dphi-genetic-bacteria-identification-decision-tree.ipynb

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       "      <td>0.000005</td>\n",
       "      <td>9.460000e-06</td>\n",
       "      <td>4.630000e-08</td>\n",
       "      <td>Salmonella_enterica</td>\n",
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       "      <th>1</th>\n",
       "      <td>28056</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-5.540000e-06</td>\n",
       "      <td>3.080000e-06</td>\n",
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       "      <td>0.000012</td>\n",
       "      <td>3.250000e-05</td>\n",
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       "      <td>1.710000e-05</td>\n",
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       "      <td>-0.000020</td>\n",
       "      <td>7.970000e-05</td>\n",
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       "      <td>0.000036</td>\n",
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       "      <td>0.000021</td>\n",
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       "      <td>Streptococcus_pneumoniae</td>\n",
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       "      <th>3</th>\n",
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       "      <td>4.630000e-08</td>\n",
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       "      <td>0.000022</td>\n",
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       "      <td>0.000025</td>\n",
       "      <td>0.000039</td>\n",
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       "      <td>0.000002</td>\n",
       "      <td>4.460000e-06</td>\n",
       "      <td>4.630000e-08</td>\n",
       "      <td>Escherichia_coli</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166342</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>4.630000e-07</td>\n",
       "      <td>-1.290000e-05</td>\n",
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       "      <td>0.000066</td>\n",
       "      <td>-0.000033</td>\n",
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       "      <td>0.000010</td>\n",
       "      <td>4.050000e-05</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>Escherichia_fergusonii</td>\n",
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       "<p>5 rows × 288 columns</p>\n",
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       "2  7.970000e-05 -0.000030  0.000036 -0.000043  ...  0.000054  0.000097   \n",
       "3  2.970000e-05  0.000036  0.000022  0.000003  ...  0.000048  0.000025   \n",
       "4 -3.260000e-07 -0.000040  0.000066 -0.000033  ...  0.000134  0.000087   \n",
       "\n",
       "   A8T1G0C1  A8T1G1C0  A8T2G0C0  A9T0G0C1  A9T0G1C0      A9T1G0C0  \\\n",
       "0  0.000043  0.000055  0.000028  0.000001  0.000005  9.460000e-06   \n",
       "1  0.000142  0.000125  0.000093  0.000009  0.000012  3.250000e-05   \n",
       "2  0.000064  0.000114 -0.000003 -0.000010  0.000021  4.630000e-07   \n",
       "3  0.000039  0.000042  0.000019  0.000004  0.000002  4.460000e-06   \n",
       "4  0.000134  0.000134  0.000037  0.000010  0.000010  4.050000e-05   \n",
       "\n",
       "      A10T0G0C0                    target  \n",
       "0  4.630000e-08       Salmonella_enterica  \n",
       "1  4.630000e-08          Escherichia_coli  \n",
       "2 -9.540000e-07  Streptococcus_pneumoniae  \n",
       "3  4.630000e-08          Escherichia_coli  \n",
       "4 -9.540000e-07    Escherichia_fergusonii  \n",
       "\n",
       "[5 rows x 288 columns]"
      ]
     },
     "execution_count": 1,
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('../input/dphi-genetic-bacteria-identification/train_dataset.csv')\n",
    "train.head()"
   ]
  },
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       "      <th>count</th>\n",
       "      <td>24000.000000</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>2.400000e+04</td>\n",
       "      <td>2.400000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100068.060208</td>\n",
       "      <td>8.391232e-07</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1.436741e-05</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>0.000591</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>9.362193e-05</td>\n",
       "      <td>1.195599e-04</td>\n",
       "      <td>3.514586e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57588.661702</td>\n",
       "      <td>9.421035e-05</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>0.000714</td>\n",
       "      <td>8.288626e-04</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.000088</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000711</td>\n",
       "      <td>0.001099</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>5.685651e-04</td>\n",
       "      <td>6.837408e-04</td>\n",
