dass21-train-test-FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b820fd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85f7e4e8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Hard_to_wind_down</th>\n",
       "      <th>Dryness_of_mouth</th>\n",
       "      <th>could_nt_experience_any_positive _feeling</th>\n",
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       "      <th>Intolerant_to_getting_what_I_was_doing</th>\n",
       "      <th>Close_to_panic</th>\n",
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       "<p>580 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "     Hard_to_wind_down  Dryness_of_mouth  \\\n",
       "0                    3                 2   \n",
       "1                    0                 0   \n",
       "2                    3                 0   \n",
       "3                    3                 1   \n",
       "4                    3                 2   \n",
       "..                 ...               ...   \n",
       "575                  0                 0   \n",
       "576                  1                 2   \n",
       "577                  0                 1   \n",
       "578                  0                 3   \n",
       "579                  1                 0   \n",
       "\n",
       "     could_nt_experience_any_positive _feeling   Breathing_difficulty  \\\n",
       "0                                             2                     2   \n",
       "1                                             0                     0   \n",
       "2                                             2                     1   \n",
       "3                                             0                     1   \n",
       "4                                             1                     0   \n",
       "..                                          ...                   ...   \n",
       "575                                           0                     0   \n",
       "576                                           3                     2   \n",
       "577                                           0                     0   \n",
       "578                                           0                     0   \n",
       "579                                           1                     0   \n",
       "\n",
       "     Difficult_to_work_up_the_initiative_to_do_things  \\\n",
       "0                                                   1   \n",
       "1                                                   1   \n",
       "2                                                   0   \n",
       "3                                                   0   \n",
       "4                                                   1   \n",
       "..                                                ...   \n",
       "575                                                 1   \n",
       "576                                                 2   \n",
       "577                                                 1   \n",
       "578                                                 2   \n",
       "579                                                 2   \n",
       "\n",
       "     Overreact_to_situations  Experienced_trembling  nervous_energy  \\\n",
       "0                          2                      2               3   \n",
       "1                          1                      1               0   \n",
       "2                          0                      0               0   \n",
       "3                          0                      1               3   \n",
       "4                          1                      1               1   \n",
       "..                       ...                    ...             ...   \n",
       "575                        2                      1               0   \n",
       "576                        2                      2               2   \n",
       "577                        2                      0               1   \n",
       "578                        0                      1               2   \n",
       "579                        0                      0               0   \n",
       "\n",
       "     Worried_about_panic_and_make_a_fool_of_myself  \\\n",
       "0                                                2   \n",
       "1                                                1   \n",
       "2                                                2   \n",
       "3                                                1   \n",
       "4                                                0   \n",
       "..                                             ...   \n",
       "575                                              0   \n",
       "576                                              1   \n",
       "577                                              2   \n",
       "578                                              3   \n",
       "579                                              3   \n",
       "\n",
       "     Nothing_to_look_forward_to  ...  Difficult_to_relax  \\\n",
       "0                             1  ...                   3   \n",
       "1                             0  ...                   0   \n",
       "2                             2  ...                   1   \n",
       "3                             1  ...                   1   \n",
       "4                             0  ...                   1   \n",
       "..                          ...  ...                 ...   \n",
       "575                           0  ...                   0   \n",
       "576                           3  ...                   3   \n",
       "577                           0  ...                   0   \n",
       "578                           0  ...                   0   \n",
       "579                           0  ...                   0   \n",
       "\n",
       "     Down_hearted_and_blue  Intolerant_to_getting_what_I_was_doing   \\\n",
       "0                        3                                        1   \n",
       "1                        0                                        0   \n",
       "2                        2                                        1   \n",
       "3                        2                                        1   \n",
       "4                        0                                        1   \n",
       "..                     ...                                      ...   \n",
       "575                      0                                        0   \n",
       "576                      3                                        3   \n",
       "577                      0                                        0   \n",
       "578                      1                                        3   \n",
       "579                      0                                        0   \n",
       "\n",
       "     Close_to_panic  Unable_to_become_enthusiastic_about_anything  \\\n",
       "0                 3                                             0   \n",
       "1                 0                                             0   \n",
       "2                 3                                             0   \n",
       "3                 3                                             1   \n",
       "4                 1                                             0   \n",
       "..              ...                                           ...   \n",
       "575               0                                             0   \n",
       "576               2                                             3   \n",
       "577               1                                             0   \n",
       "578               2                                             1   \n",
