diff --git a/notebooks/regression/Regression_Preprocessed.ipynb b/notebooks/regression/Regression_Preprocessed.ipynb
deleted file mode 100644
index 81f6380cf4b46aba1992f48f516be1b3aa6bfaea..0000000000000000000000000000000000000000
--- a/notebooks/regression/Regression_Preprocessed.ipynb
+++ /dev/null
@@ -1,1850 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import plotly.express as px\n",
-    "\n",
-    "%matplotlib inline\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "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>date</th>\n",
-       "      <th>quarter</th>\n",
-       "      <th>department</th>\n",
-       "      <th>day</th>\n",
-       "      <th>team</th>\n",
-       "      <th>targeted_productivity</th>\n",
-       "      <th>smv</th>\n",
-       "      <th>wip</th>\n",
-       "      <th>over_time</th>\n",
-       "      <th>incentive</th>\n",
-       "      <th>idle_time</th>\n",
-       "      <th>idle_men</th>\n",
-       "      <th>no_of_style_change</th>\n",
-       "      <th>no_of_workers</th>\n",
-       "      <th>actual_productivity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1/1/2015</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sweing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>26.16</td>\n",
-       "      <td>1108.0</td>\n",
-       "      <td>7080</td>\n",
-       "      <td>98</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.940725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1/1/2015</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.886500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1/1/2015</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sweing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1/1/2015</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sweing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1/1/2015</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sweing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>25.90</td>\n",
-       "      <td>1170.0</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.800382</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1192</th>\n",
-       "      <td>3/11/2015</td>\n",
-       "      <td>Quarter2</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Wednesday</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>2.90</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.628333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1193</th>\n",
-       "      <td>3/11/2015</td>\n",
-       "      <td>Quarter2</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Wednesday</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>3.90</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.625625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1194</th>\n",
-       "      <td>3/11/2015</td>\n",
-       "      <td>Quarter2</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Wednesday</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>3.90</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.625625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1195</th>\n",
-       "      <td>3/11/2015</td>\n",
-       "      <td>Quarter2</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Wednesday</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>2.90</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>0.505889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1196</th>\n",
-       "      <td>3/11/2015</td>\n",
-       "      <td>Quarter2</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Wednesday</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>2.90</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>720</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.394722</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1197 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           date   quarter  department        day  team  targeted_productivity  \\\n",
-       "0      1/1/2015  Quarter1      sweing   Thursday     8                   0.80   \n",
-       "1      1/1/2015  Quarter1  finishing    Thursday     1                   0.75   \n",
-       "2      1/1/2015  Quarter1      sweing   Thursday    11                   0.80   \n",
-       "3      1/1/2015  Quarter1      sweing   Thursday    12                   0.80   \n",
-       "4      1/1/2015  Quarter1      sweing   Thursday     6                   0.80   \n",
-       "...         ...       ...         ...        ...   ...                    ...   \n",
-       "1192  3/11/2015  Quarter2   finishing  Wednesday    10                   0.75   \n",
-       "1193  3/11/2015  Quarter2   finishing  Wednesday     8                   0.70   \n",
-       "1194  3/11/2015  Quarter2   finishing  Wednesday     7                   0.65   \n",
-       "1195  3/11/2015  Quarter2   finishing  Wednesday     9                   0.75   \n",
-       "1196  3/11/2015  Quarter2   finishing  Wednesday     6                   0.70   \n",
-       "\n",
-       "        smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
-       "0     26.16  1108.0       7080         98        0.0         0   \n",
-       "1      3.94     NaN        960          0        0.0         0   \n",
-       "2     11.41   968.0       3660         50        0.0         0   \n",
-       "3     11.41   968.0       3660         50        0.0         0   \n",
-       "4     25.90  1170.0       1920         50        0.0         0   \n",
-       "...     ...     ...        ...        ...        ...       ...   \n",
-       "1192   2.90     NaN        960          0        0.0         0   \n",
