capgimini.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "import calendar\n",
    "import math\n",
    "\n",
    "from sklearn.metrics import mean_squared_error,r2_score,accuracy_score\n",
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso,RidgeCV\n",
    "from sklearn.ensemble import RandomForestRegressor,BaggingRegressor,GradientBoostingRegressor,AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split,cross_validate,KFold,GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.metrics import mean_squared_log_error,mean_squared_error, r2_score,mean_absolute_error \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat=pd.read_excel(\"C:\\\\Users\\\\rishi\\\\Desktop\\\\Demandv1.1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dat.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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trRZAvxTYjMwSHkrW039Q0rLlpdVsjHLN+R4wHXndQFI/gIgYBlwNPAXMV7+WVKsbHEDvDKXW/ShJAyVtJul3khaWNKSaTGn1GM2sR+lNrni6iCwddjWwJHCnA+hmZtaTORPdzKzJJJ1CBsFn/QafmYvMQv8YeDwiRlcNA7+rcVr3ImlbYEdgPeCecg6sCFwO/B44uATCnDlqXyDpBmBaYOGIGCqpX0QML/vuBp6PiHVaOkhrqVI3/x5gAmAysi/HZcCeEfGmVyWY2depXydKKbErgUXIjPRf18sWmpmZ9UTOLDEza74RwABJg8Z2gKTeSuuVgNfDEXF3RDxagqe9HEDvOLOT5VueLOfAtGQpjguAI0sAvZdrpFsXLgYGA6dJmiwihpfryyxkpvqTrR2etUJDr40DgFeBXwOTAJcCywDHS/pedd9pwTDNrAcoz7RPSvoxjGlE/TEwEOgDTC9pYAuHaGZm9n/mh2EzsyapBTZfBb4P/HRsx5byHeMARwFbdLHfGYEdogQ7ewOzAh9FxLuSZgAeBP4ObBERn0raGTjcmehWqa45EXEGcCYwD3CXpB2Bg8q2D4FDWzZIa5mIGClpkKQNycndMyLiwYh4hywhdjGwMHCcpEkcSDezrzA7cDv5jFs5kSzrcg/ZxHqN0qfDzMysR/KDsJlZk9QCm2eRgavtJY3b1bEl+LUU8AL58mEdojGLPNIo4HpgXknrkefEjcBmEfGxpGmAucn7ev9mj9m6p3o964jYA9gDeAY4BFgB+Dcwbwmmur9ChynXmj+QkymbkfcbJA2IiNERsTNwITnhe2xZxeAJXDP7koi4G9gqIt6XdIqkhSPitlLCZTXgPuBoYLUqI13SAEnLSJqihUM3MzP7nzmIbmbWRKXcxlBy6fzSwEmSJmg4RmT94l2A/wIPN32g1hKSeleTLZIGNwTU7yazRc8C/hERa0TEB5K+B+xFZhmfGBGfNX3g1hL/S1ZwPXs4Is6KiGWB6cnzZd0SQO/T2NjY2l+51lxY/gwB1izbP601oK0C6asBW7doqGbWA5Sycj8iS0FdL2nesv1DYHXgXuBIYH1Js5PZ6WcDvv+YmVmP4MaiZmYtIGky4DfATsCdwCnAzUBfsgnTDmQdyblLkMtN3dpcQ0Ouw8jszzeBGyPipLJ9C2A/4HXgVGA8Mhi6CLBYRDzairFb81WNhSX1B6rGw+/8