Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "df['Target']=le.fit_transform(df['Target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1421, 35)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropout=df[df.Target==0]\n",
    "dropout.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital status                                      6\n",
       "Application mode                                   18\n",
       "Application order                                   8\n",
       "Course                                             17\n",
       "Daytime/evening attendance                          2\n",
       "Previous qualification                             17\n",
       "Nacionality                                        21\n",
       "Mother's qualification                             29\n",
       "Father's qualification                             34\n",
       "Mother's occupation                                32\n",
       "Father's occupation                                46\n",
       "Displaced                                           2\n",
       "Educational special needs                           2\n",
       "Debtor                                              2\n",
       "Tuition fees up to date                             2\n",
       "Gender                                              2\n",
       "Scholarship holder                                  2\n",
       "Age at enrollment                                  46\n",
       "International                                       2\n",
       "Curricular units 1st sem (credited)                21\n",
       "Curricular units 1st sem (enrolled)                23\n",
       "Curricular units 1st sem (evaluations)             35\n",
       "Curricular units 1st sem (approved)                23\n",
       "Curricular units 1st sem (grade)                  805\n",
       "Curricular units 1st sem (without evaluations)     11\n",
       "Curricular units 2nd sem (credited)                19\n",
       "Curricular units 2nd sem (enrolled)                22\n",
       "Curricular units 2nd sem (evaluations)             30\n",
       "Curricular units 2nd sem (approved)                20\n",
       "Curricular units 2nd sem (grade)                  786\n",
       "Curricular units 2nd sem (without evaluations)     10\n",
       "Unemployment rate                                  10\n",
       "Inflation rate                                      9\n",
       "GDP                                                10\n",
       "Target                                              3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['Target']==1].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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>Marital status</th>\n",
       "      <th>Application mode</th>\n",
       "      <th>Application order</th>\n",
       "      <th>Course</th>\n",
       "      <th>Daytime/evening attendance</th>\n",
       "      <th>Previous qualification</th>\n",
       "      <th>Nacionality</th>\n",
       "      <th>Mother's qualification</th>\n",
       "      <th>Father's qualification</th>\n",
       "      <th>Mother's occupation</th>\n",
       "      <th>...</th>\n",
       "      <th>Curricular units 1st sem (without evaluations)</th>\n",
       "      <th>Curricular units 2nd sem (credited)</th>\n",
       "      <th>Curricular units 2nd sem (enrolled)</th>\n",
       "      <th>Curricular units 2nd sem (evaluations)</th>\n",
