D3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "060fd927",
   "metadata": {
    "id": "060fd927"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd87eeb",
   "metadata": {},
   "source": [
    "## Loading and insights on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "AohuaqoC6gay",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "AohuaqoC6gay",
    "outputId": "00227b9e-a554-43f3-97c7-faefe3177394"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>fee_range</th>\n",
       "      <th>fee_slab</th>\n",
       "      <th>loan</th>\n",
       "      <th>req_branch</th>\n",
       "      <th>alloted_branch</th>\n",
       "      <th>campus_req</th>\n",
       "      <th>campus_allotted</th>\n",
       "      <th>entrance_rank</th>\n",
       "      <th>admission_acceptance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>QAHF7680</td>\n",
       "      <td>Male</td>\n",
       "      <td>42967.60</td>\n",
       "      <td>37590.75</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>971</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KAFN0165</td>\n",
       "      <td>Male</td>\n",
       "      <td>75113.66</td>\n",
       "      <td>45560.81</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>578</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FPVA8631</td>\n",
       "      <td>Male</td>\n",
       "      <td>50439.62</td>\n",
       "      <td>28703.58</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>927</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YICC8687</td>\n",
       "      <td>Male</td>\n",
       "      <td>61341.87</td>\n",
       "      <td>24546.13</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>625</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NZJO8685</td>\n",
       "      <td>Female</td>\n",
       "      <td>61221.07</td>\n",
       "      <td>33893.19</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>720</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  admission_id  gender  fee_range  fee_slab loan              req_branch  \\\n",
       "0     QAHF7680    Male   42967.60  37590.75   No  Mechanical Engineering   \n",
       "1     KAFN0165    Male   75113.66  45560.81   No  Mechanical Engineering   \n",
       "2     FPVA8631    Male   50439.62  28703.58   No  Mechanical Engineering   \n",
       "3     YICC8687    Male   61341.87  24546.13   No        Computer Science   \n",
       "4     NZJO8685  Female   61221.07  33893.19   No  Mechanical Engineering   \n",
       "\n",
       "           alloted_branch   campus_req  campus_allotted  entrance_rank  \\\n",
       "0        Computer Science  City Campus      City Campus            971   \n",
       "1  Information Technology  City Campus  Suburban Campus            578   \n",
       "2  Electrical Engineering  Main Campus      City Campus            927   \n",
       "3  Information Technology  City Campus      Main Campus            625   \n",
       "4  Mechanical Engineering  Main Campus      City Campus            720   \n",
       "\n",
       "  admission_acceptance  \n",
       "0                   No  \n",
       "1                   No  \n",
       "2                   No  \n",
       "3                   No  \n",
       "4                   No  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm=pd.read_csv('D3.csv')\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "GJPMaMBW7DLI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GJPMaMBW7DLI",
    "outputId": "7bf1f351-95d1-4b6e-b378-0cc1363bd0ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['admission_id', 'gender', 'fee_range', 'fee_slab', 'loan', 'req_branch',\n",
       "       'alloted_branch', 'campus_req', 'campus_allotted', 'entrance_rank',\n",
       "       'admission_acceptance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75fd5c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admission_id            0\n",
       "gender                  0\n",
       "fee_range               0\n",
       "fee_slab                0\n",
       "loan                    0\n",
       "req_branch              0\n",
       "alloted_branch          0\n",
       "campus_req              0\n",
       "campus_allotted         0\n",
       "entrance_rank           0\n",
       "admission_acceptance    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c872b2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   admission_id          1000 non-null   object \n",
      " 1   gender                1000 non-null   object \n",
      " 2   fee_range             1000 non-null   float64\n",
      " 3   fee_slab              1000 non-null   float64\n",
      " 4   loan                  1000 non-null   object \n",
      " 5   req_branch            1000 non-null   object \n",
      " 6   alloted_branch        1000 non-null   object \n",
      " 7   campus_req            1000 non-null   object \n",
      " 8   campus_allotted       1000 non-null   object \n",
      " 9   entrance_rank         1000 non-null   int64  \n",
      " 10  admission_acceptance  1000 non-null   object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 86.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dfm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "24800485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admission_id             object\n",
       "gender                   object\n",
       "fee_range               float64\n",
       "fee_slab                float64\n",
       "loan                     object\n",
       "req_branch               object\n",
       "alloted_branch           object\n",
       "campus_req               object\n",
       "campus_allotted          object\n",
       "entrance_rank             int64\n",
       "admission_acceptance     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba5df3b",
   "metadata": {},
   "source": [
    "## Understanding on the features and basic renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bacebb01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>fee_range</th>\n",
       "      <th>fee_slab</th>\n",
       "      <th>loan</th>\n",
       "      <th>req_branch</th>\n",
       "      <th>alloted_branch</th>\n",
       "      <th>campus_req</th>\n",
       "      <th>campus_allotted</th>\n",
       "      <th>entrance_rank</th>\n",
       "      <th>admission_acceptance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>QAHF7680</td>\n",
       "      <td>Male</td>\n",
       "      <td>42967.60</td>\n",
       "      <td>37590.75</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>971</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KAFN0165</td>\n",
       "      <td>Male</td>\n",
       "      <td>75113.66</td>\n",
       "      <td>45560.81</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>578</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FPVA8631</td>\n",
       "      <td>Male</td>\n",
       "      <td>50439.62</td>\n",
       "      <td>28703.58</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>927</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YICC8687</td>\n",
       "      <td>Male</td>\n",
       "      <td>61341.87</td>\n",
       "      <td>24546.13</td>\n",
       "      <td>No</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>625</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NZJO8685</td>\n",
       "      <td>Female</td>\n",
       "      <td>61221.07</td>\n",
       "      <td>33893.19</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>720</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>KVGH9633</td>\n",
       "      <td>Male</td>\n",
       "      <td>35050.14</td>\n",
       "      <td>47725.23</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Chemical Engineering</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>799</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>KWSC6731</td>\n",
       "      <td>Female</td>\n",
       "      <td>70374.72</td>\n",
       "      <td>39900.08</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>641</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>TYKI9800</td>\n",
       "      <td>Female</td>\n",
       "      <td>37509.33</td>\n",
       "      <td>46348.11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>Computer Science</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>113</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>SNRT1371</td>\n",
       "      <td>Male</td>\n",
       "      <td>59358.78</td>\n",
       "      <td>27068.52</td>\n",
       "      <td>No</td>\n",
       "      <td>Electrical Engineering</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>City Campus</td>\n",
       "      <td>Main Campus</td>\n",
       "      <td>971</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>EYLV1017</td>\n",
       "      <td>Female</td>\n",
       "      <td>49105.59</td>\n",
