Gamma ray sources classification into pulsars and AGNs.ipynb

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    "# Gamma ray sources classification into pulsars and AGNs\n",
    "\n",
    "In the mid-1960s, researchers first discovered pulsars, which emit regular\n",
    "bursts of radio waves and were initially considered enigmatic. It was later\n",
    "found that they were rotating neutron stars emitting radiation that was\n",
    "observed across a broad range of wavelengths, including gamma rays. Since\n",
    "then, gamma-ray astronomy has continued to be a field of active research, and\n",
    "a significant fraction of gamma-ray sources remain unidentified. The goal of\n",
    "this model is to develop a machine learning model that can\n",
    "accurately classify gamma-ray sources based on their characteristics and\n",
    "properties, using data from various space-based observatories. Gamma-ray\n",
    "sources can be broadly classified into two types: AGNs and pulsars. The pulsars\n",
    "can be further classified as millisecond pulsars or young pulsars. Similarly some\n",
    "common types of AGNs include quasars, Seyfert galaxies, and blazars.\n"
   ]
  },
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   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn import metrics\n",
    "\n",
    "#making sure all the columns are visible\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipmQpFinOOU-"
   },
   "source": [
    "\n",
    "\n",
    "---                                       \n",
    "\n",
    "## 1.Importing and inspecting the gamma ray sources data"
   ]
  },
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       "      <th></th>\n",
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       "      <th>Name</th>\n",
       "      <th>RA (deg)</th>\n",
       "      <th>DEC (deg)</th>\n",
       "      <th>Signif_Avg</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Flux1000</th>\n",
       "      <th>Energy_Flux100</th>\n",
       "      <th>PL_Flux_Density</th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>LP_Flux_Density</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>LP_beta</th>\n",
       "      <th>LP_SigCurv</th>\n",
       "      <th>PLEC_Flux_Density</th>\n",
       "      <th>PLEC_IndexS</th>\n",
       "      <th>PLEC_ExpfactorS</th>\n",
       "      <th>PLEC_Exp_Index</th>\n",
       "      <th>PLEC_SigCurv</th>\n",
       "      <th>Npred</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "      <th>CLASS1</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4FGL J0000.3-7355</td>\n",
       "      <td>0.0983</td>\n",
       "      <td>0.0983</td>\n",
       "      <td>7.459561</td>\n",
       "      <td>2329.5903</td>\n",
       "      <td>1.392216e-10</td>\n",
       "      <td>1.581099e-12</td>\n",
       "      <td>2.610434e-14</td>\n",
       "      <td>2.179690</td>\n",
       "      <td>2.904301e-14</td>\n",
       "      <td>2.063685</td>\n",
       "      <td>0.091939</td>\n",
       "      <td>0.749253</td>\n",
       "      <td>2.918480e-14</td>\n",
       "      <td>1.993013</td>\n",
       "      <td>0.150609</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.292339</td>\n",
       "      <td>305.21463</td>\n",
       "      <td>14.602470</td>\n",
       "      <td>0.215777</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4FGL J0000.5+0743</td>\n",
       "      <td>0.1375</td>\n",
       "      <td>0.1375</td>\n",
       "      <td>5.271726</td>\n",
       "      <td>1304.1947</td>\n",
       "      <td>1.951913e-10</td>\n",
       "      <td>1.121896e-12</td>\n",
       "      <td>1.039931e-13</td>\n",
       "      <td>2.349694</td>\n",
       "      <td>1.575568e-13</td>\n",
       "      <td>2.069591</td>\n",
       "      <td>0.454411</td>\n",
       "      <td>2.549887</td>\n",
       "      <td>1.475405e-13</td>\n",
       "      <td>1.998095</td>\n",
       "      <td>0.586220</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.532814</td>\n",
       "      <td>197.24410</td>\n",
       "      <td>25.399525</td>\n",
       "      <td>0.687574</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4FGL J0000.7+2530</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>4.177483</td>\n",
       "      <td>6318.9400</td>\n",
       "      <td>7.202151e-11</td>\n",
       "      <td>8.571274e-13</td>\n",
       "      <td>2.058057e-15</td>\n",
       "      <td>1.852803</td>\n",
       "      <td>3.043761e-15</td>\n",
       "      <td>1.646164</td>\n",
       "      <td>0.376358</td>\n",
       "      <td>1.471138</td>\n",
       "      <td>3.079466e-15</td>\n",
       "      <td>1.507830</td>\n",
       "      <td>0.720244</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.562938</td>\n",
       "      <td>87.70652</td>\n",
       "      <td>13.650518</td>\n",
       "      <td>0.000000</td>\n",
       "      <td></td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4FGL J0001.2+4741</td>\n",
       "      <td>0.3126</td>\n",
       "      <td>0.3126</td>\n",
       "      <td>4.692600</td>\n",
       "      <td>2420.5752</td>\n",
       "      <td>1.259796e-10</td>\n",
       "      <td>1.499454e-12</td>\n",
       "      <td>2.156031e-14</td>\n",
       "      <td>2.271696</td>\n",
       "      <td>2.196550e-14</td>\n",
       "      <td>2.254081</td>\n",
       "      <td>0.012156</td>\n",
       "      <td>0.083893</td>\n",
       "      <td>2.228697e-14</td>\n",
       "      <td>2.219642</td>\n",
       "      <td>0.029462</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.337723</td>\n",
       "      <td>293.14978</td>\n",
       "      <td>25.313953</td>\n",
       "      <td>0.675882</td>\n",
       "      <td>bcu</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4FGL J0001.2-0747</td>\n",
       "      <td>0.3151</td>\n",
       "      <td>0.3151</td>\n",
       "      <td>23.782486</td>\n",
       "      <td>1721.6039</td>\n",
       "      <td>7.471219e-10</td>\n",
       "      <td>8.339171e-12</td>\n",
       "      <td>2.656894e-13</td>\n",
       "      <td>2.116692</td>\n",
       "      <td>2.842831e-13</td>\n",
       "      <td>2.078927</td>\n",
       "      <td>0.051182</td>\n",
       "      <td>1.697737</td>\n",
       "      <td>2.813444e-13</td>\n",
       "      <td>2.046604</td>\n",
       "      <td>0.063212</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.892928</td>\n",
       "      <td>1192.35190</td>\n",
       "      <td>46.780693</td>\n",
       "      <td>0.406565</td>\n",
       "      <td>bll</td>\n",
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       "   Unnamed: 0                Name  RA (deg)  DEC (deg)  Signif_Avg  \\\n",
       "0           0  4FGL J0000.3-7355     0.0983     0.0983    7.459561   \n",
       "1           1  4FGL J0000.5+0743     0.1375     0.1375    5.271726   \n",
       "2           2  4FGL J0000.7+2530     0.1878     0.1878    4.177483   \n",
       "3           3  4FGL J0001.2+4741     0.3126     0.3126    4.692600   \n",
       "4           4  4FGL J0001.2-0747     0.3151     0.3151   23.782486   \n",
       "\n",
       "   Pivot_Energy      Flux1000  Energy_Flux100  PL_Flux_Density  PL_Index  \\\n",
       "0     2329.5903  1.392216e-10    1.581099e-12     2.610434e-14  2.179690   \n",
       "1     1304.1947  1.951913e-10    1.121896e-12     1.039931e-13  2.349694   \n",
       "2     6318.9400  7.202151e-11    8.571274e-13     2.058057e-15  1.852803   \n",
       "3     2420.5752  1.259796e-10    1.499454e-12     2.156031e-14  2.271696   \n",
       "4     1721.6039  7.471219e-10    8.339171e-12     2.656894e-13  2.116692   \n",
       "\n",
       "   LP_Flux_Density  LP_Index   LP_beta  LP_SigCurv  PLEC_Flux_Density  \\\n",
       "0     2.904301e-14  2.063685  0.091939    0.749253       2.918480e-14   \n",
       "1     1.575568e-13  2.069591  0.454411    2.549887       1.475405e-13   \n",
       "2     3.043761e-15  1.646164  0.376358    1.471138       3.079466e-15   \n",
       "3     2.196550e-14  2.254081  0.012156    0.083893       2.228697e-14   \n",
       "4     2.842831e-13  2.078927  0.051182    1.697737       2.813444e-13   \n",
       "\n",
       "   PLEC_IndexS  PLEC_ExpfactorS  PLEC_Exp_Index  PLEC_SigCurv       Npred  \\\n",
       "0     1.993013         0.150609        0.666667      1.292339   305.21463   \n",
       "1     1.998095         0.586220        0.666667      2.532814   197.24410   \n",
       "2     1.507830         0.720244        0.666667      1.562938    87.70652   \n",
       "3     2.219642         0.029462        0.666667      0.337723   293.14978   \n",
       "4     2.046604         0.063212        0.666667      1.892928  1192.35190   \n",
       "\n",
       "   Variability_Index  Frac_Variability CLASS1      CLASS2  \n",
       "0          14.602470          0.215777                     \n",
       "1          25.399525          0.687574                     \n",
       "2          13.650518          0.000000                     \n",
       "3          25.313953          0.675882  bcu                \n",
       "4          46.780693          0.406565  bll                "
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   ],
   "source": [
    "#Loading the dataset\n",
    "GS = pd.read_csv(\"4FGL_DR3_Dataset_Enigma.csv\")\n",
    "\n",
    "GS.head()"
   ]
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       "      <td>1.292339</td>\n",
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       "      <td>14.602470</td>\n",
       "      <td>0.215777</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4FGL J0000.5+0743</td>\n",
       "      <td>0.1375</td>\n",
       "      <td>0.1375</td>\n",
       "      <td>5.271726</td>\n",
       "      <td>1304.1947</td>\n",
       "      <td>1.951913e-10</td>\n",
       "      <td>1.121896e-12</td>\n",
       "      <td>1.039931e-13</td>\n",
       "      <td>2.349694</td>\n",
       "      <td>1.575568e-13</td>\n",
       "      <td>2.069591</td>\n",
       "      <td>0.454411</td>\n",
       "      <td>2.549887</td>\n",
       "      <td>1.475405e-13</td>\n",
       "      <td>1.998095</td>\n",
       "      <td>0.586220</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.532814</td>\n",
       "      <td>197.24410</td>\n",
       "      <td>25.399525</td>\n",
       "      <td>0.687574</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4FGL J0000.7+2530</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>0.1878</td>\n",
       "      <td>4.177483</td>\n",
       "      <td>6318.9400</td>\n",
       "      <td>7.202151e-11</td>\n",
       "      <td>8.571274e-13</td>\n",
       "      <td>2.058057e-15</td>\n",
       "      <td>1.852803</td>\n",
       "      <td>3.043761e-15</td>\n",
       "      <td>1.646164</td>\n",
       "      <td>0.376358</td>\n",
       "      <td>1.471138</td>\n",
       "      <td>3.079466e-15</td>\n",
       "      <td>1.507830</td>\n",
       "      <td>0.720244</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.562938</td>\n",
       "      <td>87.70652</td>\n",
       "      <td>13.650518</td>\n",
       "      <td>0.000000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4FGL J0001.2+4741</td>\n",
       "      <td>0.3126</td>\n",
       "      <td>0.3126</td>\n",
       "      <td>4.692600</td>\n",
       "      <td>2420.5752</td>\n",
       "      <td>1.259796e-10</td>\n",
       "      <td>1.499454e-12</td>\n",
       "      <td>2.156031e-14</td>\n",
       "      <td>2.271696</td>\n",
       "      <td>2.196550e-14</td>\n",
       "      <td>2.254081</td>\n",
       "      <td>0.012156</td>\n",
       "      <td>0.083893</td>\n",
       "      <td>2.228697e-14</td>\n",
       "      <td>2.219642</td>\n",
       "      <td>0.029462</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.337723</td>\n",
       "      <td>293.14978</td>\n",
       "      <td>25.313953</td>\n",
       "      <td>0.675882</td>\n",
       "      <td>bcu</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4FGL J0001.2-0747</td>\n",
       "      <td>0.3151</td>\n",
       "      <td>0.3151</td>\n",
       "      <td>23.782486</td>\n",
       "      <td>1721.6039</td>\n",
       "      <td>7.471219e-10</td>\n",
       "      <td>8.339171e-12</td>\n",
       "      <td>2.656894e-13</td>\n",
       "      <td>2.116692</td>\n",
       "      <td>2.842831e-13</td>\n",
       "      <td>2.078927</td>\n",
       "      <td>0.051182</td>\n",
