diff --git a/notebooks/regression/preprocessing_Shivasmi.ipynb b/notebooks/regression/preprocessing_Shivasmi.ipynb
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+++ b/notebooks/regression/preprocessing_Shivasmi.ipynb
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "500a99ff-4d32-45b6-b5f4-4a9b7ae022dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>number</th>\n",
+       "      <th>incident_state</th>\n",
+       "      <th>active</th>\n",
+       "      <th>reassignment_count</th>\n",
+       "      <th>reopen_count</th>\n",
+       "      <th>sys_mod_count</th>\n",
+       "      <th>made_sla</th>\n",
+       "      <th>caller_id</th>\n",
+       "      <th>opened_by</th>\n",
+       "      <th>opened_at</th>\n",
+       "      <th>...</th>\n",
+       "      <th>u_priority_confirmation</th>\n",
+       "      <th>notify</th>\n",
+       "      <th>problem_id</th>\n",
+       "      <th>rfc</th>\n",
+       "      <th>vendor</th>\n",
+       "      <th>caused_by</th>\n",
+       "      <th>closed_code</th>\n",
+       "      <th>resolved_by</th>\n",
+       "      <th>resolved_at</th>\n",
+       "      <th>closed_at</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>INC0000045</td>\n",
+       "      <td>New</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Caller 2403</td>\n",
+       "      <td>Opened by  8</td>\n",
+       "      <td>29/2/2016 01:16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Do Not Notify</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>code 5</td>\n",
+       "      <td>Resolved by 149</td>\n",
+       "      <td>29/2/2016 11:29</td>\n",
+       "      <td>5/3/2016 12:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>INC0000045</td>\n",
+       "      <td>Resolved</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Caller 2403</td>\n",
+       "      <td>Opened by  8</td>\n",
+       "      <td>29/2/2016 01:16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Do Not Notify</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>code 5</td>\n",
+       "      <td>Resolved by 149</td>\n",
+       "      <td>29/2/2016 11:29</td>\n",
+       "      <td>5/3/2016 12:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>INC0000045</td>\n",
+       "      <td>Resolved</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Caller 2403</td>\n",
+       "      <td>Opened by  8</td>\n",
+       "      <td>29/2/2016 01:16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Do Not Notify</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>code 5</td>\n",
+       "      <td>Resolved by 149</td>\n",
+       "      <td>29/2/2016 11:29</td>\n",
+       "      <td>5/3/2016 12:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>INC0000045</td>\n",
+       "      <td>Closed</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Caller 2403</td>\n",
+       "      <td>Opened by  8</td>\n",
+       "      <td>29/2/2016 01:16</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Do Not Notify</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>code 5</td>\n",
+       "      <td>Resolved by 149</td>\n",
+       "      <td>29/2/2016 11:29</td>\n",
+       "      <td>5/3/2016 12:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>INC0000047</td>\n",
+       "      <td>New</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Caller 2403</td>\n",
+       "      <td>Opened by  397</td>\n",
+       "      <td>29/2/2016 04:40</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Do Not Notify</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>code 5</td>\n",
+       "      <td>Resolved by 81</td>\n",
+       "      <td>1/3/2016 09:52</td>\n",
+       "      <td>6/3/2016 10:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       number incident_state  active  reassignment_count  reopen_count  \\\n",
+       "0  INC0000045            New    True                   0             0   \n",
+       "1  INC0000045       Resolved    True                   0             0   \n",
+       "2  INC0000045       Resolved    True                   0             0   \n",
+       "3  INC0000045         Closed   False                   0             0   \n",
+       "4  INC0000047            New    True                   0             0   \n",
+       "\n",
+       "   sys_mod_count  made_sla    caller_id       opened_by        opened_at  ...  \\\n",
+       "0              0      True  Caller 2403    Opened by  8  29/2/2016 01:16  ...   \n",
+       "1              2      True  Caller 2403    Opened by  8  29/2/2016 01:16  ...   \n",
+       "2              3      True  Caller 2403    Opened by  8  29/2/2016 01:16  ...   \n",
+       "3              4      True  Caller 2403    Opened by  8  29/2/2016 01:16  ...   \n",
+       "4              0      True  Caller 2403  Opened by  397  29/2/2016 04:40  ...   \n",
+       "\n",
+       "  u_priority_confirmation         notify problem_id rfc vendor caused_by  \\\n",
