D4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": 50,
   "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>Year</th>\n",
       "      <th>10th Marks</th>\n",
       "      <th>12th Marks</th>\n",
       "      <th>12th Division</th>\n",
       "      <th>AIEEE Rank</th>\n",
       "      <th>College</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>IIT Bombay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>IIT delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>IIT kanpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>IIT kharagpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>IIT guwahati</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  10th Marks  12th Marks  12th Division  AIEEE Rank        College\n",
       "0  2019          90          89              3          98     IIT Bombay\n",
       "1  2015          95          92              2         100      IIT delhi\n",
       "2  2018          91          80              6         260     IIT kanpur\n",
       "3  2017          88          85              2         222  IIT kharagpur\n",
       "4  2016          89          84              1         600   IIT guwahati"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm=pd.read_csv('D4.csv')\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "GJPMaMBW7DLI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GJPMaMBW7DLI",
    "outputId": "7bf1f351-95d1-4b6e-b378-0cc1363bd0ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', '10th Marks', '12th Marks', '12th Division', 'AIEEE Rank',\n",
       "       'College'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "75fd5c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year             0\n",
       "10th Marks       0\n",
       "12th Marks       0\n",
       "12th Division    0\n",
       "AIEEE Rank       0\n",
       "College          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c872b2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1004 entries, 0 to 1003\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Year           1004 non-null   int64 \n",
      " 1   10th Marks     1004 non-null   int64 \n",
      " 2   12th Marks     1004 non-null   int64 \n",
      " 3   12th Division  1004 non-null   int64 \n",
      " 4   AIEEE Rank     1004 non-null   int64 \n",
      " 5   College        1004 non-null   object\n",
      "dtypes: int64(5), object(1)\n",
      "memory usage: 47.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dfm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "24800485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year              int64\n",
       "10th Marks        int64\n",
       "12th Marks        int64\n",
       "12th Division     int64\n",
       "AIEEE Rank        int64\n",
       "College          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "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": 55,
   "id": "e109425b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique colleges: 38\n",
      "\n",
      "Count of each unique college:\n",
      "College\n",
      "IIT palakkad                  50\n",
      "Ahemedabad IT                 49\n",
      "S O A  university             49\n",
      "SSN college of ENGG           45\n",
      "Manipal IT                    43\n",
      "IIT jodhpur                   43\n",
      "NMIMS                         42\n",
      "IIT jammu                     35\n",
      "SRMIST chennai                32\n",
      "IIT bhilai                    32\n",
      "IIT goa                       30\n",
      "IIT indore                    30\n",
      "IIEST shibpur                 29\n",
      "KLEF hydrabad                 29\n",
      "University college of ENGG    29\n",
      "BIT Mesra                     29\n",
      "MNIT jaipur                   29\n",
      "BMS college of ENGG           29\n",
      "MNNIT allahabad               29\n",
      "HBUT kanpur                   25\n",
      "IIT kanpur                    21\n",
      "IIT Bombay                    20\n",
      "IIT hydrabad                  19\n",
      "MSIT                          19\n",
      "IIT guwahati                  18\n",
      "IIT kharagpur                 18\n",
      "IIT delhi                     17\n",
      "IIIT hydrabad                 17\n",
      "IIT mandi                     17\n",
      "IIT ropar                     17\n",
      "DTU delhi                     17\n",
      "VIT vellore                   17\n",
      "BITS pilani                   14\n",
      "NIT trichy                    14\n",
      "Netaji Subhas IT              14\n",
      "IIT tirupati                  13\n",
      "NIT warangal                  13\n",
      "Jadavpur Univversity          11\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "college_counts = dfm['College'].value_counts()\n",
    "\n",
    "# Print the count of unique colleges\n",
    "print(\"Number of unique colleges:\", len(college_counts))\n",
    "print(\"\\nCount of each unique college:\")\n",
    "print(college_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4730d01d",
   "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>Year</th>\n",
       "      <th>10th Marks</th>\n",
       "      <th>12th Marks</th>\n",
       "      <th>12th Division</th>\n",
       "      <th>AIEEE Rank</th>\n",
       "      <th>College</th>\n",
