predicting-suicide-and-word-analysis.ipynb

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   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import zipfile\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
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   "id": "53d92d45",
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    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Ex Wife Threatening SuicideRecently I left my ...</td>\n",
       "      <td>suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Am I weird I don't get affected by compliments...</td>\n",
       "      <td>non-suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Finally 2020 is almost over... So I can never ...</td>\n",
       "      <td>non-suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>i need helpjust help me im crying so hard</td>\n",
       "      <td>suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>I’m so lostHello, my name is Adam (16) and I’v...</td>\n",
       "      <td>suicide</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                               text        class\n",
       "0           2  Ex Wife Threatening SuicideRecently I left my ...      suicide\n",
       "1           3  Am I weird I don't get affected by compliments...  non-suicide\n",
       "2           4  Finally 2020 is almost over... So I can never ...  non-suicide\n",
       "3           8          i need helpjust help me im crying so hard      suicide\n",
       "4           9  I’m so lostHello, my name is Adam (16) and I’v...      suicide"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#INCLUDE YOUR DATASET OR THE ONE USED HERE IS MENTIONED IN THE DATASET PART IN COMMENT TO NOT INTERFERE DIRECTLY WITH CODE\n",
    "\n",
    "#zf = zipfile.ZipFile('Suicide_Detection.zip') \n",
    "#Suicide = pd.read_csv(zf.open('Suicide_Detection.csv'))\n",
    "\n",
    "#Suicide.head()"
   ]
  },
  {
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   "execution_count": 7,
   "id": "deb3abd4",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 232074 entries, 0 to 232073\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   text    232074 non-null  object\n",
      " 1   class   232074 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Suicide = Suicide.drop('Unnamed: 0',axis=1)\n",
    "Suicide.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aca9fce",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Am I weird I don't get affected by compliments if it's coming from someone I know irl but I feel really good when internet strangers do it\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Suicide['text'][1]"
   ]
  },
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   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Suicide['class'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a37f4452",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:53.176733Z",
     "iopub.status.busy": "2024-05-11T02:15:53.175970Z",
     "iopub.status.idle": "2024-05-11T02:15:54.638005Z",
     "shell.execute_reply": "2024-05-11T02:15:54.638515Z",
     "shell.execute_reply.started": "2022-03-07T11:15:49.892626Z"
    },
    "papermill": {
     "duration": 1.529979,
     "end_time": "2024-05-11T02:15:54.638661",
     "exception": false,
     "start_time": "2024-05-11T02:15:53.108682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    232074.000000\n",
       "mean        131.546438\n",
       "std         222.081581\n",
       "min           1.000000\n",
       "25%          26.000000\n",
       "50%          60.000000\n",
       "75%         154.000000\n",
       "max       15632.000000\n",
       "Name: Length, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Suicide['Length'] = Suicide['text'].apply(lambda x: len(x.split(' ')))\n",
    "Suicide['Length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58daf024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:54.770346Z",
     "iopub.status.busy": "2024-05-11T02:15:54.769781Z",
     "iopub.status.idle": "2024-05-11T02:15:54.802504Z",
     "shell.execute_reply": "2024-05-11T02:15:54.802061Z",
     "shell.execute_reply.started": "2022-03-07T11:15:51.913142Z"
    },
    "papermill": {
     "duration": 0.099208,
     "end_time": "2024-05-11T02:15:54.802628",
     "exception": false,
     "start_time": "2024-05-11T02:15:54.703420",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0\n",
      "22.0\n",
      "30.0\n",
      "42.0\n",
      "60.0\n",
      "86.0\n",
      "126.0\n",
      "190.0\n",
      "317.0\n"
     ]
    }
   ],
   "source": [
    "for i in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:\n",
    "    print(Suicide['Length'].quantile(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9689d1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:54.936429Z",
     "iopub.status.busy": "2024-05-11T02:15:54.935838Z",
     "iopub.status.idle": "2024-05-11T02:15:54.965275Z",
     "shell.execute_reply": "2024-05-11T02:15:54.965709Z",
     "shell.execute_reply.started": "2022-03-07T11:15:51.966093Z"
    },
    "papermill": {
     "duration": 0.098733,
     "end_time": "2024-05-11T02:15:54.965856",
     "exception": false,
     "start_time": "2024-05-11T02:15:54.867123",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "339.0\n",
      "363.0\n",
      "393.0\n",
      "427.0\n",
      "470.0\n",
      "526.0\n",
      "604.0\n",
      "721.0\n",
      "960.0\n"
     ]
    }
   ],
   "source": [
    "for i in [0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99]:\n",
    "    print(Suicide['Length'].quantile(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cebe2838",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:55.097610Z",
     "iopub.status.busy": "2024-05-11T02:15:55.096393Z",
     "iopub.status.idle": "2024-05-11T02:15:55.099182Z",
     "shell.execute_reply": "2024-05-11T02:15:55.099634Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.019333Z"
    },
    "papermill": {
     "duration": 0.069596,
     "end_time": "2024-05-11T02:15:55.099760",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.030164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Cutting off the post length at 317 words. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5dc1554",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:55.233663Z",
     "iopub.status.busy": "2024-05-11T02:15:55.233117Z",
     "iopub.status.idle": "2024-05-11T02:15:55.251355Z",
     "shell.execute_reply": "2024-05-11T02:15:55.251849Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.025788Z"
    },
    "papermill": {
     "duration": 0.087898,
     "end_time": "2024-05-11T02:15:55.252015",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.164117",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Suicide = Suicide[Suicide.Length<=317]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "352947fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:55.383973Z",
     "iopub.status.busy": "2024-05-11T02:15:55.383320Z",
     "iopub.status.idle": "2024-05-11T02:15:55.578153Z",
     "shell.execute_reply": "2024-05-11T02:15:55.578636Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.060847Z"
    },
    "papermill": {
     "duration": 0.261804,
     "end_time": "2024-05-11T02:15:55.578801",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.316997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Suicide['class'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "194e0fb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:55.721988Z",
     "iopub.status.busy": "2024-05-11T02:15:55.721435Z",