+       "0                   False  Do Not Notify          ?   ?      ?         ?   \n",
+       "1                   False  Do Not Notify          ?   ?      ?         ?   \n",
+       "2                   False  Do Not Notify          ?   ?      ?         ?   \n",
+       "3                   False  Do Not Notify          ?   ?      ?         ?   \n",
+       "4                   False  Do Not Notify          ?   ?      ?         ?   \n",
+       "\n",
+       "  closed_code      resolved_by      resolved_at       closed_at  \n",
+       "0      code 5  Resolved by 149  29/2/2016 11:29  5/3/2016 12:00  \n",
+       "1      code 5  Resolved by 149  29/2/2016 11:29  5/3/2016 12:00  \n",
+       "2      code 5  Resolved by 149  29/2/2016 11:29  5/3/2016 12:00  \n",
+       "3      code 5  Resolved by 149  29/2/2016 11:29  5/3/2016 12:00  \n",
+       "4      code 5   Resolved by 81   1/3/2016 09:52  6/3/2016 10:00  \n",
+       "\n",
+       "[5 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "df = pd.read_csv(\"incident_event_log.csv\")\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5295dd7a-bb01-407b-ae01-9d4b0ed9e7d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert opened_at and resolved_at to datetime\n",
+    "df[\"opened_at\"] = pd.to_datetime(df[\"opened_at\"], format=\"%d/%m/%Y %H:%M\", errors=\"coerce\")\n",
+    "df[\"resolved_at\"] = pd.to_datetime(df[\"resolved_at\"], format=\"%d/%m/%Y %H:%M\", errors=\"coerce\")\n",
+    "\n",
+    "# Now calculate time_to_resolution (in hours)\n",
+    "df[\"time_to_resolution\"] = (df[\"resolved_at\"] - df[\"opened_at\"]).dt.total_seconds() / 3600\n",
+    "df.dtypes[\"time_to_resolution\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5ed1dbc4-08a2-4f59-9a95-91c79d313da4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "number                        0\n",
+       "incident_state                0\n",
+       "active                        0\n",
+       "reassignment_count            0\n",
+       "reopen_count                  0\n",
+       "sys_mod_count                 0\n",
+       "made_sla                      0\n",
+       "caller_id                     0\n",
+       "opened_by                     0\n",
+       "opened_at                     0\n",
+       "sys_created_by                0\n",
+       "sys_created_at                0\n",
+       "sys_updated_by                0\n",
+       "sys_updated_at                0\n",
+       "contact_type                  0\n",
+       "location                      0\n",
+       "category                      0\n",
+       "subcategory                   0\n",
+       "u_symptom                     0\n",
+       "cmdb_ci                       0\n",
+       "impact                        0\n",
+       "urgency                       0\n",
+       "priority                      0\n",
+       "assignment_group              0\n",
+       "assigned_to                   0\n",
+       "knowledge                     0\n",
+       "u_priority_confirmation       0\n",
+       "notify                        0\n",
+       "problem_id                    0\n",
+       "rfc                           0\n",
+       "vendor                        0\n",
+       "caused_by                     0\n",
+       "closed_code                   0\n",
+       "resolved_by                   0\n",
+       "resolved_at                3141\n",
+       "closed_at                     0\n",
+       "time_to_resolution         3141\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57133d6b-adb1-4a71-8e88-6687e4e043ca",
+   "metadata": {},
+   "source": [
+    "Time_to_resolution depends directly on resolved_at, those two missing together mean that incident was never happened. Date and time can be imputed but in this case imputing would mean artifically creating target values which is data leakeage and leads to garbage prediction. Therefore, dropping missing values would be better choice in this case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f7e48ed-656f-4fff-92b7-9b18045b2363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna(subset=['resolved_at'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "480a9211-91b1-41e8-8414-cbcd60d2b66f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "number                          0\n",
+       "incident_state                  0\n",
+       "active                          0\n",
+       "reassignment_count              0\n",
+       "reopen_count                    0\n",
+       "sys_mod_count                   0\n",
+       "made_sla                        0\n",
+       "caller_id                      29\n",
+       "opened_by                    4714\n",
+       "opened_at                       0\n",
+       "sys_created_by              49943\n",
+       "sys_created_at              49943\n",
+       "sys_updated_by                  0\n",
+       "sys_updated_at                  0\n",
