Lung Cancer Prediction .ipynb

{
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   "source": [
    "import pandas as pd\n",
    "import matplotlib. pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2377dd1f",
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   "outputs": [],
   "source": [
    "lung_data = pd.read_csv(\"survey lung cancer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th>GENDER</th>\n",
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       "    GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0        M   69        1               2        2              1   \n",
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       "0                  1        2        1         2                  2         2   \n",
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       "     SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
       "0                      2                      2           2         YES  \n",
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       "..                   ...                    ...         ...         ...  \n",
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   ],
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   ]
  },
  {
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       "  GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0      M   69        1               2        2              1   \n",
       "1      M   74        2               1        1              1   \n",
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       "3      M   63        2               2        2              1   \n",
       "4      F   63        1               2        1              1   \n",
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       "   CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1        2        1         2                  2         2   \n",
       "1                2        2        2         1                  1         1   \n",
       "2                1        2        1         2                  1         2   \n",
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       "4                1        1        1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
       "0                    2                      2           2         YES  \n",
       "1                    2                      2           2         YES  \n",
       "2                    2                      1           2          NO  \n",
       "3                    1                      2           2          NO  \n",
       "4                    2                      1           1          NO  "
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     "execution_count": 4,
     "metadata": {},
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   ],
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    "lung_data.head()"
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  },
  {
   "cell_type": "code",
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   "id": "1f77a74d",
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   "outputs": [
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       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>M</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>M</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "304      F   56        1               1        1              2   \n",
       "305      M   70        2               1        1              1   \n",
       "306      M   58        2               1        1              1   \n",
       "307      M   67        2               1        2              1   \n",
       "308      M   62        1               1        1              2   \n",
       "\n",
       "     CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "304                2        2        1         1                  2         2   \n",
       "305                1        2        2         2                  2         2   \n",
       "306                1        1        2         2                  2         2   \n",
       "307                1        2        2         1                  2         2   \n",
       "308                1        2        2         2                  2         1   \n",
       "\n",
       "     SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN LUNG_CANCER  \n",
       "304                    2                      2           1         YES  \n",
       "305                    2                      1           2         YES  \n",
       "306                    1                      1           2         YES  \n",
       "307                    2                      1           2         YES  \n",
       "308                    1                      2           1         YES  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27526252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
      "0        M   69        1               2        2              1   \n",
      "1        M   74        2               1        1              1   \n",
      "2        F   59        1               1        1              2   \n",
      "3        M   63        2               2        2              1   \n",
      "4        F   63        1               2        1              1   \n",
      "..     ...  ...      ...             ...      ...            ...   \n",
      "304      F   56        1               1        1              2   \n",
      "305      M   70        2               1        1              1   \n",
      "306      M   58        2               1        1              1   \n",
      "307      M   67        2               1        2              1   \n",
      "308      M   62        1               1        1              2   \n",
      "\n",
      "     CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
      "0                  1        2        1         2                  2         2   \n",
      "1                  2        2        2         1                  1         1   \n",
      "2                  1        2        1         2                  1         2   \n",
      "3                  1        1        1         1                  2         1   \n",
      "4                  1        1        1         2                  1         2   \n",
      "..               ...      ...      ...       ...                ...       ...   \n",
      "304                2        2        1         1                  2         2   \n",
      "305                1        2        2         2                  2         2   \n",
      "306                1        1        2         2                  2         2   \n",
      "307                1        2        2         1                  2         2   \n",
      "308                1        2        2         2                  2         1   \n",
      "\n",
      "     SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \n",