       "      <th>is_IIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>IIT Bombay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>IIT delhi</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>IIT kanpur</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>IIT kharagpur</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>IIT guwahati</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  10th Marks  12th Marks  12th Division  AIEEE Rank        College  \\\n",
       "0  2019          90          89              3          98     IIT Bombay   \n",
       "1  2015          95          92              2         100      IIT delhi   \n",
       "2  2018          91          80              6         260     IIT kanpur   \n",
       "3  2017          88          85              2         222  IIT kharagpur   \n",
       "4  2016          89          84              1         600   IIT guwahati   \n",
       "\n",
       "   is_IIT  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm['is_IIT'] = np.where(\n",
    "    dfm['College'].str.startswith('IIT') |\n",
    "    dfm['College'].str.startswith('NMIMS') |\n",
    "    dfm['College'].str.startswith('NIT') |\n",
    "    dfm['College'].str.startswith('BITS'),\n",
    "    1,\n",
    "    0\n",
    ")\n",
    "\n",
    "# Print the updated DataFrame\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "35e21e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>10th Marks</th>\n",
       "      <th>12th Marks</th>\n",
       "      <th>12th Division</th>\n",
       "      <th>AIEEE Rank</th>\n",
       "      <th>is_IIT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>260</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>88</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  10th Marks  12th Marks  12th Division  AIEEE Rank  is_IIT\n",
       "0  2019          90          89              3          98       1\n",
       "1  2015          95          92              2         100       1\n",
       "2  2018          91          80              6         260       1\n",
       "3  2017          88          85              2         222       1\n",
       "4  2016          89          84              1         600       1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.drop('College', axis=1, inplace=True)\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e6d5662d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of 1s (IIT): 463\n",
      "Count of 0s (Non-IIT): 541\n"
     ]
    }
   ],
   "source": [
    "# Count the occurrences of each value in the 'is_IIT' column\n",
    "is_iit_counts = dfm['is_IIT'].value_counts()\n",
    "\n",
    "# Print the count of 1s and 0s in the 'is_IIT' column\n",
    "print(\"Count of 1s (IIT):\", is_iit_counts[1])\n",
    "print(\"Count of 0s (Non-IIT):\", is_iit_counts[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4d789bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 6)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=dfm.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7a7b3f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 60,
     "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": 61,
   "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['10th Marks'], dfm['is_IIT'])\n",
    "plt.xlabel('10th Marks')\n",
    "plt.ylabel('is_IIT')\n",
    "plt.title('Scatter Plot between 10th Marks and is_IIT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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['12th Marks'], dfm['is_IIT'])\n",
    "plt.xlabel('12th Marks')\n",
    "plt.ylabel('is_IIT')\n",
    "plt.title('Scatter Plot between 12th Marks and is_IIT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2693162",
   "metadata": {},
   "source": [
    "## Splitting of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "_QNQGBTZ6m71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_QNQGBTZ6m71",
    "outputId": "6be845dd-2eb7-4ee7-ff13-b24c078e3ea4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1004, 6)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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(['is_IIT'], axis = 1), dfm['is_IIT'], test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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: (803, 5)\n",
      "X_test shape: (201, 5)\n",
      "y_train shape: (803,)\n",
      "y_test shape: (201,)\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": 66,
   "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": 67,
   "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": 68,
   "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: 0.961389751552795\n",
      "Test Score: 0.9601990049751243\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": 69,
   "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": 70,
   "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 0.96\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": 71,
   "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       0.98      0.95      0.97       119\n",
      "           1       0.93      0.98      0.95        82\n",
      "\n",
      "    accuracy                           0.96       201\n",
      "   macro avg       0.96      0.96      0.96       201\n",
      "weighted avg       0.96      0.96      0.96       201\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": 72,
   "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": 73,