     "iopub.status.idle": "2024-05-11T02:15:55.724885Z",
     "shell.execute_reply": "2024-05-11T02:15:55.724397Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.482622Z"
    },
    "papermill": {
     "duration": 0.071066,
     "end_time": "2024-05-11T02:15:55.724999",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.653933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208955"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Suicide)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2de3fe4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:55.859322Z",
     "iopub.status.busy": "2024-05-11T02:15:55.858660Z",
     "iopub.status.idle": "2024-05-11T02:15:55.860332Z",
     "shell.execute_reply": "2024-05-11T02:15:55.860745Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.492002Z"
    },
    "papermill": {
     "duration": 0.0706,
     "end_time": "2024-05-11T02:15:55.860862",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.790262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#As we can see we still get a good distribution of the target class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67eee252",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:15:56.000332Z",
     "iopub.status.busy": "2024-05-11T02:15:55.994834Z",
     "iopub.status.idle": "2024-05-11T02:16:00.549565Z",
     "shell.execute_reply": "2024-05-11T02:16:00.549049Z",
     "shell.execute_reply.started": "2022-03-07T11:15:52.510419Z"
    },
    "papermill": {
     "duration": 4.62243,
     "end_time": "2024-05-11T02:16:00.549679",
     "exception": false,
     "start_time": "2024-05-11T02:15:55.927249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16688702"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words = list()\n",
    "for i in Suicide['text']:\n",
    "    for j in i.split(' '):\n",
    "        Words.append(j.upper())\n",
    "len(Words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d5b3fb8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:00.685440Z",
     "iopub.status.busy": "2024-05-11T02:16:00.684873Z",
     "iopub.status.idle": "2024-05-11T02:16:00.687340Z",
     "shell.execute_reply": "2024-05-11T02:16:00.686872Z",
     "shell.execute_reply.started": "2022-03-07T11:15:58.628295Z"
    },
    "papermill": {
     "duration": 0.072252,
     "end_time": "2024-05-11T02:16:00.687442",
     "exception": false,
     "start_time": "2024-05-11T02:16:00.615190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6991175a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:00.822179Z",
     "iopub.status.busy": "2024-05-11T02:16:00.821672Z",
     "iopub.status.idle": "2024-05-11T02:16:03.742473Z",
     "shell.execute_reply": "2024-05-11T02:16:03.742900Z",
     "shell.execute_reply.started": "2022-03-07T11:15:58.633934Z"
    },
    "papermill": {
     "duration": 2.989445,
     "end_time": "2024-05-11T02:16:03.743045",
     "exception": false,
     "start_time": "2024-05-11T02:16:00.753600",
     "status": "completed"
    },
    "tags": []
   },
   "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>890895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TO</td>\n",
       "      <td>558762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AND</td>\n",
       "      <td>464789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>THE</td>\n",
       "      <td>350903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>A</td>\n",
       "      <td>339305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>CAN'T</td>\n",
       "      <td>35968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>HAD</td>\n",
       "      <td>35618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>DON’T</td>\n",
       "      <td>35336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>I'VE</td>\n",
       "      <td>35261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>ONLY</td>\n",
       "      <td>34925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Word  Frequency\n",
       "4         I     890895\n",
       "18       TO     558762\n",
       "16      AND     464789\n",
       "90      THE     350903\n",
       "30        A     339305\n",
       "...     ...        ...\n",
       "404   CAN'T      35968\n",
       "296     HAD      35618\n",
       "460   DON’T      35336\n",
       "1328   I'VE      35261\n",
       "158    ONLY      34925\n",
       "\n",
       "[70 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq = Counter(Words)\n",
    "Words_Freq = pd.DataFrame({'Word':list(Words_Freq.keys()),'Frequency':list(Words_Freq.values())})\n",
    "Words_Freq.sort_values('Frequency',ascending=False,inplace=True)\n",
    "Words_Freq.head(70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0e8921a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:03.876448Z",
     "iopub.status.busy": "2024-05-11T02:16:03.875973Z",
     "iopub.status.idle": "2024-05-11T02:16:03.879604Z",
     "shell.execute_reply": "2024-05-11T02:16:03.880077Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.658729Z"
    },
    "papermill": {
     "duration": 0.072068,
     "end_time": "2024-05-11T02:16:03.880206",
     "exception": false,
     "start_time": "2024-05-11T02:16:03.808138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "428444"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Words_Freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff76beae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.015882Z",
     "iopub.status.busy": "2024-05-11T02:16:04.015384Z",
     "iopub.status.idle": "2024-05-11T02:16:04.029572Z",
     "shell.execute_reply": "2024-05-11T02:16:04.030090Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.667032Z"
    },
    "papermill": {
     "duration": 0.083635,
     "end_time": "2024-05-11T02:16:04.030239",
     "exception": false,
     "start_time": "2024-05-11T02:16:03.946604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    428444.000000\n",
       "mean         38.951886\n",
       "std        2288.110751\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max      890895.000000\n",
       "Name: Frequency, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq['Frequency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "57f4e683",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.167122Z",
     "iopub.status.busy": "2024-05-11T02:16:04.166607Z",
     "iopub.status.idle": "2024-05-11T02:16:04.214385Z",
     "shell.execute_reply": "2024-05-11T02:16:04.213909Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.697904Z"
    },
    "papermill": {
     "duration": 0.117895,
     "end_time": "2024-05-11T02:16:04.214528",
     "exception": false,
     "start_time": "2024-05-11T02:16:04.096633",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "228.0\n",
      "270.0\n",
      "325.8989999999758\n",
      "406.0\n",
      "528.0\n",
      "709.0\n",
      "1035.0\n",
      "1825.0\n",
      "4104.140000000596\n"
     ]
    }
   ],
   "source": [
    "for i in [0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999]:\n",
    "    print(Words_Freq['Frequency'].quantile(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca28f936",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.354742Z",
     "iopub.status.busy": "2024-05-11T02:16:04.354171Z",
     "iopub.status.idle": "2024-05-11T02:16:04.355721Z",
     "shell.execute_reply": "2024-05-11T02:16:04.356126Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.786864Z"
    },
    "papermill": {
     "duration": 0.07343,
     "end_time": "2024-05-11T02:16:04.356273",
     "exception": false,