       "      <th>Curricular units 2nd sem (approved)</th>\n",
       "      <th>Curricular units 2nd sem (grade)</th>\n",
       "      <th>Curricular units 2nd sem (without evaluations)</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Inflation rate</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.261084</td>\n",
       "      <td>8.342013</td>\n",
       "      <td>1.593244</td>\n",
       "      <td>9.898663</td>\n",
       "      <td>0.854328</td>\n",
       "      <td>3.059113</td>\n",
       "      <td>1.250528</td>\n",
       "      <td>13.102745</td>\n",
       "      <td>16.716397</td>\n",
       "      <td>6.916256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192118</td>\n",
       "      <td>0.449683</td>\n",
       "      <td>5.780436</td>\n",
       "      <td>7.173821</td>\n",
       "      <td>1.940183</td>\n",
       "      <td>5.899339</td>\n",
       "      <td>0.237861</td>\n",
       "      <td>11.616397</td>\n",
       "      <td>1.283955</td>\n",
       "      <td>-0.150859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.134903</td>\n",
       "      <td>5.824808</td>\n",
       "      <td>1.851064</td>\n",
       "      <td>9.959258</td>\n",
       "      <td>0.909009</td>\n",
       "      <td>2.226799</td>\n",
       "      <td>1.237211</td>\n",
       "      <td>12.208692</td>\n",
       "      <td>16.629697</td>\n",
       "      <td>7.403803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088275</td>\n",
       "      <td>0.666818</td>\n",
       "      <td>6.628339</td>\n",
       "      <td>8.142146</td>\n",
       "      <td>6.177003</td>\n",
       "      <td>12.697276</td>\n",
       "      <td>0.080579</td>\n",
       "      <td>11.639339</td>\n",
       "      <td>1.197918</td>\n",
       "      <td>0.081833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Marital status  Application mode  Application order    Course  \\\n",
       "Target                                                                  \n",
       "0             1.261084          8.342013           1.593244  9.898663   \n",
       "2             1.134903          5.824808           1.851064  9.959258   \n",
       "\n",
       "        Daytime/evening attendance  Previous qualification  Nacionality  \\\n",
       "Target                                                                    \n",
       "0                         0.854328                3.059113     1.250528   \n",
       "2                         0.909009                2.226799     1.237211   \n",
       "\n",
       "        Mother's qualification  Father's qualification  Mother's occupation  \\\n",
       "Target                                                                        \n",
       "0                    13.102745               16.716397             6.916256   \n",
       "2                    12.208692               16.629697             7.403803   \n",
       "\n",
       "        ...  Curricular units 1st sem (without evaluations)  \\\n",
       "Target  ...                                                   \n",
       "0       ...                                        0.192118   \n",
       "2       ...                                        0.088275   \n",
       "\n",
       "        Curricular units 2nd sem (credited)  \\\n",
       "Target                                        \n",
       "0                                  0.449683   \n",
       "2                                  0.666818   \n",