       "579               0                                             0   \n",
       "\n",
       "     Wasnt_worth_much_as_a_person   I_felt_that_I_was_rather_touchy  \\\n",
       "0                               2                                 1   \n",
       "1                               0                                 1   \n",
       "2                               0                                 1   \n",
       "3                               2                                 1   \n",
       "4                               0                                 1   \n",
       "..                            ...                               ...   \n",
       "575                             0                                 1   \n",
       "576                             3                                 1   \n",
       "577                             0                                 1   \n",
       "578                             0                                 1   \n",
       "579                             0                                 1   \n",
       "\n",
       "     Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                    3                  \n",
       "1                                                    1                  \n",
       "2                                                    0                  \n",
       "3                                                    3                  \n",
       "4                                                    0                  \n",
       "..                                                 ...                  \n",
       "575                                                  3                  \n",
       "576                                                  3                  \n",
       "577                                                  2                  \n",
       "578                                                  0                  \n",
       "579                                                  0                  \n",
       "\n",
       "     Scared_without_any_good_reason  Life_was_meaningless  \n",
       "0                                 3                     1  \n",
       "1                                 0                     0  \n",
       "2                                 0                     1  \n",
       "3                                 0                     1  \n",
       "4                                 1                     0  \n",
       "..                              ...                   ...  \n",
       "575                               0                     0  \n",
       "576                               2                     3  \n",
       "577                               0                     0  \n",
       "578                               1                     2  \n",
       "579                               0                     0  \n",
       "\n",
       "[580 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dass = pd.read_csv(r\"D:\\\\Study\\\\Thesis Backup\\\\Thesis Codes\\\\dass_wo_target.csv\")\n",
    "dass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80b45700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hard_to_wind_down                                                   0\n",
       "Dryness_of_mouth                                                    0\n",
       "could_nt_experience_any_positive _feeling                           0\n",
       "Breathing_difficulty                                                0\n",
       "Difficult_to_work_up_the_initiative_to_do_things                    0\n",
       "Overreact_to_situations                                             0\n",
       "Experienced_trembling                                               0\n",
       "nervous_energy                                                      0\n",
       "Worried_about_panic_and_make_a_fool_of_myself                       0\n",
       "Nothing_to_look_forward_to                                          0\n",
       "Getting_agitated                                                    0\n",
       "Difficult_to_relax                                                  0\n",
       "Down_hearted_and_blue                                               0\n",
       "Intolerant_to_getting_what_I_was_doing                              0\n",
       "Close_to_panic                                                      0\n",
       "Unable_to_become_enthusiastic_about_anything                        0\n",
       "Wasnt_worth_much_as_a_person                                        0\n",
       " I_felt_that_I_was_rather_touchy                                    0\n",
       "Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion    0\n",
       "Scared_without_any_good_reason                                      0\n",
       "Life_was_meaningless                                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dass.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f887ce22",
   "metadata": {},
   "outputs": [],
   "source": [
    "dass_anxiety_data = pd.DataFrame()\n",
    "dass_anxiety_data[\"Dryness_of_mouth\"] = dass.Dryness_of_mouth\n",
    "dass_anxiety_data[\"Breathing_difficulty\"] = dass.Breathing_difficulty\n",
    "dass_anxiety_data[\"Experienced_trembling\"] = dass.Experienced_trembling\n",
    "dass_anxiety_data[\"Worried_about_panic_and_make_a_fool_of_myself\"] = dass.Worried_about_panic_and_make_a_fool_of_myself\n",
    "dass_anxiety_data[\"Close_to_panic\"] = dass.Close_to_panic\n",
    "dass_anxiety_data[\"Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion\"] = dass.Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion\n",
    "dass_anxiety_data[\"Scared_without_any_good_reason\"] = dass.Scared_without_any_good_reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6dee880c",
   "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>Dryness_of_mouth</th>\n",
       "      <th>Breathing_difficulty</th>\n",
       "      <th>Experienced_trembling</th>\n",
       "      <th>Worried_about_panic_and_make_a_fool_of_myself</th>\n",
       "      <th>Close_to_panic</th>\n",
       "      <th>Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion</th>\n",
       "      <th>Scared_without_any_good_reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>572</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>573</th>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "</table>\n",
       "<p>174 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
       "19                  1                     0                      1   \n",
       "23                  0                     0                      1   \n",
       "31                  1                     0                      1   \n",
       "36                  1                     0                      0   \n",
       "37                  1                     0                      0   \n",
       "..                ...                   ...                    ...   \n",
       "571                 1                     0                      0   \n",
       "572                 2                     0                      0   \n",
       "573                 2                     1                      3   \n",
       "574                 1                     0                      0   \n",
       "576                 2                     2                      2   \n",
       "\n",
       "     Worried_about_panic_and_make_a_fool_of_myself  Close_to_panic  \\\n",
       "19                                               0               1   \n",
       "23                                               0               0   \n",