       "      <td>2.338609e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-2.403260e-04</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50283.750000</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-2.403260e-04</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99779.500000</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-2.393260e-04</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>4.630000e-07</td>\n",
       "      <td>4.630000e-07</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150028.750000</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>6.740000e-07</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>5.950000e-05</td>\n",
       "      <td>7.050000e-05</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199997.000000</td>\n",
       "      <td>9.999046e-03</td>\n",
       "      <td>0.009990</td>\n",
       "      <td>0.009957</td>\n",
       "      <td>0.009886</td>\n",
       "      <td>0.019800</td>\n",
       "      <td>1.975967e-02</td>\n",
       "      <td>0.009800</td>\n",
       "      <td>0.009886</td>\n",
       "      <td>0.009957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009957</td>\n",
       "      <td>0.019914</td>\n",
       "      <td>0.019957</td>\n",
       "      <td>0.019914</td>\n",
       "      <td>0.019914</td>\n",
       "      <td>0.019957</td>\n",
       "      <td>0.009990</td>\n",
       "      <td>9.990463e-03</td>\n",
       "      <td>1.999046e-02</td>\n",
       "      <td>9.990460e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 287 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              row_id     A0T0G0C10      A0T0G1C9      A0T0G2C8      A0T0G3C7  \\\n",
       "count   24000.000000  2.400000e+04  24000.000000  24000.000000  24000.000000   \n",
       "mean   100068.060208  8.391232e-07     -0.000002     -0.000014     -0.000010   \n",
       "std     57588.661702  9.421035e-05      0.000148      0.000287      0.000425   \n",
       "min         1.000000 -9.540000e-07     -0.000010     -0.000043     -0.000114   \n",
       "25%     50283.750000 -9.540000e-07     -0.000010     -0.000043     -0.000114   \n",
       "50%     99779.500000 -9.540000e-07     -0.000010     -0.000043     -0.000114   \n",
       "75%    150028.750000 -9.540000e-07     -0.000003     -0.000013     -0.000004   \n",
       "max    199997.000000  9.999046e-03      0.009990      0.009957      0.009886   \n",
       "\n",
       "           A0T0G4C6      A0T0G5C5      A0T0G6C4      A0T0G7C3      A0T0G8C2  \\\n",
       "count  24000.000000  2.400000e+04  24000.000000  24000.000000  24000.000000   \n",
       "mean       0.000009  1.436741e-05      0.000010     -0.000010     -0.000028   \n",
       "std        0.000714  8.288626e-04      0.000733      0.000437      0.000088   \n",
       "min       -0.000200 -2.403260e-04     -0.000200     -0.000114     -0.000043   \n",
       "25%       -0.000200 -2.403260e-04     -0.000200     -0.000114     -0.000043   \n",
       "50%       -0.000200 -2.393260e-04     -0.000200     -0.000114     -0.000043   \n",
       "75%       -0.000010  6.740000e-07     -0.000030      0.000004     -0.000028   \n",
       "max        0.019800  1.975967e-02      0.009800      0.009886      0.009957   \n",
       "\n",
       "       ...      A8T0G0C2      A8T0G1C1      A8T0G2C0      A8T1G0C1  \\\n",
       "count  ...  24000.000000  24000.000000  24000.000000  24000.000000   \n",
       "mean   ...      0.000136      0.000341      0.000254      0.000461   \n",
       "std    ...      0.000711      0.001099      0.000924      0.001221   \n",
       "min    ...     -0.000043     -0.000086     -0.000043     -0.000086   \n",
       "25%    ...     -0.000043     -0.000086     -0.000043     -0.000086   \n",
       "50%    ...      0.000014      0.000063      0.000047      0.000124   \n",
       "75%    ...      0.000108      0.000453      0.000297      0.000674   \n",
       "max    ...      0.009957      0.019914      0.019957      0.019914   \n",
       "\n",
       "           A8T1G1C0      A8T2G0C0      A9T0G0C1      A9T0G1C0      A9T1G0C0  \\\n",
       "count  24000.000000  24000.000000  24000.000000  2.400000e+04  2.400000e+04   \n",
       "mean       0.000591      0.000374      0.000059  9.362193e-05  1.195599e-04   \n",
       "std        0.001573      0.001156      0.000450  5.685651e-04  6.837408e-04   \n",
       "min       -0.000086     -0.000043     -0.000010 -9.540000e-06 -9.540000e-06   \n",
       "25%       -0.000086     -0.000043     -0.000010 -9.540000e-06 -9.540000e-06   \n",
       "50%        0.000116      0.000039     -0.000010  4.630000e-07  4.630000e-07   \n",
       "75%        0.000774      0.000357      0.000030  5.950000e-05  7.050000e-05   \n",
       "max        0.019914      0.019957      0.009990  9.990463e-03  1.999046e-02   \n",