      "0                      2                      2           2  \n",
      "1                      2                      2           2  \n",
      "2                      2                      1           2  \n",
      "3                      1                      2           2  \n",
      "4                      2                      1           1  \n",
      "..                   ...                    ...         ...  \n",
      "304                    2                      2           1  \n",
      "305                    2                      1           2  \n",
      "306                    1                      1           2  \n",
      "307                    2                      1           2  \n",
      "308                    1                      2           1  \n",
      "\n",
      "[309 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "#dependent_variable \n",
    "x = lung_data.iloc[:,0:-1] \n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c53880d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    LUNG_CANCER\n",
      "0           YES\n",
      "1           YES\n",
      "2            NO\n",
      "3            NO\n",
      "4            NO\n",
      "..          ...\n",
      "304         YES\n",
      "305         YES\n",
      "306         YES\n",
      "307         YES\n",
      "308         YES\n",
      "\n",
      "[309 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#independent_variable\n",
    "y = lung_data. iloc[:,-1:]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e96d026",
   "metadata": {},
   "outputs": [],
   "source": [
    "lung_data.GENDER = lung_data.GENDER.map({\"M\":1,\"F\":2})\n",
    "lung_data.LUNG_CANCER = lung_data.LUNG_CANCER.map({\"YES\":1,\"NO\":2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b9ea8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 16)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "51a5e5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GENDER                   0\n",
       "AGE                      0\n",
       "SMOKING                  0\n",
       "YELLOW_FINGERS           0\n",
       "ANXIETY                  0\n",
       "PEER_PRESSURE            0\n",
       "CHRONIC DISEASE          0\n",
       "FATIGUE                  0\n",
       "ALLERGY                  0\n",
       "WHEEZING                 0\n",
       "ALCOHOL CONSUMING        0\n",
       "COUGHING                 0\n",
       "SHORTNESS OF BREATH      0\n",
       "SWALLOWING DIFFICULTY    0\n",
       "CHEST PAIN               0\n",
       "LUNG_CANCER              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b3b56be0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GENDER                   int64\n",
       "AGE                      int64\n",
       "SMOKING                  int64\n",
       "YELLOW_FINGERS           int64\n",
       "ANXIETY                  int64\n",
       "PEER_PRESSURE            int64\n",
       "CHRONIC DISEASE          int64\n",
       "FATIGUE                  int64\n",
       "ALLERGY                  int64\n",
       "WHEEZING                 int64\n",
       "ALCOHOL CONSUMING        int64\n",
       "COUGHING                 int64\n",
       "SHORTNESS OF BREATH      int64\n",
       "SWALLOWING DIFFICULTY    int64\n",
       "CHEST PAIN               int64\n",
       "LUNG_CANCER              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44f38592",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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      "text/plain": [
       "   GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0       1   69        1               2        2              1   \n",
       "1       1   74        2               1        1              1   \n",
       "2       2   59        1               1        1              2   \n",
       "3       1   63        2               2        2              1   \n",
       "4       2   63        1               2        1              1   \n",
       "\n",
       "   CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                1        2        1         2                  2         2   \n",
       "1                2        2        2         1                  1         1   \n",
       "2                1        2        1         2                  1         2   \n",
       "3                1        1        1         1                  2         1   \n",
       "4                1        1        1         2                  1         2   \n",
       "\n",
       "   SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "0                    2                      2           2            1  \n",
       "1                    2                      2           2            1  \n",
       "2                    2                      1           2            2  \n",
       "3                    1                      2           2            2  \n",
       "4                    2                      1           1            2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2592d4e3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "     GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "304       2   56        1               1        1              2   \n",
       "305       1   70        2               1        1              1   \n",
       "306       1   58        2               1        1              1   \n",
       "307       1   67        2               1        2              1   \n",
       "308       1   62        1               1        1              2   \n",
       "\n",
       "     CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "304                2        2        1         1                  2         2   \n",
       "305                1        2        2         2                  2         2   \n",
       "306                1        1        2         2                  2         2   \n",
       "307                1        2        2         1                  2         2   \n",
       "308                1        2        2         2                  2         1   \n",
       "\n",
       "     SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "304                    2                      2           1            1  \n",
       "305                    2                      1           2            1  \n",
       "306                    1                      1           2            1  \n",
       "307                    2                      1           2            1  \n",
       "308                    1                      2           1            1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9e7cdf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
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       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
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       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.475728</td>\n",