       "31                                               0               1   \n",
       "36                                               1               1   \n",
       "37                                               1               1   \n",
       "..                                             ...             ...   \n",
       "571                                              1               0   \n",
       "572                                              1               0   \n",
       "573                                              3               3   \n",
       "574                                              1               1   \n",
       "576                                              1               2   \n",
       "\n",
       "     Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "19                                                   0                  \n",
       "23                                                   0                  \n",
       "31                                                   0                  \n",
       "36                                                   1                  \n",
       "37                                                   1                  \n",
       "..                                                 ...                  \n",
       "571                                                  1                  \n",
       "572                                                  0                  \n",
       "573                                                  0                  \n",
       "574                                                  1                  \n",
       "576                                                  3                  \n",
       "\n",
       "     Scared_without_any_good_reason  \n",
       "19                                0  \n",
       "23                                0  \n",
       "31                                0  \n",
       "36                                1  \n",
       "37                                1  \n",
       "..                              ...  \n",
       "571                               1  \n",
       "572                               0  \n",
       "573                               3  \n",
       "574                               1  \n",
       "576                               2  \n",
       "\n",
       "[174 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_rows = dass_anxiety_data[dass_anxiety_data.duplicated()]\n",
    "duplicate_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "707e95ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "dass_without_duplicates = dass_anxiety_data.drop_duplicates()\n",
    "\n",
    "# Save the dataset without duplicates to a new file\n",
    "dass_without_duplicates.to_csv(r'D:\\Study\\Thesis Backup\\Thesis Codes\\preprocessed\\dass_without_duplicates.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b0e5ca1",
   "metadata": {},
   "outputs": [
    {
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       "     Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
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       "..                ...                   ...                    ...   \n",
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       "577                 1                     0                      0   \n",
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       "579                 0                     0                      0   \n",
       "\n",
       "     Worried_about_panic_and_make_a_fool_of_myself  Close_to_panic  \\\n",
       "0                                                2               3   \n",
       "1                                                1               0   \n",
       "2                                                2               3   \n",
       "3                                                1               3   \n",
       "4                                                0               1   \n",
       "..                                             ...             ...   \n",
       "569                                              1               3   \n",
       "575                                              0               0   \n",
       "577                                              2               1   \n",
       "578                                              3               2   \n",
       "579                                              3               0   \n",
       "\n",
       "     Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                    3                  \n",
       "1                                                    1                  \n",
       "2                                                    0                  \n",
       "3                                                    3                  \n",
       "4                                                    0                  \n",
       "..                                                 ...                  \n",
       "569                                                  1                  \n",
       "575                                                  3                  \n",
       "577                                                  2                  \n",
       "578                                                  0                  \n",
       "579                                                  0                  \n",
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       "2                                 0  \n",
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       "4                                 1  \n",
       "..                              ...  \n",
       "569                               0  \n",
       "575                               0  \n",
       "577                               0  \n",
       "578                               1  \n",
       "579                               0  \n",
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       "[406 rows x 7 columns]"
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    "dass_without_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "028494b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scores(dass_without_duplicates):\n",
    "    col = list(dass_without_duplicates)\n",
    "    dass_without_duplicates[\"Scores\"] = dass_without_duplicates[col].sum(axis=1)\n",
    "    return dass_without_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "141bc782",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saleh\\AppData\\Local\\Temp\\ipykernel_8960\\2986473366.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dass_without_duplicates[\"Scores\"] = dass_without_duplicates[col].sum(axis=1)\n"
     ]
    },
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      "text/plain": [
       "     Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
       "0                   2                     2                      2   \n",
       "1                   0                     0                      1   \n",
       "2                   0                     1                      0   \n",
       "3                   1                     1                      1   \n",
       "4                   2                     0                      1   \n",
       "..                ...                   ...                    ...   \n",
       "569                 2                     1                      0   \n",
       "575                 0                     0                      1   \n",
       "577                 1                     0                      0   \n",
       "578                 3                     0                      1   \n",
       "579                 0                     0                      0   \n",