L+dAveSPy/90jrHdRyTNSjYnXgn4fUQcVLbXG9BuA5ziyRYz+zqSFgcOJEu8LBoR95ft4wHnA8uRZV/6ActFxIOtGquZmdk34SC6mVmLSJoU+BWwD9nc70OgN/ASWWZhjVqWqJuIdghJp5MvmLcA8wITApdFxBZl/xrkebME8BrwKHBIRDzVmhFbs5UVC6MkDSHL/ExW/oisQXu6zwerq0269ANmACYHngdeiogRJSt0T3KF1GERcWD5XP+IGFb7nt4OpJsZfPX1QNISZCB9VmqB9LJvK3JF/HUR8VxTBmtmZvYtcBDdzKzFSjmX5clA+sdk+ZaHShkGB9DbXJUJXEq3fI9c2nx0RNwgaRJgf7LMwuURsXH5TB9gUKk9OiZb1DqHpAFkiZ/3gYOBz8gyLaeRNa53jYi3WzdC6y4aJl3+SpYLmxx4F7gDWDsiPpE0B1k3fyng0Ig4uGWDNrNurR5Al7QWMBXwGPBERLxcto81kG5mZtYT9Wn1AMzM2k1XS+a/Yhl9r4h4FzhvLPscQG9jDVlcfcgg+juUOvgR8aak/YCRwLol3r5JySj9pHxuRNMHbt3BmmT5px0i4hEYs7oF4HEH0K1SAugDyNJhb5NlxP4FbAmsCtwvab6IeETSAWR94gMlvRIRf2rZwM2s26oF0C8ma6AHMC5wpaSjI+L2iPh7ae1yIHCjpGVLA1IzM7MeyUF0M7NvUcOS+WnIoOgjwKfAl4LoX1Xn3DXQ21vJQK9eQo8n61qPSwZGpwLeAoiIVyUdWD62hqQLI2KtiBhR9ntJWQfoYiJueqA/8J+yf03gAmCPiDhW0kTAbBFxS/NHa93QqsDEwKbAwyWwviPwEHAQcK6kNSPiMUmHAs+S55OZ2RgNGegrkFnmK5FZ6L8kG4cOkXRQRNxcAukBnAxcKmlaYJifXczMrCdyEN3M7FtSXixGliXzV/P5kvnXgRMkXRIR/2npIK1baGgiehawJHAPWc5nCWADSS+UVQpExGslQ3QwsKikySPitRYN35qkKtUjqW+pW12vT90LGBwRH5RARhVAP6SUBloXWEHSUz5XOoekeYBZIuLshl0zAgMi4oFyXN9SwuV8svfCisAUwItlZUO1usElxcxsjFoAfVPgB2SJqNvLdeJsScOAY4A9JVEC6TdJ2py8vnzWssGbmZn9H/Vq9QDMzNpFyewbCNxFZp3vBPwMuJTM9Ntb0ngtHKJ1AyUDvQqgz0Rmnq8TEauRgc8DgN8Au0gav/pcRAwFdgUWcFC0/UkaDPxO0tolgD4e8Lakrcsh1wK9Jd0O/AX4XUQcUvbNTAZFnwKGNnvs1nxKA4DTyZUsjV4Axpe0KEA5p/pGxCdkHf2JgR82fsgBdLPOJmmQpPUkTVzbti5wArAZ2Zx4pKT+ABFxIbADMAuwq6Sly/ZbI+L55v8GZmZm3x5nopuZfQuq5pDAemRdyB2Bx0pz0AmA7YC7I+L9Vo7TWq9awizpWGA2YBIy2ElEvCHpcHISZu9y3KER8V7Z/3pLBm2tMIAMah5YriG/Ae4Hrin7HwGuANYCHgCOlTQOMCeZBRjA9lXTWi+db2/l3/dTSUtHxOsloL5URFxZDvk32W9hW0mvR8S/SiC9D/Bj4L+AJ+fMrNGuwF7kpO7ZZYXcxcB8wObARpLOi4h3qlVTEXGhpNHA+eR16Y4yYWdmZtajOYhuZvYtqAWoZgb6AU+WAPq6wJ+AvSLitJJNOgtwf1XT2jrWhMACwEdkwLSajPlQ0pHlmN2AQZL29gRMZylNZQ8FJgOOJxtB/qIExXtFxEeS9iJXFf6SrGH9CTkB8145dmRD81prY+XfuppoOwdYUtKOEXFWRNxezqdD81AdD9xGlnLZkpzIe7YlAzez7mw/YErgYKCXpHPLhP+OZGPzjYATJW0dEe/WAukXSxpJNrp2AN3MzNqCnJhkZvZ/V2oQA5wILBwRs0laGziPz+sU9wL2IGtI7lJlF1tnqdcYLkGtnYGLgF0j4uXacYOB3wObAD+OiDdaMV5rLUnXAfMD4wGbRsSZ5XrTp2QSDyFXNCxNNjB+BvhLKS/letZtrmSSj4KczK3Vz5+ZzALtDxwVEaeV43cmrzkTA++TEy//Je9bI7poYGtmHa5cZ84A1iSfY6tAeh/gKLJx8W1AFUjvFxHDWzdiMzOz74aD6GZm/x/GFmiQtAzZZOkCYA3gDxFxUNn3E+Bk4CGyfrEvwB3g6zKBJR1HlgG6FNivi0D6OBHx1nc/UutuSoBiMWA4Wc5lZWCziDijBNJ7je3ccgZ6+5M0LrA+8K/SuG8wcB+we0RcJelHwJ/Jlaf1QPpPgenICd1ngUs96WJmla6ecf/HQPpNZBmxd5o9ZjMzs2ZwORczs2+oCjRI6kfWLJ4ceBgYGRHXSTobWBu4LiIOKs2WqjrFkFnorlPcAeqBTEmrANOT5Tf+HBHPAkTEdmWVwjrluH0j4r9l30dkuRfrAI2B7xLQvLHsq8r5nCaJiDgDGCVpSmDliDiu/l0OoHeE3uQky/6SNgEOA94EHijn0lOSViMD6TuW8+a0iLgbuLv+ReV4B9DNOpikgcCEEfHfru5H5ToDcBB5/zmnZJ7vCIwEtgE+k7SFV7SYmVk7cia6mdk3UL1UlBIK1wDTkLUi/0sunT8SGEQ2hdwYuJ2sfT2SLLWwaFky7yzRNlfP5JJ0ITAXWXbhYzKYvnJE3FI7/gRgdeBmYMeIeLX5o7ZWqU3ODQQ2BGYAXgBujYhHyzFzAnuSGek7kVnHh5djJ3XQorOU1QiTkg1mZweeBlaoVrPU7ldVRnpv4PCIOKtVYzaz7qlM5l8OrABMHxH/6epZtWSen0neh1aPiL/Wtu8PnBkR/27u6M3MzJrDQXQzs2+oBLnuITP+/kg2/NsM2IIMrK9BBtIXBJYBPgMeJbOPvWS+w0g6FVgS2DAibpN0NLA9GUxfMyKurR17NrAI8NOIeK0V47Xma5icu4W8fgwF5gYeAU6OiAvLsXMCvwXWJa9BzwGLlMk5r27pMCVw9RDwE+AdsrzLjbUJvHog/SJgamDd+nXHzAxA0hpkL5YJyT4Jz40lkD4ecDXZW2GuiPis+aM1s57GSWTWDhxENzP7hiRtDOxIBiseL8Gr1YCLgd9ExIlVMKsxqOWHh/bVVQBT0tLAH4D9I+JvknYCDibriS4DzAusGhE31D4zWUQMbeLQrRuQNIBchfApsFFEvCjpJmAhMsP4oIi4uBz7fWBGYCrgfE/Oda5y3ixI1s0/mFzlsjFwQ62UVK+IGF2aje5Knl++D5kZ8MXnF0krkCucxgMWagyk155vNyfLuvwsIv7VssGbWY/QcB1ZHfg+mSjyZES83tLBmX0DvVo9ADOzHmg2oH9EPFwC6OuSGX57lgD6uMDSksZp/KADF+2pBLJukLRAw65PgauA68t58ntg/Yg4nFzFMBg4V9KK1QccQO9YWwLDyPPjRUl/IQOim5LZw/tLWgsgIl6JiFsi4k8lgO561h1CUu/6zxHxaUTcDNxFnivPkqUWlqodNpmklSLiiYhYvzpnmjdqM+vOqqSP8vergF2A94G7JE1Xv2bUkgX6kyvqPmjFmK21Svkfs/9ZLYB+MXAaWf7pJuA4SfO1cmxm34QvfmZmX6F6aaheLsrS+c/I2taUoNafyAD6weX4LYFfA4NcWqFj/BiYCbhE0lzVxoi4HTij/LgRGdy6ouy7BHgcCOBkSYObOmLrbh4EbikN3Y4jJ+tWiYjzyGvKD4GtJW3a+EFPznWGstpglKQBktaUtF/57/TlXvMsWVrsWbIB7dqSFiIn8vas7mPgc8bMvqghkH4lGUh/jwykz1C/ZkiaFPgF8AQOoncMpT4AtZJhk7d2VNbd1SftJa1Meb4FpiWbES8F7Cvpp60Zodk34yC6mdlXqAIWwLWSvl+yPe8AZpR0PhlA3ysiDikfmQH4JRlof6clg7ZW+AewDvAGcI2kuasdEfEmWTf0R8CnVe3QkrX+MVnbeq6I+Kjpo7amkjRI0lQl8LmYpNmqfRFxB3CQpO8BiwFHkJMsAC+TWerzAT9r9rit9arVBqVu/h1kg9m1yCbWF0qaqwS5ngE2J0sAnQdcBowGFqwHyczMGnURSN+VfJa9U9KvJE1Znl2OABYFdo6ID