       "      <td>1.697737</td>\n",
       "      <td>2.813444e-13</td>\n",
       "      <td>2.046604</td>\n",
       "      <td>0.063212</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.892928</td>\n",
       "      <td>1192.35190</td>\n",
       "      <td>46.780693</td>\n",
       "      <td>0.406565</td>\n",
       "      <td>bll</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo                Name  RA (deg)  DEC (deg)  Signif_Avg  Pivot_Energy  \\\n",
       "0    0  4FGL J0000.3-7355     0.0983     0.0983    7.459561     2329.5903   \n",
       "1    1  4FGL J0000.5+0743     0.1375     0.1375    5.271726     1304.1947   \n",
       "2    2  4FGL J0000.7+2530     0.1878     0.1878    4.177483     6318.9400   \n",
       "3    3  4FGL J0001.2+4741     0.3126     0.3126    4.692600     2420.5752   \n",
       "4    4  4FGL J0001.2-0747     0.3151     0.3151   23.782486     1721.6039   \n",
       "\n",
       "       Flux1000  Energy_Flux100  PL_Flux_Density  PL_Index  LP_Flux_Density  \\\n",
       "0  1.392216e-10    1.581099e-12     2.610434e-14  2.179690     2.904301e-14   \n",
       "1  1.951913e-10    1.121896e-12     1.039931e-13  2.349694     1.575568e-13   \n",
       "2  7.202151e-11    8.571274e-13     2.058057e-15  1.852803     3.043761e-15   \n",
       "3  1.259796e-10    1.499454e-12     2.156031e-14  2.271696     2.196550e-14   \n",
       "4  7.471219e-10    8.339171e-12     2.656894e-13  2.116692     2.842831e-13   \n",
       "\n",
       "   LP_Index   LP_beta  LP_SigCurv  PLEC_Flux_Density  PLEC_IndexS  \\\n",
       "0  2.063685  0.091939    0.749253       2.918480e-14     1.993013   \n",
       "1  2.069591  0.454411    2.549887       1.475405e-13     1.998095   \n",
       "2  1.646164  0.376358    1.471138       3.079466e-15     1.507830   \n",
       "3  2.254081  0.012156    0.083893       2.228697e-14     2.219642   \n",
       "4  2.078927  0.051182    1.697737       2.813444e-13     2.046604   \n",
       "\n",
       "   PLEC_ExpfactorS  PLEC_Exp_Index  PLEC_SigCurv       Npred  \\\n",
       "0         0.150609        0.666667      1.292339   305.21463   \n",
       "1         0.586220        0.666667      2.532814   197.24410   \n",
       "2         0.720244        0.666667      1.562938    87.70652   \n",
       "3         0.029462        0.666667      0.337723   293.14978   \n",
       "4         0.063212        0.666667      1.892928  1192.35190   \n",
       "\n",
       "   Variability_Index  Frac_Variability CLASS1      CLASS2  \n",
       "0          14.602470          0.215777                     \n",
       "1          25.399525          0.687574                     \n",
       "2          13.650518          0.000000                     \n",
       "3          25.313953          0.675882  bcu                \n",
       "4          46.780693          0.406565  bll                "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.rename(columns={'Unnamed: 0':'SNo'},inplace=True)\n",
    "GS.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1677508102940,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "LXFSnbttgGhr",
    "outputId": "f01df10a-9cb3-4657-8a9c-43a0110696e9"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>SNo</th>\n",
       "      <th>RA (deg)</th>\n",
       "      <th>DEC (deg)</th>\n",
       "      <th>Signif_Avg</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Flux1000</th>\n",
       "      <th>Energy_Flux100</th>\n",
       "      <th>PL_Flux_Density</th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>LP_Flux_Density</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>LP_beta</th>\n",
       "      <th>LP_SigCurv</th>\n",
       "      <th>PLEC_Flux_Density</th>\n",
       "      <th>PLEC_IndexS</th>\n",
       "      <th>PLEC_ExpfactorS</th>\n",
       "      <th>PLEC_Exp_Index</th>\n",
       "      <th>PLEC_SigCurv</th>\n",
       "      <th>Npred</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6.658000e+03</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6.658000e+03</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3329.000000</td>\n",
       "      <td>190.437892</td>\n",
       "      <td>190.437892</td>\n",
       "      <td>15.639615</td>\n",
       "      <td>2737.386617</td>\n",
       "      <td>1.453465e-09</td>\n",
       "      <td>1.266817e-11</td>\n",
       "      <td>3.625125e-11</td>\n",
       "      <td>2.283192</td>\n",
       "      <td>2.767488e-11</td>\n",
       "      <td>2.215740</td>\n",
       "      <td>0.230219</td>\n",
       "      <td>2.975724</td>\n",
       "      <td>2.785301e-11</td>\n",
       "      <td>2.142938</td>\n",
       "      <td>0.417266</td>\n",
       "      <td>0.665748</td>\n",
       "      <td>2.977417</td>\n",
       "      <td>1352.746395</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1922.432053</td>\n",
       "      <td>99.673564</td>\n",
       "      <td>99.673564</td>\n",
       "      <td>28.926910</td>\n",
       "      <td>4208.618377</td>\n",
       "      <td>1.913959e-08</td>\n",
       "      <td>1.322343e-10</td>\n",
       "      <td>2.853454e-09</td>\n",
       "      <td>0.295798</td>\n",
       "      <td>2.129364e-09</td>\n",
       "      <td>0.385238</td>\n",
       "      <td>0.255427</td>\n",
       "      <td>5.916809</td>\n",
       "      <td>2.146587e-09</td>\n",
       "      <td>0.374804</td>\n",
       "      <td>0.624839</td>\n",
       "      <td>0.019202</td>\n",
       "      <td>5.987263</td>\n",
       "      <td>5375.139223</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098300</td>\n",
       "      <td>0.098300</td>\n",
       "      <td>1.623841</td>\n",
       "      <td>49.677998</td>\n",
       "      <td>3.740937e-13</td>\n",
       "      <td>3.707689e-13</td>\n",
       "      <td>2.682798e-17</td>\n",
       "      <td>1.014061</td>\n",
       "      <td>3.623631e-17</td>\n",
       "      <td>-0.048980</td>\n",
       "      <td>-0.151934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000388e-17</td>\n",
       "      <td>-0.159668</td>\n",
       "      <td>-0.368838</td>\n",
       "      <td>0.207654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.918154</td>\n",
       "      <td>-inf</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1664.500000</td>\n",
       "      <td>107.921600</td>\n",
       "      <td>107.921600</td>\n",
       "      <td>5.441673</td>\n",
       "      <td>1098.925400</td>\n",
       "      <td>1.584481e-10</td>\n",
       "      <td>1.846526e-12</td>\n",
       "      <td>2.747724e-14</td>\n",
       "      <td>2.064106</td>\n",
       "      <td>3.177824e-14</td>\n",
       "      <td>1.948876</td>\n",
       "      <td>0.054827</td>\n",
       "      <td>0.984587</td>\n",
       "      <td>3.141187e-14</td>\n",
       "      <td>1.891283</td>\n",
       "      <td>0.056860</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.006731</td>\n",
       "      <td>299.277260</td>\n",
       "      <td>9.496667e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3329.000000</td>\n",
       "      <td>203.322700</td>\n",
       "      <td>203.322700</td>\n",
       "      <td>7.901763</td>\n",
       "      <td>1764.813600</td>\n",
       "      <td>3.025504e-10</td>\n",
       "      <td>3.295702e-12</td>\n",
       "      <td>1.422794e-13</td>\n",
       "      <td>2.313304</td>\n",
       "      <td>1.799026e-13</td>\n",
       "      <td>2.224967</td>\n",
       "      <td>0.138951</td>\n",
       "      <td>2.038423</td>\n",
       "      <td>1.755163e-13</td>\n",
       "      <td>2.164486</td>\n",
       "      <td>0.170415</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.033031</td>\n",
       "      <td>549.045170</td>\n",
       "      <td>1.419961e+01</td>\n",
       "      <td>0.182504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4993.500000</td>\n",
       "      <td>272.013650</td>\n",
       "      <td>272.013650</td>\n",
       "      <td>14.593597</td>\n",
       "      <td>3105.348600</td>\n",
       "      <td>6.986070e-10</td>\n",
       "      <td>7.068370e-12</td>\n",
       "      <td>5.537922e-13</td>\n",
       "      <td>2.508057</td>\n",
       "      <td>7.314475e-13</td>\n",
       "      <td>2.492634</td>\n",
       "      <td>0.332463</td>\n",
       "      <td>3.520772</td>\n",
       "      <td>7.245303e-13</td>\n",
       "      <td>2.412008</td>\n",
       "      <td>0.550371</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.507771</td>\n",
       "      <td>1009.155375</td>\n",
       "      <td>2.525354e+01</td>\n",
       "      <td>0.471393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6658.000000</td>\n",
       "      <td>359.990800</td>\n",
       "      <td>359.990800</td>\n",
       "      <td>884.642940</td>\n",
       "      <td>195054.750000</td>\n",
       "      <td>1.333140e-06</td>\n",
       "      <td>9.292083e-09</td>\n",
       "      <td>2.328326e-07</td>\n",
       "      <td>3.371371</td>\n",
       "      <td>1.737617e-07</td>\n",
       "      <td>3.444003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>239.436390</td>\n",
       "      <td>1.751540e-07</td>\n",
       "      <td>3.318735</td>\n",
       "      <td>5.448646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244.335510</td>\n",
       "      <td>261898.020000</td>\n",
       "      <td>8.434076e+04</td>\n",
       "      <td>2.910003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SNo     RA (deg)    DEC (deg)   Signif_Avg   Pivot_Energy  \\\n",
       "count  6659.000000  6659.000000  6659.000000  6658.000000    6659.000000   \n",
       "mean   3329.000000   190.437892   190.437892    15.639615    2737.386617   \n",
       "std    1922.432053    99.673564    99.673564    28.926910    4208.618377   \n",
       "min       0.000000     0.098300     0.098300     1.623841      49.677998   \n",
       "25%    1664.500000   107.921600   107.921600     5.441673    1098.925400   \n",
       "50%    3329.000000   203.322700   203.322700     7.901763    1764.813600   \n",
       "75%    4993.500000   272.013650   272.013650    14.593597    3105.348600   \n",
       "max    6658.000000   359.990800   359.990800   884.642940  195054.750000   \n",
       "\n",
       "           Flux1000  Energy_Flux100  PL_Flux_Density     PL_Index  \\\n",
       "count  6.659000e+03    6.659000e+03     6.658000e+03  6658.000000   \n",
       "mean   1.453465e-09    1.266817e-11     3.625125e-11     2.283192   \n",
       "std    1.913959e-08    1.322343e-10     2.853454e-09     0.295798   \n",
       "min    3.740937e-13    3.707689e-13     2.682798e-17     1.014061   \n",
       "25%    1.584481e-10    1.846526e-12     2.747724e-14     2.064106   \n",
       "50%    3.025504e-10    3.295702e-12     1.422794e-13     2.313304   \n",
       "75%    6.986070e-10    7.068370e-12     5.537922e-13     2.508057   \n",
       "max    1.333140e-06    9.292083e-09     2.328326e-07     3.371371   \n",
       "\n",
       "       LP_Flux_Density     LP_Index      LP_beta   LP_SigCurv  \\\n",
       "count     6.659000e+03  6659.000000  6659.000000  6659.000000   \n",
       "mean      2.767488e-11     2.215740     0.230219     2.975724   \n",
       "std       2.129364e-09     0.385238     0.255427     5.916809   \n",
       "min       3.623631e-17    -0.048980    -0.151934     0.000000   \n",
       "25%       3.177824e-14     1.948876     0.054827     0.984587   \n",
       "50%       1.799026e-13     2.224967     0.138951     2.038423   \n",
       "75%       7.314475e-13     2.492634     0.332463     3.520772   \n",
       "max       1.737617e-07     3.444003     1.000000   239.436390   \n",
       "\n",
       "       PLEC_Flux_Density  PLEC_IndexS  PLEC_ExpfactorS  PLEC_Exp_Index  \\\n",
       "count       6.658000e+03  6658.000000      6658.000000     6658.000000   \n",
       "mean        2.785301e-11     2.142938         0.417266        0.665748   \n",
       "std         2.146587e-09     0.374804         0.624839        0.019202   \n",
       "min         4.000388e-17    -0.159668        -0.368838        0.207654   \n",
       "25%         3.141187e-14     1.891283         0.056860        0.666667   \n",
       "50%         1.755163e-13     2.164486         0.170415        0.666667   \n",
       "75%         7.245303e-13     2.412008         0.550371        0.666667   \n",
       "max         1.751540e-07     3.318735         5.448646        1.000000   \n",
       "\n",