       "\n",
       "     Worried_about_panic_and_make_a_fool_of_myself  Close_to_panic  \\\n",
       "0                                                2               3   \n",
       "1                                                1               0   \n",
       "2                                                2               3   \n",
       "3                                                1               3   \n",
       "4                                                0               1   \n",
       "..                                             ...             ...   \n",
       "569                                              1               3   \n",
       "575                                              0               0   \n",
       "577                                              2               1   \n",
       "578                                              3               2   \n",
       "579                                              3               0   \n",
       "\n",
       "     Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                    3                  \n",
       "1                                                    1                  \n",
       "2                                                    0                  \n",
       "3                                                    3                  \n",
       "4                                                    0                  \n",
       "..                                                 ...                  \n",
       "569                                                  1                  \n",
       "575                                                  3                  \n",
       "577                                                  2                  \n",
       "578                                                  0                  \n",
       "579                                                  0                  \n",
       "\n",
       "     Scared_without_any_good_reason  Scores  \n",
       "0                                 3      17  \n",
       "1                                 0       3  \n",
       "2                                 0       6  \n",
       "3                                 0      10  \n",
       "4                                 1       5  \n",
       "..                              ...     ...  \n",
       "569                               0       8  \n",
       "575                               0       4  \n",
       "577                               0       6  \n",
       "578                               1      10  \n",
       "579                               0       3  \n",
       "\n",
       "[406 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data = scores(dass_without_duplicates)\n",
    "score_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e836a91b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
       "0                  2                     2                      2   \n",
       "1                  0                     0                      1   \n",
       "2                  0                     1                      0   \n",
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       "0                                               2               3   \n",
       "1                                               1               0   \n",
       "2                                               2               3   \n",
       "3                                               1               3   \n",
       "4                                               0               1   \n",
       "5                                               0               1   \n",
       "6                                               1               1   \n",
       "7                                               3               0   \n",
       "8                                               2               2   \n",
       "9                                               1               2   \n",
       "10                                              1               1   \n",
       "11                                              0               0   \n",
       "12                                              1               0   \n",
       "13                                              3               3   \n",
       "14                                              0               0   \n",
       "15                                              2               1   \n",
       "16                                              3               3   \n",
       "17                                              3               3   \n",
       "18                                              2               1   \n",
       "20                                              0               0   \n",
       "\n",
       "    Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                   3                  \n",
       "1                                                   1                  \n",
       "2                                                   0                  \n",
       "3                                                   3                  \n",
       "4                                                   0                  \n",
       "5                                                   0                  \n",
       "6                                                   0                  \n",
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       "8                                                   2                  \n",
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       "10                                                  1                  \n",
       "11                                                  0                  \n",
       "12                                                  0                  \n",
       "13                                                  3                  \n",
       "14                                                  2                  \n",
       "15                                                  0                  \n",
       "16                                                  0                  \n",
       "17                                                  0                  \n",
       "18                                                  2                  \n",
       "20                                                  1                  \n",
       "\n",
       "    Scared_without_any_good_reason  Scores  \n",
       "0                                3      17  \n",
       "1                                0       3  \n",
       "2                                0       6  \n",
       "3                                0      10  \n",
       "4                                1       5  \n",
       "5                                0       3  \n",
       "6                                0       4  \n",
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       "8                                0       8  \n",
       "9                                2      12  \n",
       "10                               1       5  \n",
       "11                               0       1  \n",
       "12                               0       4  \n",
       "13                               3      19  \n",
       "14                               1      10  \n",
       "15                               0       7  \n",
       "16                               3      13  \n",
       "17                               3      17  \n",
       "18                               2      10  \n",