-       "1193   3.90     NaN        960          0        0.0         0   \n",
-       "1194   3.90     NaN        960          0        0.0         0   \n",
-       "1195   2.90     NaN       1800          0        0.0         0   \n",
-       "1196   2.90     NaN        720          0        0.0         0   \n",
-       "\n",
-       "      no_of_style_change  no_of_workers  actual_productivity  \n",
-       "0                      0           59.0             0.940725  \n",
-       "1                      0            8.0             0.886500  \n",
-       "2                      0           30.5             0.800570  \n",
-       "3                      0           30.5             0.800570  \n",
-       "4                      0           56.0             0.800382  \n",
-       "...                  ...            ...                  ...  \n",
-       "1192                   0            8.0             0.628333  \n",
-       "1193                   0            8.0             0.625625  \n",
-       "1194                   0            8.0             0.625625  \n",
-       "1195                   0           15.0             0.505889  \n",
-       "1196                   0            6.0             0.394722  \n",
-       "\n",
-       "[1197 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"C:/Users/ritwi/garments_worker_productivity.csv\")\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1197 entries, 0 to 1196\n",
-      "Data columns (total 15 columns):\n",
-      " #   Column                 Non-Null Count  Dtype  \n",
-      "---  ------                 --------------  -----  \n",
-      " 0   date                   1197 non-null   object \n",
-      " 1   quarter                1197 non-null   object \n",
-      " 2   department             1197 non-null   object \n",
-      " 3   day                    1197 non-null   object \n",
-      " 4   team                   1197 non-null   int64  \n",
-      " 5   targeted_productivity  1197 non-null   float64\n",
-      " 6   smv                    1197 non-null   float64\n",
-      " 7   wip                    691 non-null    float64\n",
-      " 8   over_time              1197 non-null   int64  \n",
-      " 9   incentive              1197 non-null   int64  \n",
-      " 10  idle_time              1197 non-null   float64\n",
-      " 11  idle_men               1197 non-null   int64  \n",
-      " 12  no_of_style_change     1197 non-null   int64  \n",
-      " 13  no_of_workers          1197 non-null   float64\n",
-      " 14  actual_productivity    1197 non-null   float64\n",
-      "dtypes: float64(6), int64(5), object(4)\n",
-      "memory usage: 140.4+ KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Impute missing 'wip' with median\n",
-    "df['wip'] = df['wip'].fillna(df['wip'].median())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "date                     0\n",
-       "quarter                  0\n",
-       "department               0\n",
-       "day                      0\n",
-       "team                     0\n",
-       "targeted_productivity    0\n",
-       "smv                      0\n",
-       "wip                      0\n",
-       "over_time                0\n",
-       "incentive                0\n",
-       "idle_time                0\n",
-       "idle_men                 0\n",
-       "no_of_style_change       0\n",
-       "no_of_workers            0\n",
-       "actual_productivity      0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#checking for any existing null values\n",
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "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>team</th>\n",
-       "      <th>targeted_productivity</th>\n",
-       "      <th>smv</th>\n",
-       "      <th>wip</th>\n",
-       "      <th>over_time</th>\n",
-       "      <th>incentive</th>\n",
-       "      <th>idle_time</th>\n",
-       "      <th>idle_men</th>\n",
-       "      <th>no_of_style_change</th>\n",
-       "      <th>no_of_workers</th>\n",
-       "      <th>actual_productivity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "      <td>1197.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>6.426901</td>\n",
-       "      <td>0.729632</td>\n",
-       "      <td>15.062172</td>\n",
-       "      <td>1126.437761</td>\n",
-       "      <td>4567.460317</td>\n",
-       "      <td>38.210526</td>\n",
-       "      <td>0.730159</td>\n",
-       "      <td>0.369256</td>\n",
-       "      <td>0.150376</td>\n",
-       "      <td>34.609858</td>\n",
-       "      <td>0.735091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>3.463963</td>\n",
-       "      <td>0.097891</td>\n",
-       "      <td>10.943219</td>\n",
-       "      <td>1397.653191</td>\n",
-       "      <td>3348.823563</td>\n",
-       "      <td>160.182643</td>\n",
-       "      <td>12.709757</td>\n",
-       "      <td>3.268987</td>\n",
-       "      <td>0.427848</td>\n",
-       "      <td>22.197687</td>\n",
-       "      <td>0.174488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.070000</td>\n",
-       "      <td>2.900000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.233705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>3.940000</td>\n",
-       "      <td>970.000000</td>\n",
-       "      <td>1440.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>0.650307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>15.260000</td>\n",
-       "      <td>1039.000000</td>\n",
-       "      <td>3960.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>34.000000</td>\n",
-       "      <td>0.773333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>24.260000</td>\n",
