AI_SCE (1).ipynb

{
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   "id": "a3abb18c-842f-436f-99f5-b0670d077d05",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Studid</th>\n",
       "      <th>Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Studid  Placement\n",
       "0       1          1\n",
       "1       2          1\n",
       "2       3          1\n",
       "3       4          1\n",
       "4       5          1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_soln = pd.read_csv(\"solution.csv\")\n",
    "df_soln.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17d725c8-0942-48f1-810a-a1ca9c200c19",
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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>Name</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "      <th>Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MOHITE AARTI RAMESH</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WALUNJKAR SIDDHI SHAHURAO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KABADI KAIVALYA SUDHIR</td>\n",
       "      <td>92.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULLA MOIN ADILSHAH</td>\n",
       "      <td>91.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>9.80</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BHAPKAR SHRUTI MOHANRAO</td>\n",
       "      <td>84.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  10th %age  12th %age  College %age  Amcat  \\\n",
       "0        MOHITE AARTI RAMESH       92.0       82.0          9.50   87.0   \n",
       "1  WALUNJKAR SIDDHI SHAHURAO        NaN        NaN           NaN    NaN   \n",
       "2     KABADI KAIVALYA SUDHIR       92.0       86.0          9.00   51.0   \n",
       "3        MULLA MOIN ADILSHAH       91.0       84.0          9.80   64.0   \n",
       "4    BHAPKAR SHRUTI MOHANRAO       84.5        NaN          9.41    0.0   \n",
       "\n",
       "   Placement  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d547e21b-7593-42ac-bc76-57ff8c2d0b4c",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Studid</th>\n",
       "      <th>Name</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BHALERAO KAJAL SHANKAR</td>\n",
       "      <td>72.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>BHATEWARA JINESH JAYANT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KANSAGARA JEET ASHWIN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DESHPANDE ISHAN SHIRISH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>BHANUSHALI HARSHIT DHIRAJ</td>\n",
       "      <td>90.0</td>\n",
       "      <td>82.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Studid                       Name  10th %age  12th %age  College %age  \\\n",
       "0       1     BHALERAO KAJAL SHANKAR       72.4        NaN          88.0   \n",
       "1       2    BHATEWARA JINESH JAYANT        NaN        NaN           NaN   \n",
       "2       3      KANSAGARA JEET ASHWIN        NaN        NaN           NaN   \n",
       "3       4    DESHPANDE ISHAN SHIRISH        NaN        NaN           NaN   \n",
       "4       5  BHANUSHALI HARSHIT DHIRAJ       90.0      82.46           9.4   \n",
       "\n",
       "   Amcat  \n",
       "0    7.0  \n",
       "1    NaN  \n",
       "2    NaN  \n",
       "3    NaN  \n",
       "4   94.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(\"test.csv\")\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "643f833f-bb65-4dfd-bf91-90e1d10cd942",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253 entries, 0 to 252\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Name          253 non-null    object \n",
      " 1   10th %age     135 non-null    float64\n",
      " 2   12th %age     113 non-null    float64\n",
      " 3   College %age  135 non-null    float64\n",
      " 4   Amcat         135 non-null    float64\n",
      " 5   Placement     253 non-null    int64  \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 12.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd721d7f-8649-4c7d-9b8e-04b543e86276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73 entries, 0 to 72\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Studid        73 non-null     int64  \n",
      " 1   Name          73 non-null     object \n",
      " 2   10th %age     44 non-null     float64\n",
      " 3   12th %age     39 non-null     float64\n",
      " 4   College %age  44 non-null     float64\n",
      " 5   Amcat         44 non-null     float64\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 3.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa505f09-112e-4211-bfa2-c89bf9bbd7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(253, 6)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "574b5d80-cebf-4146-9e3a-19e26ff9bf08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6f9e2ae-8929-4dfe-a888-edc3a0e6d265",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "      <th>Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.727926</td>\n",
       "      <td>79.640619</td>\n",
       "      <td>13.7800</td>\n",
       "      <td>43.251852</td>\n",
       "      <td>0.770751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.189111</td>\n",
       "      <td>10.600583</td>\n",
       "      <td>18.8244</td>\n",
       "      <td>32.960564</td>\n",
       "      <td>0.421183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>8.8250</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>91.300000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>9.1100</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>89.6000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        10th %age   12th %age  College %age       Amcat   Placement\n",