       "\n",
       "        Curricular units 2nd sem (enrolled)  \\\n",
       "Target                                        \n",
       "0                                  5.780436   \n",
       "2                                  6.628339   \n",
       "\n",
       "        Curricular units 2nd sem (evaluations)  \\\n",
       "Target                                           \n",
       "0                                     7.173821   \n",
       "2                                     8.142146   \n",
       "\n",
       "        Curricular units 2nd sem (approved)  Curricular units 2nd sem (grade)  \\\n",
       "Target                                                                          \n",
       "0                                  1.940183                          5.899339   \n",
       "2                                  6.177003                         12.697276   \n",
       "\n",
       "        Curricular units 2nd sem (without evaluations)  Unemployment rate  \\\n",
       "Target                                                                      \n",
       "0                                             0.237861          11.616397   \n",
       "2                                             0.080579          11.639339   \n",
       "\n",
       "        Inflation rate       GDP  \n",
       "Target                            \n",
       "0             1.283955 -0.150859  \n",
       "2             1.197918  0.081833  \n",
       "\n",
       "[2 rows x 34 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Target').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x78341ab4c9a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 500x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DNX/+fMXExLhkMAAAUM1QL1u2zNVzAACAG6hWqCtkZWXp8OHDkqROnTqpe/fuLhkKAABcV61Qnz17VmPGjFFGRoZ8fX0lSQUFBRo4cKBWrVqlli1bunJGAADqrGq96nv69OkqLi7WoUOHdOHCBV24cEE5OTkqKirSP//zP7t6RgAA6qxqPaJOTU3V1q1bFRERYV/WsWNHJScn82IyAABcqFqPqMvLyyt9BrUkNWzYUOXl5Xc9FAAAuK5aof7Zz36m5557TqdOnbIv++abb/T8889r0KBBLhsOAIC6rlqh/u1vf6uioiK1bt1abdu2Vdu2bRUWFqaioiK98847rp4RAIA6q1rPUYeEhGjfvn3aunWrjhw5IkmKiIhQdHS0S4cDAKCuq9Ij6m3btqljx44qKiqSw+HQ4MGDNX36dE2fPl29e/dWp06dtGvXrpqaFQCAOqdKoX7zzTc1ZcoUeXt7V9rm4+OjX/ziF0pKSnLZcAAA1HVVCvUXX3yhuLi4m26PiYlRVlbWXQ8FAACuq1Koz5w5c8O3ZVVo0KCBvv3227seCgAAXFelUP/kJz9RTk7OTbcfOHBAQUFBdz0UAAC4rkqhHjp0qGbPnq3Lly9X2vb9999r7ty5evTRR102HAAAdV2V3p71yiuvaO3atWrfvr0SEhIUHh4uSTpy5IiSk5NVVlamf/3Xf62RQQEAqIuqFOqAgAB98sknmjZtmmbNmiXLsiRJDodDsbGxSk5OVkBAQI0MCgBAXVTlX3gSGhqqjz76SN99952OHTsmy7J0//33q3nz5jUxHwAAdVq1fjOZJDVv3ly9e/d25SwAAOBHqvW7vgEAQO0g1AAAGMytod65c6eGDx+u4OBgORwOrV+/3mm7ZVmaM2eOgoKC1KhRI0VHR+vo0aNOay5cuKCxY8fK29tbvr6+mjRpkkpKSpzWHDhwQP3795eXl5dCQkK0cOHCmt41AABcwq2hvnjxorp27ark5OQbbl+4cKHefvttLVmyRHv37lWTJk0UGxvr9D7usWPH6tChQ0pLS9PGjRu1c+dOTZ061d5eVFSkmJgYhYaGKisrS6+//rrmzZund999t8b3DwCAu1XtF5O5wpAhQzRkyJAbbrMsS2+++aZeeeUVjRgxQpL0pz/9SQEBAVq/fr3GjBmjw4cPKzU1VZ999pl69eolSXrnnXc0dOhQLVq0SMHBwVq+fLmuXLmipUuXysPDQ506dVJ2draSkpKcgg4AgImMfY76xIkTys/Pd/qMax8fH0VGRiozM1OSlJmZKV9fXzvSkhQdHa169epp79699pqHH35YHh4e9prY2Fjl5ubqu+++q6W9AQCgetz6iPpW8vPzJanSL1AJCAiwt+Xn58vf399pe4MGDeTn5+e0JiwsrNJ1VGy70fu/S0tLVVpaap8vKiq6y70BAKB6jH1E7U4LFiyQj4+PfQoJCXH3SACAOsrYUAcGBkq6/tGaP3TmzBl7W2BgoM6ePeu0/dq1a7pw4YLTmhtdxw9v48dmzZqlwsJC+3Ty5Mm73yEAAKrB2FCHhYUpMDBQ6enp9mVFRUXau3evoqKiJElRUVEqKChQVlaWvWbbtm0qLy9XZGSkvWbnzp26evWqvSYtLU3h4eE3/bWnnp6e8vb2djoBAOAObg11SUmJsrOzlZ2dLen6C8iys7OVl5cnh8OhGTNm6Ne//rX+8pe/6ODBg5