       "20                               0       1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb8c9a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saleh\\AppData\\Local\\Temp\\ipykernel_8960\\3965042791.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dass_without_duplicates[\"d_score\"] = dass_without_duplicates['Scores']*2\n"
     ]
    }
   ],
   "source": [
    "dass_without_duplicates[\"d_score\"] = dass_without_duplicates['Scores']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a35c37c",
   "metadata": {},
   "outputs": [
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       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
       "0                  2                     2                      2   \n",
       "1                  0                     0                      1   \n",
       "2                  0                     1                      0   \n",
       "3                  1                     1                      1   \n",
       "4                  2                     0                      1   \n",
       "5                  1                     0                      1   \n",
       "6                  1                     1                      0   \n",
       "7                  3                     1                      2   \n",
       "8                  0                     1                      1   \n",
       "9                  2                     2                      2   \n",
       "10                 1                     0                      0   \n",
       "11                 0                     0                      1   \n",
       "12                 3                     0                      0   \n",
       "13                 1                     3                      3   \n",
       "14                 3                     2                      2   \n",
       "15                 2                     1                      1   \n",
       "16                 1                     0                      3   \n",
       "17                 2                     3                      3   \n",
       "18                 3                     0                      0   \n",
       "20                 0                     0                      0   \n",
       "\n",
       "    Worried_about_panic_and_make_a_fool_of_myself  Close_to_panic  \\\n",
       "0                                               2               3   \n",
       "1                                               1               0   \n",
       "2                                               2               3   \n",
       "3                                               1               3   \n",
       "4                                               0               1   \n",
       "5                                               0               1   \n",
       "6                                               1               1   \n",
       "7                                               3               0   \n",
       "8                                               2               2   \n",
       "9                                               1               2   \n",
       "10                                              1               1   \n",
       "11                                              0               0   \n",
       "12                                              1               0   \n",
       "13                                              3               3   \n",
       "14                                              0               0   \n",
       "15                                              2               1   \n",
       "16                                              3               3   \n",
       "17                                              3               3   \n",
       "18                                              2               1   \n",
       "20                                              0               0   \n",
       "\n",
       "    Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                   3                  \n",
       "1                                                   1                  \n",
       "2                                                   0                  \n",
       "3                                                   3                  \n",
       "4                                                   0                  \n",
       "5                                                   0                  \n",
       "6                                                   0                  \n",
       "7                                                   2                  \n",
       "8                                                   2                  \n",
       "9                                                   1                  \n",
       "10                                                  1                  \n",
       "11                                                  0                  \n",
       "12                                                  0                  \n",
       "13                                                  3                  \n",
       "14                                                  2                  \n",
       "15                                                  0                  \n",
       "16                                                  0                  \n",
       "17                                                  0                  \n",
       "18                                                  2                  \n",
       "20                                                  1                  \n",
       "\n",
       "    Scared_without_any_good_reason  Scores  d_score  \n",
       "0                                3      17       34  \n",
       "1                                0       3        6  \n",
       "2                                0       6       12  \n",
       "3                                0      10       20  \n",
       "4                                1       5       10  \n",
       "5                                0       3        6  \n",
       "6                                0       4        8  \n",
       "7                                1      12       24  \n",
       "8                                0       8       16  \n",
       "9                                2      12       24  \n",
       "10                               1       5       10  \n",
       "11                               0       1        2  \n",
       "12                               0       4        8  \n",
       "13                               3      19       38  \n",
       "14                               1      10       20  \n",
       "15                               0       7       14  \n",
       "16                               3      13       26  \n",
       "17                               3      17       34  \n",
       "18                               2      10       20  \n",
       "20                               0       1        2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dass_without_duplicates.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16ee94df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saleh\\AppData\\Local\\Temp\\ipykernel_8960\\1794641890.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dass_without_duplicates['condition']=dass_without_duplicates['d_score'].apply(condition)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dryness_of_mouth</th>\n",
       "      <th>Breathing_difficulty</th>\n",
       "      <th>Experienced_trembling</th>\n",
       "      <th>Worried_about_panic_and_make_a_fool_of_myself</th>\n",
       "      <th>Close_to_panic</th>\n",
       "      <th>Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion</th>\n",
