dphi-liver-disease.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
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       "      <th>N_Days</th>\n",
       "      <th>Status</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ascites</th>\n",
       "      <th>Hepatomegaly</th>\n",
       "      <th>Spiders</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Bilirubin</th>\n",
       "      <th>Cholesterol</th>\n",
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       "      <th>Copper</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>116.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3135</td>\n",
       "      <td>1872</td>\n",
       "      <td>C</td>\n",
       "      <td>D-penicillamine</td>\n",
       "      <td>21111</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
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       "      <td>0.3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>306.0</td>\n",
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       "     ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly  \\\n",
       "0  7135    1654     CL  D-penicillamine  19581   F       N            N   \n",
       "1  7326      41      C  D-penicillamine  22880   F     NaN            N   \n",
       "2  7254     297      D              NaN  27957   F       N            N   \n",
       "3  3135    1872      C  D-penicillamine  21111   F     NaN            Y   \n",
       "4  2483     939     CL  D-penicillamine  18061   F     NaN          NaN   \n",
       "\n",
       "  Spiders Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
       "0       Y     N        0.3        279.0     2.96    84.0    1500.8   99.43   \n",
       "1     NaN     N        0.3          NaN     2.96     NaN    1835.4   26.35   \n",
       "2     NaN     N        0.3        328.0     2.64     4.0       NaN     NaN   \n",
       "3       Y     N        0.3        302.0     2.02    49.0       NaN   26.35   \n",
       "4     NaN     N        0.5        344.0     3.11    91.0       NaN  104.56   \n",
       "\n",
       "   Tryglicerides  Platelets  Prothrombin  Stage  \n",
       "0          109.0      293.0         10.2    4.0  \n",
       "1          131.0      308.0         10.8    1.0  \n",
       "2          116.0      194.0         10.3    3.0  \n",
       "3            NaN        NaN         10.5    4.0  \n",
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   ],
   "source": [
    "train = pd.read_csv('../input/no-title/train_dataset.csv')\n",
    "train.head()"
   ]
  },
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       "      <td>NaN</td>\n",
       "      <td>43.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296.0</td>\n",
       "      <td>10.3</td>\n",
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       "      <th>3</th>\n",
       "      <td>722</td>\n",
       "      <td>1969</td>\n",
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       "      <td>17039</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>617.1</td>\n",
       "      <td>113.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125.0</td>\n",
       "      <td>10.9</td>\n",
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       "      <th>4</th>\n",
       "      <td>1000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>N</td>\n",
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       "      <td>2.36</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1782.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>10.6</td>\n",
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      "text/plain": [
       "     ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly  \\\n",
       "0  3870      41      C          Placebo  22553   F       N          NaN   \n",
       "1  3462    1811      C  D-penicillamine  16223   F       N            Y   \n",
       "2  1632     954      C  D-penicillamine  27100   F       N            N   \n",
       "3   722    1969      D          Placebo  17039   F       N            Y   \n",
       "4  1000    2721      D  D-penicillamine  17738   F     NaN          NaN   \n",
       "\n",
       "  Spiders Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
       "0       N     N        1.4        247.0     3.62     NaN       NaN  108.65   \n",
       "1       N     N        0.3        311.0     2.80    92.0    1748.1     NaN   \n",
       "2       N     N        0.4          NaN     3.56     NaN       NaN   43.52   \n",
       "3       N     N        1.2          NaN     3.16     NaN     617.1  113.76   \n",
       "4     NaN     N        3.2          NaN     2.36    89.0    1782.4     NaN   \n",
       "\n",
       "   Tryglicerides  Platelets  Prothrombin  \n",
       "0            NaN      169.0         11.6  \n",
       "1          129.0      321.0         11.5  \n",
       "2            NaN      296.0         10.3  \n",
       "3            NaN      125.0         10.9  \n",
       "4          129.0      138.0         10.6  "
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   ],
   "source": [
    "test = pd.read_csv('../input/no-title/test_dataset.csv')\n",
    "test.head()"
   ]
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     "data": {
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       "ID                  0\n",
       "N_Days              0\n",
       "Status              0\n",
