NoteBook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2335bf09",
   "metadata": {},
   "source": [
    "### `Import main Libraries and Frameworks`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3500decc",
   "metadata": {},
   "outputs": [],
   "source": [
    "## main libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from scipy.stats import boxcox\n",
    "plt.style.use('ggplot')\n",
    "import joblib\n",
    "\n",
    "## for preprocessing and preparing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import utils\n",
    "\n",
    "## Models -- sklearn\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "## Models -- Xgboost\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af87fbf",
   "metadata": {},
   "source": [
    "### `Reading the Dataset and Look at the big Picture`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c177df8",
   "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>DELTA</th>\n",
       "      <th>AGE</th>\n",
       "      <th>FC%</th>\n",
       "      <th>LC(ft/mile)</th>\n",
       "      <th>TC(ft/mile)</th>\n",
       "      <th>RUT(in)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1584</th>\n",
       "      <td>1.385827</td>\n",
       "      <td>3.99</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.157480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>28.976378</td>\n",
       "      <td>13.46</td>\n",
       "      <td>10.295164</td>\n",
       "      <td>7431.965741</td>\n",
       "      <td>1806.1887</td>\n",
       "      <td>0.236220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>6.488189</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.157480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>0.125984</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.078740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>7.622047</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.590551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DELTA    AGE        FC%  LC(ft/mile)  TC(ft/mile)   RUT(in)\n",
       "1584   1.385827   3.99   0.000000     0.000000       0.0000  0.157480\n",
       "249   28.976378  13.46  10.295164  7431.965741    1806.1887  0.236220\n",
       "1142   6.488189   3.39   0.000000     0.000000       0.0000  0.157480\n",
       "809    0.125984   0.85   0.000000     0.000000       0.0000  0.078740\n",
       "657    7.622047   0.62   0.000000     0.000000       0.0000  0.590551"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reading the Dataset\n",
    "df = pd.read_excel('file.xlsx', sheet_name='Sheet1')\n",
    "## Shuffling the Dataset to split randomly --> helping the model training \n",
    "df = utils.shuffle(df, random_state=123)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74395362",
   "metadata": {},
   "source": [
    "### `Exploring the Dataset and Feature Engineering`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbf42844",
   "metadata": {},
   "outputs": [],
   "source": [
    "## take a copy from the main DataFrame for changing of Feature Engineering\n",
    "df_new = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b9c60d",
   "metadata": {},
   "source": [
    "### `Explore the (DELTA) Column (target)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bded3e02",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Explore the Target\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='DELTA', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('DELTA', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of DELTA (target) in The Dataset', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6296ce19",
   "metadata": {},
   "source": [
    ">`It seems that it takes (exponential Distribution) -- it is preferable to take the log, but the column contanis zeros so we will take log(value + 1)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af5c6e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvwy1Wo2HHnoIWq32or/Ye/fujVWrVmHt2rVISkrCBx98gM2bN1+0FNYWDz30EG688UYMHToUV1xxBb799lu8++67TYaftZYoio3V0NXV1Y3D5Kqrq7FmzZom+zqdziZV1mecPXyrvLy82Zd1eHh4i80c5eXlWLNmDT7++ONmvarvuOMOXHrppThy5AgSExObHdu7d298+OGH2Lp1K0wmE/75z38iPz8fgwcPvuD9xsfHY8OGDcjIyIBKpYLBYMCCBQswefJk9OzZE1OnToVCocD+/fuxfft2LF269ILna+n8a9euxaFDh2A0GqHX69G7d28UFBTg3XffxaWXXop169add/iZRqPBl19+icmTJ+Pyyy/H+vXrERIS4lEMZ9x777247bbbkJ6ejjFjxuDzzz/Hr7/+6vFn8oEHHsDLL7+M1atXY8KECY3be/fujf/85z8YPnw4LBYLHn744WZDB1t63uPGjUNlZSWeeuop3HTTTdi0aVOrxsobDAaYTCa8/vrriIuLQ0FBAR566CGPf4QBpztpnvs51Wq1uOWWW7Bw4ULceuutePLJJ2E2mzF79mxcd911jbVX9957L5YuXYrevXsjNTUVr776KoqKihAVFeVxHN0ZS/B0XkFBQXj22WcxbNgwDBkyBLt378batWuh1WoBAE8++SROnjyJxMTExlLokCFD8PHHH+PTTz9F//79MX/+fMyfP7/J5CyrVq1CbGwsxo0bh2uuuQa33HILwsPDoVarLxjP7NmzMXXqVEybNg3p6ek4duwYHnjggU659ylTpuCf//wnli9fjtTUVLzwwgt46aWXcPXVV3t8LqvViqioKERHR2PYsGF4/vnncfXVV2P//v1NxsADwKFDhxAVFdXs7+wS1IcffojBgwc3+Ttfz+133nkHKpUKEydObPbe8OHDERcXd95S/OOPP45hw4bhd7/7HcaOHYvAwMDzVuefbdmyZfjhhx8QFxfX+GNg4sSJ+Prrr/HDDz9g2LBhGDZsGJYsWYIePXpc9HznuvPOO9G3b9/Gvhg//fQTrr76ajz00EO47777MHDgQHz//fd48sknz3sOjUaDr776Cnq9HpdffvlFm6vO56abbsLf/vY3zJ8/H4MHD8b+/fsxZ86ci36Wz6XT6bBw4cJmfSLeeust1NXVYejQobjppptw++23NxuC19Lz7tu3L15++WW89tprjc/j3BELLZHJZPjwww+xd+9e9O/fH3PnzsXf//53qFQqj+4HON2Wf+7ndNq0adBqtVi3bh1qamowbNgw/P73v8ell16Kt956q/HYBx98EDNmzMBtt92GESNGAACuvfZaj59rdyeInjSKEnWC8vJyREdH4/3338f1118vdThE7XLttdfC6XQ2DqmkjjFkyBCMGjUK//znP6UOxWewip663MaNG1FbW4sBAwagtLQUjz32GEwmE6688kqpQyPyiNVqxcsvv4wrr7wSCoUCn376Kb744gt8+umnUofm044fP45169YhIyMDTqcTr732Gvbs2YPXXntN6tB8ChM8dbmGhgY8/vjjOHr0KLRaLYYPH44tW7a0uQc1kVQEQcDatWvx1FNPwWazITk5Gf/+979x7bXXSh2aT5PJZHjnnXfw0EMPwe12IzU1FWvXrm3XMMruiFX0REREfoid7IiIiPwQEzwREZEfYoInIiLyQ37Xya41M6J5wmQyNa7cRJ7j82sfPr/24fNrHz6/9umq59fSJFcAS/BERER+iQmeiIjIDzHBExER+SEmeCIiIj/EBE9EROSHmOCJiIj8EBM8ERGRH2KCJyIi8kNM8ERERH6ICZ6IiMgPMcETERH5ISZ4IiIiP8QET0RE5Ie6bDW5l156Cbt27YJer8eyZcsAAHV1dVi+fDnKysoQFhaGefPmQafTAQA+//xzbNy4ETKZDLfddhsGDRrUVaESeR27S4DV6fboGK1CBrVc7KSIiMjbdVmCHzduHK688kqsWLGicdvq1asxYMAATJkyBatXr8bq1asxffp0nDp1Ctu2bcPzzz8Ps9mMv//973jhhRcgk7HCgbonq9ONdYc8W3ZyYooJarnQSRERkbfrsoyZmpraWDo/IysrCxkZGQCAjIwMZGVlNW4fOXIklEolwsPDERkZiby8vK4KlYiIyOd1WQm+JdXV1TAYDAAAg8GAmpoaAEBlZSWSk5Mb9wsNDUVlZWWL51i/fj3Wr18PAFiyZAlMJlOHxqhQKDr8nN0Jn1/7nHl+lvIaaLVaj45VqVQwmYI7KTLfwM9f+/D5tY/Uz0/SBH8+otj6dsPMzExkZmY2vi4v96wa82JMJlOHn7M74fNrnzPPz+EQYbVaPTrW4dB2+2fPz1/78Pm1T1c9v+jo6Ba3S9qordfrYTabAQBmsxnBwadLG0ajERUVFY37VVZWIjQ0VJIYiYiIfJGkCT4tLQ2bN28GAGzevBnp6emN27dt24aGhgaUlpaiqKgISUlJUoZKRETkU7qsiv4f//gHsrOzUVtbizlz5mDq1KmYMmUKli9fjo0bN8JkMuH+++8HAMTFxeHSSy/F/fffD5lMhjvuuIM96ImIiDwgiJ40ePuAwsLCDj0f26Dah8+vfc48v0qH2KZhcqGq7j1Mjp+/9uHza59u3QZPREREncMre9ETUcdzuUWcqHbgSKUdx6scKKlrgNnmRF29G25RhCAAgUo59Go5InVKxASr0NukRi+DGgpZ964JIPJFTPBEfqza7sT2U3XIKqjD/hIrLA2np7sNkAuI0gXAoJEjLFAJpUyASxRhbXDDbHMiu9QG23+nxlXJBQyMDMSwWB1GxgVBp5JLeUtE1EpM8ER+xuUWUVRbjxd+KsC+4jq4RMCkVWBkjyD0j9Cit1GDCJ0S8vOUyu0uAZYGFyptThyttCOnzIq9RRZkFdTh1awSDI3RYXRPPVIjtJAJ/zsH574n8i5M8ER+oq7ehdwKO/LNDtS7RISo5bimTyjGxgejl0EFQWhdNbvV6cZ3h/83D0W4Vo4JCUEw213INzvwW2Edfj1ZC61Sht5GNRJD1VDKBc59T+RlmOCJfFy5tQGHyu04VV0PAIjVB6CXQYVbh0QhTNMx/WgFQUCoRoFQjQKDIrUoqKlHbqUdu4utyC6zIdmoxqh4F0JV/Eoh8hb8v5HIB4miiOK6BhwotaHc6oRSJqBPmBrJRjW0ytNt5Oergm8vuUxAjxAVeoSoUG5pwMEyGw6U2vDgN0dwTZ9QXJsaisAAttMTSY0Jnrq9tqy1DkjT5nwmse8vtaHC6oRWKcOQKC16GU5Xk59NJshQ6fD8vlweHGIKVGJMoBJVdicqbW58fKAC3+aacX0/Iyb1NkCl4EhcIqkwwVO315a11oGuXW9dFE93nNtfYkOF7XRiT4sORC+D6rwldZvTjU15nt/XuCTPV78KUSvwh0tMuNniwL93l2HVb2X4MseM6YPCMK5XcJPOeETUNZjgibzcgVIr3txZiiOV9lYldiklhKqx8LI47C+xYtVvpXjh5yKsPWzGn9IjkGzUSB0eUbfCBE/UhTxpDiiqceDTzXux81QNQtQKr07s5+ofocXSiT2xKb8Gb/9Wige/PY4JCXrcOigMIZr2f+209jlaymvgcJxuRuEwPupumOCJulBrmgNsDW7sL7XiaKUDCpmAAREa3DksFtuOVXZRlB1DJgi4LEGPEXE6fLSvAl8eqsTPJ2vxhwFGXNU7tFmfAU+0tllFq9XCarUC6NomFSJvwARP5CWcbhE5ZTbklNvgcgNJRjXSeoZCbHAgwIc7q2mVcswaEo7Lk0Lw5s4SrNxVho1Ha/CXEZGstifqREzwRF6gsKYeOwstsDS4ERscgEsitQhSyaFRymFtkDq6jhETHIAF4+Pw66lavLq9BA+vO45r+oRi2kATe9sTdQImeCIJWepd+K3IilM19QhWyTG+VzAidEqpw+pUw2OD0D9ci7d/K8Pqg5X49VQt7h0Rhb7hWqlDI/Ir/NlMJAFRFJFbYcfa3CoU1dZjYIQWE5P0fp/czwgMkOOu4ZH4+4Q4iCLw6PoT+GR/BdwiO8ERdRSW4Im6mLXBhe2nLCiua0CkTon0mMBuO/PbwMhALJ8Uj5d+Lca/95RhX4kF80ZGd0hPe6LujiV4oi4iiiK2Ha/G2sPVKLM0IC06EBnxQd02uZ+hVcrxwKhozB0eiewyG+77Jh/7SixSh0Xk85jgibqAw+nGi78U4/WsYujVclyZHIIko7rVK7z5O0EQcEVSCJ6d2BOBAXIs3HAS3+VVSR0WkU9jPRhRJyupq8eSLQU4anbgmr5GqOWiX07d2pa57wPkMtSfNfl9sFaFv47vgZd/KcSKX4uRX1WPG/qbmv0Q8mS+fKLuigmeqBPtKqzDsp8KIYrA4xmxSAwLbNO8976gLXPfj0sytXhMapga9gYXvjlUiZxSC9JiApv8KGrLfPlE3Q0TPFEn+TKnEm/uLEXPEBXmj41BVFAAKh3sJd4aMkHA0OhABMhlyC6zwekWMSJO55c1H0SdhQmeqIOJooh3dpfhs+xKjIjTYd7IaKg5kYvHBEHAwEgtlHIBe4qtkAl1GB6rY78FolZigifqQE63iH/9UoQf8mvwu+QQ3JkW4ROLw3izvmEauEUR+0psUMgsGBodKHVIRD6BCZ6og9idbjyzpQC7iiy4ZaAJN/Y3srTZQfqFa+F0izhYZodWKcf4ZKkjIvJ+TPBEHcDhdOPvP5xEdpkNc4dH4oqkEKlD8jsDI7Sw1ruxt8SK7SdrpA6HyOuxYZConRxONxZvPnV6kpZLo5jcO4kgCBgWq0NYoALv7CpGpc0pdUhEXo0JnqgdGlxuLNlSgL3FVvxlRBQyeumlDsmvyWUCRvUIQpBKjq3Ha+FwckA80fkwwRO1kdMt4pkfC7GryIK7hkfisgQm966gVsgwZ3gM7E43fj5ZB5EL1BC1iAmeqA1EUcQbWUXIKqjD7PQIVst3sZ4GNYZEB6K4rgGHyu1Sh0PklZjgidpgb4kNv56sxfRLTJjU2yB1ON1SokGF2OAA7C2xwsz2eKJmmOCJPJRXacfBMhsyeulxQz+j1OF0W4IgID0mECq5gJ9P1sHlZlU90dmY4Ik8UFhbj50FFkTplJgxOILj3CWmUsgwLFaHGocLB0ptUodD5FWY4IlayWxzYtuJWujVcozsEcQZ6rxEVFAAehlUOFhmY1U90VmY4IlaweF048fjtVDKZRgbHwSlnMndmwyO0kKlELD9VB3c7FVPBIAJnuii3KKIbSfqYHe6MaZHELRKudQh0TkC5DIMjQ6E2e5CXgV71RMBTPBEF7W32IoSSwPSogMRquXszt4qNjgAETol9pXYYOcEOESci57oQk5UOZBTbkdSqAoJoeom78kEGSodniUSF/NOpxEEAUOjAvFtXhX2FFsxPFYndUhEkmKCJzqPKrsT2wvqYNIqMDiq+RKlNqcbm/LKPTrnuCRTR4VHLQhWy9HbqEZOuR3JRjW0WqkjIpIOq+iJWtDgEvHT8Voo/jv3OXvM+45+4Rqo5AJ+K7JwGlvq1pjgiVqws9CCuno3Lo0LgkbJ/018iVIuQ/8ILcosTpysYoc76r74zUV0jnyzHceqHEgN1yBCp5Q6HGqDxFAVglQyZJ2o4rA56raY4InOUmN3YUeBBeGBCvQL10gdDrWRTBBwSYQW1XYnjlfVSx0OkSSY4In+y+kWse3k6Xb3S+OCIOM0tD4tJjgARq0SB0qtLMVTt8QET/Rfu4ssqLK7MDxOx3Z3PyAIAgbH6lFX78Yxs0PqcIi6HL/FiHB6EZm8SgdSTGpEBwVIHQ51kLgQNQwaOQ6U2uDkanPUzTDBU7dX63Bi+6k66FVyDIzgwGl/IggCBoRrYWlwY9vxaqnDIepSTPDUrYmiiHd2laDeJWJEnI7j3f1QVJASoRoF1hysQIOLpXjqPpjgqVvblF+DHQV1GBChhUHDiR39kSAI6B+hQYXViY1HWYqn7oMJnrqt0roGvLajBMlGDVJM6osfQD4rSqdEYqgaH+8vZymeug0meOqW3KKIF34pglsE7hwWySFxfk4QBPw+NQxlVifW5lWj0iG2+s/u4meDfBPrJKlb+jLHjP0lVvxlRCTCAtlrvjtINmmgV8vxyb4y1NnrIbTyR93EFBPUciZ58j1ekeC/+uorbNy4EYIgIC4uDnfddRfq6+uxfPlylJWVISwsDPPmzYNOx+Ufqf2OVznwzu4yDI/VYUKCHmZOdNYtCIKAPiYNfj1Vh6K6Bg6HJL8neRV9ZWUl1q5diyVLlmDZsmVwu93Ytm0bVq9ejQEDBuDFF1/EgAEDsHr1aqlDJT/gdItYvq0QgQEy3DU8stWlOPIPPfQB0ChkyCmzSR0KUaeTPMEDgNvtRn19PVwuF+rr62EwGJCVlYWMjAwAQEZGBrKysiSOkvzBJwcqkG92YO6wSISovaICi7qQXCYgxaRGqcWJSptT6nCIOpXk33ChoaG4+uqr8ec//xkBAQG45JJLcMkll6C6uhoGgwEAYDAYUFNTI3Gk5OuOme34eH85xvYMxvC4IKnDIYkkhqpwoNSGnDIbRvbg54D8l+QJvq6uDllZWVixYgW0Wi2ef/55bNmypdXHr1+/HuvXrwcALFmyBCaTqUPjUygUHX7O7qStz6+i1oo6h+clLJ1KAWNQ89nonC43Xvp+D4JUSjwysS9CNP9bBtZSXgOt1vMZ7ORymcfHeXqMTHZ6/664lhTHdfa1zjy/c4/pE+HE/qJauGQBCLpITY5KpYLJFOxxjP6A33/tI/XzkzzB79u3D+Hh4QgOPv0/0PDhw3H48GHo9XqYzWYYDAaYzebG98+VmZmJzMzMxtfl5eUdGp/JZOrwc3YnbX1+lQ4R6w55ftzEFBNEh7XZ9o/3l+NQqQWPjImG01KNcsv/3nM4RFitzY+5GJdL6/Fxnh6j1Z7evyuuJcVxnX2tM8/v3GMS9AocKAZ2nzJjaHTgBc/hcGi77XcAv//ap6ueX3R0dIvbJW+DN5lMyM3NhcPhgCiK2LdvH2JiYpCWlobNmzcDADZv3oz09HSJIyVfdaLKgQ/2VWBUjyCM7NE9S2LUlEYpQ88QFY5W2uFwuqUOh6hTSF6CT05OxogRI/DII49ALpcjPj4emZmZsNvtWL58OTZu3AiTyYT7779f6lDJB7ncIl78pQhapQyz0yOkDoe8SIpJjXyzA0fNDvQN00gdDlGHkzzBA8DUqVMxderUJtuUSiUWLFggUUTkL744WIncCjseHBUNPXvN01lC1AqEByqQW2FHiknN2QzJ70heRU/UWU5VO/De3nJcGqfD6J7sLU3N9TZqYG1wo6CGsx2R/2GCJ790pmperRAwJ50T2lDLooOV0CplOFxhlzoUog7HBE9+6atDZhwqt+OPaREI4TKwdB4yQUBvoxplFifMnPiG/AwTPPmdgpp6/GdPGYbF6pARz17zdGEJoSrIBbAUT36HCZ78ilsU8a9fiqCUC/jzMFbN08UFyGXoZVDheJWDQ+bIrzDBk1/ZkFeF7DIb/jg0AqGsmqdWSjaq4RaBI5UOqUMh6jBM8OQ3ah0ufLK/DEOjAzG+F6vmqfX0agUidErkVtrhFkWpwyHqEEzw5BdEUcT2gjrIBYHLwFKb9DaqYWtw4xSHzJGfYIInv5BX6UCZxYmbLgmDSau8+AFE54gKOj1kLped7chPMMGTz6urd2FPsQWROiXGxOulDod8lEwQkPzfIXNVdg6ZI9/HBE8+TRRFbD9VB0BAekwgq+apXRIMKsgEII+lePIDTPDk045UOlBqcWJQpBaBAXKpwyEfp1KcXmXuWJUD9S4OmSPfxgRPPstS78LuYivCAxVIDFVJHQ75ieRQNZxu4JiZQ+bItzHBk08SRRFZBRYAIobF6lg1Tx0mVKtAqEaB3Eo7RA6ZIx/GBE8+Kd/sQHFdAy6JDISOVfPUwZKNatQ63CixNEgdClGbMcGTz7E1uPFbkRVhgQoksWqeOkEPfQBUcoFD5sinMcGTTxFFETsK6+AWRQyLYdU8dQ65TEBCqBqFNQ0oZymefBQTPPmUk9X1KKhpwIAILYJUrJqnznOmduiHo1XSBkLURkzw5DPsTjd2FloQqpGjt0ktdTjk5wID5IgOVmJLfjWHzJFPYoInn7Gr0IIG9+le8zJWzVMXSDaqUVfvwk/Ha6UOhchjTPDkEwpq6nGiuh6pYRqEqLkMLHWNiEAlIoMC8PVhs9ShEHmMCZ68Xr3LjR0FddCr5egbppE6HOpGBEHAhMQQ5FbYkVthkzocIo+wKESdzu4SYHV61oZ5dpPn7iIr7E4RY3rqIJdduGpeJshQ6Wj7tYjONbJnMD7dX4ZvDptx76X8gUm+gwmeOp3V6ca6Q+UeHTMuyQQAKK6rx1GzA31MaoRqL/5xtTnd2JTXtmsRtUSrlGN8Lz3WH6nGbYOdCGYTEfkIVtGT12pwicg6ZUFQgAz9I7RSh0Pd2KTeBjS4RXx/pFrqUIhajQmevNa+EissDW4Mi9VBcZGqeaLO1CNEhf4RWnyba4bLzfnpyTcwwZNXyquw4nCFHclGNcIClVKHQ4RJvUNQanFiZ2Gd1KEQtQoTPHkdl1vEv3eVQKuUYSCr5slLDI8NQqhGga8PV0kdClGrMMGT19lfakVJXT3SYwKhlLNqnryDQibgyuQQ7C6yoKCmXupwiC6KCZ68SqXNiZwyO0b2CEZUUIDU4RA1cUVSCBQyYC0nviEfwARPXsPlFrH9VB1UCgHXDwiXOhyiZgwaBUbGBWPj0WrYGjiBAnk3JnjyGgfLbKiyu5AWo0NgAFeKI+80qXcILA1ubD7GIXPk3ZjgyStU253ILrOhhz4AscGsmifv1SdMg14GFb45XAVR5JA58l5M8CQ5URSRVWCBQiZgSHSg1OEQXZAgCJjU24DjVQ5kl3F+evJeTPAkubxKB8qtTgyOCoRawY8keb+M+GAEBsjw9SF2tiPvxW9TkpS1wYU9xVZE6JSID2HVPPkGlUKGzAQ9fjlZiwprg9ThELWICZ4ktavQClEUkRYdCEHgmHfyHb/rbYBLBL7Lq5I6FKIWMcGTZE5VO3Cqph79I7QIUrHXPPmWqKAADI0OxLq8ajg5Pz15ISZ4kkS9y42dhRaEqOVIMamlDoeoTSb1NsBsc+KXk7VSh0LUDBM8SWJvsRU2p4j0GB1krJonHzU4KhAROiU725FXYoKnLlduaUBepQO9jWoYtQqpwyFqM7lMwO+SQ5BdZsMxs13qcIiaYIKnLuVynx7zrlXKMIArxZEfyEwMQYBcwDdcZY68DBM8damcchuqHS6kRXOlOPIPQSo5xsYHY1N+NerqXVKHQ9SICZ66TI3DhQOlNsTpAxDN6WjJj0zqbYDDJWLjUc5PT96DDaDUJU5PR1sHuSBgSBSnoyXfIRNkqHRceOU4Q6AKiaFqfHXIjJHxIdAp5VDLOXSOpOVRgh80aBD++Mc/4pZbboHBYOismMgPHTU7UGZxIj0mEBolK47Id9icbmzKK7/ofmFaBY5U2vHOziLMSouGmk1QJDGPvmmvuuoqLF26FNHR0bj55puxYcOGzoqL/Eitw4k9xVaEaRVIMKikDoeoU8TpA6BSCDhczt705B08SvCLFy/G8ePH8dlnn8HlcuGqq65CfHw8nnzySZw4caKzYiQf9/G+cjS4RKTFcDpa8l9ymYDkUDWK6hpQWOOQOhwizzvZCYKA3/3ud/joo49QWFiI2bNn46mnnkJCQgImTpyIb7/9tjPiJB+1t7AGPx6rRopJDb2aXT7IvyUZ1ZAJwHe5nPiGpNfmxtBffvkF8+fPx5IlSxAdHY2FCxciMTERN9xwA+67774ODJF8ldMt4tmNeQjVKNAvnGPeyf+pFTLEh6iw7XgNauxOqcOhbs6jBF9aWornnnsO/fr1w7hx41BVVYVPPvkER48exd/+9je89NJLWLNmDd54443Oipd8yFeHKnG0wopbBoVzzDt1GykmNRrcIr7NrZI6FOrmPKozjY2NRVJSEu644w7MnDkTJpOp2T5paWlIT0/vsADJN5VZGvD+3nKM6mXA4GgdvjvMNknqHvRqBQZEBuLrw2ZcmxoKpZyjRkgaHiX4DRs2YMyYMRfcJzg4GD/88EO7giLf9+bOErhF4L6MRNTVcaUt6l4mJhvw3I+nsOVYDSYkhkgdDnVTHiX40NBQ7N27FwMHDmyyfe/evVAoFEhNTW1TEBaLBa+88gpOnjwJQRDw5z//GdHR0Vi+fDnKysoQFhaGefPmQafTten81LV2FNTh55N1mH6JCdF6NQ4zwVM3kxquRc8QFb7IMeOyBD1Hj5AkPKo7+tOf/oT9+/c3256dnY0//elPbQ5i5cqVGDRoEP7xj3/g2WefRUxMDFavXo0BAwbgxRdfxIABA7B69eo2n5+6jsPpxus7ShAbHIApfY1Sh0MkCUEQ8Ps+BhyvcmBPsVXqcKib8ijB7927F8OGDWu2PT09Hfv27WtTAFarFQcPHsRll10GAFAoFAgMDERWVhYyMjIAABkZGcjKymrT+alrfXKgAsV1DZidHsGOddStjY0PRohajjU5lVKHQt2UR1X0crkc1dXNF1Mwm80QxbbNu1xaWorg4GC89NJLOH78OBISEjBr1ixUV1c3TodrMBhQU1PT4vHr16/H+vXrAQBLlixpseNfeygUig4/p786brbi84OVuCIlDJf17wng9PNTqVTQaj0bJieXyzw+pq3HefO1ZLLT+3v7fbX1uM6+1pnn1xXXOptKpUKUKRg3Drbj9Z9PoFbQopfR94aK8vuvfaR+fh4l+IyMDCxevBgff/wx5HI5AMDpdGLx4sUYO3ZsmwJwuVzIz8/H7bffjuTkZKxcudKj6vjMzExkZmY2vi4vv/ic0Z4wmUwdfk5/JIoilmw8iQCZgGn99I3PzGQyweFwwGr1rJrS5dJ6fExbj/Pma2m1p/f39vtq63Gdfa0zz68rrnU2h0OL8vJyjI0OwNtyAe/8cgRzh0d5fG2p8fuvfbrq+UVHR7e43aMEv3TpUowePRpJSUkYPXo0AGDr1q2oq6vDli1b2hSY0WiE0WhEcnIyAGDEiBFYvXo19Ho9zGYzDAYDzGYzgoOD23R+6ho/Hq/F3mIr/pQWAYOGM9YRAUCwWoHxvfTYeLQa0y8J42yO1KU8aoNPSUnB3r17MW3aNFRWVqKiogK33HIL9uzZg759+7YpgJCQEBiNRhQWFgIA9u3bh9jYWKSlpWHz5s0AgM2bN3NsvRez1Lvw1s4SJIaqcWVyiNThEHmVa/oY0OAWsfZwldShUDfj8c/JqKgoLF68uEODuP322/Hiiy/C6XQiPDwcd911F0RRxPLly7Fx40aYTCbcf//9HXpN6jjv7i1Hld2Fx8bFQi5jxzqis8XqVUiP+d/ENyoFJ76hruFxgrdardi9ezdKS0vhdrubvHfddde1KYj4+HgsWbKk2fYFCxa06XzUdY5U2rH2sBlXJocg2aiROhwir3RtqhGPfn8CG49W43e9DVKHQ92ERwl+/fr1uPnmm1FRUdHsPUEQ4HK5Oiww6lx2lwCr033xHc+iVciglv9vtITLLeLl7cUIUskxfVBYR4dI5DdSwzRIMamx+mAlrkgKYU0XdQmPEvy9996Lq666Ck899dR5e+2Rb7A63Vh3yLPenRNTTFCfNbb9u7wq5FbYMW9kFHQB8o4OkchvCIKAa1ONWLKlAD+frMXonuw0TJ3PowR/7NgxrFmzhsmdUGV34t97yjAgQouMeH5ZEV3M8FgdooMC8Fl2JUb1COL0tdTpPOrtMWrUKBw6dKizYiEfsmpXKRxON2anR/CLiqgVZIKAa1NDcaTSjr0lLY+rt7sEVDpEj/7sLv7/Ry3zqAQ/Z84cPPjggygsLMSAAQOgVCqbvD9kyJAODY680/4SK37Ir8EN/YyI06ukDofIZ4zrFYz39pThs+xKXBIZ2Oz9jmg6IzrDowR/ww03AECLC8uwk1330OAS8UpWMcIDFZjan4vJEHkiQC7D1X1C8c7uMhyttCMhVC11SOTHPErw+fn5nRUH+Ygvcypxsroej2XEcDwvURtMTA7Bx/sr8Hl2JR4Yzf5M1Hk8SvA9e/bsrDjIB5RbGvDBvnIMj9VhWGyQ1OEQ+SRdgBwTk0OwJqcS0+tMiNAFSB0S+SmPi2Br167F5MmTkZqaipMnTwIA3njjDWzYsKHDgyPv8v6eUgDAH4dGSBwJkW+7po8BMgH4IscsdSjkxzxK8O+++y6mTp2K5ORk5Ofno6GhAcDpFeGWLl3aKQGSdyioqceuwjr8YYAJ4TrlxQ8govMyapXIiNfj+7wq1NidUodDfsqjBL906VK8/vrrWL58ORSK/9XujxgxArt37+7o2MhLON0idhVaEB0cgGv6hEodDpFfmJIainqXiG+4CA11Eo8SfG5uLi699NJm23U6HWpqajosKPIu2aU2WBrcuHVwBJQcjkPUIXroVUiP0eGrw2Y4PJw2mqg1PErw0dHROHz4cLPtW7ZsQWJiYocFRd6jxu5CTrkN8SEqpIRppQ6HyK9cnxqKWocL649USx0K+SGPetH/6U9/wj333IM33ngDAHDy5En8+OOPePjhh7Fo0aLOiI8kJIoidhTWQS4IGBTF5E7UWjJBhkrHxUvlEXoNkoxqfJZdiWE99ABYQ0Ydx6ME//DDD6O6uhqXX3457HY7xo8fD5VKhQcffBBz587trBhJIvlmB0otTqTFBELNMe9ErWZzurEpr3Uz0kXqlMirsOPNrELMTIvp5MioO/F4PfjFixfjscceQ3Z2NtxuN1JTU6HT6TojNpKQrcGN3cVWhAUqkGjgdLREnSUmSIlglRwHy2wQRfHiBxC1kscJHgC0Wi3S0tI6OhbyIruKLHC6RaTH6LiYDFEnEgQBfUxqbC+wILu05UVoiNrCowR/zTXXXPD9NWvWtCsY8g4FNfU4WV2PAREaBKu4zjtRZ+sZosK+EhvWHa7AEPZ3oQ7iUYI3GpsuLtLQ0IA9e/bg5MmTuO666zo0MJJGg8uNHQUW6NVy9DFppA6HqFuQywT0CVPjtyIreuiVMGk5mRS1n0cJfuXKlS1uf+CBBxAUxLnJ/cHeYitsTjdG9wyGXMaqeaKukmBQ41C5HQfLbBjTkwme2q9DukbPnj0bL730UkeciiRUZmlAbqUDvY1qGFmCIOpSSrmA8YkGFNQ0oJrT11IH6JAEf+jQoY44DUnI5RaRVWCBVinDgAi2ARJJYXxCCOQCkFNulzoU8gMeVdHfc889TV6LooiioiKsXbsWt99+e4cGRl0ru8yGGocLY+ODOB0tkUR0KgUSQ9XIrbCjf7gGgQHs5Ept51GC37dvX5PXMpkMYWFhWL58ORO8DzPbnDhYZkNPfQCig7g2NZGUUkynE/yhcjuGRAdKHQ75MI8S/A8//NBZcZBEXG4Rv56qQ4Bc4JcJkRcIDJCjZ4gKRyrt6BeugYqzSFIb8ZPTzR0otaHK7kJ6jI5fJEReom+YGi4ROFzBtnhqO49K8OPHj2/1rGYbN25sU0DUdSqsDThYZkMvgwoxwayaJ/IWerUCMcFK5FbY0cekYb8YahOPEnzfvn3x7rvvIjIyEsOHDwcAbN++HcXFxZg2bRrkcnYI8RX1Ljd+OVUHjVKGwZw5i8jr9A3ToKCmBkfNdqRw0ilqA48SvEqlwsyZM/HCCy80Kcnfd999EEURL7zwQocHSJ3j033lqHW4Ma5XMALkrJon8jYmrRJhgQrklNuRFKrmxFPkMY++2d955x3cfffdzarp77rrLvz73//u0MCo8+wusuD7PDOSQ1WI1HFCGyJvlRqmga3BjePVDqlDIR/kUYIXRbHZUDmg+fA58l7Vdif+sa0QUcEBuCSKveaJvFmkTokQtRw5ZXYuJUse86iK/vbbb8cf//hH5ObmYsSIEQCAX375BUuXLsVtt93WKQFSxxFFES/+XIS6ejfmjY5Fdkmd1CER0QUIgoC+YRr8fLIOBTX1iNWrpA6JfIhHCX7p0qUIDw/HCy+8gEcffRQAEBUVhfnz5+OBBx7olACp43x1yIwdhRbcmRaOuBA1EzyRD4jTB2BviQzZZXbEBAe0eiQTkUcJXiaT4eGHH8bDDz+MmpoaAEBwcHCnBEYdK99sx6rfypAeE4irehtgrpc6IiJqDZkgoK9Jgx2FFpRanIhgvxlqJY8S/Bk7duzAkSNHMHnyZACAxWKBSqWCQtGm01EnszvdeG5rIYJUcvxlRNR/SwCet+fJBBkqHW6PjrGU18Dl2SFEdI5eBhX2l1pxsMzGBE+t5lFGLikpwTXXXIOsrCwIgoDc3FwkJCTg/vvvh1qt5jA5LySKIlb8WoyCmno8MSEOenXbf4TZnG5syiv36BitVoth0RxnT9QecpmAFJMGe4qtqLQ5EaphYYouzqNe9PPmzUNkZCQqKiqg1f7vS/vGG2/Ed9991+HBUfutza3ClmM1mDbQhEsi2WueyFclhqqglAk4WGaTOhTyER79DNywYQM2bNgAg8HQZHtiYiJOnDjRoYFR+x0ut+HNnSUYGh2IG/obpQ6HiNohQC5DklGFnDI7ah0uBKk4cyhdmEcleJvNhoCA5nOWl5WVQa1Wd1hQ1H41diee+bEAoRol5o2Mhow9b4l8Xm+jBoIA5JSzFE8X51GCHzt2LFatWtX4WhAEuFwuPPPMM5gwYUJHx0Zt5HKLWLatCNV2Fx4ZE8Nf+kR+QqOUIcGgQr7ZAVsDe6/ShXk8Dj4jIwNZWVlwOBx44IEHcODAAVRXV+Onn37qrBh9jt0lwOr0/H8+rUIGtbz9s1W9s7sMu4ssmDs8EklG1qwQ+ZMUkwZHKh04VG7DIM5GSRfgUYJPTU3Fvn378PLLL0OlUsFut+PGG2/E3LlzERUV1Vkx+hyr0411hzzrbQ4AE1NMULdzWcjv86qw+mAlruodgiuSQtp1LiLyPkEqOeL0AcirtCM1nKvM0fm1OsE3NDRg9OjReOedd/DEE090ZkzURvtKLHh5ezEGRwXijqERUodDRJ2kb5gGJ6rrkVthlzoU8mKtboNXKpXIz8/nNIleqqi2Hs9sKUBUUAAeGh3NpSWJ/JhBo0CUTonD5XbUcyYpOg+POtnNnDkTr7/+emfFQm1U53Dh75tOAYKAx8fFIjCAneqI/F3fcA0cLhFbj1VLHQp5KY/a4C0WC9599118//33GDp0KAIDm3bwePHFFzs0OLo4p1vE0q0FKKmrx5MTeiAqqPkwRiLyP2FaBYxaBdYeMuPaPgbW2lEzrUrwe/fuRb9+/XDw4EEMGTIEAHD06NEm+7DqvuuJoojXd5RgT7EV94yIRL9wTglL1F0IgoDUMA1+PF6LrcdrkNFLL3VI