+       "contact_type                    0\n",
+       "location                       73\n",
+       "category                       78\n",
+       "subcategory                   108\n",
+       "u_symptom                   32155\n",
+       "cmdb_ci                    138128\n",
+       "impact                          0\n",
+       "urgency                         0\n",
+       "priority                        0\n",
+       "assignment_group            14204\n",
+       "assigned_to                 27346\n",
+       "knowledge                       0\n",
+       "u_priority_confirmation         0\n",
+       "notify                          0\n",
+       "problem_id                 136276\n",
+       "rfc                        137580\n",
+       "vendor                     138327\n",
+       "caused_by                  138548\n",
+       "closed_code                   703\n",
+       "resolved_by                    71\n",
+       "resolved_at                     0\n",
+       "closed_at                       0\n",
+       "time_to_resolution              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "df.replace(\"?\", np.nan, inplace=True)\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cb9a621a-8cd4-4f8d-98a9-c054c25b08b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols_to_drop = [\n",
+    "    \"sys_created_by\", \"sys_created_at\", \"cmdb_ci\", \"problem_id\", \"sys_updated_by\", \"sys_updated_at\" , \"active\" , \"made_sla\",\n",
+    "    \"rfc\", \"vendor\", \"caused_by\"\n",
+    "]\n",
+    "df.drop(columns=cols_to_drop, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4958fabf-d9b2-4435-b473-0164ead2a174",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 138571 entries, 0 to 141711\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column                   Non-Null Count   Dtype         \n",
+      "---  ------                   --------------   -----         \n",
+      " 0   number                   138571 non-null  object        \n",
+      " 1   incident_state           138571 non-null  object        \n",
+      " 2   reassignment_count       138571 non-null  int64         \n",
+      " 3   reopen_count             138571 non-null  int64         \n",
+      " 4   sys_mod_count            138571 non-null  int64         \n",
+      " 5   caller_id                138542 non-null  object        \n",
+      " 6   opened_by                133857 non-null  object        \n",
+      " 7   opened_at                138571 non-null  datetime64[ns]\n",
+      " 8   contact_type             138571 non-null  object        \n",
+      " 9   location                 138498 non-null  object        \n",
+      " 10  category                 138493 non-null  object        \n",
+      " 11  subcategory              138463 non-null  object        \n",
+      " 12  u_symptom                106416 non-null  object        \n",
+      " 13  impact                   138571 non-null  object        \n",
+      " 14  urgency                  138571 non-null  object        \n",
+      " 15  priority                 138571 non-null  object        \n",
+      " 16  assignment_group         124367 non-null  object        \n",
+      " 17  assigned_to              111225 non-null  object        \n",
+      " 18  knowledge                138571 non-null  bool          \n",
+      " 19  u_priority_confirmation  138571 non-null  bool          \n",
+      " 20  notify                   138571 non-null  object        \n",
+      " 21  closed_code              137868 non-null  object        \n",
+      " 22  resolved_by              138500 non-null  object        \n",
+      " 23  resolved_at              138571 non-null  datetime64[ns]\n",
+      " 24  closed_at                138571 non-null  object        \n",
+      " 25  time_to_resolution       138571 non-null  float64       \n",
+      "dtypes: bool(2), datetime64[ns](2), float64(1), int64(3), object(18)\n",
+      "memory usage: 26.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbad0fef-fe33-463b-97a9-855cdaba37ad",
+   "metadata": {},
+   "source": [
+    "If assigned_to and caller_id impact how fast tickets are resolved (and they likely do), we shouldn't just drop them without trying to extract useful, generalizable signal.Instead of using raw names, extract performance-related metrics, like:\n",
+    "    assigned_to_avg_resolution_time\n",
+    "    caller_avg_resolution_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dea59cf2-1238-469a-9da5-738f502b33f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fallback: global average for safety\n",
+    "global_avg = df[\"time_to_resolution\"].mean()\n",
+    "\n",
+    "# 1. caller_id → caller_avg_resolution\n",
+    "caller_avg = df.groupby(\"caller_id\")[\"time_to_resolution\"].mean()\n",
+    "df[\"caller_avg_resolution\"] = df[\"caller_id\"].map(caller_avg).fillna(global_avg)\n",
+    "\n",