       "      <td>62.673139</td>\n",
       "      <td>1.563107</td>\n",
       "      <td>1.569579</td>\n",
       "      <td>1.498382</td>\n",
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       "      <td>1.556634</td>\n",
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       "      <td>1.640777</td>\n",
       "      <td>1.469256</td>\n",
       "      <td>1.556634</td>\n",
       "      <td>1.126214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500221</td>\n",
       "      <td>8.210301</td>\n",
       "      <td>0.496806</td>\n",
       "      <td>0.495938</td>\n",
       "      <td>0.500808</td>\n",
       "      <td>0.500808</td>\n",
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       "      <td>0.497588</td>\n",
       "      <td>0.494474</td>\n",
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       "      <td>0.499863</td>\n",
       "      <td>0.497588</td>\n",
       "      <td>0.332629</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>2.000000</td>\n",
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       "      <td>2.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
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       "      <td>2.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
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      "text/plain": [
       "           GENDER         AGE     SMOKING  YELLOW_FINGERS     ANXIETY  \\\n",
       "count  309.000000  309.000000  309.000000      309.000000  309.000000   \n",
       "mean     1.475728   62.673139    1.563107        1.569579    1.498382   \n",
       "std      0.500221    8.210301    0.496806        0.495938    0.500808   \n",
       "min      1.000000   21.000000    1.000000        1.000000    1.000000   \n",
       "25%      1.000000   57.000000    1.000000        1.000000    1.000000   \n",
       "50%      1.000000   62.000000    2.000000        2.000000    1.000000   \n",
       "75%      2.000000   69.000000    2.000000        2.000000    2.000000   \n",
       "max      2.000000   87.000000    2.000000        2.000000    2.000000   \n",
       "\n",
       "       PEER_PRESSURE  CHRONIC DISEASE     FATIGUE     ALLERGY    WHEEZING  \\\n",
       "count     309.000000       309.000000  309.000000  309.000000  309.000000   \n",
       "mean        1.501618         1.504854    1.673139    1.556634    1.556634   \n",
       "std         0.500808         0.500787    0.469827    0.497588    0.497588   \n",
       "min         1.000000         1.000000    1.000000    1.000000    1.000000   \n",
       "25%         1.000000         1.000000    1.000000    1.000000    1.000000   \n",
       "50%         2.000000         2.000000    2.000000    2.000000    2.000000   \n",
       "75%         2.000000         2.000000    2.000000    2.000000    2.000000   \n",
       "max         2.000000         2.000000    2.000000    2.000000    2.000000   \n",
       "\n",
       "       ALCOHOL CONSUMING    COUGHING  SHORTNESS OF BREATH  \\\n",
       "count         309.000000  309.000000           309.000000   \n",
       "mean            1.556634    1.579288             1.640777   \n",
       "std             0.497588    0.494474             0.480551   \n",
       "min             1.000000    1.000000             1.000000   \n",
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       "50%             2.000000    2.000000             2.000000   \n",
       "75%             2.000000    2.000000             2.000000   \n",
       "max             2.000000    2.000000             2.000000   \n",
       "\n",
       "       SWALLOWING DIFFICULTY  CHEST PAIN  LUNG_CANCER  \n",
       "count             309.000000  309.000000   309.000000  \n",
       "mean                1.469256    1.556634     1.126214  \n",
       "std                 0.499863    0.497588     0.332629  \n",
       "min                 1.000000    1.000000     1.000000  \n",
       "25%                 1.000000    1.000000     1.000000  \n",
       "50%                 1.000000    2.000000     1.000000  \n",
       "75%                 2.000000    2.000000     1.000000  \n",
       "max                 2.000000    2.000000     2.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the describe() method returns description of data in DataFrame\n",
    "lung_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18426938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 16 columns):\n",
      " #   Column                 Non-Null Count  Dtype\n",
      "---  ------                 --------------  -----\n",
      " 0   GENDER                 309 non-null    int64\n",
      " 1   AGE                    309 non-null    int64\n",
      " 2   SMOKING                309 non-null    int64\n",
      " 3   YELLOW_FINGERS         309 non-null    int64\n",
      " 4   ANXIETY                309 non-null    int64\n",
      " 5   PEER_PRESSURE          309 non-null    int64\n",
      " 6   CHRONIC DISEASE        309 non-null    int64\n",
      " 7   FATIGUE                309 non-null    int64\n",
      " 8   ALLERGY                309 non-null    int64\n",
      " 9   WHEEZING               309 non-null    int64\n",
      " 10  ALCOHOL CONSUMING      309 non-null    int64\n",
      " 11  COUGHING               309 non-null    int64\n",
      " 12  SHORTNESS OF BREATH    309 non-null    int64\n",
      " 13  SWALLOWING DIFFICULTY  309 non-null    int64\n",
      " 14  CHEST PAIN             309 non-null    int64\n",
      " 15  LUNG_CANCER            309 non-null    int64\n",
      "dtypes: int64(16)\n",
      "memory usage: 38.8 KB\n"
     ]
    }
   ],
   "source": [
    "#the info() method prints information of the database\n",
    "lung_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a37db6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the Dataset: Training and Testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=1/3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfead0c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    270\n",
       "2     39\n",
       "Name: LUNG_CANCER, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lung_data['LUNG_CANCER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d9286c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "309"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lung_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa00fa40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37951afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4cfb72b5",
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GENDER  AGE  SMOKING  YELLOW_FINGERS  ANXIETY  PEER_PRESSURE  \\\n",
       "0         1   69        1               2        2              1   \n",
       "1         1   74        2               1        1              1   \n",
       "2         2   59        1               1        1              2   \n",