owYYKCg4M1cuRISVJERITi4uI0ZcoUffrpp9q9e7cSEhI0ZswYBQcHS5J+/vOfy8PDQ5MmTdKhQ4e0evVqvfXWW5o5c6ab9hoAgDvn1heTff755xo4cKB9viKeEydOVEpKil566SVdvHhRU6dOVUFBgR566CGlpqbKy8vL/prly5crISFBgwYNUr169TR69Gi9/fbb9nYfHx9t2bJF8fHx6tmzp1q0aKE5c+bw1iwAwD3BraEeMGCA/VGZN+JwODR//nzNnz//pmv8/Py0YsWKW95Oly5dtGvXrmrPCQCAuxj7HDUAACDUAAAYjVADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABjM61PPmzZPD4XA6dejQwd5++fJlxcfH67777lPTpk01evRonTlzxuk68vLyNGzYMDVu3Fj+/v568cUXde3atdreFQAAqqWBuwe4nU6dOmnr1q32+QYN/jby888/r02bNunDDz+Uj4+PEhISNGrUKO3evVuSVFZWpmHDhikwMFCffPKJTp8+rQkTJqhhw4b693//91rfFwAAqsr4UDdo0ECBgYGVLi8sLNR7772nFStW6Gc/+5kkadmyZYqIiNCePXvUt29fbdmyRV9++aW2bt2qgIAAdevWTa+++qpefvllzZs3Tx4eHrW9OwAAVInRP/qWpKNHjyo4OFht2rTR2LFjlZeXJ0nKysrS1atXFR0dba/t0KGDWrVqpczMTElSZmamOnfurICAAHtNbGysioqKdOjQodrdEQAAqsHoR9SRkZFKSUlReHi4Tp8+rcTERPXv3185OTnKz8+Xh4eHfH19nb4mICBA+fn5kqT8/HynSFdsr9h2M6WlpSotLbXPFxUVuWiPAACoGqNDPWTIEPvPXbp0UWRkpEJDQ7VmzRo1atSoxm53wYIFSkxMrLHrBwDgThn/o+8f8vX1Vfv27XXs2DEFBgbqypUrKigocFpz5swZ+zntwMDASq8Crzh/o+e9K8yaNUuFhYX26eTJk67dEQAA7tA9FeqSkhIdP35cQUFB6tmzpxo2bKj09HR7e25urvLy8hQVFSVJioqK0sGDB3X27Fl7TVpamry9vdWxY8eb3o6np6e8vb2dTgAAuIPRP/p+4YUXNHz4cIWGhurUqVOaO3eu6tevr6eeeko+Pj6aNGmSZs6cKT8/P3l7e2v69OmKiopS3759JUkxMTHq2LGjxo8fr4ULFyo/P1+vvPKK4uPj5enp6ea9AwDg9owO9ddff62nnnpK58+fV8uWLfXQQw9pz549atmypSRp8eLFqlevnkaPHq3S0lLFxsbqd7/7nf319evX18aNGzVt2jRFRUWpSZMmmjhxoubPn++uXQIAoEqMDvWqVatuud3Ly0vJyclKTk6+6ZrQ0FB99NFHrh4NAIBacU89Rw0AQF1DqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxGqAEAMBihBgDAYIQaAACDEWoAAAxWp0KdnJys1q1by8vLS5GRkfr000/dPRIAALdUZ0K9evVqzZw5U3PnztW+ffvUtWtXxcbG6uzZs+4eDQCAm6ozoU5KStKUKVP0zDPPqGPHjlqyZIkaN26spUuXuns0AABuqk6E+sqVK8rKylJ0dLR9Wb169RQdHa3MzEw3TgYAwK01cPcAteHcuXMqKytTQECA0+UBAQE6cuRIpfWlpaUqLS21zxcWFkqSioqK7nqWkpISSVJxcZbKykru+vpu59KlXElSVlaWfdu1oV69eiovL+f27uHb/Hu/PXfc5t/77dX2bebmXr9/q637U+lv96klJSUuaUKzZs3kcDhuuaZOhLqqFixYoMTExEqXh4SEuOw2/vd/p7rsuu7E1Km1e3sAUFtq+/5Ukh555BGXXE9hYaG8vb1vuaZOhLpFixaqX7++zpw543T5mTNnFBgYWGn9rFmzNHPmTPt8eXm5Lly4oPvuu++2//K5naKiIoWEhOjkyZO3/csxCXPXLuauXcxdu+7VuSXXz96sWbPbrqkTofbw8FDPnj2Vnp6ukSNHSroe3/T0dCUkJFRa7+npKU9PT6fLfH19XTqTt7f3PfcNKjF3bWPu2sXctetenVuq3dnrRKglaebMmZo4caJ69eqlPn366M0339TFixf1zDPPuHs0AABuqs6E+sknn9S3336rOXPmKD8/X926dVNqamqlF5gBAGCSOhNqSUpISLjhj7prk6enp+bOnVvpR+umY+7axdy1i7lr1706t+Se2R2WZVm1dmsAAKBK6sQvPAEA4F5FqAEAMBihBgDAYIT6LlX1ozM//PBDdejQQV5eXurcubM++ugjp+2WZWnOnDkKCgpSo0aNFB0draNHj7p17j/+8Y/q37+/mjdvrubNmys6OrrS+qeffloOh8PpFBcX5/K5qzp7SkpKpbm8vLyc1ph4zAcMGFBpbofDoWHDhtlravqY79y5U8OHD1dwcLAcDofWr19/26/JyMhQjx495OnpqXbt2iklJaXSmpr+uNmqzr127VoNHjxYLVu2lLe3t6KiorR582anNfPmzat0rDt06ODSuasze0ZGxg2/T/Lz853WmXbMb/S963A41KlTJ3tNTR/zBQsWqHfv3mrWrJn8/f01cuRI+1eS3oo77sMJ9V2o6kdnfvLJJ3rqqac0adIk7d+/XyNHjtTIkSOVk5Njr1m4cKHefvttLVmyRHv37lWTJk0UGxury5cvu23ujIwMPfXUU9q+fbsyMzMVEhKimJgYffPNN07r4uLidPr0afu0cuVKl81c3dml67+Y4Idz/fWvf3XabuIxX7t2rdPMOTk5ql+/vv7xH//RaV1NHvOLFy+qa9euSk5OvqP1J06c0LBhwzRw4EBlZ2drxowZmjx5slP0auPjZqs6986dOzV48GB99NFHysrK0sCBAzV8+HDt37/faV2nTp2cjvX//M//uGzm6s5eITc312k2f39/e5uJx/ytt95ymvfkyZPy8/Or9P1dk8d8x44dio+P1549e5SWlqarV68qJiZGFy9evOnXuO0+3EK19enTx4qPj7fPl5WVWcHBwdaCBQtuuP6JJ56whg0b5nRZZGSk9Ytf/MKyLMsqLy+3AgMDrddff93eXlBQYHl6elorV65029w/du3aNatZs2bW+++/b182ceJEa8SIES6b8WaqOvuyZcssHx+fm17fvXLMFy9ebDVr1swqKSmxL6utY25ZliXJWrdu3S3XvPTSS1anTp2cLnvyySet2NhY+/zdHoequpO5b6Rjx45WYmKifX7u3LlW165dXTfYHbiT2bdv325Jsr777rubrrkXjvm6dessh8NhffXVV/ZltX3Mz549a0myduzYcdM17roP5xF1NVXnozMzMzOd1ktSbGysvf7EiRPKz893WuPj46PIyEiXfRynKz7y89KlS7p69ar8/PycLs/IyJC/