Star_Types.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Star Types.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyONdSs/gPYnr9L0U/B14nXA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Star_Types.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "gHy3kJTdw1Mc"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "trcR5t1SwudE",
        "outputId": "b0b8b135-c62a-4479-a43e-ab8eddf3f470"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>Temperature (K)</th>\n",
              "      <th>Luminosity(L/Lo)</th>\n",
              "      <th>Radius(R/Ro)</th>\n",
              "      <th>Absolute magnitude(Mv)</th>\n",
              "      <th>Star type</th>\n",
              "      <th>Star color</th>\n",
              "      <th>Spectral Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3068</td>\n",
              "      <td>0.002400</td>\n",
              "      <td>0.1700</td>\n",
              "      <td>16.12</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3042</td>\n",
              "      <td>0.000500</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>16.60</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2600</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.1020</td>\n",
              "      <td>18.70</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2800</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>16.65</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1939</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.1030</td>\n",
              "      <td>20.06</td>\n",
              "      <td>0</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>38940</td>\n",
              "      <td>374830.000000</td>\n",
              "      <td>1356.0000</td>\n",
              "      <td>-9.93</td>\n",
              "      <td>5</td>\n",
              "      <td>Blue</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>30839</td>\n",
              "      <td>834042.000000</td>\n",
              "      <td>1194.0000</td>\n",
              "      <td>-10.63</td>\n",
              "      <td>5</td>\n",
              "      <td>Blue</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>8829</td>\n",
              "      <td>537493.000000</td>\n",
              "      <td>1423.0000</td>\n",
              "      <td>-10.73</td>\n",
              "      <td>5</td>\n",
              "      <td>White</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>9235</td>\n",
              "      <td>404940.000000</td>\n",
              "      <td>1112.0000</td>\n",
              "      <td>-11.23</td>\n",
              "      <td>5</td>\n",
              "      <td>White</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>37882</td>\n",
              "      <td>294903.000000</td>\n",
              "      <td>1783.0000</td>\n",
              "      <td>-7.80</td>\n",
              "      <td>5</td>\n",
              "      <td>Blue</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>240 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Temperature (K)  Luminosity(L/Lo)  ...  Star color  Spectral Class\n",
              "0               3068          0.002400  ...         Red               M\n",
              "1               3042          0.000500  ...         Red               M\n",
              "2               2600          0.000300  ...         Red               M\n",
              "3               2800          0.000200  ...         Red               M\n",
              "4               1939          0.000138  ...         Red               M\n",
              "..               ...               ...  ...         ...             ...\n",
              "235            38940     374830.000000  ...        Blue               O\n",
              "236            30839     834042.000000  ...        Blue               O\n",
              "237             8829     537493.000000  ...       White               A\n",
              "238             9235     404940.000000  ...       White               A\n",
              "239            37882     294903.000000  ...        Blue               O\n",
              "\n",
              "[240 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/stars%20type.csv'\n",
        "stars = pd.read_csv(url,error_bad_lines=False)\n",
        "stars"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stars.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVmPjLtxxrhz",
        "outputId": "f20f2cde-6933-403e-9d22-a51dc5c9170f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 240 entries, 0 to 239\n",
            "Data columns (total 7 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Temperature (K)         240 non-null    int64  \n",
            " 1   Luminosity(L/Lo)        240 non-null    float64\n",
            " 2   Radius(R/Ro)            240 non-null    float64\n",
            " 3   Absolute magnitude(Mv)  240 non-null    float64\n",
            " 4   Star type               240 non-null    int64  \n",
            " 5   Star color              240 non-null    object \n",
            " 6   Spectral Class          240 non-null    object \n",
            "dtypes: float64(3), int64(2), object(2)\n",
            "memory usage: 13.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stars.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0adOJL4xt-O",
        "outputId": "e4220a23-4670-4ffb-db2d-f3fd981946d9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Temperature (K)           0\n",
              "Luminosity(L/Lo)          0\n",
              "Radius(R/Ro)              0\n",
              "Absolute magnitude(Mv)    0\n",
              "Star type                 0\n",
              "Star color                0\n",