     "start_time": "2024-05-11T02:16:04.282843",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Cutting off the words frequency at above 99 quantile and below 31905"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1450970",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.494613Z",
     "iopub.status.busy": "2024-05-11T02:16:04.493749Z",
     "iopub.status.idle": "2024-05-11T02:16:04.505129Z",
     "shell.execute_reply": "2024-05-11T02:16:04.505577Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.792359Z"
    },
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     "duration": 0.082959,
     "end_time": "2024-05-11T02:16:04.505741",
     "exception": false,
     "start_time": "2024-05-11T02:16:04.422782",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4281"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq = Words_Freq[Words_Freq['Frequency']>Words_Freq['Frequency'].quantile(0.99)]\n",
    "len(Words_Freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9589c3f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.650020Z",
     "iopub.status.busy": "2024-05-11T02:16:04.646905Z",
     "iopub.status.idle": "2024-05-11T02:16:04.653101Z",
     "shell.execute_reply": "2024-05-11T02:16:04.653528Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.818842Z"
    },
    "papermill": {
     "duration": 0.079093,
     "end_time": "2024-05-11T02:16:04.653699",
     "exception": false,
     "start_time": "2024-05-11T02:16:04.574606",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      4281.000000\n",
       "mean       3504.923616\n",
       "std       22625.909153\n",
       "min         195.000000\n",
       "25%         294.000000\n",
       "50%         528.000000\n",
       "75%        1378.000000\n",
       "max      890895.000000\n",
       "Name: Frequency, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq['Frequency'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "08bf5acb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:04.974233Z",
     "iopub.status.busy": "2024-05-11T02:16:04.973643Z",
     "iopub.status.idle": "2024-05-11T02:16:04.977897Z",
     "shell.execute_reply": "2024-05-11T02:16:04.977443Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.830614Z"
    },
    "papermill": {
     "duration": 0.255056,
     "end_time": "2024-05-11T02:16:04.978006",
     "exception": false,
     "start_time": "2024-05-11T02:16:04.722950",
     "status": "completed"
    },
    "tags": []
   },
   "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>SOME</td>\n",
       "      <td>31882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>ME.</td>\n",
       "      <td>31630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>FILLER</td>\n",
       "      <td>31016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>WE</td>\n",
       "      <td>30745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HAS</td>\n",
       "      <td>29485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word  Frequency\n",
       "726     SOME      31882\n",
       "145      ME.      31630\n",
       "3986  FILLER      31016\n",
       "1254      WE      30745\n",
       "11       HAS      29485"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq = Words_Freq[Words_Freq['Frequency'] < 31905]\n",
    "Words_Freq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9d91a29b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.117868Z",
     "iopub.status.busy": "2024-05-11T02:16:05.117206Z",
     "iopub.status.idle": "2024-05-11T02:16:05.120697Z",
     "shell.execute_reply": "2024-05-11T02:16:05.120233Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.845691Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4202"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Words_Freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a4031a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.261102Z",
     "iopub.status.busy": "2024-05-11T02:16:05.260568Z",
     "iopub.status.idle": "2024-05-11T02:16:05.262727Z",
     "shell.execute_reply": "2024-05-11T02:16:05.263088Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.852182Z"
    },
    "papermill": {
     "duration": 0.073842,
     "end_time": "2024-05-11T02:16:05.263211",
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     "start_time": "2024-05-11T02:16:05.189369",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#We have to reduce the number of words for processing capacity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eb0c2f6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.413640Z",
     "iopub.status.busy": "2024-05-11T02:16:05.409311Z",
     "iopub.status.idle": "2024-05-11T02:16:05.416533Z",
     "shell.execute_reply": "2024-05-11T02:16:05.416033Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.864739Z"
    },
    "papermill": {
     "duration": 0.083633,
     "end_time": "2024-05-11T02:16:05.416642",
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     "start_time": "2024-05-11T02:16:05.333009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19803.076999999972\n",
      "20529.751999999964\n",
      "21670.046999999926\n",
      "22609.074000000095\n",
      "24278.224999999984\n",
      "25391.77599999999\n",
      "26753.605999999985\n",
      "27639.58599999999\n",
      "29412.64000000032\n"
     ]
    }
   ],
   "source": [
    "for i in [0.991,0.992,0.993,0.994,0.995,0.996,0.997,0.998,0.999]:\n",
    "    print(Words_Freq['Frequency'].quantile(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8cdfde0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.563494Z",
     "iopub.status.busy": "2024-05-11T02:16:05.562836Z",
     "iopub.status.idle": "2024-05-11T02:16:05.566510Z",
     "shell.execute_reply": "2024-05-11T02:16:05.565989Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.886596Z"
    },
    "papermill": {
     "duration": 0.081482,
     "end_time": "2024-05-11T02:16:05.566617",
     "exception": false,
     "start_time": "2024-05-11T02:16:05.485135",
     "status": "completed"
    },
    "tags": []
   },
   "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>Word</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>SUPPOSED</td>\n",
       "      <td>3718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>HEAR</td>\n",
       "      <td>3711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>FRIENDS.</td>\n",
       "      <td>3684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3339</th>\n",
       "      <td>MOTHER</td>\n",
       "      <td>3668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>LOVED</td>\n",
       "      <td>3617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Word  Frequency\n",
       "71    SUPPOSED       3718\n",
       "117       HEAR       3711\n",
       "2096  FRIENDS.       3684\n",
       "3339    MOTHER       3668\n",
       "2387     LOVED       3617"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Words_Freq = Words_Freq[Words_Freq['Frequency'] >= 3615.8130000000237]\n",
    "Words_Freq.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "33a9f8a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.709670Z",
     "iopub.status.busy": "2024-05-11T02:16:05.708879Z",
     "iopub.status.idle": "2024-05-11T02:16:05.713335Z",
     "shell.execute_reply": "2024-05-11T02:16:05.713827Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.898325Z"
    },
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     "exception": false,
     "start_time": "2024-05-11T02:16:05.635250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Words_Freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d02d0d6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:05.857380Z",