v7/Cw8M1bdo0nT9/3iUz3+3sJSUlCg0NVUhIiEaMGKFDhw7Z2+6VY/7ee+9pzJgxatKkidPlNX3Mq+J239/3ysfNlpeXq7i4uNL399GjRxUcHKw2bdpo7NixysvLc9OElXXr1k1BQUEaPHiwdu/ebV9+rxzz9957T9HR0QoNDXW6vDaPecWnJP747/2H3HUfTqir6VYfnfnj54cq5Ofn33J9xX+rcp21MfePvfzyywoODnb6ZoyLi9Of/vQnpaen6ze/+Y127NihIUOGqKyszCVzV3f28PBwLV26VBs2bNAHH3yg8vJy9evXT19//bWke+OYf/rpp8rJydHkyZOdLq+NY14VN/v+Lioq0vfff++S773asGjRIpWUlOiJJ56wL4uMjFRKSopSU1P1+9//XidOnFD//v1VXFzsxkmloKAgLVmyRH/+85/15z//WSEhIRowYID27dsnyTX/v9e0U6dO6eOPP670/V2bx7y8vFwzZszQgw8+qAceeOCm69x1H16nfjMZ7t5rr72mVatWKSMjw+lFWWPGjLH/3LlzZ3Xp0kVt27ZVRkaGBg0a5I5RJUlRUVGKioqyz/fr108RERH6wx/+oFdffdVtc1XFe++9p86dO6tPnz5Ol5t6zO9lK1asUGJiojZs2OD0PO+QIUPsP3fp0kWRkZEKDQ3VmjVrNGnSJHeMKun6P0TDw8Pt8/369dPx48e1ePFi/ed//qfb5qqK999/X76+vvYHJlWozWMeHx+vnJycGnndgSvwiLqaqvrRmZIUGBh4y/UV/63KddbG3BUWLVqk1157TVu2bFGXLl1uubZNmzZq0aKFjh07dtczV7ib2Ss0bNhQ3bt3t+cy/ZhfvHhRq1atuqM7ppo45lVxs+9vb29vNWrUyCV/fzVp1apVmjx5stasWVPpx5s/5uvrq/bt27vtWN9Knz597LlMP+aWZWnp0qUaP368PDw8brm2po55QkKCNm7cqO3bt+unP/3pLde66z6cUFfTDz86s0LFR2f+8BHcD0VFRTmtl6S0tDR7fVhYmAIDA53WFBUVae/evTe9ztqYW7r+SsZXX31Vqamp6tWr121v5+uvv9b58+cVFBTkkrml6s/+Q2VlZTp48KA9l8nHXLr+VpDS0lKNGzfutrdTE8e8Km73/e2Kv7+asnLlSj3zzDNauXKl01vgbqakpETHjx9327G+lezsbHsuk4+5dP2V18eOHbujf4i6+phblqWEhAStW7dO27ZtU1hY2G2/xm334dV+GRqsVatWWZ6enlZKSor15ZdfWlOnTrV8fX2t/Px8y7Isa/z48davfvUre/3u3butBg0aWIsWLbIOHz5szZ0712rYsKF18OBBe81rr71m+fr6Whs2bLAOHDhgjRgxwgoLC7O+//57t8392muvWR4eHtZ//dd/WadPn7ZPxcXFlmVZVnFxsfXCCy9YmZmZ1okTJ6ytW7daPXr0sO6//37r8uXLLpu7OrMnJiZamzdvto4fP25lZWVZY8aMsby8vKxDhw457Z9px7zCQw89ZD355JOVLq+NY15cXGzt37/f2r9/vyXJSkpKsvbv32/99a9/tSzLsn71q19Z48ePt9f/3//9n9W4cWPrxRdftA4fPmwlJydb9evXt1JTU+/4OLhj7uXLl1sNGjSwkpOTnb6/CwoK7DW//OUvrYyMDOvEiRPW7t27rejoaKtFixbW2bNnXTZ3dWZfvHixtX79euvo0aPWwYMHreeee86qV6+etXXrVnuNice8wrhx46zIyMgbXmdNH/Np06ZZPj4+VkZGhtPf+6VLl+w1ptyHE+q79M4771itWrWyPDw8rD59+lh79uyxtz3yyCPWxIkTndavWbPGat++veXh4WF16tTJ2rRpk9P28vJya/bs2VZAQIDl6elpDRo0yMrNzXXr3KGhoZakSqe5c+dalmVZly5dsmJiYqyWLVtaDRs2tEJDQ60pU6a49I6gurPPmDHDXhsQEGANHTrU2rdvn9P1mXjMLcuyjhw5YkmytmzZUum6auOYV7z158enijknTpxoPfLII5W+plu3bpaHh4fVpk0ba9myZZWu91bHwR1zP/LII7dcb1nX32YWFBRkeXh4WD/5yU+sJ5980jp27JhL567O7L/5zW+stm3bWl5eXpafn581YMAAa9u2bZWu17RjblnX37bUqFEj6913373hddb0Mb/RvJKcvmdNuQ/n07MAADAYz1EDAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg0AgMEINQAABiPUAAAYjFADAGAwQg3UYQ6H45anefPmuXW29evXu+32AVPwedRAHXb69Gn7z6tXr9acOXOUm5trX9a0adMqXd+VK1du+3GFAKqGR9RAHRYYGGiffHx85HA47PMXL17U2LFjFRAQoKZNm6p3797aunWr09e3bt1ar776qiZMmCBvb29NnTpVkvTHP/5RISEhaty4sR577DElJSXJ19fX6Ws3bNigHj16yMvLS23atFFiYqKuXbtmX68kPfbYY3I4HPZ5oC4i1ABuqKSkREOHDlV6err279+vuLg4DR8+XHl5eU7rFi1apK5du2r//v2aPXu2du/erWeffVbPPfecsrOzNXjwYP3bv/2b09fs2rVLEyZM0HPPPacvv/xSf/jDH5SSkmKv++yzzyRJy5Yt0+nTp+3zQF3Ep2cBkCSlpKRoxowZKigouOmaBx54QM8++6wSEhIkXX/k2717d61bt85eM2bMGJWUlGjjxo32ZePGjdPGjRvt646OjtagQYM0a9Yse80HH3ygl156SadOnZJ0/TnqdevWaeTIka7bSeAexCNqADdUUlKiF154QREREfL19VXTpk11+PDhSo+oe/Xq5XQ+NzdXffr0cbrsx+e/+OILzZ8/X02bNrVPU6ZM0enTp3Xp0qWa2SHgHsWLyQDc0AsvvKC0tDQtWrRI7dq1U6NGjfT444/rypUrTuuaNGlS5esuKSlRYmKiRo0aVWmbl5dXtWcG/h4RagA3tHv3bj399NN67LHHJF2P61dffXXbrwsPD6/0nPKPz/fo0UO5ublq167dTa+nYcOGKisrq/rgwN8ZQg3ghu6//36tXbtWw4cPl8Ph0OzZs1VeXn7br5s+fboefvhhJSUlafjw4dq2bZs+/vhjORwOe82cOXP06KOPqlWrVnr88cdVr149ffHFF8rJydGvf/1rSdef/05PT9eDDz4oT09PNW/evMb2FTAZz1EDuKGkpCQ1b95c/fr10/DhwxUbG6sePXrc9usefPBBLVmyRElJSeratatSU1P1/PPPO/1IOzY2Vhs3btSWLVvUu3dv9e3bV4sXL1ZoaKi95o033lBaWppCQkLUvXv3GtlH4F7Aq74B1LgpU6boyJEj2rVrl7tHAe45/OgbgMstWrRIgwcPVpMmTfTxxx/r/fff1+9+9zt3jwXck3hEDcDlnnjiCWVkZKi4uFht2rTR9OnT9eyzz7p7LOCeRKgBADAYLyYDAMBghBoAAIMRagAADEaoAQAwGKEGAMBghBoAAIMRagAADEaoAQAwGKEGAMBg/w+ljovZleO1MAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(5,10))\n",