       "count  135.000000  113.000000      135.0000  135.000000  253.000000\n",
       "mean    89.727926   79.640619       13.7800   43.251852    0.770751\n",
       "std      6.189111   10.600583       18.8244   32.960564    0.421183\n",
       "min     68.000000    8.000000        7.8000   -2.000000    0.000000\n",
       "25%     87.000000   75.000000        8.8250   12.000000    1.000000\n",
       "50%     91.300000   82.000000        9.1100   39.000000    1.000000\n",
       "75%     94.000000   86.000000        9.5000   73.500000    1.000000\n",
       "max    100.000000   93.100000       89.6000  100.000000    1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a77db6fb-2943-462b-b440-b10bf608f6de",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Studid</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>90.951136</td>\n",
       "      <td>79.101538</td>\n",
       "      <td>14.643409</td>\n",
       "      <td>44.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.217131</td>\n",
       "      <td>5.043533</td>\n",
       "      <td>13.687290</td>\n",
       "      <td>20.306082</td>\n",
       "      <td>33.079466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>89.975000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>8.975000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>79.540000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>87.075000</td>\n",
       "      <td>9.612500</td>\n",
       "      <td>76.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>99.200000</td>\n",
       "      <td>93.080000</td>\n",
       "      <td>89.500000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Studid  10th %age  12th %age  College %age      Amcat\n",
       "count  73.000000  44.000000  39.000000     44.000000  44.000000\n",
       "mean   37.000000  90.951136  79.101538     14.643409  44.931818\n",
       "std    21.217131   5.043533  13.687290     20.306082  33.079466\n",
       "min     1.000000  72.400000   8.000000      8.000000  -2.000000\n",
       "25%    19.000000  89.975000  75.000000      8.975000   7.000000\n",
       "50%    37.000000  92.200000  79.540000      9.400000  48.500000\n",
       "75%    55.000000  94.075000  87.075000      9.612500  76.250000\n",
       "max    73.000000  99.200000  93.080000     89.500000  98.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83529a21-7c00-4b91-a622-a718ccd580a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask0 = df_train['College %age'] > 10\n",
    "mask1 = df_test['College %age'] > 10\n",
    "\n",
    "df_train.loc[mask0, 'College %age'] /= 10\n",
    "df_test.loc[mask1, 'College %age'] /= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "644a539d-e360-484f-ae1a-5cb60dfcbabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "      <th>Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>135.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>253.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>89.727926</td>\n",
       "      <td>79.640619</td>\n",
       "      <td>9.061733</td>\n",
       "      <td>43.251852</td>\n",
       "      <td>0.770751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.189111</td>\n",
       "      <td>10.600583</td>\n",
       "      <td>0.482183</td>\n",
       "      <td>32.960564</td>\n",
       "      <td>0.421183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>91.300000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>9.470000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        10th %age   12th %age  College %age       Amcat   Placement\n",
       "count  135.000000  113.000000    135.000000  135.000000  253.000000\n",
       "mean    89.727926   79.640619      9.061733   43.251852    0.770751\n",
       "std      6.189111   10.600583      0.482183   32.960564    0.421183\n",
       "min     68.000000    8.000000      7.800000   -2.000000    0.000000\n",
       "25%     87.000000   75.000000      8.800000   12.000000    1.000000\n",
       "50%     91.300000   82.000000      9.000000   39.000000    1.000000\n",
       "75%     94.000000   86.000000      9.470000   73.500000    1.000000\n",
       "max    100.000000   93.100000     10.000000  100.000000    1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a57547c1-2e40-4a89-bd5e-6214a5c423aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Studid</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>90.951136</td>\n",
       "      <td>79.101538</td>\n",
       "      <td>9.192273</td>\n",
       "      <td>44.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.217131</td>\n",
       "      <td>5.043533</td>\n",
       "      <td>13.687290</td>\n",
       "      <td>0.457002</td>\n",
       "      <td>33.079466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.400000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>89.975000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>79.540000</td>\n",
       "      <td>9.165000</td>\n",
       "      <td>48.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>87.075000</td>\n",
       "      <td>9.592500</td>\n",
       "      <td>76.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73.000000</td>\n",
       "      <td>99.200000</td>\n",
       "      <td>93.080000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Studid  10th %age  12th %age  College %age      Amcat\n",
