6EMeOn7ytrl4Vbv2X//SUvgutl151e89VgbGEY8/8pH3N6jHe9qr1/NneqdN+36XU18ZyLuX1F9LPZT0d3cZpvvW1elUgnLTOB7Fcba3LJHuramK5dSx6mKiq/gZ4aoEuVzITFFVV9wNrk7wY2ArcW1VPNQFwL7BtuYUuZue+KV7zkS+w+cb/zWs+8gV27pvq11sBM789zP1BmT52nFt3H+g6fa7O/nc+8Pi8fbq1t2HQY9ap23I9U5wYk1m37j5wyk4Z4NgzNW/fhdZLr7q95zPFSUEw+/p3PvB4K++7lPr6+X69bL/9eu82dRun+dbXsWfqlHU+iOXq97rsVRt/dLYe6PzJPty0dWs/RZLrgOsAzj///CUXMDdZp45Os+Oe/QB9S/Qnjk4v2N5terf+x7t8QuvWfrqGMWadFlquuWO30Fj22rfX9bHc/t2WZ6mv06u2lnOpr9/2PP3Wraal/Fz1e7n6vS57NRInkKvq9qqaqKqJ8fGl/zX1MJL1vLVrFmzvNr1b/7Fk3und2k/XsH8bWWi55o7dQmPZa99e18dy+3dbnqW+Tq/aWs6lvn7b8/Rbt5qW8nPV7+Xq97rsVRthMAVs7Hi+oWnr1t66YSTrDVsvYs3qsZPa1qweO3ESar7pc3X2v+ayjfP26dZ+uob920i35VoVTozJrBu2XsTqsVN/eFevyrx9F1ovver2nqvSHFue8/rXXLaxlfddSn39fL9ett9+vXebuo3TfOtr9aqcss4HsVz9Xpe9auMw0S7g+iR3MXMC+ftV9WSS3cDvJDmn6Xc5sKOF9zvFeWvXMDXPTqyfyTp7KKXbFQDzTV/oCozZk8SDuppoGGPWaXa5ermaaPZ5L1cTLbZeerXQe3Z7/YkLzh3YFSFtLWevr79SryZaaJzmW1/d+g6rxkHq5WqiO4E3AOuAbwMfBFYDVNXHm0tL/4iZk8M/BH6tqiabeX8d+HfNS324qv77YgUt52qifl9ZcSZyzKQzy+leTbToJ4OqumaR6QW8u8u0O4A7llda70YlWVcSx0xSp0U/GQzaSvk7A0kaJX3/OwNJ0pnPMJAkjd5/OluuUbi3xyhwHIbv2bIORnU5+1HXUu9Dtth7juLYnRHnDHbum+J9n3uQZzraVgEf/VeXDH2Al3KTrDY2WK8QGq4218Eo7jBmjeq21o+65nvNMHPDtfXNZbZLuS9Vv8bOcwbAjnsePikIAJ5p2odpdqVPHZ2m+MktH27auX/e9tO9N9Cw/6pY7a2DbtvOIO8ftZBR3db6Udd8rzn7K/TU0Wk+e/+3lvSeozp2Z0QYTB+bGwULtw/KUm6S1cbGMOy/KlZ762BUdxizRnVb60ddi83b7djKUmsZ9tidEWEwqpZ6k6zT3RhG5R4nz2ZtrYNR3WHMGtVtrR91LXfepdYy7LE7I8JgVZd7TnVrH5Sl3iTrdDeGUbnHybNZW+tgVHcYs0Z1W+tHXb3cp2nuT/RC7zmqY3dGhMHbL5v/ttfd2gdlKTfJamNjuGrLem65+hWsX7uGMHNya9gn9J5t2loHo7rDmDWq21o/6up8TZh/x/8rrz6/5/cc1bE7I64mgtH9l5GDvJpIZxa3kdE0quvldK8mOmPCQJKezby0VJJ02gwDSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkiR7DIMm2JAeSHExy4zzTL0jy+SQPJ/likg0d044nebD52tVm8ZKkdpy1WIckY8BtwJuAw8CeJLuq6tGObr8HfLqqPpXknwO3AP+6mTZdVZe0XLdWgFH9j1CSTrVoGACXAger6hBAkruAK4HOMLgYeF/z+D5gZ5tFaviWumPfuW+KHffsZ/rYcQCmjk6z4579AAaCNIJ6OUy0Hni84/nhpq3TQ8DVzeO3As9L8sLm+XOTTCa5P8lV871BkuuaPpNHjhxZQvkahNkd+9TRaYqf7Nh37pvqOs+tuw+cCIJZ08eOc+vuA32uVtJytHUC+f3A65PsA14PTAGze4ILmv/L+XbgD5K8dO7MVXV7VU1U1cT4+HhLJakty9mxP3F0ekntkoarlzCYAjZ2PN/QtJ1QVU9U1dVVtQX4QNN2tPk+1Xw/BHwR2HL6ZWuQlrNjP2/tmiW1SxquXsJgD3Bhks1Jzga2AyddFZRkXZLZ19oB3NG0n5PkObN9gNdw8rkGrQDL2bHfsPUi1qweO6ltzeoxbth6Uau1SWrHomFQVU8D1wO7ga8Cd1fVI0luTvKWptsbgANJvg68CPhw0/5yYDLJQ8ycWP7InKuQtAIsZ8d+1Zb13HL1K1i/dg0B1q9dwy1Xv8KTx9KISlUNu4aTTExM1OTk5LDL0BxeJiqNtiR7m/Ozy9LLpaUSV21Z785fOoN5OwpJkmEgSTIMJEkYBpIkDANJEl5NpBHlpazSYBkGGjne8VQaPA8TaeR4x1Np8AwDjRzveCoNnmGgkeMdT6XBMww0crzjqTR4nkDWyJk9SezVRNLgGAYaSd4YTxosDxNJkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJLoMQySbEtyIMnBJDfOM/2CJJ9P8nCSLybZ0DHt2iTfaL6ubbN4SVI7Fg2DJGPAbcCbgYuBa5JcPKfb7wGfrqqfB24GbmnmPRf4IHAZcCnwwSTntFe+JKkNvXwyuBQ4WFWHqurHwF3AlXP6XAx8oXl8X8f0rcC9VfVUVX0PuBfYdvplS5La1EsYrAce73h+uGnr9BBwdfP4rcDzkrywx3lJcl2SySSTR44c6bV2SVJL2jqB/H7g9Un2Aa8HpoDjvc5cVbdX1URVTYyPj7dUkiSpV73828spYGPH8w1N2wlV9QTNJ4MkPwO8raqOJpkC3jBn3i+eRr2SpD7o5ZPBHuDCJJuTnA1sB3Z1dkiyLsnsa+0A7mge7wYuT3JOc+L48qZNkjRCFg2DqnoauJ6ZnfhXgbur6pEkNyd5S9PtDcCBJF8HXgR8uJn3KeA/MhMoe4CbmzZJ0ghJVQ27hpNMTEzU5OTksMuQpBUlyd6qmlju/P4FsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJHoMgyTbkhxIcjDJjfNMPz/JfUn2JXk4yRVN+6Yk00kebL4+3vYCSJJO31mLdUgyBtwGvAk4DOxJsquqHu3odhNwd1V9LMnFwF8Am5ppj1XVJe2WLUlqUy+fDC4FDlbVoar6MXAXcOWcPgU8v3n8AuCJ9kqUJPVbL2GwHni84/nhpq3Th4B3JDnMzKeC93RM29wcPvrrJK+d7w2SXJdkMsnkkSNHeq9ektSKtk4gXwN8sqo2AFcAn0myCngSOL+qtgDvA/4kyfPnzlxVt1fVRFVNjI+Pt1SSJKlXvYTBFLCx4/mGpq3Tu4C7AarqS8BzgXVV9aOq+m7Tvhd4DHjZ6RYtSWpXL2GwB7gwyeYkZwPbgV1z+nwLeCNAkpczEwZHkow3J6BJ8hLgQuBQW8VLktqx6NVEVfV0kuuB3cAYcEdVPZLkZmCyqnYBvwV8Isl7mTmZ/M6qqiSvA25Ocgx4BviNqnqqb0sjSVqWVNWwazjJxMRETU5ODrsMSVpRkuytqonlzu9fIEuSDANJkmEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSaLHMEiyLcmBJAeT3DjP9POT3JdkX5KHk1zRMW1HM9+BJFvbLF6S1I6zFuuQZAy4DXgTcBjYk2RXVT3a0e0m4O6q+liSi4G/ADY1j7cDPwecB/xVkpdV1fG2F0SStHy9fDK4FDhYVYeq6sfAXcCVc/oU8Pzm8QuAJ5rHVwJ3VdWPqurvgIPN60mSRkgvYbAeeLzj+eGmrdOHgHckOczMp4L3LGFeklyXZDLJ5JEjR3osXZLUlrZOIF8DfLKqNgBXAJ9J0vNrV9XtVTVRVRPj4+MtlSRJ6tWi5wyAKWBjx/MNTVundwHbAKrqS0meC6zrcV5J0pD18tv7HuDCJJuTnM3MCeFdc/p8C3gjQJKXA88FjjT9tid5TpLNwIXA37ZVvCSpHYt+Mqiqp5NcD+wGxoA7quqRJDcDk1W1C/gt4BNJ3svMyeR3VlUBjyS5G3gUeBp4t1cSSdLoycw+e3RMTEzU5OTksMuQpBUlyd6qmlju/P4FsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJHoMgyTbkhxIcjDJjfNM//0kDzZfX09ytGPa8Y5pu9osXpLUjrMW65BkDLgNeBNwGNiTZFdVPTrbp6re29H/PcCWjpeYrqpL2itZktS2Xj4ZXAocrKpDVfVj4C7gygX6XwPc2UZxkqTB6CUM1gOPdzw/3LSdIskFwGbgCx3Nz00ymeT+JFd1me+6ps/kkSNHeixdktSWtk8gbwf+tKqOd7RdUFUTwNuBP0jy0rkzVdXtVTVRVRPj4+MtlyRJWkwvYTAFbOx4vqFpm8925hwiqqqp5vsh4IucfD5BkjQCegmDPcCFSTYnOZuZHf4pVwUl+VngHOBLHW3nJHlO83gd8Brg0bnzSpKGa9Griarq6STXA7uBMeCOqnokyc3AZFXNBsN24K6qqo7ZXw781yTPMBM8H+m8CkmSNBpy8r57+CYmJmpycnLYZUjSipJkb3N+dln8C2RJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJNHD7SgkSb3ZuW+KW3cf4Imj05y3dg03bL2Iq7bMe8f/kWMYSFILdu6bYsc9+5k+NnMH/6mj0+y4Zz/AiggEDxNJUgtu3X3gRBDMmj52nFt3HxhSRUtjGEhSC544Or2k9lFjGEhSC85bu2ZJ7aPGMJCkFtyw9SLWrB47qW3N6jFu2HrRkCpaGk8gS1ILZk8SezWRJD3LXbVl/YrZ+c/lYSJJkmEgSTIMJEkYBpIkDANJEpCqGnYNJ0lyBPj7YdfRxTrgH4ddRA+ss10roc6VUCNYZ9s667ygqsaX+0IjFwajLMlkVU0Mu47FWGe7VkKdK6FGsM62tVmnh4kkSYaBJMkwWKrbh11Aj6yzXSuhzpVQI1hn21qr03MGkiQ/GUiSDANJEobBCUk2JrkvyaNJHknyb5r2DyWZSvJg83VFxzw7khxMciDJ1gHW+s0k+5t6Jpu2c5Pcm+QbzfdzmvYk+S9NnQ8neeWAaryoY8weTPKDJL85CuOZ5I4k30nylY62JY9fkmub/t9Icu2A6rw1ydeaWv48ydqmfVOS6Y5x/XjHPK9qtpeDzbJkAHUueT0n2da0HUxyY5s1LlDn5zpq/GaSB5v2oYznAvuh/m+fVeXXzHmTFwOvbB4/D/g6cDHwIeD98/S/GHgIeA6wGXgMGBtQrd8E1s1p+0/Ajc3jG4HfbR5fAfwfIMCrgQeGMLZjwD8AF4zCeAKvA14JfGW54wecCxxqvp/TPD5nAHVeDpzVPP7djjo3dfab8zp/29SeZlnePIA6l7Sem6/HgJcAZzd9Lu53nXOm/2fg3w9zPBfYD/V9+/STQaOqnqyqLzeP/y/wVWChG5NfCdxVVT+qqr8DDgKX9r/SBev5VPP4U8BVHe2frhn3A2uTvHjAtb0ReKyqFvrL8oGNZ1X9DfDUPO+/lPHbCtxbVU9V1feAe4Ft/a6zqv6yqp5unt4PbFjoNZpan19V99fMXuLT/GTZ+lbnArqt50uBg1V1qKp+DNzV9B1Inc1v978M3LnQa/R7PBfYD/V9+zQM5pFkE7AFeKBpur75CHbH7MczZlbQ4x2zHWbh8GhTAX+ZZG+S65q2F1XVk83jfwBe1DweZp2ztnPyD9mojScsffyGXS/ArzPzW+GszUn2JfnrJK9t2tY3tc0aZJ1LWc/DHs/XAt+uqm90tA11POfsh/q+fRoGcyT5GeDPgN+sqh8AHwNeClwCPMnMR8lh+4WqeiXwZuDdSV7XObH5jWUkrhlOcjbwFuB/NE2jOJ4nGaXx6ybJB4Cngc82TU8C51fVFuB9wJ8kef6w6mMFrOc5ruHkX1iGOp7z7IdO6Nf2aRh0SLKamRXw2aq6B6Cqvl1Vx6vqGeAT/OTQxRSwsWP2DU1b31XVVPP9O8CfNzV9e/bwT/P9O8Ous/Fm4MtV9W0YzfFsLHX8hlZvkncCvwj8SrNjoDns8t3m8V5mjr+/rKmp81DSQOpcxnoe5nieBVwNfG62bZjjOd9+iAFsn4ZBozlm+N+Ar1bVRzvaO4+vvxWYvRJhF7A9yXOSbAYuZObEUr/r/Okkz5t9zMwJxa809cxeMXAt8D876vzV5qqDVwPf7/i4OQgn/cY1auPZYanjtxu4PMk5zSGQy5u2vkqyDfi3wFuq6ocd7eNJxprHL2Fm/A41tf4gyaubbfxXO5atn3UudT3vAS5Msrn5NLm96TsI/wL4WlWdOPwzrPHsth9iENtnW2fBV/oX8AvMfPR6GHiw+boC+Aywv2nfBby4Y54PMPMbwwFavkJjgTpfwsyVFg8BjwAfaNpfCHwe+AbwV8C5TXuA25o69wMTAxzTnwa+C7ygo23o48lMOD0JHGPmWOq7ljN+zByzP9h8/dqA6jzIzLHg2W30403ftzXbw4PAl4Ff6nidCWZ2xo8Bf0Rz54E+17nk9dz8vH29mfaBQYxn0/5J4Dfm9B3KeNJ9P9T37dPbUUiSPEwkSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJAv4/WRGOkTMh1AEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMo2ghPBjgj4"
      },
      "source": [
        "#Rectified Parameters taken for Grid Search(XGB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C73O2kOji0jH"
      },
      "source": [
        "#Taken from Visualising the hyperparameters from the Random Search of XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fU8j2tJt9QN_"
      },
      "source": [
        "gbm_param_grid_final = {\n",
        "    'n_estimators': range(90,125),\n",
        "    'max_depth': range(1,5)\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3TpoxETRieM"
      },
      "source": [
        "#GRID SEARCH"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n57D8drfC2RF"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccKc-uu3Au88"
      },
      "source": [
        "Grid_mse = GridSearchCV(estimator=model,param_grid=gbm_param_grid_final, scoring='balanced_accuracy', cv=4, verbose=1,n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SbGeG9zC1KE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "08f0628f-8217-455a-9aef-5163665a21f1"
      },
      "source": [
        "Grid_mse.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 4 folds for each of 140 candidates, totalling 560 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   11.9s\n",
            "[Parallel(n_jobs=-1)]: Done 196 tasks      | elapsed:  1.0min\n",
            "[Parallel(n_jobs=-1)]: Done 446 tasks      | elapsed:  2.8min\n",
            "[Parallel(n_jobs=-1)]: Done 560 out of 560 | elapsed:  3.7min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, error_score=nan,\n",
              "             estimator=XGBClassifier(base_score=0.5, booster='gbtree',\n",
              "                                     colsample_bylevel=1, colsample_bynode=1,\n",
              "                                     colsample_bytree=1, gamma=0,\n",
              "                                     learning_rate=0.1, max_delta_step=0,\n",
              "                                     max_depth={'scale_pos_weight': [1, 10, 25,\n",
              "                                                                     50, 75, 99,\n",
              "                                                                     100,\n",
              "                                                                     1000]},\n",
              "                                     min_child_weight=1, missing=None,\n",
              "                                     n_estimators=100, n_jobs=1, nthread=None,\n",