1s2YGuaknm+JrC2pL5l24XA5pL6tXRw1q3VMtA3JJugX0kmjbwRESeTJVLnAX7vQLr1BA6im5l9vfmAJYBly8/XAceTAYzrgRMk9S43/rOAccgXCwcsOkDJDg3gTvJB8FUykD5PdUxEvAG8BCwpaaGybwNyRcNDbiLa/krA/CQyAHoOuYT1NklHlIk6ImIEMCUwM/BeRAwr15CJyAm7Och619ZhyoTuQODvZHboihExAzkRNwe5Cma+kh34FJnZtRGwMxlAH1G7VplZh6syims/V8HzxkD69sCjZODrcXJybm6yofUTTR20tUy5tywInA1sJela8t3o6ogY3sqxWfcnaX3gFGAL4MXyTNMfICLOIp9V5iZXzS3SupGafT03FjUz+xplGdo9ZFBrqbLtR8BWwG+Au4EJycZuHwGLlYCFm4i2qfLgN2NEPN6wvReZKXwUMAXwq4h4sOybnSzl8kMys2sUsHREPNLEoVsLlAm2i4H7yMmWm8jyPxsCy5HBie0i4mVJUwKXkIkOu5evOAB4NSJWK9/na0uHKUGt35ElFLYsdfMvBRYATiRfTEcAq0fEP7r4vM8Zsw5XnlEmLhP71bbFS1+FxmPrzUb7AiuQzzUvAQ9ExKtNGrZ1I5KuIoPnw4CVIuLW1o7IeoKSLHIk2bvlH8CSEfGBpL4lgaQKtJ8NXA6sFxGftmq8Zl/FQXQzsxpJvao6f+XnfhExXNJKZCboRhFxadknMjt9fqAP8DBVavU9AAAgAElEQVTwlzK73seN/tqTpEHAzcCsZEPZJ4ELgfcj4qMy6TIfcDIwGRlIf6B8dhJgbTKT9PaIeL4Fv4I1kaSfAzeSGThHRsRLtX1Tkita9gH+FhGrlu3bAluTJYDeBF4AFq5eNKwzlaXQE0bEUZKOJ0uHrRARj0vanyzx8gywWSkPZGY2hqRVyQDoORFxTwmI9gPWjgiXILSxqoKdyqaQq5GTtnsBfyzB0DGTLtbZGt+la9vHIUtBrQ1cA2wTER9W79rlmLXIFbrPNHXQZt+Ag+jWERpv7L7RW1eq86Jk3IxfallX+2Ygb/h/j4htSjZPdHUeOeOvvUnahiznM4IspfAO8H3gATJ74o6IeKgsR9yLrP+3bEQ81KIhW4uU2vgPAPuSAfSPyvYx1whJE5JZxAcCB0XEXmX7/MDU5ATdxZ6c6ywNWaBjXkjLKpjvkRN5h5HBsOGS5iTvUb3Ia9DqLRq6mXVTkpYjJ/0fJIPn05IT/Q+3dGDWbTW+05SVuL3IYOgvyL4cp0bEey0aonUjDc+3PyGfV14GPo6IoSUj/RjgV2SCybaNgXSz7s5BdGt7VdChZIf2I8/7T1o9LuueSnOc+4FPyJIKfyLLuIyWtAeZMTpbRDzlyZjOJelAYEWy/vkhZEPZlchGWyPJpYp3khnnW5GNZtepSrtY+5M0GDiVbMS1SkRcUa8523Ds1MD5wKTAEhHxYhff58m5DlH9W1fB8/pzTNm+OFkbfeGIuLucVxuQmemnAzf6XDGzrpSVleeRgdDtI+LUst3PtPYFDQHRnYAPgGsj4pWy7W/A4mQg/eQSDB1Ilqn7R0Q816KhWws0TPifSa56mZIsX3kzcHhE/L0E0o8Gfg38DdghIj5o0bDNvjE3FrW2Vi7mI0sw4wrgXuARSWtKGq/Fw7NuqMyCnw08DxxE1i4+RdJUwC1k6Y7VSya6m4Z2mDIZR0TsCfyVrGu9BXB+RCxNZp1vS9bGX508hwaRQfaTqiY61v5K1vmpwA3ApZKWG1uAogTNLyJXNIwzlmMcFO0AtUD5YOBkSX8BzpQ0be0ceBx4DfhdKQm0ENlEdGhEXFc+37s1v4E1S3kOqf/sZxIbq9r5Mjk52f8psFbVxK+sxHRswIAx79BVAP0CssTcpMDw6jyJiGXJd6N9gR0kzUP2BDqeTB6xDlILoJ9DrlLYh+zb8geyH9RZkpYptc5/C1wGrAMc4vuX9STORLe2VSvNMQ6ZWfwhGQD9ATlrfiA5a/56C4dpLTa27M5S93pKYEfg52Rt67+R2cb3kA1RRjlzp/M0ZOYcQjaHvJtsDPnf2nE