+    "# 2. assigned_to → assigned_avg_resolution\n",
+    "assigned_avg = df.groupby(\"assigned_to\")[\"time_to_resolution\"].mean()\n",
+    "df[\"assigned_avg_resolution\"] = df[\"assigned_to\"].map(assigned_avg).fillna(global_avg)\n",
+    "\n",
+    "# 3. opened_by → opened_by_avg_resolution\n",
+    "opened_by_avg = df.groupby(\"opened_by\")[\"time_to_resolution\"].mean()\n",
+    "df[\"opened_by_avg_resolution\"] = df[\"opened_by\"].map(opened_by_avg).fillna(global_avg)\n",
+    "\n",
+    "# 4. resolved_by → resolved_by_avg_resolution\n",
+    "resolved_avg = df.groupby(\"resolved_by\")[\"time_to_resolution\"].mean()\n",
+    "df[\"resolved_by_avg_resolution\"] = df[\"resolved_by\"].map(resolved_avg).fillna(global_avg)\n",
+    "\n",
+    "# 5. u_symptom → symptom_avg_resolution\n",
+    "symptom_avg = df.groupby(\"u_symptom\")[\"time_to_resolution\"].mean()\n",
+    "df[\"symptom_avg_resolution\"] = df[\"u_symptom\"].map(symptom_avg).fillna(global_avg)\n",
+    "\n",
+    "# 6. closed_code → closed_code_avg_resolution\n",
+    "closed_code_avg = df.groupby(\"closed_code\")[\"time_to_resolution\"].mean()\n",
+    "df[\"closed_code_avg_resolution\"] = df[\"closed_code\"].map(closed_code_avg).fillna(global_avg)\n",
+    "\n",
+    "# 7. location → location_avg_resolution\n",
+    "loc_avg = df.groupby(\"location\")[\"time_to_resolution\"].mean()\n",
+    "df[\"location_avg_resolution\"] = df[\"location\"].map(loc_avg).fillna(global_avg)\n",
+    "\n",
+    "# 8. category → category_avg_resolution\n",
+    "cat_avg = df.groupby(\"category\")[\"time_to_resolution\"].mean()\n",
+    "df[\"category_avg_resolution\"] = df[\"category\"].map(cat_avg).fillna(global_avg)\n",
+    "\n",
+    "# 9. subcategory → subcategory_avg_resolution\n",
+    "subcat_avg = df.groupby(\"subcategory\")[\"time_to_resolution\"].mean()\n",
+    "df[\"subcategory_avg_resolution\"] = df[\"subcategory\"].map(subcat_avg).fillna(global_avg)\n",
+    "# 10. assignment_group → assignment_group_avg_resolution\n",
+    "assignment_avg = df.groupby(\"assignment_group\")[\"time_to_resolution\"].mean()\n",
+    "df[\"assignment_group_avg_resolution\"] = df[\"assignment_group\"].map(assignment_avg).fillna(global_avg)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d7014cdc-3a56-4285-a0f7-e8325e3fce08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\n",
+    "    \"caller_id\", \"assigned_to\", \"opened_by\", \"resolved_by\", \"closed_at\", \"number\", \"resolved_at\", \"opened_at\",\n",
+    "    \"u_symptom\", \"closed_code\", \"location\", \"category\", \"subcategory\", \"assignment_group\"], inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a974899a-5ec0-461d-98b2-3caa3ea3a74e",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'opened_at'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'opened_at'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Convert if not already datetime\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopened_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mopened_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm/\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m, errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Feature Engineering from opened_at\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m#time-based signals that can affect resolution speed (e.g., weekend delays, night shift backlog).\u001b[39;00m\n\u001b[0;32m      6\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopened_hour\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopened_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mdt\u001b[38;5;241m.\u001b[39mhour                  \u001b[38;5;66;03m# Hour of day (0–23)\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'opened_at'"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert if not already datetime\n",
+    "df[\"opened_at\"] = pd.to_datetime(df[\"opened_at\"], format=\"%d/%m/%Y %H:%M\", errors=\"coerce\")\n",
+    "\n",
+    "# Feature Engineering from opened_at\n",
+    "#time-based signals that can affect resolution speed (e.g., weekend delays, night shift backlog).\n",
+    "df[\"opened_hour\"] = df[\"opened_at\"].dt.hour                  # Hour of day (0–23)\n",
+    "df[\"opened_dayofweek\"] = df[\"opened_at\"].dt.dayofweek        # Day of week (0 = Monday)\n",
+    "df[\"opened_month\"] = df[\"opened_at\"].dt.month                # Month (1–12)\n",
+    "df[\"opened_weekend\"] = df[\"opened_dayofweek\"].isin([5, 6]).astype(int)  # 1 if Saturday/Sunday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d3fef0-f7c3-45ea-b05e-ea56ba3fcdca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Any missing values left?\\n\", df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f85aa7f-3760-4a7d-acdb-b3d8857d72b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"processed_incident_event_log1.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93488637-b399-4eaa-ad34-a4791b4519c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c04552f7-5836-49fc-85d2-8d02439feb98",