       "Drug             2025\n",
       "Age                 0\n",
       "Sex                 0\n",
       "Ascites          2246\n",
       "Hepatomegaly     2427\n",
       "Spiders          2590\n",
       "Edema               0\n",
       "Bilirubin           0\n",
       "Cholesterol      3101\n",
       "Albumin             0\n",
       "Copper           2156\n",
       "Alk_Phos         2498\n",
       "SGOT             2102\n",
       "Tryglicerides    2812\n",
       "Platelets         338\n",
       "Prothrombin       155\n",
       "Stage               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "train.isnull().sum()"
   ]
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    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "N_Days              0\n",
       "Status              0\n",
       "Drug              963\n",
       "Age                 0\n",
       "Sex                 0\n",
       "Ascites          1087\n",
       "Hepatomegaly     1102\n",
       "Spiders          1200\n",
       "Edema               0\n",
       "Bilirubin           0\n",
       "Cholesterol      1435\n",
       "Albumin             0\n",
       "Copper           1006\n",
       "Alk_Phos         1168\n",
       "SGOT              987\n",
       "Tryglicerides    1286\n",
       "Platelets         164\n",
       "Prothrombin        64\n",
       "dtype: int64"
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     "execution_count": 5,
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    "test.isnull().sum()"
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       "      <td>84.0</td>\n",
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       "      <td>99.43</td>\n",
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       "      <th>1</th>\n",
       "      <td>7326</td>\n",
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       "      <td>289.0</td>\n",
       "      <td>26.35</td>\n",
       "      <td>116.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>3135</td>\n",
       "      <td>1872</td>\n",
       "      <td>C</td>\n",
       "      <td>D-penicillamine</td>\n",
       "      <td>21111</td>\n",
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       "      <td>302.0</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>130.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2483</td>\n",
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       "      <td>N</td>\n",
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       "      <td>289.0</td>\n",
       "      <td>104.56</td>\n",
       "      <td>130.0</td>\n",
       "      <td>306.0</td>\n",
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      "text/plain": [
       "     ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly  \\\n",
       "0  7135    1654     CL  D-penicillamine  19581   F       N            N   \n",
       "1  7326      41      C  D-penicillamine  22880   F       N            N   \n",
       "2  7254     297      D  D-penicillamine  27957   F       N            N   \n",
       "3  3135    1872      C  D-penicillamine  21111   F       N            Y   \n",
       "4  2483     939     CL  D-penicillamine  18061   F       N            N   \n",
       "\n",
       "  Spiders Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
       "0       Y     N        0.3        279.0     2.96    84.0    1500.8   99.43   \n",
       "1       N     N        0.3        120.0     2.96     4.0    1835.4   26.35   \n",
       "2       N     N        0.3        328.0     2.64     4.0     289.0   26.35   \n",
       "3       Y     N        0.3        302.0     2.02    49.0     289.0   26.35   \n",
       "4       N     N        0.5        344.0     3.11    91.0     289.0  104.56   \n",
       "\n",
       "   Tryglicerides  Platelets  Prothrombin  Stage  \n",
       "0          109.0      293.0         10.2    4.0  \n",
       "1          131.0      308.0         10.8    1.0  \n",
       "2          116.0      194.0         10.3    3.0  \n",
       "3          130.0      563.0         10.5    4.0  \n",
       "4          130.0      306.0         11.4    2.0  "
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    {
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       "ID               0\n",
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       "Cholesterol      0\n",
       "Albumin          0\n",
       "Copper           0\n",
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       "dtype: int64"
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       "      <td>2.80</td>\n",
       "      <td>92.0</td>\n",
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       "      <td>125.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>10.3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>722</td>\n",
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       "      <td>D</td>\n",
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       "      <td>3.16</td>\n",
       "      <td>4.0</td>\n",
       "      <td>617.1</td>\n",
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       "      <td>125.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
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       "      <td>D-penicillamine</td>\n",
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       "      <td>89.0</td>\n",
       "      <td>1782.4</td>\n",