/JGujLwXMQjYDdBPRDtBQimNBYH9gMbJ+9bUN+6ug6RrAucCCLv3T2Uom3wNks+rPgKmAIcCKEXFLWTG1GXlejU+uePkPee64bn4HaLgPzR4Rj7Z6TNY9dVWfWNIQMgv0KPLdaJ8oDSJLMEtd1TS29tdF+ZazyPfmdYEnuqqdL+kaMvv8TSCA5f0c05lKabmryNUJ59buUyuSky2fAutHxDMlRnMgWVffNdCtx3AQ3dpSLSjRm7zx/6b8eYmsL3sAsDOZJXq8A+mdqbZEfgBZmmMKMmhxDvBhrT56L2B7Mst4ZaA/WcPtpBYN3Vqsi0D6euRk3dYR8VrZXk3k9QZ6u9ZfZ/lfAukwJvuvN3AiMCNZP39Yq8ZtrdFwTdkUWIXM/HuJzOTal5yUWzUibigTvdOQ59TbuG5+x2g4V04EfgxcGBGntXZk1t00nCvjApPUS2xI2gA4HHgC+ENE3F4CW78FXoqI81sxbms+SeNExGcN2+Yn34l2joiry7bJyID6AOCZiLi4bF8f6A3cFhH/aergrenKRNza5Ercj2rblyP7s8wWEf9UaUhb9m1NrlJYKiJuasW4zb4NDqJb2yqB0UuBYcCoiFitYf/BwK7kDOgJDqR3llqt2SHA7cC45DLFfsArwG7AXyPiw9pnBgEzA2cAz0bESs0fuXUXYwmk30cG0oc6mGVfF0ivHTc3GUT/a0Ts15LBWsuVe8wBQF/g/VI2qsoMnYNsJLoAWWP/hi4+77r5ba7hmnIJMA/we7KZ7ItdHWedqYvJlgWA2cjSljdExP5l33pkk8hnyfIKPwLWB+aIiKdaMXZrrlJq8D7ggog4rLZ9PrL/xlpkTeuVgeOA98n3pmHkKsurmz5oaylJp5ONiZepJwmViZfbgd2Bo6uEtIgYURJGPgb2i4iDWjJws2+B655ZOxtNvnSuCExYbazVcdudbAi4G7CHpAm7+hJrTyWA3p8s0fI+WZNtRmBZsh76ycAykBnr5WOfRcT95MTLryXN1vSBW1PV/u2/pF53OCJ2I0txzA8cL2kKB9CtvDxUGef3kMtbbwGuKtk6SJqGfCkdRl5bXNu4c81B9lTYmpzQBfI8ioh/ALuQ5cQulvTLxg87gN7+agH035P3mzWBSyLiRUkDJE1YAhYOoHe4WgD9QrLp8OVkGag3gB0lXVaOO5dcrTsx+U60OFkaygH0zjGE7Ad1SsP2YcBj5GqFu8jyP+cC85Lv1/3JFVHWYSJiU+CXETFc0jK1OMqz5Hv0+sCCJWmtCqDPDrwDuHSL9WgOoltbqQIP5QViGDlDeh+wmKStJPUrwdMqkL4H2fhtbuDdVo3bmqchKDov+fB3RETcGxGvluVlqwOPAodKGlIFQ2sBineAt4CBTRy6NYmk3pK+D1D920v6VVfHdhFIPxv4FXnuuLFfB5A0t6TNyhL4L/maQPpGwGlkXeslqvPJAbDO0MU14iFgefIF9FeNE7W1QPrzZIkx60ClJv7MwHURcX8JYswGXA3cATykbPDnCbkOV55dFgI2B46KiLPJFS3jAm+VVbtExCXACmQAfeFyrbEOUFatvBURx0TEB5KOk3QpQGSvhUPIa8vVwEYRsUNEvA28CLwKfNCywVtLlHsQEfGJpO2AvwKbSJqonBvrke/XR5KrGAB+AmxJNjW+v/mjNvv2OIhubaEWxIry3xHlv8PIpfNPkMvo15PUvyGQvg3ws3qgw9pLCXL9CTIoWgukT17+vFuOq86jt4CDycaiazV8Vx8yW30gWavW2s/PgQslVSsRrgfOlPS9rg5uCKTvSZ47+zkrtP2Vf/dNgD8C65fVLV/SRSB9L3KJ9BnkNWiOkqnTx+dN5yjXjoGS9pI0oNSjvR3YBpgAOE7Sjxo+8wiwBrB080dsrdA42VKWzk8IzCNpAUkHkkGJIANdkOWhxjwXW8eaCRgF3B0Rn5brybXAJcBvy7afAUTE0xHxz4gY2sLxWpPVrxHKuvnjA4tKOq3svwbYLSL+EBF/LcdNRZaRGkSWebE2V0tU7FMv3xIRx5H3nX2BjUsg/QEyIWBq8v3pY+DKsm2FiPD7s/VoY12