    "sns.displot(df['Target'],color='Blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7834184aac70>,\n",
       "  <matplotlib.patches.Wedge at 0x7834184b4700>],\n",
       " [Text(-0.5175392024887888, 0.9706457509757453, 'Male'),\n",
       "  Text(0.5175392933672003, -0.970645702520224, 'Female')],\n",
       " [Text(-0.2822941104484302, 0.529443136895861, '65.59%'),\n",
       "  Text(0.28229416001847285, -0.5294431104655767, '34.41%')])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ratio of Total male and female\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"Gender\")\n",
    "plt.pie(df['Gender'].value_counts(),labels=['Male','Female'],autopct='%1.2f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x7834186fd220>,\n",
       "  <matplotlib.patches.Wedge at 0x7834186fdaf0>],\n",
       " [Text(-0.023101508978009465, 1.099757391556401, 'Male'),\n",
       "  Text(0.023101508978009083, -1.099757391556401, 'Female')],\n",
       " [Text(-0.012600823078914252, 0.5998676681216732, '50.67%'),\n",
       "  Text(0.012600823078914044, -0.5998676681216732, '49.33%')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ratio of dropout boys and girls\n",
    "df1=df.drop(df[df['Target']==2].index)\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"Gender\")\n",
    "plt.pie(df1['Gender'].value_counts(),labels=['Male','Female'],autopct='%1.2f%%',shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital status                                   -0.100479\n",
       "Application mode                                 -0.233888\n",
       "Application order                                 0.094355\n",
       "Course                                            0.006814\n",
       "Daytime/evening attendance                        0.084496\n",
       "Previous qualification                           -0.102795\n",
       "Nacionality                                      -0.003823\n",
       "Mother's qualification                           -0.048459\n",
       "Father's qualification                           -0.003850\n",
       "Mother's occupation                               0.064195\n",
       "Father's occupation                               0.073238\n",
       "Displaced                                         0.126113\n",
       "Educational special needs                        -0.007254\n",
       "Debtor                                           -0.267207\n",
       "Tuition fees up to date                           0.442138\n",
       "Gender                                           -0.251955\n",
       "Scholarship holder                                0.313018\n",
       "Age at enrollment                                -0.267229\n",
       "International                                     0.006181\n",
       "Curricular units 1st sem (credited)               0.046900\n",
       "Curricular units 1st sem (enrolled)               0.161074\n",
       "Curricular units 1st sem (evaluations)            0.059786\n",
       "Curricular units 1st sem (approved)               0.554881\n",
       "Curricular units 1st sem (grade)                  0.519927\n",
       "Curricular units 1st sem (without evaluations)   -0.074642\n",
       "Curricular units 2nd sem (credited)               0.052402\n",