       "      <th>Scared_without_any_good_reason</th>\n",
       "      <th>Scores</th>\n",
       "      <th>d_score</th>\n",
       "      <th>condition</th>\n",
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       "      <td>17</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>20</td>\n",
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       "      <th>4</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>406 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dryness_of_mouth  Breathing_difficulty  Experienced_trembling  \\\n",
       "0                   2                     2                      2   \n",
       "1                   0                     0                      1   \n",
       "2                   0                     1                      0   \n",
       "3                   1                     1                      1   \n",
       "4                   2                     0                      1   \n",
       "..                ...                   ...                    ...   \n",
       "569                 2                     1                      0   \n",
       "575                 0                     0                      1   \n",
       "577                 1                     0                      0   \n",
       "578                 3                     0                      1   \n",
       "579                 0                     0                      0   \n",
       "\n",
       "     Worried_about_panic_and_make_a_fool_of_myself  Close_to_panic  \\\n",
       "0                                                2               3   \n",
       "1                                                1               0   \n",
       "2                                                2               3   \n",
       "3                                                1               3   \n",
       "4                                                0               1   \n",
       "..                                             ...             ...   \n",
       "569                                              1               3   \n",
       "575                                              0               0   \n",
       "577                                              2               1   \n",
       "578                                              3               2   \n",
       "579                                              3               0   \n",
       "\n",
       "     Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion  \\\n",
       "0                                                    3                  \n",
       "1                                                    1                  \n",
       "2                                                    0                  \n",
       "3                                                    3                  \n",
       "4                                                    0                  \n",
       "..                                                 ...                  \n",
       "569                                                  1                  \n",
       "575                                                  3                  \n",
       "577                                                  2                  \n",
       "578                                                  0                  \n",
       "579                                                  0                  \n",
       "\n",
       "     Scared_without_any_good_reason  Scores  d_score  condition  \n",
       "0                                 3      17       34          4  \n",
       "1                                 0       3        6          0  \n",
       "2                                 0       6       12          2  \n",
       "3                                 0      10       20          4  \n",
       "4                                 1       5       10          2  \n",
       "..                              ...     ...      ...        ...  \n",
       "569                               0       8       16          3  \n",
       "575                               0       4        8          1  \n",
       "577                               0       6       12          2  \n",
       "578                               1      10       20          4  \n",
       "579                               0       3        6          0  \n",
       "\n",
       "[406 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def condition(x):\n",
    "    if x<=7:\n",
    "        return 0 # 'Normal'\n",
    "    if  8<=x<=9:\n",
    "        return 1 # 'Mild'\n",
    "    if 10<=x<=14:\n",
    "        return 2 # 'Moderate'\n",
    "    if 15<=x<=19:\n",
    "        return 3 # 'Severe'\n",
    "    if x>=20:\n",
    "        return 4 # 'Extremely Severe'\n",
    "\n",
    "dass_without_duplicates['condition']=dass_without_duplicates['d_score'].apply(condition)\n",
    "dass_without_duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "652b481d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dryness_of_mouth                                                    0\n",
       "Breathing_difficulty                                                0\n",
       "Experienced_trembling                                               0\n",
       "Worried_about_panic_and_make_a_fool_of_myself                       0\n",
       "Close_to_panic                                                      0\n",
       "Aware_of_the_action_of_heart_in_the_absence_of_physical_exertion    0\n",
       "Scared_without_any_good_reason                                      0\n",
       "Scores                                                              0\n",
       "d_score                                                             0\n",
       "condition                                                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dass_without_duplicates.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50a18f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "dass_without_duplicates = dass_without_duplicates.drop('Scores',axis=1)\n",
    "dass_without_duplicates = dass_without_duplicates.drop('d_score',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c6fb0be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dass_without_duplicates.drop('condition',axis=1)\n",
    "y = dass_without_duplicates['condition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dceb3421",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=116,random_state=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee3f9a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After OverSampling, the shape of train_x: (540, 7)\n",
      "After OverSampling, the shape of train_y: (540,)\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "xtrain_ros, ytrain_ros = RandomOverSampler(random_state=2).fit_resample(xtrain, ytrain)\n",
    "\n",
    "print('After OverSampling, the shape of train_x: {}'.format(xtrain_ros.shape))\n",
    "print('After OverSampling, the shape of train_y: {}'.format(ytrain_ros.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "673c0e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "models = []\n",
    "models.append(['XGBClassifier',XGBClassifier()])\n",
    "models.append(['KNeigbors',KNeighborsClassifier()])\n",
    "models.append(['GaussianNB',GaussianNB()])\n",
    "models.append(['SVM',SVC()])\n",
    "models.append(['DecisionTree',DecisionTreeClassifier(random_state=0)])\n",