-       "      <td>1083.000000</td>\n",
-       "      <td>6960.000000</td>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>57.000000</td>\n",
-       "      <td>0.850253</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>12.000000</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>54.560000</td>\n",
-       "      <td>23122.000000</td>\n",
-       "      <td>25920.000000</td>\n",
-       "      <td>3600.000000</td>\n",
-       "      <td>300.000000</td>\n",
-       "      <td>45.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>89.000000</td>\n",
-       "      <td>1.120437</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              team  targeted_productivity          smv           wip  \\\n",
-       "count  1197.000000            1197.000000  1197.000000   1197.000000   \n",
-       "mean      6.426901               0.729632    15.062172   1126.437761   \n",
-       "std       3.463963               0.097891    10.943219   1397.653191   \n",
-       "min       1.000000               0.070000     2.900000      7.000000   \n",
-       "25%       3.000000               0.700000     3.940000    970.000000   \n",
-       "50%       6.000000               0.750000    15.260000   1039.000000   \n",
-       "75%       9.000000               0.800000    24.260000   1083.000000   \n",
-       "max      12.000000               0.800000    54.560000  23122.000000   \n",
-       "\n",
-       "          over_time    incentive    idle_time     idle_men  \\\n",
-       "count   1197.000000  1197.000000  1197.000000  1197.000000   \n",
-       "mean    4567.460317    38.210526     0.730159     0.369256   \n",
-       "std     3348.823563   160.182643    12.709757     3.268987   \n",
-       "min        0.000000     0.000000     0.000000     0.000000   \n",
-       "25%     1440.000000     0.000000     0.000000     0.000000   \n",
-       "50%     3960.000000     0.000000     0.000000     0.000000   \n",
-       "75%     6960.000000    50.000000     0.000000     0.000000   \n",
-       "max    25920.000000  3600.000000   300.000000    45.000000   \n",
-       "\n",
-       "       no_of_style_change  no_of_workers  actual_productivity  \n",
-       "count         1197.000000    1197.000000          1197.000000  \n",
-       "mean             0.150376      34.609858             0.735091  \n",
-       "std              0.427848      22.197687             0.174488  \n",
-       "min              0.000000       2.000000             0.233705  \n",
-       "25%              0.000000       9.000000             0.650307  \n",
-       "50%              0.000000      34.000000             0.773333  \n",
-       "75%              0.000000      57.000000             0.850253  \n",
-       "max              2.000000      89.000000             1.120437  "
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([735599., 735613., 735630., 735644., 735658.]),\n",
-       " <a list of 5 Text xticklabel objects>)"
-      ]
-     },
-     "execution_count": 268,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Type Conversion of 'Date' column\n",
-    "df['date'] = pd.to_datetime(df['date'], format='%m/%d/%Y')\n",
-    "\n",
-    "# Time Series Plot of 'Productivity'\n",
-    "plt.figure(figsize=(12,5))\n",
-    "df.groupby('date')['actual_productivity'].mean().plot(marker='o', color='purple')\n",
-    "plt.title('Productivity Trend Over Time')\n",
-    "plt.xlabel('Date')\n",
-    "plt.ylabel('Average Productivity')\n",
-    "plt.xticks(rotation=45)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**The time series plot of average productivity reveals noticeable fluctuations over the observed period. While productivity initially starts high in early January, a gradual decline is seen mid-month, followed by a brief spike around early February. However, productivity dips again in mid-February and shows slight recovery toward early March. These trends may reflect seasonal, operational, or workforce-related factors, indicating that time-dependent patterns should be considered in productivity modeling.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 900x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "# Group and sort average productivity by day\n",
-    "day_order = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Saturday']\n",
-    "avg_by_day = df.groupby('day')['actual_productivity'].mean().reindex(day_order)\n",
-    "\n",
-    "# Define color palette\n",
-    "colors = plt.cm.viridis(np.linspace(0.2, 0.9, len(avg_by_day)))\n",
-    "\n",
-    "# Create the donut chart\n",
-    "fig, ax = plt.subplots(figsize=(6, 6), dpi=150)\n",
-    "wedges, texts, autotexts = ax.pie(\n",
-    "    avg_by_day,\n",
-    "    labels=day_order,\n",
-    "    autopct=lambda pct: f'{pct * avg_by_day.sum() / 100:.2f}',\n",
-    "    startangle=90,\n",
-    "    colors=colors,\n",
-    "    wedgeprops={'width': 0.3, 'edgecolor': 'white'},\n",
-    "    textprops={'fontsize': 11}\n",
-    ")\n",
-    "\n",
-    "# Optional: add a center label (e.g., Total)\n",
-    "ax.text(0, 0, '100%', ha='center', va='center', fontsize=13, fontweight='bold')\n",
-    "\n",
-    "# Final formatting\n",
-    "ax.set_title('Productivity Share by Day (Donut Chart)', fontsize=14, fontweight='bold', pad=20)\n",
-    "ax.axis('equal')  # Keeps it circular\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Average productivity remains stable throughout the week, ranging narrowly between 0.72 and 0.75.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quarter1' 'Quarter2' 'Quarter3' 'Quarter4' 'Quarter5']\n",
-      "['sweing' 'finishing ' 'finishing']\n",