       "3         1   63        2               2        2              1   \n",
       "4         2   63        1               2        1              1   \n",
       "..      ...  ...      ...             ...      ...            ...   \n",
       "304       2   56        1               1        1              2   \n",
       "305       1   70        2               1        1              1   \n",
       "306       1   58        2               1        1              1   \n",
       "307       1   67        2               1        2              1   \n",
       "308       1   62        1               1        1              2   \n",
       "\n",
       "     CHRONIC DISEASE  FATIGUE  ALLERGY  WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "0                  1        2        1         2                  2         2   \n",
       "1                  2        2        2         1                  1         1   \n",
       "2                  1        2        1         2                  1         2   \n",
       "3                  1        1        1         1                  2         1   \n",
       "4                  1        1        1         2                  1         2   \n",
       "..               ...      ...      ...       ...                ...       ...   \n",
       "304                2        2        1         1                  2         2   \n",
       "305                1        2        2         2                  2         2   \n",
       "306                1        1        2         2                  2         2   \n",
       "307                1        2        2         1                  2         2   \n",
       "308                1        2        2         2                  2         1   \n",
       "\n",
       "     SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \n",
       "0                      2                      2           2  \n",
       "1                      2                      2           2  \n",
       "2                      2                      1           2  \n",
       "3                      1                      2           2  \n",
       "4                      2                      1           1  \n",
       "..                   ...                    ...         ...  \n",
       "304                    2                      2           1  \n",
       "305                    2                      1           2  \n",
       "306                    1                      1           2  \n",
       "307                    2                      1           2  \n",
       "308                    1                      2           1  \n",
       "\n",
       "[309 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dependent_variable\n",
    "x = lung_data.iloc[:,0:-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7f6d150",
   "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>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     LUNG_CANCER\n",
       "0              1\n",
       "1              1\n",
       "2              2\n",
       "3              2\n",
       "4              2\n",
       "..           ...\n",
       "304            1\n",
       "305            1\n",
       "306            1\n",
       "307            1\n",
       "308            1\n",
       "\n",
       "[309 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#independent_variable\n",
    "y = lung_data.iloc[:,-1:] \n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4d4e3ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c5fd1145",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=1/3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ad4cc5",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f97e35e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "#Fitting simple linear regression to the training test\n",
    "Model1 = LogisticRegression()\n",
    "Model1.fit(x_train, y_train)\n",
    "#Predicting the test set results\n",
    "prediction1 = Model1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d5172ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1,\n",
       "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6001a0ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[85,  2],\n",
       "       [10,  6]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "confusion_matrix(y_test,prediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a045421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.883495145631068"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " accuracy_score(y_test,prediction1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7690c64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89473684, 0.75      ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction1, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6310670c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.883495145631068\n",
      "Precision: 0.8947368421052632\n",
      "Recall: 0.9770114942528736\n",
      "F1 score: 0.9340659340659342\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction1)\n",
    "precision = precision_score(y_test, prediction1)\n",
    "recall = recall_score(y_test, prediction1)\n",
    "f1 = f1_score(y_test, prediction1)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9f209fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "061a23a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97701149, 0.375     ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction1, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7bff9f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93406593, 0.5       ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " f1_score(y_test, prediction1, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9db6e38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction1)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1a93084",
   "metadata": {},
   "source": [
    "KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a644d5cb",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aaad0759",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting K-NN to the Training set\n",
    "classifier = KNeighborsClassifier(n_neighbors = 3, metric = \"minkowski\", p = 2)\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b6747f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicting the Test set result\n",
    "prediction2 = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5b29cd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e0338e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[86,  1],\n",
       "       [12,  4]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "confusion_matrix(y_test,prediction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "240a8f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8737864077669902\n",
      "Precision: 0.8775510204081632\n",
      "Recall: 0.9885057471264368\n",
      "F1 score: 0.9297297297297297\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction2)\n",