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       "      <td>129.0</td>\n",
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       "      <td>10.6</td>\n",
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      "text/plain": [
       "     ID  N_Days Status             Drug    Age Sex Ascites Hepatomegaly  \\\n",
       "0  3870      41      C          Placebo  22553   F       N            N   \n",
       "1  3462    1811      C  D-penicillamine  16223   F       N            Y   \n",
       "2  1632     954      C  D-penicillamine  27100   F       N            N   \n",
       "3   722    1969      D          Placebo  17039   F       N            Y   \n",
       "4  1000    2721      D  D-penicillamine  17738   F       N            N   \n",
       "\n",
       "  Spiders Edema  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
       "0       N     N        1.4        247.0     3.62     4.0     289.0  108.65   \n",
       "1       N     N        0.3        311.0     2.80    92.0    1748.1   26.35   \n",
       "2       N     N        0.4        120.0     3.56     4.0     289.0   43.52   \n",
       "3       N     N        1.2        120.0     3.16     4.0     617.1  113.76   \n",
       "4       N     N        3.2        120.0     2.36    89.0    1782.4   26.35   \n",
       "\n",
       "   Tryglicerides  Platelets  Prothrombin  \n",
       "0          125.0      169.0         11.6  \n",
       "1          129.0      321.0         11.5  \n",
       "2          125.0      296.0         10.3  \n",
       "3          125.0      125.0         10.9  \n",
       "4          129.0      138.0         10.6  "
      ]
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     "execution_count": 8,
     "metadata": {},
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    }
   ],
   "source": [
    "test = test.fillna(test.mode().iloc[0])\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e069524",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3200, 19)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
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   "id": "b5d14e07",
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "ID               0\n",
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       "Status           0\n",
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       "Sex              0\n",
       "Ascites          0\n",
       "Hepatomegaly     0\n",
       "Spiders          0\n",
       "Edema            0\n",
       "Bilirubin        0\n",
       "Cholesterol      0\n",
       "Albumin          0\n",
       "Copper           0\n",
       "Alk_Phos         0\n",
       "SGOT             0\n",
       "Tryglicerides    0\n",
       "Platelets        0\n",
       "Prothrombin      0\n",
       "dtype: int64"
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     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "test.isnull().sum()"
   ]
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   "cell_type": "code",
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       "      <td>26.35</td>\n",
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       "      <td>194.0</td>\n",
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      ],
      "text/plain": [
       "     ID  N_Days  Status_1  Status_2  Status_3  Drug_1  Drug_2    Age  Sex_1  \\\n",
       "0  7135    1654         1         0         0       1       0  19581      1   \n",
       "1  7326      41         0         1         0       1       0  22880      1   \n",
       "2  7254     297         0         0         1       1       0  27957      1   \n",
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       "4  2483     939         1         0         0       1       0  18061      1   \n",
       "\n",
       "   Sex_2  ...  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos    SGOT  \\\n",
       "0      0  ...        0.3        279.0     2.96    84.0    1500.8   99.43   \n",
       "1      0  ...        0.3        120.0     2.96     4.0    1835.4   26.35   \n",
       "2      0  ...        0.3        328.0     2.64     4.0     289.0   26.35   \n",
       "3      0  ...        0.3        302.0     2.02    49.0     289.0   26.35   \n",
       "4      0  ...        0.5        344.0     3.11    91.0     289.0  104.56   \n",
       "\n",
       "   Tryglicerides  Platelets  Prothrombin  Stage  \n",
       "0          109.0      293.0         10.2    4.0  \n",
       "1          131.0      308.0         10.8    1.0  \n",
       "2          116.0      194.0         10.3    3.0  \n",
       "3          130.0      563.0         10.5    4.0  \n",
       "4          130.0      306.0         11.4    2.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "ce_OHE = ce.OneHotEncoder(cols=['Status','Drug','Sex','Ascites','Hepatomegaly','Spiders','Edema'])\n",
    "\n",
    "train = ce_OHE.fit_transform(train)\n",
    "train.head()"
   ]
  },
  {
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   "outputs": [
    {
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       "      <td>43.52</td>\n",
       "      <td>125.0</td>\n",
       "      <td>296.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     ID  N_Days  Status_1  Status_2  Status_3  Drug_1  Drug_2    Age  Sex_1  \\\n",
       "0  3870      41         1         0         0       1       0  22553      1   \n",