       "       PLEC_SigCurv          Npred  Variability_Index  Frac_Variability  \n",
       "count   6659.000000    6659.000000       6.659000e+03       6659.000000  \n",
       "mean       2.977417    1352.746395               -inf              -inf  \n",
       "std        5.987263    5375.139223                NaN               NaN  \n",
       "min        0.000000       5.918154               -inf              -inf  \n",
       "25%        1.006731     299.277260       9.496667e+00          0.000000  \n",
       "50%        2.033031     549.045170       1.419961e+01          0.182504  \n",
       "75%        3.507771    1009.155375       2.525354e+01          0.471393  \n",
       "max      244.335510  261898.020000       8.434076e+04          2.910003  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's look at the statistical aspects of the dataframe\n",
    "GS.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1677508102941,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "Qa_A-7vFJnpH",
    "outputId": "1432fda4-7bf9-46c2-87d4-bc14287987bc"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>RA (deg)</th>\n",
       "      <th>DEC (deg)</th>\n",
       "      <th>Signif_Avg</th>\n",
       "      <th>Pivot_Energy</th>\n",
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       "      <th>Energy_Flux100</th>\n",
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       "      <th>PL_Index</th>\n",
       "      <th>LP_Flux_Density</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>LP_beta</th>\n",
       "      <th>LP_SigCurv</th>\n",
       "      <th>PLEC_Flux_Density</th>\n",
       "      <th>PLEC_IndexS</th>\n",
       "      <th>PLEC_ExpfactorS</th>\n",
       "      <th>PLEC_Exp_Index</th>\n",
       "      <th>PLEC_SigCurv</th>\n",
       "      <th>Npred</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6.658000e+03</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6.659000e+03</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6.658000e+03</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6658.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "      <td>6659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3329.000000</td>\n",
       "      <td>190.437892</td>\n",
       "      <td>190.437892</td>\n",
       "      <td>15.639615</td>\n",
       "      <td>2737.386617</td>\n",
       "      <td>1.453465e-09</td>\n",
       "      <td>1.266817e-11</td>\n",
       "      <td>3.625125e-11</td>\n",
       "      <td>2.283192</td>\n",
       "      <td>2.767488e-11</td>\n",
       "      <td>2.215740</td>\n",
       "      <td>0.230219</td>\n",
       "      <td>2.975724</td>\n",
       "      <td>2.785301e-11</td>\n",
       "      <td>2.142938</td>\n",
       "      <td>0.417266</td>\n",
       "      <td>0.665748</td>\n",
       "      <td>2.977417</td>\n",
       "      <td>1352.746395</td>\n",
       "      <td>141.873469</td>\n",
       "      <td>0.285620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1922.432053</td>\n",
       "      <td>99.673564</td>\n",
       "      <td>99.673564</td>\n",
       "      <td>28.926910</td>\n",
       "      <td>4208.618377</td>\n",
       "      <td>1.913959e-08</td>\n",
       "      <td>1.322343e-10</td>\n",
       "      <td>2.853454e-09</td>\n",
       "      <td>0.295798</td>\n",
       "      <td>2.129364e-09</td>\n",
       "      <td>0.385238</td>\n",
       "      <td>0.255427</td>\n",
       "      <td>5.916809</td>\n",
       "      <td>2.146587e-09</td>\n",
       "      <td>0.374804</td>\n",
       "      <td>0.624839</td>\n",
       "      <td>0.019202</td>\n",
       "      <td>5.987263</td>\n",
       "      <td>5375.139223</td>\n",
       "      <td>1688.776708</td>\n",
       "      <td>0.350881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098300</td>\n",
       "      <td>0.098300</td>\n",
       "      <td>1.623841</td>\n",
       "      <td>49.677998</td>\n",
       "      <td>3.740937e-13</td>\n",
       "      <td>3.707689e-13</td>\n",
       "      <td>2.682798e-17</td>\n",
       "      <td>1.014061</td>\n",
       "      <td>3.623631e-17</td>\n",
       "      <td>-0.048980</td>\n",
       "      <td>-0.151934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000388e-17</td>\n",
       "      <td>-0.159668</td>\n",
       "      <td>-0.368838</td>\n",
       "      <td>0.207654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.918154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1664.500000</td>\n",
       "      <td>107.921600</td>\n",
       "      <td>107.921600</td>\n",
       "      <td>5.441673</td>\n",
       "      <td>1098.925400</td>\n",
       "      <td>1.584481e-10</td>\n",
       "      <td>1.846526e-12</td>\n",
       "      <td>2.747724e-14</td>\n",
       "      <td>2.064106</td>\n",
       "      <td>3.177824e-14</td>\n",
       "      <td>1.948876</td>\n",
       "      <td>0.054827</td>\n",
       "      <td>0.984587</td>\n",
       "      <td>3.141187e-14</td>\n",
       "      <td>1.891283</td>\n",
       "      <td>0.056860</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.006731</td>\n",
       "      <td>299.277260</td>\n",
       "      <td>9.496667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3329.000000</td>\n",
       "      <td>203.322700</td>\n",
       "      <td>203.322700</td>\n",
       "      <td>7.901763</td>\n",
       "      <td>1764.813600</td>\n",
       "      <td>3.025504e-10</td>\n",
       "      <td>3.295702e-12</td>\n",
       "      <td>1.422794e-13</td>\n",
       "      <td>2.313304</td>\n",
       "      <td>1.799026e-13</td>\n",
       "      <td>2.224967</td>\n",
       "      <td>0.138951</td>\n",
       "      <td>2.038423</td>\n",
       "      <td>1.755163e-13</td>\n",
       "      <td>2.164486</td>\n",
       "      <td>0.170415</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.033031</td>\n",
       "      <td>549.045170</td>\n",
       "      <td>14.199614</td>\n",
       "      <td>0.182504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4993.500000</td>\n",
       "      <td>272.013650</td>\n",
       "      <td>272.013650</td>\n",
       "      <td>14.593597</td>\n",
       "      <td>3105.348600</td>\n",
       "      <td>6.986070e-10</td>\n",
       "      <td>7.068370e-12</td>\n",
       "      <td>5.537922e-13</td>\n",
       "      <td>2.508057</td>\n",
       "      <td>7.314475e-13</td>\n",
       "      <td>2.492634</td>\n",
       "      <td>0.332463</td>\n",
       "      <td>3.520772</td>\n",
       "      <td>7.245303e-13</td>\n",
       "      <td>2.412008</td>\n",
       "      <td>0.550371</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.507771</td>\n",
       "      <td>1009.155375</td>\n",
       "      <td>25.253543</td>\n",
       "      <td>0.471393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6658.000000</td>\n",
       "      <td>359.990800</td>\n",
       "      <td>359.990800</td>\n",
       "      <td>884.642940</td>\n",
       "      <td>195054.750000</td>\n",
       "      <td>1.333140e-06</td>\n",
       "      <td>9.292083e-09</td>\n",
       "      <td>2.328326e-07</td>\n",
       "      <td>3.371371</td>\n",
       "      <td>1.737617e-07</td>\n",
       "      <td>3.444003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>239.436390</td>\n",
       "      <td>1.751540e-07</td>\n",
       "      <td>3.318735</td>\n",
       "      <td>5.448646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244.335510</td>\n",
       "      <td>261898.020000</td>\n",
       "      <td>84340.760000</td>\n",
       "      <td>2.910003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SNo     RA (deg)    DEC (deg)   Signif_Avg   Pivot_Energy  \\\n",
       "count  6659.000000  6659.000000  6659.000000  6658.000000    6659.000000   \n",
       "mean   3329.000000   190.437892   190.437892    15.639615    2737.386617   \n",
       "std    1922.432053    99.673564    99.673564    28.926910    4208.618377   \n",
       "min       0.000000     0.098300     0.098300     1.623841      49.677998   \n",
       "25%    1664.500000   107.921600   107.921600     5.441673    1098.925400   \n",
       "50%    3329.000000   203.322700   203.322700     7.901763    1764.813600   \n",
       "75%    4993.500000   272.013650   272.013650    14.593597    3105.348600   \n",
       "max    6658.000000   359.990800   359.990800   884.642940  195054.750000   \n",
       "\n",
       "           Flux1000  Energy_Flux100  PL_Flux_Density     PL_Index  \\\n",
       "count  6.659000e+03    6.659000e+03     6.658000e+03  6658.000000   \n",
       "mean   1.453465e-09    1.266817e-11     3.625125e-11     2.283192   \n",
       "std    1.913959e-08    1.322343e-10     2.853454e-09     0.295798   \n",
       "min    3.740937e-13    3.707689e-13     2.682798e-17     1.014061   \n",
       "25%    1.584481e-10    1.846526e-12     2.747724e-14     2.064106   \n",
       "50%    3.025504e-10    3.295702e-12     1.422794e-13     2.313304   \n",
       "75%    6.986070e-10    7.068370e-12     5.537922e-13     2.508057   \n",
       "max    1.333140e-06    9.292083e-09     2.328326e-07     3.371371   \n",
       "\n",
       "       LP_Flux_Density     LP_Index      LP_beta   LP_SigCurv  \\\n",
       "count     6.659000e+03  6659.000000  6659.000000  6659.000000   \n",
       "mean      2.767488e-11     2.215740     0.230219     2.975724   \n",
       "std       2.129364e-09     0.385238     0.255427     5.916809   \n",
       "min       3.623631e-17    -0.048980    -0.151934     0.000000   \n",
       "25%       3.177824e-14     1.948876     0.054827     0.984587   \n",
       "50%       1.799026e-13     2.224967     0.138951     2.038423   \n",
       "75%       7.314475e-13     2.492634     0.332463     3.520772   \n",
       "max       1.737617e-07     3.444003     1.000000   239.436390   \n",
       "\n",
       "       PLEC_Flux_Density  PLEC_IndexS  PLEC_ExpfactorS  PLEC_Exp_Index  \\\n",
       "count       6.658000e+03  6658.000000      6658.000000     6658.000000   \n",
       "mean        2.785301e-11     2.142938         0.417266        0.665748   \n",
       "std         2.146587e-09     0.374804         0.624839        0.019202   \n",
       "min         4.000388e-17    -0.159668        -0.368838        0.207654   \n",
       "25%         3.141187e-14     1.891283         0.056860        0.666667   \n",
       "50%         1.755163e-13     2.164486         0.170415        0.666667   \n",
       "75%         7.245303e-13     2.412008         0.550371        0.666667   \n",
       "max         1.751540e-07     3.318735         5.448646        1.000000   \n",
       "\n",
       "       PLEC_SigCurv          Npred  Variability_Index  Frac_Variability  \n",
       "count   6659.000000    6659.000000        6659.000000       6659.000000  \n",
       "mean       2.977417    1352.746395         141.873469          0.285620  \n",
       "std        5.987263    5375.139223        1688.776708          0.350881  \n",
       "min        0.000000       5.918154           0.000000          0.000000  \n",
       "25%        1.006731     299.277260           9.496667          0.000000  \n",
       "50%        2.033031     549.045170          14.199614          0.182504  \n",
       "75%        3.507771    1009.155375          25.253543          0.471393  \n",
       "max      244.335510  261898.020000       84340.760000          2.910003  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace -np.inf with a specific value, 0\n",
    "GS.replace(-np.inf, 0, inplace=True)\n",
    "GS.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1677508102941,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "zQIlGXCoJnpJ",
    "outputId": "9a688e07-0c4b-454c-8a60-6c44de996f68",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6659 entries, 0 to 6658\n",
      "Data columns (total 24 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   SNo                6659 non-null   int64  \n",
      " 1   Name               6659 non-null   object \n",
      " 2   RA (deg)           6659 non-null   float64\n",
      " 3   DEC (deg)          6659 non-null   float64\n",
      " 4   Signif_Avg         6658 non-null   float64\n",
      " 5   Pivot_Energy       6659 non-null   float64\n",
      " 6   Flux1000           6659 non-null   float64\n",
      " 7   Energy_Flux100     6659 non-null   float64\n",
      " 8   PL_Flux_Density    6658 non-null   float64\n",