    "precision = precision_score(y_test, prediction2)\n",
    "recall = recall_score(y_test, prediction2)\n",
    "f1 = f1_score(y_test, prediction2)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52397e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8737864077669902"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "250b49e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87755102, 0.8       ])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction2, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "51acb9ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98850575, 0.25      ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction2, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1e05028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92972973, 0.38095238])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, prediction2, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dcce3018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction2)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6627e1a",
   "metadata": {},
   "source": [
    "Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4ee2f7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(random_state = 0,criterion = \"entropy\")\n",
    "tree.fit(x_train, y_train)\n",
    "prediction3 = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4d144687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1eb70491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[86,  1],\n",
       "       [12,  4]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "113e06b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8737864077669902\n",
      "Precision: 0.8775510204081632\n",
      "Recall: 0.9885057471264368\n",
      "F1 score: 0.9297297297297297\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction3)\n",
    "precision = precision_score(y_test, prediction3)\n",
    "recall = recall_score(y_test, prediction3)\n",
    "f1 = f1_score(y_test, prediction3)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1ee956ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8737864077669902"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1c0481b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87755102, 0.8       ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction3, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b4ad4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98850575, 0.25      ])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction3, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0ef6eb27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92972973, 0.38095238])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, prediction3, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "55503779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction3)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e52430",
   "metadata": {},
   "source": [
    "SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5494adba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Support Vector Machine\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "svm = OneVsRestClassifier(BaggingClassifier(SVC(C=10,kernel='rbf',random_state=9,probability=True),n_jobs=-1))\n",
    "svm.fit(x_train, y_train)\n",
    "prediction4 = svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ca742075",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[87,  0],\n",
       "       [16,  0]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "788c9771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8446601941747572\n",
      "Precision: 0.8446601941747572\n",
      "Recall: 1.0\n",
      "F1 score: 0.9157894736842105\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction4)\n",
    "precision = precision_score(y_test, prediction4)\n",
    "recall = recall_score(y_test, prediction4)\n",
    "f1 = f1_score(y_test, prediction4)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "56fb954d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446601941747572"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "553761fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.84466019, 0.        ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction4, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6baec25b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction4, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aab6a107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91578947, 0.        ])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, prediction4, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a7698178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction4)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1656a9",
   "metadata": {},
   "source": [
    "Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3063bc8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nbcla = GaussianNB()\n",
    "nbcla.fit(x_train, y_train)\n",
    "prediction5 = nbcla.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "02e2e56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[81,  6],\n",
       "       [ 8,  8]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "confusion_matrix(y_test,prediction5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7379ab57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8640776699029126\n",
      "Precision: 0.9101123595505618\n",
      "Recall: 0.9310344827586207\n",
      "F1 score: 0.9204545454545454\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction5)\n",
    "precision = precision_score(y_test, prediction5)\n",
    "recall = recall_score(y_test, prediction5)\n",
    "f1 = f1_score(y_test, prediction5)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4601e7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8640776699029126"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "534adff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91011236, 0.57142857])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction5, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ef970ecc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93103448, 0.5       ])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction5, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ae742763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92045455, 0.53333333])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, prediction5, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6324e83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction5)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6e18fd",
   "metadata": {},
   "source": [
    "RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c33d6921",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_9784\\4118252254.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf_classifier.fit(x_train, y_train)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Initialize the classifier\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the model using training dataset\n",