       "\n",
       "          A10T0G0C0  \n",
       "count  2.400000e+04  \n",
       "mean   3.514586e-07  \n",
       "std    2.338609e-05  \n",
       "min   -9.540000e-07  \n",
       "25%   -9.540000e-07  \n",
       "50%   -9.540000e-07  \n",
       "75%   -9.540000e-07  \n",
       "max    9.990460e-04  \n",
       "\n",
       "[8 rows x 287 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a00b4c49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:12.315572Z",
     "iopub.status.busy": "2022-03-06T15:40:12.314939Z",
     "iopub.status.idle": "2022-03-06T15:40:12.608388Z",
     "shell.execute_reply": "2022-03-06T15:40:12.604763Z",
     "shell.execute_reply.started": "2022-03-06T15:37:30.960972Z"
    },
    "papermill": {
     "duration": 0.322115,
     "end_time": "2022-03-06T15:40:12.608651",
     "exception": false,
     "start_time": "2022-03-06T15:40:12.286536",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "train['target'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0c0350c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:12.698226Z",
     "iopub.status.busy": "2022-03-06T15:40:12.697450Z",
     "iopub.status.idle": "2022-03-06T15:40:12.723723Z",
     "shell.execute_reply": "2022-03-06T15:40:12.724766Z",
     "shell.execute_reply.started": "2022-03-06T15:37:31.225932Z"
    },
    "papermill": {
     "duration": 0.075412,
     "end_time": "2022-03-06T15:40:12.724953",
     "exception": false,
     "start_time": "2022-03-06T15:40:12.649541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id       0\n",
       "A0T0G0C10    0\n",
       "A0T0G1C9     0\n",
       "A0T0G2C8     0\n",
       "A0T0G3C7     0\n",
       "            ..\n",
       "A9T0G0C1     0\n",
       "A9T0G1C0     0\n",
       "A9T1G0C0     0\n",
       "A10T0G0C0    0\n",
       "target       0\n",
       "Length: 288, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7aa08b47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:12.835308Z",
     "iopub.status.busy": "2022-03-06T15:40:12.834535Z",
     "iopub.status.idle": "2022-03-06T15:40:12.842531Z",
     "shell.execute_reply": "2022-03-06T15:40:12.843558Z",
     "shell.execute_reply.started": "2022-03-06T15:37:31.253354Z"
    },
    "papermill": {
     "duration": 0.075286,
     "end_time": "2022-03-06T15:40:12.843738",
     "exception": false,
     "start_time": "2022-03-06T15:40:12.768452",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24000, 287), (24000,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X  = train.iloc[:, :-1]\n",
    "y = train.iloc[:, -1]\n",
    "#X_test= test\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0537e756",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:12.934260Z",
     "iopub.status.busy": "2022-03-06T15:40:12.933385Z",
     "iopub.status.idle": "2022-03-06T15:40:13.710417Z",
     "shell.execute_reply": "2022-03-06T15:40:13.709939Z",
     "shell.execute_reply.started": "2022-03-06T15:37:31.279135Z"
    },
    "papermill": {
     "duration": 0.822811,
     "end_time": "2022-03-06T15:40:13.710566",
     "exception": false,
     "start_time": "2022-03-06T15:40:12.887755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b62001e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:13.763426Z",
     "iopub.status.busy": "2022-03-06T15:40:13.762770Z",
     "iopub.status.idle": "2022-03-06T15:40:13.765229Z",
     "shell.execute_reply": "2022-03-06T15:40:13.765663Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.140849Z"
    },
    "papermill": {
     "duration": 0.031203,
     "end_time": "2022-03-06T15:40:13.765789",
     "exception": false,
     "start_time": "2022-03-06T15:40:13.734586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16800, 287)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "606e714f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:13.815828Z",
     "iopub.status.busy": "2022-03-06T15:40:13.814156Z",
     "iopub.status.idle": "2022-03-06T15:40:13.818773Z",
     "shell.execute_reply": "2022-03-06T15:40:13.818253Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.148548Z"
    },
    "papermill": {
     "duration": 0.029681,
     "end_time": "2022-03-06T15:40:13.818892",
     "exception": false,
     "start_time": "2022-03-06T15:40:13.789211",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#scaler = StandardScaler().fit(X_train)\n",
    "#X_train = scaler.transform(X_train)\n",
    "#X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd21309c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:13.872059Z",
     "iopub.status.busy": "2022-03-06T15:40:13.871275Z",
     "iopub.status.idle": "2022-03-06T15:40:13.896505Z",
     "shell.execute_reply": "2022-03-06T15:40:13.896919Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.157391Z"
    },
    "papermill": {
     "duration": 0.054513,