     "iopub.status.busy": "2024-05-11T02:16:05.856691Z",
     "iopub.status.idle": "2024-05-11T02:16:05.860335Z",
     "shell.execute_reply": "2024-05-11T02:16:05.860794Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.911908Z"
    },
    "papermill": {
     "duration": 0.076263,
     "end_time": "2024-05-11T02:16:05.860960",
     "exception": false,
     "start_time": "2024-05-11T02:16:05.784697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#We get a good list of words. However we might have to reduce this list further based on capacity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdab1c05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:16:06.004050Z",
     "iopub.status.busy": "2024-05-11T02:16:06.003346Z",
     "iopub.status.idle": "2024-05-11T02:16:06.007487Z",
     "shell.execute_reply": "2024-05-11T02:16:06.007977Z",
     "shell.execute_reply.started": "2022-03-07T11:16:02.922732Z"
    },
    "papermill": {
     "duration": 0.077077,
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     "exception": false,
     "start_time": "2024-05-11T02:16:05.931081",
     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#We will create separate variables for each of these words based on their presence in the posts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "be57aea7",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "         class  Length  SOME  ME.  FILLER  WE  HAS  FUCKING  WHY  BEING  ...  \\\n",
       "0      suicide     143     0    0       0   0    1        0    0      0  ...   \n",
       "1  non-suicide      27     0    0       0   0    0        0    0      0  ...   \n",
       "2  non-suicide      26     0    0       0   0    1        1    0      0  ...   \n",
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       "\n",
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       "\n",
       "[5 rows x 382 columns]"
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     },
     "execution_count": 35,
     "metadata": {},
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    }
   ],
   "source": [
    "Final_Words = list(Words_Freq.Word)\n",
    "for i in Final_Words:\n",
    "    Suicide[i] = Suicide['text'].apply(lambda x: 1 if i in x.upper().split(' ') else 0)\n",
    "Suicide.drop('text',axis=1,inplace=True)\n",
    "Suicide.head()"
   ]
  },
  {
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   "id": "e3b04f66",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LEAST\n",
       "0    203642\n",
       "1      5313\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Suicide['LEAST'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "405c7d9b",
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       "   class  Length  SOME  ME.  FILLER  WE  HAS  FUCKING  WHY  BEING  ...  WEIRD  \\\n",
       "0      1     143     0    0       0   0    1        0    0      0  ...      0   \n",
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       "3      1       9     0    0       0   0    0        0    0      0  ...      0   \n",
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       "\n",
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     },
     "execution_count": 37,
     "metadata": {},
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    }
   ],
   "source": [
    "Suicide['class'] = Suicide['class'].apply(lambda x: 1 if x == 'suicide' else 0)\n",
    "Suicide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b004f05",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a776b76d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:01.752906Z",
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     "shell.execute_reply.started": "2022-03-07T11:26:10.866984Z"
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x='class',y='Length',data=Suicide)\n",
    "plt.title('Class vs Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "64bc698d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:04.589451Z",
     "iopub.status.busy": "2024-05-11T02:24:04.588858Z",
     "iopub.status.idle": "2024-05-11T02:24:04.591478Z",
     "shell.execute_reply": "2024-05-11T02:24:04.591981Z",
     "shell.execute_reply.started": "2022-03-07T11:26:13.980889Z"
    },
    "papermill": {
     "duration": 0.076955,
     "end_time": "2024-05-11T02:24:04.592133",
     "exception": false,
     "start_time": "2024-05-11T02:24:04.515178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Since there are a still quite a lot of words we will check only the words with the highest correlation with the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ba33414d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:04.737007Z",
     "iopub.status.busy": "2024-05-11T02:24:04.736521Z",
     "iopub.status.idle": "2024-05-11T02:24:04.739334Z",
     "shell.execute_reply": "2024-05-11T02:24:04.739799Z",
     "shell.execute_reply.started": "2022-03-07T11:26:13.985962Z"
    },
    "papermill": {
     "duration": 0.076739,
     "end_time": "2024-05-11T02:24:04.739915",
     "exception": false,
     "start_time": "2024-05-11T02:24:04.663176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e340b4b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:04.886697Z",
     "iopub.status.busy": "2024-05-11T02:24:04.886201Z",
     "iopub.status.idle": "2024-05-11T02:24:06.018400Z",
     "shell.execute_reply": "2024-05-11T02:24:06.018924Z",
     "shell.execute_reply.started": "2022-03-07T11:26:13.998576Z"
    },
    "papermill": {
     "duration": 1.207,
     "end_time": "2024-05-11T02:24:06.019092",
     "exception": false,
     "start_time": "2024-05-11T02:24:04.812092",
     "status": "completed"
    },
    "tags": []
   },
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Length</td>\n",
       "      <td>0.458362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KILL</td>\n",
       "      <td>0.309071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ME.</td>\n",
       "      <td>0.279329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>SUICIDE</td>\n",
       "      <td>0.271519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>END</td>\n",
       "      <td>0.257212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>ANYMORE.</td>\n",
       "      <td>0.247672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>LIVE</td>\n",
       "      <td>0.228209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>MYSELF.</td>\n",
       "      <td>0.222832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IT.</td>\n",
       "      <td>0.222543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>SUICIDAL</td>\n",
       "      <td>0.219771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Correlation\n",
       "0      Length     0.458362\n",
       "23       KILL     0.309071\n",
       "2         ME.     0.279329\n",
       "63    SUICIDE     0.271519\n",
       "37        END     0.257212\n",
       "73   ANYMORE.     0.247672\n",
       "47       LIVE     0.228209\n",
       "119   MYSELF.     0.222832\n",
       "13        IT.     0.222543\n",
       "132  SUICIDAL     0.219771"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Correlations = dict()\n",
    "var = list(Suicide.columns)\n",
    "var.remove('class')\n",
    "for i in var:\n",
    "    Correlations[i] = pearsonr(Suicide[i],Suicide['class'])[0]\n",
    "Correlations = pd.DataFrame({'Feature':list(Correlations.keys()),'Correlation':list(Correlations.values())})\n",
    "Correlations.sort_values('Correlation',ascending=False,inplace=True)\n",
    "Correlations.set_index('Feature')\n",