    "rf_classifier.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on test dataset\n",
    "prediction6 = rf_classifier.predict(x_test)\n",
    "\n",
    "# Evaluate the accuracy of the model\n",
    "#accuracy = rf_classifier.score(x_test, y_test)\n",
    "#print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "20710a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[85,  2],\n",
       "       [ 9,  7]], dtype=int64)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,prediction6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b4a1091e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8932038834951457\n",
      "Precision: 0.9042553191489362\n",
      "Recall: 0.9770114942528736\n",
      "F1 score: 0.9392265193370166\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "# assuming your predicted and actual labels are stored in variables y_pred and y_true, respectively\n",
    "accuracy = accuracy_score(y_test, prediction6)\n",
    "precision = precision_score(y_test, prediction6)\n",
    "recall = recall_score(y_test, prediction6)\n",
    "f1 = f1_score(y_test, prediction6)\n",
    "\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"F1 score:\", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a6ee17a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8932038834951457"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,prediction6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a009bbc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.90425532, 0.77777778])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs = Model1.predict_proba(x_test)\n",
    "precision_score(y_test, prediction6, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8a221dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97701149, 0.4375    ])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, prediction6, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "91e39486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.93922652, 0.56      ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, prediction6, average = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ec409b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_true = y_test, y_pred = prediction6)\n",
    "#plot_confusion_matrix(cm,level,title = \"confusion_matrix\")\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", fmt=\"d\")\n",
    "plt.xlabel(\"Predicted Labels\")\n",
    "plt.ylabel(\"True Labels\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bfe22b",
   "metadata": {},
   "source": [
    "========================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "69577070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SMOKING</th>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <th>ANXIETY</th>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <th>FATIGUE</th>\n",
       "      <th>ALLERGY</th>\n",
       "      <th>WHEEZING</th>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <th>COUGHING</th>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <th>LUNG_CANCER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GENDER</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021306</td>\n",
       "      <td>-0.036277</td>\n",
       "      <td>0.212959</td>\n",
       "      <td>0.152127</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.204606</td>\n",
       "      <td>0.083560</td>\n",
       "      <td>-0.154251</td>\n",
       "      <td>-0.141207</td>\n",
       "      <td>-0.454268</td>\n",
       "      <td>-0.133303</td>\n",
       "      <td>0.064911</td>\n",
       "      <td>0.078161</td>\n",
       "      <td>-0.362958</td>\n",
       "      <td>0.067254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>-0.021306</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084475</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>0.053170</td>\n",
       "      <td>0.018685</td>\n",
       "      <td>-0.012642</td>\n",
       "      <td>0.012614</td>\n",
       "      <td>0.027990</td>\n",
       "      <td>0.055011</td>\n",
       "      <td>0.058985</td>\n",
       "      <td>0.169950</td>\n",
       "      <td>-0.017513</td>\n",
       "      <td>-0.001270</td>\n",
       "      <td>-0.018104</td>\n",
       "      <td>-0.089465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMOKING</th>\n",
       "      <td>-0.036277</td>\n",
       "      <td>-0.084475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014585</td>\n",
       "      <td>0.160267</td>\n",
       "      <td>-0.042822</td>\n",
       "      <td>-0.141522</td>\n",
       "      <td>-0.029575</td>\n",
       "      <td>0.001913</td>\n",
       "      <td>-0.129426</td>\n",
       "      <td>-0.050623</td>\n",
       "      <td>-0.129471</td>\n",
       "      <td>0.061264</td>\n",
       "      <td>0.030718</td>\n",
       "      <td>0.120117</td>\n",
       "      <td>-0.058179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YELLOW_FINGERS</th>\n",
       "      <td>0.212959</td>\n",
       "      <td>0.005205</td>\n",
       "      <td>-0.014585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.565829</td>\n",
       "      <td>0.323083</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>-0.118058</td>\n",
       "      <td>-0.144300</td>\n",
       "      <td>-0.078515</td>\n",
       "      <td>-0.289025</td>\n",
       "      <td>-0.012640</td>\n",
       "      <td>-0.105944</td>\n",
       "      <td>0.345904</td>\n",
       "      <td>-0.104829</td>\n",
       "      <td>-0.181339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANXIETY</th>\n",
       "      <td>0.152127</td>\n",
       "      <td>0.053170</td>\n",
       "      <td>0.160267</td>\n",
       "      <td>0.565829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216841</td>\n",
       "      <td>-0.009678</td>\n",
       "      <td>-0.188538</td>\n",
       "      <td>-0.165750</td>\n",
       "      <td>-0.191807</td>\n",
       "      <td>-0.165750</td>\n",
       "      <td>-0.225644</td>\n",
       "      <td>-0.144077</td>\n",
       "      <td>0.489403</td>\n",
       "      <td>-0.113634</td>\n",
       "      <td>-0.144947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEER_PRESSURE</th>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.018685</td>\n",
       "      <td>-0.042822</td>\n",
       "      <td>0.323083</td>\n",
       "      <td>0.216841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048515</td>\n",
       "      <td>0.078148</td>\n",
       "      <td>-0.081800</td>\n",
       "      <td>-0.068771</td>\n",
       "      <td>-0.159973</td>\n",
       "      <td>-0.089019</td>\n",
       "      <td>-0.220175</td>\n",
       "      <td>0.366590</td>\n",
       "      <td>-0.094828</td>\n",
       "      <td>-0.186388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRONIC DISEASE</th>\n",
       "      <td>0.204606</td>\n",
       "      <td>-0.012642</td>\n",