     "end_time": "2022-03-06T15:40:13.897034",
     "exception": false,
     "start_time": "2022-03-06T15:40:13.842521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.000138</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>4.630000e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6468</th>\n",
       "      <td>146199</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>1.050000e-05</td>\n",
       "      <td>4.710000e-05</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8917</th>\n",
       "      <td>28866</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>0.001957</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13468</th>\n",
       "      <td>172353</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19883</th>\n",
       "      <td>160979</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10006</th>\n",
       "      <td>151457</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>4.630000e-07</td>\n",
       "      <td>7.080000e-06</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23814</th>\n",
       "      <td>122209</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>192800</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8688</th>\n",
       "      <td>66290</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9944</th>\n",
       "      <td>42764</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "      <td>-9.540000e-06</td>\n",
       "      <td>-4.290000e-05</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000240</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.000914</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>-0.000086</td>\n",
       "      <td>0.001914</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-9.540000e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16800 rows × 287 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       row_id     A0T0G0C10      A0T0G1C9      A0T0G2C8  A0T0G3C7  A0T0G4C6  \\\n",
       "18318   99455  4.630000e-08  3.460000e-06  8.470000e-08 -0.000015  0.000007   \n",
       "6468   146199 -9.540000e-07  1.050000e-05  4.710000e-05  0.000046  0.000080   \n",
       "8917    28866 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "13468  172353 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "19883  160979 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "...       ...           ...           ...           ...       ...       ...   \n",
       "10006  151457 -9.540000e-07  4.630000e-07  7.080000e-06 -0.000024  0.000040   \n",
       "23814  122209 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "1361   192800 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "8688    66290 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "9944    42764 -9.540000e-07 -9.540000e-06 -4.290000e-05 -0.000114 -0.000200   \n",
       "\n",
       "       A0T0G5C5  A0T0G6C4  A0T0G7C3  A0T0G8C2  ...  A8T0G0C2  A8T0G1C1  \\\n",
       "18318 -0.000005 -0.000005  0.000003  0.000003  ...  0.000041  0.000138   \n",
       "6468   0.000230  0.000150  0.000096 -0.000023  ...  0.000027  0.000034   \n",
       "8917  -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043 -0.000086   \n",
       "13468 -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043 -0.000086   \n",
       "19883 -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043 -0.000086   \n",
       "...         ...       ...       ...       ...  ...       ...       ...   \n",
       "10006  0.000060  0.000040  0.000016 -0.000013  ...  0.000027  0.000094   \n",
       "23814 -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043 -0.000086   \n",
       "1361  -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043 -0.000086   \n",
       "8688   0.000760  0.000800  0.000886 -0.000043  ... -0.000043 -0.000086   \n",
       "9944  -0.000240 -0.000200 -0.000114 -0.000043  ... -0.000043  0.000914   \n",
       "\n",
       "       A8T0G2C0  A8T1G0C1  A8T1G1C0  A8T2G0C0  A9T0G0C1  A9T0G1C0  A9T1G0C0  \\\n",
       "18318  0.000077  0.000203  0.000216  0.000160  0.000027  0.000041  0.000041   \n",
       "6468   0.000057  0.000034  0.000034 -0.000033 -0.000010  0.000010 -0.000010   \n",
       "8917   0.001957 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "13468 -0.000043 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "19883 -0.000043 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "10006  0.000087  0.000134  0.000224  0.000037  0.000010  0.000010  0.000021   \n",
       "23814 -0.000043 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "1361  -0.000043 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "8688  -0.000043 -0.000086 -0.000086 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "9944   0.000957 -0.000086  0.001914 -0.000043 -0.000010 -0.000010 -0.000010   \n",
       "\n",
       "          A10T0G0C0  \n",