    "Correlations.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d58d3917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:06.204076Z",
     "iopub.status.busy": "2024-05-11T02:24:06.197058Z",
     "iopub.status.idle": "2024-05-11T02:24:06.398321Z",
     "shell.execute_reply": "2024-05-11T02:24:06.398930Z",
     "shell.execute_reply.started": "2022-03-07T11:26:15.88275Z"
    },
    "papermill": {
     "duration": 0.287028,
     "end_time": "2024-05-11T02:24:06.399078",
     "exception": false,
     "start_time": "2024-05-11T02:24:06.112050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Correlations.head(15)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Correlation,y=temp.Feature)\n",
    "plt.title('Feature vs Correlation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e53c7268",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:06.552207Z",
     "iopub.status.busy": "2024-05-11T02:24:06.551720Z",
     "iopub.status.idle": "2024-05-11T02:24:06.553852Z",
     "shell.execute_reply": "2024-05-11T02:24:06.554218Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.200522Z"
    },
    "papermill": {
     "duration": 0.078458,
     "end_time": "2024-05-11T02:24:06.554338",
     "exception": false,
     "start_time": "2024-05-11T02:24:06.475880",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#As we can see the correlations are not high considering the non linear relationship between the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6d04f80a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:06.716514Z",
     "iopub.status.busy": "2024-05-11T02:24:06.716033Z",
     "iopub.status.idle": "2024-05-11T02:24:06.724812Z",
     "shell.execute_reply": "2024-05-11T02:24:06.724314Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.210691Z"
    },
    "papermill": {
     "duration": 0.084721,
     "end_time": "2024-05-11T02:24:06.724918",
     "exception": false,
     "start_time": "2024-05-11T02:24:06.640197",
     "status": "completed"
    },
    "tags": []
   },
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>WANNA</td>\n",
       "      <td>-0.029934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>WEIRD</td>\n",
       "      <td>-0.031056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>IDK</td>\n",
       "      <td>-0.032433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WE</td>\n",
       "      <td>-0.032647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>US</td>\n",
       "      <td>-0.032701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>CLASS</td>\n",
       "      <td>-0.047545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>GIRL</td>\n",
       "      <td>-0.047838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>REDDIT</td>\n",
       "      <td>-0.048870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>KINDA</td>\n",
       "      <td>-0.054916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>PLAY</td>\n",
       "      <td>-0.059581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>GIRLS</td>\n",
       "      <td>-0.064615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>YOUR</td>\n",
       "      <td>-0.070825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>U</td>\n",
       "      <td>-0.078854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FILLER</td>\n",
       "      <td>-0.100234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>GUYS</td>\n",
       "      <td>-0.101048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature  Correlation\n",
       "106   WANNA    -0.029934\n",
       "371   WEIRD    -0.031056\n",
       "246     IDK    -0.032433\n",
       "4        WE    -0.032647\n",
       "230      US    -0.032701\n",
       "344   CLASS    -0.047545\n",
       "151    GIRL    -0.047838\n",
       "281  REDDIT    -0.048870\n",
       "270   KINDA    -0.054916\n",
       "286    PLAY    -0.059581\n",
       "334   GIRLS    -0.064615\n",
       "18     YOUR    -0.070825\n",
       "298       U    -0.078854\n",
       "3    FILLER    -0.100234\n",
       "160    GUYS    -0.101048"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Correlations.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "efcccfc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:06.880194Z",
     "iopub.status.busy": "2024-05-11T02:24:06.879395Z",
     "iopub.status.idle": "2024-05-11T02:24:07.085914Z",
     "shell.execute_reply": "2024-05-11T02:24:07.085362Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.225792Z"
    },
    "papermill": {
     "duration": 0.288349,
     "end_time": "2024-05-11T02:24:07.086037",
     "exception": false,
     "start_time": "2024-05-11T02:24:06.797688",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Correlations.tail(15)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Correlation,y=temp.Feature)\n",
    "plt.title('Feature vs Correlation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5b2a4c34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:07.239695Z",
     "iopub.status.busy": "2024-05-11T02:24:07.239111Z",
     "iopub.status.idle": "2024-05-11T02:24:07.241143Z",
     "shell.execute_reply": "2024-05-11T02:24:07.240708Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.509818Z"
    },
    "papermill": {
     "duration": 0.079531,
     "end_time": "2024-05-11T02:24:07.241259",
     "exception": false,
     "start_time": "2024-05-11T02:24:07.161728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#here we get the highest negative correlations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4b359f02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:07.391223Z",
     "iopub.status.busy": "2024-05-11T02:24:07.390755Z",
     "iopub.status.idle": "2024-05-11T02:24:07.393358Z",
     "shell.execute_reply": "2024-05-11T02:24:07.392981Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.515881Z"
    },
    "papermill": {
     "duration": 0.078474,
     "end_time": "2024-05-11T02:24:07.393482",
     "exception": false,
     "start_time": "2024-05-11T02:24:07.315008",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2e569392",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:07.543779Z",
     "iopub.status.busy": "2024-05-11T02:24:07.543191Z",
     "iopub.status.idle": "2024-05-11T02:24:07.545853Z",
     "shell.execute_reply": "2024-05-11T02:24:07.545420Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.529221Z"
    },
    "papermill": {
     "duration": 0.078895,
     "end_time": "2024-05-11T02:24:07.545953",
     "exception": false,
     "start_time": "2024-05-11T02:24:07.467058",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#We will do this by taking a sample of the data for training and using the rest for prediction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ee60a7b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:07.699390Z",
     "iopub.status.busy": "2024-05-11T02:24:07.698811Z",
     "iopub.status.idle": "2024-05-11T02:24:07.700664Z",
     "shell.execute_reply": "2024-05-11T02:24:07.699998Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.542468Z"
    },
    "papermill": {
     "duration": 0.080461,
     "end_time": "2024-05-11T02:24:07.700798",
     "exception": false,
     "start_time": "2024-05-11T02:24:07.620337",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Also since we are mostly interested in the words we will drop the variable 'Length' for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4f4bb2df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:07.853745Z",
     "iopub.status.busy": "2024-05-11T02:24:07.853089Z",
     "iopub.status.idle": "2024-05-11T02:24:08.132989Z",