-      "['Thursday' 'Saturday' 'Sunday' 'Monday' 'Tuesday' 'Wednesday']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df['quarter'].unique())\n",
-    "print(df['department'].unique())\n",
-    "print(df['day'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1. Fix department issues\n",
-    "df['department'] = df['department'].str.strip().str.lower()\n",
-    "df['department'] = df['department'].replace({'sweing': 'sewing'})\n",
-    "\n",
-    "# 2. (Optional) Standardize quarter\n",
-    "df['quarter'] = df['quarter'].str.strip().str.capitalize()  # e.g., Quarter1 → Quarter1\n",
-    "\n",
-    "# 3. Strip and standardize 'day' names\n",
-    "df['day'] = df['day'].str.strip().str.capitalize()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Quarter1' 'Quarter2' 'Quarter3' 'Quarter4' 'Quarter5']\n",
-      "['sewing' 'finishing']\n",
-      "['Thursday' 'Saturday' 'Sunday' 'Monday' 'Tuesday' 'Wednesday']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df['quarter'].unique())\n",
-    "print(df['department'].unique())\n",
-    "print(df['day'].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Clean department name (fix typo)\n",
-    "df['department'] = df['department'].str.replace('sweing', 'sewing', case=False)\n",
-    "\n",
-    "# Group by department and team, then calculate average productivity\n",
-    "team_avg = df.groupby(['department', 'team'], as_index=False)['actual_productivity'].mean()\n",
-    "\n",
-    "# Plotting\n",
-    "plt.figure(figsize=(14, 6))\n",
-    "ax = sns.barplot(\n",
-    "    data=team_avg,\n",
-    "    x='team',\n",
-    "    y='actual_productivity',\n",
-    "    hue='department',\n",
-    "    palette='Set2'\n",
-    ")\n",
-    "\n",
-    "# Titles and labels\n",
-    "plt.title('Average Productivity by Team and Department')\n",
-    "plt.xlabel('Team')\n",
-    "plt.ylabel('Average Productivity')\n",
-    "plt.legend(title='Department', loc='upper right')\n",
-    "plt.ylim(0, 1)  # Optional: Set y-axis max for consistency\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Optional: Add value labels on top of bars\n",
-    "for bar in ax.patches:\n",
-    "    height = bar.get_height()\n",
-    "    ax.annotate(f'{height:.2f}',\n",
-    "                xy=(bar.get_x() + bar.get_width() / 2, height),\n",
-    "                xytext=(0, 3),  # Offset text slightly above bar\n",
-    "                textcoords=\"offset points\",\n",
-    "                ha='center', va='bottom', fontsize=8)\n",
-    "\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**This grouped bar chart compares the average productivity of each team across the finishing and sewing departments. While productivity levels are relatively close between departments for most teams, certain teams (e.g., Team 3 and Team 5) in the finishing department outperform their counterparts, indicating possible differences in efficiency or workflow.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Actual Productivity')"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot for Average 'Productivity' by 'Department'\n",
-    "plt.figure(figsize=(8,5))\n",
-    "sns.barplot(x=df['department'], y=df['actual_productivity'], estimator=np.mean, palette='coolwarm')\n",
-    "plt.title('Average Productivity by Department')\n",
-    "plt.xlabel('Department')\n",
-    "plt.ylabel('Actual Productivity')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Bar chart compares average productivity between departments, showing a slightly higher performance in the finishing department.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot for 'Targeted' & 'Actual Productivity' Distribution by Department\n",
-    "plt.figure(figsize=(10,6))\n",
-    "df_melted = df.melt(id_vars=['department'], value_vars=['targeted_productivity', 'actual_productivity'], var_name=\"Type\", value_name=\"Productivity\")\n",
-    "\n",
-    "sns.boxplot(x='department', y='Productivity', hue='Type', data=df_melted, palette='coolwarm')\n",
-    "plt.title(\"Targeted vs. Actual Productivity Distribution by Department\")\n",
-    "plt.xlabel(\"Department\")\n",
-    "plt.ylabel(\"Productivity\")\n",
-    "plt.legend(title=\"Productivity Type\")\n",
-    "plt.xticks(rotation=45)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**This boxplot compares targeted and actual productivity distributions for each department. While median actual productivity in both departments is close to the target, the finishing department shows higher variability, with several instances exceeding the target. In contrast, sewing is more consistent but includes more low-performing outliers.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 1. Bin 'over_time' values\n",
-    "df['overtime_bin'] = pd.cut(\n",
-    "    df['over_time'],\n",
-    "    bins=[-1, 500, 1000, 2000, 5000, 10000, 20000, float('inf')],\n",
-    "    labels=['0-500', '501-1000', '1001-2000', '2001-5000', '5001-10000', '10001-20000', '20001+']\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# 2. Group and average\n",
-    "incentive_avg = df.groupby('overtime_bin')['incentive'].mean().reset_index()\n",
-    "\n",
-    "# 3. Plot\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.barplot(data=incentive_avg, x='overtime_bin', y='incentive', palette='Blues')\n",
-    "\n",
-    "plt.title('Average Incentive by Overtime Bin')\n",
-    "plt.xlabel('Overtime (Minutes, Binned)')\n",