       "count  73.000000  44.000000  39.000000     44.000000  44.000000\n",
       "mean   37.000000  90.951136  79.101538      9.192273  44.931818\n",
       "std    21.217131   5.043533  13.687290      0.457002  33.079466\n",
       "min     1.000000  72.400000   8.000000      8.000000  -2.000000\n",
       "25%    19.000000  89.975000  75.000000      8.900000   7.000000\n",
       "50%    37.000000  92.200000  79.540000      9.165000  48.500000\n",
       "75%    55.000000  94.075000  87.075000      9.592500  76.250000\n",
       "max    73.000000  99.200000  93.080000     10.000000  98.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb35cc7a-1c28-477c-a413-bca81ce98938",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_fill = ['10th %age', '12th %age', 'College %age', 'Amcat'] \n",
    "\n",
    "# df_train[columns_to_fill] = df_train[columns_to_fill].fillna(df_train[columns_to_fill].mode().iloc[0])\n",
    "# df_test[columns_to_fill] = df_test[columns_to_fill].fillna(df_test[columns_to_fill].mode().iloc[0])\n",
    "\n",
    "df_train[columns_to_fill] = df_train[columns_to_fill].fillna(df_train[columns_to_fill].median())\n",
    "df_test[columns_to_fill] = df_test[columns_to_fill].fillna(df_test[columns_to_fill].median())\n",
    "\n",
    "# df_train[columns_to_fill] = df_train[columns_to_fill].fillna(df_train[columns_to_fill].mean())\n",
    "# df_test[columns_to_fill] = df_test[columns_to_fill].fillna(df_test[columns_to_fill].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88241ca9-f6af-4787-802c-96ee675cf3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Name</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "      <th>Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MOHITE AARTI RAMESH</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WALUNJKAR SIDDHI SHAHURAO</td>\n",
       "      <td>91.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KABADI KAIVALYA SUDHIR</td>\n",
       "      <td>92.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MULLA MOIN ADILSHAH</td>\n",
       "      <td>91.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>9.80</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BHAPKAR SHRUTI MOHANRAO</td>\n",
       "      <td>84.5</td>\n",
       "      <td>82.0</td>\n",
       "      <td>9.41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  10th %age  12th %age  College %age  Amcat  \\\n",
       "0        MOHITE AARTI RAMESH       92.0       82.0          9.50   87.0   \n",
       "1  WALUNJKAR SIDDHI SHAHURAO       91.3       82.0          9.00   39.0   \n",
       "2     KABADI KAIVALYA SUDHIR       92.0       86.0          9.00   51.0   \n",
       "3        MULLA MOIN ADILSHAH       91.0       84.0          9.80   64.0   \n",
       "4    BHAPKAR SHRUTI MOHANRAO       84.5       82.0          9.41    0.0   \n",
       "\n",
       "   Placement  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "945bfed6-f12f-4ccb-b5b4-1585c4232aa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Studid</th>\n",
       "      <th>Name</th>\n",
       "      <th>10th %age</th>\n",
       "      <th>12th %age</th>\n",
       "      <th>College %age</th>\n",
       "      <th>Amcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BHALERAO KAJAL SHANKAR</td>\n",
       "      <td>72.4</td>\n",
       "      <td>79.54</td>\n",
       "      <td>8.800</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>BHATEWARA JINESH JAYANT</td>\n",
       "      <td>92.2</td>\n",
       "      <td>79.54</td>\n",
       "      <td>9.165</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KANSAGARA JEET ASHWIN</td>\n",
       "      <td>92.2</td>\n",
       "      <td>79.54</td>\n",
       "      <td>9.165</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>DESHPANDE ISHAN SHIRISH</td>\n",
       "      <td>92.2</td>\n",
       "      <td>79.54</td>\n",
       "      <td>9.165</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>BHANUSHALI HARSHIT DHIRAJ</td>\n",
       "      <td>90.0</td>\n",
       "      <td>82.46</td>\n",
       "      <td>9.400</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Studid                       Name  10th %age  12th %age  College %age  \\\n",
       "0       1     BHALERAO KAJAL SHANKAR       72.4      79.54         8.800   \n",
       "1       2    BHATEWARA JINESH JAYANT       92.2      79.54         9.165   \n",
       "2       3      KANSAGARA JEET ASHWIN       92.2      79.54         9.165   \n",
       "3       4    DESHPANDE ISHAN SHIRISH       92.2      79.54         9.165   \n",
       "4       5  BHANUSHALI HARSHIT DHIRAJ       90.0      82.46         9.400   \n",
       "\n",
       "   Amcat  \n",
       "0    7.0  \n",
       "1   48.5  \n",
       "2   48.5  \n",
       "3   48.5  \n",
       "4   94.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "94402f25-00d9-4dd7-846d-89034a23cdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, '10th %age'),\n",
       " Text(1.5, 0, '12th %age'),\n",
       " Text(2.5, 0, 'College %age'),\n",
       " Text(3.5, 0, 'Amcat'),\n",
       " Text(4.5, 0, 'Placement')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr_df = df_train.drop(['Name'], axis=1).corr(method=\"pearson\")\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "# cannot write each value of dataset in cell\n",
    "heatmap = sns.heatmap(corr_df, annot=True, fmt=\".1g\", vmin=-1, vmax=1, center=0, cmap=\"inferno\", linewidths=1, linecolor=\"Black\")\n",