5GValeAHDx6MoqIi/PDDDwCAq666Cm+88QZ7zncwTxdz+T7XjG9zq3BdaigmJIZ0XmBE5JWig5SIDg7Ap9mVGBsfzIIWNdGqBC+KTcdm//jjj7DZOJNSR/NkMZfC2nr8eKwWQ6J1mDEorJMjIyJvJAgCJqWE4o2sYuwstCAtRid1SORFPOpkd8a5CZ+6VpXdiW0n6qBXy3HnsChOQ0vUjQ2PC4ZJq8CnByqkDoW8TKsSvCAIzap+WBUkDbvTjR+P1UIhA8bGB0GtaNNvNCLyEwqZgCl9Q5FdZkN2qVXqcMiLtLqKfvr06VCpVAAAu92OO++8s8mSsQCwZs2ajo+QGrncIrYer4Xd6cZlCXpolRwOR0TA5Ukh+HB/BT7LrkAqO9vSf7Uqwc+cObPJ6+nTp3d4IG63G/Pnz0doaCjmz5+Puro6LF++HGVlZQgLC8O8efOg03Xf9iVRFJFVUIdyqxMje+hg1Ho0wpGI/JhaIcPkFAPe31uOY2Y74g1cg4JameBXrlzZ2XHgm2++QUxMTGPnvdWrV2PAgAGYMmUKVq9ejdWrV3fKDwtfkV1mw7GqevQP16CHXiV1OETkZa7qbcDn2RX4PLsS80ZFSx0OeQGvaMCtqKjArl27miw5m5WVhYyMDABoXMGuuzpZ7cC+Eht66gPQj4tLEFELglRyTEwKwZbjNSipq5c6HPICXpHgV61ahenTpzfpuFddXQ2DwQAAMBgMqKmpkSo8SVXZnPj1VB2MGgWGxerYuZGIzuuavqGQCcAXByulDoW8gOQNuTt37oRer0dCQgIOHDjg8fHr16/H+vXrAQBLliyByWTq0PgUCoXH57SU1zTrgNgacrmsyXH2Bhe2Hq5CgFyOy/uEQ9vCHPMqlQomU7DH12pLjOfG1xoymaxNx7XlmLYe583XkslO7+/t99XW4zr7WmeeX1dcq73HtPW4s78DTACu7FOH7w6V4c8ZvWHQtm/q6rZ8/9H/SP38JE/whw4dwo4dO/Dbb7+hvr4eNpsNL774IvR6PcxmMwwGA8xmM4KDW05imZmZyMzMbHxdXu75OuwXYjKZPD6nwyHCavV8uIrLpW08zi2K2JRfA2u9CxMSggGnA1ZnS9fStume2xLj2fG1llarhcvl7pJrtfU4b76WVnt6f2+/r7Ye19nXOvP8uuJa7T2mrced+x3wuwQtvs524+1tRzC9nZNgteX7j/6nq55fdHTLfS4kr6KfNm0aXnnlFaxYsQL33Xcf+vfvj3vuuQdpaWnYvHkzAGDz5s1IT0+XONKu9VuRFaUWJ9JjdDBqlVKHQ0Q+Ilavwog4Hb7JNcPa4JI6HJKQ5An+fKZMmYK9e/finnvuwd69ezFlyhSpQ+oyR8125FbYkWJSo5eBPeaJyDPXpRphqXdjXW6V1KGQhCSvoj9bv3790K9fPwBAUFAQFixYIHFEXa/a7sTOAgvCAxW4JJITVhCR53qbNBgYocUXOWZMTjFAKffashx1Iv6rexGH042fTtRBIRdwaVwQ55gnoja7vp8RZpsTP+R3zxFIxATvVT7cW4oahwuXxumgUfKfhoja7pJILRJDVfg8uwIuNxcI646YRbzEMbMD245XIzVMg0hd+4a2EBEJgoDrU40orG3AL6dqpQ6HJMAE7wVqHC7sKKxDklGD/hGcqY6IOsaIuCBEBynx6YEKLvPdDTHBS8wtivjlZC1kgoA70rm2OxF1HLlMwLWpRhypdGBPMZeS7W6Y4CV2qNyOSpsLadGBMGg43p2IOtb4XsEwaBT49ECF1KFQF2OCl1CN3YV9JVbEBgcgTs92dyLqeEq5DL/vY8DeEisOl9ukDoe6EBO8RNyiiF8L6qCQCRgaHdjmRWRkggyVDtHjP5e7g2+IiLzWxOQQ6AJk+Gg/S/HdiVdNdNOd5FbYUWF1YkRs+4bE2ZxubMrzfK7jcUlcQIKou9Aq5bimTyje21uOo5V2JISqpQ6JugBL8BKodbiwt9iK6CAleoawap6IOt9VKQZolTJ8tJ+Lx3QXTPBdTBRFZBXUQSYISItpe9U8EZEndAFyTE4x4OeTdThe5ZA6HOoCTPBd7ER1PUotTgyM1EKrbL6+OxFRZ7m6TyjUChk+Zim+W2CC70INLhG7iywwqOVIDOUqcUTUtYJVckzqHYKtx2txqoaleH/HBN+FDpRaYXOKSIsJ5IQ2RNQhPB1Jk5FggFIu4MN9lVKHTp2Mvei7SK3DhcMVdvQyqGDUckIbIuoYbRlJ0ytEha3HqzFtoBFRQezo669Ygu8ie4qtkAnAwAiu8U5E0uoTpoFMEPAJZ7fza0zwXaDM0oBTNfXoY9JwGVgikpxGKcPYXnr8cLQapXUNUodDnYTZppOJoojdRVZoFDL0CeNKcUTkHSalhEIQgM+yWYr3V0zwnayotgEVNif6R2igkLFjHRF5B6NWiQkJIfj+SDUqrCzF+yMm+E4kiiL2lVgRGCBDLwOHxRGRd7m+XyhEUWRbvJ9igu9EBTX1MNtd6B+u4bA4IvI6EboAZCaG4Lu8apRZWIr3N0zwneR06d2GoAAZeoaw9E5E3unG/kYA4Bz1fojj4C+iotaKSofo0TEuN3Cyuh7VDhdGxOlYeicirxUWqMTE5BCsPWzGdakcF+9PmOAvos7hxLpDnv2yHZtoxP5SG4JVcvTQ838WIvJuN/Qz4vu8KnywrxzzRkZLHQ51EFbRd4KskzWocbgwIIJt70Tk/UI1CkzqbcCWYzU4Wc056v0FE3wHc4sivs6pQIhajthglt6JyDdclxqKALmA9/eyLd5fMMF3sIKaepRaGpAaruFa70TkM/RqBa5OCcVPJ2pxzGyXOhzqAEzwHUgUReSU2WEKVLL0TkQ+Z0rfUAQqZXiPpXi/wATfgcqsTlTYnMhMMrDtnYh8jk4lx+/7huLXU3XIrbBJHQ61ExN8B8ops0ElFzCyh17qUIiI2uTqPgYEBcjw3h6W4n0dE3wHqbY7UVjbgGSjGgEKPlYi8k1apRzXpRqxq8iCvYU1UodD7cBM1EFyyu2QC0CyUS11KERE7TIpxYAQtRyv/HQMoujZRF/kPZjgO4CtwY3jVQ4kGFRQsfRORD5OrZDhpgEm7CmswY4Ci9ThUBsxG3WAQ+U2iCKQwvXeichPXJ4UgrgQNd7ZXQqXm6V4X8QE304NLhFHKh2I1QdAFyCXOhwiog6hkAmYPTIeJ6rr8UN+tdThUBswwbfTsSoHGtwiUtj2TkR+ZlySEclGNd7bUw6H0y11OOQhJvh2EEURuRV2GNRyGLVct4eI/IsgCJg1OBwVNie+OmSWOhzyEBN8O5RYnKhxuJBsUnNaWiLyS/0jtEiLDsSnBypQ63BJHQ55gAm+HXIrTk9s01OvkjoUIqJOc+vgcFgb3Ph4Pye/8SVM8G1UV+9CYU0DEkLVkMtYeici/9UzRIUJiXp8fdiMwpp6qcOhVmKCb6O8itOrLSWFsvRORP5v+iVhUMhkWPVbqdShUCuxZ1gbON0ijpodiAkOQCCHxhGRD5IJMlQ6Ltwz3lJeA4fjv2PgZXJMSTXig71l2FtswcDIwC6IktqDCb4Njlc5UO8SOS0tEfksm9ONTXkXblPXarWwWq2Nry9LCsXGI2a8ubMUz/8uns2TXo5V9B46MzROr5IjPJC/j4io+1DKZZg5OBzHqhzYcJST33g7JngPVVidqLK7kGzk0Dgi6n5G9QhC3zAN/rOnDNYGDpvzZkzwHsqrdEAhO92rlIiouxEEAXcMDUe13YWP91dIHQ5dABO8B+pdbpysdiA+RAWlnKV3Iuqeko0aXJYQjDU5lThV7ZA6HDoPJngP5JsdcIlAYig71xFR9zZzUDhUchle3VHCNeO9FBN8K4ni6VXjjBoFDBp2riOi7i1Eo8Atl4Rhb7EVP52olTocagETfCuVWU/PO59oZNs7EREAXJkcggSDCm/uLGWHOy/EBN9KeRV2KGUCenDeeSIiAIBcJmDOsEhU2pz4cB873HkbJvhWsDvdOFVTj14GFRSc2IGIqFGKSYPMRD2+zKnEiSp2uPMmTPCtkG92wC0CiZx3noiomVsHhUGjlOGVrGK42eHOa0jeW6y8vBwrVqxAVVUVBEFAZmYmJk2ahLq6OixfvhxlZWUICwvDvHnzoNPpujw+tyjiSKUdYVoF9GrJHxcRkWTOO3+9IMeNA8KwcmcJVudUYVxCSJO3tQoZ1HIm/q4mecaSy+WYMWMGEhISYLPZMH/+fAwcOBCbNm3CgAEDMGXKFKxevRqrV6/G9OnTuzy+A8V1qKt3o3+EtsuvTUTkTS40f70oiggPVOC93aWostZDo/xfBfHEFBPUnDuky0leRW8wGJCQkAAA0Gg0iImJQWVlJbKyspCRkQEAyMjIQFZWliTxbcitRIBcQFxwgCTXJyLyBYIgID1GB7coYkdhHcfGewHJS/BnKy0tRX5+PpKSklBdXQ2DwQDg9I+AmpqaFo9Zv3491q9fDwBYsmQJTCZTh8VTbqnHzlM56BsZhCBd65dGlMtl0Go9L/G35Thvv5ZMJvPL++qqa8lkp/f39vtq63Gdfa0zz68rrtXeY7zxWmc/v9Yco9UCg2NF7DhZjVK7gF7G0/uqVCqYTMEex+jrFApFh+Ykj68v2ZXPYbfbsWzZMsyaNcujD2tmZiYyMzMbX5eXX3j5Q098tL8cLhHoGSRvsmTixbhcWo/2b89x3n4trVYLl8vtd/fVVdc6s1ynt99XW4/r7GudvdypP91XV13r3OViW3NMgl6OI2Vy/HysEoYANwLkMjgc2g79bvYVJpOpS+47Ojq6xe2SV9EDgNPpxLJlyzBmzBgMHz4cAKDX62E2mwEAZrMZwcFd++vP5RbxXW4V+kXqEKSSd+m1iYh8lUwQMCxWB4dTxG9Fnv/woI4jeYIXRRGvvPIKYmJiMHny5MbtaWlp2Lx5MwBg8+bNSE9P79K4TlY7UO1wYUJSaJdel4jI1xk0CvQJ0yDf7EBBTb3U4XRbklfRHzp0CFu2bEGPHj3w0EMPAQBuvvlmTJkyBcuXL8fGjRthMplw//33d2lc8QY1Vl6bBJdSjW+qLV16bSIiX9c/XIOi2npsL6jDDQOdCFUppQ6p25E8wffp0wcfffRRi+8tWLCgi6NpSqeSwwIO7SAi8pRcJmBEnA7f5VXj7Z0lWDAuBoLA79OuJHkVPRER+acQtQIDI7TYVViHDUerpQ6n22GCJyKiTpNiUiPFpMEbO0pRUsf2+K7EBE9ERJ1GEAT8MT0KAPCPbUVwuTkBTldhgiciok5lClRizrAIZJfZ8MG+7jceXipM8ERE1OnG9dIjM1GPj/dX4LcijkzqCkzwRETUJf6UFoE4fQCW/1SICmuD1OH4PSZ4IiLqEiqFDA+PiYHd6caynwrZHt/JmOCJiKjLxOlVmDMsEgdKbXhvL9vjOxMTPBERdanLEk63x39yoAK/nKyVOhy/xQRPRERd7k9pEUgKVWP5tiIcr3JIHY5fYoInIqIup1LI8GhGDDQKAYs3n0KNwyV1SH6HCZ6IiCRh1Crx14xYVFidePbHAjjZ6a5DMcETEZFkUkwa3DUsAntLrHhrV6nU4fgVyVeTIyIi/yYTZKh0uM/7/uBYPa5IcuDrQ2bo1UpcnmyAViGDWs4SfXswwRMRUaeyOd3YlHfhIXEhagExwUq8t6cUx81W/HFYDNRyLi/bHqyiJyIiyckEAZfGBcGkVeDnk3U4XGaVOiSfxwRPREReQSETMKZnEAIDZHhhWwFOVHP4XHswwRMRkddQKWTIiA+GUi7giY0nUWbhnPVtxQRPREReRRcgx7xRsbA1uPG3DSe4ME0bMcETEZHX6WlQY8H4OJhtLizYcBJVdqfUIfkcJngiIvJKfcI0WDAuFqWWBizYcJKz3XmICZ6IiLxWvwgtHsuIRWFNPRZtPIE6JvlWY4InIiKvNigqEPPHxuB4VT0e33CC1fWtxARPREReLy1Gh8cyYlBQU4/HvmfHu9ZggiciIp8wJFqHRePjUGF14q/fn0BJXb3UIXk1JngiIvI6p+evF5v9RYVo8ODYWNQ5XHh43QnsK7M3ed/u4vS2Z3AueiIi8joXm79+TM8gbDpWgyfWH8eYnkEI1ykBABNTTJzD/r9YgiciIp8TolEgM1EPjVKGTcdqcKKK09qeiwmeiIh8ki5AjgkJwQjVKLDtZB1yymwQRS4xewYTPBER+SyVQobxvYIRpw/A7mIr3vmtBA0uJnmACZ6IiHycXCZgZJwOfcPU2HS0Ggs4Vh4AEzwREfkBQRBwSWQgZg+LQl6lHQ+sPYajlXapw5IUEzwREfmNET2C8fTlPSECeOS749iUXy11SJJhgiciIr+SZFTj+SvjkWxUY/m2IvzrlyI4nG6pw+pyHAdPRER+4/QEOW5AJse80XFYnV2Or3IqcbDchruGRyMqWNXsGK1CBrXc/zrmMcETEZHfOHeCHJ1SQEZ8EH45WYcF649hSHQgeoWoIAj/mwzHXyfHYRU9ERH5taigAExM1sOgUWD7KQu2nqiFvRtU2TPBExGR39Mq5RjfKxiDIrUoqm3A2sNVOFXt34vVMMETEVG3IBME9AnT4IokPbRKGbaeqMXPJ2pR46dj5pngiYioWwlRn57Hvl+4Bidr6vHXdflYl1sFt59Nc8sET0RE3Y5cJmBAhBYTk/SI06vw0vZi/PW7Ezhm9p/JcZjgiYio29KrFXgkIw73XhqFgtp6zFt7DP/8pQgV1gapQ2s3DpMjIqJuTRAEXJagR3qMDh/vL8fXh8348VgNrks1YkpqKNQK3ywL+2bUREREHSxIJcftQyPwr8kJSIvR4f195fjTF0fwWXYFbA2+N6yOJXgiIqKzRAUF4OExMcgps+H9feV4+7cyfJZdid/3MeCqFAO0SrnUIbYKEzwREVEL+oRp8MRlcThUbsOH+8rxnz3l+Cy7EhMS9biqtwFRQQFSh3hBTPBEREQXkGLSYMH4OORW2LAmx4xvDpnxVY4ZaTGBmNTbgEsiAyGXed9Ut0zwRERErZBs1OCBURrMGhyGb3OrsC63ClkFp2DUKDCuVzAuS9AjVt98MRupMMETERF5wKhV4pZLwjC1vxHbT9Vhw9FqfH6wEp9mVyLZqMbIHkG4NC4IJpO0cTLBExERtYFSLsOonsEY1TMYZpsTm49VY8uxGrz9Wxne/q0MSaZipEVpMCRah6RQdZdX4zPBExFRt9a4hryHAuQy1Lv+e5xMjrEJoRibEIpySwN2FtTityILPtxXgQ/2VUCrlCE1XIvBUTqMjQ9CsKrze+IzwRMRUbd27hryrTUuyXTB40YlGJFqUqGkrgFFdQ04UGrFjoI6DIjQMMEDwO7du7Fy5Uq43W5MmDABU6ZMkTokIiKiVlEpZOgRokKPEBVEUcQl0cHooe+a4XVePZOd2+3Gm2++iUcffRTLly/HTz/9hFOnTkkdFhERkccEQUBUsAqC0DVt8V6d4PPy8hAZGYmIiAgoFAqMHDkSWVlZUodFRETk9bw6wVdWVsJoNDa+NhqNqKyslDAiIiIi3yCIoveucP/zzz9jz549mDNnDgBgy5YtyMvLw+233964z/r167F+/XoAwJIlSySJk4iIyNt4dQneaDSioqKi8XVFRQUMBkOTfTIzM7FkyZJOS+7z58/vlPN2F3x+7cPn1z58fu3D59c+Uj8/r07wiYmJKCoqQmlpKZxOJ7Zt24a0tDSpwyIiIvJ6Xj1MTi6X4/bbb8fixYvhdrsxfvx4xMXFSR0WERGR1/PqBA8AQ4YMwZAhQyS7fmZmpmTX9gd8fu3D59c+fH7tw+fXPlI/P6/uZEdERERt49Vt8ERERNQ2Xl9FLxVOkds+L730Enbt2gW9Xo9ly5ZJHY5PKS8vx4oVK1BVVQVBEJCZmYlJkyZJHZbPqK+vx8KFC+F0OuFyuTBixAhMnTpV6rB8jtvtxvz58xEaGip5b3BfM3fuXKjVashkMsjlcsmGcDPBt+DMFLmPP/44jEYj/vrXvyItLQ2xsbFSh+Yzxo0bhyuvvBIrVqyQOhSfI5fLMWPGDCQkJMBms2H+/PkYOHAgP3+tpFQqsXDhQqjVajidTixYsACDBg1C7969pQ7Np3zzzTeIiYmBzWaTOhSftHDhQgQHB0saA6voW8ApctsvNTUVOp1O6jB8ksFgQEJCAgBAo9EgJiaGMzh6QBAEqNVqAIDL5YLL5eqyub/9RUVFBXbt2oUJEyZIHQq1A0vwLWhpitzc3FwJI6LuqrS0FPn5+UhKSpI6FJ/idrvxyCOPoLi4GBMnTkRycrLUIfmUVatWYfr06Sy9t8PixYsBAJdffrlkvemZ4FvQ0sAClgCoq9ntdixbtgyzZs2CVquVOhyfIpPJ8Oyzz8JiseC5557DiRMn0KNHD6nD8gk7d+6EXq9HQkICDhw4IHU4Punvf/87QkNDUV1djf/7v/9DdHQ0UlNTuzwOJvgWtGaKXKLO5HQ6sWzZMowZMwbDhw+XOhyfFRgYiNTUVOzevZsJvpUOHTqEHTt24LfffkN9fT1sNhtefPFF3HPPPVKH5jNCQ0MBAHq9Hunp6cjLy5MkwbMNvgWcIpekJIoiXnnlFcTExGDy5MlSh+NzampqYLFYAJzuUb9v3z7ExMRIHJXvmDZtGl555RWsWLEC9913H/r378/k7gG73d7YtGG327F3717JflyyBN8CTpHbfv/4xz+QnZ2N2tpazJkzB1OnTsVll10mdVg+4dChQ9iyZQt69OiBhx56CABw8803Szqjoy8xm81YsWIF3G43RFHEpZdeiqFDh0odFnUT1dXVeO655wCc7uQ5evRoDBo0SJJYOJMdERGRH2IVPRERkR9igiciIvJDTPBERER+iAmeiIjIDzHBExER+SEmeCIiIj/EBE/UTc2aNQuCIEAQBCiVSoSHh2P8+PFYsWIFGhoaGvcbN25c435n/910002N+wiCgE8++aTx9aJFi1o85uy/Y8eOAQB+++03yOVyjBo1qsvunag7YIIn6sYyMzNRVFSEY8eO4bvvvsPVV1+NhQsXYsyYMY2zwQHAbbfdhqKioiZ/r7766nnP++CDDzbZNyUlBQ888ECTbWcmj3r99ddx1113Yf/+/Th48GCn3zNRd8GZ7Ii6MZVKhcjISABATEwMBg0ahCuuuAJDhgzB0qVL8cQTTwAAtFpt436todPpmiwXrFAooNPpmp3DZrPhvffew5YtW2C1WvHmm282zgJGRO3DEjwRNdG/f39ceeWV+PTTTzv9Wp988gl69uyJgQMHYsaMGXjnnXeaNA8QUdsxwRNRM6mpqTh69Gjj69dee62xVH7m76WXXmr3dd544w3MmDEDAJCRkQGtVos1a9a0+7xExCp6ImqBKIoQBKHx9R/+8AcsXLiwyT5hYWHtukZeXh5++uknvP/++wBOd9S75ZZb8MYbb+D6669v17mJiAmeiFqQnZ2NhISExtd6vR5JSUkdeo033ngDLperyVKaZ9a+OnnyJFdwJGonVtETURP79+/Ht99+ixtuuKHTruF0OvH222/j6aefxu7duxv/9uzZg4EDB2LlypWddm2i7oIleKJuzOFwoLi4GG63G2VlZdiwYQOeeuopDB06FA8++GDjflarFcXFxU2ODQgIQGhoaOPrY8eOYffu3U32SUhIQHBwcLPrfv311ygvL8edd94Jo9HY5L2bbroJL7/8Mh5//HHIZCyDELUV14Mn6qZmzZqFt99+GwAgl8sREhKC/v374/rrr8fs2bMREBAA4PREN5s3b252/KhRo7B161YAaNJef7Yvv/wSkydPRv/+/XHDDTdg0aJFAIBrrrkGdrsd3333XbNjjh49isTERKxbtw5XXHFFR9wqUbfEBE9EROSHWP9FRETkh5jgiYiI/BATPBERkR9igiciIvJDTPBERER+iAmeiIjIDzHBExER+SEmeCIiIj/EBE9EROSH/h/xjFGt8vMVOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## taking the natural log for this column --> will be almost Normal Distribution\n",
    "df_new['DELTA'] = np.log(df_new['DELTA']+1)\n",
    "\n",
    "\n",
    "## check Distribution again\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(df_new['DELTA'], bins=30, kde=True)\n",
    "plt.title('Histogram of DELTA after taking Natural Log', fontsize=14, color='k')\n",
    "plt.xlabel('DELTA', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8ef7cf",
   "metadata": {},
   "source": [
    "### `Explore the (AGE) Column`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2236fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Explore the AGE Column\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='AGE', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('AGE', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of AGE in The Dataset', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0271328d",
   "metadata": {},
   "source": [
    ">`I will try using (box-cox) transformation for this column`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453fa224",
   "metadata": {},
   "source": [
    "<img src='https://miro.medium.com/max/884/1*_nHvGg-OQYtNSSZph-424g.png' alt=\"boxcox\" width=400>\n",
    "\n",
    "#### [Great Article for box cox](https://onlinestatbook.com/2/transformations/box-cox.html)\n",
    "#### [How do box-cox in Excel](https://www.statology.org/box-cox-transformation-excel/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b535b35b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Let's try Boxcox  ---> Normal Distribution\n",
    "df_new['AGE'], lmd_age = boxcox(df_new['AGE'])\n",
    "\n",
    "## Check again\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='AGE', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('AGE', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of AGE in The Dataset after box-cox Transformation', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183b6038",
   "metadata": {},
   "source": [
    "### `Explore the (FC%) Column`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78dc5dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Explore the AGE Column\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='FC%', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('FC%', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of (FC%) in The Dataset', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d325b4",
   "metadata": {},
   "source": [
    "> * `It seems that it takes (exponential Distribution) -- I tried box-cox and natural-log but none of them was useful, box-cox can't be useful because of zeros`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f84438",
   "metadata": {},
   "source": [