-    "plt.ylabel('Average Incentive (BDT)')\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**This bar chart shows the average incentive paid across different overtime bins. While incentives remain relatively low across all overtime levels, there is a slight upward trend, suggesting limited correlation between increased overtime and incentive payout.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "def preprocess_data(df, model_type='svm'):\n",
-    "    df = df.copy()\n",
-    "\n",
-    "    # Clip productivity values\n",
-    "    df['actual_productivity'] = df['actual_productivity'].clip(upper=1.0)\n",
-    "\n",
-    "    # Create day_num from date\n",
-    "    df['day_num'] = df['date'].dt.weekday\n",
-    "\n",
-    "    # Shared encodings\n",
-    "    if model_type in ['svm', 'perceptron']:\n",
-    "        # ✅ Cyclical encoding for 'day'\n",
-    "        df['day_sin'] = np.sin(2 * np.pi * df['day_num'] / 7)\n",
-    "        df['day_cos'] = np.cos(2 * np.pi * df['day_num'] / 7)\n",
-    "\n",
-    "        # ✅ Target encoding\n",
-    "        df['department_encoded'] = df['department'].map(df.groupby('department')['actual_productivity'].mean())\n",
-    "        df['team_encoded'] = df['team'].map(df.groupby('team')['actual_productivity'].mean())\n",
-    "        df['quarter_encoded'] = df['quarter'].map(df.groupby('quarter')['actual_productivity'].mean())\n",
-    "\n",
-    "        # ✅ Drop original categorical columns\n",
-    "        df = df.drop(['day', 'department', 'team', 'quarter'], axis=1)\n",
-    "\n",
-    "    elif model_type == 'decision_tree':\n",
-    "        # ✅ One-hot encoding for 'day'\n",
-    "        df = pd.get_dummies(df, columns=['day'], drop_first=False)\n",
-    "\n",
-    "        # ✅ Label encoding for 'department' and 'quarter'\n",
-    "        le = LabelEncoder()\n",
-    "        df['department_label'] = le.fit_transform(df['department'])\n",
-    "        df['quarter_label'] = le.fit_transform(df['quarter'])\n",
-    "\n",
-    "        # ✅ Drop original columns that were encoded\n",
-    "        df = df.drop(['department', 'quarter'], axis=1)\n",
-    "\n",
-    "    else:\n",
-    "        raise ValueError(\"Invalid model_type. Choose 'svm', 'perceptron', or 'decision_tree'.\")\n",
-    "\n",
-    "    return df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x20e62196388>"
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr_wip = df[['wip', 'smv', 'over_time', 'idle_time', 'actual_productivity']].corr()\n",
-    "sns.heatmap(corr_wip, annot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**No strong linear correlations found with actual productivity, suggesting limited predictive power from individual features.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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OjYiXAi8B7sjMW+odliRJ0vAN6Yv+IuLvgXOAVwH/FhEfrnVUkiRJIzDUbzJ+FzA7M08AXg0cWd+QJEmSRmaogdPIzLUAmbkGWFPfkCRJkkZmqBcZXxMRFwKLgIOAa+sbkiRJ0shs8ghORLwfOBn4OrANcFVmfqTugUmSJA3XoIETEfOBNwCTM/Ny4BvAayPilDEYmyRJ0rBs6gjOXOAdmbkKIDPvpnWB8V/VPC5JkqRh21TgPJ6Z/W+yuQZYWd+QJEmSRmZTgfNERLyg74LqeXMj20uSJI27TX2K6iTg0oj4CXAXsBNwKHB03QOTJEkarkGP4GTmr4HZwBJgS+Bm4NWZuWQMxiZJkjQsm/wenMx8jNanpyRJkp4RhvpNxpIkSc8YBo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4kwa7R1GxGTga8AuwFTgU8Ay4DygCSwFjs/Mnog4FTgcWAuckJk3jPZ4JEnSxFPHEZx3A49k5mxgLnAmcAYwr1rWAI6IiH2Ag4EDgKOAs2oYiyRJmoDqCJzvA6f0eb4W2Be4qnq+ADgEOAhYmJnNzLwXmBQRM2sYjyRJmmBG/RRVZj4OEBEzgAuBecBpmdmsNlkJbANsDTzS56W9yx8abP+dnQ26uqaP9rCrfXeM+r6nTZ1c23gnijrmRSPnvLQn56X9OCfjY9QDByAidgQuAb6Umd+OiM/1WT0DWA6sqB73Xz6o7u4my5evGs3hrtPVNX3U9/3k6jW1jXeiqGNeNHLOS3tyXtqPc1KvmTNnDLh81E9RRcT2wELgpMz8WrV4SUTMqR7PBRYB1wKHRkRHROwEdGTmw6M9HkmSNPHUcQTnY8CzgFMiovdanA8CX4yIKcBtwIWZ2R0Ri4DraYXW8TWMRZIkTUB1XIPzQVpB09/BA2w7H5g/2mOQJEkTm1/0J0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTgGjiRJKo6BI0mSimPgSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSqOgSNJkopj4EiSpOIYOJIkqTiT6tpxRBwA/EtmzomIFwLnAU1gKXB8ZvZExKnA4cBa4ITMvKGu8UiSpImjliM4EXEicC4wrVp0BjAvM2cDDeCIiNgHOBg4ADgKOKuOsUiSpImnriM4dwJvA75ZPd8XuKp6vAB4A5DAwsxsAvdGxKSImJmZDw22487OBl1d02sZdGdnx6jve9rUybWNd6KoY140cs5Le3Je2o9zMj5qCZzMvCgidumzqFGFDMBKYBtga+CRPtv0Lh80cLq7myxfvmoUR7teV9f0Ud/3k6vX1DbeiaKOedHIOS/tyXlpP85JvWbOnDHg8rG6yLinz+MZwHJgRfW4/3JJkqQRGavAWRIRc6rHc4FFwLXAoRHRERE7AR2Z+fAYjUeSJBWstk9R9fMh4JyImALcBlyYmd0RsQi4nlZoHT9GY5EkSYWrLXAy827gwOrxHbQ+MdV/m/nA/LrGIEmSJia/6E+SJBXHwJEkScUxcCRJUnEMHEmSVBwDR5IkFcfAkSRJxTFwJElScQwcSZJUHANHkiQVx8CRJEnFMXAkSVJxDBxJklQcA0eSJBXHwJEkScUxcCRJUnEMHEmSVBwDR5IkFcfAkSRJxTFwJElScQwcSZJUHANHkiQVx8CRJEnFMXAkSVJxDBxJklQcA0eSJBXHwJEkScUxcCRJUnEMHEmSVBwDR5IkFcfAkSRJxTFwJElScQwcSZJUHANHkiQVx8CRJEnFMXAkSVJxDBxJklQcA0eSJBXHwJEkScUxcCRJUnEMHEmSVBwDR5IkFcfAkSRJxTFwJElScQycGq3t7uHPq9awprtnvIciSdKEMmm8B1Cia+56hG/ccB/LHnyc1Wt7WLDsT7xx9+14x8tnsfO208d7eJIkFc/AGUVru3s465q7+dbi37PTs7bgbXvuwAMrn6Cz0cnFv/ojly19gM+8+SW8+vnbjvdQJUkqmoEzSp5c080Jlyzlpvse4+177cAJc3Zl6qQOvnHjPbznFTvzp5Wr+cdLf82HLlnKSYe8iLfuucN4D1mSpGIZOKNgTXcPJ/1gGTff9xjz3xgcvsf269Z1NOCCxfeytge+cuSenPyD2/jnK3/D5M4Gb9rjOeM4akmSyuVFxiPU3dPk1AXJdb/7Mye//kUbxM26bZpNuptNtpwyiTPesgev2KmLTy/8DTfdt3wcRixJUvkMnBH64tV3cWU+xN/Pfv4Gp52+eeO9A24/qbODz755d57XNY0TL1vGPY+uGquhSpI0YRg4I/C9Jffz7Zvu58i9Z/Ge/XfcYF13s7nR1209bTKff+tL6Ww0+IdLlrJ81Zq6hypJ0oRi4AzTVb99mNN/diev2fXZ/MOcXdct/+aN9/Kdm55+9KajAeffsH7587q24LS37MGDK1dz4mW/5qm1fleOJEmjxcAZhp/f/Sgn//A2dt9+Bp86/MV0djTWreu93qa/jgY0Gs0NTl3tOWtrTn1jsOT+FXzyiqRnkKM+kiRp6AyczXTTfcv58H8sY5tpk/jC217KFpM7N1jfp3UG1Dd+Llh8L39c8STHH7QLV9z+EJ/98W9oGjmSJI2YgbMZfpwP8cGLlzJr62kcvsf2bLPF5HXrNnZRcV8djQ1PVfUe7Tl6/x05Zv8dueRXD3D6z+40ciRJGiEDZwh6mk3Oue4eTv7hbew2cyu+/M492Wpq68hNb9g0Gk0uWLw+cnq//2YgTVqnqnqP9jQaDf7uoF14177P5btL/sC8y2/niTXd9b4pSZIKZuD0cfbVdz5t2e0PruS937mFf7v+Hg7fY3vOfueePHvLKesCpsn6sOl/7c1gn6RqsuG6RqPBs7aYxPEH7cKV+RDv/c4tfoRckqRh8puM++jpWR8dyx5YyfeW3M+C2/5E1xaT+cTcYPmqJ/nukt+vO