      " 9   PL_Index           6658 non-null   float64\n",
      " 10  LP_Flux_Density    6659 non-null   float64\n",
      " 11  LP_Index           6659 non-null   float64\n",
      " 12  LP_beta            6659 non-null   float64\n",
      " 13  LP_SigCurv         6659 non-null   float64\n",
      " 14  PLEC_Flux_Density  6658 non-null   float64\n",
      " 15  PLEC_IndexS        6658 non-null   float64\n",
      " 16  PLEC_ExpfactorS    6658 non-null   float64\n",
      " 17  PLEC_Exp_Index     6658 non-null   float64\n",
      " 18  PLEC_SigCurv       6659 non-null   float64\n",
      " 19  Npred              6659 non-null   float64\n",
      " 20  Variability_Index  6659 non-null   float64\n",
      " 21  Frac_Variability   6659 non-null   float64\n",
      " 22  CLASS1             6659 non-null   object \n",
      " 23  CLASS2             6659 non-null   object \n",
      "dtypes: float64(20), int64(1), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# Let's see the type of each column\n",
    "GS.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SVN0mA1kO_bE"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "## 2. Data preperation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1677508102942,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "bTAsVwSQJnpN",
    "outputId": "b4b27b9f-caa5-4932-ace2-a20c5b0ee5e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['     ', 'bcu  ', 'bll  ', 'fsrq ', 'unk  ', 'spp  ', 'PSR  ',\n",
       "       'rdg  ', 'agn  ', 'FSRQ ', 'MSP  ', 'glc  ', 'snr  ', 'gal  ',\n",
       "       'sbg  ', 'BLL  ', 'GAL  ', 'sfr  ', 'bin  ', 'SNR  ', 'HMB  ',\n",
       "       'RDG  ', 'nlsy1', 'msp  ', 'lmb  ', 'NOV  ', 'LMB  ', 'css  ',\n",
       "       'AGN  ', 'PWN  ', 'pwn  ', 'BCU  ', 'hmb  ', 'ssrq ', 'NLSY1',\n",
       "       'psr  ', 'SFR  ', 'BIN  ', 'sey  ', 'GC   '], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the unique values of the class\n",
    "GS['CLASS1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1677508102942,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "Qcc57wcTJnpO",
    "outputId": "ec87d796-07ba-4f89-c530-352bddb72f8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'bcu', 'bll', 'fsrq', 'unk', 'spp', 'PSR', 'rdg', 'agn',\n",
       "       'FSRQ', 'MSP', 'glc', 'snr', 'gal', 'sbg', 'BLL', 'GAL', 'sfr',\n",
       "       'bin', 'SNR', 'HMB', 'RDG', 'nlsy1', 'msp', 'lmb', 'NOV', 'LMB',\n",
       "       'css', 'AGN', 'PWN', 'pwn', 'BCU', 'hmb', 'ssrq', 'NLSY1', 'psr',\n",
       "       'SFR', 'BIN', 'sey', 'GC'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#and removing empty spaces\n",
    "GS['CLASS1'] = GS['CLASS1'].str.strip()\n",
    "GS['CLASS1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1677508102942,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "mC9RTJh3JnpP",
    "outputId": "121aecb0-47ea-4327-9ec7-405c32de9cbd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['          ', 'agn       ', 'unk       ', 'sey       ',\n",
       "       'clust     ', 'psr       ', 'sbg       ', 'msp       ',\n",
       "       'bin       ', 'glc       '], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS['CLASS2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1677508102943,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "Mhq8EXeeJnpQ",
    "outputId": "09c7fadd-fc1d-4de1-ba3e-1f14f17121c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'agn', 'unk', 'sey', 'clust', 'psr', 'sbg', 'msp', 'bin',\n",
       "       'glc'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS['CLASS2'] = GS['CLASS2'].str.strip()\n",
    "GS['CLASS2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1677508102943,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "mHzlNIF0JnpS",
    "outputId": "78f50c19-18e3-4ee4-c18c-257a3acc0964"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'bcu', 'bll', 'fsrq', 'unk', 'spp', 'psr', 'rdg', 'agn', 'msp',\n",
       "       'glc', 'snr', 'gal', 'sbg', 'sfr', 'bin', 'hmb', 'nlsy1', 'lmb',\n",
       "       'nov', 'css', 'pwn', 'ssrq', 'sey', 'gc'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making all the categorical columns lowercase to avoid duplicates\n",
    "for col in ['CLASS1','CLASS2']:\n",
    "    GS[col] = GS[col].str.lower()\n",
    "GS['CLASS1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1677508102943,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "b2xtwqUiJnpU",
    "outputId": "44e8ae9a-139c-49f7-8e7e-1770b3009f5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'bcu', 'bll', 'fsrq', 'unk', 'spp', 'psr', 'agn', 'rdg', 'msp',\n",
       "       'glc', 'snr', 'gal', 'sbg', 'sfr', 'sey', 'clust', 'bin', 'hmb',\n",
       "       'nlsy1', 'lmb', 'nov', 'css', 'pwn', 'ssrq', 'gc'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS['CLASS'] = GS['CLASS1']+GS['CLASS2']\n",
    "GS.drop(['CLASS1','CLASS2'],axis=1,inplace=True)\n",
    "GS['CLASS'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 865,
     "status": "ok",
     "timestamp": 1677508103794,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "3CNeeSRxJnpV",
    "outputId": "751858ef-e544-4c58-8f04-d31edfaf3792"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'bcu', 'bll', 'fsrq', 'unk', 'spp', 'psr', 'agn', 'rdg',\n",
       "       'msp', 'glc', 'snr', 'gal', 'sbg', 'sfr', 'sey', 'clust', 'bin',\n",
       "       'hmb', 'nlsy1', 'lmb', 'nov', 'css', 'pwn', 'ssrq', 'gc'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace empty strings with NaN\n",
    "GS.replace('', np.nan, inplace=True)\n",
    "GS['CLASS'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nt-P4hzzPjQ5"
   },
   "source": [
    "### ***Now here we can see abbrivations of different types of gamma ray sources in the class variable***\n",
    "\n",
    "***Here's a brief explaination:-***\n",
    "\n",
    "\n",
    "1. bcu: BL Lacertae object of uncertain type. A type of blazar with a featureless optical spectrum and strong radio emission.\n",
    "\n",
    "2. bll: BL Lacertae object. A type of blazar with a featureless optical spectrum and strong radio emission.\n",
    "\n",
    "3. fsrq: Flat Spectrum Radio Quasar. A type of blazar with a steep radio spectrum and a flat gamma-ray spectrum.\n",
    "\n",
    "4. unk: Unknown. Refers to a gamma-ray source whose nature and origin are still unclear.\n",
    "\n",
    "5. spp: pectrally peculiar stars. A group of stars that exhibit unusual spectral features, often due to abnormal abundances of certain elements on their surfaces.\n",
    "\n",
    "6. psr: Pulsar. A highly magnetized rotating neutron star that emits beams of electromagnetic radiation, including gamma rays, from its magnetic poles.\n",
    "\n",
    "7. agn: Active Galactic Nucleus. A compact region at the center of a galaxy that emits high levels of radiation across the electromagnetic spectrum, including gamma rays.\n",
    "\n",
    "8. rdg: Radio Galaxy. A type of galaxy that emits large amounts of radio waves, often as a result of an active galactic nucleus.\n",
    "\n",
    "9. msp: Millisecond Pulsar. A type of pulsar with a very short period of rotation, usually less than 30 milliseconds.\n",
    "\n",
    "10. glc: Globular Cluster. A spherical collection of stars bound together by gravity that orbits a galaxy.\n",
    "\n",
    "11. snr: Supernova Remnant. The leftover material from a supernova explosion that emits radiation, including gamma rays.\n",
    "\n",
    "12. gal: Galaxy. A large group of stars, gas, and dust bound together by gravity.\n",
    "\n",
    "13. sbg: Starburst Galaxy. A type of galaxy that is undergoing a burst of star formation.\n",
    "\n",
    "14. sfr: Star Formation Region. A region of space where new stars are being formed.\n",
    "\n",
    "15. sey: Seyfert Galaxy. A type of active galaxy with a bright, compact nucleus that emits radiation across the electromagnetic spectrum, including gamma rays.\n",
    "\n",
    "16. clust: Cluster of Galaxies. A group of galaxies that are gravitationally bound together.\n",
    "\n",
    "17. bin: Binary System. A system of two objects, such as stars, that orbit around a common center of mass.\n",
    "\n",
    "18. hmb: High-Mass Binary. A binary system consisting of two high-mass stars.\n",
    "\n",
    "19. nlsy1: Narrow-Line Seyfert 1. A type of Seyfert galaxy with a narrow emission line in its spectrum.\n",
    "\n",
    "20. lmb: Low-Mass Binary. A binary system consisting of two low-mass stars.\n",
    "\n",
    "21. nov: Nova. A type of stellar explosion that occurs in a binary system where one of the stars is a white dwarf.\n",
    "\n",
    "22. css: Compact Steep-Spectrum Source. A type of radio source with a steep radio spectrum and a compact size.\n",
    "\n",
    "23. pwn: Pulsar Wind Nebula. A nebula that forms when the wind of high-energy particles from a pulsar interacts with the surrounding gas and dust.\n",
    "\n",
    "24. ssrq: Soft Spectral Radio Quasar. A type of quasar with a steep radio spectrum and a soft gamma-ray spectrum.\n",
    "\n",
    "25. gc: Galactic Center. The central region of the Milky Way galaxy, which is a strong source of gamma rays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 55,
     "status": "ok",
     "timestamp": 1677508103795,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "5Uf6UDVAJnpY",
    "outputId": "262fed29-b630-4a71-df3d-4b0e6621a315",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6659 entries, 0 to 6658\n",
      "Data columns (total 23 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   SNo                6659 non-null   int64  \n",
      " 1   Name               6659 non-null   object \n",
      " 2   RA (deg)           6659 non-null   float64\n",
      " 3   DEC (deg)          6659 non-null   float64\n",
      " 4   Signif_Avg         6658 non-null   float64\n",
      " 5   Pivot_Energy       6659 non-null   float64\n",
      " 6   Flux1000           6659 non-null   float64\n",
      " 7   Energy_Flux100     6659 non-null   float64\n",
      " 8   PL_Flux_Density    6658 non-null   float64\n",
      " 9   PL_Index           6658 non-null   float64\n",
      " 10  LP_Flux_Density    6659 non-null   float64\n",
      " 11  LP_Index           6659 non-null   float64\n",
      " 12  LP_beta            6659 non-null   float64\n",
      " 13  LP_SigCurv         6659 non-null   float64\n",
      " 14  PLEC_Flux_Density  6658 non-null   float64\n",
      " 15  PLEC_IndexS        6658 non-null   float64\n",
      " 16  PLEC_ExpfactorS    6658 non-null   float64\n",
      " 17  PLEC_Exp_Index     6658 non-null   float64\n",
      " 18  PLEC_SigCurv       6659 non-null   float64\n",
      " 19  Npred              6659 non-null   float64\n",
      " 20  Variability_Index  6659 non-null   float64\n",
      " 21  Frac_Variability   6659 non-null   float64\n",
      " 22  CLASS              4811 non-null   object \n",
      "dtypes: float64(20), int64(1), object(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking for missing values\n",
    "GS.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 50,
     "status": "ok",
     "timestamp": 1677508103797,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "NE9lnGAKJnpa",
    "outputId": "b92f6225-bac8-4d4f-ba80-66178226aba9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                     0\n",
       "Name                    0\n",
       "RA (deg)                0\n",
       "DEC (deg)               0\n",
       "Signif_Avg              1\n",
       "Pivot_Energy            0\n",
       "Flux1000                0\n",
       "Energy_Flux100          0\n",
       "PL_Flux_Density         1\n",
       "PL_Index                1\n",
       "LP_Flux_Density         0\n",
       "LP_Index                0\n",
       "LP_beta                 0\n",
       "LP_SigCurv              0\n",
       "PLEC_Flux_Density       1\n",
       "PLEC_IndexS             1\n",
       "PLEC_ExpfactorS         1\n",
       "PLEC_Exp_Index          1\n",
       "PLEC_SigCurv            0\n",
       "Npred                   0\n",
       "Variability_Index       0\n",
       "Frac_Variability        0\n",
       "CLASS                1848\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "GS.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 44,
     "status": "ok",
     "timestamp": 1677508103798,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "BD9OJGZlJnpb",
    "outputId": "f8dfb413-b9cd-448a-bab7-8c7b7cf16365"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                  0\n",