       "      <td>-0.141522</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>-0.009678</td>\n",
       "      <td>0.048515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110529</td>\n",
       "      <td>0.106386</td>\n",
       "      <td>-0.049967</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.175287</td>\n",
       "      <td>-0.026459</td>\n",
       "      <td>0.075176</td>\n",
       "      <td>-0.036938</td>\n",
       "      <td>-0.110891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FATIGUE</th>\n",
       "      <td>0.083560</td>\n",
       "      <td>0.012614</td>\n",
       "      <td>-0.029575</td>\n",
       "      <td>-0.118058</td>\n",
       "      <td>-0.188538</td>\n",
       "      <td>0.078148</td>\n",
       "      <td>-0.110529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.141937</td>\n",
       "      <td>-0.191377</td>\n",
       "      <td>0.146856</td>\n",
       "      <td>0.441745</td>\n",
       "      <td>-0.132790</td>\n",
       "      <td>-0.010832</td>\n",
       "      <td>-0.150673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLERGY</th>\n",
       "      <td>-0.154251</td>\n",
       "      <td>0.027990</td>\n",
       "      <td>0.001913</td>\n",
       "      <td>-0.144300</td>\n",
       "      <td>-0.165750</td>\n",
       "      <td>-0.081800</td>\n",
       "      <td>0.106386</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173867</td>\n",
       "      <td>0.344339</td>\n",
       "      <td>0.189524</td>\n",
       "      <td>-0.030056</td>\n",
       "      <td>-0.061508</td>\n",
       "      <td>0.239433</td>\n",
       "      <td>-0.327766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHEEZING</th>\n",
       "      <td>-0.141207</td>\n",
       "      <td>0.055011</td>\n",
       "      <td>-0.129426</td>\n",
       "      <td>-0.078515</td>\n",
       "      <td>-0.191807</td>\n",
       "      <td>-0.068771</td>\n",
       "      <td>-0.049967</td>\n",
       "      <td>0.141937</td>\n",
       "      <td>0.173867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265659</td>\n",
       "      <td>0.374265</td>\n",
       "      <td>0.037834</td>\n",
       "      <td>0.069027</td>\n",
       "      <td>0.147640</td>\n",
       "      <td>-0.249300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALCOHOL CONSUMING</th>\n",
       "      <td>-0.454268</td>\n",
       "      <td>0.058985</td>\n",
       "      <td>-0.050623</td>\n",
       "      <td>-0.289025</td>\n",
       "      <td>-0.165750</td>\n",
       "      <td>-0.159973</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.191377</td>\n",
       "      <td>0.344339</td>\n",
       "      <td>0.265659</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.202720</td>\n",
       "      <td>-0.179416</td>\n",
       "      <td>-0.009294</td>\n",
       "      <td>0.331226</td>\n",
       "      <td>-0.288533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COUGHING</th>\n",
       "      <td>-0.133303</td>\n",
       "      <td>0.169950</td>\n",
       "      <td>-0.129471</td>\n",
       "      <td>-0.012640</td>\n",
       "      <td>-0.225644</td>\n",
       "      <td>-0.089019</td>\n",
       "      <td>-0.175287</td>\n",
       "      <td>0.146856</td>\n",
       "      <td>0.189524</td>\n",
       "      <td>0.374265</td>\n",
       "      <td>0.202720</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277385</td>\n",
       "      <td>-0.157586</td>\n",
       "      <td>0.083958</td>\n",
       "      <td>-0.248570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHORTNESS OF BREATH</th>\n",
       "      <td>0.064911</td>\n",
       "      <td>-0.017513</td>\n",
       "      <td>0.061264</td>\n",
       "      <td>-0.105944</td>\n",
       "      <td>-0.144077</td>\n",
       "      <td>-0.220175</td>\n",
       "      <td>-0.026459</td>\n",
       "      <td>0.441745</td>\n",
       "      <td>-0.030056</td>\n",
       "      <td>0.037834</td>\n",
       "      <td>-0.179416</td>\n",
       "      <td>0.277385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.161015</td>\n",
       "      <td>0.024256</td>\n",
       "      <td>-0.060738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWALLOWING DIFFICULTY</th>\n",
       "      <td>0.078161</td>\n",
       "      <td>-0.001270</td>\n",
       "      <td>0.030718</td>\n",
       "      <td>0.345904</td>\n",
       "      <td>0.489403</td>\n",
       "      <td>0.366590</td>\n",
       "      <td>0.075176</td>\n",
       "      <td>-0.132790</td>\n",
       "      <td>-0.061508</td>\n",
       "      <td>0.069027</td>\n",
       "      <td>-0.009294</td>\n",
       "      <td>-0.157586</td>\n",
       "      <td>-0.161015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069027</td>\n",
       "      <td>-0.259730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEST PAIN</th>\n",
       "      <td>-0.362958</td>\n",
       "      <td>-0.018104</td>\n",
       "      <td>0.120117</td>\n",
       "      <td>-0.104829</td>\n",
       "      <td>-0.113634</td>\n",
       "      <td>-0.094828</td>\n",
       "      <td>-0.036938</td>\n",
       "      <td>-0.010832</td>\n",
       "      <td>0.239433</td>\n",
       "      <td>0.147640</td>\n",
       "      <td>0.331226</td>\n",
       "      <td>0.083958</td>\n",
       "      <td>0.024256</td>\n",
       "      <td>0.069027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LUNG_CANCER</th>\n",
       "      <td>0.067254</td>\n",
       "      <td>-0.089465</td>\n",
       "      <td>-0.058179</td>\n",
       "      <td>-0.181339</td>\n",
       "      <td>-0.144947</td>\n",
       "      <td>-0.186388</td>\n",
       "      <td>-0.110891</td>\n",
       "      <td>-0.150673</td>\n",
       "      <td>-0.327766</td>\n",
       "      <td>-0.249300</td>\n",
       "      <td>-0.288533</td>\n",
       "      <td>-0.248570</td>\n",
       "      <td>-0.060738</td>\n",
       "      <td>-0.259730</td>\n",
       "      <td>-0.190451</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         GENDER       AGE   SMOKING  YELLOW_FINGERS   ANXIETY  \\\n",
       "GENDER                 1.000000 -0.021306 -0.036277        0.212959  0.152127   \n",
       "AGE                   -0.021306  1.000000 -0.084475        0.005205  0.053170   \n",
       "SMOKING               -0.036277 -0.084475  1.000000       -0.014585  0.160267   \n",
       "YELLOW_FINGERS         0.212959  0.005205 -0.014585        1.000000  0.565829   \n",
       "ANXIETY                0.152127  0.053170  0.160267        0.565829  1.000000   \n",
       "PEER_PRESSURE          0.275564  0.018685 -0.042822        0.323083  0.216841   \n",
       "CHRONIC DISEASE        0.204606 -0.012642 -0.141522        0.041122 -0.009678   \n",
       "FATIGUE                0.083560  0.012614 -0.029575       -0.118058 -0.188538   \n",