9XUGzDdzeYmr73pq++2kzrWH+npAY47YCd2224r5l1+O0edfxPv3HsW7ztwZ2ZMc6okSRqqcf9/zYjoAL4E7AWsBt6Xmb8dj7E8sOJJzrnuHhbd9Qi3Pfg4W0zu4Mi9n8v/emUrML61+B66m61jL5sTNJvSG0i91+ccvf9OfO+YfTn72nv4zk338/1b/sDc3bfjsJdsz16ztmZSpwfeJEkazLgHDvAWYFpmvjIiDgROB44Y60Es+f1jfOfG39MA9thhBrNfsC2fPOzF/GDpH7hs6R/oaUKjMbph019Ho3W9zwWL76WnCfMO3Y137j2Lf7oi+XE+zGVLH2TqpA5esv1W7LbdVszaZhrP2XoaW03pZMupk9b9ObWzg44O6Gg0aND6s/Ux9fV/SpJUsnYInIOAHwFk5s8jYr/xGMRLnjODI/d7Ll1TOtlyaidru5tsNXX9X0/rCEsrGHqf982Evs+H8nhj6zr7tceN9/2ZQ2ImR+79XK773aPccv8Kbv3jCi7+1R9Z0z38T1v1H8vTDBJBg71usHYa/HXDj67hjmciWdvdZFL//3KN8PWNRmPAT/z5IcD6DOWvttFwDtRe3vKy5/Dh175wzH9uY7w/khwR5wIXZeaC6vm9wAsyc+1GXvIQcM9YjU+SJLW1nYGZ/Re2wxGcFcCMPs87BokbGOBNSJIk9dUOV6teCxwGUF2Dc+v4DkeSJD3TtcMRnEuA10fEdbQupzh2nMcjSZKe4cb9GhxJkqTR1g6nqCRJkkaVgSNJkopj4EiSpOK0w0XG466dbhcxkUTEEuCx6unvgK8AXwDWAgsz8xMbm5vqE3cbbDvmb6AwEXEA8C+ZOSciXgicR+u75ZYCx2dmT0ScChxO6+/9hMy8YXO2HfM3VYB+87IP8APgN9XqL2fmd52XsRMRk4GvAbsAU4FPAcvw96XteASnZd3tIoCP0rpdhGoUEdMAMnNO9Z9jgbOBd9H6dusDqv8x39jcDLSthikiTgTOBaZVi84A5mXmbFqfbjyi+js+GDgAOAo4axjbajMMMC/7AGf0+b35rvMy5t4NPFL9vc4FzsTfl7Zk4LRscLsIYFxuFzHB7AVMj4iFEfHTiHgNMDUz78zMJnAF8DoGmJuI2Hoj22r47gTe1uf5vsBV1eMFwCG05mJhZjYz815gUkTM3MxttXkGmpfDI+LqiPhqRMzAeRlr3wdO6fN8Lf6+tCUDp2Vr1p8qAeiOCE/f1WsVcBpwKPAB4OvVsl4rgW0YYG6qZSsG2FbDlJkXAWv6LGpU8Qgbn4ve5ZuzrTbDAPNyA/CRzHwNcBdwKs7LmMrMxzNzZRWXFwLz8PelLRk4LZt7uwiN3B3At6p/sdxB65d72z7rZwDLGWBuBljWu61GT0+fxxubi97lm7OtRuaSzLyp9zGwN87LmIuIHYGfAd/MzG/j70tbMnBavF3E2DuO6nqaiJgFTAf+OyJ2jYgGrSM7ixhgbjJzBfDUANtq9CyJiDnV47msn4tDI6IjInai9Q+BhzdzW43MFRGxf/X4dcBNOC9jKiK2BxYCJ2Xm16rF/r60IU/DtHi7iLH3VeC8iLiG1qcJjqP1L5sLgE5a56N/ERE3MvDcfKD/tmP9Bgr3IeCciJgC3AZcmJndEbEIuJ7WP46OH8a2Gpm/Bc6MiKeAB4D3Z+YK52VMfQx4FnBKRPRei/NB4Iv+vrQXb9UgSZKK4ykqSZJUHANHkiQVx8CRJEnFMXAkSVJxDBxJklQcPyYuqa1ExE+Bj1Y3JpwCPAT8U2aeVq2/CniU1s1Zbwd+BdxM6+sGpgE/y8yPjcvgJbUNj+BIajcLgdnV49m07jV2OKy7SeuObPi19suqG0/+JfBq4C8jYs8xHK+kNmTgSGo3V7I+cA6jdTftrojYBngl629UOJAtgKlseF8zSROQX/Qnqa1ERAewDNid1s0lDwI+Bfwc2LNaNxf4d55+iqqb1v2Bzh/7kUtqJ16DI6mtZGZPRPwSeCPwQGaujogFwJuAvYAv0AqcXssyc87Yj1RSO/MUlaR2dCWte/4sqJ5fA+wDkJmPjtegJD1zGDiS2tGVtE5N/SdAZj4FLAeuHs9BSXrm8BocSZJUHI/gSJKk4hg4kiSpOAaOJEkqjoEjSZKKY+BIkqTiGDiSJKk4Bo4kSSrO/weDfevcFideswAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.histplot(df['wip'], kde=True)\n",
-    "plt.title(\"Distribution of WIP\")\n",
-    "plt.xlabel(\"WIP\")\n",
-    "plt.ylabel(\"Count\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The distribution of the `wip` (Work In Progress) feature is heavily right-skewed, with the majority of observations concentrated below 2000 units. However, a small number of extreme values exceed 20,000, which could disproportionately impact the regression model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['wip_log'] = np.log1p(df['wip'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['wip_log'], kde=True, color='skyblue', bins=30)\n",
-    "plt.title(\"Log-Transformed WIP Distribution\", fontsize=14, fontweight='bold')\n",
-    "plt.xlabel(\"Log of WIP\")\n",
-    "plt.ylabel(\"Count\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Keep wip_log as wip not needed now to ensure consistency\n",
-    "df = df.drop(columns=['wip'])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x20e5d1a0f08>"
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr_wip = df[['wip_log', 'smv', 'over_time', 'idle_time', 'actual_productivity']].corr()\n",
-    "sns.heatmap(corr_wip, annot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After applying log transformation to the `wip` feature, the distribution becomes nearly symmetric and well-centered. This transformation reduces skewness and minimizes the impact of extreme outliers, making the data more suitable for regression models.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))  \n",
-    "sns.histplot(df['over_time'], kde=True, bins=30)\n",
-    "plt.title('Distribution of Overtime')\n",
-    "plt.xlabel('Overtime')\n",
-    "plt.ylabel('Count')\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**The distribution of 'over_time' is heavily right-skewed and multimodal, with a large number of instances clustered at lower overtime values (under 2000 minutes). There are multiple peaks, notably around 0, 2000, 7000, and 10000 minutes, suggesting distinct overtime patterns among employees or production units. A small number of extreme outliers beyond 15,000 minutes are also present, which may warrant further investigation or transformation.**\n",
-    "\n",
-    "**This skewed and non-normal distribution suggests the need for log transformation or binning to reduce its impact on linear models.**\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Count')"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.histplot(df['actual_productivity'], bins=20, kde=True)\n",
-    "plt.title(\"Distribution of Actual Productivity\")\n",
-    "plt.title('Distribution of Actual Productivity')\n",
-    "plt.xlabel('Actual Productivity')\n",
-    "plt.ylabel('Count')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**The distribution of actual productivity is slightly right-skewed, with most values concentrated between 0.6 and 0.9. The peak occurs around 0.8, indicating that a large number of employees perform close to this level. While the distribution is roughly unimodal, there are some lower and higher productivity outliers present. Overall, the distribution suggests that the majority of the workforce maintains consistent productivity, which is favorable for regression modeling.**\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "numerical_cols = ['wip_log', 'over_time', 'incentive', 'smv']\n",
-    "\n",