       "      <td>22187.36</td>\n",
       "      <td>No</td>\n",
       "      <td>Mechanical Engineering</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>Suburban Campus</td>\n",
       "      <td>992</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    admission_id  gender  fee_range  fee_slab loan              req_branch  \\\n",
       "0       QAHF7680    Male   42967.60  37590.75   No  Mechanical Engineering   \n",
       "1       KAFN0165    Male   75113.66  45560.81   No  Mechanical Engineering   \n",
       "2       FPVA8631    Male   50439.62  28703.58   No  Mechanical Engineering   \n",
       "3       YICC8687    Male   61341.87  24546.13   No        Computer Science   \n",
       "4       NZJO8685  Female   61221.07  33893.19   No  Mechanical Engineering   \n",
       "..           ...     ...        ...       ...  ...                     ...   \n",
       "995     KVGH9633    Male   35050.14  47725.23  Yes        Computer Science   \n",
       "996     KWSC6731  Female   70374.72  39900.08   No  Mechanical Engineering   \n",
       "997     TYKI9800  Female   37509.33  46348.11  Yes        Computer Science   \n",
       "998     SNRT1371    Male   59358.78  27068.52   No  Electrical Engineering   \n",
       "999     EYLV1017  Female   49105.59  22187.36   No  Mechanical Engineering   \n",
       "\n",
       "             alloted_branch       campus_req  campus_allotted  entrance_rank  \\\n",
       "0          Computer Science      City Campus      City Campus            971   \n",
       "1    Information Technology      City Campus  Suburban Campus            578   \n",
       "2    Electrical Engineering      Main Campus      City Campus            927   \n",
       "3    Information Technology      City Campus      Main Campus            625   \n",
       "4    Mechanical Engineering      Main Campus      City Campus            720   \n",
       "..                      ...              ...              ...            ...   \n",
       "995    Chemical Engineering  Suburban Campus      Main Campus            799   \n",
       "996  Information Technology      Main Campus  Suburban Campus            641   \n",
       "997        Computer Science      City Campus      City Campus            113   \n",
       "998  Information Technology      City Campus      Main Campus            971   \n",
       "999  Information Technology  Suburban Campus  Suburban Campus            992   \n",
       "\n",
       "    admission_acceptance  \n",
       "0                     No  \n",
       "1                     No  \n",
       "2                     No  \n",
       "3                     No  \n",
       "4                     No  \n",
       "..                   ...  \n",
       "995                   No  \n",
       "996                   No  \n",
       "997                  Yes  \n",
       "998                   No  \n",
       "999                   No  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e109425b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initialize LabelEncoder\n",
    "from sklearn.calibration import LabelEncoder\n",
    "\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "# List of columns to be encoded\n",
    "categorical_columns = ['admission_id','gender', 'loan', 'req_branch', 'alloted_branch', 'campus_req', 'campus_allotted', 'admission_acceptance']\n",
    "\n",
    "# Check if specified columns exist in the DataFrame\n",
    "for col in categorical_columns:\n",
    "    if col not in dfm.columns:\n",
    "        print(f\"Error: Column '{col}' not found in the DataFrame.\")\n",
    "        exit()\n",
    "\n",
    "# Check if specified columns are of type 'object'\n",
    "for col in categorical_columns:\n",
    "    if dfm[col].dtype != 'object':\n",
    "        print(f\"Error: Column '{col}' is not of type 'object'.\")\n",
    "        exit()\n",
    "\n",
    "# Apply label encoding to categorical features\n",
    "dfm[categorical_columns] = dfm[categorical_columns].apply(lambda col: le.fit_transform(col))\n",
    "\n",
    "# Convert all columns to float64\n",
    "dfm = dfm.astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06eecb58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>admission_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>fee_range</th>\n",
       "      <th>fee_slab</th>\n",
       "      <th>loan</th>\n",
       "      <th>req_branch</th>\n",
       "      <th>alloted_branch</th>\n",
       "      <th>campus_req</th>\n",
       "      <th>campus_allotted</th>\n",
       "      <th>entrance_rank</th>\n",
       "      <th>admission_acceptance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>629.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42967.60</td>\n",
       "      <td>37590.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>380.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75113.66</td>\n",
       "      <td>45560.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>221.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50439.62</td>\n",
       "      <td>28703.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>927.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>940.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61341.87</td>\n",
       "      <td>24546.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>545.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61221.07</td>\n",
       "      <td>33893.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>420.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35050.14</td>\n",
       "      <td>47725.23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>799.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>423.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70374.72</td>\n",
       "      <td>39900.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>641.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>782.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37509.33</td>\n",
       "      <td>46348.11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>725.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59358.78</td>\n",
       "      <td>27068.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>201.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49105.59</td>\n",
       "      <td>22187.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     admission_id  gender  fee_range  fee_slab  loan  req_branch  \\\n",
       "0           629.0     1.0   42967.60  37590.75   0.0         2.0   \n",
       "1           380.0     1.0   75113.66  45560.81   0.0         2.0   \n",
       "2           221.0     1.0   50439.62  28703.58   0.0         2.0   \n",
       "3           940.0     1.0   61341.87  24546.13   0.0         0.0   \n",
       "4           545.0     0.0   61221.07  33893.19   0.0         2.0   \n",
       "..            ...     ...        ...       ...   ...         ...   \n",
       "995         420.0     1.0   35050.14  47725.23   1.0         0.0   \n",
       "996         423.0     0.0   70374.72  39900.08   0.0         2.0   \n",
       "997         782.0     0.0   37509.33  46348.11   1.0         0.0   \n",
       "998         725.0     1.0   59358.78  27068.52   0.0         1.0   \n",
       "999         201.0     0.0   49105.59  22187.36   0.0         2.0   \n",
       "\n",
       "     alloted_branch  campus_req  campus_allotted  entrance_rank  \\\n",
       "0               2.0         0.0              0.0          971.0   \n",
       "1               4.0         0.0              2.0          578.0   \n",
       "2               3.0         1.0              0.0          927.0   \n",
       "3               4.0         0.0              1.0          625.0   \n",
       "4               5.0         1.0              0.0          720.0   \n",
       "..              ...         ...              ...            ...   \n",
       "995             0.0         2.0              1.0          799.0   \n",
       "996             4.0         1.0              2.0          641.0   \n",
       "997             2.0         0.0              0.0          113.0   \n",
       "998             4.0         0.0              1.0          971.0   \n",
       "999             4.0         2.0              2.0          992.0   \n",
       "\n",
       "     admission_acceptance  \n",
       "0                     0.0  \n",
       "1                     0.0  \n",
       "2                     0.0  \n",
       "3                     0.0  \n",
       "4                     0.0  \n",
       "..                    ...  \n",
       "995                   0.0  \n",
       "996                   0.0  \n",
       "997                   1.0  \n",
       "998                   0.0  \n",