     "shell.execute_reply": "2024-05-11T02:24:08.132481Z",
     "shell.execute_reply.started": "2022-03-07T11:26:16.555038Z"
    },
    "papermill": {
     "duration": 0.35794,
     "end_time": "2024-05-11T02:24:08.133112",
     "exception": false,
     "start_time": "2024-05-11T02:24:07.775172",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Suicide_Pred = Suicide.drop('Length',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a2055844",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:08.286504Z",
     "iopub.status.busy": "2024-05-11T02:24:08.285954Z",
     "iopub.status.idle": "2024-05-11T02:24:08.289166Z",
     "shell.execute_reply": "2024-05-11T02:24:08.288650Z",
     "shell.execute_reply.started": "2022-03-07T11:26:17.235601Z"
    },
    "papermill": {
     "duration": 0.081909,
     "end_time": "2024-05-11T02:24:08.289274",
     "exception": false,
     "start_time": "2024-05-11T02:24:08.207365",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208955"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Suicide_Pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff661f5f",
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       "   class  SOME  ME.  FILLER  WE  HAS  FUCKING  WHY  BEING  ANY  ...  WEIRD  \\\n",
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     "metadata": {},
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   ],
   "source": [
    "Suicide_Pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6bcecb9e",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "be380e5c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "        SOME  ME.  FILLER  WE  HAS  FUCKING  WHY  BEING  ANY  MORE  ...  \\\n",
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     "execution_count": 55,
     "metadata": {},
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   "source": [
    "X = Suicide_Pred.drop('class',axis=1)\n",
    "y = Suicide_Pred['class']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.01,random_state=100)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2089"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6a5596b6",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2024-05-11T02:24:09.909710Z",
     "iopub.status.idle": "2024-05-11T02:24:09.913250Z",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "#Checking the class distirbution of the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "61790463",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "0    1172\n",
       "1     917\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c16834ec",
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   "outputs": [],
   "source": [
    "#As we can see we get a balanced data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c866d265",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:10.391872Z",
     "iopub.status.busy": "2024-05-11T02:24:10.391345Z",
     "iopub.status.idle": "2024-05-11T02:24:10.579993Z",
     "shell.execute_reply": "2024-05-11T02:24:10.580403Z",
     "shell.execute_reply.started": "2022-03-07T11:26:18.323177Z"
    },
    "papermill": {
     "duration": 0.269429,
     "end_time": "2024-05-11T02:24:10.580598",
     "exception": false,
     "start_time": "2024-05-11T02:24:10.311169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "601c7474",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:24:10.738100Z",
     "iopub.status.busy": "2024-05-11T02:24:10.737596Z",
     "iopub.status.idle": "2024-05-11T02:26:20.160547Z",
     "shell.execute_reply": "2024-05-11T02:26:20.160968Z",
     "shell.execute_reply.started": "2022-03-07T11:26:18.402159Z"
    },
    "papermill": {
     "duration": 129.50529,
     "end_time": "2024-05-11T02:26:20.161107",
     "exception": false,
     "start_time": "2024-05-11T02:24:10.655817",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 70 candidates, totalling 350 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 10, 12, 15, 17, 20],\n",
       "                         &#x27;max_features&#x27;: [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;n_estimators&#x27;: [200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 10, 12, 15, 17, 20],\n",
       "                         &#x27;max_features&#x27;: [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;n_estimators&#x27;: [200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=100)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=100)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [3, 5, 10, 12, 15, 17, 20],\n",
       "                         'max_features': [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         'n_estimators': [200]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = RandomForestClassifier(random_state=100,n_jobs=-1)\n",
    "\n",
    "params = {'n_estimators':[200],\n",
    "          'max_depth':[3,5,10,12,15,17,20],\n",
    "          'max_features':[0.1,0.15,0.2,0.25,0.3],\n",
    "          'criterion':['gini','entropy']}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=Model,param_grid=params,verbose=1,n_jobs=-1,scoring='accuracy')\n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8c07a2a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:20.319074Z",
     "iopub.status.busy": "2024-05-11T02:26:20.318489Z",
     "iopub.status.idle": "2024-05-11T02:26:20.320796Z",
     "shell.execute_reply": "2024-05-11T02:26:20.321292Z",
     "shell.execute_reply.started": "2022-03-07T11:29:18.991558Z"
    },
    "papermill": {
     "duration": 0.083342,
     "end_time": "2024-05-11T02:26:20.321441",
     "exception": false,
     "start_time": "2024-05-11T02:26:20.238099",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Model_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d97301b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:20.484329Z",
     "iopub.status.busy": "2024-05-11T02:26:20.478608Z",
     "iopub.status.idle": "2024-05-11T02:26:22.723877Z",
     "shell.execute_reply": "2024-05-11T02:26:22.724329Z",
     "shell.execute_reply.started": "2022-03-07T11:29:18.998065Z"
    },
    "papermill": {
     "duration": 2.326406,
     "end_time": "2024-05-11T02:26:22.724532",
     "exception": false,
     "start_time": "2024-05-11T02:26:20.398126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy score : 0.9305887984681666\n",
      "Test accuracy score : 0.8374841684955479\n",
      "Train recall score : 0.8451472191930207\n",
      "Test recall score : 0.767300992258898\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = Model_best.predict(X_train)\n",
    "y_test_pred = Model_best.predict(X_test)\n",
    "\n",
    "print('Train accuracy score :',accuracy_score(y_train,y_train_pred))\n",
    "print('Test accuracy score :',accuracy_score(y_test,y_test_pred))\n",
    "print('Train recall score :',recall_score(y_train,y_train_pred))\n",
    "print('Test recall score :',recall_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "22af100f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:22.883285Z",
     "iopub.status.busy": "2024-05-11T02:26:22.882809Z",
     "iopub.status.idle": "2024-05-11T02:26:22.993184Z",
     "shell.execute_reply": "2024-05-11T02:26:22.992683Z",
     "shell.execute_reply.started": "2022-03-07T11:29:21.888769Z"
    },
    "papermill": {
     "duration": 0.192431,
     "end_time": "2024-05-11T02:26:22.993293",
     "exception": false,
     "start_time": "2024-05-11T02:26:22.800862",
     "status": "completed"
    },
    "tags": []
   },
   "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>Importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUICIDE</th>\n",