       "18318  4.630000e-08  \n",
       "6468  -9.540000e-07  \n",
       "8917  -9.540000e-07  \n",
       "13468 -9.540000e-07  \n",
       "19883 -9.540000e-07  \n",
       "...             ...  \n",
       "10006 -9.540000e-07  \n",
       "23814 -9.540000e-07  \n",
       "1361  -9.540000e-07  \n",
       "8688  -9.540000e-07  \n",
       "9944  -9.540000e-07  \n",
       "\n",
       "[16800 rows x 287 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9586c97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:13.948560Z",
     "iopub.status.busy": "2022-03-06T15:40:13.948006Z",
     "iopub.status.idle": "2022-03-06T15:40:13.950950Z",
     "shell.execute_reply": "2022-03-06T15:40:13.951389Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.193018Z"
    },
    "papermill": {
     "duration": 0.030382,
     "end_time": "2022-03-06T15:40:13.951531",
     "exception": false,
     "start_time": "2022-03-06T15:40:13.921149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import LogisticRegression\n",
    "#model = LogisticRegression()\n",
    "#model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9631a3e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.004415Z",
     "iopub.status.busy": "2022-03-06T15:40:14.002912Z",
     "iopub.status.idle": "2022-03-06T15:40:14.005034Z",
     "shell.execute_reply": "2022-03-06T15:40:14.005424Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.198005Z"
    },
    "papermill": {
     "duration": 0.030175,
     "end_time": "2022-03-06T15:40:14.005567",
     "exception": false,
     "start_time": "2022-03-06T15:40:13.975392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#pred = model.predict(X_test)\n",
    "#print('Accuracy score',accuracy_score(pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "360c5331",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.056742Z",
     "iopub.status.busy": "2022-03-06T15:40:14.056100Z",
     "iopub.status.idle": "2022-03-06T15:40:14.059022Z",
     "shell.execute_reply": "2022-03-06T15:40:14.059386Z",
     "shell.execute_reply.started": "2022-03-06T15:37:47.192929Z"
    },
    "papermill": {
     "duration": 0.030015,
     "end_time": "2022-03-06T15:40:14.059531",
     "exception": false,
     "start_time": "2022-03-06T15:40:14.029516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#type(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cbed06a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.111139Z",
     "iopub.status.busy": "2022-03-06T15:40:14.110403Z",
     "iopub.status.idle": "2022-03-06T15:40:14.551302Z",
     "shell.execute_reply": "2022-03-06T15:40:14.550451Z",
     "shell.execute_reply.started": "2022-03-06T15:37:59.160139Z"
    },
    "papermill": {
     "duration": 0.46782,
     "end_time": "2022-03-06T15:40:14.551478",
     "exception": false,
     "start_time": "2022-03-06T15:40:14.083658",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('../input/dphi-genetic-bacteria-identification/test_dataset.csv')\n",
    "#target = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "087ecd1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.607776Z",
     "iopub.status.busy": "2022-03-06T15:40:14.607039Z",
     "iopub.status.idle": "2022-03-06T15:40:14.609135Z",
     "shell.execute_reply": "2022-03-06T15:40:14.609533Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.317156Z"
    },
    "papermill": {
     "duration": 0.031962,
     "end_time": "2022-03-06T15:40:14.609671",
     "exception": false,
     "start_time": "2022-03-06T15:40:14.577709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#type(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "baed253b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.661467Z",
     "iopub.status.busy": "2022-03-06T15:40:14.660621Z",
     "iopub.status.idle": "2022-03-06T15:40:14.663198Z",
     "shell.execute_reply": "2022-03-06T15:40:14.662817Z",
     "shell.execute_reply.started": "2022-03-06T15:37:32.318887Z"
    },
    "papermill": {
     "duration": 0.029685,
     "end_time": "2022-03-06T15:40:14.663312",
     "exception": false,
     "start_time": "2022-03-06T15:40:14.633627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.DataFrame(target, columns = ['target'])\n",
    "#df.to_csv('Output2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b877c979",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.734148Z",
     "iopub.status.busy": "2022-03-06T15:40:14.733006Z",
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     "exception": false,
     "start_time": "2022-03-06T15:40:14.687166",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24000, 287), (24000,), (6000, 287))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train.iloc[:, :-1]\n",
    "X_test = test\n",
    "y_train = train.iloc[:,-1]\n",