       "Name                 0\n",
       "RA (deg)             0\n",
       "DEC (deg)            0\n",
       "Signif_Avg           0\n",
       "Pivot_Energy         0\n",
       "Flux1000             0\n",
       "Energy_Flux100       0\n",
       "PL_Flux_Density      0\n",
       "PL_Index             0\n",
       "LP_Flux_Density      0\n",
       "LP_Index             0\n",
       "LP_beta              0\n",
       "LP_SigCurv           0\n",
       "PLEC_Flux_Density    0\n",
       "PLEC_IndexS          0\n",
       "PLEC_ExpfactorS      0\n",
       "PLEC_Exp_Index       0\n",
       "PLEC_SigCurv         0\n",
       "Npred                0\n",
       "Variability_Index    0\n",
       "Frac_Variability     0\n",
       "CLASS                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# impute missing values with median and checking again\n",
    "GS.dropna(inplace=True)\n",
    "GS.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CYRMXrGYUF2P"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "## 3. Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "executionInfo": {
     "elapsed": 38,
     "status": "ok",
     "timestamp": 1677508103799,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "tly1jcz9Jnpe",
    "outputId": "2e2ea49e-4703-4088-cce0-d544d16b3de1"
   },
   "outputs": [
    {
     "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>RA (deg)</th>\n",
       "      <th>DEC (deg)</th>\n",
       "      <th>Signif_Avg</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Flux1000</th>\n",
       "      <th>Energy_Flux100</th>\n",
       "      <th>PL_Flux_Density</th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>LP_Flux_Density</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>LP_beta</th>\n",
       "      <th>LP_SigCurv</th>\n",
       "      <th>PLEC_Flux_Density</th>\n",
       "      <th>PLEC_IndexS</th>\n",
       "      <th>PLEC_ExpfactorS</th>\n",
       "      <th>PLEC_Exp_Index</th>\n",
       "      <th>PLEC_SigCurv</th>\n",
       "      <th>Npred</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4.810000e+03</td>\n",
       "      <td>4.810000e+03</td>\n",
       "      <td>4.810000e+03</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4.810000e+03</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4.810000e+03</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "      <td>4810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>182.355077</td>\n",
       "      <td>182.355077</td>\n",
       "      <td>18.811330</td>\n",
       "      <td>2652.431183</td>\n",
       "      <td>1.762063e-09</td>\n",
       "      <td>1.527858e-11</td>\n",
       "      <td>5.005888e-11</td>\n",
       "      <td>2.254534</td>\n",
       "      <td>3.813039e-11</td>\n",
       "      <td>2.181060</td>\n",
       "      <td>0.187287</td>\n",
       "      <td>3.113043</td>\n",
       "      <td>3.837233e-11</td>\n",
       "      <td>2.120971</td>\n",
       "      <td>0.320614</td>\n",
       "      <td>0.665395</td>\n",
       "      <td>3.130517</td>\n",
       "      <td>1625.278195</td>\n",
       "      <td>191.752099</td>\n",
       "      <td>0.345808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.411838</td>\n",
       "      <td>101.411838</td>\n",
       "      <td>33.348128</td>\n",
       "      <td>4026.303113</td>\n",
       "      <td>2.249212e-08</td>\n",
       "      <td>1.552055e-10</td>\n",
       "      <td>3.357141e-09</td>\n",
       "      <td>0.298521</td>\n",
       "      <td>2.505421e-09</td>\n",
       "      <td>0.376522</td>\n",
       "      <td>0.221611</td>\n",
       "      <td>6.845041</td>\n",
       "      <td>2.525496e-09</td>\n",
       "      <td>0.368820</td>\n",
       "      <td>0.527601</td>\n",
       "      <td>0.022582</td>\n",
       "      <td>6.926288</td>\n",
       "      <td>6277.673159</td>\n",
       "      <td>1984.823787</td>\n",
       "      <td>0.374629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.312600</td>\n",
       "      <td>0.312600</td>\n",
       "      <td>2.229098</td>\n",
       "      <td>49.677998</td>\n",
       "      <td>3.740937e-13</td>\n",
       "      <td>3.707689e-13</td>\n",
       "      <td>6.224186e-17</td>\n",
       "      <td>1.058625</td>\n",
       "      <td>7.244539e-17</td>\n",
       "      <td>-0.048980</td>\n",
       "      <td>-0.116156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.593357e-17</td>\n",
       "      <td>-0.159668</td>\n",
       "      <td>-0.143681</td>\n",
       "      <td>0.207654</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.056292</td>\n",
       "      <td>1.590456</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>96.102175</td>\n",
       "      <td>96.102175</td>\n",
       "      <td>6.125386</td>\n",
       "      <td>1033.339925</td>\n",
       "      <td>1.611237e-10</td>\n",
       "      <td>1.933204e-12</td>\n",
       "      <td>2.843842e-14</td>\n",
       "      <td>2.027785</td>\n",
       "      <td>3.224470e-14</td>\n",
       "      <td>1.917850</td>\n",
       "      <td>0.047469</td>\n",
       "      <td>0.934793</td>\n",
       "      <td>3.211871e-14</td>\n",
       "      <td>1.867406</td>\n",
       "      <td>0.049310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.974149</td>\n",
       "      <td>317.541123</td>\n",
       "      <td>10.584725</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>185.939700</td>\n",
       "      <td>185.939700</td>\n",
       "      <td>9.559812</td>\n",
       "      <td>1712.585150</td>\n",
       "      <td>3.040690e-10</td>\n",
       "      <td>3.528937e-12</td>\n",
       "      <td>1.524306e-13</td>\n",
       "      <td>2.263703</td>\n",
       "      <td>1.830221e-13</td>\n",
       "      <td>2.183158</td>\n",
       "      <td>0.112786</td>\n",
       "      <td>1.888546</td>\n",
       "      <td>1.802256e-13</td>\n",
       "      <td>2.134784</td>\n",
       "      <td>0.131771</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.929566</td>\n",
       "      <td>586.498080</td>\n",
       "      <td>16.736631</td>\n",
       "      <td>0.273666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>266.978625</td>\n",
       "      <td>266.978625</td>\n",
       "      <td>18.431345</td>\n",
       "      <td>3082.919050</td>\n",
       "      <td>7.469120e-10</td>\n",
       "      <td>7.838020e-12</td>\n",
       "      <td>7.083509e-13</td>\n",
       "      <td>2.483710</td>\n",
       "      <td>8.859359e-13</td>\n",
       "      <td>2.445840</td>\n",
       "      <td>0.248677</td>\n",
       "      <td>3.442299</td>\n",
       "      <td>8.749478e-13</td>\n",
       "      <td>2.384490</td>\n",
       "      <td>0.371141</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.420921</td>\n",
       "      <td>1195.791075</td>\n",
       "      <td>35.496843</td>\n",
       "      <td>0.552566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>359.990800</td>\n",
       "      <td>359.990800</td>\n",
       "      <td>884.642940</td>\n",
       "      <td>195054.750000</td>\n",
       "      <td>1.333140e-06</td>\n",
       "      <td>9.292083e-09</td>\n",
       "      <td>2.328326e-07</td>\n",
       "      <td>3.371371</td>\n",
       "      <td>1.737617e-07</td>\n",
       "      <td>3.444003</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>239.436390</td>\n",
       "      <td>1.751540e-07</td>\n",
       "      <td>3.212312</td>\n",
       "      <td>5.448646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>244.335510</td>\n",
       "      <td>261898.020000</td>\n",
       "      <td>84340.760000</td>\n",
       "      <td>2.910003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RA (deg)    DEC (deg)   Signif_Avg   Pivot_Energy      Flux1000  \\\n",
       "count  4810.000000  4810.000000  4810.000000    4810.000000  4.810000e+03   \n",
       "mean    182.355077   182.355077    18.811330    2652.431183  1.762063e-09   \n",
       "std     101.411838   101.411838    33.348128    4026.303113  2.249212e-08   \n",
       "min       0.312600     0.312600     2.229098      49.677998  3.740937e-13   \n",
       "25%      96.102175    96.102175     6.125386    1033.339925  1.611237e-10   \n",
       "50%     185.939700   185.939700     9.559812    1712.585150  3.040690e-10   \n",
       "75%     266.978625   266.978625    18.431345    3082.919050  7.469120e-10   \n",
       "max     359.990800   359.990800   884.642940  195054.750000  1.333140e-06   \n",
       "\n",
       "       Energy_Flux100  PL_Flux_Density     PL_Index  LP_Flux_Density  \\\n",
       "count    4.810000e+03     4.810000e+03  4810.000000     4.810000e+03   \n",
       "mean     1.527858e-11     5.005888e-11     2.254534     3.813039e-11   \n",
       "std      1.552055e-10     3.357141e-09     0.298521     2.505421e-09   \n",
       "min      3.707689e-13     6.224186e-17     1.058625     7.244539e-17   \n",
       "25%      1.933204e-12     2.843842e-14     2.027785     3.224470e-14   \n",
       "50%      3.528937e-12     1.524306e-13     2.263703     1.830221e-13   \n",
       "75%      7.838020e-12     7.083509e-13     2.483710     8.859359e-13   \n",
       "max      9.292083e-09     2.328326e-07     3.371371     1.737617e-07   \n",
       "\n",
       "          LP_Index      LP_beta   LP_SigCurv  PLEC_Flux_Density  PLEC_IndexS  \\\n",
       "count  4810.000000  4810.000000  4810.000000       4.810000e+03  4810.000000   \n",
       "mean      2.181060     0.187287     3.113043       3.837233e-11     2.120971   \n",
       "std       0.376522     0.221611     6.845041       2.525496e-09     0.368820   \n",
       "min      -0.048980    -0.116156     0.000000       7.593357e-17    -0.159668   \n",
       "25%       1.917850     0.047469     0.934793       3.211871e-14     1.867406   \n",
       "50%       2.183158     0.112786     1.888546       1.802256e-13     2.134784   \n",
       "75%       2.445840     0.248677     3.442299       8.749478e-13     2.384490   \n",
       "max       3.444003     0.999999   239.436390       1.751540e-07     3.212312   \n",
       "\n",
       "       PLEC_ExpfactorS  PLEC_Exp_Index  PLEC_SigCurv          Npred  \\\n",
       "count      4810.000000     4810.000000   4810.000000    4810.000000   \n",
       "mean          0.320614        0.665395      3.130517    1625.278195   \n",
       "std           0.527601        0.022582      6.926288    6277.673159   \n",
       "min          -0.143681        0.207654      0.000000       9.056292   \n",
       "25%           0.049310        0.666667      0.974149     317.541123   \n",
       "50%           0.131771        0.666667      1.929566     586.498080   \n",
       "75%           0.371141        0.666667      3.420921    1195.791075   \n",
       "max           5.448646        1.000000    244.335510  261898.020000   \n",
       "\n",
       "       Variability_Index  Frac_Variability  \n",
       "count        4810.000000       4810.000000  \n",
       "mean          191.752099          0.345808  \n",
       "std          1984.823787          0.374629  \n",
       "min             1.590456          0.000000  \n",
       "25%            10.584725          0.000000  \n",
       "50%            16.736631          0.273666  \n",
       "75%            35.496843          0.552566  \n",
       "max         84340.760000          2.910003  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# putting input variales to X\n",
    "X = GS[['RA (deg)','DEC (deg)','Signif_Avg','Pivot_Energy','Flux1000','Energy_Flux100','PL_Flux_Density','PL_Index',\n",
    "            'LP_Flux_Density','LP_Index','LP_beta','LP_SigCurv','PLEC_Flux_Density','PLEC_IndexS','PLEC_ExpfactorS',\n",
    "            'PLEC_Exp_Index','PLEC_SigCurv','Npred','Variability_Index','Frac_Variability' ]]\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1677508103801,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "Pciq5btiJnpf",
    "outputId": "6d147997-c3ec-4e5f-959c-ecf147eb049c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bcu', 'bll', 'fsrq', 'unk', 'spp', 'psr', 'agn', 'rdg', 'msp',\n",
       "       'glc', 'snr', 'gal', 'sbg', 'sfr', 'sey', 'clust', 'bin', 'hmb',\n",
       "       'nlsy1', 'lmb', 'nov', 'css', 'pwn', 'ssrq', 'gc'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = GS['CLASS']\n",
    "Y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1677508103802,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "hiUxr7WKJnpg"