              "Spectral Class            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig , ax = plt.subplots(figsize = (13,10))\n",
        "\n",
        "R = stars[stars[\"Star type\"] == 0]\n",
        "B = stars[stars[\"Star type\"] == 1]\n",
        "W = stars[stars[\"Star type\"] == 2]\n",
        "M = stars[stars[\"Star type\"] == 3]\n",
        "S = stars[stars[\"Star type\"] == 4]\n",
        "H = stars[stars[\"Star type\"] == 5]\n",
        "\n",
        "ax.scatter(np.log(R[\"Temperature (K)\"]), np.log(R[\"Luminosity(L/Lo)\"]),5, label = 'Red dwarfs')\n",
        "ax.scatter(np.log(B[\"Temperature (K)\"]), np.log(B[\"Luminosity(L/Lo)\"]),7, label = 'Brown dwarfs')\n",
        "ax.scatter(np.log(W[\"Temperature (K)\"]), np.log(W[\"Luminosity(L/Lo)\"]),10, label = 'White dwarfs')\n",
        "ax.scatter(np.log(M[\"Temperature (K)\"]), np.log(M[\"Luminosity(L/Lo)\"]),15, label = 'Main sequence stars')\n",
        "ax.scatter(np.log(S[\"Temperature (K)\"]), np.log(S[\"Luminosity(L/Lo)\"]),30, label = 'Supergiants')\n",
        "ax.scatter(np.log(H[\"Temperature (K)\"]), np.log(H[\"Luminosity(L/Lo)\"]),50, label = 'Hypergiants')\n",
        "\n",
        "ax.invert_xaxis()\n",
        "ax.legend()\n",
        "plt.xlabel(\"Log Temperature\")\n",
        "plt.ylabel(\"Log Luminosity\")\n",
        "ax.grid()\n",
        "ax.set_facecolor(\"black\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "B3Ov3wIwxvPy",
        "outputId": "77b491fd-c326-4256-b0b7-82d6aa00cf5e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 936x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stars = stars.sample(frac=1) # shuffle the data\n",
        "x = stars.drop(\"Star type\", axis=1)\n",
        "y = stars[\"Star type\"]"
      ],
      "metadata": {
        "id": "hCjJyoAzx0Im"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features = [\"Spectral Class\",\"Star color\"]\n",
        "one_hot = OneHotEncoder()\n",
        "transformer = ColumnTransformer([(\"one_hot\",\n",
        "                                 one_hot,\n",
        "                                 categorical_features)],\n",
        "                                remainder = \"passthrough\")\n",
        "transformed_x = transformer.fit_transform(stars)\n",
        "transformed_x "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lcKmBSPGx3m3",
        "outputId": "cea276fc-74bd-4fce-a23d-c42775856a04"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<240x31 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 1640 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(transformed_x,y,test_size=0.2)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8k0XrWiIx5ez",
        "outputId": "a44c1163-4357-41cb-9a29-2ead83412811"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<192x31 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 1312 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestClassifier()\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6d4Xcfcx7jE",
        "outputId": "7b4783cd-97c7-495c-906a-87e393e6cbdf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMpdzqWBx-Qo",
        "outputId": "34fde930-1b68-40c5-ed14-9da8026f571d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4u9pQNQx__g",
        "outputId": "07f1df55-0260-496b-e7d1-642bbeda8f15"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<48x31 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 328 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = model.predict(x_test)\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iX_LXDK6yCnV",
        "outputId": "e02713ae-fd56-4fc5-b5cf-9e5ef2ba8c03"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 5, 4, 0, 3, 3, 1, 1, 0, 5, 4, 2, 0, 3, 4, 5, 4, 4, 3, 1, 2, 1,\n",
              "       0, 0, 4, 5, 4, 1, 5, 5, 3, 3, 2, 2, 3, 1, 0, 4, 2, 0, 1, 1, 1, 2,\n",
              "       5, 3, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5lNxwW6yEwU",
        "outputId": "b97f3a21-d119-42bc-8713-ee7018ef0c2c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "72     1\n",
              "234    5\n",
              "40     4\n",
              "183    0\n",
              "211    3\n",
              "157    3\n",
              "135    1\n",
              "14     1\n",
              "180    0\n",
              "174    5\n",
              "220    4\n",
              "207    2\n",
              "123    0\n",
              "219    3\n",
              "45     4\n",
              "173    5\n",
              "103    4\n",
              "229    4\n",
              "93     3\n",
              "198    1\n",
              "26     2\n",
              "19     1\n",
              "182    0\n",
              "120    0\n",
              "42     4\n",
              "119    5\n",
              "49     4\n",
              "138    1\n",
              "57     5\n",
              "113    5\n",
              "150    3\n",
              "216    3\n",
              "81     2\n",
              "83     2\n",
              "212    3\n",
              "77     1\n",
              "181    0\n",
              "164    4\n",
              "88     2\n",
              "184    0\n",
              "137    1\n",
              "12     1\n",
              "13     1\n",
              "209    2\n",
              "111    5\n",
              "38     3\n",
              "9      0\n",
              "17     1\n",
              "Name: Star type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.score(x_test,y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eht6nKtbyG6x",
        "outputId": "8ee159e1-1c7c-4471-b496-6baa919b7e3b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qjsu1Xs0yJAD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}