diff --git a/notebooks/regression/Regression_Preprocessed.ipynb b/notebooks/regression/Regression_Preprocessed.ipynb
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@@ -0,0 +1,2314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "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": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Loaded dataset from: c:\\Users\\ritwi\\mlmavericks_coursework\\data\\raw\\garments_worker_productivity.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "project_root = Path.cwd().parent.parent  # Adjust if needed\n",
+    "df = load_dataset(\"garments_worker_productivity.csv\", folder=\"data/raw\", base_path=project_root)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "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",
+       "  </tbody>\n",
+       "</table>\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",
+       "     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",
+       "   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  "
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### 📊 Dataset Overview\n",
+    "\n",
+    "The dataset contains **1197 rows** and **15 columns**, capturing various aspects of garment manufacturing productivity. Here's a summary of the key columns:\n",
+    "\n",
+    "| Column                  | Description                          |\n",
+    "|-------------------------|--------------------------------------|\n",
+    "| `date`                 | Record date                          |\n",
+    "| `quarter`              | Quarter of the year (e.g., Q1, Q2)   |\n",
+    "| `department`           | Department name                      |\n",
+    "| `day`                  | Day of the week                      |\n",
+    "| `team`                 | Team number                          |\n",
+    "| `targeted_productivity`| Expected productivity                |\n",
+    "| `smv`                  | Standard Minute Value                |\n",
+    "| `wip`                  | Work in progress (has missing values)|\n",
+    "| `over_time`            | Overtime minutes                     |\n",
+    "| `incentive`            | Incentive amount                     |\n",
+    "| `idle_time`            | Minutes of idle time                 |\n",
+    "| `idle_men`             | Number of idle workers               |\n",
+    "| `no_of_style_change`   | Style changes per day                |\n",
+    "| `no_of_workers`        | Number of workers on the team        |\n",
+    "| `actual_productivity`  | Measured productivity                |\n",
+    "\n",
+    "### 🛠 Notes:\n",
+    "- `wip` has missing values (691 non-null).\n",
+    "- Categorical columns like `date`, `quarter`, `department`, and `day` may need to be encoded.\n",
+    "- Numerical features dominate the dataset, making it suitable for machine learning workflows.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Handling Missing Values\n",
+    "\n",
+    "We impute missing values in the `wip` column using the **median** of the available values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Impute missing 'wip' with median\n",
+    "df['wip'] = df['wip'].fillna(df['wip'].median())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### checking null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "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": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for any existing null values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "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": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Productivity Over Time\n",
+    "\n",
+    "We first convert the `date` column to `datetime` format for accurate time-based analysis:\n",
+    "\n",
+    "Then, we plot the average actual_productivity over time:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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)\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Productivity Trend Over Time\n",
+    "\n",
+    "This time series plot shows how the average `actual_productivity` changes over the dataset's timeline.\n",
+    "\n",
+    "**Key Points:**\n",
+    "- The x-axis represents the date (starting from January 2015).\n",
+    "- The y-axis shows the average productivity recorded on each day.\n",
+    "- The plot highlights some fluctuations and dips, particularly around mid-February and early March.\n",
+    "- There may be weekly or operational cycles affecting performance that could be explored further (e.g., team shifts, deadlines, resource availability).\n",
+    "\n",
+    "This trend helps in understanding operational efficiency and identifying periods of unusually low or high productivity.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Productivity Share by Day (Donut Chart)\n",
+    "\n",
+    "This donut chart represents the average share of `actual_productivity` across different days of the week."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x750 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=(5, 5), 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 by Day of the Week\n",
+    "\n",
+    "This donut chart visualizes the **average `actual_productivity`** for each day recorded in the dataset.\n",
+    "\n",
+    "**Key Observations:**\n",
+    "- Productivity is fairly consistent across days, with values ranging from **0.72 to 0.75**.\n",
+    "- **Saturday** shows the highest average productivity (~0.75), while **Thursday** has the lowest (~0.72).\n",
+    "- The differences are subtle, but this view can still guide scheduling or shift optimization decisions.\n",
+    "\n",
+    "The use of a donut chart gives a clear, aesthetic snapshot of how productivity is distributed through the week.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### inspecting the unique values in categorical columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Value counts for quarter:\n",
+      "quarter\n",
+      "Quarter1    360\n",
+      "Quarter2    335\n",
+      "Quarter4    248\n",
+      "Quarter3    210\n",
+      "Quarter5     44\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Value counts for department:\n",
+      "department\n",
+      "sweing        691\n",
+      "finishing     257\n",
+      "finishing     249\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Value counts for day:\n",
+      "day\n",
+      "Wednesday    208\n",
+      "Sunday       203\n",
+      "Tuesday      201\n",
+      "Thursday     199\n",
+      "Monday       199\n",
+      "Saturday     187\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in ['quarter', 'department', 'day']:\n",
+    "    print(f\"\\nValue counts for {col}:\\n{df[col].value_counts(dropna=False)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Overview of Categorical Feature Distributions\n",
+    "\n",
+    "####  Quarter\n",
+    "- Most data entries fall within Quarter 1 and Quarter 2.\n",
+    "- An unexpected entry, \"Quarter5\", is present — this may be a data entry error and should be reviewed.\n",
+    "\n",
+    "####  Department\n",
+    "- The dataset is primarily composed of records from the \"sewing\" department.\n",
+    "- \"finishing\" appears duplicated — likely due to inconsistent entries that need to be standardized during preprocessing.\n",
+    "\n",
+    "#### Day of the Week\n",
+    "- Data is relatively evenly distributed across days, with no records for Friday.\n",
+    "- This might indicate a non-operational day.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### fixing inconsistencies in values "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### checking quarter values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "quarter\n",
+       "Quarter1    360\n",
+       "Quarter2    335\n",
+       "Quarter4    248\n",
+       "Quarter3    210\n",
+       "Quarter5     44\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['quarter'].value_counts()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "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>498</th>\n",
+       "      <td>2015-01-29</td>\n",
+       "      <td>Quarter5</td>\n",
+       "      <td>sewing</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>22.52</td>\n",
+       "      <td>1416.0</td>\n",
+       "      <td>6840</td>\n",
+       "      <td>113</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>1.000230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>2015-01-29</td>\n",
+       "      <td>Quarter5</td>\n",