   },
   "outputs": [],
   "source": [
    "# Splitting the data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=0.9, test_size=0.1, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8x3oP12DUhy1"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "## 4. Feature Scaling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1677508103803,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "6tP1sTpYJnph",
    "outputId": "fb17c63c-bd9e-46d1-fcc4-41b2bfbb081c"
   },
   "outputs": [
    {
     "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>RA (deg)</th>\n",
       "      <th>DEC (deg)</th>\n",
       "      <th>Signif_Avg</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Flux1000</th>\n",
       "      <th>Energy_Flux100</th>\n",
       "      <th>PL_Flux_Density</th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>LP_Flux_Density</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>LP_beta</th>\n",
       "      <th>LP_SigCurv</th>\n",
       "      <th>PLEC_Flux_Density</th>\n",
       "      <th>PLEC_IndexS</th>\n",
       "      <th>PLEC_ExpfactorS</th>\n",
       "      <th>PLEC_Exp_Index</th>\n",
       "      <th>PLEC_SigCurv</th>\n",
       "      <th>Npred</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.570733</td>\n",
       "      <td>-0.570733</td>\n",
       "      <td>-0.415835</td>\n",
       "      <td>-0.434848</td>\n",
       "      <td>-0.069486</td>\n",
       "      <td>-0.076751</td>\n",
       "      <td>-0.015558</td>\n",
       "      <td>1.538051</td>\n",
       "      <td>-0.015754</td>\n",
       "      <td>1.965922</td>\n",
       "      <td>0.492912</td>\n",
       "      <td>-0.085265</td>\n",
       "      <td>-0.015735</td>\n",
       "      <td>1.638250</td>\n",
       "      <td>0.450416</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>-0.107052</td>\n",
       "      <td>-0.152713</td>\n",
       "      <td>-0.092753</td>\n",
       "      <td>-0.919634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.063332</td>\n",
       "      <td>-1.063332</td>\n",
       "      <td>0.418282</td>\n",
       "      <td>-0.438068</td>\n",
       "      <td>-0.038866</td>\n",
       "      <td>-0.010919</td>\n",
       "      <td>-0.015094</td>\n",
       "      <td>0.964902</td>\n",
       "      <td>-0.015162</td>\n",
       "      <td>0.944771</td>\n",
       "      <td>-0.675863</td>\n",
       "      <td>-0.202430</td>\n",
       "      <td>-0.015158</td>\n",
       "      <td>1.081470</td>\n",
       "      <td>-0.543128</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>-0.249411</td>\n",
       "      <td>0.257959</td>\n",
       "      <td>-0.004839</td>\n",
       "      <td>0.430416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.536070</td>\n",
       "      <td>-1.536070</td>\n",
       "      <td>-0.373653</td>\n",
       "      <td>-0.310933</td>\n",
       "      <td>-0.070906</td>\n",
       "      <td>-0.085762</td>\n",
       "      <td>-0.015649</td>\n",
       "      <td>0.155493</td>\n",
       "      <td>-0.015941</td>\n",
       "      <td>0.376243</td>\n",
       "      <td>-1.082326</td>\n",
       "      <td>-0.316766</td>\n",
       "      <td>-0.015905</td>\n",
       "      <td>0.822484</td>\n",
       "      <td>0.639545</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>-0.440622</td>\n",
       "      <td>-0.198104</td>\n",
       "      <td>-0.090423</td>\n",
       "      <td>-0.919634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.464382</td>\n",
       "      <td>0.464382</td>\n",
       "      <td>-0.321229</td>\n",
       "      <td>-0.257451</td>\n",
       "      <td>-0.070159</td>\n",
       "      <td>-0.084784</td>\n",
       "      <td>-0.015654</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>-0.015942</td>\n",
       "      <td>0.094177</td>\n",
       "      <td>-0.585856</td>\n",
       "      <td>-0.354696</td>\n",
       "      <td>-0.015920</td>\n",
       "      <td>0.330362</td>\n",
       "      <td>-0.588698</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>-0.417683</td>\n",
       "      <td>-0.193640</td>\n",
       "      <td>-0.078500</td>\n",
       "      <td>1.330491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.257720</td>\n",
       "      <td>-0.257720</td>\n",
       "      <td>-0.122886</td>\n",
       "      <td>2.641326</td>\n",
       "      <td>0.135701</td>\n",
       "      <td>0.252411</td>\n",
       "      <td>-0.015666</td>\n",
       "      <td>-0.287231</td>\n",
       "      <td>-0.015961</td>\n",
       "      <td>-0.049264</td>\n",
       "      <td>-0.908881</td>\n",
       "      <td>-0.359289</td>\n",
       "      <td>-0.015937</td>\n",
       "      <td>0.197107</td>\n",
       "      <td>-0.655555</td>\n",
       "      <td>0.054052</td>\n",
       "      <td>-0.334667</td>\n",
       "      <td>0.319076</td>\n",
       "      <td>-0.089688</td>\n",
       "      <td>-0.919634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RA (deg)  DEC (deg)  Signif_Avg  Pivot_Energy  Flux1000  Energy_Flux100  \\\n",
       "0 -0.570733  -0.570733   -0.415835     -0.434848 -0.069486       -0.076751   \n",
       "1 -1.063332  -1.063332    0.418282     -0.438068 -0.038866       -0.010919   \n",
       "2 -1.536070  -1.536070   -0.373653     -0.310933 -0.070906       -0.085762   \n",
       "3  0.464382   0.464382   -0.321229     -0.257451 -0.070159       -0.084784   \n",
       "4 -0.257720  -0.257720   -0.122886      2.641326  0.135701        0.252411   \n",
       "\n",
       "   PL_Flux_Density  PL_Index  LP_Flux_Density  LP_Index   LP_beta  LP_SigCurv  \\\n",
       "0        -0.015558  1.538051        -0.015754  1.965922  0.492912   -0.085265   \n",
       "1        -0.015094  0.964902        -0.015162  0.944771 -0.675863   -0.202430   \n",
       "2        -0.015649  0.155493        -0.015941  0.376243 -1.082326   -0.316766   \n",
       "3        -0.015654 -0.000033        -0.015942  0.094177 -0.585856   -0.354696   \n",
       "4        -0.015666 -0.287231        -0.015961 -0.049264 -0.908881   -0.359289   \n",
       "\n",
       "   PLEC_Flux_Density  PLEC_IndexS  PLEC_ExpfactorS  PLEC_Exp_Index  \\\n",
       "0          -0.015735     1.638250         0.450416        0.054052   \n",
       "1          -0.015158     1.081470        -0.543128        0.054052   \n",
       "2          -0.015905     0.822484         0.639545        0.054052   \n",
       "3          -0.015920     0.330362        -0.588698        0.054052   \n",
       "4          -0.015937     0.197107        -0.655555        0.054052   \n",
       "\n",
       "   PLEC_SigCurv     Npred  Variability_Index  Frac_Variability  \n",
       "0     -0.107052 -0.152713          -0.092753         -0.919634  \n",
       "1     -0.249411  0.257959          -0.004839          0.430416  \n",
       "2     -0.440622 -0.198104          -0.090423         -0.919634  \n",
       "3     -0.417683 -0.193640          -0.078500          1.330491  \n",
       "4     -0.334667  0.319076          -0.089688         -0.919634  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)\n",
    "X_test = pd.DataFrame(scaler.fit_transform(X_test), columns=X_test.columns)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OXrZAsoGU93r"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "## 5. Let's see the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 679
    },
    "executionInfo": {
     "elapsed": 3112,
     "status": "ok",
     "timestamp": 1677508106889,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "2nFPetmmJnpi",
    "outputId": "cd22ac92-0958-425b-efbf-def753311ed5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (20,10))        # Size of the figure\n",
    "sns.heatmap(GS.corr(),annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kacY2UGPVLHG"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "## 6. Model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1832,
     "status": "ok",
     "timestamp": 1677508108707,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "gtq2nVErJnpi",
    "outputId": "cc631239-f233-4b2e-8479-39fa8bc77b44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=300)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = LogisticRegression(max_iter=300)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2861,
     "status": "ok",
     "timestamp": 1677508111561,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "FK4w649JJnpk",
    "outputId": "2731a685-2bd0-4d57-fd7e-e8bfdc8f5fdc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=300)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 71,
     "status": "ok",
     "timestamp": 1677508111562,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "hgWrxhuZJnpl",
    "outputId": "b8f9406c-310c-41f5-d5c3-8297ffd07bc1",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['unk', 'fsrq', 'bcu', 'bcu', 'snr', 'fsrq', 'bcu', 'fsrq', 'bll',\n",
       "       'bcu'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the predicted values on the train set\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_train_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yb4mDccUVmRP"
   },
   "source": [
    "\n",
    "\n",
    "---\n",
    "\n",
    "## 7. Assessing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 63,
     "status": "ok",
     "timestamp": 1677508111562,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "2PBNRGm8Jnpm",
    "outputId": "8b4b80d4-7ffe-4b36-c0e1-c766348542c6"
   },
   "outputs": [
    {
     "data": {
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       "array([[1.28874980e-01, 2.96328966e-01, 5.39265652e-03, 5.63044583e-03,\n",
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       "        1.27733577e-05, 1.15819860e-04, 2.33226678e-04, 1.10690096e-04,\n",
       "        2.07431852e-04, 5.55290196e-04, 2.65220069e-04, 1.26311444e-05,\n",
       "        3.67168846e-04, 2.83744927e-03, 1.09115674e-03, 8.71456654e-04,\n",
       "        2.67222137e-05, 7.88198170e-05, 1.07091669e-03, 3.04927532e-04,\n",
       "        3.53482612e-03],\n",
       "       [4.34427050e-03, 6.43224976e-02, 3.15485738e-04, 1.02924053e-02,\n",
       "        3.82260201e-05, 1.47688095e-04, 1.12585249e-04, 1.03624543e-03,\n",
       "        1.69835132e-03, 1.88050417e-04, 2.73419752e-02, 8.12240938e-05,\n",
       "        6.30798813e-07, 1.42166924e-05, 7.30312763e-06, 3.60352311e-02,\n",
       "        1.26746304e-01, 4.52020547e-02, 7.56185168e-04, 4.24587769e-03,\n",
       "        1.59225933e-02, 2.70762096e-01, 2.31439197e-01, 5.43190754e-05,\n",
       "        1.58894985e-01]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_prob = model.predict_proba(X_train)\n",
    "y_train_prob[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 57,
     "status": "ok",
     "timestamp": 1677508111562,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "pjBb-wrGJnpm",
    "outputId": "16f2f126-b07d-40cd-909b-02f6ddb01d91",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>True</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>SNo</th>\n",
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       "      <td>fsrq</td>\n",
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       "      <td>1128</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fsrq</td>\n",
       "      <td>bcu</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bll</td>\n",
       "      <td>bcu</td>\n",
       "      <td>3833</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>4324</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bcu</td>\n",
       "      <td>5786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4325</th>\n",
       "      <td>unk</td>\n",
       "      <td>unk</td>\n",
       "      <td>2379</td>\n",
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       "    <tr>\n",
       "      <th>4326</th>\n",
       "      <td>bcu</td>\n",
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       "      <th>4327</th>\n",
       "      <td>bll</td>\n",
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       "      <td>102</td>\n",
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       "    <tr>\n",
       "      <th>4328</th>\n",
       "      <td>bll</td>\n",
       "      <td>bll</td>\n",
       "      <td>5445</td>\n",
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       "  </tbody>\n",