       "999                   0.0  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4d789bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=dfm.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a7b3f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(corr,cbar=False, square= True, fmt='.2%', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10908dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['fee_range'], dfm['admission_acceptance'])\n",
    "plt.xlabel('fee_range')\n",
    "plt.ylabel('admission_acceptance')\n",
    "plt.title('Scatter Plot between admission_acceptance and admission_acceptance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3d3f1ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['req_branch'], dfm['admission_acceptance'])\n",
    "plt.xlabel('req_branch')\n",
    "plt.ylabel('admission_acceptance')\n",
    "plt.title('Scatter Plot between req_branch and admission_acceptance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4152e088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dfm['entrance_rank'], dfm['admission_acceptance'])\n",
    "plt.xlabel('entrance_rank')\n",
    "plt.ylabel('admission_acceptance')\n",
    "plt.title('Scatter Plot between entrance_rank and admission_acceptance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2693162",
   "metadata": {},
   "source": [
    "## Splitting of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "_QNQGBTZ6m71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_QNQGBTZ6m71",
    "outputId": "6be845dd-2eb7-4ee7-ff13-b24c078e3ea4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 11)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "AxjVxtNV8CAB",
   "metadata": {
    "id": "AxjVxtNV8CAB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(dfm.drop(['admission_acceptance'], axis = 1), dfm['admission_acceptance'], test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "hi9kuinU8RxA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hi9kuinU8RxA",
    "outputId": "c2ad3a6d-e9b3-4d6a-8645-7c2d7f9d0674"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (800, 10)\n",
      "X_test shape: (200, 10)\n",
      "y_train shape: (800,)\n",
      "y_test shape: (200,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:\",X_train.shape)\n",
    "print(\"X_test shape:\",X_test.shape)\n",
    "print(\"y_train shape:\",y_train.shape)\n",
    "print(\"y_test shape:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "q6MCQpBg8Ued",
   "metadata": {
    "id": "q6MCQpBg8Ued"
   },
   "outputs": [],
   "source": [
    "# To store results of models\n",
    "result_dict_train = {}\n",
    "result_dict_test = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a081fa1c",
   "metadata": {},
   "source": [
    "## Gradient Boost Classfier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "MKLLo0y38WKs",
   "metadata": {
    "id": "MKLLo0y38WKs"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eiUhAbUZ8YWd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eiUhAbUZ8YWd",
    "outputId": "0dd111c0-c4dd-496b-8552-b83e1ae4d4f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.91625\n",
      "Test Score: 0.92\n"
     ]
    }
   ],
   "source": [
    "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
    "accuracies = cross_val_score(gradient_booster, X_train, y_train, cv=5)\n",
    "gradient_booster.fit(X_train,y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",gradient_booster.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "gRRqpD_o8eDI",
   "metadata": {
    "id": "gRRqpD_o8eDI"
   },
   "outputs": [],
   "source": [
    "result_dict_train[\"Gradient-Boost Default Train Score\"] = np.mean(accuracies)\n",
    "result_dict_test[\"Gradient-Boost Default Test Score\"] = gradient_booster.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "XVplzJnN8ge6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XVplzJnN8ge6",
    "outputId": "9da698f4-adf7-4224-e434-995b114eebef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBC accuracy is 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print(\"GBC accuracy is %2.2f\" % accuracy_score(\n",
    "     y_test, gradient_booster.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "nZnLatcG8tcy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nZnLatcG8tcy",
    "outputId": "e22a1926-1dce-462a-d326-777be9196be0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.96      0.96      0.96       181\n",
      "         1.0       0.58      0.58      0.58        19\n",
      "\n",
      "    accuracy                           0.92       200\n",
      "   macro avg       0.77      0.77      0.77       200\n",
      "weighted avg       0.92      0.92      0.92       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "pred=gradient_booster.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ddef00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = gradient_booster.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aYPFbr2W8B7p",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aYPFbr2W8B7p",
    "outputId": "2c590387-5600-4a12-88ed-016ee4fee89d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'learning_rate': 0.1, 'n_estimators': 200}\n",
      "Train Score: 0.92875\n",
      "Test Score: 0.925\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "    'learning_rate':[0.01,0.05,0.1],\n",
    "    'n_estimators':np.arange(100,500,100),\n",
    "}\n",
    "\n",
    "gb = GradientBoostingClassifier()\n",
    "gb_cv = GridSearchCV(gb, grid, cv = 4)\n",
    "gb_cv.fit(X_train,y_train)\n",
    "print(\"Best Parameters:\",gb_cv.best_params_)\n",
    "print(\"Train Score:\",gb_cv.best_score_)\n",
    "print(\"Test Score:\",gb_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "TXAWu3S48B06",
   "metadata": {
    "id": "TXAWu3S48B06"
   },
   "outputs": [],
   "source": [
    "result_dict_train[\"Gradient-Boost GridSearch Train Score\"] = gb_cv.best_score_\n",
    "result_dict_test[\"Gradient-Boost GridSearch Test Score\"] = gb_cv.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7pXqQLNc8Bt_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7pXqQLNc8Bt_",
    "outputId": "7af0e216-1c05-4378-e703-9a14f07011cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'max_depth': 2}\n",
      "Train Score: 0.9262500000000001\n",
      "Test Score: 0.925\n"
     ]
    }
   ],
   "source": [
    "grid = {'max_depth':[2,3,4,5,6,7] }\n",
    "\n",
    "gb = GradientBoostingClassifier(learning_rate=0.1,n_estimators=400)\n",
    "\n",
    "gb_cv = GridSearchCV(gb, grid, cv = 4)\n",
    "\n",
    "gb_cv.fit(X_train,y_train)\n",
    "\n",
    "print(\"Best Parameters:\",gb_cv.best_params_)\n",
    "\n",
    "print(\"Train Score:\",gb_cv.best_score_)\n",
    "\n",
    "print(\"Test Score:\",gb_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22f32995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = gb_cv.predict(X_test)\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OsayFTAQ95H1",
   "metadata": {
    "id": "OsayFTAQ95H1"
   },
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2C8esZf18Bov",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2C8esZf18Bov",
    "outputId": "5f5382fc-140d-4bdc-b55f-9915f3c310dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.89875\n",
      "Test Score: 0.905\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(random_state = 101)\n",
    "accuracies = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "svc.fit(X_train,y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",svc.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9HRt4IMX8Bjr",
   "metadata": {
    "id": "9HRt4IMX8Bjr"
   },
   "outputs": [],
   "source": [
    "result_dict_train[\"SVM Default Train Score\"] = np.mean(accuracies)\n",
    "result_dict_test[\"SVM Default Test Score\"] = svc.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "h5opFJ7z8BaT",
   "metadata": {
    "id": "h5opFJ7z8BaT"
   },
   "outputs": [],
   "source": [
    "# grid = {\n",
    "#     'C':[0.01,0.1,1,10],\n",
    "#     'kernel' : [\"linear\",\"poly\",\"rbf\",\"sigmoid\"],\n",
    "#     'degree' : [1,3,5,7],\n",
    "#     'gamma' : [0.01,1]\n",
    "# }\n",
    "\n",
    "# svm  = SVC ()\n",
    "# svm_cv = GridSearchCV(svm, grid, cv = 5)\n",
    "# svm_cv.fit(X_train,y_train)\n",
    "# print(\"Best Parameters:\",svm_cv.best_params_)\n",
    "# print(\"Train Score:\",svm_cv.best_score_)\n",