+       "      <td>finishing</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>1039.0</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.989000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>500</th>\n",
+       "      <td>2015-01-29</td>\n",
+       "      <td>Quarter5</td>\n",
+       "      <td>sewing</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>22.52</td>\n",
+       "      <td>1287.0</td>\n",
+       "      <td>6840</td>\n",
+       "      <td>100</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>0.950186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>501</th>\n",
+       "      <td>2015-01-29</td>\n",
+       "      <td>Quarter5</td>\n",
+       "      <td>sewing</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>22.52</td>\n",
+       "      <td>1444.0</td>\n",
+       "      <td>6900</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.5</td>\n",
+       "      <td>0.900800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>2015-01-29</td>\n",
+       "      <td>Quarter5</td>\n",
+       "      <td>sewing</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>22.52</td>\n",
+       "      <td>1088.0</td>\n",
+       "      <td>6720</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.900130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date   quarter department       day  team  targeted_productivity  \\\n",
+       "498 2015-01-29  Quarter5     sewing  Thursday     2                    0.8   \n",
+       "499 2015-01-29  Quarter5  finishing  Thursday     4                    0.8   \n",
+       "500 2015-01-29  Quarter5     sewing  Thursday     3                    0.8   \n",
+       "501 2015-01-29  Quarter5     sewing  Thursday     4                    0.8   \n",
+       "502 2015-01-29  Quarter5     sewing  Thursday    10                    0.8   \n",
+       "\n",
+       "       smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
+       "498  22.52  1416.0       6840        113        0.0         0   \n",
+       "499   4.30  1039.0       1200          0        0.0         0   \n",
+       "500  22.52  1287.0       6840        100        0.0         0   \n",
+       "501  22.52  1444.0       6900         88        0.0         0   \n",
+       "502  22.52  1088.0       6720         88        0.0         0   \n",
+       "\n",
+       "     no_of_style_change  no_of_workers  actual_productivity  \n",
+       "498                   0           57.0             1.000230  \n",
+       "499                   0           10.0             0.989000  \n",
+       "500                   0           57.0             0.950186  \n",
+       "501                   0           57.5             0.900800  \n",
+       "502                   0           56.0             0.900130  "
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['quarter'] == 'Quarter5'].head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fixing Quarter5 Label\n",
+    "\n",
+    "All rows labeled as `Quarter5` are dated **January 29, 2015**, which falls within **Quarter1**. This confirms that `Quarter5` is a mislabel.\n",
+    "\n",
+    "We'll replace Quarter5 to Quarter 1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['quarter'] = df['quarter'].replace({'Quarter5': 'Quarter1'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Value counts for quarter:\n",
+      "quarter\n",
+      "Quarter1    404\n",
+      "Quarter2    335\n",
+      "Quarter4    248\n",
+      "Quarter3    210\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Value counts for department:\n",
+      "department\n",
+      "sewing       691\n",
+      "finishing    506\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Value counts for day:\n",
+      "day\n",
+      "Wednesday    208\n",
+      "Sunday       203\n",
+      "Tuesday      201\n",
+      "Thursday     199\n",
+      "Monday       199\n",
+      "Saturday     187\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in ['quarter', 'department', 'day']:\n",
+    "    print(f\"\\nValue counts for {col}:\\n{df[col].value_counts(dropna=False)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Cleaned Categorical Feature Summary\n",
+    "\n",
+    "After cleaning:\n",
+    "\n",
+    "- **Quarter5** entries were correctly relabeled as **Quarter1**, reflecting accurate date alignment.\n",
+    "- **Department** names were standardized, correcting typos and casing issues.\n",
+    "- **Day** names are consistently formatted, with no stray or missing labels (aside from the expected absence of \"Friday\").\n",
+    "\n",
+    "The dataset is now ready for further analysis, modeling, or feature engineering without inconsistencies in key categorical fields.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Average Productivity by Team and Department"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\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 Actual Productivity by Team and Department – Garment Workers')\n",
+    "plt.xlabel('Team')\n",
+    "plt.ylabel('Average Productivity')\n",
+    "plt.ylim(0, 1)\n",
+    "plt.xticks(rotation=0)  # Keep team numbers horizontal\n",
+    "plt.legend(title='Department', loc='upper right')\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Add value labels on bars\n",
+    "for bar in ax.patches:\n",
+    "    height = bar.get_height()\n",
+    "    if not np.isnan(height) and height > 0:  # Avoid labeling bars that shouldn't be shown\n",
+    "        ax.annotate(f'{height:.2f}',\n",
+    "                    xy=(bar.get_x() + bar.get_width() / 2, height),\n",
+    "                    xytext=(0, 3),\n",
+    "                    textcoords=\"offset points\",\n",
+    "                    ha='center', va='bottom', fontsize=8)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Team-Level Productivity \n",
+    "\n",
+    "To better understand team performance across departments, we calculate the **average actual productivity per team**. \n",
+    "\n",
+    "The grouped bar chart below shows how different teams perform within the `finishing` and `sewing` departments. We've also annotated each bar with its exact value to make comparison easier and more precise.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Actual Productivity')"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": [
+    "### Average Productivity by Department\n",
+    "\n",
+    "This plot compares the **average actual productivity** between the `sewing` and `finishing` departments. \n",
+    "\n",
+    "It provides a high-level view of departmental performance. Error bars are included to indicate variability or confidence in the average values.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Targeted vs. Actual Productivity by Department\n",
+    "\n",
+    "To evaluate how well each department meets its targets, we compare the **distribution of targeted and actual productivity** using boxplots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([0, 1], [Text(0, 0, 'sewing'), Text(1, 0, 'finishing')])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing Targeted vs. Actual Productivity Distributions\n",
+    "\n",
+    "This boxplot compares the distribution of **targeted** and **actual productivity** across departments.\n",
+    "\n",
+    "- The boxes show the **interquartile range (IQR)** and median values.\n",
+    "- Dots outside the whiskers represent **outliers** — either exceptionally high or low productivity.\n",
+    "- This comparison helps us understand how closely teams are hitting their targets, and whether one department consistently outperforms or underperforms relative to expectations.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Median incentive by overtime bin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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",
+    "# Median incentive per overtime bin (robust to outliers)\n",
+    "incentive_median = df.groupby('overtime_bin')['incentive'].median().reset_index()\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.barplot(data=incentive_median, x='overtime_bin', y='incentive', palette='Blues')\n",
+    "\n",
+    "plt.title('Median Incentive by Overtime Bin')\n",
+    "plt.xlabel('Overtime (Minutes, Binned)')\n",
+    "plt.ylabel('Median Incentive (BDT)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Median Incentive by Overtime Bin\n",
+    "\n",
+    "This bar chart shows the **median incentive** received by workers in each overtime range. \n",
+    "\n",
+    "By using the **median** instead of the mean, we reduce the impact of extreme values (outliers) and get a clearer picture of what a **typical worker** in each overtime group actually earns.\n",
+    "\n",
+    "Key takeaways:\n",