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       "<p>4329 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      True Predicted   SNo\n",
       "0      unk       unk  1928\n",
       "1     fsrq      fsrq  1128\n",
       "2     fsrq       bcu   421\n",
       "3      bll       bcu  3833\n",
       "4      sfr       snr  2463\n",
       "...    ...       ...   ...\n",
       "4324   bcu       bcu  5786\n",
       "4325   unk       unk  2379\n",
       "4326   bcu       bcu   437\n",
       "4327   bll       bll   102\n",
       "4328   bll       bll  5445\n",
       "\n",
       "[4329 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# True and predicted values\n",
    "matrix_df = pd.DataFrame({'True':y_train.values, 'Predicted':y_train_pred})\n",
    "matrix_df['SNo'] = y_train.index\n",
    "matrix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 55,
     "status": "ok",
     "timestamp": 1677508111563,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "iERfsbVvJnpp",
    "outputId": "3c396b06-e64a-4440-dd19-e4254ed16769"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1 104   0  17   0   0  22   0   0   0   0   0   1   0   0   2   0   0\n",
      "    0   0   0   0   0   0  16]\n",
      " [  4 824   0 319   0   0 141   0   0   0   0   0  10   0   0   7   0   0\n",
      "    0   0   0   0   2   0  29]\n",
      " [  0   3   0   0   0   0   0   0   0   0   0   0   3   0   0   0   0   0\n",
      "    0   0   0   0   1   0   1]\n",
      " [  0 304   0 978   0   0  33   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   2   0   0   2]\n",
      " [  0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0   3   0   0   0   0   2   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0 212   0  53   0   0 443   0   0   0   0   0   0   0   0   1   0   0\n",
      "    0   0   0   0   0   0   3]\n",
      " [  0   3   0   1   0   0   0   1   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   1   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   1   0   0   0]\n",
      " [  0  21   0   0   0   0   1   0   0   0   0   0   8   0   0   2   0   0\n",
      "    0   0   0   0   0   0   2]\n",
      " [  0   3   0   0   0   0   0   0   0   0   1   0   0   0   0   4   0   0\n",
      "    0   0   0   1   0   0   1]\n",
      " [  0   3   0   1   0   0   1   0   0   0   0   0   3   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0  31   0   1   0   0   1   0   0   0   0   0 101   0   0   6   0   0\n",
      "    0   0   0   0   0   0   8]\n",
      " [  0   1   0   0   0   0   6   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0   3   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0  11   0   0   0   0   1   0   0   0   0   0  19   0   0  84   0   0\n",
      "    0   0   0   2   0   0  17]\n",
      " [  0   4   0   4   0   0   0   0   0   0   0   0   0   0   0   1   8   0\n",
      "    0   0   0   0   2   0   0]\n",
      " [  0  17   0  16   0   0   6   0   0   0   0   0   0   0   0   1   0   0\n",
      "    0   0   0   0   0   0   0]\n",
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      "    0   0   0   0   0   0   0]\n",
      " [  0   8   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  0   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   1   1   0   0   0]\n",
      " [  0   5   0  10   0   0   0   0   0   0   0   0   0   0   0   6   1   1\n",
      "    0   0   0   7   2   0   5]\n",
      " [  0  49   0   1   0   0   1   0   0   0   0   0   2   0   0   3   1   0\n",
      "    0   0   0   2   5   0  35]\n",
      " [  0   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0]\n",
      " [  1 128   0   7   0   0   0   0   0   0   0   0   2   0   0   3   0   0\n",
      "    0   0   0   1   3   0  68]]\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "confusion = metrics.confusion_matrix(matrix_df['True'], matrix_df['Predicted'])\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 48,
     "status": "ok",
     "timestamp": 1677508111563,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "yBWSn9tVJnpr",
    "outputId": "c7c6e5b1-9d7c-4db4-bfc4-1fc0f7a2f58c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         agn       0.17      0.01      0.01       163\n",
      "         bcu       0.47      0.62      0.53      1336\n",
      "         bin       0.00      0.00      0.00         8\n",
      "         bll       0.69      0.74      0.72      1319\n",
      "       clust       0.00      0.00      0.00         1\n",
      "         css       0.00      0.00      0.00         5\n",
      "        fsrq       0.67      0.62      0.65       712\n",
      "         gal       1.00      0.17      0.29         6\n",
      "          gc       0.00      0.00      0.00         1\n",
      "         glc       0.00      0.00      0.00        34\n",
      "         hmb       1.00      0.10      0.18        10\n",
      "         lmb       0.00      0.00      0.00         8\n",
      "         msp       0.68      0.68      0.68       148\n",
      "       nlsy1       0.00      0.00      0.00         7\n",
      "         nov       0.00      0.00      0.00         4\n",
      "         psr       0.70      0.63      0.66       134\n",
      "         pwn       0.80      0.42      0.55        19\n",
      "         rdg       0.00      0.00      0.00        40\n",
      "         sbg       0.00      0.00      0.00        10\n",
      "         sey       0.00      0.00      0.00         9\n",
      "         sfr       1.00      0.25      0.40         4\n",
      "         snr       0.39      0.19      0.25        37\n",
      "         spp       0.33      0.05      0.09        99\n",
      "        ssrq       0.00      0.00      0.00         2\n",
      "         unk       0.36      0.32      0.34       213\n",
      "\n",
      "    accuracy                           0.58      4329\n",
      "   macro avg       0.33      0.19      0.21      4329\n",
      "weighted avg       0.56      0.58      0.56      4329\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#printing the classification report\n",
    "print(metrics.classification_report(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 42,
     "status": "ok",
     "timestamp": 1677508111563,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "42BXu2bPJnps",
    "outputId": "908cd2cd-fd2d-4061-ab3e-e24f634341c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5825825825825826\n"
     ]
    }
   ],
   "source": [
    "# Let's check the overall accuracy.\n",
    "print(metrics.accuracy_score(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GT7iZn1kWMfL"
   },
   "source": [
    "*As you can see our model has an accuracy of 58%*\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "## 8. Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1677508111564,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "b2aIkn55Jnpt",
    "outputId": "15288e6c-b2fc-4722-da9c-0e6c1bc50f6d"
   },
   "outputs": [
    {
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       "      <th>RA (deg)</th>\n",
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       "      <th>Variability_Index</th>\n",
       "      <th>Frac_Variability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.710862</td>\n",
       "      <td>-1.710862</td>\n",
       "      <td>-0.409208</td>\n",
       "      <td>0.319550</td>\n",
       "      <td>-0.242647</td>\n",
       "      <td>-0.283676</td>\n",
       "      <td>-0.285043</td>\n",
       "      <td>-0.746813</td>\n",
       "      <td>-0.282818</td>\n",
       "      <td>-0.631044</td>\n",
       "      <td>-0.027647</td>\n",
       "      <td>-0.313467</td>\n",
       "      <td>-0.285382</td>\n",
       "      <td>-0.625180</td>\n",
       "      <td>-0.336277</td>\n",
       "      <td>0.073835</td>\n",
       "      <td>-0.386274</td>\n",
       "      <td>-0.352231</td>\n",
       "      <td>-0.160966</td>\n",
       "      <td>-0.186350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.046791</td>\n",
       "      <td>1.046791</td>\n",
       "      <td>-0.351492</td>\n",
       "      <td>-0.483898</td>\n",
       "      <td>-0.195925</td>\n",
       "      <td>-0.197928</td>\n",
       "      <td>-0.214998</td>\n",
       "      <td>1.246638</td>\n",
       "      <td>-0.200268</td>\n",
       "      <td>1.772410</td>\n",
       "      <td>0.838241</td>\n",
       "      <td>0.191379</td>\n",
       "      <td>-0.200123</td>\n",
       "      <td>1.526625</td>\n",
       "      <td>0.774721</td>\n",
       "      <td>0.073835</td>\n",
       "      <td>0.055805</td>\n",
       "      <td>-0.177968</td>\n",
       "      <td>-0.080191</td>\n",
       "      <td>1.635442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.271401</td>\n",
       "      <td>-0.271401</td>\n",
       "      <td>-0.061306</td>\n",
       "      <td>-0.009471</td>\n",
       "      <td>-0.189197</td>\n",
       "      <td>-0.205878</td>\n",
       "      <td>-0.274663</td>\n",
       "      <td>-0.406430</td>\n",
       "      <td>-0.275430</td>\n",
       "      <td>-0.049432</td>\n",
       "      <td>-1.006547</td>\n",
       "      <td>-0.344988</td>\n",
       "      <td>-0.276715</td>\n",
       "      <td>0.033265</td>\n",
       "      <td>-0.647847</td>\n",
       "      <td>0.073835</td>\n",
       "      <td>-0.678316</td>\n",
       "      <td>-0.201769</td>\n",
       "      <td>-0.144487</td>\n",
       "      <td>-0.026748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.156032</td>\n",
       "      <td>1.156032</td>\n",
       "      <td>5.446181</td>\n",
       "      <td>-0.276063</td>\n",
       "      <td>1.697262</td>\n",
       "      <td>2.591379</td>\n",
       "      <td>0.258083</td>\n",
       "      <td>-1.504647</td>\n",
       "      <td>0.182631</td>\n",
       "      <td>-1.111385</td>\n",
       "      <td>-0.684526</td>\n",
       "      <td>0.208078</td>\n",
       "      <td>0.188071</td>\n",
       "      <td>-0.959591</td>\n",
       "      <td>-0.616565</td>\n",
       "      <td>0.073835</td>\n",
       "      <td>0.173668</td>\n",
       "      <td>2.829953</td>\n",
       "      <td>1.615038</td>\n",
       "      <td>0.343816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.558330</td>\n",
       "      <td>0.558330</td>\n",
       "      <td>-0.313056</td>\n",
       "      <td>0.106106</td>\n",
       "      <td>-0.195516</td>\n",
       "      <td>-0.215643</td>\n",
       "      <td>-0.278356</td>\n",
       "      <td>-0.493885</td>\n",
       "      <td>-0.277232</td>\n",
       "      <td>-0.296797</td>\n",
       "      <td>-0.580691</td>\n",
       "      <td>-0.481729</td>\n",
       "      <td>-0.279408</td>\n",
       "      <td>-0.245763</td>\n",
       "      <td>-0.511403</td>\n",
       "      <td>0.073835</td>\n",
       "      <td>-0.441831</td>\n",
       "      <td>-0.281045</td>\n",
       "      <td>-0.153554</td>\n",
       "      <td>0.095068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RA (deg)  DEC (deg)  Signif_Avg  Pivot_Energy  Flux1000  Energy_Flux100  \\\n",
       "0 -1.710862  -1.710862   -0.409208      0.319550 -0.242647       -0.283676   \n",
       "1  1.046791   1.046791   -0.351492     -0.483898 -0.195925       -0.197928   \n",
       "2 -0.271401  -0.271401   -0.061306     -0.009471 -0.189197       -0.205878   \n",
       "3  1.156032   1.156032    5.446181     -0.276063  1.697262        2.591379   \n",
       "4  0.558330   0.558330   -0.313056      0.106106 -0.195516       -0.215643   \n",
       "\n",
       "   PL_Flux_Density  PL_Index  LP_Flux_Density  LP_Index   LP_beta  LP_SigCurv  \\\n",
       "0        -0.285043 -0.746813        -0.282818 -0.631044 -0.027647   -0.313467   \n",
       "1        -0.214998  1.246638        -0.200268  1.772410  0.838241    0.191379   \n",
       "2        -0.274663 -0.406430        -0.275430 -0.049432 -1.006547   -0.344988   \n",
       "3         0.258083 -1.504647         0.182631 -1.111385 -0.684526    0.208078   \n",
       "4        -0.278356 -0.493885        -0.277232 -0.296797 -0.580691   -0.481729   \n",
       "\n",
       "   PLEC_Flux_Density  PLEC_IndexS  PLEC_ExpfactorS  PLEC_Exp_Index  \\\n",
       "0          -0.285382    -0.625180        -0.336277        0.073835   \n",
       "1          -0.200123     1.526625         0.774721        0.073835   \n",
       "2          -0.276715     0.033265        -0.647847        0.073835   \n",
       "3           0.188071    -0.959591        -0.616565        0.073835   \n",
       "4          -0.279408    -0.245763        -0.511403        0.073835   \n",