+   "metadata": {},
+   "source": [
+    "ONE HOT ENCODING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c06d9f6-bcb0-4be4-84fd-dc9ca464691d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_cols = df.select_dtypes(include=[\"object\"]).columns.tolist()\n",
+    "print(cat_cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0abbf79-fc2c-492d-b931-98fe84e2df18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encode_cols = ['incident_state', 'contact_type', 'notify']\n",
+    "df = pd.get_dummies(df, columns=encode_cols, drop_first=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec6e15dd-55a9-4673-b752-9df59ff60f96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-run just to be safe\n",
+    "encode_cols = ['incident_state', 'contact_type', 'notify']\n",
+    "\n",
+    "# Count how many new one-hot columns exist for each\n",
+    "for col in encode_cols:\n",
+    "    one_hot_cols = [c for c in df.columns if col + \"_\" in c]\n",
+    "    print(f\"{col}: {len(one_hot_cols)} new columns\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a83b710-a730-4370-a924-11649c505b3f",
+   "metadata": {},
+   "source": [
+    "label-encoding. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0206b6f6-8a9f-47a4-af54-b4aa627fdcb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols_to_clean = [\"impact\", \"urgency\", \"priority\"]\n",
+    "\n",
+    "for col in cols_to_clean:\n",
+    "    df[col] = df[col].astype(str).str.extract(r\"(\\d+)\").astype(int)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "972a0795-d809-48d6-896e-dbd881c96662",
+   "metadata": {},
+   "source": [
+    "boolean encoding "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9349f21d-3aef-4d7b-8ed3-7a247696dc01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bool_cols = df.select_dtypes(include=\"bool\").columns\n",
+    "\n",
+    "for col in bool_cols:\n",
+    "    df[col] = df[col].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55d5723a-43be-4b6e-b626-baa1de64cf25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd0b59ea-978b-414f-95da-d4c8afeac470",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"processed_incident_event_log2.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ae681af8-6cf3-4f2f-8e57-e28afc8282aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=['incident_state', 'contact_type', 'impact', 'urgency', 'priority', 'notify'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1a70d5ca-9a2b-47a1-98cc-f09e467b8c85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Only use numeric features\n",
+    "numeric_df = df.select_dtypes(include=[\"int\", \"float\"])\n",
+    "\n",
+    "# Optional: Remove the target column if you want a clean correlation matrix\n",
+    "# numeric_df = numeric_df.drop(columns=[\"time_to_resolution\"])\n",
+    "\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "plt.title(\"Correlation Heatmap (Before One-Hot Encoding)\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fcba2062-cb53-4942-899e-d3e8ece396c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=[\"resolved_by_avg_resolution\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ccbcc0c8-f752-422a-90dd-1ad5ab0eefa5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 138571 entries, 0 to 141711\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column                           Non-Null Count   Dtype  \n",
+      "---  ------                           --------------   -----  \n",
+      " 0   reassignment_count               138571 non-null  int64  \n",
+      " 1   reopen_count                     138571 non-null  int64  \n",
+      " 2   sys_mod_count                    138571 non-null  int64  \n",
+      " 3   knowledge                        138571 non-null  bool   \n",
+      " 4   u_priority_confirmation          138571 non-null  bool   \n",
+      " 5   time_to_resolution               138571 non-null  float64\n",
+      " 6   caller_avg_resolution            138571 non-null  float64\n",
+      " 7   assigned_avg_resolution          138571 non-null  float64\n",
+      " 8   opened_by_avg_resolution         138571 non-null  float64\n",
+      " 9   symptom_avg_resolution           138571 non-null  float64\n",
+      " 10  closed_code_avg_resolution       138571 non-null  float64\n",
+      " 11  location_avg_resolution          138571 non-null  float64\n",
+      " 12  category_avg_resolution          138571 non-null  float64\n",
+      " 13  subcategory_avg_resolution       138571 non-null  float64\n",
+      " 14  assignment_group_avg_resolution  138571 non-null  float64\n",
+      "dtypes: bool(2), float64(10), int64(3)\n",
+      "memory usage: 15.1 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42d3b12f-42e3-4210-b786-b24e320f79b3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}