+    "- Workers with **0–500 minutes** of overtime have the **highest typical incentive**, possibly due to high-efficiency or bonus structures.\n",
+    "- Incentives generally **increase with overtime**, peaking around `10001–20000` minutes.\n",
+    "- A slight **drop in the final bin** (`20001+`) may indicate diminishing returns or overwork not translating into proportional incentives.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model-Specific Feature Engineering\n",
+    "\n",
+    "Not all models treat features the same way — particularly when it comes to **scale** and **categorical variables**.\n",
+    "\n",
+    "In this function, we apply **model-aware preprocessing**:\n",
+    "- For models like **SVM, Perceptron, Linear Regression**:\n",
+    "  - We apply a **log transformation** to `wip` to reduce skew.\n",
+    "  - We use **cyclical encoding** for day of the week.\n",
+    "  - We apply **target encoding** to categorical variables.\n",
+    "\n",
+    "- For **tree-based models** (e.g. Decision Trees):\n",
+    "  - No log transformation is applied (trees handle scale naturally).\n",
+    "  - We use **one-hot encoding** for `day`.\n",
+    "  - We use **label encoding** for `department` and `quarter`.\n",
+    "\n",
+    "This setup ensures each model gets the features it understands best — improving both interpretability and performance.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_specific_encoding(df, model_type='svm'):\n",
+    "    df = df.copy()\n",
+    "\n",
+    "    # Create day_num if needed\n",
+    "    if 'day_num' not in df.columns:\n",
+    "        df['day_num'] = df['date'].dt.weekday\n",
+    "\n",
+    "    # Log-transform WIP for non-tree models\n",
+    "    if model_type in ['svm', 'perceptron', 'linear']:\n",
+    "        df['wip_log'] = np.log1p(df['wip'])\n",
+    "        df.drop(columns=['wip'], inplace=True)\n",
+    "\n",
+    "    # Model-specific feature encoding\n",
+    "    if model_type in ['svm', 'perceptron', 'linear']:\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",
+    "        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",
+    "        df.drop(['day', 'department', 'team', 'quarter'], axis=1, inplace=True)\n",
+    "\n",
+    "    elif model_type == 'decision_tree':\n",
+    "        df = pd.get_dummies(df, columns=['day'], drop_first=False)\n",
+    "\n",
+    "        le = LabelEncoder()\n",
+    "        df['department_label'] = le.fit_transform(df['department'])\n",
+    "        df['quarter_label'] = le.fit_transform(df['quarter'])\n",
+    "\n",
+    "        df.drop(['department', 'quarter'], axis=1, inplace=True)\n",
+    "\n",
+    "    else:\n",
+    "        raise ValueError(\"Invalid model_type. Choose from 'svm', 'perceptron', 'linear', or 'decision_tree'.\")\n",
+    "\n",
+    "    return df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Applying Model-Specific Preprocessing\n",
+    "\n",
+    "Now that we've defined our flexible encoding function, we can preprocess our data depending on the model we're training.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select numerical features for correlation (adjust 'wip' if you used 'wip_log')\n",
+    "features = ['wip', 'smv', 'over_time', 'idle_time','idle_men', 'actual_productivity']\n",
+    "\n",
+    "# If you used log-transformed wip:\n",
+    "# features[0] = 'wip_log'\n",
+    "\n",
+    "# Calculate correlation matrix\n",
+    "corr_matrix = df[features].corr()\n",
+    "\n",
+    "# Plot heatmap\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5, square=True)\n",
+    "plt.title(\"🔗 Correlation Heatmap of Key Features\", fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Correlation Analysis\n",
+    "\n",
+    "This heatmap reveals the linear relationships between selected numeric features and `actual_productivity`.\n",
+    "\n",
+    "Key insights:\n",
+    "- None of the features show strong linear correlation with the target variable.\n",
+    "- `wip` has a slight positive relationship with productivity.\n",
+    "- `smv`, `over_time`, and `idle_time` show weak or negative correlations.\n",
+    "- This suggests that simple linear models may struggle, and more complex models (like decision trees or ensemble methods) may be better suited to capture non-linear interactions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 4))\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Distribution of WIP (Work In Progress)\n",
+    "\n",
+    "This histogram shows the distribution of `wip` (Work In Progress) values across all observations.\n",
+    "\n",
+    "- The distribution is **highly right-skewed**, with a majority of values clustered near the lower end.\n",
+    "- A few large values act as **outliers**, which may influence models that are sensitive to scale.\n",
+    "- This insight supports the idea of using a **log transformation** (e.g., `wip_log`) to normalize the distribution and reduce the effect of extreme values.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['wip_log'] = np.log1p(df['wip'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 4))\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": "markdown",
+   "metadata": {},
+   "source": [
+    "### Result: Log Transformation Effectiveness\n",
+    "\n",
+    "As shown above, the `wip_log` distribution is much more **centered and symmetrical** compared to the raw `wip` values.\n",
+    "\n",
+    "This transformation helps:\n",
+    "- Reduce the impact of extreme values\n",
+    "- Normalize the scale for better performance in models like **SVMs** and **linear regression**\n",
+    "- Improve convergence and interpretability\n",
+    "\n",
+    "We will use `wip_log` in our modeling workflow for all non-tree-based models.\n"
+   ]
+  },
+  {
+   "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": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rl1RVVrbZMUNCQ2V7ZibBGwAAAIDXIHSjWaqHWwXuGx9+QRLSuhh+vLysPTLnL9P1cQndAAAAALwFoRsnpAJ3Src+rm4GAAAAAHgkCqkBAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAADeGLrfeOMN6d+/v0RGRurLiBEj5JtvvnE+Xl1dLffdd5/ExMRIeHi4TJ48WfLy8pq8RlZWlkycOFFCQ0MlPj5epk+fLvX19S54NwAAAAAAuFHoTklJkf/3//6frFu3TtauXSujR4+WK664QrZu3aoff/DBB+Wrr76STz75RJYtWyaHDx+Wq666yvl8i8WiA3dtba0sX75c3nvvPZk9e7Y8/vjjLnxXAAAAAADY+YsLXXbZZU1u//nPf9a93ytXrtSB/O2335a5c+fqMK68++670qtXL/348OHD5bvvvpNt27bJwoULJSEhQQYOHChPP/20PPzwwzJz5kwJDAx00TsDAAAAAMCN5nSrXusPP/xQKioq9DBz1ftdV1cnY8eOde7Ts2dPSUtLkxUrVujb6rpfv346cDuMHz9eSktLnb3lzampqdH7NL4AAAAAAOB1oXvz5s16vnZQUJDcfffd8tlnn0nv3r0lNzdX91RHR0c32V8FbPWYoq4bB27H447HWvLcc89JVFSU85KammrIewMAAAAA+DaXh+4ePXpIRkaGrFq1Su655x659dZb9ZBxI82YMUNKSkqcl+zsbEOPBwAAAADwTS6d062o3uyuXbvq7cGDB8uaNWvk1VdflWuvvVYXSCsuLm7S262qlycmJuptdb169eomr+eobu7YpzmqV11dAAAAAADw6p7uY1mtVj3nWgXwgIAAWbRokfOxHTt26CXC1JxvRV2r4en5+fnOfb7//nu9/Jgaog4AAAAAgM/2dKth3hMmTNDF0crKynSl8qVLl8q3336r51pPmTJFpk2bJu3bt9dB+v7779dBW1UuV8aNG6fD9c033yzPP/+8nsf96KOP6rW96ckGAAAAAPh06FY91Lfccovk5OTokN2/f38duC+66CL9+Msvvyxms1kmT56se79VZfLXX3/d+Xw/Pz+ZN2+enguuwnhYWJieE/7UU0+58F0BAAAAAOAGoVutw30iwcHBMmvWLH1pSXp6usyfP9+A1gEAAAAA4GVzugEAAAAA8BaEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAG0P3c889J0OHDpWIiAiJj4+XSZMmyY4dO5rsM2rUKDGZTE0ud999d5N9srKyZOLEiRIaGqpfZ/r06VJfX9/G7wYAAAAAgKb8xYWWLVsm9913nw7eKiT/6U9/knHjxsm2bdskLCzMud8dd9whTz31lPO2CtcOFotFB+7ExERZvny55OTkyC233CIBAQHy7LPPtvl7AgAAAADALUL3ggULmtyePXu27qlet26dXHDBBU1CtgrVzfnuu+90SF+4cKEkJCTIwIED5emnn5aHH35YZs6cKYGBgYa/DwAAAAAA3H5Od0lJib5u3759k/vnzJkjsbGx0rdvX5kxY4ZUVlY6H1uxYoX069dPB26H8ePHS2lpqWzdurXZ49TU1OjHG18AAAAAAPCqnu7GrFarPPDAA3LuuefqcO1www03SHp6uiQnJ8umTZt0D7aa9/3pp5/qx3Nzc5sEbsVxWz3W0lzyJ5980tD3AwAAAACA24RuNbd7y5Yt8tNPPzW5/84773Ruqx7tpKQkGTNmjOzZs0e6dOlyRsdSveXTpk1z3lY93ampqb+g9QAAAAAAuOnw8qlTp8q8efNkyZIlkpKScsJ9hw0bpq93796tr9Vc77y8vCb7OG63NA88KChIIiMjm1wAAAAAAPCq0G2z2XTg/uyzz2Tx4sXSqVOnkz4nIyNDX6seb2XEiBGyefNmyc/Pd+7z/fff6yDdu3dvA1sPAAAAAIAbDy9XQ8rnzp0rX3zxhV6r2zEHOyoqSkJCQvQQcvX4JZdcIjExMXpO94MPPqgrm/fv31/vq5YYU+H65ptvlueff16/xqOPPqpfW/VoAwAAAADgkz3db7zxhq5YPmrUKN1z7bh89NFH+nG13JdaCkwF6549e8rvf/97mTx5snz11VfO1/Dz89ND09W16vW+6aab9Drdjdf1BgAAAADA53q61fDyE1HFzZYtW3bS11HVzefPn9+KLQMAAAAAwEsKqQEAAAAA4I0I3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAN4bu5557ToYOHSoRERESHx8vkyZNkh07djTZp7q6Wu677z6JiYmR8PBwmTx5suTl5TXZJysrSyZOnCihoaH6daZPny719fVt/G4AAAAAAHCj0L1s2TIdqFeuXCnff/+91NXVybhx46SiosK5z4MPPihfffWVfPLJJ3r/w4cPy1VXXeV83GKx6MBdW1sry5cvl/fee09mz54tjz/+uIveFQAAAAAAdv7iQgsWLGhyW4Vl1VO9bt06ueCCC6SkpETefvttmTt3rowePVrv8+6770qvXr10UB8+fLh89913sm3bNlm4cKEkJCTIwIED5emnn5aHH35YZs6cKYGBgS56dwAAAAAAX+dWc7pVyFbat2+vr1X4Vr3fY8eOde7Ts2dPSUtLkxUrVujb6rpfv346cDuMHz9eSktLZevWrc0ep6amRj/e+AIAAAAAgNeGbqvVKg888ICce+650rdvX31fbm6u7qmOjo5usq8K2Ooxxz6NA7fjccdjLc0lj4qKcl5SU1MNelcAAAAAAF/mNqFbze3esmWLfPjhh4Yfa8aMGbpX3XHJzs42/JgAAAAAAN/j0jndDlOnTpV58+bJDz/8ICkpKc77ExMTdYG04uLiJr3dqnq5esyxz+rVq5u8nqO6uWOfYwUFBekLAAAAAABe29Nts9l04P7ss89k8eLF0qlTpyaPDx48WAICAmTRokXO+9SSYmqJsBEjRujb6nrz5s2Sn5/v3EdVQo+MjJTevXu34bsBAAAAAMCNerrVkHJVmfyLL77Qa3U75mCredYhISH6esqUKTJt2jRdXE0F6fvvv18HbVW5XFFLjKlwffPNN8vzzz+vX+PRRx/Vr01vNgAAAADAZ0P3G2+8oa9HjRrV5H61LNivf/1rvf3yyy+L2WyWyZMn66rjqjL566+/7tzXz89PD02/5557dBgPCwuTW2+9VZ566qk2fjcAAAAAALhR6FbDy08mODhYZs2apS8tSU9Pl/nz57dy6wAAAAAA8JLq5QAAAAAAeBtCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAO4Uujt37ixHjx497v7i4mL9GAAAAAAAOMPQvX//frFYLMfdX1NTI4cOHWqNdgEAAAAA4PH8T2fnL7/80rn97bffSlRUlPO2CuGLFi2Sjh07tm4LAQAAAADwhdA9adIkfW0ymeTWW29t8lhAQIAO3H/9619bt4UAAAAAAPhC6LZarfq6U6dOsmbNGomNjTWqXQAAAAAA+Fbodti3b1/rtwQAAAAAAC9zRqFbUfO31SU/P9/ZA+7wzjvvtEbbAAAAAADwvdD95JNPylNPPSVDhgyRpKQkPccbAAAAAAC0Quh+8803Zfbs2XLzzTefydMBAAAAAPAJZ7ROd21trZxzzjmt3xoAAAAAAHw9dN9+++0yd+7c1m8NAAAAAAC+Pry8urpa3nrrLVm4cKH0799fr9Hd2EsvvdRa7QMAAAAAwLdC96ZNm2TgwIF6e8uWLU0eo6gaAAAAAAC/IHQvWbLkTJ4GAAAAAIBPOaM53QAAAAAAwKCe7gsvvPCEw8gXL158Ji8LAAAAAIBXOaPQ7ZjP7VBXVycZGRl6fvett97aWm2DB8opqZLaequktAsVPzPz+wEAAAD4tjMK3S+//HKz98+cOVPKy8t/aZvggUqr62TZjiOyt6BC3w4J8JPuCeHSMzFSEiKDKLAHAAAAwCe16pzum266Sd55553WfEm4OavVJusPFMn7Kw/owK06t1XgrqqzyMaDJfLR2mz5YE22VNTUu7qpAAAAAOAZPd0tWbFihQQHB7fmS8KNVddZ5LMNhyS/rEbfTo4KltE946VdaKBkFVXK9pwy2XOkXI6U1cg3W3LlykEdGHIOAAAAwKecUei+6qqrmty22WySk5Mja9eulccee6y12gY399PuAh24g/zNcl63WOmTFOkcRt4xJkxfCitq5aM12XKouEp+3lMgF3SLc3WzAQAAAMC9Q3dUVFST22azWXr06CFPPfWUjBs3rrXaBjemQvTWw6V6+7L+ydKhXUiz+7UPC5SLeifI15tzZENWsSRGBkv3hIg2bi0AAAAAeFDofvfdd1u/JfAYFqtNlmzP19t9kiNbDNwOXePDZXB6O1l3oEgWZuZJTFigxIQHtVFrAQAAAMBD53SvW7dOMjMz9XafPn1k0KBBrdUuuLH1WUVytKJWF0w7t2vsKT3nnM4xkldaLQeLqmTe5hy5bmiqBPn7Gd5WAAAAAPC40J2fny/XXXedLF26VKKjo/V9xcXFcuGFF8qHH34ocXHM2/VWJVV1smpfod4+v1usDt6nwmw2yYS+ifLB6mwprqzTvd7ndDm1wA4AAAAAPrVk2P333y9lZWWydetWKSws1JctW7ZIaWmp/Pa3v239VsItqIJ5S3bk6+HlKe1CpGfi6c3NDg30l5Hd7V/IbMwukZo6i0EtBQAAAAAPDt0LFiyQ119/XXr16uW8r3fv3jJr1iz55ptvWrN9cCP7CirkwNFK8TOZZHSPeGel8tPRJS5MF1ertVj1Ot4AAAAA4M3OKHRbrVYJCAg47n51n3oM3mlTQ0gekBol7cICz+g1VFAf2rGd3t6QXSS19ZwvAAAAALzXGYXu0aNHy+9+9zs5fPiw875Dhw7Jgw8+KGPGjGnN9sFNlFbVyYHCSr3dr0PTJeNOV/f4CIkKCZDqOqtsOURvNwAAAADvdUah++9//7uev92xY0fp0qWLvnTq1Enf99prr7V+K+FyjjW5U9uFSHTomfVyNy6q5ujtXpdVJPUWersBAAAAeKczql6empoq69evl4ULF8r27dv1fWp+99ixY1u7fXADVqtNtuXYQ3ffX9jL7dAzMVJXQS+rrteBPqZVXhUAAAAAPLine/HixbpgmurRVnNzL7roIl3JXF2GDh2q1+r+8ccfjWstXGJ/YYWU19RLcIBZOseFtcpr+plNMjjd3tu99kCRWG2t8rIAAAAA4Lmh+5VXXpE77rhDIiMjj3ssKipK7rrrLnnppZdas31wA1sP2Xu5eyVFir/5jGYkNKtPUqSEBvrpQH+govVeFwAAAADcxWklnY0bN8rFF1/c4uPjxo2TdevWtUa74Caq6kX2Ha3Q232TW2douYO/n1kGpUXr7f3lhG4AAAAA3ue0kk5eXl6zS4U5+Pv7y5EjR1qjXXATqgfaZhNJjgrW62u3tl6JkaKW+y6sNYt/+5RWf30AAAAA8JjQ3aFDB9myZUuLj2/atEmSkpJao11wCybZV+7XqgXUjhUW5C8dY+zzxMP7stwcAAAAAB8O3Zdccok89thjUl1dfdxjVVVV8sQTT8ill17amu2DCwWn95dKi0kC/c3SNT7csOP0SozQ12F9R4uFimoAAAAAfHXJsEcffVQ+/fRT6d69u0ydOlV69Oih71fLhs2aNUssFos88sgjRrUVbSysz2h93TMhQgL8jJtz3SkuTALMNpGIGNmcXytDDTsSAAAAALhx6E5ISJDly5fLPffcIzNmzBCbmuyrBiGbTDJ+/HgdvNU+8HyqxzmkyxC93S3BuF5uRVVETw21yt5yP1myv1J+Y+jRAAAAAMBNQ7eSnp4u8+fPl6KiItm9e7cO3t26dZN27exrLsM77CqsE7/QKAkw2SQ5KsTw46WH2UP3qkPVUlpdJ5HBLRfsg2/LysqSgoKCNjtebGyspKWltdnxAAAA4OOh20GF7KFDGQjsrdYets/bTwyxitlsMvx47QJtUluQJRKbJvM35ch1ZxNy0Hzg7tmrl1RVVrbZMUNCQ2V7ZibBGwAAAG0buuHd1ubU6OvEkLYpbKaWDavYvEgCL7xN/rvuIKEbzVI93Cpw3/jwC5KQ1sXw4+Vl7ZE5f5muj0voBgAAwJkgdOM42YWVklVSLzarRRKDrW123IptSyRm9G2y9kCR7C+okI6x9qXEgGOpwJ3SrY+rmwEAAACclHElqeGxFm/P19c1B7dJoH2Z7jZhKS+UAQlBevv/1h9suwMDAAAAgEEI3TjOoobQXbVndZsfe1RHe9G2rzYedlbHBwAAAABP5dLQ/cMPP8hll10mycnJetmxzz//vMnjv/71r/X9jS8XX3xxk30KCwvlxhtvlMjISImOjpYpU6ZIeXl5G78T71FeUy8r9xzV25W717T58YckBUmgv1n2H62UHXllbX58AAAAAPCa0F1RUSEDBgzQ63u3RIXsnJwc5+WDDz5o8rgK3Fu3bpXvv/9e5s2bp4P8nXfe2Qat904/7SqQWotVEsP9pL6w7Yd4hwSY5YJucXr7m825bX58AAAAAPCaQmoTJkzQlxMJCgqSxMTEZh/LzMyUBQsWyJo1a2TIkCH6vtdee00uueQSefHFF3UPOk7Posw8fT0kKVhWuagNE/omysLMPPl2a648eFF3F7UCAAAAAHxgTvfSpUslPj5eevToIffcc48cPWof+qysWLFCDyl3BG5l7NixYjabZdWqliNjTU2NlJaWNrlAxGq1yZId9vncQ5LtBc1cYWyvBPE3m2R7bpnsK6hwWTsAAAAAwKtDtxpa/u9//1sWLVokf/nLX2TZsmW6Z9xisejHc3NzdSBvzN/fX9q3b68fa8lzzz0nUVFRzktqaqrh78UTbDxYLAXltRIR5C+9YgNd1o6o0AAZ0SVGb3+zJcdl7QAAAAAArw7d1113nVx++eXSr18/mTRpkp6zrYaSq97vX2LGjBlSUlLivGRnZ7dam71hqbALusdJgJ/JpW25uK99SsG3W5jXDQAAAMBzuXXoPlbnzp0lNjZWdu/erW+rud75+fag6FBfX68rmrc0D9wxT1xVO298gcjSHUf09YU9m44ecIVxvRPFZFK97yVyqLjK1c0BAAAAAO8P3QcPHtRzupOSkvTtESNGSHFxsaxbt865z+LFi8VqtcqwYcNc2FLPU1JVJ1sPl+jt87rGuro5EhcRJEPT2+ttersBAAAAeCqXhm61nnZGRoa+KPv27dPbWVlZ+rHp06fLypUrZf/+/Xpe9xVXXCFdu3aV8ePH6/179eql533fcccdsnr1avn5559l6tSpelg6lctPz9r9hWK1iXSKDZPEqGBxB44h5gsI3QAAAAA8lEtD99q1a2XQoEH6okybNk1vP/744+Ln5yebNm3Sc7q7d+8uU6ZMkcGDB8uPP/6oh4c7zJkzR3r27CljxozRS4Wdd9558tZbb7nwXXmmlXvtVeGHd7b3LruD8Q2he82BQjlSVuPq5gAAAACAZ63TPWrUKLHZbC0+/u233570NVSl8rlz57Zyy3zPyr2F+np4Z3vVcHfQITpEBqRE6Xnd323LlRuHpbu6SQAAAADgvXO6YYzS6v/N5x7WyX1Cd+PeboaYAwAAAPBEhG645Xxuh4v72EP3ij1H9ZcDAAAAAOBJCN1oNLTcfeZzO3SOC5fOcWFSb7XJDzvtS5oBAAAAgKcgdKNRETX3GlruMLZXgr5enNl0TXYAAAAAcHeEbh+nhmxvOeSe87kdxvSM19dLduSLRY2DBwAAAAAPQej2cY753B1jQt1uPrfD4PR2EhUSIEWVdbI+q8jVzQEAAACAU0bo9nHuuFTYsfz9zDKqR5zeXpiZ5+rmAAAAAMApI3T7OHefz+0wpmFe9yLmdQMAAADwIIRuH1bWeD63G1Yub2xk9zjxN5tkd365HDha4ermAAAAAMApIXT7sLX7i5zzuZOiQsSdqTndQzvavxigtxsAAACApyB0+zBPGVruMKaXvYr5ou3M6wYAAADgGQjdPszzQrd9XveqvYV6qTMAAAAAcHeEbh9VVWuRLYdL9fbZndx7PrdDp9gw6RwXJvVWm/yw84irmwMAAAAAJ0Xo9lFbDpeIxWqThMggSY527/ncjY2lijkAAAAAD0Lo9lEZWcX6emBqtHiSMT3t87qX7MiXeovV1c0BAAAAgBMidPuojGx76B7gYaF7cHo7Xcm8uLJONjS8BwAAAABwV4RuHw/dntbT7e9nllE94vT2wkyqmAMAAABwb4RuH3SkrEYOFVeJySTSP8WzQnfjKubM6wYAAADg7gjdPtzL3S0+XMKD/MXTjOweJ/5mk+zOL5cDRytc3RwAAAAAaBGh2wdlZBd55NByBzWne2hH+zJnC+ntBgAAAODGCN0+aGN2ib4emNpOPNWYXvYq5ouY1w0AAADAjRG6fYzVapONzsrlUeKpHOt1r95XKKXVda5uDgAAAAA0i9DtY/YWlEtZTb2EBPhJj4QI8VQdY8Okc1yY1Ftt8sPOI65uDgAAAAA0i9DtYzIahpb36xCll9/yZI7ebqqYAwAAAHBXnp26cMZF1Dx5aLnDmJ72ed1LduRLvcXq6uYAAAAAwHE8b70otMpyYZ5cRM1hcHo7Xcm8uLJO1mcVy9md7BXNgVNhs9mkuKpO1zmw2uy3A/zNEh0SICa1iD0AAADQCgjdPqS6ziLbc8r09sA0z1wurDE1PP7CHnHyecZhXcWc0I1TVVRZK/M350hBee1xj/VIjNCjKAIaTb/IzMxss7bFxsZKWlpamx0PAAAAxiJ0+5Cth0t04bHY8CBJjgoWbzC6V4I9dG/PlxmX9HJ1c+ABduWV6fXday1W8TObJNDPLKpj22wySUVtvezILZOC8hq5tF+SlBbai/TddNNNbda+kNBQ2Z6ZSfAGAADwEoRuH7IhyzG0PNprhs+O7B4n/maT7M4vl/0FFbqqOdAci9UmP+46IhsP2osJdogOkYv7Jkp40P8+Bg8VVcn8LTlytLxWPliTLV3q7bUCJt71iPToP9jwNuZl7ZE5f5kuBQUFhG4AAAAvQej2IY6wMcgLhpY7qDndalj58j1HZWFmntx+fmdXNwluGrg/33BIDhZXOesBnNM5Rszmpl8+dWgXItefnaaHnueUVEumpEr4gPESk5wuKd36uKj1AAAA8GRUL/fByuWqp9ubjOttXzrsu615rm4K3NTqfYU6cKuh5JcNSJLzusYeF7gdVM/35LNSpH+KvcJ/u9F3SJXVr41bDAAAAG9B6PYRR8trJLuwSs9d7dcQJrzFRX0S9fXaA4V6Li7Q2KHiKlmzv1Bvj+kVL51jw0/6HDXXe1T3OImUCjEHBsuu2ihd3RwAAAA4XYRuH7HxoH0+d5e4cIkMDhBvoubm9u0QqZd9WpyZ7+rmwI3U1Fvku625ouJyr8QI6Z4QccrPVXUPukmu2OprpcgarAusAQAAAKeL0O0jMhoVUfNG43rbe7u/25br6qbAjSzbcURKq+slMthfRvaIO+3nh0itFP/8gf21dh2Rytp6A1oJAAAAb0bo9hEbsu2he4C3hu4+9nndP+wqkIoaghFEduaVSWZumaiZ2+P7JEqQ/5nNyy5d/amEmeqkus4qP+4qaPV2AgAAwLsRun2Amou6sSF0D/LS0N0jIULS2odKbb0KRva1leG7VI/04u32qQZDO7aX5OiQM38xq0W6B9r//9meWyYHjla0VjMBAADgAwjdPmBfQYUeYhvkb5Yeiac+p9WTqPm3VDGHw7oDRVJTb5W48CC9pNwvFelX55ya8cPOAoqqAQAA4JQRun1ARkMvd98OURLg572/8nENVcwXbc+XOovV1c2Bi5TX1DvXpD+na4yuRN4ahndur5ccK6yslazCylZ5TQAAAHg/701gcHIMLffWImoOg9PbSUxYoJRU1el1meGb1PJgFqtNkqKCJb19aKu9rpoT3js5skmNBAAAAOBkCN0+1NPt7aFb9WiO7eUYYk4Vc19UWlUnWw419HJ3idHTDlqT4/+hA0crpbCitlVfGwAAAN6J0O3lqusssi2n1CdCd+Mq5t9ty2PerQ9avb9Qr9ee2i5EUtq1Xi+3Q1RIgHSODWvyZRYAAABwIoRuL5eZUyp1Fpsedp3S7hdUcPYQ53aNldBAP8kpqZYth+xfNsA3FFfWOr9gGtElxrDjOL68Uv9vqS+1AAAAgBMhdPvQ0PLWHmrrjoID/GRk9zi9PX9Ljqubgza0al+hqMENHWNCJSnKuC+Y1JdXseGBUm+1ydbDfLEDAACAEyN0+0joHuADQ8sdLumXpK/nbTrMEHMfoeZXqzW0leGdjevlVtSXV47ebvX/l1WNZwcAAABaQOj2cr5SubyxMb3iJSTAT7ILq2RTw9JR8G4bsor0tZpvnRAZbPjxeiRE6HNMLU+250i54ccDAACA5yJ0e7GiilrZf9S+nvCAFN8J3aGB/jK2t72g2lcbD7u6OTCYmlft6OU+K61dmxzT388s/TpE6W2WDwMAAMCJELq9WMbBYmfvX1RogPiSS/s7hpjnMPzXy6klwtT86rjwIEmONr6X26F/SpSoMgmqaF9RJcuHAQAAoHmEbi+WkeV7Q8sdVDG1iCB/yS2tlnUNQ4/hfdQXKhsbphC0dbHAsCB/SWtYlmxnnr2nHQAAADgWoduLbWzo6R6Y5nuhW1UxH9cnUW8zxNx7qfnUal61ml/dPSG8zY/fPTFCX+/MLadoHwAAAJpF6PZSKgA4iqj50nzuxi4dYB9iPn9zjtRbrK5uDgycQqHmV6t51m2tS1yY+JlMUlhZKwXlDDEHAADA8QjdXurA0UopqqyTQH+z9EqKFF90XtdYiQ4N0GFIreEM75JfVi2Hi6vFbBLpl2IvatbWgvz9pGMsQ8wBAADQMkK3lw8t75McqYO3LwrwM8uEvvbeboaYe+8a9F3jwyU8yN9l7eie0DDEPK+MIeYAAAA4jm+mMR+wIcu3h5Y7XNYwxPybLblSW88Qc29RbbHPo1YGpbbNMmEt6RQbJv5mk5RW10teaY1L2wIAAAD3Q+j28l7AQT5YRK2xYZ1iJC4iSEqq6uSn3Udc3Ry0kv3lZrHYbJIYGSyJUW23TFhLIyo6x4XpbYaYAwAA4FiEbi+kenS3HS712eXCGvMzm2RiP3tv9+cbGGLuHUyyr9zPuVa2O+jhGGKezxBzAAAANEXo9kKZOaVSa7FKu9AASWtvL/Lky646q4O+XrA1V4orqTDt6YLT+0ulxaRrFaj53O4gLSZUt6eixqKLuwEAAAAOhG4vHlo+IDVaTCaT+Dq1nFTvpEg9AuCzDYdc3Rz8QuEDxuvrngkRemi3O/A3m6VrnP0LgB0MMQcAAEAj7vEXK1qVY31uXx9a7qC+eLju7FS9/eHqbIb/erDSGquEdhuht/t2cI+h5Q7dE+yhe3d+uVisnGMAAACwI3R7eU837K4Y0EGC/M26F9Lx84HnWba/Ukz+ARIdaNUF8txJartQCQnwk6o6NcS8ytXNAQAAgJsgdHuZkso62VtQobcH+vhyYY1FhQY4C6p9tCbb1c3BGVAjFBbus4fZTmHut/yb2WySjrH2Ggr7Gv4fBAAAAAjdXmbjQXsvbseYUGkXFujq5riVa4fah5h/ufGwlNfUu7o5OE3rs4olu7RerHXVkuqGoduxZrdC6AYAAIADodvLMLS8ZWd3ai+dY8OkstYi8zayfJin+WhNlr6u3P6TBLjpJ5daLcBsEimuqpMiKuUDAADA1aH7hx9+kMsuu0ySk5N1savPP//8uOGkjz/+uCQlJUlISIiMHTtWdu3a1WSfwsJCufHGGyUyMlKio6NlypQpUl5eLr4euimidjx1jjl6uz9kiLlHKauuk6825ujt8o3firsK8veTlHYMMQcAAICbhO6KigoZMGCAzJo1q9nHn3/+efnb3/4mb775pqxatUrCwsJk/PjxUl39v3VwVeDeunWrfP/99zJv3jwd5O+8807xRepLCiqXn9jkwSnibzbpLye255a6ujk4RSpwqwJlHSL8pOZQprgz5xDzI4RuAAAAiPi78uATJkzQl5YC5CuvvCKPPvqoXHHFFfq+f//735KQkKB7xK+77jrJzMyUBQsWyJo1a2TIkCF6n9dee00uueQSefHFF3UPui85WFQlRytqJcDPJL2SIl3dHLcUGx4kF/VOkG+25Orlw2Ze3sfVTcJpDC0f2zlUlot7U/UUlonIoZIqqamzSFCAn6ub5DaysrKkoKCgzY4XGxsraWlpbXY8AAAAtwvdJ7Jv3z7Jzc3VQ8odoqKiZNiwYbJixQodutW1GlLuCNyK2t9sNuue8SuvvFJ8yYaGXu7eSZESzB/6Lbru7DQduj9Zmy0Pju2uK5vDfW07XCobD5boL5NGpoeIu4sODZT2oYFSWFkrBworpXtChKub5DaBu2evXlJVWdlmxwwJDZXtmZkEbwAA4FJuG7pV4FZUz3Zj6rbjMXUdHx/f5HF/f39p3769c5/m1NTU6ItDaal3DDNmaPmpuaBbrPRIiNBrdr+/6oDcd2FXVzcJJ/DxWvv8ezVCITrYJJ5ADTEvzKrV87oJ3Xaqh1sF7hsffkES0roYfry8rD0y5y/T9XEJ3QAAwJXcNnQb6bnnnpMnn3xSvI03VC5XUwbawq/6RckzeWXyzk/7ZMp5nRgZ4Kaq6yzy6fqDevu6oWki5dkeE7rXZRXJ/oIKsdpsYjZ5xpcFbUEF7pRuTOsAAAC+w21Dd2Jior7Oy8vT1csd1O2BAwc698nPz2/yvPr6el3R3PH85syYMUOmTZvWpKc7NdVe1dpT1VmssuVQicf2dJcWHtHXN910U5scLyQsXPrP+D/JLauVT9YdlJuHp7fJcXF6FmzJldLqeukQHSLndY2VjAzPCN1JUcES5G+W6nqr5JRU6/YDAADAN7lt6O7UqZMOzosWLXKGbBWO1Vzte+65R98eMWKEFBcXy7p162Tw4MH6vsWLF4vVatVzv1sSFBSkL95kR26Z1NRbJTLY31k92ZNUlduH+E+86xHp0d/+uzR62OmlXUPkXxvq5K0f9sj1Q1PF389NF3/2YR82FFC7ZkiqmNUC2B5CtbVjTJiewqCGmBO6AQAAfJdLQ7daT3v37t1NiqdlZGToOdlqDt4DDzwgzzzzjHTr1k2H8Mcee0xXJJ80aZLev1evXnLxxRfLHXfcoZcVq6urk6lTp+oia75WuXxDo6Hlaj1qTxWTnN5mQ0/HdAqVT3dWS3ZhlXy9OUeuGNihTY6LU6PC6sq9haJO518NSRFPo778UqFbDTFXvfQAAADwTS7t2lu7dq0MGjRIXxQ15FttP/744/r2H/7wB7n//vv1uttDhw7VIV0tERYcHOx8jTlz5kjPnj1lzJgxeqmw8847T9566y3xNRlZ9tA9yAOHlrtKkL9Jbjuno95+Y+kevUwd3MdHa+xDyUd2j5NkD+wpTo8J1V8YqGX8SqrqXN0cAAAA+GJP96hRo04YdFSP7VNPPaUvLVG94nPnzhVft/FgQ+XyNEL36bhlREd5c9ke2Z5bJkt3HpELezSthg/X1Sj477pGBdQ8kCrOlxwVIoeKq2T/0QoZkML/mwAAAL6ISaxeoLS6TvYcKdfb/GF/etQa3TcMs4e615fsprfbTSzeni8F5TUSGx4oY3p57hchaTGh+jq7sO3WpgYAAIB7IXR7gU3ZJaKyYmr7EIkJ964CcW1hynmdJdDfLGv2F8l32/Jc3RyoAmqr7QXUJg9OkQAPLnCX3t4RuqvEauULHQAAAF/kuX/Nwikju0hfD0xt5+qmeKTEqGC54/xOevuZr7fptaHhOmo49rKd9iXkrh3i2Uv5xUUESbC/WWotVsktrXZ1cwAAAOAChG4vkJFtX597QEqUq5vise4d1VUSIoN0j+TbP+1zdXPE1wuoqU7h4Z3bS+e4cPFkZpNJUht6u7MYYg4AAOCTCN0eTs1BzmhYLmwQRdTOWFiQv8yY0Etvz1qyW3JL6JV0hXqLVT5uqFp+w7B08QZphG4AAACfRuj2gqG4quCUv9kkfZLp6f4lrhiYLIPT20llrUX+smC7q5vjk5buOKKHYbcLDZDxfRLEm0K3el819UxdAAAA8DWEbg+3sWFoec+kCL1EEc6cWqJu5mV99NrKn204JOsO2OfKo+180FBA7erBKRLk7x3nc2RIgESHBuhihweLqlzdHAAAALQxQrfXFFFjaHlr6JcSJdcMthfvmvnlVrFQcbrNHC6ukiU78vX29Wd75trcJ6tifuAoQ8wBAAB8DaHbwznmc1O5vPVMv7iHRAT5y+ZDJfK3Rbtc3Ryf4U0F1I7FvG4AAADfRej28KJTKhgqA1OZz91aYsOD5OlJffX23xbvkp93F7i6Sb5RQG1ttlf2cisd2oWI2SRSUlWnLwAAAPAdhG4PtiOvTKrrrBIR7C+dY72rZ9DVJg3qINcNTdXzcH/3YYbkl1HN3OgCajkl9gJqF/dNFG+j5qcnRgbrbXq7AQAAfAuh2wuGlg9IiRaz6kZDq5p5eR/pmRihq8P/7oMM5ncbyBsLqB0rLaZhiDnzugEAAHwKoduDbXSEboaWG0JVg//7DWdJaKCfrNh7lPndBskurHQWULvOC4eWHzuvO7uoUqxqCAUAAAB8AqHbg1FEzXhd48Pl2Sv7Oed3z9+c4+omeZ1/r9ivC6id1zVWunhZAbXGEiKCJdDfLDX1VskvrXF1cwAAANBG/NvqQGhdZdV1siu/XG/T0238/O41+wtlzqos+e0HG8RsMp32vOOsrCwpKGi7gmyxsbGSlub+vcYVNfXy4Rp7AbXbzu0o3kxNAUltFyJ7jlToed2JUfY53gAAAPBuhG4PpaqWqxGqHaJDJD6CP96N9tQVfaWy1iKfbTgkU+eul9dvPEvG9Uk85cDds1cvqapsu7m8IaGhsj0z0+2D96frD0pZdb10jAmVC3vEi7dTQ8xV6D5QWCFnd2rv6uYAAACgDRC6PX5oebSrm+IT/MwmefFXA/Rc3C8yDst9c9fLGzcOlrG9E076XNXDrQL3jQ+/IAlpXQxva17WHpnzl+n6uO4cuq1Wm7y7fL/evvWcjj5RDDA9JkxEjkhuSbXU1lv1cHMAAAB4N0K3h8rIooiaK4L3X3XwFvlq42G5Z846eeHqAXr4+alQgTulWx/D2+kpfth1RPYeqZDwIH9dtdwXRIUE6Itaq/tgUaV09uI57AAAALCjm8UD2Ww2WZ9VpLfPSqOIWlvy9zPLy9cMkIn9k6TOYpMHPsqQP322WarrLK5umsd592d7L/evhqRIRHCA+ApHFXPW6wYAAPANhG4PlF1YJQXltRLgZ5K+HejpdkXw/tt1g+S3o7uKySQyd1WWXP3mctZfPg2788tl2c4j+uf363O8u4DasQjdAAAAvoXQ7YEcvdx9kqP0WtJwzVDzaeN6yOzbzpZ2oQGy5VCpTHztR/l8wyE9EgEn9l7DXO4xPRMa5jn7DlXBXM1eL6qsk9LqOlc3BwAAAAYjdHsghpa7j5Hd4+Tr354vZ6VF6yrcarj5dW+tlJ15Za5umtsqqayT/1t/UG//xsuXCWtOUICfJETaVxygtxsAAMD7Ebo90LoDDaE7ncrl7iA5OkQ+vHOE/P6i7hIcYJZV+wplwqs/yjPztun11NHUu8v36eXXeiZGyIguMeKL0mLsQ8yzmZIAAADg9QjdHqaytl6259p7UQen09PtLtTST/eP6SbfPzhSxvVOEIvVJv/6aZ+MfGGpfLmjXEz+ga5uoltQw6nf+Wmf3p6q58R7/zJhJ5zXXVTJdAQAAAAvR+j2MBuzS3SgS4oKlqSoEFc3B8dIbR8qb90yRGbfNlQ6x4ZJYUWtzN5YJsl3/lP2lpn1786Xzf55v5RW10vX+HCZ0DdJfFViZLAE+pmlus4q+WU1rm4OAAAADETo9jDM5/YMo3rEy3cPXiB/mdxPYkPN4h8RIxuK/OXfK/ZLZk6pWH2wd1MNtX+7oZf7/tFddTE6X6Xee0o7+5dmzOsGAADwboRuD7O+YT73oDTmc3vC0mLXDk2TWRPipfD7NyXIbNO9vN9ty5M5K7NkV16ZTw0t/veKA1JSVSdd4sLk0v7J4utYOgwAAMA3+Lu6ATh1KqBtyC7W28zn/uUyMzPb5Di7d26XsvXz5LbbfiNHgpJ0IbzCylqZvyVX4iKCZETnGOkYE+rV85vLa+rlnz/u1dv3j+7m073cxxZTO1xcJXUWqwT48R0oAACANyJ0e5D9Ryv1HGFVtEut0Y0zU1p4RF/fdNNNbXrc6spyGdqjvfTvECXrs4plQ3aRHCmrkS83HtZz9M/pEiMp7exBzNuoYfXFlXV6nvtlA+jlVqJDAiQi2F8vNXeoqEo6xvrWeuUAAAC+gtDtgUuF9esQpYM3zkxVeam+nnjXI9Kj/2DDj5e5epl8896rUl1d7VynWS2VNSA1Sv9ONx4skZySavm/9Yf0kOPzu8VKbHiQeIsK1cv9w15nxXJ6ue3UyAb1+956uFQOFFYSugEAALwUodsji6gxn7s1xCSnS0q3PoYfJy9rT7P3hwb6y/nd4mRQajtZvb9Qth4u0fN7567OkkGp0TKsU4xXfLny1g97paiyTg+hv5xe7ibSG0I387oBAAC8l+f/Re+DRdSYz+1dwoP9ZXTPeLllREddZEzVVlPDz/+9cr/s9PBia1lHK+WNZfYvHaaP76mLy6HpEnOKmjaiqrsDAADA+/AXsIdQf5CrAKawXJh3igoJ0FW9VW+w2q6oscg3W3L1nG9ViMwTPfnVVqmtt8q5XWPkkn6Jrm6O2wkO8JOESPtUAnq7AQAAvBOh20NszC4Rq02kQ3SIxEcGu7o5MFCn2DC5aViaDOvUXvxMJl1A7/2VB2R7bqlH9Xov3JYni7bnS4CfSZ68vK9XV2f/JdLb2+dyE7oBAAC8E6Hb0+ZzM7TcJ6hh2MM7x8j1Z6dKfESQ1NRb5dutefL15hxdmMzdVddZ5Ml5W/X2b87rJF3jw13dJLdfOkyFbg/6TgUAAACniNDtISii5ptiwoPkmiGpMrxze1FFv/ccqZA5q7JkX0GFuLM3l+2R7MIqSYwMlt+O7ubq5rg19TMK9DNLdZ1VimsZDQAAAOBtCN0eQA0pzsgu1tsUUfM9aoktVcn8uqFpEhMWKFV1Fj3Pe+mOfKm3WMUdi6e9vtRePO3RS3tJWBCLJJzs95vSLkRv51UTugEAALwNodsDqLmwSx8aJe/eNlR6JUW6ujlwkbiIILluaKoMTLGPdlDre3+4JlsKymvEXaiiaQ9+nOEsnjaxX5Krm+RRQ8zzqvlIBgAA8Db8hechokMD5cIe8RLAkkvi63O9R/aIkysGJEtIgJ8crajVwVuNhHCHImvPzs+UdQeKJCLYX/48qR/F005jvW7laI1JTIH2Xm8AAAB4BxIc4IE6xobJjcPSJD0mVCxWmyzbeUQPOa+sdV2RtS8yDsns5fv19kvXDNRtxKl/qaaWibOJSYJT+7q6OQAAAGhFhG7AQ6m50qrHe2T3OD0v2L60WJbkVrV977JaQ/6P/7dZb987qotc1Duhzdvg6dIaeruDOw5ydVMAAADQigjdgAdTw7cHpkbrud6OIms/HwmQdmPukFpL2ww3L6uuk7v/s04f+7yusfL7cT3a5LjeGrpDOp3l6qYAAACgFRG6AS8QG24vsjYgJUrfjhxyhTy8sED3QBupvKZe7nl/vewtqJDkqGB59bqButcdpy+1fYiYxCYBMSmSX+H+a7EDAADg1LCWD+BFRdZG9YiXsJoC+XF/hRyQaLnstZ/kT5f0kpuHp4u5lcNwfmm13DZ7jWw9XCpBfib53ZAwObBzqxwQ42RmZoq3CvL3k/ZBNl1MbWNurVzs6gYBAACgVRC6AS+TFGKTw+9Olcuf+UQ25NbIE19ulf9bf1CeuKy3DE5v3yrH2J1fLre+s1oOFVeJtbJY9n8yU657dre0lfLycvFGCcFWOVpjlow891kGDgAAAL8MoRvwQtaKYnnk/Haypbq9vPjdTtl0sEQmv7FCrhiYLH+c0FOSos58WaqVe4/K3e+vk+LKOkkK95O1/3hIrr3r95KQ1kWMlrl6mXzz3qtSXV0t3ig+2CbbSkQ25tVIncXKEoEAAABegNANeCmzySS/PreTTOyfLC9+u0M+XpctX2Qclm+35sqVgzrIDWenS7+GOeCnYvPBEnll4U5ZtD1f3x6QGi0PDAqU0Y/l6sCd0q2PGC0va494s/aBNrFUlkhlaJRe73x45xhXNwkAAAC/EN0ogJeLiwiSv1zdX7687zwZkt5Oquus8sHqbLns7z/Jpa/9KO+vPCC788uktt563HOLKmpl+Z4Cuf29tXp/FbjV1PBrhqTIB3cMk6hgP5e8J29lMolU7V2nt5c0fLkBAAAAz0ZPN+AjVK/2J3ePkJV7C+WD1VmyYEuubDlUKo8e2qIfV1XH1bJVHWNCpaSqTvYVVEhRZZ3z+SpsXzGwg9w/uqt0jgt34TvxblV71kh439GyeHu+zLikl6ub49YOFlXKoaIqKamuk9Kqeimttp+v3eLDpV0tVfQBAIB7IHQDPrau94guMfpSWFErn64/KF9typHdeWVSUWvRQVtdGlNLgalhzvde2FW6xhO2jVa1b73+gmNXfrlkF1ZKasP63fif0qo6+WHXEdlzpOm56rA+q1hEAiTpttfkyx3l0rufRYIDGJUBAABcg9AN+Kj2YYFy+/md9cVms0l+WY3sOVIu+wsqJSLYXzrHhUmn2DAJDeRjoi3ZaiqkZ2ygbDtSq3u7bz2no6ub5DbqrVYdqNfsK5R6q00Px+8eH6HP5cgQf4kMDpCqOotszymTvUfKJDC+k8zeWCYbi1bKWzcP0VMtAAAA2hp/TQPQPeAJkcH6co7xRchxEoOTggjdzfRuf55xyDnloUN0iIzqESex4ccH6S5x4bJ3R6HMfvufknbpVNmQVSyTZv0s/7p1iPRKinRB6wEAgC+jkBoAuJkhSfYguWLvUamsrRdfV1ZdJ59usAfu0EA/Gd8nQSaf1aHZwO0QaBYpz/hG/jI2Vo/YUGvKT35juSzcltembQcAACB0A4CbSYn0l5R2Ibqi/M+7j4ovq6ipl0/XH9LF/aJCAuS6oanSMzFSj844FckR/vLZvefIOV1ipLLWInf8Z618sjbb8HYDAAA4ELoBwM2oQDm6Z7zeVkPMfTlw/9/6g1JcVSeRwf5y1VkdJCI44LRfJzo0UN77zdly/dlpYrOJPPLZFtmQVWRImwEAAI5F6AYAN3RhQ+hW63WrQne+prrOIp81DCkPD1KBO0UXSjtTAX5mefbKvnpoeq3FKve8v16OlNW0apsBAACaQ+gGADc0onOMhAT4SW5ptWzLKRVfor5k+H5bnhytqJWwID89f1sNLW+NEQQv/mqAdIkL0z/X++aslzqLtVXaDAAA0BJCNwC4IbWu9LldY5y93b5k48ES2VtQIX4mk1wxoIMeHt5a1PD0t24ZonvPV+8vlD9/ndlqrw0AANAcQjcAuPkQc1+a151fVi0/7SrQ2+d3izVkbW21pNhL1wzQ27OX75fPNhxs9WMAAAA4ELoBwE05iqltyC72ifnHqlr7N5tzxWKzSefYMOmfEmXYscb1SZT7R3fV249/sVWHfQAAACMQugHATSVFhejgqeqofbs119XNMdzSHfm6Urka+n1R74RTXhbsTD0wtrv06xAlZdX18vQ8hpkDAABjELoBwI1d0i9JX3+zJUe82fbcUsnMLRMVsy/um6jntBvNz2yS567qJ2aTyFcbD8uynUcMPyYAAPA9hG4AcGOX9LWH7hV7jsrRcu8cYl5Va3EG3mGd2kuH6JA2O3bfDlHy63M66e1HP9+s2wIAAOAzoXvmzJl6eGHjS8+ePZ2PV1dXy3333ScxMTESHh4ukydPlry8PJe2GQBaU1pMqPTtEClWm8h327zz8+3H3Uekus4qMeGBMqRj+zY//rRx3SUpKliyC6vktcW72vz4AADAu7l16Fb69OkjOTk5zstPP/3kfOzBBx+Ur776Sj755BNZtmyZHD58WK666iqXthcAWtuEht7u+Zu9b4h5dmGlZOaU6e0xPeP1kO+2puaQz7y8j95+64e9sjPP3h4AAIDW4C9uzt/fXxITE4+7v6SkRN5++22ZO3eujB49Wt/37rvvSq9evWTlypUyfPhw8TZZWVlSUGBfSsdomZkUFQLcaV73C9/ukOV7jkpRRa20C2u9datdqd5ilcU77MuhqYJmqnCcqz7L4kTk7OQgWX24Rn73nxXyzIUxp1XILTY2VtLS0n5BSwEAgLdy+9C9a9cuSU5OluDgYBkxYoQ899xz+g+bdevWSV1dnYwdO9a5rxp6rh5bsWKF14VuFbh79uolVZWVbXrc8vLyNj0egON1ig2TXkmRkplTKt9vy5NrhqaKN1hzoEiKK+skNNBPzu0a06qvXVponyN+0003nfJz/CJiJfn2NyWzQOT86++Xql0rTvm5IaGhsj0zk+ANAAA8K3QPGzZMZs+eLT169NBDy5988kk5//zzZcuWLZKbmyuBgYESHR3d5DkJCQn6sROpqanRF4fS0lJxd6qHWwXuGx9+QRLSuhh+vMzVy+Sb917V8+YBuN7Efok6dH+9OccrQndhRa2s3V+ot0d1j5Mg/9atVl5Vbv9cn3jXI9Kj/+BTft7WYj/ZXirS+Zo/yUVJdbqy+cnkZe2ROX+Zrj+nCd0AAMCjQveECROc2/3799chPD09XT7++GMJCTnzYYiqt1wFeE+kAndKN/vcQyOpPyIBuI8J/ZLkxe92ys+7C6Sksk6iQgPEU9lsNlmyI18Xh+sYEypd48MNO1ZMcvppfWbG1VvkwPIDUl5nkZKwFD3sHQAAwKsLqTWmerW7d+8uu3fv1vO8a2trpbi4uMk+qnp5c3PAG5sxY4aeE+64ZGdnG9xyAPhlusSFS8/ECKm32uS7bScezePudueXy8GiKl007cIe8ac1d9poqsf97E72Cuor9x6VOovV1U0CAAAezqNCt5pfvGfPHklKSpLBgwdLQECALFq0yPn4jh079NxnNff7RIKCgiQyMrLJBQA8pYr5N1s8N3TXW0V+2GUvCDkkvZ1Ehrhfj71aoi0y2F8qay2yIavpF7sAAABeFbofeughvRTY/v37Zfny5XLllVeKn5+fXH/99RIVFSVTpkyRadOmyZIlS3Rhtdtuu00Hbm8rogYAysT+9lE8P+46IiVVdeKJdpb6SXlNvUQE+8vg9HbijvzNZjmnS6zeXnegSCpr613dJAAA4MHcOnQfPHhQB2xVSO2aa66RmJgYvRxYXJxa3EXk5ZdflksvvVQmT54sF1xwgR5W/umnn7q62QBgiK7xEdI9IVzqLDb5epPnrdntFxkvO8rs/+yc3zVWAvzc958g9XOOjwiSWotV1uwrcnVzAACAB3Pfv3hE5MMPP5TDhw/rSuMqgKvbXbr8r3K3WkZs1qxZUlhYKBUVFTpwn2w+NwB4sl8Ntlcu/2hNlniadqOniNVmkpR2IYYWT2sNap75uV3tvd2bDhVLqYeOLAAAAK7n1qEbANDUlWd1kAA/k2w8WCLbDrv/cocOm/JqJKzHuWISm4zsHudWxdNaktY+VFLbh+gq66sbljcDAAA4XYRuAPAgseFBclHvBL398VrPWHlBVQB/e4P9C4LO4Vb9HjzF8E4x+lqtke6p8+gBAIBrEboBwMNcOzRNX3+6/qBU11nE3b2/8oBkl9aLpbJEeke7f3sbS44O0T3eurd7H73dAADg9BG6AcDDnNc1VjpEh0hpdb18u9W9lw87Wl4jL32/U28X//BvCfTAf3WGd7av252ZS283AAA4fR745w8A+DY/s0l+NSRFb3+42r2HmL/43Q4pq66XTtH+Ur7pe/FESVEhkt4+VGz0dgMAgDNA6AYAD/SrIamiapGt2HtU9hdUiDvafLBEPlxj/1Lg9rOiRGxW8VTDOzfM7c4tleLKWlc3BwAAeBBCNwB4IDW8/IJucW5bUM1ms8kTX27RvcOTBiZLr9hA8WSJUcGSHtPQ200lcwAAcBoI3QDgoa4bal+z+5N1B6Xe4l69yJ9nHJL1WcUSGugnf5zQS7yBo5L59pwyKaK3GwAAnCJCNwB4qDG9EiQmLFCOlNXIwsx8cRdl1XXy3Pztenvq6K66l9gbqPfRUfV2C3O7AQDAqSN0A4CHCvQ3y7UNvd2vL92th3S7gxe+3SH5ZTU6oE45r5N4E8fc7h25ZVJUQW83AAA4OUI3AHiw35zXSUIC/GTTwRJZuvOIq5sj6w4UyX9WHtDbz17ZT4L8/cSbJEQGS6fYMHtvN3O7AQDAKSB0A4AHiw0PkpuGp+ntvy3a5dLe7jqLVf706WZdbOzqwSlyTtdY8UbDOtnX7aa3GwAAnApCNwB4uDsu6CxB/mbZkFUsP+8+6rJ2vPXDXtmRVybtwwLlkUu8o3haS73dnRt6u1cxtxsAAJwEoRsAPFx8RLDcMMze2/3qop0u6e3eV1Ahry7apbcfv7S3tAvz7CXCTmZY54be7rwyKa1zdWsAAIA783d1AwAYIzMz0yuOgVNz98guMmdVlqzZXyQr9xbKiC72gl9tQYX8Rz7bLLX1Vjm/W6xcMTBZfOGLji5xYbLnSIVklnjXvHUAANC6CN2AlykttBfTuummm9rsmOXl5W12LLQ85Fmt2/3vFQf03O62DN3qmMv3HJXgALP8eVI/MZlM4guGdYrRoftgpVkCYu0jDQAAAI5F6Aa8TFV5qb6eeNcj0qP/YEOPlbl6mXzz3qtSXV1t6HFw6r3dH6zOkhV7j+p1pM9uKPhlpC2HSuTPX9tHPDx8cU9JiwkVXxEXEeTs7Y465zpXNwcAALgpQjfgpWKS0yWlWx9Dj5GXtcfQ18fpSY4OkV8NSZW5q7Lk8S+2yJdTz9NreRulvKZeps5dL7UWq1zUO0F+fU5H8TWO3u7QnudJVkmdnOXqBgEAALdDITUA8CIPju2uq4dvzy3Tw8yNnse9/2ilJEcFywtX9/eZYeXH9nZ3CLGKyWSWj7cyzQIAAByP0A0AXhYCn5nUV2+/vnS3bMgqMuQ4n6w9KF9kHBY/s0leu2GQRId6d7XyE+kVZdHXyw9Wy/Zc+/QOAAAAB0I3AHiZS/ol6QriVpvI7z/ZKNV19lDYWrYdLpXHv9yit6dd1F0Gpxs/d9ydRQXapGL7j3r71YXGjS4AAACeidANAF7oycv7SHxEkOw9UiEvfLuj1V53d36Z3Pz2Kqmusy8Pds/ILq322p6s5OcPRA2u/2ZLrv5SAgAAwIHQDQBeSA33/svk/nr7nZ/3yYo9R3/xa+4rqJAb/rlKjlbUSp/kSPn79WeJ2ex787ibU1eQJeekButtI+fSAwAAz0PoBgAvdWHPeL12t80mcud/1uplxM5UdmGl3PDPlZJfViM9EyPk/SnDJCo0oFXb6+mu6R0uqpbcgq25svVwiaubAwAA3AShGwC82KOX9pYh6e2krLpebnp7lXy7Nfe0XyPraKVc/8+VklNSLV3jw+X924dJuzDfLZzWktSoALm0f7Lefum7na5uDgAAcBOEbgDwYuFB/vKfKcNkbK94qa23yj3vr5MPVmed0nPrLVZ564c9Mv6VH+RgUZV0ig2TubcPk9jwIMPb7akeHNtNV3RftD1fVu795UP6AQCA5yN0A4CXCwn0kzdvGizXDknVFc1nfLpZnvhii+zKK2vxORnZxXLZ33+WZ+dvl6o6iwzr1F7m3jFM4iPt85bRvM5x4XLD2Wl6+9n5mWJVP3AAAODT/F3dAACA8fz9zPL/JvfT63j/fclueW/FAX3pnRQpkwYlS8/ESNl7pFx25ZfL7vxyWb2/UM8FjwoJkEcm9pJfDU4Rk5qwjJP67Zhu8un6g7LpYInM25wjlw+wDzkHAAC+idANAD5CheaHxveQganR8uGaLFm644hsyynVl+ZcOaiDDtwMJz896ouNu0d2kb9+v1Ne+Ha7jO+TIEH+fq5uFgAAcBFCNwD4mLG9E/SlqKJWvt6cI19tPCwF5TXSJS5cF0pTl74doqR7QoSrm+qxppzfSf6z8oBkF1bJ+yuzZMp5nVzdJAAA4CKEbgDwUaoC+U3D0/UFrSs00F9+P667PPx/m+W1xbvk6sEpeqg+AADwPRRSAwDAAFcPTpXuCeFSXFknry/Z7ermAAAAFyF0AwBgALV02IwJvfT2Oz/v0wXqAACA7yF0AwBgkFE94mRMz3ips9jksc+3iE2VhAcAAD6F0A0AgIEV42de3keCA8yyYu9R+SLjsKubBAAA2hihGwAAA6W2D5X7R3fT2898nSklVXWubhIAAGhDhG4AAAx2+/mdpHNcmF6a7a/f7XB1cwAAQBsidAMAYLAgfz955oq+elut3735YImrmwQAANoIoRsAgDZwTtdYuWJgsqhaan/6bLPUW6yubhIAAGgDhG4AANrIIxN7SUSwv2w+VCKvLWbtbgAAfAGhGwCANhIfESzPTLIPM//7kt2y7kCRq5sEAAAMRugGAKANXTGwgx5mbrHa5MGPMqS8pt7VTQIAAAYidAMA0MaeuqKvdIgOkazCSnnqq62ubg4AADAQoRsAgDYWFRIgL10zQEwmkY/XHpQFW3Jc3SQAAGAQQjcAAC4wrHOM3D2yi97+46ebJbuw0tVNAgAABiB0AwDgIg+O7S79U6KkuLJObn9vrZRV17m6SQAAoJURugEAcJFAf7P84+bBEh8RJDvyyuR3H2boAmsAAMB7ELoBAHChpKgQ+ectQyTI3yyLt+fLs/MzXd0kAADQigjdAAC42IDUaHnpmoF6++2f9sncVVmubhIAAGglhG4AANzAxP5JMu2i7nr78S+2yMJtea5uEgAAaAWEbgAA3MT9o7vKpIHJUm+1yd3vr5NvNrOUGAAAno7QDQCAmzCZTPLirwbI5QPswXvqBxvki4xDrm4WAAD4BQjdAAC4EX8/s7x87UC5enCKrmT+wEcZ8snabFc3CwAAnCH/M30iAMAYmZltU706NjZW0tLS2uRYOD1+ZpM8P7m/XlJMFVWb/t9NcqS8Ru6+oIuYzSZXNw8AAJwGQjcAuInSwiP6+qabbmqT44WEhsr2zEyCt5tS4frPk/rqpcTe/Xm/PL9gh6w/UCx//dUAiQoNcHXzAADAKSJ0A4CbqCov1dcT73pEevQfbOix8rL2yJy/TJeCggJCt5vP8X780t7SPSFCnvhyqyzMzJNL//6jvHHjYOnbIcrVzQMAAKeA0A0AbiYmOV1SuvVxdTPgRsH7+rPTpG9ylNw7d51kF1bJVW8slz+M7yG3ntNRAvwozwIAgDvjX2oAADxAv5QomTf1fBnbK15q663yzNeZMvFvP8ry3QWubhoAADgBQjcAAB5CzeV+6+Yh8txV/aRdaIDszCuXG/61Su6bs14OFlW6unkAAKAZhG4AADyswJoabr7koVFyy4h0UcXMv96cIyNfWCq//WCDbMwudnUTAQBAI4RuAAA8UHRooDx1RV+Zd//5cm7XGL2m95cbD8sVs36WX725XOZtOiyVtfWubiYAAD6PQmoA4MPaak3wtjqOL+qdHClzbh8uWw6VyDs/7dPBe83+In0JDjDLhT3iZUK/JBndM17Cg/hnHwCAtsa/vgDgg9p6TXCH8vLyNj2eL1FLiL107UD5w8U95T8r98sXGYflYFGVfLMlV18C/Ex6n6Ed2+vLkPR20i4sUHxdVlaWXjqvrcTGxrJMHwD4GK8J3bNmzZIXXnhBcnNzZcCAAfLaa6/J2Wef7epmAYD4+prgSubqZfLNe69KdXW14cfydYlRwTJ9fE95aFwP2Xq4VOZvztGX/UcrZUNWsb689cNevW9CZJBeA9x+CZfUdqGSHB2iXyM4wE+8mc1mk/0HsqRP375SVVUtYlLLs6lZd2pDxGa1iFgsas9WPW5IaKhsz8wkeAOAD/GK0P3RRx/JtGnT5M0335Rhw4bJK6+8IuPHj5cdO3ZIfHy8q5sHAOLra4LnZe0x/Bi+5HR6Z8fGi4wZHSn5FWGyraBWthfUyrYjtXKozCJ5pTX68uOu418rOtisL5GBZomNDJHUuGiJCA6QkEA/CQnw09ehgX46nIc23Bfk7ycmk4jZZBKzWWVXky70ptYad9xvsVqlzmKTeotN6qxWfV1vsUqdteFa3W64v67hdm29RWotVqmps9qv66162bSaeove/t9tdW055vb/9nXcV2/9X5COn/rhSX6CNpXB7e+j0bWfScTPbBN/dd1wsW/bRC2drrb1pWEff7NI+dEcWfLB67Ji52GpDW6vh/uHBamfn7/4qRcGAHglrwjdL730ktxxxx1y22236dsqfH/99dfyzjvvyB//+EdXNw8A4APaat56Tk6OXP2rX0l1VdUveh1TYKgExKZJYGyavlYX/8g48YuME3NAsBRXW/VFy68V2V0ivsmk+7otjpzeuOPbcrpBOVUSr39OHl54VGThsiaPqC8twhpCeFigvw7koWo7yF/CA+3b9pDecAlseEztF9josUD7a/ir5A8AHjwlJ9aLpuN4fOiura2VdevWyYwZM5z3mc1mGTt2rKxYscKlbQMAeD9XzY+/8rdPSacefVv9dW02kVprrVRZTFJtETmYtU82LF8qfiFRYgoKFXNAkJgCgsXkr66DdEBX1/riH2R/Ed2zrbqEG4Kfydxw2yQ2q1XEWt8wfNt+bbPW66HcNn27XqTxfWrf+lr7Y/q6Tl+Lvq6z3264z37d9HaT/Rz7qGOrdug3bJVbn3hDug8YrHvmVTMVq80mqkPcalXXNv1zsTiurTZ9UT3xqtfc0VOvbzt67lUPvb6297Sr6/KKCsk7fFCS0zpKrdUsFbUW/TpKVZ1FXwpaqexBkL+5mRDvL4F+Zj2/X4XyALO6brxt1rcDzA3X6rb5f6MUFHXtuG3/laqfmvzvvoZ9pPHthlEPx+7fmuyv2sqvacDgg7YYz9DShAh17h6/r+009m1uv1OffvGLj3+KbWqpXc3u28ILNNeu0zv+mb9mi697qu9JfX6pz6iGzy/1uaU+Zxzb/7vP/jnn+DxzbP/vvkaPNzyvvKJSFi5arG/r6Thm9dmu/gdvuDhv2z//TWa/hsdMzv3t/x403m74t8KsRkrpT4f/vRHTXrlv5BF5+Arjp8EZzeNDt/qmxWKxSEJCQpP71e3t27c3+5yamhp9cSgpsX97X1pqn+PojhzFhw7u2io1VZVtNhQ0d/9O2RMW6lXH8+b31tbH8+b35u3H8+b31tbH279tg74eOuFaSenUTYyWtXOzrFv4hVSUlRr670FIwyX/yE4pW/tlw/tLP+bPPTVHv5l5+uqhU/lbXE0b92v+/TX9eQY2XFr3d+k41qGdGyU04MzjkPpqQX3d0PCVw/HUS/uLHCnbJxlvPyZPvvWW9OjRQweDeqtIVb1VquttUl2vttW147ZNqutsUm353+2qOpvUqGtL032qLPb71espVTUiVRUibVciDoAvCOjQWwLa6mA2m+Tn57l1RnO07WRfQJlsp/MVlRs6fPiwdOjQQZYvXy4jRoxw3v+HP/xBli1bJqtWrTruOTNnzpQnn3yyjVsKAAAAAPA22dnZkpKS4r093Wqsv5+fn+Tl5TW5X91OTExs9jlqKLoqvOZgtVqlsLBQYmJiGoY1uOe3KKmpqfoXGhkZ6ermwAdxDsKVOP/gSpx/cDXOQbgS51/LVP91WVmZJCcnn2AvLwjdgYGBMnjwYFm0aJFMmjTJGaLV7alTpzb7nKCgIH1pLDo6WjyBOtE52eFKnINwJc4/uBLnH1yNcxCuxPnXvKioKDkZjw/diuq1vvXWW2XIkCF6bW61ZFhFRYWzmjkAAAAAAK7gFaH72muvlSNHjsjjjz8uubm5MnDgQFmwYMFxxdUAAAAAAGhLXhG6FTWUvKXh5N5ADYd/4oknjhsWD7QVzkG4EucfXInzD67GOQhX4vz75Ty+ejkAAAAAAO5KLS0JAAAAAAAMQOgGAAAAAMAghG4AAAAAAAxC6PYQs2bNko4dO0pwcLAMGzZMVq9e7eomwcPMnDlTTCZTk0vPnj2dj1dXV8t9990nMTExEh4eLpMnT5a8vLwmr5GVlSUTJ06U0NBQiY+Pl+nTp0t9fX2TfZYuXSpnnXWWLrbRtWtXmT17dpu9R7iXH374QS677DJJTk7W59vnn3/e5HFVUkStOpGUlCQhISEyduxY2bVrV5N9CgsL5cYbb9TrgkZHR8uUKVOkvLy8yT6bNm2S888/X38+pqamyvPPP39cWz755BN9vqt9+vXrJ/PnzzfoXcNTzr9f//rXx30mXnzxxU324fzDmXruuedk6NChEhERof+9nDRpkuzYsaPJPm357y5/R/qWUzn/Ro0addxn4N13391kH86/VqQKqcG9ffjhh7bAwEDbO++8Y9u6davtjjvusEVHR9vy8vJc3TR4kCeeeMLWp08fW05OjvNy5MgR5+N33323LTU11bZo0SLb2rVrbcOHD7edc845zsfr6+ttffv2tY0dO9a2YcMG2/z5822xsbG2GTNmOPfZu3evLTQ01DZt2jTbtm3bbK+99prNz8/PtmDBgjZ/