    "> * `Explore the LC(ft/mile) & TC(ft/mile) Columns, -- > It seems that it takes (exponential Distribution) -- I tried box-cox and natural-log but none of them was useful, box-cox can't be useful because of zeros`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ce3c1b",
   "metadata": {},
   "source": [
    "### `Explore (RUT(in)) Column`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "42e612cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Explore the RUT(in) Column\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='RUT(in)', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('RUT(in)', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of (RUT(in)) in The Dataset', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9b3f258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Let's try Box-cox Transformation --> Normal Distribution\n",
    "df_new['RUT(in)'], lmd_rut = boxcox(df_new['RUT(in)'])\n",
    "\n",
    "\n",
    "## Explore the RUT(in) again\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.histplot(x='RUT(in)', data=df_new, bins=20, kde=True)\n",
    "plt.xlabel('RUT(in)', fontsize=14, color='k')\n",
    "plt.ylabel('Frequency', fontsize=14, color='k')\n",
    "plt.title('Histogram of (RUT(in)) in The Dataset', fontsize=14, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f128ca8",
   "metadata": {},
   "source": [
    "### `Check perason Correlation`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd8bfebc",
   "metadata": {},
   "source": [
    "#### `Before Transformation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "67b55d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking the (pearson) correlation before transformation we did --> (df)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8ba43a",
   "metadata": {},
   "source": [
    "#### `After Transformation`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b97187b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Checking the (pearson) correlation after transformation we did --> (df_new)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df_new.corr(), annot=True, cmap='Blues');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f00f190",
   "metadata": {},
   "source": [
    "### `Split the Dataset using the ordinary Method`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87492c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape => (1363, 5)\n",
      "y_train shape => (1363,)\n",
      "==============================\n",
      "X_test shape => (341, 5)\n",
      "y_test shape => (341,)\n"
     ]
    }
   ],
   "source": [
    "## split the dataset --> make the test set work as (validation and testing) as the dataset is very small\n",
    "## It is enough to take 0.20 of the Dataset as a validation Dataset \n",
    "\n",
    "## Split to target and Features --> Note Impotant --> split the original data and then do the transformation on splitted data\n",
    "X = df.drop(columns=['DELTA'], axis=1)\n",
    "y = df['DELTA']\n",
    "\n",
    "## split to training and testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123, test_size=0.2)\n",
    "\n",
    "\n",
    "## check shapes after splitting\n",
    "print('X_train shape =>', X_train.shape)\n",
    "print('y_train shape =>', y_train.shape)\n",
    "print('=='*15)\n",
    "print('X_test shape =>', X_test.shape)\n",
    "print('y_test shape =>', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02573a9e",
   "metadata": {},
   "source": [
    "### `Preparing before Training Models`\n",
    "* `Apply box-cox Transformation for (AGE & (RUT(in)) Columns`\n",
    "* `Apply natural log (value+1) for target (DELTA)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f94005",
   "metadata": {},
   "source": [
    "#### [Kaggle Notes on box-cox](https://www.kaggle.com/rtatman/boxcox-transform-on-train-test-data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7bc0b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "## BoxCox for both (X_train) and (X_test) for both two columns\n",
    "X_train['AGE'], lmd_age_train = boxcox(X_train['AGE'])            ## age  -- train\n",
    "X_train['RUT(in)'], lmd_rut_train = boxcox(X_train['RUT(in)'])    ## rut -- train\n",
    "\n",
    "## use the lambda that calculate from trained date and use it to test\n",
    "X_test['AGE'] = boxcox(X_test['AGE'], lmbda=lmd_age_train)            ## age  -- test\n",
    "X_test['RUT(in)'] = boxcox(X_test['RUT(in)'], lmbda=lmd_rut_train)    ## rut -- test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9954ea07",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Scaling the Features to (mean=0, std=1) --> standard Normal Distr.\n",
    "scaler = StandardScaler()\n",
    "X_train_final = scaler.fit_transform(X_train)\n",
    "X_test_final = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "529ee34a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## for target --> taking the log again, we almost ready to train Models\n",
    "y_train_final = np.log(y_train + 1)\n",
    "y_test_final = np.log(y_test + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ac7fd27",
   "metadata": {},
   "source": [
    "### `Building ML Models`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcd42dc",
   "metadata": {},
   "source": [
    "### `LinearRegression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21ce76dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Try simple LinearRegression\n",
    "lin_reg = LinearRegression(fit_intercept=True)\n",
    "lin_reg.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ec2784b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_lin : 0.24150722490108556\n",
      "score_test_lin : 0.19396285752150366\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using LinearRegression\n",
    "y_pred_train_lin = np.exp(lin_reg.predict(X_train_final)) - 1\n",
    "y_pred_test_lin = np.exp(lin_reg.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_lin = r2_score(y_train, y_pred_train_lin)\n",
    "score_test_lin = r2_score(y_test, y_pred_test_lin)\n",
    "\n",
    "print('score_train_lin :', score_train_lin)\n",
    "print('score_test_lin :', score_test_lin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbf5da3",
   "metadata": {},
   "source": [
    "### `SGD Regressor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0f90274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Try SGD Regression\n",
    "sgd_reg = SGDRegressor()\n",
    "sgd_reg.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd7d2041",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_sgd : 0.24216177526066196\n",