       "ALLERGY               -0.154251  0.027990  0.001913       -0.144300 -0.165750   \n",
       "WHEEZING              -0.141207  0.055011 -0.129426       -0.078515 -0.191807   \n",
       "ALCOHOL CONSUMING     -0.454268  0.058985 -0.050623       -0.289025 -0.165750   \n",
       "COUGHING              -0.133303  0.169950 -0.129471       -0.012640 -0.225644   \n",
       "SHORTNESS OF BREATH    0.064911 -0.017513  0.061264       -0.105944 -0.144077   \n",
       "SWALLOWING DIFFICULTY  0.078161 -0.001270  0.030718        0.345904  0.489403   \n",
       "CHEST PAIN            -0.362958 -0.018104  0.120117       -0.104829 -0.113634   \n",
       "LUNG_CANCER            0.067254 -0.089465 -0.058179       -0.181339 -0.144947   \n",
       "\n",
       "                       PEER_PRESSURE  CHRONIC DISEASE   FATIGUE   ALLERGY  \\\n",
       "GENDER                      0.275564         0.204606  0.083560 -0.154251   \n",
       "AGE                         0.018685        -0.012642  0.012614  0.027990   \n",
       "SMOKING                    -0.042822        -0.141522 -0.029575  0.001913   \n",
       "YELLOW_FINGERS              0.323083         0.041122 -0.118058 -0.144300   \n",
       "ANXIETY                     0.216841        -0.009678 -0.188538 -0.165750   \n",
       "PEER_PRESSURE               1.000000         0.048515  0.078148 -0.081800   \n",
       "CHRONIC DISEASE             0.048515         1.000000 -0.110529  0.106386   \n",
       "FATIGUE                     0.078148        -0.110529  1.000000  0.003056   \n",
       "ALLERGY                    -0.081800         0.106386  0.003056  1.000000   \n",
       "WHEEZING                   -0.068771        -0.049967  0.141937  0.173867   \n",
       "ALCOHOL CONSUMING          -0.159973         0.002150 -0.191377  0.344339   \n",
       "COUGHING                   -0.089019        -0.175287  0.146856  0.189524   \n",
       "SHORTNESS OF BREATH        -0.220175        -0.026459  0.441745 -0.030056   \n",
       "SWALLOWING DIFFICULTY       0.366590         0.075176 -0.132790 -0.061508   \n",
       "CHEST PAIN                 -0.094828        -0.036938 -0.010832  0.239433   \n",
       "LUNG_CANCER                -0.186388        -0.110891 -0.150673 -0.327766   \n",
       "\n",
       "                       WHEEZING  ALCOHOL CONSUMING  COUGHING  \\\n",
       "GENDER                -0.141207          -0.454268 -0.133303   \n",
       "AGE                    0.055011           0.058985  0.169950   \n",
       "SMOKING               -0.129426          -0.050623 -0.129471   \n",
       "YELLOW_FINGERS        -0.078515          -0.289025 -0.012640   \n",
       "ANXIETY               -0.191807          -0.165750 -0.225644   \n",
       "PEER_PRESSURE         -0.068771          -0.159973 -0.089019   \n",
       "CHRONIC DISEASE       -0.049967           0.002150 -0.175287   \n",
       "FATIGUE                0.141937          -0.191377  0.146856   \n",
       "ALLERGY                0.173867           0.344339  0.189524   \n",
       "WHEEZING               1.000000           0.265659  0.374265   \n",
       "ALCOHOL CONSUMING      0.265659           1.000000  0.202720   \n",
       "COUGHING               0.374265           0.202720  1.000000   \n",
       "SHORTNESS OF BREATH    0.037834          -0.179416  0.277385   \n",
       "SWALLOWING DIFFICULTY  0.069027          -0.009294 -0.157586   \n",
       "CHEST PAIN             0.147640           0.331226  0.083958   \n",
       "LUNG_CANCER           -0.249300          -0.288533 -0.248570   \n",
       "\n",
       "                       SHORTNESS OF BREATH  SWALLOWING DIFFICULTY  CHEST PAIN  \\\n",
       "GENDER                            0.064911               0.078161   -0.362958   \n",
       "AGE                              -0.017513              -0.001270   -0.018104   \n",
       "SMOKING                           0.061264               0.030718    0.120117   \n",
       "YELLOW_FINGERS                   -0.105944               0.345904   -0.104829   \n",
       "ANXIETY                          -0.144077               0.489403   -0.113634   \n",
       "PEER_PRESSURE                    -0.220175               0.366590   -0.094828   \n",
       "CHRONIC DISEASE                  -0.026459               0.075176   -0.036938   \n",
       "FATIGUE                           0.441745              -0.132790   -0.010832   \n",
       "ALLERGY                          -0.030056              -0.061508    0.239433   \n",
       "WHEEZING                          0.037834               0.069027    0.147640   \n",
       "ALCOHOL CONSUMING                -0.179416              -0.009294    0.331226   \n",
       "COUGHING                          0.277385              -0.157586    0.083958   \n",
       "SHORTNESS OF BREATH               1.000000              -0.161015    0.024256   \n",
       "SWALLOWING DIFFICULTY            -0.161015               1.000000    0.069027   \n",
       "CHEST PAIN                        0.024256               0.069027    1.000000   \n",
       "LUNG_CANCER                      -0.060738              -0.259730   -0.190451   \n",
       "\n",
       "                       LUNG_CANCER  \n",
       "GENDER                    0.067254  \n",
       "AGE                      -0.089465  \n",
       "SMOKING                  -0.058179  \n",
       "YELLOW_FINGERS           -0.181339  \n",
       "ANXIETY                  -0.144947  \n",
       "PEER_PRESSURE            -0.186388  \n",
       "CHRONIC DISEASE          -0.110891  \n",
       "FATIGUE                  -0.150673  \n",
       "ALLERGY                  -0.327766  \n",
       "WHEEZING                 -0.249300  \n",
       "ALCOHOL CONSUMING        -0.288533  \n",
       "COUGHING                 -0.248570  \n",
       "SHORTNESS OF BREATH      -0.060738  \n",
       "SWALLOWING DIFFICULTY    -0.259730  \n",
       "CHEST PAIN               -0.190451  \n",
       "LUNG_CANCER               1.000000  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Correlation\n",
    "cn=lung_data.corr()\n",
    "cn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8a5293e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation \n",
    "cmap=sns.diverging_palette(260,-10,s=50, l=75, n=6,\n",
    "as_cmap=True)\n",
    "plt.subplots(figsize=(18,18))\n",
    "sns.heatmap(cn,cmap=\"Blues\",annot=True, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "766a0bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x3000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_list = list(lung_data.columns)\n",
    "\n",
    "fig = plt.figure(figsize=(10,30))\n",
    "\n",
    "for i in range(len(num_list)):\n",
    "    plt.subplot(8,2,i+1)\n",
    "    plt.title(num_list[i])\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.hist(lung_data[num_list[i]],color='blue',alpha=0.5)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66d802aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}