       "1  3462    1811         1         0         0       0       1  16223      1   \n",
       "2  1632     954         1         0         0       0       1  27100      1   \n",
       "3   722    1969         0         1         0       1       0  17039      1   \n",
       "4  1000    2721         0         1         0       0       1  17738      1   \n",
       "\n",
       "   Sex_2  ...  Edema_3  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos  \\\n",
       "0      0  ...        0        1.4        247.0     3.62     4.0     289.0   \n",
       "1      0  ...        0        0.3        311.0     2.80    92.0    1748.1   \n",
       "2      0  ...        0        0.4        120.0     3.56     4.0     289.0   \n",
       "3      0  ...        0        1.2        120.0     3.16     4.0     617.1   \n",
       "4      0  ...        0        3.2        120.0     2.36    89.0    1782.4   \n",
       "\n",
       "     SGOT  Tryglicerides  Platelets  Prothrombin  \n",
       "0  108.65          125.0      169.0         11.6  \n",
       "1   26.35          129.0      321.0         11.5  \n",
       "2   43.52          125.0      296.0         10.3  \n",
       "3  113.76          125.0      125.0         10.9  \n",
       "4   26.35          129.0      138.0         10.6  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = ce_OHE.fit_transform(test)\n",
    "test.head()"
   ]
  },
  {
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   "source": [
    "X = train\n",
    "y = test"
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   "source": [
    "X_train = X.iloc[:, :-1]\n",
    "y_train = X.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
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    "X_test = y"
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   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8db94344",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  int64\n",
       "N_Days              int64\n",
       "Status_1            int64\n",
       "Status_2            int64\n",
       "Status_3            int64\n",
       "Drug_1              int64\n",
       "Drug_2              int64\n",
       "Age                 int64\n",
       "Sex_1               int64\n",
       "Sex_2               int64\n",
       "Ascites_1           int64\n",
       "Ascites_2           int64\n",
       "Hepatomegaly_1      int64\n",
       "Hepatomegaly_2      int64\n",
       "Spiders_1           int64\n",
       "Spiders_2           int64\n",
       "Edema_1             int64\n",
       "Edema_2             int64\n",
       "Edema_3             int64\n",
       "Bilirubin         float64\n",
       "Cholesterol       float64\n",
       "Albumin           float64\n",
       "Copper            float64\n",
       "Alk_Phos          float64\n",
       "SGOT              float64\n",
       "Tryglicerides     float64\n",
       "Platelets         float64\n",
       "Prothrombin       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ff8831b",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>N_Days</th>\n",
       "      <th>Status_1</th>\n",
       "      <th>Status_2</th>\n",
       "      <th>Status_3</th>\n",
       "      <th>Drug_1</th>\n",
       "      <th>Drug_2</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Sex_2</th>\n",
       "      <th>...</th>\n",
       "      <th>Edema_3</th>\n",
       "      <th>Bilirubin</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Copper</th>\n",
       "      <th>Alk_Phos</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Tryglicerides</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>Prothrombin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7135</td>\n",
       "      <td>1654</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19581</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>279.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1500.8</td>\n",
       "      <td>99.43</td>\n",
       "      <td>109.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7326</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.96</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1835.4</td>\n",
       "      <td>26.35</td>\n",
       "      <td>131.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7254</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>328.0</td>\n",
       "      <td>2.64</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>26.35</td>\n",
       "      <td>116.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3135</td>\n",
       "      <td>1872</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21111</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>302.0</td>\n",
       "      <td>2.02</td>\n",
       "      <td>49.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>26.35</td>\n",
       "      <td>130.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2483</td>\n",
       "      <td>939</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18061</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.11</td>\n",
       "      <td>91.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>104.56</td>\n",
       "      <td>130.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  N_Days  Status_1  Status_2  Status_3  Drug_1  Drug_2    Age  Sex_1  \\\n",
       "0  7135    1654         1         0         0       1       0  19581      1   \n",