      "score_test_sgd : 0.19785540662719237\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using LinearRegression\n",
    "y_pred_train_sgd = np.exp(sgd_reg.predict(X_train_final)) - 1\n",
    "y_pred_test_sgd = np.exp(sgd_reg.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_sgd = r2_score(y_train, y_pred_train_sgd)\n",
    "score_test_sgd = r2_score(y_test, y_pred_test_sgd)\n",
    "\n",
    "print('score_train_sgd :', score_train_sgd)\n",
    "print('score_test_sgd :', score_test_sgd)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f96c68",
   "metadata": {},
   "source": [
    "### `Try KNN`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "87c9be4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define the Model\n",
    "knn_reg = KNeighborsRegressor(n_neighbors=5)\n",
    "knn_reg.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4a934657",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_knn : 0.6352612645686759\n",
      "score_test_knn : 0.45130582139238806\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using KNN\n",
    "y_pred_train_knn = np.exp(knn_reg.predict(X_train_final)) - 1\n",
    "y_pred_test_knn = np.exp(knn_reg.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_knn = r2_score(y_train, y_pred_train_knn)\n",
    "score_test_knn = r2_score(y_test, y_pred_test_knn)\n",
    "\n",
    "print('score_train_knn :', score_train_knn)\n",
    "print('score_test_knn :', score_test_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b74ad07d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters:  {'n_neighbors': 3}\n",
      "Best Model KNN:  KNeighborsRegressor(n_neighbors=3)\n"
     ]
    }
   ],
   "source": [
    "## Tuning\n",
    "params_best_knn = {'n_neighbors': np.arange(1, 10, 2)}\n",
    "\n",
    "grid_knn = GridSearchCV(estimator=knn_reg, param_grid=params_best_knn, cv=5, scoring='r2', n_jobs=-1)\n",
    "grid_knn.fit(X_train_final, y_train_final)\n",
    "\n",
    "print('Best Parameters: ', grid_knn.best_params_)\n",
    "\n",
    "## best Model with best parameters\n",
    "best_knn = grid_knn.best_estimator_\n",
    "print('Best Model KNN: ', best_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cfaaf880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_knn : 0.7598180599033904\n",
      "score_test_knn : 0.45503424930298697\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using KNN\n",
    "y_pred_train_knn = np.exp(best_knn.predict(X_train_final)) - 1\n",
    "y_pred_test_knn = np.exp(best_knn.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_knn = r2_score(y_train, y_pred_train_knn)\n",
    "score_test_knn = r2_score(y_test, y_pred_test_knn)\n",
    "\n",
    "print('score_train_knn :', score_train_knn)\n",
    "print('score_test_knn :', score_test_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475b814d",
   "metadata": {},
   "source": [
    "### `Try RandomForest`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed1954c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=20, n_estimators=200)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define the Model\n",
    "forest_reg = RandomForestRegressor(n_estimators=200, max_depth=20)\n",
    "forest_reg.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39a21184",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_forest : 0.8855495889936613\n",
      "score_test_forest : 0.5884463335686965\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using RandomForest\n",
    "y_pred_train_forest = np.exp(forest_reg.predict(X_train_final)) - 1\n",
    "y_pred_test_forest = np.exp(forest_reg.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_forest = r2_score(y_train, y_pred_train_forest)\n",
    "score_test_forest = r2_score(y_test, y_pred_test_forest)\n",
    "\n",
    "print('score_train_forest :', score_train_forest)\n",
    "print('score_test_forest :', score_test_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8ad25f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
      "Best Parameters:  {'max_depth': 32, 'n_estimators': 140}\n",
      "Best Model Forest:  RandomForestRegressor(max_depth=32, n_estimators=140)\n"
     ]
    }
   ],
   "source": [
    "params_best_forest = {'n_estimators': np.arange(100, 200, 20),\n",
    "                    'max_depth': np.arange(20, 40, 2),\n",
    "                     }\n",
    "\n",
    "grid_forest = GridSearchCV(estimator=forest_reg, param_grid=params_best_forest, cv=5, scoring='r2', n_jobs=-1, verbose=6)\n",
    "grid_forest.fit(X_train_final, y_train_final)\n",
    "\n",
    "print('Best Parameters: ', grid_forest.best_params_)\n",
    "\n",
    "## best Model with best parameters\n",
    "best_forest = grid_forest.best_estimator_\n",
    "print('Best Model Forest: ', best_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dab2f9c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_forest : 0.884958471320873\n",
      "score_test_forest : 0.5827951269898759\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using RandomForest\n",
    "y_pred_train_forest = np.exp(best_forest.predict(X_train_final)) - 1\n",
    "y_pred_test_forest = np.exp(best_forest.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_forest = r2_score(y_train, y_pred_train_forest)\n",
    "score_test_forest = r2_score(y_test, y_pred_test_forest)\n",
    "\n",
    "print('score_train_forest :', score_train_forest)\n",
    "print('score_test_forest :', score_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461f8abc",
   "metadata": {},
   "source": [
    "### `Xgboost`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98ccaa0",
   "metadata": {},
   "source": [
    "### [Understand GBRT](https://towardsdatascience.com/machine-learning-part-18-boosting-algorithms-gradient-boosting-in-python-ef5ae6965be4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a82a36f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.1, max_delta_step=0,\n",
       "             max_depth=5, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=300, n_jobs=8,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=2, scale_pos_weight=1, subsample=0.9,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define Xgboost -- our Hero\n",
    "xgb_reg = XGBRegressor(max_depth=5, learning_rate=0.1, n_estimators=300, reg_lambda=2, subsample=0.9)\n",
    "\n",
    "## fitting\n",