-    "plt.figure(figsize=(14, 6))\n",
-    "for i, col in enumerate(numerical_cols):\n",
-    "    plt.subplot(1, len(numerical_cols), i + 1)\n",
-    "    sns.boxplot(y=df[col], color='lightblue')\n",
-    "    plt.title(col)\n",
-    "    plt.tight_layout()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The boxplots reveal strong outliers in `wip`, `over_time`, `incentive`, and `idle_time`, while `smv` appears more stable. These outliers can distort regression models, especially linear ones, and may require log transformation or capping to reduce their influence.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def replace_outliers_with_median(df, column):\n",
-    "    Q1 = df[column].quantile(0.25)\n",
-    "    Q3 = df[column].quantile(0.75)\n",
-    "    IQR = Q3 - Q1\n",
-    "    if IQR == 0:\n",
-    "        print(f\"Skipping '{column}' — no variability (IQR = 0)\")\n",
-    "        return\n",
-    "    lower_bound = Q1 - 1.5 * IQR\n",
-    "    upper_bound = Q3 + 1.5 * IQR\n",
-    "    median_value = df[column].median()\n",
-    "    df[column] = np.where((df[column] < lower_bound) | (df[column] > upper_bound), median_value, df[column])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x432 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "numerical_cols = ['wip_log', 'over_time', 'incentive', 'smv']\n",
-    "\n",
-    "plt.figure(figsize=(14, 6))\n",
-    "for i, col in enumerate(numerical_cols):\n",
-    "    plt.subplot(1, len(numerical_cols), i + 1)\n",
-    "    sns.boxplot(y=df[col], color='lightblue')\n",
-    "    plt.title(col)\n",
-    "    plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Due to the highly skewed and multimodal distribution of the `over_time` feature, extreme outliers were handled by imputing values above the 95th percentile with the median. This approach reduces the influence of rare, extreme values while preserving the original scale of the feature, making it more stable for regression modeling.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "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>date</th>\n",
-       "      <th>quarter</th>\n",
-       "      <th>department</th>\n",
-       "      <th>day</th>\n",
-       "      <th>team</th>\n",
-       "      <th>targeted_productivity</th>\n",
-       "      <th>smv</th>\n",
-       "      <th>over_time</th>\n",
-       "      <th>incentive</th>\n",
-       "      <th>idle_time</th>\n",
-       "      <th>idle_men</th>\n",
-       "      <th>no_of_style_change</th>\n",
-       "      <th>no_of_workers</th>\n",
-       "      <th>actual_productivity</th>\n",
-       "      <th>overtime_bin</th>\n",
-       "      <th>wip_log</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>26.16</td>\n",
-       "      <td>7080</td>\n",
-       "      <td>98</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.940725</td>\n",
-       "      <td>5001-10000</td>\n",
-       "      <td>7.011214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.886500</td>\n",
-       "      <td>501-1000</td>\n",
-       "      <td>6.946976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "      <td>2001-5000</td>\n",
-       "      <td>6.876265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "      <td>2001-5000</td>\n",
-       "      <td>6.876265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>25.90</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.800382</td>\n",
-       "      <td>1001-2000</td>\n",
-       "      <td>7.065613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date   quarter department       day  team  targeted_productivity  \\\n",
-       "0 2015-01-01  Quarter1     sewing  Thursday     8                   0.80   \n",
-       "1 2015-01-01  Quarter1  finishing  Thursday     1                   0.75   \n",
-       "2 2015-01-01  Quarter1     sewing  Thursday    11                   0.80   \n",
-       "3 2015-01-01  Quarter1     sewing  Thursday    12                   0.80   \n",
-       "4 2015-01-01  Quarter1     sewing  Thursday     6                   0.80   \n",
-       "\n",
-       "     smv  over_time  incentive  idle_time  idle_men  no_of_style_change  \\\n",
-       "0  26.16       7080         98        0.0         0                   0   \n",
-       "1   3.94        960          0        0.0         0                   0   \n",
-       "2  11.41       3660         50        0.0         0                   0   \n",
-       "3  11.41       3660         50        0.0         0                   0   \n",
-       "4  25.90       1920         50        0.0         0                   0   \n",
-       "\n",
-       "   no_of_workers  actual_productivity overtime_bin   wip_log  \n",
-       "0           59.0             0.940725   5001-10000  7.011214  \n",
-       "1            8.0             0.886500     501-1000  6.946976  \n",
-       "2           30.5             0.800570    2001-5000  6.876265  \n",
-       "3           30.5             0.800570    2001-5000  6.876265  \n",
-       "4           56.0             0.800382    1001-2000  7.065613  "
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     1179\n",
-       "30       3\n",
-       "20       3\n",
-       "15       3\n",
-       "10       3\n",
-       "35       2\n",
-       "45       1\n",
-       "40       1\n",
-       "37       1\n",
-       "25       1\n",
-       "Name: idle_men, dtype: int64"
-      ]
-     },
-     "execution_count": 292,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['idle_men'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Dropping these columns as more than 95% are 0 values suggesting no variance and therefore does not contribute significantly\n",