    "# print(\"Test Score:\",svm_cv.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5e45f6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uu84EBAQYSaWWEajoGstzdumAzZs3m9tvv90EBQWZSy+91IwYMcKcOnXK6fHUH5YOOFvXoEGDTHh4uPHz8zONGzc2SUlJprCw0NHn+PHjZuzYsaZp06bG19fXhIWFmS5dupjXXnvNnDlzxhhjzPz5881NN91kIiIijK+vr6lfv7657777zOHDhy2PDagINmP+MAcOAPhLefbZZ/Xcc88pJydHYWFh7i4HqHa4ZwkAAMACYQkAAMACYQkAAMAC9ywBAABYYGYJAADAAmEJAADAAotSVgC73a5Dhw4pKCjovL8kDgAAqgdjjI4fP67o6GjL394kLFWAQ4cOKSYmxt1lAACAC7B//37LHwYnLFWAoKAgSb8NdnBwsJurAQAAzsjPz1dMTIzjPH4uhKUKcPbSW3BwMGEJAAAPc75baLjBGwAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwAJhCQAAwILHhaXJkyerYcOG8vf3V8eOHfXdd99Z9p83b55atmwpf39/xcbGKjU19Zx977//ftlsNk2cOLGCqwYAAJ7Ko8LS3LlzlZycrJSUFGVmZuqKK65QQkKCjhw5Um7/tWvXqn///ho6dKg2btyo3r17q3fv3vrxxx/L9P3000+1bt06RUdHV/ZhAAAAD+JRYemNN97Qvffeq7vvvlutW7fW1KlTdckll+j9998vt/9bb72l7t2767HHHlOrVq30wgsv6Morr9SkSZNK9Tt48KBGjhypWbNmqUaNGlVxKAAAwEN4TFg6c+aMMjIyFB8f72jz8vJSfHy80tPTy90nPT29VH9JSkhIKNXfbrdr4MCBeuyxx3TZZZdVTvEAAMBj+bi7AGcdPXpUJSUlioyMLNUeGRmprVu3lrtPVlZWuf2zsrIcz1955RX5+Pho1KhRTtdSWFiowsJCx/P8/Hyn9wUAAJ7FY2aWKkNGRobeeustTZ8+XTabzen9JkyYoJCQEMcjJiamEqsEAADu5DFhKSwsTN7e3srOzi7Vnp2draioqHL3iYqKsuy/evVqHTlyRPXr15ePj498fHy0d+9ePfLII2rYsOE5axk7dqzy8vIcj/379/+5gwMAANWWx4QlX19ftWvXTmlpaY42u92utLQ0de7cudx9OnfuXKq/JC1btszRf+DAgfrvf/+r77//3vGIjo7WY489pqVLl56zFj8/PwUHB5d6AACAi5PH3LMkScnJyRo8eLDat2+vDh06aOLEiSooKNDdd98tSRo0aJDq1q2rCRMmSJJGjx6tuLg4vf7667r55ps1Z84cbdiwQdOmTZMk1a5dW7Vr1y71HjVq1FBUVJRatGhRtQcHAACqJY8KS3379lVOTo6eeeYZZWVlqW3btlqyZInjJu59+/bJy+t/k2VdunTR7NmzNW7cOD355JNq1qyZFi1apDZt2rjrEAAAgIexGWOMu4vwdPn5+QoJCVFeXh6X5AAA8BDOnr895p4lAAAAdyAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWCAsAQAAWPC4sDR58mQ1bNhQ/v7+6tixo7777jvL/vPmzVPLli3l7++v2NhYpaamOrYVFRXpiSeeUGxsrAIDAxUdHa1Bgwbp0KFDlX0YAADAQ3hUWJo7d66Sk5OVkpKizMxMXXHFFUpISNCRI0fK7b927Vr1799fQ4cO1caNG9W7d2/17t1bP/74oyTp5MmTyszM1NNPP63MzEwtXLhQ27Zt06233lqVhwUAAKoxmzHGuLsIZ3Xs2FFXXXWVJk2aJEmy2+2KiYnRyJEjNWbMmDL9+/btq4KCAi1evNjR1qlTJ7Vt21ZTp04t9z3Wr1+vDh06aO/evapfv75TdeXn5yskJER5eXkKDg6+gCMDAABVzdnzt8fMLJ05c0YZGRmKj493tHl5eSk+Pl7p6enl7pOenl6qvyQlJCScs78k5eXlyWazKTQ0tELqBgAAns3H3QU46+jRoyopKVFkZGSp9sjISG3durXcfbKyssrtn5WVVW7/06dP64knnlD//v0tE2ZhYaEKCwsdz/Pz8509DAAA4GE8ZmapshUVFalPnz4yxmjKlCmWfSdMmKCQkBDHIyYmpoqqBAAAVc1jwlJYWJi8vb2VnZ1dqj07O1tRUVHl7hMVFeVU/7NBae/evVq2bNl57zsaO3as8vLyHI/9+/dfwBEBAABP4DFhydfXV+3atVNaWpqjzW63Ky0tTZ07dy53n86dO5fqL0nLli0r1f9sUNqxY4eWL1+u2rVrn7cWPz8/BQcHl3oAAICLk8fcsyRJycnJGjx4sNq3b68OHTpo4sSJKigo0N133y1JGjRokOrWrasJEyZIkkaPHq24uDi9/vrruvnmmzVnzhxt2LBB06ZNk/RbULr99tuVmZmpxYsXq6SkxHE/U61ateTr6+ueAwUAANWGR4Wlvn37KicnR88884yysrLUtm1bLVmyxHET9759++Tl9b/Jsi5dumj27NkaN26cnnzySTVr1kyLFi1SmzZtJEkHDx7UZ599Jklq27ZtqfdauXKlrr/++io5LgAAUH151DpL1RXrLAEA4HkuunWWAAAA3IGwBAAAYIGwBAAAYIGwBAAAYIGwBAAAYIGwBAAAYIGwBAAAYMHlsLRkyRKtWbPG8Xzy5Mlq27at7rzzTv36668VWhwAAIC7uRyWHnvsMeXn50uSNm3apEceeUQ9e/bU7t27lZycXOEFAgAAuJPLP3eye/dutW7dWpK0YMEC3XLLLRo/frwyMzPVs2fPCi8QAADAnVyeWfL19dXJkyclScuXL9dNN90k6bcfnj074wQAAHCxcHlm6ZprrlFycrKuvvpqfffdd5o7d64kafv27apXr16FFwgAAOBOLs8sTZo0ST4+Ppo/f76mTJmiunXrSpK++OILde/evcILBAAAcCebMca4uwhP5+yvFgMAgOrD2fP3Ba2ztHPnTo0bN079+/fXkSNHJP02s/TTTz9dWLUAAADVlMth6euvv1ZsbKy+/fZbLVy4UCdOnJAk/fDDD0pJSanwAgEAANzJ5bA0ZswYvfjii1q2bJl8fX0d7V27dtW6desqtDgAAAB3czksbdq0Sf/4xz/KtEdEROjo0aMVUhQAAEB14XJYCg0N1eHDh8u0b9y40fHNOAAAgIuFy2GpX79+euKJJ5SVlSWbzSa73a5vvvlGjz76qAYNGlQZNQIAALiNy2Fp/PjxatmypWJiYnTixAm1bt1a1113nbp06aJx48ZVRo0AAABuc8HrLO3fv1+bNm3SiRMn9Le//U3NmjWr6No8BussAQDgeZw9f7v8cydnxcTEKCYm5kJ3BwAA8AguX4ZLTEzUK6+8Uqb91Vdf1R133FEhRQEAAFQXLoelVatWqWfPnmXae/TooVWrVlVIUQAAANWFy2HpxIkTpRajPKtGjRrKz8+vkKIAAACqC5fDUmxsrObOnVumfc6cOWrdunWFFAUAAFBduHyD99NPP63bbrtNO3fuVNeuXSVJaWlp+vjjjzVv3rwKLxAAAMCdXA5LvXr10qJFizR+/HjNnz9fAQEBuvzyy7V8+XLFxcVVRo0AAABuc8HrLOF/WGcJAADP4+z52+V7lgAAAP5KnLoMV6tWLW3fvl1hYWG69NJLZbPZztn32LFjFVYcAACAuzkVlt58800FBQVJkiZOnFiZ9QAAAFQrToWlwYMHS5KKi4tls9mUkJCgyMjISi0MAACgOnDpniUfHx/df//9On36dGXVAwAAUK24fIN3hw4dtHHjxsqoBQAAoNpxeZ2lBx98UI888ogOHDigdu3aKTAwsNT2yy+/vMKKAwAAcDeX11ny8io7GWWz2WSMkc1mU0lJSYUV5ylYZwkAAM/j7Pnb5Zml3bt3/6nCAAAAPInLYalBgwaVUQcAAEC15HJYkqRt27bpnXfe0ZYtWyRJrVq10siRI9WiRYsKLQ4AAMDdXP423IIFC9SmTRtlZGToiiuu0BVXXKHMzEy1adNGCxYsqIwaAQAA3MblG7ybNGmiAQMG6Pnnny/VnpKSopkzZ2rnzp0VWqAn4AZvAAA8T6X9kO7hw4c1aNCgMu133XWXDh8+7OrLAQAAVGsuh6Xrr79eq1evLtO+Zs0aXXvttRVSFAAAQHXh8g3et956q5544gllZGSoU6dOkqR169Zp3rx5eu655/TZZ5+V6gsAAODJKmRRynJf+C+0QCX3LAEA4HkqbVFKu93+pwoDAADwJC7fswQAAPBXQlgCAACwQFgCAACwQFgCAACwQFgCAACw4NS34fLz851+Qb46DwAALiZOhaXQ0FDZbDanXvCvsrYSAAD4a3AqLK1cudLxz3v27NGYMWM0ZMgQde7cWZKUnp6uGTNmaMKECZVTJQAAgJu4vIJ3t27dNGzYMPXv379U++zZszVt2jR99dVXFVmfR2AFbwAAPI+z52+Xb/BOT09X+/bty7S3b99e3333nasv57LJkyerYcOG8vf3V8eOHc/7nvPmzVPLli3l7++v2NhYpaamltpujNEzzzyjOnXqKCAgQPHx8dqxY0dlHgIAAPAgLoelmJgYvffee2Xa//3vfysmJqZCijqXuXPnKjk5WSkpKcrMzNQVV1yhhIQEHTlypNz+a9euVf/+/TV06FBt3LhRvXv3Vu/evfXjjz86+rz66qt6++23NXXqVH377bcKDAxUQkKCTp8+XanHAgAAPIPLl+FSU1OVmJiopk2bqmPHjpKk7777Tjt27NCCBQvUs2fPSilUkjp27KirrrpKkyZNkvTb79TFxMRo5MiRGjNmTJn+ffv2VUFBgRYvXuxo69Spk9q2baupU6fKGKPo6Gg98sgjevTRRyVJeXl5ioyM1PTp09WvXz+n6qqMy3DGGP1ScKZCXgsAAE9W6xJfeXk590UzV1TaD+n27NlT27dv15QpU7R161ZJUq9evXT//fdX6szSmTNnlJGRobFjxzravLy8FB8fr/T09HL3SU9PV3Jycqm2hIQELVq0SJK0e/duZWVlKT4+3rE9JCREHTt2VHp6+jnDUmFhoQoLCx3PXVlawVm/FJxR+xeXV/jrAgDgiXaN71kpgckZLocl6bdLcePHj6/oWiwdPXpUJSUlioyMLNUeGRnpCG1/lJWVVW7/rKwsx/azbefqU54JEyboueeec/kYAACA57mgsLR69Wr961//0q5duzRv3jzVrVtXH330kRo1aqRrrrmmomusdsaOHVtqxio/P7/CZ9VqB/pqw7j483cEAOAiV1mX4ZzlclhasGCBBg4cqAEDBigzM9NxOSovL0/jx48v822zihIWFiZvb29lZ2eXas/OzlZUVFS5+0RFRVn2P/u/2dnZqlOnTqk+bdu2PWctfn5+8vPzu5DDcJrNZlNYzcp9DwAAcH4ufxvuxRdf1NSpU/Xee++pRo0ajvarr75amZmZFVrc7/n6+qpdu3ZKS0tztNntdqWlpTkWx/yjzp07l+ovScuWLXP0b9SokaKiokr1yc/P17fffnvO1wQAAH8tLs8sbdu2Tdddd12Z9pCQEOXm5lZETeeUnJyswYMHq3379urQoYMmTpyogoIC3X333ZKkQYMGqW7duo6VxEePHq24uDi9/vrruvnmmzVnzhxt2LBB06ZNk/Tb7M1DDz2kF198Uc2aNVOjRo309NNPKzo6Wr17967UYwEAAJ7B5bAUFRWln3/+WQ0bNizVvmbNGjVu3Lii6ipX3759lZOTo2eeeUZZWVlq27atlixZ4rhBe9++ffLy+t9kWZcuXTR79myNGzdOTz75pJo1a6ZFixapTZs2jj6PP/64CgoKNHz4cOXm5uqaa67RkiVL5O/vX6nHAgAAPIPL6yxNmDBBM2fO1Pvvv68bb7xRqamp2rt3rx5++GE9/fTTGjlyZGXVWm3xcycAAHieSltnacyYMbLb7erWrZtOnjyp6667Tn5+fnr00Uf/kkEJAABc3FyeWTrrzJkz+vnnn3XixAm1bt1aNWvWrOjaPAYzSwAAeJ5K+yHde+65R8ePH5evr69at26tDh06qGbNmiooKNA999zzp4oGAACoblwOSzNmzNCpU6fKtJ86dUoffvhhhRQFAABQXTh9z1J+fr6MMTLG6Pjx46W+LVZSUqLU1FRFRERUSpEAAADu4nRYCg0Nlc1mk81mU/Pmzctst9ls/F4aAAC46DgdllauXCljjLp27aoFCxaoVq1ajm2+vr5q0KCBoqOjK6VIAAAAd3E6LMXFxUmSdu/erfr168tmc98P2gEAAFQVl2/wXrFihebPn1+mfd68eZoxY0aFFAUAAFBduByWJkyYoLCwsDLtERERGj9+fIUUBQAAUF24HJb27dunRo0alWlv0KCB9u3bVyFFAQAAVBcuh6WIiAj997//LdP+ww8/qHbt2hVSFAAAQHXhcljq37+/Ro0apZUrV6qkpEQlJSVasWKFRo8erX79+lVGjQAAAG7j8g/pvvDCC9qzZ4+6desmH5/fdrfb7Ro0aBD3LAEAgIvOBf+Q7vbt2/XDDz8oICBAsbGxatCgQUXX5jH4IV0AADyPs+dvl2eWzmrevHm5K3kDAABcTJwKS8nJyXrhhRcUGBio5ORky75vvPFGhRQGAABQHTgVljZu3KiioiLHP58Lq3oDAICLzQXfs4T/4Z4lAAA8j7Pnb5eXDgAAAPgrceoy3G233eb0Cy5cuPCCiwEAAKhunJpZCgkJcTyCg4OVlpamDRs2OLZnZGQoLS1NISEhlVYoAACAOzg1s/TBBx84/vmJJ55Qnz59NHXqVHl7e0uSSkpK9OCDD3K/DgAAuOi4fIN3eHi41qxZoxYtWpRq37Ztm7p06aJffvmlQgv0BNzgDQCA56m0G7yLi4u1devWMu1bt26V3W539eUAAACqNZdX8L777rs1dOhQ7dy5Ux06dJAkffvtt3r55Zd19913V3iBAAAA7uRyWHrttdcUFRWl119/XYcPH5Yk1alTR4899pgeeeSRCi8QAADAnf7UopT5+fmS9Je/T4d7lgAA8DyVuihlcXGxli9fro8//tjxEyeHDh3SiRMnLqxaAACAasrly3B79+5V9+7dtW/fPhUWFurGG29UUFCQXnnlFRUWFmrq1KmVUScAAIBbuDyzNHr0aLVv316//vqrAgICHO3/+Mc/lJaWVqHFAQAAuJvLM0urV6/W2rVr5evrW6q9YcOGOnjwYIUVBgAAUB24PLNkt9tVUlJSpv3AgQMKCgqqkKIAAACqC5fD0k033aSJEyc6nttsNp04cUIpKSnq2bNnRdYGAADgdi4vHbB//351795dxhjt2LFD7du3144dOxQWFqZVq1YpIiKismqttlg6AAAAz+Ps+fuC1lkqLi7W3Llz9cMPP+jEiRO68sorNWDAgFI3fP+VEJYAAPA8lRKWioqK1LJlSy1evFitWrWqkEIvBoQlAAA8T6UsSlmjRg2dPn36TxcHAADgKVy+wTspKUmvvPKKiouLK6MeAACAasXldZbWr1+vtLQ0ffnll4qNjVVgYGCp7QsXLqyw4gAAANzN5bAUGhqqxMTEyqgFAACg2nE5LH3wwQeVUQcAAEC15PQ9S3a7Xa+88oquvvpqXXXVVRozZoxOnTpVmbUBAAC4ndNh6aWXXtKTTz6pmjVrqm7dunrrrbeUlJRUmbUBAAC4ndNh6cMPP9S7776rpUuXatGiRfr88881a9Ys2e32yqwPAADArZwOS/v27Sv122/x8fGy2Ww6dOhQpRQGAABQHTgdloqLi+Xv71+qrUaNGioqKqrwogAAAKoLp78NZ4zRkCFD5Ofn52g7ffq07r///lJrLbHOEgAAuJg4HZYGDx5cpu2uu+6q0GIAAACqG6fDEusrAQCAvyKXfxsOAADgr4SwBAAAYIGwBAAAYIGwBAAAYIGwBAAAYIGwBAAAYMFjwtKxY8c0YMAABQcHKzQ0VEOHDtWJEycs9zl9+rSSkpJUu3Zt1axZU4mJicrOznZs/+GHH9S/f3/FxMQoICBArVq10ltvvVXZhwIAADyIx4SlAQMG6KefftKyZcu0ePFirVq1SsOHD7fc5+GHH9bnn3+uefPm6euvv9ahQ4d02223ObZnZGQoIiJCM2fO1E8//aSnnnpKY8eO1aRJkyr7cAAAgIewGWOMu4s4ny1btqh169Zav3692rdvL0lasmSJevbsqQMHDig6OrrMPnl5eQoPD9fs2bN1++23S5K2bt2qVq1aKT09XZ06dSr3vZKSkrRlyxatWLHC6fry8/MVEhKivLw8BQcHX8ARAgCAqubs+dsjZpbS09MVGhrqCEqSFB8fLy8vL3377bfl7pORkaGioiLFx8c72lq2bKn69esrPT39nO+Vl5enWrVqWdZTWFio/Pz8Ug8AAHBx8oiwlJWVpYiIiFJtPj4+qlWrlrKyss65j6+vr0JDQ0u1R0ZGnnOftWvXau7cuee9vDdhwgSFhIQ4HjExMc4fDAAA8ChuDUtjxoyRzWazfGzdurVKavnxxx/197//XSkpKbrpppss+44dO1Z5eXmOx/79+6ukRgAAUPWc/iHdyvDII49oyJAhln0aN26sqKgoHTlypFR7cXGxjh07pqioqHL3i4qK0pkzZ5Sbm1tqdik7O7vMPps3b1a3bt00fPhwjRs37rx1+/n5yc/P77z9AACA53NrWAoPD1d4ePh5+3Xu3Fm5ubnKyMhQu3btJEkrVqyQ3W5Xx44dy92nXbt2qlGjhtLS0pSYmChJ2rZtm/bt26fOnTs7+v3000/q2rWrBg8erJdeeqkCjgoAAFxMPOLbcJLUo0cPZWdna+rUqSoqKtLdd9+t9u3ba/bs2ZKkgwcPqlu3bvrwww/VoUMHSdIDDzyg1NRUTZ8+XcHBwRo5cqSk3+5Nkn679Na1a1clJCTon//8p+O9vL29nQpxZ/FtOAAAPI+z52+3ziy5YtasWRoxYoS6desmLy8vJSYm6u2333ZsLyoq0rZt23Ty5ElH25tvvunoW1hYqISEBL377ruO7fPnz1dOTo5mzpypmTNnOtobNGigPXv2VMlxAQCA6s1jZpaqM2aWAADwPBfVOksAAADuQlgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACwQFgCAACw4DFh6dixYxowYICCg4MVGhqqoUOH6sSJE5b7nD59WklJSapdu7Zq1qypxMREZWdnl9v3l19+Ub169WSz2ZSbm1sJRwAAADyRx4SlAQMG6KefftKyZcu0ePFirVq1SsOHD7fc5+GHH9bnn3+uefPm6euvv9ahQ4d02223ldt36NChuvzyyyujdAAA4MFsxhjj7iLOZ8uWLWrdurXWr1+v9u3bS5KWLFminj176sCBA4qOji6zT15ensLDwzV79mzdfvvtkqStW7eqVatWSk9PV6dOnRx9p0yZorlz5+qZZ55Rt27d9Ouvvyo0NNTp+vLz8xUSEqK8vDwFBwf/uYMFAABVwtnzt0fMLKWnpys0NNQRlCQpPj5eXl5e+vbbb8vdJyMjQ0VFRYqPj3e0tWzZUvXr11d6erqjbfPmzXr++ef14YcfysvLueEoLCxUfn5+qQcAALg4eURYysrKUkRERKk2Hx8f1apVS1lZWefcx9fXt8wMUWRkpGOfwsJC9e/fX//85z9Vv359p+uZMGGCQkJCHI+YmBjXDggAAHgMt4alMWPGyGazWT62bt1aae8/duxYtWrVSnfddZfL++Xl5Tke+/fvr6QKAQCAu/m4880feeQRDRkyxLJP48aNFRUVpSNHjpRqLy4u1rFjxxQVFVXuflFRUTpz5oxyc3NLzS5lZ2c79lmxYoU2bdqk+fPnS5LO3r4VFhamp556Ss8991y5r+3n5yc/Pz9nDhEAAHg4t4al8PBwhYeHn7df586dlZubq4yMDLVr107Sb0HHbrerY8eO5e7Trl071ahRQ2lpaUpMTJQkbdu2Tfv27VPnzp0lSQsWLNCpU6cc+6xfv1733HOPVq9erSZNmvzZwwMAABcBt4YlZ7Vq1Urdu3fXvffeq6lTp6qoqEgjRoxQv379HN+EO3jwoLp166YPP/xQHTp0UEhIiIYOHark5GTVqlVLwcHBGjlypDp37uz4JtwfA9HRo0cd7+fKt+EAAMDFyyPCkiTNmjVLI0aMULdu3eTl5aXExES9/fbbju1FRUXatm2bTp486Wh78803HX0LCwuVkJCgd9991x3lAwAAD+UR6yxVd6yzBACA57mo1lkCAABwF8ISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABcISAACABR93F3AxMMZIkvLz891cCQAAcNbZ8/bZ8/i5EJYqwPHjxyVJMTExbq4EAAC46vjx4woJCTnndps5X5zCedntdh06dEhBQUGy2WwV9rr5+fmKiYnR/v37FRwcXGGvi9IY56rBOFcdxrpqMM5VozLH2Rij48ePKzo6Wl5e574ziZmlCuDl5aV69epV2usHBwfzL2IVYJyrBuNcdRjrqsE4V43KGmerGaWzuMEbAADAAmEJAADAAmGpGvPz81NKSor8/PzcXcpFjXGuGoxz1WGsqwbjXDWqwzhzgzcAAIAFZpYAAAAsEJYAAAAsEJYAAAAsEJYAAAAsEJbcbPLkyWrYsKH8/f3VsWNHfffdd5b9582bp5YtW8rf31+xsbFKTU2toko9myvj/N577+naa6/VpZdeqksvvVTx8fHn/VzwG1f/ns+aM2eObDabevfuXbkFXiRcHefc3FwlJSWpTp068vPzU/Pmzflvh5NcHeuJEyeqRYsWCggIUExMjB5++GGdPn26iqr1TKtWrVKvXr0UHR0tm82mRYsWnXefr776SldeeaX8/PzUtGlTTZ8+vXKLNHCbOXPmGF9fX/P++++bn376ydx7770mNDTUZGdnl9v/m2++Md7e3ubVV181mzdvNuPGjTM1atQwmzZtquLKPYur43znnXeayZMnm40bN5otW7aYIUOGmJCQEHPgwIEqrtyzuDrOZ+3evdvUrVvXXHvttebvf/971RTrwVwd58LCQtO+fXvTs2dPs2bNGrN7927z1Vdfme+//76KK/c8ro71rFmzjJ+fn5k1a5bZvXu3Wbp0qalTp455+OGHq7hyz5Kammqeeuops3DhQiPJfPrpp5b9d+3aZS655BKTnJxsNm/ebN555x3j7e1tlixZUmk1EpbcqEOHDiYpKcnxvKSkxERHR5sJEyaU279Pnz7m5ptvLtXWsWNHc99991VqnZ7O1XH+o+LiYhMUFGRmzJhRWSVeFC5knIuLi02XLl3Mv//9bzN48GDCkhNcHecpU6aYxo0bmzNnzlRViRcNV8c6KSnJdO3atVRbcnKyufrqqyu1zouJM2Hp8ccfN5dddlmptr59+5qEhIRKq4vLcG5y5swZZWRkKD4+3tHm5eWl+Ph4paenl7tPenp6qf6SlJCQcM7+uLBx/qOTJ0+qqKhItWrVqqwyPd6FjvPzzz+viIgIDR06tCrK9HgXMs6fffaZOnfurKSkJEVGRqpNmzYaP368SkpKqqpsj3QhY92lSxdlZGQ4LtXt2rVLqamp6tmzZ5XU/FfhjnMhP6TrJkePHlVJSYkiIyNLtUdGRmrr1q3l7pOVlVVu/6ysrEqr09NdyDj/0RNPPKHo6Ogy/3Lify5knNesWaP/9//+n77//vsqqPDicCHjvGvXLq1YsUIDBgxQamqqfv75Zz344IMqKipSSkpKVZTtkS5krO+8804dPXpU11xzjYwxKi4u1v33368nn3yyKkr+yzjXuTA/P1+nTp1SQEBAhb8nM0uAhZdffllz5szRp59+Kn9/f3eXc9E4fvy4Bg4cqPfee09hYWHuLueiZrfbFRERoWnTpqldu3bq27evnnrqKU2dOtXdpV10vvrqK40fP17vvvuuMjMztXDhQv3nP//RCy+84O7S8Ccxs+QmYWFh8vb2VnZ2dqn27OxsRUVFlbtPVFSUS/1xYeN81muvvaaXX35Zy5cv1+WXX16ZZXo8V8d5586d2rNnj3r16uVos9vtkiQfHx9t27ZNTZo0qdyiPdCF/D3XqVNHNWrUkLe3t6OtVatWysrK0pkzZ+Tr61upNXuqCxnrp59+WgMHDtSwYcMkSbGxsSooKNDw4cP11FNPycuL+YmKcK5zYXBwcKXMKknMLLmNr6+v2rVrp7S0NEeb3W5XWlqaOnfuXO4+nTt3LtVfkpYtW3bO/riwcZakV199VS+88IKWLFmi9u3bV0WpHs3VcW7ZsqU2bdqk77//3vG49dZbdcMNN+j7779XTExMVZbvMS7k7/nqq6/Wzz//7AijkrR9+3bVqVOHoGThQsb65MmTZQLR2ZBq+BnWCuOWc2Gl3TqO85ozZ47x8/Mz06dPN5s3bzbDhw83oaGhJisryxhjzMCBA82YMWMc/b/55hvj4+NjXnvtNbNlyxaTkpLC0gFOcHWcX375ZePr62vmz59vDh8+7HgcP37cXYfgEVwd5z/i23DOcXWc9+3bZ4KCgsyIESPMtm3bzOLFi01ERIR58cUX3XUIHsPVsU5JSTFBQUHm448/Nrt27TJffvmladKkienTp4+7DsEjHD9+3GzcuNFs3LjRSDJvvPGG2bhxo9m7d68xxpgxY8aYgQMHOvqfXTrgscceM1u2bDGTJ09m6YCL3TvvvGPq169vfH19TYcOHcy6desc2+Li4szgwYNL9f/kk09M8+bNja+vr7nsssvMf/7znyqu2DO5Ms4NGjQwkso8UlJSqr5wD+Pq3/PvEZac5+o4r1271nTs2NH4+fmZxo0bm5deeskUFxdXcdWeyZWxLioqMs8++6xp0qSJ8ff3NzExMebBBx80v/76a9UX7kFWrlxZ7n9zz47t4MGDTVxcXJl92rZta3x9fU3jxo3NBx98UKk12oxhbhAAAOBcuGcJAADAAmEJAADAAmEJAADAAmEJAADAAmEJAADAAmEJAADAAmEJAADAAmEJACQ1bNhQEydOdHcZAKohwhKAi86QIUNks9lks9nk6+urpk2b6vnnn1dxcfE591m/fr2GDx9ehVUC8BQ+7i4AACpD9+7d9cEHH6iwsFCpqalKSkpSjRo1NHbs2FL9zpw5I19fX4WHh7upUgDVHTNLAC5Kfn5+ioqKUoMGDfTAAw8oPj5en332mYYMGaLevXvrpZdeUnR0tFq0aCGp7GW43Nxc3XfffYqMjJS/v7/atGmjxYsXO7avWbNG1157rQICAhQTE6NRo0apoKDAsf3dd99Vs2bN5O/vr8jISN1+++1VduwAKhYzSwD+EgICAvTLL79IktLS0hQcHKxly5aV29dut6tHjx46fvy4Zs6cqSZNmmjz5s3y9vaWJO3cuVPdu3fXiy++qPfff185OTkaMWKERowYoQ8++EAbNmzQqFGj9NFHH6lLly46duyYVq9eXWXHCqBiEZYAXNSMMUpLS9PSpUs1cuRI5eTkKDAwUP/+97/l6+tb7j7Lly/Xd999py1btqh58+aSpMaNGzu2T5gwQQMGDNBDDz0kSWrWrJnefvttxcXFacqUKdq3b58CAwN1yy23KCgoSA0aNNDf/va3Sj9WAJWDy3AALkqLFy9WzZo15e/vrx49eqhv37569tlnJUmxsbHnDEqS9P3336tevXqOoPRHP/zwg6ZPn66aNWs6HgkJCbLb7dq9e7duvPFGNWjQQI0bN9bAgQM1a9YsnTx5sjIOE0AVYGYJwEXphhtu0JQpU+Tr66vo6Gj5+PzvP3eBgYGW+wYEBFhuP3HihO677z6NGjWqzLb69evL19dXmZmZ+uqrr/Tll1/qmWee0bPPPqv169crNDT0go4HgPsQlgBclAIDA9W0adML2vfyyy/XgQMHtH379nJnl6688kpt3rzZ8vV9fHwUHx+v+Ph4paSkKDQ0VCtWrNBtt912QTUBcB/CEgD8QVxcnK677jolJibqjTfeUNOmTbV161bZbDZ1795dTzzxhDp16qQRI0Zo2LBhCgwM1ObNm7Vs2TJNmjRJixcv1q5du3Tdddfp0ksvVWpqqux2u+ObdwA8C/csAUA5FixYoKuuukr9+/dX69at9fjjj6ukpETSbzNPX3/9tbZv365rr71Wf/vb3/TMM88oOjpakhQaGqqFCxeqa9euatWqlaZOnaqPP/5Yl112mTsPCcAFshljjLuLAAAAqK6YWQIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALBAWAIAALDw/wE0Fxo5KM8VfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred=svc.predict(X_test)\n",
    "plt.plot(y_test, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f0364a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.91      1.00      0.95       181\n",
      "         1.0       0.00      0.00      0.00        19\n",
      "\n",
      "    accuracy                           0.91       200\n",
      "   macro avg       0.45      0.50      0.48       200\n",
      "weighted avg       0.82      0.91      0.86       200\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhargava\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\bhargava\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\bhargava\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "pred=svc.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "658b74a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = svc.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "OThr97ak-JRa",
   "metadata": {
    "id": "OThr97ak-JRa"
   },
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "h5jlkw9Z8Aur",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h5jlkw9Z8Aur",
    "outputId": "40eced78-81ce-48d4-8864-50583ae09153"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.9375\n",
      "Test Score: 0.915\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "classifier = GaussianNB()\n",
    "accuracies = cross_val_score(classifier, X_train, y_train, cv=5)\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train Score:\",np.mean(accuracies))\n",
    "print(\"Test Score:\",classifier.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73480160",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dict_train[\"Naive Bayes Default Train Score\"] = np.mean(accuracies)\n",
    "result_dict_test[\"Naive Bayes Default Test Score\"] = classifier.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2935debb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "plt.plot(y_test, y_pred)\n",
    "plt.xlabel(\"Admission decission\")\n",
    "plt.ylabel(\"Predicted Admission decission\")\n",
    "plt.title(\"Actual Admission decission vs Predicted Admission decission\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9b086f3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.96      0.94      0.95       181\n",
      "         1.0       0.55      0.63      0.59        19\n",
      "\n",
      "    accuracy                           0.92       200\n",
      "   macro avg       0.75      0.79      0.77       200\n",
      "weighted avg       0.92      0.92      0.92       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "pred=classifier.