    "heatmap.set_title(\"Correlation Heatmap Between Variables\")\n",
    "heatmap.set_xticklabels(heatmap.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d5d2c048-8d70-4bbd-9311-f66b202e7b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X contains the features, y contains the target variable\n",
    "\n",
    "X_train = df_train[['College %age', 'Amcat']]\n",
    "y_train = df_train['Placement']\n",
    "\n",
    "X_test = df_test[['College %age', 'Amcat']]\n",
    "y_test = df_soln['Placement']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "02d7a45f-3ec8-44e3-a9fd-1c0a48d12798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the training data into training and testing sets\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(\n",
    "    # X, y, test_size=0.2\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a29baa9-fab2-43c2-8d28-de1fbd3b5170",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bda94f6d-2081-484d-9d56-29caeae18445",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5fcc2852-4ff4-47e3-bb65-6ab9c3dce8cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the Logistic Regression model\n",
    "\n",
    "logistic_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f78f3a77-91ea-49dc-ad8c-8d82bd9ef264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model on the training set\n",
    "\n",
    "logistic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88f5ab77-4580-4310-9e2e-8fc3695bd68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on the testing set\n",
    "# logistic_model.score(X_test, y_test)\n",
    "\n",
    "y_pred1 = logistic_model.predict(X_test)\n",
    "np.array(y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01c57a3f-8f5a-451a-8b47-7f386391d146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_soln['Placement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb0497ec-aba4-42db-ae46-2809994958ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression: 0.84\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "\n",
    "accuracy1 = accuracy_score(y_test, y_pred1)\n",
    "print(f'Accuracy of Logistic Regression: {accuracy1:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e85bf13b-b61a-45f7-b2c9-3a43926213b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 12],\n",
       "       [ 0, 61]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b563b472-6d12-4faa-86fb-3fa32a468253",
   "metadata": {},
   "source": [
    "             Actual Positive   Actual Negative\n",
    "Predicted Positive          60                21\n",
    "Predicted Negative         0 1               0 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "577c7818-40df-4782-8fb6-9f66febbf10e",
   "metadata": {},
   "source": [
    "Support Vector machine SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "94d7cd55-627e-49d0-b381-dd6a04ff54d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train0 = df_train[df_train.Placement == 0]\n",
    "df_train1 = df_train[df_train.Placement == 1]\n",
    "# df_train0.head()\n",
    "# df_train1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21329981-e2e9-47db-8f9f-3973c3fe331e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1754cf06f50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_train0['Amcat'], df_train0['College %age'], color='red', marker='+')\n",
    "plt.scatter(df_train1['Amcat'], df_train1['College %age'], color='green', marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18f28de6-0be9-4e6f-ba53-51ee7e2feacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1754cf5b390>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_train0['10th %age'], df_train0['12th %age'], color='red', marker='+')\n",
    "plt.scatter(df_train1['10th %age'], df_train1['12th %age'], color='green', marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a829cc94-ff76-49f7-a591-e5b9c0e7c9af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e88ea118-69e8-47fe-ace0-e3bde2b091f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b8e0cffa-b9f9-4520-980d-ee31ccb86264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8356164383561644"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "37f94047-3f4a-40e5-8bdc-9bc567066fae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred2 = svc_model.predict(X_test)\n",
    "np.array(y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "77a80d85-a0e4-4b35-a42c-c6f44ac96c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_soln['Placement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "18501fd9-55d2-4666-b001-c915de8b9ab4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM: 0.84\n"
     ]
    }
   ],
   "source": [
    "accuracy2 = accuracy_score(y_test, y_pred2)\n",
    "print(f'Accuracy of SVM: {accuracy2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "919db4d0-c0a8-44da-a2cf-81cdeb298d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 12],\n",
       "       [ 0, 61]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615efb8f-7d42-4727-a730-c0037f499f77",
   "metadata": {},
   "source": [
    "Decission Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1749357-662b-4987-adef-0863b4e875d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "decission_model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7bc922ed-80dd-47cc-a0f0-cf0f799f3307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decission_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d1a46c1-8f19-4729-b48f-c1f9577d49af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8904109589041096"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decission_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "76b5e5ef-1c50-45c3-af02-975fdb53c648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred3 = decission_model.predict(X_test)\n",