v3A9dY488sgjtk8//VQV7LR99tlnTR7/f//v/9mioqJsn3/+uW3jxo22yy+/3NapUydbVVWVc5+LL77YNmDAANvKlSttP/74o61r166266+/3vl4SUmJLSEhwXbjjTfatmzZYvvggw9sISEhtn/84x/OfX7++Wd9Hj7//PP6vHz00UdtAQEBts2bN7fRTwLueP7deuut+vxq/JlYWFjYZB/OP5yp8ePH29599119XmRkZNguueQSW1pamq28vLzN/93l70jfcyrn38iRI/W50PgzUH2mOXD+tS5Ctwc4++yzbffdd5/ztsVisSUnJ9uee+45l7YLnhe61R+PzSkuLtZ/BH7yySfO+zIzM/UfqitWrNC31Yet2Wy25ebmOvd54403bJGRkbaamhp9+w9/+IMO9o1de+21+sMfvu3Y0GO1Wm2JiYm2F154ocl5GBQUpIOLov4BV89bs2aNc59vvvnGZjKZbIcOHdK3X3/9dVu7du2c56Dy8MMP23r06OG8fc0119gmTpzYpD3Dhg2z3XXXXQa9W7iblkL3FVdc0eJzOP/QmvLz8/X5tGzZsjb/d5e/I3Hs+ecI3b/73e9afA7nX+tieLmbq62tlXXr1ulhlw5ms1nfXrFihUvbBs+jhu6qoZadO3fWQybVsCFFnWN1dXVNzjM1FDItLc15nqlrNSwyISHBuc/48eOltLRUtm7d6tyn8Ws49uFcxbH27dsnubm5Tc6XqKgoPeys8TmnhvQOGTLEuY/aX30Grlq1yrnPBRdcIIGBgU3OOTWMrqioyLkP5yWao4ZFqiGTPXr0kHvuuUeOHj3qfIzzD62ppKREX7dv375N/93l70g0d/45zJkzR2JjY6Vv374yY8YMqaysdD7G+de6/Fv59dDKCgoKxGKxNDnhFXV7+/btLmsXPI8KM2qejfrjMicnR5588kk9D3HLli06/Kg/GtUfmMeeZ+oxRV03dx46HjvRPuoDuqqqSs/bBRqfM82dL43PJxWIGvP399d/NDTep1OnTse9huOxdu3atXheOl4DvknN377qqqv0+bNnzx7505/+JBMmTNB/CPr5+XH+odVYrVZ54IEH5Nxzz9XhRmmrf3fVlz/8Henbmjv/lBtuuEHS09N1Z4yqTfHwww/rLww//fRT/TjnX+sidAM+Qv0x6dC/f38dwtWH7ccff0wYBuBzrrvuOue26s1Rn4tdunTRvd9jxoxxadvgXVSxNPUF908//eTqpsAHtXT+3XnnnU0+A1VRU/XZp76EVJ+FaF0ML3dzasiH+sb92GqW6nZiYqLL2gXPp75d7969u+zevVufS2oIUHFxcYvnmbpu7jx0PHaifVTlX4I9GnOcMyf6bFPX+fn5TR5XVVNVRenWOC/5DEVjatqN+jdXfSYqnH9oDVOnTpV58+bJkiVLJCUlxXl/W/27y9+Rvq2l8685qjNGafwZyPnXegjdbk4NPRo8eLAsWrSoyTARdXvEiBEubRs8m1r2Rn2bqb7ZVOdYQEBAk/NMDTFSc74d55m63rx5c5M/Qr///nv9wdq7d2/nPo1fw7EP5yqOpYbkqn9wG58vajiamivb+JxTf5Cq+WAOixcv1p+Bjj8O1D5qaSg1N7LxOaemUaihvY59OC9xMgcPHtRzutVnosL5h19C1e9Tgeezzz7T582x0xDa6t9d/o70TSc7/5qTkZGhrxt/BnL+taJWLswGA6hS+6qi7+zZs3U11TvvvFOX2m9cTRA4md///ve2pUuX2vbt26eXsFFLQKilH1RFS8fSJWo5icWLF+ulS0aMGKEvxy4dMW7cOL38hFoOIi4urtmlI6ZPn66rsM6aNYslw3xYWVmZXmZEXdQ/Ny+99JLePnDggHPJMPVZ9sUXX9g2bdqkK0k3t2TYoEGDbKtWrbL99NNPtm7dujVZsklVAFZLNt188816aRT1eanOwWOXbPL397e9+OKL+rxUlfxZssm3zz/12EMPPaSrRKvPxIULF9rOOussfX5VV1c7X4PzD2fqnnvu0Usiqn93Gy/JVFlZ6dynrf7d5e9I33Oy82/37t22p556Sp936jNQ/TvcuXNn2wUXXOB8Dc6/1kXo9hBq3Tv1wazWuVOl99WaocDpUEs4JCUl6XOoQ4cO+rb60HVQQefee+/Vy9+oD9Arr7xSf0A3tn//ftuECRP0OrQqsKsgX1dX12SfJUuW2AYOHKiPoz7A1TqR8E3qXFBh59iLWqrJsWzYY489pkOL+gd5zJgxth07djR5jaNHj+qQEx4erpcpue2223Rgakyt8X3eeefp11Dntgrzx/r4449t3bt31+elWt7k66+/Nvjdw53PP/WHp/pDUv0BqQJwenq6Xjv22D8COf9wppo799Sl8b+JbfnvLn9H+paTnX9ZWVk6YLdv315/dnXt2lUH58brdCucf63HpP7Tmj3nAAAAAADAjjndAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAOCMdezYUV555RVXNwMAALdF6AYAwENkZ2fLb37zG0lOTpbAwEBJT0+X3/3ud3L06FHDjz179myJjo4+7v41a9bInXfeafjxAQDwVIRuAAA8wN69e2XIkCGya9cu+eCDD2T37t3y5ptvyqJFi2TEiBFSWFho2LHr6upafCwuLk5CQ0MNOzYAAJ6O0A0AgAe47777dO/2d999JyNHjpS0tDSZMGGCLFy4UA4dOiSPPPKI/OlPf5Jhw4Yd99wBAwbIU0895bz9r3/9S3r16iXBwcHSs2dPef31152P7d+/X0wmk3z00Uf6OGqfOXPmyG233SYlJSX6MXWZOXNms8PL1WP/+Mc/5NJLL9VhXB1nxYoV+kuCUaNGSVhYmJxzzjmyZ8+eJm384osv5KyzztLH69y5szz55JNSX19v0E8TAIC2Q+gGAMDNqV7sb7/9Vu69914JCQlp8lhiYqLceOONOiSr69WrVzcJtFu3bpVNmzbJDTfcoG+rAP3444/Ln//8Z8nMzJRnn31WHnvsMXnvvfeavO4f//hHPXRd7XPhhRfqYB0ZGSk5OTn68tBDD7XY3qefflpuueUWycjI0KFeHfuuu+6SGTNmyNq1a8Vms8nUqVOd+//44496f3W8bdu26dCuhrOrNgIA4OkI3QAAuDk1pFwFVdVr3Bx1f1FRkR7qrXq1586d63xMhWzV+921a1d9+4knnpC//vWvctVVV0mnTp309YMPPqiDbmMPPPCAcx81dzwqKkr3YquQry7h4eEttlf1il9zzTXSvXt3efjhh3XvufpCYPz48bqtKlwvXbrUub/q1VYh/9Zbb9W93BdddJEO7se2CQAAT0ToBgDAQ6jgfTIq3DpCt9pfzf9W9ykVFRW6F3zKlCk6NDsuzzzzzHHDvdX88TPVv39/53ZCQoK+7tevX5P7qqurpbS0VN/euHGjHv7euE133HGH7lGvrKw843YAAOAO/F3dAAAAcGKql1r1Mquh3ldeeeVxj6v727Vrp3u6r7/+et27vH79eqmqqtIVz6+99lq9X3l5ub7+5z//edzcbz8/vya31dzrMxUQEODcVu1u6T6r1epsl+rtVj3rx1JzvAEA8GSEbgAA3FxMTIwecq0Knqmh4I3ndefm5uoh5GpOtAqzKSkpugCauk+FbvW8+Ph4Zw+zWm5MVUJ39H6fKlXEzWKxiBFUAbUdO3Y4h8ADAOBNCN0AAHiAv//977rqt5oXrYaDq7nWqkja9OnTpUOHDk2KjqlAreZu19bWyssvv9zkdVSP8m9/+1s9R/viiy+WmpoaXdxMzQmfNm1ai8dXVcpVj7RaokzNG1eVyVtrqTBV2E1VO1cV2a+++moxm816yPmWLVv0ewUAwJMxpxsAAA/QrVs3HY5VoTFVpKxLly5y55136sriakmu9u3bO/dVwfXo0aN6PvSkSZOavM7tt9+ulwx799139Txr1SuuKoWrEH8iKvDffffdeqi6Gsb+/PPPt9p7U18kzJs3Ty+HNnToUBk+fLj+skAVcAMAwNOZbKdSlQUAAAAAAJw2eroBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAAAxxv8HNv+sBrrQLxYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "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": [
+    "###  Distribution of Overtime\n",
+    "\n",
+    "This histogram shows the distribution of `over_time` (minutes) across the dataset.\n",
+    "\n",
+    "- The plot helps us identify whether overtime is normally distributed or skewed.\n",
+    "- It also allows us to detect outliers — such as workers with unusually high overtime — which may influence productivity or incentives.\n",
+    "- Depending on the shape, we may consider transformation (e.g., log) or binning for categorical treatment.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.histplot(df['actual_productivity'], bins=20, kde=True)\n",
+    "plt.title(\"Distribution of Actual Productivity\")  # You had it twice\n",
+    "plt.xlabel(\"Actual Productivity\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Target Variable Distribution: `actual_productivity`\n",
+    "\n",
+    "This plot shows the distribution of our target variable, `actual_productivity`.\n",
+    "\n",
+    "Key observations:\n",
+    "- Most values lie between **0.6 and 1.0**, with a sharp peak around **0.8**.\n",
+    "- The distribution is **right-skewed**, with fewer examples in the lower productivity range.\n",
+    "- A small number of workers have very low productivity (< 0.4), which may reflect training periods, absenteeism, or underperformance.\n",
+    "- Values are clipped at **1.0**, consistent with earlier preprocessing.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "numerical_cols = ['wip', 'over_time', 'incentive', 'smv']\n",
+    "\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i, col in enumerate(numerical_cols, 1):\n",
+    "    plt.subplot(1, len(numerical_cols), i)\n",
+    "    sns.boxplot(y=df[col], color='lightblue')\n",
+    "    plt.title(col)\n",
+    "    plt.ylabel('')  \n",
+    "\n",
+    "plt.suptitle(\"Boxplots of Numerical Features Before Outlier Treatment\", fontsize=14, fontweight='bold')\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Boxplots of Numerical Features (Before Outlier Treatment)\n",
+    "\n",
+    "The following boxplots help us visualize the distribution and presence of outliers across key numerical features.\n",
+    "\n",
+    "- **`wip`**, **`over_time`**, and **`incentive`** show strong outliers with long upper tails.\n",
+    "- **`smv`** also displays a wider range, suggesting variability in task complexity.\n",
+    "\n",
+    "These visuals support our decision to apply IQR-based outlier handling on selected features before modeling.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "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",
+    "\n",
+    "    if IQR == 0:\n",
+    "        print(f\"Skipping '{column}' — no variability (IQR = 0)\")\n",
+    "        return\n",
+    "\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    median_value = df[column].median()\n",
+    "\n",
+    "    outliers = (df[column] < lower_bound) | (df[column] > upper_bound)\n",
+    "    count_outliers = outliers.sum()\n",
+    "    \n",
+    "    df[column] = np.where(outliers, median_value, df[column])\n",
+    "    \n",
+    "    print(f\"{column}: Replaced {count_outliers} outlier(s) with median value {median_value:.2f}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 🛠 Outlier Treatment Using IQR Method\n",
+    "\n",
+    "To reduce the influence of extreme values, we apply an **IQR-based outlier detection** and replace outliers with the **column's median**.\n",
+    "\n",
+    "- This method is robust and preserves the central tendency of the feature.\n",
+    "- Outliers are defined as any value below `Q1 - 1.5*IQR` or above `Q3 + 1.5*IQR`.\n",
+    "- Replacing them with the median reduces variance without introducing artificial noise like mean imputation would.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 6))\n",
+    "for i, col in enumerate(numerical_cols, 1):\n",
+    "    plt.subplot(1, len(numerical_cols), i)\n",
+    "    sns.boxplot(y=df[col], color='lightgreen')\n",
+    "    plt.title(f\"{col} (Post-Treatment)\")\n",
+    "    plt.ylabel('')\n",
+    "\n",
+    "plt.suptitle(\"Boxplots After Outlier Replacement\", fontsize=14, fontweight='bold')\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Boxplot Analysis After Outlier Replacement\n",
+    "\n",
+    "Following IQR-based median replacement, we re-examined the distribution of key numerical features to evaluate the effectiveness of our outlier treatment.\n",