       "\n",
       "   PLEC_SigCurv     Npred  Variability_Index  Frac_Variability  \n",
       "0     -0.386274 -0.352231          -0.160966         -0.186350  \n",
       "1      0.055805 -0.177968          -0.080191          1.635442  \n",
       "2     -0.678316 -0.201769          -0.144487         -0.026748  \n",
       "3      0.173668  2.829953           1.615038          0.343816  \n",
       "4     -0.441831 -0.281045          -0.153554          0.095068  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 38,
     "status": "ok",
     "timestamp": 1677508111564,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "R6Ez9ckWJnpu",
    "outputId": "bc87d351-fe97-4485-b551-a29acd9b65e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bcu', 'bcu', 'bll', 'bll', 'bll', 'bll', 'bll', 'msp', 'psr',\n",
       "       'bcu'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = model.predict(X_test)\n",
    "y_test_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1677508111564,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "BZZxccplJnpv",
    "outputId": "b7309622-08b2-4571-896a-a51963c52f19"
   },
   "outputs": [
    {
     "data": {
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       "     0\n",
       "0  bcu\n",
       "1  bcu\n",
       "2  bll\n",
       "3  bll\n",
       "4  bll"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting y_pred to a dataframe which is an array\n",
    "y_pred_1 = pd.DataFrame(y_test_pred)\n",
    "y_pred_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1677508111565,
     "user": {
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      "userId": "00578364747584923502"
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    },
    "id": "T6crFXeaJnpw",
    "outputId": "daf5f4f9-322b-439e-a2bd-d8a46396e87e"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>bcu</td>\n",
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       "    <tr>\n",
       "      <th>5523</th>\n",
       "      <td>bcu</td>\n",
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       "    <tr>\n",
       "      <th>2506</th>\n",
       "      <td>bcu</td>\n",
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       "    <tr>\n",
       "      <th>5722</th>\n",
       "      <td>bll</td>\n",
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       "    <tr>\n",
       "      <th>4052</th>\n",
       "      <td>bcu</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     CLASS\n",
       "265    bcu\n",
       "5523   bcu\n",
       "2506   bcu\n",
       "5722   bll\n",
       "4052   bcu"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)\n",
    "y_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1677508111565,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
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    "id": "eLZdF77pJnpw",
    "outputId": "a0b0745d-4dda-4bb4-d4bd-5db1443b04c8"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLASS</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>2</th>\n",
       "      <td>bcu</td>\n",
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       "      <th>3</th>\n",
       "      <td>bll</td>\n",
       "      <td>bll</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  CLASS    0\n",
       "0   bcu  bcu\n",
       "1   bcu  bcu\n",
       "2   bcu  bll\n",
       "3   bll  bll\n",
       "4   bcu  bll"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing index for both dataframes to append them side by side \n",
    "y_pred_1.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Appending y_test_df and y_pred_1\n",
    "y_pred_final = pd.concat([y_test_df, y_pred_1],axis=1)\n",
    "\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1677508111565,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "Xod_l84BJnpx",
    "outputId": "957ed9f7-2318-4b84-9609-8fe71fdc431b"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>True</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bcu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bcu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bll</td>\n",
       "      <td>bll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>bll</td>\n",
       "      <td>bll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>unk</td>\n",
       "      <td>bcu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bcu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>bcu</td>\n",
       "      <td>bcu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>snr</td>\n",
       "      <td>snr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    True Predicted\n",
       "0    bcu       bcu\n",
       "1    bcu       bcu\n",
       "2    bcu       bll\n",
       "3    bll       bll\n",
       "4    bcu       bll\n",
       "..   ...       ...\n",
       "476  bll       bll\n",
       "477  unk       bcu\n",
       "478  bcu       bcu\n",
       "479  bcu       bcu\n",
       "480  snr       snr\n",
       "\n",
       "[481 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final= y_pred_final.rename(columns={ 0 : 'Predicted','CLASS':'True'})\n",
    "y_pred_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1677508111566,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "XwkrlwNOJnpx",
    "outputId": "d7e6fdd2-2ef6-4d88-e8bd-76642f1023f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5758835758835759"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the overall accuracy\n",
    "metrics.accuracy_score(y_test_df,y_pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1677508111566,
     "user": {
      "displayName": "Gaurav chauhan",
      "userId": "00578364747584923502"
     },
     "user_tz": -330
    },
    "id": "PSPISIcQJnpy",
    "outputId": "bb76641a-c594-423e-f90a-ed88e1d0b37c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 14,  0,  2,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  1],\n",
       "       [ 0, 97,  0, 39, 16,  0,  0,  4,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  1],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0, 32,  0, 96,  5,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  1,\n",
       "         0,  2],\n",
       "       [ 1, 20,  0,  2, 55,  0,  0,  1,  0,  2,  1,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  2,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  2,  0,  0,  0,  0,  0,  9,  0,  1,  0,  0,  0,  0,  0,  0,\n",
       "         0,  1],\n",
       "       [ 0,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0,  0, 11,  0,  0,  0,  0,  0,  0,\n",
       "         0,  2],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  2,  0,  2,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  1,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,\n",
       "         0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  2,  1,  0,  0,  0,  0,  3,\n",
       "         0,  0],\n",
       "       [ 0,  4,  0,  1,  0,  0,  0,  0,  0,  4,  1,  0,  0,  0,  0,  0,\n",
       "         0,  5],\n",
       "       [ 0, 11,  0,  2,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         1,  6]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confusioin matatrix\n",
    "metrics.confusion_matrix(y_test_df,y_pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         agn       0.00      0.00      0.00        20\n",
      "         bcu       0.52      0.62      0.56       157\n",
      "         bin       0.00      0.00      0.00         2\n",
      "         bll       0.66      0.70      0.68       137\n",
      "        fsrq       0.67      0.67      0.67        82\n",
      "         glc       0.00      0.00      0.00         3\n",
      "         hmb       0.00      0.00      0.00         1\n",
      "         msp       0.56      0.69      0.62        13\n",
      "       nlsy1       0.00      0.00      0.00         1\n",
      "         psr       0.50      0.79      0.61        14\n",
      "         pwn       0.00      0.00      0.00         0\n",
      "         rdg       0.00      0.00      0.00         5\n",
      "         sbg       0.00      0.00      0.00         2\n",
      "         sey       0.00      0.00      0.00         1\n",
      "         sfr       0.00      0.00      0.00         1\n",
      "         snr       0.60      0.50      0.55         6\n",
      "         spp       0.00      0.00      0.00        15\n",
      "         unk       0.33      0.29      0.31        21\n",
      "\n",
      "    accuracy                           0.58       481\n",
      "   macro avg       0.21      0.24      0.22       481\n",
      "weighted avg       0.52      0.58      0.55       481\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "#printing the classification report\n",
    "print(metrics.classification_report(y_test_df,y_pred_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :0.5758835758835759\n",
      "True and predicted value comparison\n",
      "    True Predicted\n",
      "0    bcu       bcu\n",
      "1    bcu       bcu\n",
      "2    bcu       bll\n",
      "3    bll       bll\n",
      "4    bcu       bll\n",
      "..   ...       ...\n",
      "476  bll       bll\n",
      "477  unk       bcu\n",
      "478  bcu       bcu\n",
      "479  bcu       bcu\n",
      "480  snr       snr\n",
      "\n",
      "[481 rows x 2 columns]\n",
      "Confusion matrix\n",
      "[[ 0 14  0  2  3  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0 97  0 39 16  0  0  4  0  0  0  0  0  0  0  0  0  1]\n",
      " [ 0  1  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0]\n",
      " [ 0 32  0 96  5  0  0  0  0  0  1  0  0  0  0  1  0  2]\n",
      " [ 1 20  0  2 55  0  0  1  0  2  1  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  0  0  0  0  2  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0]\n",
      " [ 0  2  0  0  0  0  0  9  0  1  0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0  2]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  2  0  2  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0]\n",
      " [ 0  0  0  0  0  0  0  0  0  2  1  0  0  0  0  3  0  0]\n",
      " [ 0  4  0  1  0  0  0  0  0  4  1  0  0  0  0  0  0  5]\n",
      " [ 0 11  0  2  1  0  0  0  0  0  0  0  0  0  0  0  1  6]]\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         agn       0.00      0.00      0.00        20\n",
      "         bcu       0.52      0.62      0.56       157\n",
      "         bin       0.00      0.00      0.00         2\n",
      "         bll       0.66      0.70      0.68       137\n",
      "        fsrq       0.67      0.67      0.67        82\n",
      "         glc       0.00      0.00      0.00         3\n",
      "         hmb       0.00      0.00      0.00         1\n",
      "         msp       0.56      0.69      0.62        13\n",
      "       nlsy1       0.00      0.00      0.00         1\n",
      "         psr       0.50      0.79      0.61        14\n",
      "         pwn       0.00      0.00      0.00         0\n",
      "         rdg       0.00      0.00      0.00         5\n",
      "         sbg       0.00      0.00      0.00         2\n",
      "         sey       0.00      0.00      0.00         1\n",
      "         sfr       0.00      0.00      0.00         1\n",
      "         snr       0.60      0.50      0.55         6\n",
      "         spp       0.00      0.00      0.00        15\n",
      "         unk       0.33      0.29      0.31        21\n",
      "\n",
      "    accuracy                           0.58       481\n",
      "   macro avg       0.21      0.24      0.22       481\n",
      "weighted avg       0.52      0.58      0.55       481\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "def eval():\n",
    "    print(\"Accuracy :\" + str(metrics.accuracy_score(y_test_df,y_pred_1)))\n",
    "    print('True and predicted value comparison')\n",
    "    print(y_pred_final)\n",
    "    print('Confusion matrix')\n",
    "    print(metrics.confusion_matrix(y_test_df,y_pred_1))\n",
    "    print('Classification Report')\n",
    "    print(metrics.classification_report(y_test_df,y_pred_1))\n",
    "eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FVYpDtvWWyEK"
   },
   "source": [
    "Our model gives us an accuracy of around 58 % on test data as well.\n",
    "It may not be extremely accurate but the consistent performance across the training and test data means our model is not overfitted."
   ]
  }
 ],
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