       "1  7326      41         0         1         0       1       0  22880      1   \n",
       "2  7254     297         0         0         1       1       0  27957      1   \n",
       "3  3135    1872         0         1         0       1       0  21111      1   \n",
       "4  2483     939         1         0         0       1       0  18061      1   \n",
       "\n",
       "   Sex_2  ...  Edema_3  Bilirubin  Cholesterol  Albumin  Copper  Alk_Phos  \\\n",
       "0      0  ...        0        0.3        279.0     2.96    84.0    1500.8   \n",
       "1      0  ...        0        0.3        120.0     2.96     4.0    1835.4   \n",
       "2      0  ...        0        0.3        328.0     2.64     4.0     289.0   \n",
       "3      0  ...        0        0.3        302.0     2.02    49.0     289.0   \n",
       "4      0  ...        0        0.5        344.0     3.11    91.0     289.0   \n",
       "\n",
       "     SGOT  Tryglicerides  Platelets  Prothrombin  \n",
       "0   99.43          109.0      293.0         10.2  \n",
       "1   26.35          131.0      308.0         10.8  \n",
       "2   26.35          116.0      194.0         10.3  \n",
       "3   26.35          130.0      563.0         10.5  \n",
       "4  104.56          130.0      306.0         11.4  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ca13fb0e",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [-7.73119829e-05  3.71091957e-06  3.43441580e-02  1.02319940e-02\n",
      " -4.45761520e-02 -3.24855965e-03  3.24855965e-03  7.25954569e-07\n",
      " -1.26267630e-02  1.26267630e-02  6.77717078e-03 -6.77717078e-03\n",
      "  1.87428845e-02 -1.87428845e-02 -7.70261205e-03  7.70261205e-03\n",
      " -3.61730639e-02  2.08980001e-02  1.52750637e-02 -2.22411611e-03\n",
      "  2.64528207e-04 -3.39835604e-02  2.06546305e-04  8.36979439e-07\n",
      " -5.60379202e-04 -8.99728480e-04 -1.84900104e-04  6.21679924e-03]\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xg\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "from sklearn import datasets, linear_model, metrics\n",
    "\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    " \n",
    "# train the model using the training sets\n",
    "reg.fit(X_train, y_train)\n",
    "\n",
    " \n",
    "# regression coefficients\n",
    "print('Coefficients: ', reg.coef_)\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f994d171",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ba19f942",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:34:34] WARNING: ../src/objective/regression_obj.cu:188: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Instantiation\n",
    "xgb_r = xg.XGBRegressor(objective ='reg:linear',\n",
    "                  n_estimators = 10, seed = 123)\n",
    "  \n",
    "# Fitting the model\n",
    "xgb_r.fit(X_train, y_train)\n",
    "  \n",
    "# Predict the model\n",
    "pred = xgb_r.predict(X_test)\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c89cb5d",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "961caf23",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pred, columns = ['Stage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8deb7a61",
   "metadata": {
    "execution": {
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    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.494691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.460125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.900434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.876073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.888951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Stage\n",
       "0  3.494691\n",
       "1  3.460125\n",
       "2  3.900434\n",
       "3  3.876073\n",
       "4  3.888951"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d07738b6",
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   "outputs": [],
   "source": [
    "df = df.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "69e4463b",
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    {
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       "   Stage\n",
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   "source": [
    "df.to_csv('./Output1.csv')"
   ]
  },
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   "execution_count": 28,
   "id": "ae4ce458",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "  \n",
    "# create a regressor object\n",
    "regressor = DecisionTreeRegressor(random_state = 0) \n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b2de521",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9fa28da8",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(y_pred, columns = ['Stage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3629fdca",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of       Stage\n",
       "0       4.0\n",
       "1       4.0\n",
       "2       4.0\n",
       "3       4.0\n",
       "4       4.0\n",
       "...     ...\n",
       "3195    4.0\n",
       "3196    4.0\n",
       "3197    4.0\n",
       "3198    4.0\n",
       "3199    4.0\n",
       "\n",
       "[3200 rows x 1 columns]>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "374e8649",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T11:34:36.377886Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1.to_csv('./Output_dt.csv')"
   ]
  }
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