    "xgb_reg.fit(X_train_final, y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c08f5a78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_xgb : 0.9334560541314906\n",
      "score_test_xgb : 0.67459533544896\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using Xgboost\n",
    "y_pred_train_xgb = np.exp(xgb_reg.predict(X_train_final)) - 1\n",
    "y_pred_test_xgb = np.exp(xgb_reg.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_xgb = r2_score(y_train, y_pred_train_xgb)\n",
    "score_test_xgb = r2_score(y_test, y_pred_test_xgb)\n",
    "\n",
    "print('score_train_xgb :', score_train_xgb)\n",
    "print('score_test_xgb :', score_test_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b4d76a58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
      "Best Parameters:  {'learning_rate': 0.18, 'max_depth': 4, 'n_estimators': 200, 'subsample': 0.9}\n",
      "Best Model Xgboost:  XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
      "             gamma=0, gpu_id=-1, importance_type=None,\n",
      "             interaction_constraints='', learning_rate=0.18, max_delta_step=0,\n",
      "             max_depth=4, min_child_weight=1, missing=nan,\n",
      "             monotone_constraints='()', n_estimators=200, n_jobs=8,\n",
      "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
      "             reg_lambda=2, scale_pos_weight=1, subsample=0.9,\n",
      "             tree_method='exact', validate_parameters=1, verbosity=None)\n"
     ]
    }
   ],
   "source": [
    "## Tuning xgboost\n",
    "params_best_xgb = {'max_depth': [4, 6], \n",
    "                  'learning_rate': [0.15, 0.18], \n",
    "                  'n_estimators': [100, 150, 200],\n",
    "                  'subsample': [0.9, 1]}\n",
    "\n",
    "\n",
    "grid_xgb = GridSearchCV(estimator=xgb_reg, param_grid=params_best_xgb, cv=5, scoring='r2', n_jobs=-1, verbose=6)\n",
    "grid_xgb.fit(X_train_final, y_train_final)\n",
    "\n",
    "print('Best Parameters: ', grid_xgb.best_params_)\n",
    "\n",
    "## best Model with best parameters\n",
    "best_xgb = grid_xgb.best_estimator_\n",
    "print('Best Model Xgboost: ', best_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8589b8d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score_train_xgb : 0.8925320993598865\n",
      "score_test_xgb : 0.6534438198579553\n"
     ]
    }
   ],
   "source": [
    "## Predicted values using Xgboost\n",
    "y_pred_train_xgb = np.exp(best_xgb.predict(X_train_final)) - 1\n",
    "y_pred_test_xgb = np.exp(best_xgb.predict(X_test_final)) - 1\n",
    "\n",
    "score_train_xgb = r2_score(y_train, y_pred_train_xgb)\n",
    "score_test_xgb = r2_score(y_test, y_pred_test_xgb)\n",
    "\n",
    "print('score_train_xgb :', score_train_xgb)\n",
    "print('score_test_xgb :', score_test_xgb)  ## so nelegt this Model and use the above one (xgb_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a450b529",
   "metadata": {},
   "source": [
    "### `Finally, I will use Xgboost as my Evaluation Model -- (xgb_reg)`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16547f55",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97c20ae",
   "metadata": {},
   "source": [
    "### `Inference on train, test and total Dataset`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9053bbc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score on test Dataset is : 0.67459533544896\n"
     ]
    }
   ],
   "source": [
    "## Inference on test Dataset\n",
    "y_pred_test = xgb_reg.predict(X_test_final)   ## remember, we took the natural log(value+1) --> take the exp()-1\n",
    "y_pred_test = np.exp(y_pred_test) - 1\n",
    "\n",
    "## get the score --> comparing the true with predicted\n",
    "score_test = r2_score(y_test, y_pred_test)\n",
    "print('r2 score on test Dataset is :', score_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d343e2a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score on Total Dataset is : 0.8805692787828848\n"
     ]
    }
   ],
   "source": [
    "## concatenate the train and test Datasets after processing for Features\n",
    "X_total = np.vstack((X_train_final, X_test_final))\n",
    "## concatenate the train and test Datasets after processing for target (DELTA)\n",
    "y_total = np.vstack((y_train.values.reshape(-1, 1), y_test.values.reshape(-1, 1)))\n",
    "\n",
    "## Inference on total Dataset\n",
    "y_pred_total = xgb_reg.predict(X_total)   ## remember, we took the log(value+1) --> take the exp()-1\n",
    "y_pred_total = np.exp(y_pred_total) - 1\n",
    "\n",
    "## get the score --> comparing the true with predicted\n",
    "score_total = r2_score(y_total, y_pred_total)\n",
    "print('r2 score on Total Dataset is :', score_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65876ed7",
   "metadata": {},
   "source": [
    "### `Download the Model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "933cc16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['xgboost_model.pkl']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Download the Model\n",
    "joblib.dump(xgb_reg, 'xgboost_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0816cf5",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419b3a6f",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55b49ff",
   "metadata": {},
   "source": [
    "### `The Processing PipeLine`\n",
    "* `Firstly: Splitting the Data to train and test, use test as validation as data is too small, test=20% of dataset` <br> <br>\n",
    "\n",
    "* `Secondly : For Features`\n",
    "    * `1. (AGE) Column : Applying box-cox Transformation`\n",
    "    * `2. (RUT(in)) Column : Applying box-cox Transformation`\n",
    "    * `3. For all Features : Using Standard Scaler to convert the Distr. to mean=0, std=1`\n",
    "<br> <br>\n",
    "\n",
    "* ` Thirdly : For target (DELTA)`\n",
    "    * `The Model predicted the values at log scale, you MUST take the exponential values and then subtract by 1`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9a26da",
   "metadata": {},
   "source": [
    "### `For Further Improvement`\n",
    "* `Increase the size of this Dataset`\n",
    "* `Try to get some new columns which are more correlated with the target, it requires some Domain Knowledge`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54ccd41",
   "metadata": {},
   "source": [
    "### Done  @ Mars, 2022"
   ]
  }
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