              "                                     objective='binary:logistic',\n",
              "                                     random_state=0, reg_alpha=0, reg_lambda=1,\n",
              "                                     scale_pos_weight=1, seed=None, silent=None,\n",
              "                                     subsample=1, verbosity=1),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'max_depth': range(1, 5),\n",
              "                         'n_estimators': range(90, 125)},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='balanced_accuracy', verbose=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HciifgyDzWB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b27fe370-ba2e-4714-9911-55cd2f52726d"
      },
      "source": [
        "Grid_mse.best_estimator_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=1,\n",
              "              min_child_weight=1, missing=None, n_estimators=90, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9B0UEltyFWai",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "80cbca8f-fa9f-468f-a008-2ecf4d9b14a7"
      },
      "source": [
        "Grid_mse.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9799107142857143"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ydTi-0SjRoRn"
      },
      "source": [
        "#STACKING CLASSIFIER"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkZUTb8pFcpY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "6eab326e-a934-48d5-f5e4-d59c970d182f"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from mlxtend.classifier import StackingClassifier"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTHFkB09LcHb"
      },
      "source": [
        "lr=LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJVZXy6rLPNw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "427976af-60cb-42eb-bc4a-119a1d6ad014"
      },
      "source": [
        "clf_stack=StackingClassifier(classifiers=[randomized_mse_rf.best_estimator_,Grid_mse.best_estimator_],meta_classifier=lr,use_features_in_secondary=False)\n",
        "clf_stack.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StackingClassifier(average_probas=False,\n",
              "                   classifiers=[RandomForestClassifier(bootstrap=True,\n",
              "                                                       ccp_alpha=0.0,\n",
              "                                                       class_weight=None,\n",
              "                                                       criterion='gini',\n",
              "                                                       max_depth=69,\n",
              "                                                       max_features='auto',\n",
              "                                                       max_leaf_nodes=None,\n",
              "                                                       max_samples=None,\n",
              "                                                       min_impurity_decrease=0.0,\n",
              "                                                       min_impurity_split=None,\n",
              "                                                       min_samples_leaf=7,\n",