   "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': 300}\n",
      "Train Score: 0.9725995024875622\n",
      "Test Score: 0.9850746268656716\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": 74,
   "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": 75,
   "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': 5}\n",
      "Train Score: 0.9738432835820896\n",
      "Test Score: 0.9850746268656716\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": 76,
   "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": 77,
   "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: 0.6675854037267082\n",
      "Test Score: 0.681592039800995\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": 78,
   "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": 79,
   "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": 80,
   "id": "5e45f6ca",
   "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=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": 81,
   "id": "80e80c7d",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "25d6988c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.84      0.76       119\n",
      "           1       0.66      0.45      0.54        82\n",
      "\n",
      "    accuracy                           0.68       201\n",
      "   macro avg       0.68      0.65      0.65       201\n",
      "weighted avg       0.68      0.68      0.67       201\n",
      "\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": 83,
   "id": "b7d11496",
   "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": 84,
   "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: 0.6700310559006211\n",
      "Test Score: 0.6368159203980099\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": 85,
   "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": 86,
   "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": 87,
   "id": "2d2827c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.61      0.67       119\n",
      "           1       0.54      0.67      0.60        82\n",
      "\n",
      "    accuracy                           0.64       201\n",
      "   macro avg       0.64      0.64      0.63       201\n",
      "weighted avg       0.65      0.64      0.64       201\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": 88,
   "id": "40fad276",
   "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 = 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": 89,
   "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": 90,
   "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": 91,
   "id": "d73317ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step - loss: 0.6970 - accuracy: 0.5174\n",
      "Epoch 1/50\n",
      "21/21 [==============================] - 1s 10ms/step - loss: 0.6650 - accuracy: 0.5794 - val_loss: 0.6207 - val_accuracy: 0.6894\n",
      "Epoch 2/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.6098 - accuracy: 0.7103 - val_loss: 0.5745 - val_accuracy: 0.7578\n",
      "Epoch 3/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.5688 - accuracy: 0.6916 - val_loss: 0.5451 - val_accuracy: 0.7205\n",
      "Epoch 4/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.5425 - accuracy: 0.7025 - val_loss: 0.5230 - val_accuracy: 0.7453\n",
      "Epoch 5/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.5251 - accuracy: 0.7103 - val_loss: 0.5098 - val_accuracy: 0.7516\n",
      "Epoch 6/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.5112 - accuracy: 0.7243 - val_loss: 0.5016 - val_accuracy: 0.7391\n",
      "Epoch 7/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4998 - accuracy: 0.7368 - val_loss: 0.4936 - val_accuracy: 0.7391\n",
      "Epoch 8/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4886 - accuracy: 0.7336 - val_loss: 0.4871 - val_accuracy: 0.7329\n",
      "Epoch 9/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4792 - accuracy: 0.7259 - val_loss: 0.4813 - val_accuracy: 0.7329\n",
      "Epoch 10/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4708 - accuracy: 0.7399 - val_loss: 0.4748 - val_accuracy: 0.7453\n",
      "Epoch 11/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4629 - accuracy: 0.7399 - val_loss: 0.4691 - val_accuracy: 0.7329\n",
      "Epoch 12/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4548 - accuracy: 0.7352 - val_loss: 0.4652 - val_accuracy: 0.7453\n",
      "Epoch 13/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4469 - accuracy: 0.7835 - val_loss: 0.4583 - val_accuracy: 0.7826\n",
      "Epoch 14/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4394 - accuracy: 0.7477 - val_loss: 0.4519 - val_accuracy: 0.7391\n",
      "Epoch 15/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4328 - accuracy: 0.7430 - val_loss: 0.4529 - val_accuracy: 0.7453\n",
      "Epoch 16/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4282 - accuracy: 0.7461 - val_loss: 0.4455 - val_accuracy: 0.7578\n",
      "Epoch 17/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4182 - accuracy: 0.7601 - val_loss: 0.4336 - val_accuracy: 0.7764\n",
      "Epoch 18/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.4124 - accuracy: 0.7336 - val_loss: 0.4330 - val_accuracy: 0.7702\n",
      "Epoch 19/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4033 - accuracy: 0.7897 - val_loss: 0.4266 - val_accuracy: 0.7826\n",
      "Epoch 20/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3975 - accuracy: 0.7960 - val_loss: 0.4244 - val_accuracy: 0.7826\n",