       "      <td>0.066679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KILL</th>\n",
       "      <td>0.063625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME.</th>\n",
       "      <td>0.042326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>END</th>\n",
       "      <td>0.033182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUICIDAL</th>\n",
       "      <td>0.026878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Importance\n",
       "Feature             \n",
       "SUICIDE     0.066679\n",
       "KILL        0.063625\n",
       "ME.         0.042326\n",
       "END         0.033182\n",
       "SUICIDAL    0.026878"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_Importance = pd.DataFrame({'Feature':X_train.columns,'Importance':Model_best.feature_importances_})\n",
    "Feature_Importance.sort_values(by='Importance',ascending=False,inplace=True)\n",
    "Feature_Importance.set_index('Feature',inplace=True)\n",
    "Feature_Importance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2c72f779",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:23.238314Z",
     "iopub.status.busy": "2024-05-11T02:26:23.177895Z",
     "iopub.status.idle": "2024-05-11T02:26:23.415917Z",
     "shell.execute_reply": "2024-05-11T02:26:23.415397Z",
     "shell.execute_reply.started": "2022-03-07T11:29:22.01035Z"
    },
    "papermill": {
     "duration": 0.344804,
     "end_time": "2024-05-11T02:26:23.416033",
     "exception": false,
     "start_time": "2024-05-11T02:26:23.071229",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Feature_Importance.head(20)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Importance,y=temp.index)\n",
    "plt.title('Features vs Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e5de10b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:23.582782Z",
     "iopub.status.busy": "2024-05-11T02:26:23.582172Z",
     "iopub.status.idle": "2024-05-11T02:26:23.584706Z",
     "shell.execute_reply": "2024-05-11T02:26:23.584270Z",
     "shell.execute_reply.started": "2022-03-07T11:29:22.370397Z"
    },
    "papermill": {
     "duration": 0.087017,
     "end_time": "2024-05-11T02:26:23.584830",
     "exception": false,
     "start_time": "2024-05-11T02:26:23.497813",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#We can also obtain the words/variables with the lowest importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fcd6039a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:23.829085Z",
     "iopub.status.busy": "2024-05-11T02:26:23.778251Z",
     "iopub.status.idle": "2024-05-11T02:26:24.009208Z",
     "shell.execute_reply": "2024-05-11T02:26:24.008686Z",
     "shell.execute_reply.started": "2022-03-07T11:29:22.375985Z"
    },
    "papermill": {
     "duration": 0.34403,
     "end_time": "2024-05-11T02:26:24.009321",
     "exception": false,
     "start_time": "2024-05-11T02:26:23.665291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Feature_Importance.tail(20)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Importance,y=temp.index)\n",
    "plt.title('Features vs Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6503a4a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:24.169567Z",
     "iopub.status.busy": "2024-05-11T02:26:24.168938Z",
     "iopub.status.idle": "2024-05-11T02:26:24.171765Z",
     "shell.execute_reply": "2024-05-11T02:26:24.171187Z",
     "shell.execute_reply.started": "2022-03-07T11:29:22.725343Z"
    },
    "papermill": {
     "duration": 0.083926,
     "end_time": "2024-05-11T02:26:24.171917",
     "exception": false,
     "start_time": "2024-05-11T02:26:24.087991",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#As we can see the model overfits the data slightly. However relative to the amount of data used for training and testing the \n",
    "#model has performed well. We can improve the scores by further HP tuning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc8bc6a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:24.335411Z",
     "iopub.status.busy": "2024-05-11T02:26:24.334895Z",
     "iopub.status.idle": "2024-05-11T02:26:24.339546Z",
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    "papermill": {
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     "exception": false,
     "start_time": "2024-05-11T02:26:24.251281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Now we will include the Length variable as well in our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e7a0a313",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
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       "<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>Length</th>\n",
       "      <th>SOME</th>\n",
       "      <th>ME.</th>\n",
       "      <th>FILLER</th>\n",
       "      <th>WE</th>\n",
       "      <th>HAS</th>\n",
       "      <th>FUCKING</th>\n",
       "      <th>WHY</th>\n",
       "      <th>BEING</th>\n",
       "      <th>ANY</th>\n",
       "      <th>...</th>\n",
       "      <th>WEIRD</th>\n",
       "      <th>WHAT'S</th>\n",
       "      <th>LONGER</th>\n",
       "      <th>OTHERS</th>\n",
       "      <th>THANK</th>\n",
       "      <th>SUPPOSED</th>\n",
       "      <th>HEAR</th>\n",
       "      <th>FRIENDS.</th>\n",
       "      <th>MOTHER</th>\n",
       "      <th>LOVED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>153315</th>\n",
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       "      <th>115407</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189274</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85559</th>\n",
       "      <td>37</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 381 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Length  SOME  ME.  FILLER  WE  HAS  FUCKING  WHY  BEING  ANY  ...  \\\n",
       "15131       29     0    0       0   0    0        0    0      0    0  ...   \n",
       "153315     274     1    0       0   1    1        0    0      0    0  ...   \n",
       "115407      35     0    0       0   0    0        0    0      0    0  ...   \n",
       "189274      19     0    0       0   0    0        0    0      0    0  ...   \n",
       "85559       37     1    0       0   0    0        0    0      0    0  ...   \n",
       "\n",
       "        WEIRD  WHAT'S  LONGER  OTHERS  THANK  SUPPOSED  HEAR  FRIENDS.  \\\n",
       "15131       0       0       0       0      0         0     0         0   \n",
       "153315      0       0       0       0      0         0     0         0   \n",
       "115407      0       0       0       0      0         0     0         0   \n",
       "189274      0       0       0       0      0         0     0         0   \n",
       "85559       0       0       1       0      0         0     0         0   \n",
       "\n",
       "        MOTHER  LOVED  \n",
       "15131        0      0  \n",
       "153315       0      0  \n",
       "115407       0      0  \n",
       "189274       0      0  \n",
       "85559        0      0  \n",
       "\n",
       "[5 rows x 381 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Suicide.drop('class',axis=1)\n",
    "y = Suicide['class']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.01,random_state=100)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3e561357",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:25.269871Z",
     "iopub.status.busy": "2024-05-11T02:26:25.269142Z",
     "iopub.status.idle": "2024-05-11T02:26:25.272659Z",
     "shell.execute_reply": "2024-05-11T02:26:25.272147Z",
     "shell.execute_reply.started": "2022-03-07T11:29:23.629194Z"
    },
    "papermill": {