+    "\n",
+    "#### Observations:\n",
+    "- **`wip`**: Outlier treatment was effective for common extremes, but a few very large values (e.g., >20,000) still remain. This suggests those extreme values were not captured within the IQR thresholds and may need further review (possibly manual capping or custom logic).\n",
+    "- **`over_time`**: Moderate improvement, but a single extremely high value (~26,000) is still present. This is likely a true anomaly or entry error that was not replaced by the IQR method.\n",
+    "- **`incentive`**: Still shows extreme outliers, including values above 3,000 BDT. These few high earners might reflect actual bonus payouts — but if they’re distorting model performance, consider capping at a reasonable threshold (e.g., 99th percentile).\n",
+    "- **`smv`**: This feature appears reasonably well-behaved post-treatment. Outliers are expected here due to variation in task complexity and do not need aggressive handling.\n",
+    "\n",
+    "#### Conclusion:\n",
+    "While median replacement improved the central distribution in most features, some extreme values remain because they fall just inside the IQR cutoff or represent real-world extremes. For modeling purposes:\n",
+    "- Keep the treated data for models that are sensitive to outliers.\n",
+    "- Use raw values (with optional capping) for tree-based models.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clip_outliers(df, cols, lower=0.01, upper=0.99):\n",
+    "    for col in cols:\n",
+    "        low = df[col].quantile(lower)\n",
+    "        high = df[col].quantile(upper)\n",
+    "        df[col] = df[col].clip(lower=low, upper=high)\n",
+    "        print(f\"{col}: Clipped to {low:.2f} - {high:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Outlier Capping with Percentile-Based Clipping\n",
+    "\n",
+    "To reduce the impact of rare but extreme values, we applied percentile-based capping (1st and 99th percentile) to selected features:\n",
+    "\n",
+    "- `over_time`: to handle extremely high work durations\n",
+    "- `incentive`: to tame unusually large bonuses\n",
+    "- `wip`: to smooth out high work-in-progress values\n",
+    "\n",
+    "This approach preserves the structure of the data while reducing the influence of extreme values on models that are sensitive to scale.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "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",
+       "      <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>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",
+       "      <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>1039.0</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>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",
+       "      <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>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",
+       "      <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>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",
+       "      <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     wip  over_time  incentive  idle_time  idle_men  \\\n",
+       "0  26.16  1108.0       7080         98        0.0         0   \n",
+       "1   3.94  1039.0        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",
+       "   no_of_style_change  no_of_workers  actual_productivity overtime_bin  \\\n",
+       "0                   0           59.0             0.940725   5001-10000   \n",
+       "1                   0            8.0             0.886500     501-1000   \n",
+       "2                   0           30.5             0.800570    2001-5000   \n",
+       "3                   0           30.5             0.800570    2001-5000   \n",
+       "4                   0           56.0             0.800382    1001-2000   \n",
+       "\n",
+       "    wip_log  \n",
+       "0  7.011214  \n",
+       "1  6.946976  \n",
+       "2  6.876265  \n",
+       "3  6.876265  \n",
+       "4  7.065613  "
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "idle_men\n",
+       "0     1179\n",
+       "10       3\n",
+       "15       3\n",
+       "30       3\n",
+       "20       3\n",
+       "35       2\n",
+       "37       1\n",
+       "45       1\n",
+       "25       1\n",
+       "40       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['idle_men'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "idle_time\n",
+       "0.0      1179\n",
+       "3.5         3\n",
+       "2.0         2\n",
+       "8.0         2\n",
+       "4.0         2\n",
+       "4.5         2\n",
+       "5.0         2\n",
+       "90.0        1\n",
+       "270.0       1\n",
+       "150.0       1\n",
+       "300.0       1\n",
+       "6.5         1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['idle_time'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### creating idle_men_ratio and idle_ratio, since these columns are too sparse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['idle_men_ratio'] = df['idle_men'] / (df['no_of_workers'] + 1e-5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create idle_ratio\n",
+    "df['idle_ratio'] = df['idle_time'] / (df['no_of_workers'] + 1e-5)\n",
+    "\n",
+    "# Drop raw idle_time (sparse & less useful)\n",
+    "df.drop(columns='idle_time', inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns='idle_men', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "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>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",
+       "      <th>idle_men_ratio</th>\n",
+       "      <th>idle_ratio</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>1108.0</td>\n",
+       "      <td>7080</td>\n",
+       "      <td>98</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",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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>1039.0</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",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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>968.0</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",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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>968.0</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",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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>1170.0</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",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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     wip  over_time  incentive  no_of_style_change  no_of_workers  \\\n",
+       "0  26.16  1108.0       7080         98                   0           59.0   \n",
+       "1   3.94  1039.0        960          0                   0            8.0   \n",
+       "2  11.41   968.0       3660         50                   0           30.5   \n",
+       "3  11.41   968.0       3660         50                   0           30.5   \n",
+       "4  25.90  1170.0       1920         50                   0           56.0   \n",
+       "\n",
+       "   actual_productivity overtime_bin   wip_log  idle_men_ratio  idle_ratio  \n",
+       "0             0.940725   5001-10000  7.011214             0.0         0.0  \n",
+       "1             0.886500     501-1000  6.946976             0.0         0.0  \n",
+       "2             0.800570    2001-5000  6.876265             0.0         0.0  \n",
+       "3             0.800570    2001-5000  6.876265             0.0         0.0  \n",
+       "4             0.800382    1001-2000  7.065613             0.0         0.0  "
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "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",
+       "no_of_style_change       0\n",
+       "no_of_workers            0\n",
+       "actual_productivity      0\n",
+       "overtime_bin             0\n",
+       "wip_log                  0\n",
+       "idle_men_ratio           0\n",
+       "idle_ratio               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_svm = model_specific_encoding(df, model_type='svm')\n",
+    "df_tree = model_specific_encoding(df, model_type='decision_tree')\n",
+    "\n",
+    "# Add the target column back for export\n",
+    "df_svm['actual_productivity'] = df['actual_productivity']\n",
+    "df_tree['actual_productivity'] = df['actual_productivity']\n",
+    "\n",
+    "# Export\n",
+    "df_svm.to_csv(\"svm_neuralnet_ready.csv\", index=False)\n",
+    "df_tree.to_csv(\"tree_model_ready.csv\", index=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}