      "Epoch 21/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3917 - accuracy: 0.8100 - val_loss: 0.4115 - val_accuracy: 0.8075\n",
      "Epoch 22/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3890 - accuracy: 0.8240 - val_loss: 0.4120 - val_accuracy: 0.7950\n",
      "Epoch 23/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3803 - accuracy: 0.7913 - val_loss: 0.4074 - val_accuracy: 0.8323\n",
      "Epoch 24/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3720 - accuracy: 0.8364 - val_loss: 0.4050 - val_accuracy: 0.8012\n",
      "Epoch 25/50\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.3658 - accuracy: 0.8318 - val_loss: 0.3986 - val_accuracy: 0.8261\n",
      "Epoch 26/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.3642 - accuracy: 0.7928 - val_loss: 0.4028 - val_accuracy: 0.7888\n",
      "Epoch 27/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.3580 - accuracy: 0.8396 - val_loss: 0.3908 - val_accuracy: 0.8199\n",
      "Epoch 28/50\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.3498 - accuracy: 0.8536 - val_loss: 0.3878 - val_accuracy: 0.8137\n",
      "Epoch 29/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3408 - accuracy: 0.8520 - val_loss: 0.3855 - val_accuracy: 0.8199\n",
      "Epoch 30/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3378 - accuracy: 0.8411 - val_loss: 0.3794 - val_accuracy: 0.8447\n",
      "Epoch 31/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3343 - accuracy: 0.8692 - val_loss: 0.3762 - val_accuracy: 0.8323\n",
      "Epoch 32/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3257 - accuracy: 0.8660 - val_loss: 0.3739 - val_accuracy: 0.8509\n",
      "Epoch 33/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3186 - accuracy: 0.8754 - val_loss: 0.3727 - val_accuracy: 0.8509\n",
      "Epoch 34/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.3133 - accuracy: 0.8801 - val_loss: 0.3665 - val_accuracy: 0.8509\n",
      "Epoch 35/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.3085 - accuracy: 0.8816 - val_loss: 0.3577 - val_accuracy: 0.8509\n",
      "Epoch 36/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.3018 - accuracy: 0.8863 - val_loss: 0.3550 - val_accuracy: 0.8509\n",
      "Epoch 37/50\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.2974 - accuracy: 0.8894 - val_loss: 0.3534 - val_accuracy: 0.8447\n",
      "Epoch 38/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2921 - accuracy: 0.8956 - val_loss: 0.3506 - val_accuracy: 0.8447\n",
      "Epoch 39/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2854 - accuracy: 0.8972 - val_loss: 0.3453 - val_accuracy: 0.8509\n",
      "Epoch 40/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2847 - accuracy: 0.8972 - val_loss: 0.3460 - val_accuracy: 0.8447\n",
      "Epoch 41/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2759 - accuracy: 0.9065 - val_loss: 0.3389 - val_accuracy: 0.8571\n",
      "Epoch 42/50\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.2761 - accuracy: 0.9065 - val_loss: 0.3377 - val_accuracy: 0.8820\n",
      "Epoch 43/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2672 - accuracy: 0.9159 - val_loss: 0.3369 - val_accuracy: 0.8820\n",
      "Epoch 44/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2604 - accuracy: 0.9206 - val_loss: 0.3269 - val_accuracy: 0.8571\n",
      "Epoch 45/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2559 - accuracy: 0.9081 - val_loss: 0.3244 - val_accuracy: 0.8820\n",
      "Epoch 46/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2516 - accuracy: 0.9237 - val_loss: 0.3234 - val_accuracy: 0.8820\n",
      "Epoch 47/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2463 - accuracy: 0.9206 - val_loss: 0.3261 - val_accuracy: 0.8820\n",
      "Epoch 48/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2520 - accuracy: 0.9081 - val_loss: 0.3100 - val_accuracy: 0.8820\n",
      "Epoch 49/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2470 - accuracy: 0.9159 - val_loss: 0.3166 - val_accuracy: 0.8820\n",
      "Epoch 50/50\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 0.2375 - accuracy: 0.9299 - val_loss: 0.3110 - val_accuracy: 0.8944\n",
      "7/7 [==============================] - 0s 0s/step\n",
      "Test accuracy: 51.74%\n",
      "Training accuracy: 92.99%\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",
    "test_acc = model.evaluate(X_test_scaled, y_test)[1]\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",
    "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": 92,
   "id": "d9be0a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 3ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.91      0.91       119\n",
      "           1       0.87      0.87      0.87        82\n",
      "\n",
      "    accuracy                           0.89       201\n",
      "   macro avg       0.89      0.89      0.89       201\n",
      "weighted avg       0.89      0.89      0.89       201\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": 93,
   "id": "7a357301",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 3ms/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": 94,
   "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": 95,
   "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()"
   ]
  }
 ],
 "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
}