    "np.array(y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "23374d2d-096e-48aa-930f-53849f4363c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_soln['Placement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "49732e85-b02c-428c-aade-04987f96fbec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decission tree: 0.89\n"
     ]
    }
   ],
   "source": [
    "accuracy3 = accuracy_score(y_test, y_pred3)\n",
    "print(f'Accuracy of Decission tree: {accuracy3:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3cb056fa-2083-4c51-90c5-846d73b82469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  7],\n",
       "       [ 1, 60]], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d5558d4-376a-422a-b03c-bfbf71163d3d",
   "metadata": {},
   "source": [
    "Random Forest Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dafee780-403f-4a3a-942e-3f15198b6507",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "472a1d2b-e911-43bf-be2d-f1076bbb8975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "92c87c5f-5d78-4825-a8f8-7c13038b38cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9041095890410958"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3f11001-6b38-4537-b4e1-18df39b1bee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred4 = rf_model.predict(X_test)\n",
    "np.array(y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\"Studid\": df_soln['Studid'], \"Placement\": y_pred4}\n",
    "Submission = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission.to_csv('Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a30cdedb-df86-436e-90e1-ba25775670c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_soln['Placement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e3a3cc6e-66bc-473f-86ae-239c4da7878d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest: 0.90\n"
     ]
    }
   ],
   "source": [
    "accuracy4 = accuracy_score(y_test, y_pred4)\n",
    "print(f'Accuracy of Random Forest: {accuracy4:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f1954ae1-8264-4923-af97-99e17dbb9bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  7],\n",
       "       [ 0, 61]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf569f1e-cbe2-4121-8e2a-b6b9ad280b14",
   "metadata": {},
   "source": [
    "KNN algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "eac2f8b9-cf97-4a8a-b5d7-2b338e10af4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ce83c90b-48e0-4deb-9d71-9ead83ee2f5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "abdd1404-01a4-4e3c-b3f3-ea1ec90a7a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8493150684931506"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f89c19d6-638f-48e7-a504-c93089a0c258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred5 = knn.predict(X_test)\n",
    "np.array(y_pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "30cdbbc3-7906-4f8f-9adc-c6fcd114ab75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_soln['Placement'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7c1cd76a-fb41-4255-87d4-f69460c2816c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest: 0.85\n"
     ]
    }
   ],
   "source": [
    "accuracy5 = accuracy_score(y_test, y_pred5)\n",
    "print(f'Accuracy of Random Forest: {accuracy5:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "30987b21-e3dd-4a50-a935-c9b7b99ee961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2, 10],\n",
       "       [ 1, 60]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "869ffda1-6632-4ac7-aa49-a81951d8a8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NB_model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7aadc093-a10a-4abe-8dac-251f6f7618b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fa197af8-7196-4b47-85e0-b45bb0aaf0fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8356164383561644"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9e61f8bc-b69a-4875-b14d-766ed1e2db24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred6 = NB_model.predict(X_test)\n",
    "np.array(y_pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7d166072-a913-4976-b174-622bb7886560",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "138b1de5-517c-41ac-8b75-69a9d477f3c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='logloss',\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set hyperparameters\n",
    "params = {\n",
    "    'objective': 'binary:logistic',  # for binary classification problems\n",
    "    'max_depth': 3,\n",
    "    'learning_rate': 0.1,\n",
    "    'eval_metric': 'logloss'  # use logloss for binary classification\n",
    "}\n",
    "\n",
    "# Create XGBClassifier\n",
    "xg_model = XGBClassifier(**params, n_estimators=100)\n",
    "\n",
    "# Train the model\n",
    "xg_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1713465f-3e99-432b-90a4-2d0d4634fe54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = xg_model.predict(X_test)\n",
    "np.array(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "000907a7-e29e-4661-b20c-0c26d7e07281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.863013698630137"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_model.score(X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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