              "                                                       min_samples_split=8,\n",
              "                                                       min_weight_fraction_leaf=0.0,\n",
              "                                                       n_estimators=406,\n",
              "                                                       n_job...\n",
              "                   meta_classifier=LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                      dual=False,\n",
              "                                                      fit_intercept=True,\n",
              "                                                      intercept_scaling=1,\n",
              "                                                      l1_ratio=None,\n",
              "                                                      max_iter=100,\n",
              "                                                      multi_class='auto',\n",
              "                                                      n_jobs=None, penalty='l2',\n",
              "                                                      random_state=None,\n",
              "                                                      solver='lbfgs',\n",
              "                                                      tol=0.0001, verbose=0,\n",
              "                                                      warm_start=False),\n",
              "                   store_train_meta_features=False, use_clones=True,\n",
              "                   use_features_in_secondary=False, use_probas=False,\n",
              "                   verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fufufHYoRvUu"
      },
      "source": [
        "#OVERFITTING CHECK"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-oabSM2Li_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a2ac3c16-2aad-4949-e8a0-5707f6999d8f"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy: {:0.4f}\".format(accuracy_score(y_test, clf_stack.predict(X_test))))  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JlB4gSOL323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "942d3366-3f47-4ec3-efff-a26b45087ea4"
      },
      "source": [
        "print(\"Accuracy: {:0.4f}\".format(accuracy_score(y_train, clf_stack.predict(X_train))))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGpjC-OXTRlH"
      },
      "source": [
        "#SAVING THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-OulQ3LS8Ex"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJE52mHjNhOd"
      },
      "source": [
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(clf_stack, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-o6riujfS_LM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4272919b-4e89-459a-d44a-37c686f3d387"
      },
      "source": [
        "loaded_model = pickle.load(open(filename, 'rb'))\n",
        "result = loaded_model.score(X_test,y_test)\n",
        "print(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eX2zfNcdTFcc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}