    "models.append(['RandomForest',RandomForestClassifier(random_state=0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aec6aa96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score,ConfusionMatrixDisplay,precision_score,recall_score,f1_score,classification_report,roc_curve,auc,precision_recall_curve,average_precision_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cb7945d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              objective='multi:softprob', predictor=None, ...) : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.58      0.67        12\n",
      "           1       0.07      0.17      0.10         6\n",
      "           2       0.63      0.59      0.61        37\n",
      "           3       0.44      0.50      0.47        16\n",
      "           4       1.00      0.87      0.93        45\n",
      "\n",
      "    accuracy                           0.66       116\n",
      "   macro avg       0.58      0.54      0.55       116\n",
      "weighted avg       0.73      0.66      0.69       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier() : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.92      0.81        12\n",
      "           1       0.00      0.00      0.00         6\n",
      "           2       0.68      0.51      0.58        37\n",
      "           3       0.45      0.56      0.50        16\n",
      "           4       1.00      0.84      0.92        45\n",
      "\n",
      "    accuracy                           0.66       116\n",
      "   macro avg       0.57      0.57      0.56       116\n",
      "weighted avg       0.74      0.66      0.69       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB() : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.58      0.74        12\n",
      "           1       0.25      0.83      0.38         6\n",
      "           2       0.72      0.57      0.64        37\n",
      "           3       0.38      0.62      0.48        16\n",
      "           4       1.00      0.76      0.86        45\n",
      "\n",
      "    accuracy                           0.66       116\n",
      "   macro avg       0.67      0.67      0.62       116\n",
      "weighted avg       0.79      0.66      0.70       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC() : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.75      0.86        12\n",
      "           1       0.45      0.83      0.59         6\n",
      "           2       0.91      0.81      0.86        37\n",
      "           3       0.58      0.88      0.70        16\n",
      "           4       1.00      0.87      0.93        45\n",
      "\n",
      "    accuracy                           0.84       116\n",
      "   macro avg       0.79      0.83      0.79       116\n",
      "weighted avg       0.89      0.84      0.85       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(random_state=0) : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.58      0.67        12\n",
      "           1       0.06      0.17      0.09         6\n",
      "           2       0.62      0.62      0.62        37\n",
      "           3       0.33      0.25      0.29        16\n",
      "           4       0.90      0.82      0.86        45\n",
      "\n",
      "    accuracy                           0.62       116\n",
      "   macro avg       0.54      0.49      0.50       116\n",
      "weighted avg       0.68      0.62      0.64       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(random_state=0) : \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.75      0.78        12\n",
      "           1       0.06      0.17      0.09         6\n",
      "           2       0.59      0.54      0.56        37\n",
      "           3       0.28      0.31      0.29        16\n",
      "           4       0.97      0.80      0.88        45\n",
      "\n",
      "    accuracy                           0.61       116\n",
      "   macro avg       0.54      0.51      0.52       116\n",
      "weighted avg       0.69      0.61      0.65       116\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "lst_1 = []\n",
    "for m in range(len(models)):\n",
    "    lst_2 = []\n",
    "    per_model = models[m][1]\n",
    "    per_model.fit(xtrain_ros,ytrain_ros)\n",
    "    y_pred = per_model.predict(xtest)\n",
    "    cr = classification_report(ytest,y_pred)\n",
    "    cf = confusion_matrix(ytest,y_pred)\n",
    "    cf_df = pd.DataFrame(cf,\n",
    "                    index=[0,1,2,3,4],\n",
    "                    columns=[0,1,2,3,4])\n",
    "    sns.heatmap(cf,annot=True)\n",
    "    plt.title(models[m][0])\n",
    "    plt.xlabel(\"Actual\")\n",
    "    plt.ylabel(\"Predicted\")\n",
    "    plt.show()\n",
    "    \n",
    "    precision = precision_score(ytest,y_pred,average='weighted',zero_division=0)\n",
    "    recall = recall_score(ytest,y_pred,average='weighted',zero_division=0)\n",
    "    f1 = f1_score(ytest,y_pred,average='weighted',zero_division=0)\n",
    "    \n",
    "    print(str(per_model)+\" : \\n\")\n",
    "    print(\"\")\n",
    "    print(cr)\n",
    "    print(\"-\"*25)\n",
    "    print(\"\")\n",
    "    lst_2.append(models[m][0])\n",
    "    lst_2.append(accuracy_score(ytest,y_pred)*100)\n",
    "    lst_2.append(100-accuracy_score(ytest,y_pred)*100)\n",
    "    lst_2.append(precision)\n",
    "    lst_2.append(recall)\n",
    "    lst_2.append(f1)\n",
    "    lst_1.append(lst_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f909828e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Error Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>61.206897</td>\n",
       "      <td>38.793103</td>\n",
       "      <td>0.691260</td>\n",
       "      <td>0.612069</td>\n",
       "      <td>0.646551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>62.068966</td>\n",
       "      <td>37.931034</td>\n",
       "      <td>0.677839</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.644949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>66.379310</td>\n",
       "      <td>33.620690</td>\n",
       "      <td>0.733634</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.693945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNeigbors</td>\n",
       "      <td>66.379310</td>\n",
       "      <td>33.620690</td>\n",
       "      <td>0.742303</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.694943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>66.379310</td>\n",
       "      <td>33.620690</td>\n",
       "      <td>0.788336</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.698694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVM</td>\n",
       "      <td>83.620690</td>\n",
       "      <td>16.379310</td>\n",
       "      <td>0.885319</td>\n",
       "      <td>0.836207</td>\n",
       "      <td>0.849268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Model   Accuracy  Error Rate  Precision    Recall  F1 Score\n",
       "5   RandomForest  61.206897   38.793103   0.691260  0.612069  0.646551\n",
       "4   DecisionTree  62.068966   37.931034   0.677839  0.620690  0.644949\n",
       "0  XGBClassifier  66.379310   33.620690   0.733634  0.663793  0.693945\n",
       "1      KNeigbors  66.379310   33.620690   0.742303  0.663793  0.694943\n",
       "2     GaussianNB  66.379310   33.620690   0.788336  0.663793  0.698694\n",
       "3            SVM  83.620690   16.379310   0.885319  0.836207  0.849268"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(lst_1,columns=['Model','Accuracy','Error Rate','Precision','Recall','F1 Score'])\n",
    "\n",
    "df.sort_values(by=['Accuracy'],inplace=True,ascending=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b93b7ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_models = [\n",
    "    (KNeighborsClassifier(), [{'n_neighbors': [5, 7, 8, 10], 'metric': ['euclidean', 'manhattan', 'chebyshev', 'minkowski']}]),\n",