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     "end_time": "2024-05-11T02:26:25.272791",
     "exception": false,
     "start_time": "2024-05-11T02:26:25.184558",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2032eaac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:26:25.437202Z",
     "iopub.status.busy": "2024-05-11T02:26:25.436344Z",
     "iopub.status.idle": "2024-05-11T02:28:35.807737Z",
     "shell.execute_reply": "2024-05-11T02:28:35.808164Z",
     "shell.execute_reply.started": "2022-03-07T11:29:23.63572Z"
    },
    "papermill": {
     "duration": 130.457198,
     "end_time": "2024-05-11T02:28:35.808310",
     "exception": false,
     "start_time": "2024-05-11T02:26:25.351112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 70 candidates, totalling 350 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 10, 12, 15, 17, 20],\n",
       "                         &#x27;max_features&#x27;: [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;n_estimators&#x27;: [200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 10, 12, 15, 17, 20],\n",
       "                         &#x27;max_features&#x27;: [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         &#x27;n_estimators&#x27;: [200]},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=100)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=100)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, random_state=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [3, 5, 10, 12, 15, 17, 20],\n",
       "                         'max_features': [0.1, 0.15, 0.2, 0.25, 0.3],\n",
       "                         'n_estimators': [200]},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Model = RandomForestClassifier(random_state=100,n_jobs=-1)\n",
    "\n",
    "params = {'n_estimators':[200],\n",
    "          'max_depth':[3,5,10,12,15,17,20],\n",
    "          'max_features':[0.1,0.15,0.2,0.25,0.3],\n",
    "          'criterion':['gini','entropy']}\n",
    "\n",
    "grid_search = GridSearchCV(estimator=Model,param_grid=params,verbose=1,n_jobs=-1,scoring='accuracy')\n",
    "grid_search.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "727d994d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:28:35.976753Z",
     "iopub.status.busy": "2024-05-11T02:28:35.976011Z",
     "iopub.status.idle": "2024-05-11T02:28:35.978327Z",
     "shell.execute_reply": "2024-05-11T02:28:35.977801Z",
     "shell.execute_reply.started": "2022-03-07T11:32:26.334691Z"
    },
    "papermill": {
     "duration": 0.087513,
     "end_time": "2024-05-11T02:28:35.978448",
     "exception": false,
     "start_time": "2024-05-11T02:28:35.890935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Model_best = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c6c35d87",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:28:36.145107Z",
     "iopub.status.busy": "2024-05-11T02:28:36.144435Z",
     "iopub.status.idle": "2024-05-11T02:28:38.169707Z",
     "shell.execute_reply": "2024-05-11T02:28:38.170220Z",
     "shell.execute_reply.started": "2022-03-07T11:32:26.341121Z"
    },
    "papermill": {
     "duration": 2.108489,
     "end_time": "2024-05-11T02:28:38.170367",
     "exception": false,
     "start_time": "2024-05-11T02:28:36.061878",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy score : 0.9377692675921494\n",
      "Test accuracy score : 0.8369185849777151\n",
      "Train recall score : 0.8582333696837514\n",
      "Test recall score : 0.7758680941639927\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = Model_best.predict(X_train)\n",
    "y_test_pred = Model_best.predict(X_test)\n",
    "\n",
    "print('Train accuracy score :',accuracy_score(y_train,y_train_pred))\n",
    "print('Test accuracy score :',accuracy_score(y_test,y_test_pred))\n",
    "print('Train recall score :',recall_score(y_train,y_train_pred))\n",
    "print('Test recall score :',recall_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b51be1fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:28:38.338591Z",
     "iopub.status.busy": "2024-05-11T02:28:38.338055Z",
     "iopub.status.idle": "2024-05-11T02:28:38.448693Z",
     "shell.execute_reply": "2024-05-11T02:28:38.448156Z",
     "shell.execute_reply.started": "2022-03-07T11:32:29.108587Z"
    },
    "papermill": {
     "duration": 0.196445,
     "end_time": "2024-05-11T02:28:38.448816",
     "exception": false,
     "start_time": "2024-05-11T02:28:38.252371",
     "status": "completed"
    },
    "tags": []
   },
   "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>Importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Length</th>\n",
       "      <td>0.198716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUICIDE</th>\n",
       "      <td>0.069252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KILL</th>\n",
       "      <td>0.060520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME.</th>\n",
       "      <td>0.041317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>END</th>\n",
       "      <td>0.027419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Importance\n",
       "Feature            \n",
       "Length     0.198716\n",
       "SUICIDE    0.069252\n",
       "KILL       0.060520\n",
       "ME.        0.041317\n",
       "END        0.027419"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_Importance = pd.DataFrame({'Feature':X_train.columns,'Importance':Model_best.feature_importances_})\n",
    "Feature_Importance.sort_values(by='Importance',ascending=False,inplace=True)\n",
    "Feature_Importance.set_index('Feature',inplace=True)\n",
    "Feature_Importance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3ecf6876",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-05-11T02:28:38.875332Z",
     "shell.execute_reply": "2024-05-11T02:28:38.874914Z",
     "shell.execute_reply.started": "2022-03-07T11:32:29.227309Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Feature_Importance.head(20)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Importance,y=temp.index)\n",
    "plt.title('Features vs Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5893252d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:28:39.079505Z",
     "iopub.status.busy": "2024-05-11T02:28:39.078930Z",
     "iopub.status.idle": "2024-05-11T02:28:39.303242Z",
     "shell.execute_reply": "2024-05-11T02:28:39.303680Z",
     "shell.execute_reply.started": "2022-03-07T11:32:29.593044Z"
    },
    "papermill": {
     "duration": 0.345096,
     "end_time": "2024-05-11T02:28:39.303826",
     "exception": false,
     "start_time": "2024-05-11T02:28:38.958730",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = Feature_Importance.tail(20)\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=temp.Importance,y=temp.index)\n",
    "plt.title('Features vs Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "473613d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-11T02:28:39.471185Z",
     "iopub.status.busy": "2024-05-11T02:28:39.470715Z",
     "iopub.status.idle": "2024-05-11T02:28:39.474353Z",
     "shell.execute_reply": "2024-05-11T02:28:39.473977Z",
     "shell.execute_reply.started": "2022-03-07T11:32:29.94245Z"
    },
    "papermill": {
     "duration": 0.088795,
     "end_time": "2024-05-11T02:28:39.474468",
     "exception": false,
     "start_time": "2024-05-11T02:28:39.385673",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#We get similar scores to the previous model that involve a bit of overfitting. "
   ]
  },
  {
   "cell_type": "code",
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