-    "df.drop(columns=['idle_time', 'idle_men'], inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>date</th>\n",
-       "      <th>quarter</th>\n",
-       "      <th>department</th>\n",
-       "      <th>day</th>\n",
-       "      <th>team</th>\n",
-       "      <th>targeted_productivity</th>\n",
-       "      <th>smv</th>\n",
-       "      <th>over_time</th>\n",
-       "      <th>incentive</th>\n",
-       "      <th>no_of_style_change</th>\n",
-       "      <th>no_of_workers</th>\n",
-       "      <th>actual_productivity</th>\n",
-       "      <th>overtime_bin</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2015-01-01</td>\n",
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-       "      <td>5001-10000</td>\n",
-       "      <td>7.011214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>finishing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>3.94</td>\n",
-       "      <td>960</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.886500</td>\n",
-       "      <td>501-1000</td>\n",
-       "      <td>6.946976</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "      <td>2001-5000</td>\n",
-       "      <td>6.876265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>12</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>11.41</td>\n",
-       "      <td>3660</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>0.800570</td>\n",
-       "      <td>2001-5000</td>\n",
-       "      <td>6.876265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2015-01-01</td>\n",
-       "      <td>Quarter1</td>\n",
-       "      <td>sewing</td>\n",
-       "      <td>Thursday</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>25.90</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0.800382</td>\n",
-       "      <td>1001-2000</td>\n",
-       "      <td>7.065613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        date   quarter department       day  team  targeted_productivity  \\\n",
-       "0 2015-01-01  Quarter1     sewing  Thursday     8                   0.80   \n",
-       "1 2015-01-01  Quarter1  finishing  Thursday     1                   0.75   \n",
-       "2 2015-01-01  Quarter1     sewing  Thursday    11                   0.80   \n",
-       "3 2015-01-01  Quarter1     sewing  Thursday    12                   0.80   \n",
-       "4 2015-01-01  Quarter1     sewing  Thursday     6                   0.80   \n",
-       "\n",
-       "     smv  over_time  incentive  no_of_style_change  no_of_workers  \\\n",
-       "0  26.16       7080         98                   0           59.0   \n",
-       "1   3.94        960          0                   0            8.0   \n",
-       "2  11.41       3660         50                   0           30.5   \n",
-       "3  11.41       3660         50                   0           30.5   \n",
-       "4  25.90       1920         50                   0           56.0   \n",
-       "\n",
-       "   actual_productivity overtime_bin   wip_log  \n",
-       "0             0.940725   5001-10000  7.011214  \n",
-       "1             0.886500     501-1000  6.946976  \n",
-       "2             0.800570    2001-5000  6.876265  \n",
-       "3             0.800570    2001-5000  6.876265  \n",
-       "4             0.800382    1001-2000  7.065613  "
-      ]
-     },
-     "execution_count": 294,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "date                     0\n",
-       "quarter                  0\n",
-       "department               0\n",
-       "day                      0\n",
-       "team                     0\n",
-       "targeted_productivity    0\n",
-       "smv                      0\n",
-       "over_time                0\n",
-       "incentive                0\n",
-       "no_of_style_change       0\n",
-       "no_of_workers            0\n",
-       "actual_productivity      0\n",
-       "overtime_bin             0\n",
-       "wip_log                  0\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🧼 Preprocessing Summary\n",
-    "\n",
-    "The dataset underwent several preprocessing steps to ensure data quality and model-readiness:\n",
-    "\n",
-    "1. **Handling Missing Values**\n",
-    "   - Missing values in `wip` were imputed using the median.\n",
-    "   - No remaining missing values after cleaning.\n",
-    "\n",
-    "2. **Outlier Treatment**\n",
-    "   - Applied IQR-based outlier detection.\n",
-    "   - Outliers in continuous variables (`wip`, `over_time`, `incentive`, `idle_men`) were replaced with median values.\n",
-    "   - `idle_time` was removed as over 98% of its values were zero, making it uninformative.\n",
-    "\n",
-    "3. **Feature Engineering**\n",
-    "   - Created `wip_log` (log-transformed `wip`) to reduce skew and improve performance in linear models.\n",
-    "   - Binned `over_time` into categorical `overtime_bin` for visualization and tree models.\n",
-    "\n",
-    "4. **Feature Reduction**\n",
-    "   - Removed `idle_time` due to lack of variance.\n",
-    "   - Kept `no_of_style_change` and `idle_men` after verifying they had useful variability.\n",
-    "\n",
-    "5. **Column Reordering**\n",
-    "   - Moved `actual_productivity` (target variable) to the end of the DataFrame for clarity.\n",
-    "\n",
-    "\n",
-    "This prepared dataset is now ready for encoding, feature scaling, and regression modeling.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
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-    "name": "ipython",
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