       "Curricular units 2nd sem (enrolled)               0.182897\n",
       "Curricular units 2nd sem (evaluations)            0.119239\n",
       "Curricular units 2nd sem (approved)               0.653995\n",
       "Curricular units 2nd sem (grade)                  0.605350\n",
       "Curricular units 2nd sem (without evaluations)   -0.102687\n",
       "Unemployment rate                                 0.004198\n",
       "Inflation rate                                   -0.030326\n",
       "GDP                                               0.050260\n",
       "Target                                            1.000000\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature selection\n",
    "corr_matrix=df.corr(method=\"pearson\")\n",
    "corr_matrix['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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>Marital status</th>\n",
       "      <th>Application mode</th>\n",
       "      <th>Application order</th>\n",
       "      <th>Course</th>\n",
       "      <th>Daytime/evening attendance</th>\n",
       "      <th>Previous qualification</th>\n",
       "      <th>Nacionality</th>\n",
       "      <th>Mother's qualification</th>\n",
       "      <th>Father's qualification</th>\n",
       "      <th>Mother's occupation</th>\n",
       "      <th>...</th>\n",
       "      <th>Curricular units 2nd sem (enrolled)</th>\n",
       "      <th>Curricular units 2nd sem (evaluations)</th>\n",
       "      <th>Curricular units 2nd sem (approved)</th>\n",
       "      <th>Curricular units 2nd sem (grade)</th>\n",
       "      <th>Curricular units 2nd sem (without evaluations)</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Inflation rate</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Target</th>\n",
       "      <th>Dropout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-3.12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>16.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14.345000</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marital status  Application mode  Application order  Course  \\\n",
       "1               1                 6                  1      11   \n",
       "3               1                 8                  2      15   \n",
       "4               2                12                  1       3   \n",
       "5               2                12                  1      17   \n",
       "6               1                 1                  1      12   \n",
       "\n",
       "   Daytime/evening attendance  Previous qualification  Nacionality  \\\n",
       "1                           1                       1            1   \n",
       "3                           1                       1            1   \n",
       "4                           0                       1            1   \n",
       "5                           0                      12            1   \n",
       "6                           1                       1            1   \n",
       "\n",
       "   Mother's qualification  Father's qualification  Mother's occupation  ...  \\\n",
       "1                       1                       3                    4  ...   \n",
       "3                      23                      27                    6  ...   \n",
       "4                      22                      28                   10  ...   \n",
       "5                      22                      27                   10  ...   \n",