    "X_train.shape, y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "986842f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:14.790500Z",
     "iopub.status.busy": "2022-03-06T15:40:14.789931Z",
     "iopub.status.idle": "2022-03-06T15:40:14.984138Z",
     "shell.execute_reply": "2022-03-06T15:40:14.984595Z",
     "shell.execute_reply.started": "2022-03-06T15:38:06.159582Z"
    },
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     "exception": false,
     "start_time": "2022-03-06T15:40:14.761761",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4afc31fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:15.045046Z",
     "iopub.status.busy": "2022-03-06T15:40:15.043942Z",
     "iopub.status.idle": "2022-03-06T15:40:15.140780Z",
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     "shell.execute_reply.started": "2022-03-06T15:38:10.198889Z"
    },
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     "end_time": "2022-03-06T15:40:15.140937",
     "exception": false,
     "start_time": "2022-03-06T15:40:15.010642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nb_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63582fca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:15.201492Z",
     "iopub.status.busy": "2022-03-06T15:40:15.200368Z",
     "iopub.status.idle": "2022-03-06T15:40:15.295282Z",
     "shell.execute_reply": "2022-03-06T15:40:15.294826Z",
     "shell.execute_reply.started": "2022-03-06T15:38:11.798996Z"
    },
    "papermill": {
     "duration": 0.12878,
     "end_time": "2022-03-06T15:40:15.295422",
     "exception": false,
     "start_time": "2022-03-06T15:40:15.166642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#print('Accuracy score',accuracy_score(nb_pred,y_test))\n",
    "target1 = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a056e4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:15.350722Z",
     "iopub.status.busy": "2022-03-06T15:40:15.350018Z",
     "iopub.status.idle": "2022-03-06T15:40:15.368462Z",
     "shell.execute_reply": "2022-03-06T15:40:15.367983Z",
     "shell.execute_reply.started": "2022-03-06T15:38:15.097329Z"
    },
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     "duration": 0.047887,
     "end_time": "2022-03-06T15:40:15.368577",
     "exception": false,
     "start_time": "2022-03-06T15:40:15.320690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(target1, columns = ['target'])\n",
    "df1.to_csv('Output4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d7ee4459",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:15.421803Z",
     "iopub.status.busy": "2022-03-06T15:40:15.421197Z",
     "iopub.status.idle": "2022-03-06T15:40:18.778267Z",
     "shell.execute_reply": "2022-03-06T15:40:18.777800Z",
     "shell.execute_reply.started": "2022-03-06T15:38:16.422057Z"
    },
    "papermill": {
     "duration": 3.385158,
     "end_time": "2022-03-06T15:40:18.778407",
     "exception": false,
     "start_time": "2022-03-06T15:40:15.393249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15541824",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:18.839500Z",
     "iopub.status.busy": "2022-03-06T15:40:18.832496Z",
     "iopub.status.idle": "2022-03-06T15:40:18.850859Z",
     "shell.execute_reply": "2022-03-06T15:40:18.850452Z",
     "shell.execute_reply.started": "2022-03-06T15:38:20.635124Z"
    },
    "papermill": {
     "duration": 0.047381,
     "end_time": "2022-03-06T15:40:18.850967",
     "exception": false,
     "start_time": "2022-03-06T15:40:18.803586",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(y_pred, columns = ['target'])\n",
    "df2.to_csv('Output5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "45ea9fde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-06T15:40:18.909008Z",
     "iopub.status.busy": "2022-03-06T15:40:18.907961Z",
     "iopub.status.idle": "2022-03-06T15:40:20.224497Z",
     "shell.execute_reply": "2022-03-06T15:40:20.223989Z",
     "shell.execute_reply.started": "2022-03-06T15:38:25.177342Z"
    },
    "papermill": {
     "duration": 1.348928,
     "end_time": "2022-03-06T15:40:20.224651",
     "exception": false,
     "start_time": "2022-03-06T15:40:18.875723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "clf1 = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf1 = clf1.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred1 = clf1.predict(X_test)\n",
    "\n",
    "df3 = pd.DataFrame(y_pred1, columns = ['target'])\n",
    "df3.to_csv('Output6.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2675a589",
   "metadata": {
    "papermill": {
     "duration": 0.027393,
     "end_time": "2022-03-06T15:40:20.279405",
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     "start_time": "2022-03-06T15:40:20.252012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
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