    "    (DecisionTreeClassifier(), [{'criterion': ['gini', 'entropy'], 'random_state': [0]}]),\n",
    "    (RandomForestClassifier(), [{'n_estimators': [100, 150, 200], 'criterion': ['gini', 'entropy'], 'random_state': [0]}]),\n",
    "    (XGBClassifier(), [{'n_estimators': [100, 150, 200], 'learning_rate': [0.1, 0.01, 0.001], 'max_depth': [3, 5, 7]}]),\n",
    "    (SVC(), [{'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['scale', 'auto']}]),\n",
    "    (GaussianNB(), [{}])  # No hyperparameters to tune for Gaussian Naive Bayes\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a3c8af98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " KNeighborsClassifier(): \n",
      " Best Accuracy: 79 %\n",
      " Best Param: {'metric': 'euclidean', 'n_neighbors': 5}\n",
      "\n",
      "-------------------------\n",
      "\n",
      " DecisionTreeClassifier(): \n",
      " Best Accuracy: 93 %\n",
      " Best Param: {'criterion': 'entropy', 'random_state': 0}\n",
      "\n",
      "-------------------------\n",
      "\n",
      " RandomForestClassifier(): \n",
      " Best Accuracy: 93 %\n",
      " Best Param: {'criterion': 'entropy', 'n_estimators': 100, 'random_state': 0}\n",
      "\n",
      "-------------------------\n",
      "\n",
      " XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              predictor=None, random_state=None, ...): \n",
      " Best Accuracy: 94 %\n",
      " Best Param: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}\n",
      "\n",
      "-------------------------\n",
      "\n",
      " SVC(): \n",
      " Best Accuracy: 100 %\n",
      " Best Param: {'C': 10, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "\n",
      "-------------------------\n",
      "\n",
      " GaussianNB(): \n",
      " Best Accuracy: 69 %\n",
      " Best Param: {}\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i,j in grid_models:\n",
    "    grid = GridSearchCV(estimator=i,param_grid = j, scoring = 'accuracy',cv = 10)\n",
    "    grid.fit(xtrain_ros,ytrain_ros)\n",
    "    best_accuracy = grid.best_score_\n",
    "    best_param = grid.best_params_\n",
    "    print(' {}: \\n Best Accuracy: {:.0f} %'.format(i,best_accuracy*100))\n",
    "    print(' Best Param: {}'.format(best_param))\n",
    "    print('')\n",
    "    print('-'*25)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b830a05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier: \n",
      " Best Accuracy: 79 %\n",
      "Best Params: {'metric': 'euclidean', 'n_neighbors': 5}\n",
      "\n",
      "-------------------------\n",
      "\n",
      "DecisionTreeClassifier: \n",
      " Best Accuracy: 93 %\n",
      "Best Params: {'criterion': 'entropy', 'random_state': 0}\n",
      "\n",
      "-------------------------\n",
      "\n",
      "RandomForestClassifier: \n",
      " Best Accuracy: 93 %\n",
      "Best Params: {'criterion': 'entropy', 'n_estimators': 100, 'random_state': 0}\n",
      "\n",
      "-------------------------\n",
      "\n",
      "XGBClassifier: \n",
      " Best Accuracy: 94 %\n",
      "Best Params: {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 200}\n",
      "\n",
      "-------------------------\n",
      "\n",
      "SVC: \n",
      " Best Accuracy: 100 %\n",
      "Best Params: {'C': 10, 'gamma': 'scale', 'kernel': 'linear'}\n",
      "\n",
      "-------------------------\n",
      "\n",
      "GaussianNB: \n",
      " Best Accuracy: 69 %\n",
      "Best Params: {}\n",
      "\n",
      "-------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 1.0\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1 Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming grid_models contains a list of model instances (i) and corresponding parameter grids (j)\n",
    "best_accuracy = 0\n",
    "best_params = {}\n",
    "best_model = None\n",
    "\n",
    "for i, j in grid_models:\n",
    "    grid = GridSearchCV(estimator=i, param_grid=j, scoring='accuracy', cv=10)\n",
    "    grid.fit(xtrain_ros, ytrain_ros)\n",
    "    accuracy = grid.best_score_\n",
    "    params = grid.best_params_\n",
    "    print('{}: \\n Best Accuracy: {:.0f} %'.format(i.__class__.__name__, accuracy * 100))\n",
    "    print('Best Params: {}'.format(params))\n",
    "    print('')\n",
    "    print('-' * 25)\n",
    "    print('')\n",
    "\n",
    "    if accuracy > best_accuracy:\n",
    "        best_accuracy = accuracy\n",
    "        best_params = params\n",
    "        best_model = i  # Store the best model instance, not the best_estimator_\n",
    "\n",
    "# Create the best model with the best hyperparameters\n",
    "best_model = best_model.set_params(**best_params)\n",
    "\n",
    "# Train the best model on the training data\n",
    "best_model.fit(xtrain_ros, ytrain_ros)\n",
    "\n",
    "# Make predictions on the test data\n",
    "y_pred = best_model.predict(xtest)\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "acc = accuracy_score(ytest, y_pred)\n",
    "precision = precision_score(ytest, y_pred, average='weighted', zero_division=0)\n",
    "recall = recall_score(ytest, y_pred, average='weighted', zero_division=0)\n",
    "f1 = f1_score(ytest, y_pred, average='weighted', zero_division=0)\n",
    "\n",
    "# Display confusion matrix\n",
    "cf = confusion_matrix(ytest, y_pred)\n",
    "cf_df = pd.DataFrame(cf, index=[0, 1, 2, 3, 4], columns=[0, 1, 2, 3, 4])\n",
    "sns.heatmap(cf, annot=True)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.show()\n",
    "\n",
    "# Print evaluation metrics\n",
    "print(\"Best Accuracy:\", best_accuracy)\n",
    "print(\"Accuracy:\", acc)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 Score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "215c7d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GRID ML model Prediction:\n",
      "[3 2 4 0 3 3 2 1 4 4 1 4 3 4 3 1 3 4 2 2 2 4 2 3 2 2 4 2 4 2 3 3 3 1 2 1 4\n",
      " 3 2 1 2 0 4 4 1 1 2 3 2 0 1 4 4 4 4 3 3 4 0 1 4 2 3 4 2 4 1 4 3 1 4 4 2 1\n",
      " 2 3 2 2 0 4 3 4 2 4 1 2 4 3 2 3 3 4 4 0 3 1 1 2 1 4 3 4 0 4 4 3 4 2 2 2 1\n",
      " 3 1 1 3 2]\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "path_pickle = \"D:\\\\Study\\\\Thesis Backup\\\\Thesis Codes\\\\saved models\\\\pickle\"\n",
    "\n",
    "# Saving the model\n",
    "saving_grid = open(path_pickle + \"_anxiety_grid_model_final.pickle\", \"wb\")\n",
    "pickle.dump(grid, saving_grid)\n",
    "saving_grid.close()\n",
    "\n",
    "# Loading the model\n",
    "loading_grid = open(path_pickle + \"_anxiety_grid_model_final.pickle\", \"rb\")\n",
    "grid_from_pickle = pickle.load(loading_grid)\n",
    "loading_grid.close()\n",
    "\n",
    "# Testing\n",
    "print('GRID ML model Prediction:')\n",
    "print(grid_from_pickle.predict(xtest))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "99945e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dass_anxiety = grid.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1900b548",
   "metadata": {},
   "outputs": [],
   "source": [
    "fusion_csv_txt = pd.DataFrame()\n",
    "fusion_csv_txt['txt_anxiety'] = [0 if x in [1,2,3,4] else 1 for x in dass_anxiety] # 0 = anxiety and 1 = not anxiety\n",
    "\n",
    "#[0 if x in [1,2,3,4] else 1 for x in dass_anxiety]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e7aaa929",
   "metadata": {},
   "outputs": [
    {
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2b3ae52d",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 32,
     "metadata": {},
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   ],
   "source": [
    "fusion_csv_txt.to_csv(r'D:\\Study\\Thesis Backup\\Thesis Codes\\fusion dataset\\fusion_csv_txt.csv', index=False)\n",
    "fusion_csv_txt"
   ]
  }
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