       "6                      13                      28                    8  ...   \n",
       "\n",
       "   Curricular units 2nd sem (enrolled)  \\\n",
       "1                                    6   \n",
       "3                                    6   \n",
       "4                                    6   \n",
       "5                                    5   \n",
       "6                                    8   \n",
       "\n",
       "   Curricular units 2nd sem (evaluations)  \\\n",
       "1                                       6   \n",
       "3                                      10   \n",
       "4                                       6   \n",
       "5                                      17   \n",
       "6                                       8   \n",
       "\n",
       "   Curricular units 2nd sem (approved)  Curricular units 2nd sem (grade)  \\\n",
       "1                                    6                         13.666667   \n",
       "3                                    5                         12.400000   \n",
       "4                                    6                         13.000000   \n",
       "5                                    5                         11.500000   \n",
       "6                                    8                         14.345000   \n",
       "\n",
       "   Curricular units 2nd sem (without evaluations)  Unemployment rate  \\\n",
       "1                                               0               13.9   \n",
       "3                                               0                9.4   \n",
       "4                                               0               13.9   \n",
       "5                                               5               16.2   \n",
       "6                                               0               15.5   \n",
       "\n",
       "   Inflation rate   GDP  Target  Dropout  \n",
       "1            -0.3  0.79       2        0  \n",
       "3            -0.8 -3.12       2        0  \n",
       "4            -0.3  0.79       2        0  \n",
       "5             0.3 -0.92       2        0  \n",
       "6             2.8 -4.06       2        0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dropout']=df['Target'].apply(lambda x:1 if x==0 else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15441/1176373632.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Dropout'],color='red')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Dropout', ylabel='Density'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,4))\n",
    "sns.distplot(df['Dropout'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:36].values\n",
    "X=StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Dropout'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm,annot=True)\n",
    "plt.xlabel('predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "score_lr=cross_val_score(LogisticRegression(solver='liblinear',multi_class='ovr'),iris.data,iris.target,cv=3)\n",
    "score_svc=cross_val_score(SVC(gamma='auto'),iris.data,iris.target,cv=3)\n",
    "score_rf=cross_val_score(RandomForestClassifier(n_estimators=30),iris.data,iris.target,cv=3)\n",
    "score_dt=cross_val_score(DecisionTreeClassifier(),iris.data,iris.target,cv=3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.average(score_lr)\n",
    "np.average(score_svc)\n",
    "np.average(score_lr)\n",
    "np.average(score_dt)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}