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f1593e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SgtzcXMPvvLw8ZGdnw9PTE76+vpgyZQoGDRqE0NBQhIWFYdu2bdiyZQt27twJAFAqlRgxYgQSEhLg6ekJd3d3jBs3DsHBwSbN0AQYeEREYrLSq8UOHDiAsLAww+9b9/7i4uKwZs0aPPvss0hKSoJGo8Frr72Gli1bYuPGjejWrZthn4ULF8LOzg79+/eHTqdDREQEVqxYYXItfA6PqAb4HB5Zi8Wfw+u9wOx9r21LuPdG9yH28IiIRCTgy6M5aYWIiITAHh4RkYgE/FoCA4+ISEQCDmky8IiIRMQeHhERCYGBR0REQhBwSFO8iCciIiGxh0dEJCIOaRIRkRAEHNJk4BERiYg9PCIiEgJ7eEREJAJJwMATr09LRERCYg+PiEhAIvbwGHhERCISL+8YeEREImIPj4iIhMDAIyIiIYgYeJylSUREQmAPj4hIQCL28Bh4REQiEi/vGHhERCJiD4+IiITAwCMiIiGIGHicpUlEREJgD4+ISEDs4RERkRikGiwmSE9PR9++faFWqyFJEpKTk++47ZgxYyBJEhYtWmTUfvnyZQwZMgTu7u7w8PDAiBEjUFJSYlohYOAREQlJkiSzF1OUlpaiffv2WL58+V2327RpE37++Weo1epK64YMGYKjR48iJSUFW7duRXp6OkaPHm1SHQCHNImIhGStIc3IyEhERkbedZuzZ89i3Lhx2L59O/r06WO07vjx49i2bRv279+PLl26AACWLl2Kp59+GvPnz68yIO+EPTwiIgHVpIen0+lQXFxstOh0OrPq0Ov1GDp0KKZMmYI2bdpUWp+RkQEPDw9D2AFAeHg47OzskJmZadK5GHhERGQSjUYDpVJptGg0GrOO9d5778He3h6vvfZaleu1Wi28vLyM2uzt7eHp6QmtVmvSuTikSUQkohqMaCYmJiIhIcGoTaFQmHycrKwsLF68GAcPHrTKECt7eEREAqrJkKZCoYC7u7vRYk7g7d69GwUFBfD19YW9vT3s7e3xxx9/YNKkSfD39wcAqFQqFBQUGO1348YNXL58GSqVyqTzsYdHRCSg++E5vKFDhyI8PNyoLSIiAkOHDsXw4cMBAMHBwSgsLERWVhY6d+4MAEhLS4Ner0dQUJBJ52PgEREJyFqBV1JSgtzcXMPvvLw8ZGdnw9PTE76+vmjUqJHR9g4ODlCpVGjZsiUAIDAwEL1798aoUaOQlJSE8vJyxMfHIzY21qQZmgADj4hISNYKvAMHDiAsLMzw+9a9v7i4OKxZs6Zax1i/fj3i4+PRs2dP2NnZoX///liyZInJtTDwiIio1vTo0QOyLFd7+99//71Sm6enJzZs2FDjWhh4REQisv0tPKtj4BERCeh+mLRibQw8IiIBMfCIiEgIIgYeHzwnIiIhsIdHRCQi8Tp4DLz6LKTTI5j4Yjg6tfaFTxMlBk78CFt2/mpYf+2XZVXu9/rCTVj4aSoAYOqICEQ+2QaPtngI12/cgE/oVKvUTnVf1oH9+HTNxzh+7CguXriADxYtQ1jP/71VQ5ZlJC1fik0bv8KVK8Vo36ETXp8xE75+/rYrWiAc0qR6xcVZgcMnzmKC5osq1/uHJxoto2d+Br1ej02p2YZtHB0a4JuUX7Dq691Wqprqi7Jr19CiRStM//ebVa5f+8l/8PmGdXh9xiysXf8lnJ2dMfblkWZ/ZoZMY60PwN5P2MOrx3b8dAw7fjp2x/X5l64Y/e7box127T+J389eMrTNTfoeAPBCX9PeWUcU8mQoQp4MrXKdLMvY8NmnGDl6DHr8qycAYM477+GpHiHYmfYDIiL7VLkfWU5dDi5zsYdHAAAvTzf07tYWa5MzbF0KCeDsX3/h4sULCHr8CUObm5sb2rZ7FL8eyrZdYQJhD8/KLl68iE8++QQZGRmGD/mpVCo88cQTGDZsGJo0aWLL8oTyQt8gXLlahuS0bFuXQgK4dOkCAMDzthcHN2rUGBcvXrRFSSQAm/Xw9u/fjxYtWmDJkiVQKpUIDQ1FaGgolEollixZglatWuHAgQP3PE5Vn5qX9RVWuIL65cWox/HFfw9Ad/2GrUshImuQarDUUTbr4Y0bNw7PPfcckpKSKnWRZVnGmDFjMG7cOGRk3H2ITaPRYPbs2UZtDby7wsHnMYvXXF+FdHwELQNUGDp9ta1LIUE0anRz9ObypUto0sTL0H7p0kW0bBVoq7KEUpeHJs1lsx7eoUOHMHHixCr/6JIkYeLEicjOzr7ncRITE1FUVGS02Ht3roWK66+46GBkHTuDwyfO2roUEsSDDz2Exo2bYF/m//6FtqSkBEcO/4pH23ewXWEC4T08K1KpVNi3bx9atWpV5fp9+/bB29v7nsdRKBSVPi0v2TWwSI11nYuzIx5p+r/7oP4PNsKjLR7E38VX8af2bwCAm4sTYp7qiOkLNlV5jKaqhmjo/gCa+jREAzs7PNriQQDAqT8voPTa9dq/CKqzrl4txZ9nzhh+nz37F3J+Ow53pRI+PmoMfuFF/OfDJPj6+kP94INYuWwJmjTxQo9/hd/lqGQpdTi3zGazwJs8eTJGjx6NrKws9OzZ0xBu+fn5SE1NxapVqzB//nxblVcvdGrthx3/GW/4PW9yfwDAus0/Y/TMzwAAz0V0hgQJX26r+n7pjFf6YGi/xw2/M79IBAD0GrkYu7NO1lbpVA8cO3oEo1+KM/xe8P67AIC+/aIx++13EffSSFy7dg1zZ7+JK1eK0aFjZyxLWlXpX2CpdtTlnpq5JNmUL/NZ2BdffIGFCxciKysLFRU3J5o0aNAAnTt3RkJCAgYOHGjWcZ07xluyTKI7upi51NYlkCBcHC0bUM2nbDN735Pv97ZgJdZj08cSBg0ahEGDBqG8vNwwFblx48ZwcHCwZVlERPWegB28++NNKw4ODvDx8bF1GUREwhBxSPO+CDwiIrIuAfOOgUdEJCI7O/ESj4FHRCQgEXt4fHk0EREJgT08IiIBcdIKEREJQcC8Y+AREYmIPTwiIhKCiIHHSStERAKSJPMXU6Snp6Nv375Qq9WQJAnJycmGdeXl5Zg2bRratWsHFxcXqNVqvPjiizh37pzRMS5fvowhQ4bA3d0dHh4eGDFiBEpKSky+ZgYeERHVmtLSUrRv3x7Lly+vtO7q1as4ePAgZsyYgYMHD+Kbb75BTk4O+vXrZ7TdkCFDcPToUaSkpGDr1q1IT0/H6NGjTa6FQ5pERAKy1pBmZGQkIiMjq1ynVCqRkpJi1LZs2TI89thjOHPmDHx9fXH8+HFs27YN+/fvR5cuXQAAS5cuxdNPP4358+dDrVZXuxb28IiIBFSTIU2dTofi4mKjRafTWaSuoqIiSJIEDw8PAEBGRgY8PDwMYQcA4eHhsLOzQ2ZmpknHZuAREQmoJl8812g0UCqVRotGo6lxTWVlZZg2bRqef/55uLu7AwC0Wi28vLyMtrO3t4enpye0Wq1Jx+eQJhGRgGoyopmYmIiEhASjtpp+uLe8vBwDBw6ELMtYuXJljY51Jww8IiIB1eQenkKhsOiX6W+F3R9//IG0tDRD7w4AVCoVCgoKjLa/ceMGLl++DJVKZdJ5OKRJREQ2cyvsTp48iR9++AGNGjUyWh8cHIzCwkJkZWUZ2tLS0qDX6xEUFGTSudjDIyISkLWeOy8pKUFubq7hd15eHrKzs+Hp6QkfHx8MGDAABw8exNatW1FRUWG4L+fp6QlHR0cEBgaid+/eGDVqFJKSklBeXo74+HjExsaaNEMTYOAREQnJWo8lHDhwAGFhYYbft+79xcXFYdasWdi8eTMAoEOHDkb7/fjjj+jRowcAYP369YiPj0fPnj1hZ2eH/v37Y8mSJSbXwsAjIhKQtXp4PXr0gCzLd1x/t3W3eHp6YsOGDTWuhYFHRCQgEd+lycAjIhKQgHnHWZpERCQG9vCIiATEIU0iIhKCgHnHwCMiEhF7eEREJAQGHhERCUHAvOMsTSIiEgN7eEREAuKQJhERCUHAvGPgERGJiD08IiISgoB5x8AjIhKRnYCJx1maREQkBPbwiIgEJGAHj4FHRCQiTlohIiIh2ImXdww8IiIRsYdHRERCEDDvOEuTiIjEwB4eEZGAJIjXxWPgEREJiJNWiIhICJy0QkREQhAw7xh4REQi4rs0iYiILCg9PR19+/aFWq2GJElITk42Wi/LMt588034+PjA2dkZ4eHhOHnypNE2ly9fxpAhQ+Du7g4PDw+MGDECJSUlJtfCwCMiEpAkmb+YorS0FO3bt8fy5curXD9v3jwsWbIESUlJyMzMhIuLCyIiIlBWVmbYZsiQITh69ChSUlKwdetWpKenY/To0SZfM4c0iYgEZK1JK5GRkYiMjKxynSzLWLRoEd544w1ERUUBAD799FN4e3sjOTkZsbGxOH78OLZt24b9+/ejS5cuAIClS5fi6aefxvz586FWq6tdC3t4REQCqkkPT6fTobi42GjR6XQm15CXlwetVovw8HBDm1KpRFBQEDIyMgAAGRkZ8PDwMIQdAISHh8POzg6ZmZkmnY+BR0QkIDtJMnvRaDRQKpVGi0ajMbkGrVYLAPD29jZq9/b2NqzTarXw8vIyWm9vbw9PT0/DNtXFIU0iIgHVZEAzMTERCQkJRm0KhaJmBVkBA4+IiEyiUCgsEnAqlQoAkJ+fDx8fH0N7fn4+OnToYNimoKDAaL8bN27g8uXLhv2ri0OaREQCkiTJ7MVSAgICoFKpkJqaamgrLi5GZmYmgoODAQDBwcEoLCxEVlaWYZu0tDTo9XoEBQWZdD728IiIBGStd2mWlJQgNzfX8DsvLw/Z2dnw9PSEr68vJkyYgLlz56J58+YICAjAjBkzoFarER0dDQAIDAxE7969MWrUKCQlJaG8vBzx8fGIjY01aYYmwMAjIhKStR5LOHDgAMLCwgy/b937i4uLw5o1azB16lSUlpZi9OjRKCwsRLdu3bBt2zY4OTkZ9lm/fj3i4+PRs2dP2NnZoX///liyZInJtUiyLMs1v6T7i3PHeFuXQIK4mLnU1iWQIFwcLRtQQ9cfMnvfdUPaW7AS62EPj4hIQCJ+LcGsSSu7d+/GCy+8gODgYJw9exYAsG7dOuzZs8eixREREVmKyYG3ceNGREREwNnZGb/88ovh6fqioiK88847Fi+QiIgsz04yf6mrTA68uXPnIikpCatWrYKDg4OhPSQkBAcPHrRocUREVDvuh8cSrM3ke3g5OTkIDQ2t1K5UKlFYWGiJmoiIqJbV3dgyn8k9PJVKZfRMxS179uzBww8/bJGiiIiodtXkXZp1lcmBN2rUKIwfPx6ZmZmQJAnnzp3D+vXrMXnyZLzyyiu1USMREVGNmTykOX36dOj1evTs2RNXr15FaGgoFAoFJk+ejHHjxtVGjUREZGF1uKNmNpMDT5Ik/Pvf/8aUKVOQm5uLkpIStG7dGq6urrVRHxER1YK6PPnEXGY/eO7o6IjWrVtbshYiIrISAfPO9MALCwu7678ZpKWl1aggIiKqfXV58om5TA68W98ouqW8vBzZ2dk4cuQI4uLiLFUXERHVIgHzzvTAW7hwYZXts2bNQklJSY0LIiIiqg0W+wDsCy+8gE8++cRShyMiolrEN63UQEZGhtH3i2zp7/3LbF0CCaL8ht7WJZAwLBs0Fuvt1CEmB15MTIzRb1mWcf78eRw4cAAzZsywWGFERFR76nJPzVwmB55SqTT6bWdnh5YtW2LOnDno1auXxQojIqLaU5e/emAukwKvoqICw4cPR7t27dCwYcPaqomIiGqZiIFn0jBugwYN0KtXL34VgYiI6hyT71u2bdsWp0+fro1aiIjISkScpWnWB2AnT56MrVu34vz58yguLjZaiIjo/ifiF8+rfQ9vzpw5mDRpEp5++mkAQL9+/YySXpZlSJKEiooKy1dJREQWVYc7amarduDNnj0bY8aMwY8//lib9RARkRXwXZp3IcsyAKB79+61VgwREVmHiA+em3TNdflmJRERic2k5/BatGhxz9C7fPlyjQoiIqLaJ2L/xaTAmz17dqU3rRARUd3De3j3EBsbCy8vr9qqhYiIrMRaeVdRUYFZs2bhs88+g1arhVqtxrBhw/DGG28YRgxlWcbMmTOxatUqFBYWIiQkBCtXrkTz5s0tWku17+Hx/h0RUf1hrefw3nvvPaxcuRLLli3D8ePH8d5772HevHlYunSpYZt58+ZhyZIlSEpKQmZmJlxcXBAREYGysjKLXrPJszSJiKjus9aQ5t69exEVFYU+ffoAAPz9/fH5559j3759AG5my6JFi/DGG28gKioKAPDpp5/C29sbycnJiI2NtVgt1e7h6fV6DmcSERF0Ol2lt2zpdLoqt33iiSeQmpqKEydOAAAOHTqEPXv2IDIyEgCQl5cHrVaL8PBwwz5KpRJBQUHIyMiwaN0iPopBRCQ8STJ/0Wg0UCqVRotGo6nyPNOnT0dsbCxatWoFBwcHdOzYERMmTMCQIUMAAFqtFgDg7e1ttJ+3t7dhnaVY7IvnRERUd9TknZhTExORkJBg1KZQKKrc9ssvv8T69euxYcMGtGnTBtnZ2ZgwYQLUajXi4uLML8IMDDwiIgFJMD/xFArFHQPudlOmTDH08gCgXbt2+OOPP6DRaBAXFweVSgUAyM/Ph4+Pj2G//Px8dOjQwewaq8IhTSIiAVlrlubVq1dhZ2ccNQ0aNIBerwcABAQEQKVSITU11bC+uLgYmZmZCA4OrvF1/hN7eEREArLWZ3769u2Lt99+G76+vmjTpg1++eUXLFiwAC+99BKAm4+8TZgwAXPnzkXz5s0REBCAGTNmQK1WIzo62qK1MPCIiKjWLF26FDNmzMCrr76KgoICqNVqvPzyy3jzzTcN20ydOhWlpaUYPXo0CgsL0a1bN2zbtg1OTk4WrUWS6+EDdmU3bF0BiaL8ht7WJZAg3Jwsewfq/Z2nzd53So+HLViJ9bCHR0QkoLr85XJzMfCIiAQk4tsiGXhERALi1xKIiEgIIg5p8jk8IiISAnt4REQCEnBEk4FHRCQiuxq8WqyuYuAREQmIPTwiIhKCiJNWGHhERAIS8bEEztIkIiIhsIdHRCQgATt4DDwiIhGJOKTJwCMiEpCAecfAIyISkYgTOBh4REQCkgTs4okY8kREJCD28IiIBCRe/46BR0QkJM7SJCIiIYgXdww8IiIhCdjBY+AREYmIszSJiIjqKfbwiIgEJGJvh4FHRCQgEYc0GXhERAISL+7E7NUSEQlPkiSzF1OdPXsWL7zwAho1agRnZ2e0a9cOBw4cMKyXZRlvvvkmfHx84OzsjPDwcJw8edKSlwuAgUdEJCS7Giym+PvvvxESEgIHBwf897//xbFjx/DBBx+gYcOGhm3mzZuHJUuWICkpCZmZmXBxcUFERATKyspqeplGJFmWZYse8T5QdsPWFZAoym/obV0CCcLNybL9k28OnTd735j2PtXedvr06fjpp5+we/fuKtfLsgy1Wo1JkyZh8uTJAICioiJ4e3tjzZo1iI2NNbvO27GHR0QkoJoMaep0OhQXFxstOp2uyvNs3rwZXbp0wXPPPQcvLy907NgRq1atMqzPy8uDVqtFeHi4oU2pVCIoKAgZGRkWvWYGHhGRgKQaLBqNBkql0mjRaDRVnuf06dNYuXIlmjdvju3bt+OVV17Ba6+9hrVr1wIAtFotAMDb29toP29vb8M6S+EsTSIiAdXkqYTExEQkJCQYtSkUiiq31ev16NKlC9555x0AQMeOHXHkyBEkJSUhLi7O/CLMwB4eEZGA7CCZvSgUCri7uxstdwo8Hx8ftG7d2qgtMDAQZ86cAQCoVCoAQH5+vtE2+fn5hnWWu2YiIhKOJJm/mCIkJAQ5OTlGbSdOnICfnx8AICAgACqVCqmpqYb1xcXFyMzMRHBwcI2v8584pElERLVm4sSJeOKJJ/DOO+9g4MCB2LdvHz766CN89NFHAG5OnpkwYQLmzp2L5s2bIyAgADNmzIBarUZ0dLRFa2HgEREJSLLSu1a6du2KTZs2ITExEXPmzEFAQAAWLVqEIUOGGLaZOnUqSktLMXr0aBQWFqJbt27Ytm0bnJycLFoLn8MjqgE+h0fWYunn8L4/WmD2vk+38bJgJdbDHh4RkYDsBHybJgOPiEhAAn4sgYFHRCQiEQOPjyUQEZEQ2MMjIhKQtWZp3k8YeEREArITL+8YeEREImIPj4iIhMBJK0RERPUUe3hERALikCYJJ/Kpf+HcubOV2gfFDsbrM2baoCKqLw5m7ce6NZ/g+PGjuHjhAuYvXIoe/7r5Vesb5eVYsWwxftqTjrN//QVXN1c8FhSMceMnoYlX3XxtVV3DSSsknPVffA19RYXhd27uSbw8cjieiuhtw6qoPrh27Rqat2yJftExmJLwmtG6srIy/PbbMYwc/Qqat2yFK8VFmP+eBgnjX8W6z7+2UcViYQ+PhOPp6Wn0+5P/fISmTX3RpetjNqqI6ouQbqEI6RZa5TpXNzes+PATo7apiW8gbshAaM+fg8pHbY0ShcZJKyS08uvX8d3WzYiO6Q9JxP81kE2VlFyBJElwdXO3dSlCkGqw1FUMPDJIS/sBV65cQb/oZ21dCglGp9Nh6aIPEBHZB66urrYuh+qp+zrw/vzzT7z00kt33Uan06G4uNho0el0Vqqwftm0cSNCuoXCy8vb1qWQQG6Ul2P6lImQZRnT/82JUtZiJ0lmL3XVfR14ly9fxtq1a++6jUajgVKpNFref09jpQrrj3PnziLz572IGTDA1qWQQG6Fnfb8OSz/8GP27qxIxCFNm05a2bx5813Xnz59+p7HSExMREJCglGb3EBRo7pE9O2mb+Dp2QhPhvawdSkkiFthd+bMH/jwP2vh4dHQ1iWJpS4nl5lsGnjR0dGQJAmyLN9xm3tNnlAoFFAojAOu7IZFyhOGXq/Ht5u+Qd+oaNjbc+IuWcbVq6X488wZw++zZ/9Czm/HoVQq0bhxE0ydPAE5x49h4dKVqNBX4OLFCwAApVIJBwdHW5UtDD6WYGU+Pj5YsWIFoqKiqlyfnZ2Nzp07W7kq8fycsRfnz59DdEx/W5dC9cixo0cxZmSc4ffC+e8BAJ7pF43RY+KRvjMNADB4oPEkqaT/rOVjMVZQh2/Fmc2mgde5c2dkZWXdMfDu1fsjy3gipBsOHc2xdRlUz3Tp+hgOHDp+x/V3W0dUG2waeFOmTEFpaekd1zdr1gw//vijFSsiIhKDgB08SHI97ELxHh5ZS/kNva1LIEG4OVl2Uv3+vCKz9+0aoLRgJdbDGQpERALipBUiIhICJ60QEZEQBMy7+/tNK0RERJbCwCMiEpEN3i327rvvQpIkTJgwwdBWVlaGsWPHolGjRnB1dUX//v2Rn59v/knugoFHRCQgqQb/Mcf+/fvx4Ycf4tFHHzVqnzhxIrZs2YKvvvoKu3btwrlz5xATE2OJS6yEgUdEJCBJMn8xVUlJCYYMGYJVq1ahYcP/vTO1qKgIH3/8MRYsWIB//etf6Ny5M1avXo29e/fi559/tuDV3sTAIyISUE1GNE39LNvYsWPRp08fhIeHG7VnZWWhvLzcqL1Vq1bw9fVFRkaG5S72/2PgERGJqAaJV9Vn2TSaqj/L9n//9384ePBgleu1Wi0cHR3h4eFh1O7t7Q2tVmuZ6/wHPpZAREQmqeqzbLd/tQa4+RHv8ePHIyUlBU5OTtYq744YeEREAqrJm1aq+ixbVbKyslBQUIBOnToZ2ioqKpCeno5ly5Zh+/btuH79OgoLC416efn5+VCpVGbXdycMPCIiAVnjTSs9e/bE4cOHjdqGDx+OVq1aYdq0aWjatCkcHByQmpqK/v1vfp4sJycHZ86cQXBwsMXrYeAREQnIGm9acXNzQ9u2bY3aXFxc0KhRI0P7iBEjkJCQAE9PT7i7u2PcuHEIDg7G448/bvF6GHhERCK6T94ttnDhQtjZ2aF///7Q6XSIiIjAihUrauVc/DwQUQ3w80BkLZb+PNDRs3f+Fum9tHnQxYKVWA8fSyAiIiFwSJOISED8PBAREQlBwLxj4BERCUnAxGPgEREJqCYPntdVDDwiIgGJeA+PszSJiEgI7OEREQlIwA4eA4+ISEgCJh4Dj4hIQJy0QkREQhBx0goDj4hIQALmHWdpEhGRGNjDIyISkYBdPAYeEZGAOGmFiIiEwEkrREQkBAHzjoFHRCQkAROPszSJiEgI7OEREQmIk1aIiEgInLRCRERCEDDvGHhERCJiD4+IiAQhXuJxliYREQmBPTwiIgGJOKTJHh4RkYCkGiym0Gg06Nq1K9zc3ODl5YXo6Gjk5OQYbVNWVoaxY8eiUaNGcHV1Rf/+/ZGfn1+Ty6sSA4+ISECSZP5iil27dmHs2LH4+eefkZKSgvLycvTq1QulpaWGbSZOnIgtW7bgq6++wq5du3Du3DnExMRY+IoBSZZl2eJHtbGyG7augERRfkNv6xJIEG5Olu2faIvKzd5XpXQwe98LFy7Ay8sLu3btQmhoKIqKitCkSRNs2LABAwYMAAD89ttvCAwMREZGBh5//HGzz3U79vCIiERUgzFNnU6H4uJio0Wn01XrtEVFRQAAT09PAEBWVhbKy8sRHh5u2KZVq1bw9fVFRkaGJa7UgIFHREQm0Wg0UCqVRotGo7nnfnq9HhMmTEBISAjatm0LANBqtXB0dISHh4fRtt7e3tBqtRatm7M0iYgEVJNJmomJiUhISDBqUygU99xv7NixOHLkCPbs2VODs5uPgUdEJKCaPJagUCiqFXD/FB8fj61btyI9PR0PPfSQoV2lUuH69esoLCw06uXl5+dDpVKZX2QVOKRJRCQgqQb/MYUsy4iPj8emTZuQlpaGgIAAo/WdO3eGg4MDUlNTDW05OTk4c+YMgoODLXKtt7CHR0QkIis9eD527Fhs2LAB3377Ldzc3Az35ZRKJZydnaFUKjFixAgkJCTA09MT7u7uGDduHIKDgy06QxPgYwlENcLHEshaLP1YwsUS8/+PsrFr9ftK0h3GTlevXo1hw4YBuPng+aRJk/D5559Dp9MhIiICK1assPiQJgOPqAYYeGQtdTXw7id1s2oiIqoREd+lycAjIhKQqZNP6gMGHhGRgETs4fGxBCIiEgJ7eEREAmIPj4iIqJ5iD4+ISECctEJEREIQcUiTgUdEJCAB846BR0QkJAETj5NWiIhICOzhEREJiJNWiIhICJy0QkREQhAw7xh4RERCEjDxGHhERAIS8R4eZ2kSEZEQ2MMjIhKQiJNWJFmWZVsXQban0+mg0WiQmJgIhUJh63KoHuM/a2QrDDwCABQXF0OpVKKoqAju7u62LofqMf6zRrbCe3hERCQEBh4REQmBgUdEREJg4BEAQKFQYObMmZxEQLWO/6yRrXDSChERCYE9PCIiEgIDj4iIhMDAIyIiITDwiIhICAw8wvLly+Hv7w8nJycEBQVh3759ti6J6qH09HT07dsXarUakiQhOTnZ1iWRYBh4gvviiy+QkJCAmTNn4uDBg2jfvj0iIiJQUFBg69KoniktLUX79u2xfPlyW5dCguJjCYILCgpC165dsWzZMgCAXq9H06ZNMW7cOEyfPt3G1VF9JUkSNm3ahOjoaFuXQgJhD09g169fR1ZWFsLDww1tdnZ2CA8PR0ZGhg0rIyKyPAaewC5evIiKigp4e3sbtXt7e0Or1dqoKiKi2sHAIyIiITDwBNa4cWM0aNAA+fn5Ru35+flQqVQ2qoqIqHYw8ATm6OiIzp07IzU11dCm1+uRmpqK4OBgG1ZGRGR59rYugGwrISEBcXFx6NKlCx577DEsWrQIpaWlGD58uK1Lo3qmpKQEubm5ht95eXnIzs6Gp6cnfH19bVgZiYKPJRCWLVuG999/H1qtFh06dMCSJUsQFBRk67Kontm5cyfCwsIqtcfFxWHNmjXWL4iEw8AjIiIh8B4eEREJgYFHRERCYOAREZEQGHhERCQEBh4REQmBgUdEREJg4BERkRAYeEREJAQGHlE1DRs2zOiDpT169MCECROsXsfOnTshSRIKCwutfm6iuoyBR3XesGHDIEkSJEmCo6MjmjVrhjlz5uDGjRu1et5vvvkGb731VrW2ZUgR2R5fHk31Qu/evbF69WrodDp8//33GDt2LBwcHJCYmGi03fXr1+Ho6GiRc3p6elrkOERkHezhUb2gUCigUqng5+eHV155BeHh4di8ebNhGPLtt9+GWq1Gy5YtAQB//vknBg4cCA8PD3h6eiIqKgq///674XgVFRVISEiAh4cHGjVqhKlTp+L2187ePqSp0+kwbdo0NG3aFAqFAs2aNcPHH3+M33//3fDS5IYNG0KSJAwbNgzAzc8xaTQaBAQEwNnZGe3bt8fXX39tdJ7vv/8eLVq0gLOzM8LCwozqJKLqY+BRveTs7Izr168DAFJTU5GTk4OUlBRs3boV5eXliIiIgJubG3bv3o2ffvoJrq6u6N27t2GfDz74AGvWrMEnn3yCPXv24PLly9i0adNdz/niiy/i888/x5IlS3D8+HF8+OGHcHV1RdOmTbFx40YAQE5ODs6fP4/FixcDADQaDT799FMkJSXh6NGjmDhxIl544QXs2rULwM1gjomJQd++fZGdnY2RI0di+vTptfVnI6rfZKI6Li4uTo6KipJlWZb1er2ckpIiKxQKefLkyXJcXJzs7e0t63Q6w/br1q2TW7ZsKev1ekObTqeTnZ2d5e3bt8uyLMs+Pj7yvHnzDOvLy8vlhx56yHAeWZbl7t27y+PHj5dlWZZzcnJkAHJKSkqVNf74448yAPnvv/82tJWVlckPPPCAvHfvXqNtR4wYIT///POyLMtyYmKi3Lp1a6P106ZNq3QsIro33sOjemHr1q1wdXVFeXk59Ho9Bg8ejFmzZmHs2LFo166d0X27Q4cOITc3F25ubkbHKCsrw6lTp1BUVITz588bfRPQ3t4eXbp0qTSseUt2djYaNGiA7t27V7vm3NxcXL16FU899ZRR+/Xr19GxY0cAwPHjxyt9m5BfoycyDwOP6oWwsDCsXLkSjo6OUKvVsLf/3z/aLi4uRtuWlJSgc+fOWL9+faXjNGnSxKzzOzs7m7xPSUkJAOC7777Dgw8+aLROoVCYVQcR3RkDj+oFFxcXNGvWrFrbdurUCV988QW8vLzg7u5e5TY+Pj7IzMxEaGgoAODGjRvIyspCp06dqty+Xbt20Ov12LVrF8LDwyutv9XDrKioMLS1bt0aCoUCZ86cuWPPMDAwEJs3bzZq+/nnn+99kURUCSetkHCGDBmCxo0bIyoqCrt370ZeXh527tyJ1157DX/99RcAYPz48Xj33XeRnJyM3377Da+++updn6Hz9/dHXFwcXnrpJSQnJxuO+eWXXwIA/Pz8IEkStm7digsXLqCkpARubm6YPHkyJk6ciLVr1+LUqVM4ePAgli5dirVr1wIAxowZg5MnT2LKlCnIycnBhg0bsGbNmtr+ExHVSww8Es4DDzyA9PR0+Pr6IiYmBoGBgRgxYgTKysoMPb5JkyZh6NChiIuLQ3BwMNzc3PDss8/e9bgrV67EgAED8Oqrr6JVq1YYNWoUSktLAQAPPvggZs+ejenTp8Pb2xvx8fEAgLfeegszZsyARqNBYGAgevfuje+++w4BAQEAAF9fX2zcuBHJyclo3749kpKS8M4779TiX4eo/pLkO92FJyIiqkfYwyMiIiEw8IiISAgMPCIiEgIDj4iIhMDAIyIiITDwiIhICAw8IiISAgOPiIiEwMAjIiIhMPCIiEgIDDwiIhLC/wN9wz/OcwDURQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming gradient_booster and X_test, y_test are already defined\n",
    "\n",
    "# Make predictions\n",
    "pred = classifier.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775cb036",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d4d6f8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# # Assuming 'data' is your DataFrame with the given columns\n",
    "# df = pd.read_csv('D1.csv')\n",
    "# df.head()\n",
    "# Drop unnecessary columns\n",
    "# column_to = ['name', 'id', 'dob', 'nationality', 'mother_tongue']\n",
    "# df = df.drop(columns=column_to)\n",
    "\n",
    "# # Convert 'gender' to 0 and 1\n",
    "# df['gender'] = df['gender'].replace({'Male': 0, 'Female': 1})\n",
    "\n",
    "# # Convert 'Admission_status' to 0 and 1\n",
    "# df['Admission_status'] = df['Admission_status'].replace({'Not Admitted': 0, 'Admitted': 1})\n",
    "\n",
    "# # Convert 'Admission_status' to integer type\n",
    "# # df['Admission_status'] = df['Admission_status'].astype(int)\n",
    "\n",
    "\n",
    "# # Split the data into features and target\n",
    "# X = df.drop('Admission_status', axis=1)\n",
    "# y = df['Admission_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7e5bfa03",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Ensure 'y_train' is a NumPy array with the correct data type\n",
    "y_train = np.array(y_train, dtype=np.float32)  # Use dtype=np.float32 if 'Admission_status' is float\n",
    "\n",
    "\n",
    "# Standardize the numerical features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Build the neural network model\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')  # Binary classification, so 'sigmoid' activation\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "\n",
    "# history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# # Evaluate the model on the test set\n",
    "# test_loss, test_acc = model.evaluate(X_test_scaled, y_test)\n",
    "# print(f'Test accuracy: {test_acc * 100:.2f}%')\n",
    "\n",
    "# # Print training accuracy\n",
    "# train_acc = history.history['accuracy'][-1]\n",
    "# print(f'Training accuracy: {train_acc * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d73317ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "20/20 [==============================] - 1s 12ms/step - loss: 0.4448 - accuracy: 0.8938 - val_loss: 0.3286 - val_accuracy: 0.8938\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.2847 - accuracy: 0.9000 - val_loss: 0.2603 - val_accuracy: 0.8938\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.2342 - accuracy: 0.9000 - val_loss: 0.2251 - val_accuracy: 0.8938\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.2041 - accuracy: 0.9125 - val_loss: 0.1988 - val_accuracy: 0.9125\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1849 - accuracy: 0.9219 - val_loss: 0.1849 - val_accuracy: 0.9250\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1746 - accuracy: 0.9328 - val_loss: 0.1830 - val_accuracy: 0.9250\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1691 - accuracy: 0.9406 - val_loss: 0.1773 - val_accuracy: 0.9500\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1632 - accuracy: 0.9469 - val_loss: 0.1796 - val_accuracy: 0.9438\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1588 - accuracy: 0.9469 - val_loss: 0.1790 - val_accuracy: 0.9438\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1553 - accuracy: 0.9469 - val_loss: 0.1819 - val_accuracy: 0.9375\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1513 - accuracy: 0.9500 - val_loss: 0.1782 - val_accuracy: 0.9438\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1492 - accuracy: 0.9484 - val_loss: 0.1791 - val_accuracy: 0.9375\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1463 - accuracy: 0.9500 - val_loss: 0.1811 - val_accuracy: 0.9375\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1437 - accuracy: 0.9484 - val_loss: 0.1781 - val_accuracy: 0.9438\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1404 - accuracy: 0.9516 - val_loss: 0.1816 - val_accuracy: 0.9375\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1378 - accuracy: 0.9500 - val_loss: 0.1820 - val_accuracy: 0.9375\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1350 - accuracy: 0.9516 - val_loss: 0.1824 - val_accuracy: 0.9375\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1332 - accuracy: 0.9516 - val_loss: 0.1831 - val_accuracy: 0.9375\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1304 - accuracy: 0.9516 - val_loss: 0.1831 - val_accuracy: 0.9375\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1294 - accuracy: 0.9516 - val_loss: 0.1826 - val_accuracy: 0.9375\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1256 - accuracy: 0.9516 - val_loss: 0.1849 - val_accuracy: 0.9375\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1234 - accuracy: 0.9516 - val_loss: 0.1862 - val_accuracy: 0.9375\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1211 - accuracy: 0.9516 - val_loss: 0.1858 - val_accuracy: 0.9375\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1199 - accuracy: 0.9516 - val_loss: 0.1865 - val_accuracy: 0.9375\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1174 - accuracy: 0.9531 - val_loss: 0.1886 - val_accuracy: 0.9375\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1143 - accuracy: 0.9547 - val_loss: 0.1862 - val_accuracy: 0.9375\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1127 - accuracy: 0.9516 - val_loss: 0.1875 - val_accuracy: 0.9375\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1088 - accuracy: 0.9547 - val_loss: 0.1933 - val_accuracy: 0.9250\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1076 - accuracy: 0.9594 - val_loss: 0.1881 - val_accuracy: 0.9375\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1052 - accuracy: 0.9578 - val_loss: 0.1941 - val_accuracy: 0.9312\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1020 - accuracy: 0.9578 - val_loss: 0.1916 - val_accuracy: 0.9312\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.1000 - accuracy: 0.9563 - val_loss: 0.1933 - val_accuracy: 0.9312\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0978 - accuracy: 0.9563 - val_loss: 0.1987 - val_accuracy: 0.9312\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0959 - accuracy: 0.9563 - val_loss: 0.1949 - val_accuracy: 0.9312\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0935 - accuracy: 0.9594 - val_loss: 0.2068 - val_accuracy: 0.9312\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.9609 - val_loss: 0.1979 - val_accuracy: 0.9312\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0899 - accuracy: 0.9609 - val_loss: 0.2014 - val_accuracy: 0.9312\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9609 - val_loss: 0.2030 - val_accuracy: 0.9312\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0837 - accuracy: 0.9625 - val_loss: 0.2108 - val_accuracy: 0.9312\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0826 - accuracy: 0.9688 - val_loss: 0.2116 - val_accuracy: 0.9312\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0807 - accuracy: 0.9641 - val_loss: 0.2115 - val_accuracy: 0.9312\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0784 - accuracy: 0.9703 - val_loss: 0.2147 - val_accuracy: 0.9312\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0766 - accuracy: 0.9688 - val_loss: 0.2129 - val_accuracy: 0.9250\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0744 - accuracy: 0.9703 - val_loss: 0.2127 - val_accuracy: 0.9312\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0739 - accuracy: 0.9703 - val_loss: 0.2252 - val_accuracy: 0.9312\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0699 - accuracy: 0.9719 - val_loss: 0.2212 - val_accuracy: 0.9250\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0676 - accuracy: 0.9734 - val_loss: 0.2273 - val_accuracy: 0.9312\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0666 - accuracy: 0.9734 - val_loss: 0.2280 - val_accuracy: 0.9250\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0641 - accuracy: 0.9766 - val_loss: 0.2318 - val_accuracy: 0.9312\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 3ms/step - loss: 0.0627 - accuracy: 0.9781 - val_loss: 0.2283 - val_accuracy: 0.9187\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.2614 - accuracy: 0.9150\n",
      "Test accuracy: 91.50%\n",
      "Training accuracy: 97.81%\n",
      "7/7 [==============================] - 0s 2ms/step\n",
      "Test accuracy: 91.50%\n",
      "Training accuracy: 97.81%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# ... (Previous code)\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Predict probabilities on the test set\n",
    "test_acc = model.evaluate(X_test_scaled, y_test)[1]\n",
    "print(f'Test accuracy: {test_acc*100:.2f}%')\n",
    "train_acc = history.history['accuracy'][-1]\n",
    "print(f'Training accuracy: {train_acc * 100:.2f}%')\n",
    "y_pred_proba = model.predict(X_test_scaled)\n",
    "\n",
    "# Compute ROC curve and area under the curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# # Plot ROC curve\n",
    "# plt.figure(figsize=(8, 8))\n",
    "# test_acc = model.evaluate(X_test_scaled, y_test)[1]\n",
    "print(f'Test accuracy: {test_acc*100:.2f}%')\n",
    "train_acc = history.history['accuracy'][-1]\n",
    "print(f'Training accuracy: {train_acc * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "96fa3432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.94      0.97      0.95       181\n",
      "         1.0       0.57      0.42      0.48        19\n",
      "\n",
      "    accuracy                           0.92       200\n",
      "   macro avg       0.76      0.69      0.72       200\n",
      "weighted avg       0.91      0.92      0.91       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Assuming you have trained your model as described in your code\n",
    "# and you have X_test_scaled and y_test\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_probs = model.predict(X_test_scaled)\n",
    "y_pred = (y_probs > 0.5).astype(int)  # Assuming a threshold of 0.5 for binary classification\n",
    "\n",
    "# Convert y_test to 1D array if it's not already\n",
    "y_test = np.squeeze(y_test)\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c2641288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming X_test, y_test, and model are already defined\n",
    "\n",
    "# Make predictions\n",
    "y_probs = model.predict(X_test)\n",
    "y_pred = (y_probs > 0.5).astype(int)\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(5, 4))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4331fd83",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a39aec01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # auc roc curve\n",
    "\n",
    "# from sklearn.datasets import make_classification\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# # generate two class dataset\n",
    "# X, y = make_classification(n_samples=1000, n_classes=2, n_features=20, random_state=27)\n",
    "\n",
    "# # split into train-test sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=27)\n",
    "\n",
    "\n",
    "\n",
    "# # train models\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# # logistic regression\n",
    "# model1 = LogisticRegression()\n",
    "# # knn\n",
    "# model2 = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "# # fit model\n",
    "# model1.fit(X_train, y_train)\n",
    "# model2.fit(X_train, y_train)\n",
    "\n",
    "# # predict probabilities\n",
    "# pred_prob1 = model1.predict_proba(X_test)\n",
    "# pred_prob2 = model2.predict_proba(X_test)\n",
    "\n",
    "\n",
    "# from sklearn.metrics import roc_curve\n",
    "\n",
    "# # roc curve for models\n",
    "# fpr1, tpr1, thresh1 = roc_curve(y_test, pred_prob1[:,1], pos_label=1)\n",
    "# fpr2, tpr2, thresh2 = roc_curve(y_test, pred_prob2[:,1], pos_label=1)\n",
    "\n",
    "# # roc curve for tpr = fpr \n",
    "# random_probs = [0 for i in range(len(y_test))]\n",
    "# p_fpr, p_tpr, _ = roc_curve(y_test, random_probs, pos_label=1)\n",
    "\n",
    "\n",
    "# from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# # auc scores\n",
    "# auc_score1 = roc_auc_score(y_test, pred_prob1[:,1])\n",
    "# auc_score2 = roc_auc_score(y_test, pred_prob2[:,1])\n",
    "\n",
    "# print(auc_score1, auc_score2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "KttOqs5D8AqU",
   "metadata": {
    "id": "KttOqs5D8AqU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have trained classifiers named svc, gradient_booster, and classifier\n",
    "classifiers = [svc, gradient_booster, classifier]\n",
    "labels = ['SVC', 'Gradient Boosting', 'Naive Bayes', 'Neaural Network']\n",
    "colors = ['orange', 'blue', 'green', 'yellow']\n",
    "\n",
    "# Initialize a plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Plot ROC curve for each classifier\n",
    "for classifier, label, color in zip(classifiers, labels, colors):\n",
    "    # Assuming X_test and y_test are your test data\n",
    "    if isinstance(classifier, SVC):\n",
    "        y_scores = classifier.decision_function(X_test)\n",
    "    else:\n",
    "        y_scores = classifier.predict_proba(X_test)[:, 1]\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_scores)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    # Plot ROC curve with different line styles and colors\n",
    "    plt.plot(fpr, tpr, label=f'{label} (AUC = {roc_auc:.2f})', color=color, linestyle='--' if label == 'Naive Bayes' else '-')\n",
    "\n",
    "# Plot ROC curve for the neural network\n",
    "y_probs = model.predict(X_test_scaled)\n",
    "fpr_nn, tpr_nn, thresholds_nn = roc_curve(y_test, y_probs)\n",
    "roc_auc_nn = auc(fpr_nn, tpr_nn)\n",
    "plt.plot(fpr_nn, tpr_nn, label=f'Neural Network (AUC = {roc_auc_nn:.2f})', color='purple', linestyle='-')\n",
    "\n",
    "# Plot the diagonal line for reference\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}