mbtZT6PPmkIPIDK0ZyWYn5wAPlyWu85EvGIcCIem8krmjskw6XD+yPZWXz18A65YMz3VqZTb+DYwAfgrcVcsCHQU8Rc6W92v4yonJF9WFIuLV5vwW1mSvAZ8AV0h6hKyVv3xEvDG2D9TPnYjYt1kDtdYq/+77kM20TgB6SzozumgMWr/PRMS9kg4HHgT2qSb3XAKoM+iLtct3Ln8mk/S7iPhM0s3AhuTKlj9K2jwinq4+HxH/Lt/TKyJGN3f01my1shyLAfdFxCdk7fxzyADWU8CeEXFkCXT0An5RkkbcpLhDqOueCL2BARHxYSkddjdwA7BZySJdE1hE0nN+prWSib4zmVy0bnlm2bQh0L42WcplEbJBpAOiba7cV9aWNDoiLizbbiYn5/aKiF9LupK8LyHpjIi4U9K85OqW6YDngNuj1rvDrKdyEN16tPICOVLZHPJ+Mug5jAx8XgZcIOnIiPhHCaTfCxwEDJJ0SpWxDs7WaVclyHUm+VJ5QHnJWLPsfhH4M3BgeYG4vPYC8gNgKBlQrX/fUEm71Wfhrb1ExFOStiVLK8wD7BJZC//rPucM4g4UEW9I2osMVhwL8D8G0m8CbirHO4DeIapAl6TBwBbArMCnZB30kLRzCaTfRAbSzwJOlrRdRPyz/l0OoHeOUvrpDGAbSadGxG2S5iSXzL8bEc+XQ8cDpieTBKyD1CZbdgfOKUHx54GJJe1NloC6Edg0Ij4qq+tWJHtIfdiiYVuTfdXka3k+eV3SQYCAdcojy2Zl/+Lkveo9YNGI+FfTBm6tNJC81+xXYi7LAD8Gdq+eX2uB9Kq3z1kR8QpZQ9+srchxQ+vpSnmNy4DvkU0sXi4voPsCO5V9e0XES8qatc8DD5PNMPx/gA4haSKyFtu+wKVVIL08EO4DLAjsQU60TEh2FQ+yNqSDox2g/mIhaRFy5QpkkGuNsqTVrEuSJiFL+axPBiu6DKSblZVzDwCvkBMpz5LZ6DMCF5FlFj5T1h1dnKw3elJEbNuiIVuLSZqUDE6sA/yOvL581nDMLMAOZGD0Zw5wtbf6Ctrq+UXSJsABETF57biLyF4/9wArlyDpNMCewHLAYvWVLta+ynvw9cBBEXH9WI5RmfCflDxH1gEurwXSZwZejQj3Eusgyl5Re5CT/+8Di0fWzK960Y0of7+SDLLvAZwVEe+0aMhm3xlnoluPomzy2JgtMTk5G3patcQZICL+UJYf7UVmG79UXkqnBka5hEtniYi3JVVd5/eVdElErB4RN0sKYCOybn4/MrDxDLBMQ4kXa1P1f2NJ05csv2XIFQkHAxdLWisirqp9xtnDHaqre0dEvClpz/LjV2akW8fbERgC/BZ4ojyPXA+cTPbhGFEy0oeVJdM/JYPu1gG6euYogc/dyo/HkKsWzqoC6ZJ2IGvlTwT8wgH09lYm4g6XdGFE3FHLLO4HfKDs+1TVLt6VTApZBTheWeZwInLSbjkH0DvK9GRZynMlrR4RtzYeUHs/rjLSAVavvTc90cwBW/cQEa9IGkWu7J6A7B/1aNk3ogqkl4z0y4HDgeGSTnCsxdqNG4tajyFpLuDBkj1RN4CsWdy3dmzV5O/3wL/IzMBqpnR4LTDqi3qbUkMjLshAOnA68AdgZUl/LttvAbYF5iUzuFYFlgw3+usIDQH0E4C/SPpVRLwXEY+R2RS3kc1GlyvH9SdLLGzdsoFbS1T3Dkm9JY0r6fvlfCAiXiczt/5EBtI31liajVr7kzSXpCW72PVD4KPIJn4hqV9JENiKLMOxLXCUpHFKEOy+qDUwtvZWux/9RJ83Qq+anu/O59eXDSQNKNmlb5ENI5epsgOtrc1Hrq7cXdL8te0TAG+XoPoogMgaxOuQjSA/I5tBXg/8PLJRsXWIUhJsQ+BJ4HJln4WvOn4osB9wDTCfpMm/6nhrT0q9gb8AS5Lv0sdK2q46pnpnLn9fGbgY+LtjLdaOnIluPcko4OyIeL4hC/AN4HVgKUknlcBX9QIyiGwc+RHkBb76MgdG25c+bzY7gFyqOhHwNPB4yaw4vRxaz0j/AHi8/Km+p5czjdtbuZZU14tLgdmBw8jzBYCIeLRkAB4GXFrOnymAlcmJF+sQ+mI963PJYOj05HlxXkTcVK4xVUb60cBoSec0ll6w9lVWwQ0A/k5eN25sOORZ4NeSZimB9OFVIF3ScWSAdClgN0n7V9coP7e0t4byHIcAm5MZoLfUzoE3lfWtJweOBIaTDUbPB3r7maX9lfPkFkkrA5cDfSTtExF3k8+7I+CL14sSVD+k9nkHtjpEeV7ZmizJ8mxEPCBpFzJT+DJJq5Rkour4+nVoWmB8csJmopIoYB2gnmBUzodRZCNrJL1A1sw/RhIRcVz5WB9Jq0XEhRGxVivGbdYMzkS3HiMiHo2Ig0vGzZWSFikX+PfITNEFycaRE9Q+NjW5tPFZGPNia20uvths9o/AKWSG1m2SflQeAquM9JUkXTCW73HTtjZXe1HYB5gTWJdsyPWMpIGSJi3BrceAbch6xSsDUwGzR8RDLRq6NVmZVKsC6PeQwYpDydIcq5BL638FX8pIP5msD2kdItInwCwRcYikcSQtUDvkcWBcYBNJU5TPDC/PKFOTWaLPkKVdJmry8K0FyuR/PbB5DpldfiyweH0VQkS8Rmb5DSSbjW5czjkH0DtDle35F+CXwBLAPpJ+TDYIHSZpsKS+1UqFkkk6VXmecQC9s2xETqBsI+mHABFxP9mH43EykL4YfCmAPh05UXc1MMgB9M7RsEJ3ZUk7Stpc0jwAEfEc+fx7OnC0pJ0kzU3er86XNGXLBm/WBG4saj2OpJmAW4APgI0i4h5J4wLbAXuTjSFvBEaSjXQEzOuXi/ZXyxIVcAHZbHY/8iFxTTKra0pgoYh4WtL3gE3IZl0HlPI/1oFKFvoHEbFx+Xk28gFxKnKly4ERUWVgTF2OdVOlDqNs9HguuWR+nZIVejawAvAu8CmwU0RcV46fgiwndoTvQZ2lCkaU+9HtwDRksPOGsn9f8pnlOOCMiHhc0hzAicDZwN3kvWuFcFPjtqSsW70G8GZE/L1sux64IiJOkTQ92VR2FPmMe1sp74Ok9cnVCi8DfwrXQO8I+mID9HUi4nxJvwSuIle+TAD8hOyhMAE50TK6/HkfWKqsvLQOIulAshTU8cAxEfF82T4fmZE+K7Bq7Tl3urJ9KWBhl/3pHA3XmHOBRchrxyTAq8AJEXFm2T8D2cx6K+A1MkF3+Yh4uBVjN2sWB9Gt21NDg6WSjTML+ZI5LrBuCaQPIet0HUjWSB9KvoCuUzKT3RyyA5QSLr8is4UvAK4ugYzewMJkwAJg0Yh4V9IkwLLABQ5ydSZlzeq7yAfAU4C5gd2A+8hA1ubk5NyvvTqhs0n6CZlpc1BZTn8ReV1ZCFgAuJDsw7FrY+BTbkTbMRrLJZTsrRuAF4A9I+JvZfs+ZGDjE+AdYDDwX/JcWoy8hy3tFS/tSdJ45Iq4HYDlyUn9RYFlI+KBcswM5Eq6UcAuZJLIQDJD9F1gF19XOkNDlvAVZGPQdSLikVLa5VKyvM/fydVSo4BxyPIuw4C/RsSTLRm8NZWynOkCEXFTbdvBZJPZrwqkr0yu3j4OB9A7mrJ05eLAphFxs6RjybJALwGHRsSp5bhxyWfg6YBrq/PKrJ05iG49gqSBwMHA8RHxbAmIzkoG0odQAunl2PGACcnmOUNLANXBiw5QsrrOIYPoo8hA+eNl+erwct5sTZ5LS0fEXQ2f93nS5uoZFg3bFyUDFcOAV8j+C0eWfQeTE3SLRsRHTRyutVhXk6+SlgZuJTPM9yaDGHeUfZeRpcVGA2tGxJ3NHbG1Wm1FVF8yE/T9iBhWVrfcCTwH7F5brbAsMDMZEHuSfM4ZVSZoZgZ+ERFvtOSXse+cpBnJUgvLkyssF4+Ix8u+ajXDtGRjvynIxrOfkckkP3UGemdoyA6dCjiV7LdwR/XcWstI/zOwf2QTSeswZfXTsWSD6lUj4vLavq8LpM8C/AeYCTee7ViSfk1O8O9eEkb2APYtf9YEJgb2qDLSzTqNa6Jbt6XS4bnYCNgYOEzSVCWo8TjZYfxD4DxJC5aX1/cj4vmIeK28fLg5ZIcoLxjnkYGICYAVy/aqadso8uViIDBDF5/3edLGyvWhegmdpJSGqgIVt5IBq8WAlWsB9InJl4onyQwv6xBlUm1UqSm7QgmCEhHXR8Qwsqns0+RqBZT9OiYEHiazju9p0dCtRWrnzGDgMvKFc9py7XmMXLUwHXBwCZ4TEX+LiCMiYvOIOAZYWNJZZB39dRxAb28R8Qz5HNuXTAqZobYvyv3pP8Ac5PPNy8CLOIDeUWrPLoeRdYh/ADxZVtqqnCfXAKuVP0eU5ADK59wTqkOU1QoXkbXML5W0Sm3f7mSpwt8AO0iapmyvaqQ/ST7z/swB9M7RxfXhNeCfwO2SNiBXQW0YEQcAO5HlUneR9NvmjtSse+jz9YeYNV954ayaQ/6BrGM9jAyKStIOEfGipCqQfhaZlb45cFv9u1x+oX01ZOYo0vWSPiazMHaQ9EpEnBmfN22bDXiPXDpvHUJfbJJzNLlEcUZJ/yEbEl8fES80fGYmMmNnPjIL3UH0DtFwD6p6bJwq6YlaZno/YNbaz1OSK2D2ql4+XUasc1QT9vq8qfWrwEPA8yWwroh4TNLPgDuAAyWNilIjvXzHRGRwY3oyiPF4C34Va5LyTNIPuLz82ZhsyrZuRFxWy0TvU+4/25bP9Y2IEa0bubWCpMnIZ5cpgdfi80aPvYDR5Xy5TNJKwBXAx5LujYjP6uWlrD2VVds/ioiHI+JuSfsBvYE/S1otIi6DDKSXmOmu5XPHlOSz+yXtBPw3Il5p1e9hzdXwfjReSUa8X9LT5dllfUp51PKRe8iyc4OAtSWdFRHvtWb0Zq3hci7WbZWHgQfIF9GLyIy/bcj61bcB25VAelUj/W/kssY1WjRka6Kq9IqynvV0QL961oSkRYAjyr5jyOZcUwM7ki+tCzi41RmqQET5+8XAPMBRwPXAzWQ94lPI5n4fluP2J+tBTkguh320FWO31in3oHuBN8jJ3AeqybgS2PolmRH4HnlPWphsLDp/LWjqh6wOomw8eyV5j9mYDEaMkjQ+8DEwICI+UDYQvaVsW7lkAVbfMQnQOyKGNv83sO/aV02sSZqV7OuzJFmm8LKyfRyytELVlNbXlg5Tu+/MCBxNvgsdHhFVILQXEDBmBcPy5ASea6B3gPIu/Bw5GTemT0Ip0/J7YDlgTCC97KtKuxwDnBgRzzV94NZSDQH0g8gVUddExG1l2xRkRvoREXFQmfidA9ifbIL+RES81JrRm7WOM9GtO9sImIisO/tweWG4s1zktwGOkbRdRLws6Z9k9+j/tG641izlZaLK+LsZ+CEwkaQLyBqRj0fEbSWj4mhgHzJ4fjU5GbN1CWw4S7QD1ALouwM/IUsk3Ctpe2AysonSAUCUjIoPgAfJe+RpZSm9dYhagGoz8hzYlXIPaghe3UDWjFyfXK3wKLC+ry0d7QdkFvmeEfEigKQVyGeWCYB/Sto3shHgEmR/jjFNQ8v59WYLxm1N0BCwWIqc2H8deCYinors4bI3GQw9t0zKPEI2Ht1M0mQR8YYD6O1PDf1bqn/ziHimPLv0BtaT9F5EHBwRo6tAermOXNuioVsLlOeOtfi8xM/0EfFsySjerxzWVUb6KGAPYLikvcJlLTtGuU5U96M/A3MBZ5B9NypvkCULt5B0FbnqZUtgUuC2iPikuaM26x6ciW7dlqQDgS0jYqLyc//IOrQoG26tTjbQ2b5kpFdZGg5edICSdXE5mfF3MTnhsifwBBnYuqucDwsDRwLjA0fG593Ex5xP1v4kDSDrE38SEfuUl9BDyevIX4H7gMnLtrMj4n250WxHU9alnjsiZmvY/oUs0BK4mAh4q1Z6wedNB5L0E/J68kfgMTJbdGtyNd1gYH6yieTx9XPEzy3tT18sP3cOsGjZNZCsQ3xkRFxV9s8O7AWsQtY/HwgsFxEPNX6vtZ+GyZYlyBVxfSPi/NoxM5FlC2cBToqIg8r2LpunW3trWHF5BhkQ3SYiqp4t85Ir6pYD1oiIP9c+uzdwmVctdKYyybIJ+T70YOO7saRVyXNnZmAomVyypFfoWidzEN26neoBUNJ6wMnAKhFxfdnXPyKGlYfKPwNvkZnIO5fsUWtjDS8WIidRDomIu8q2n5F1IP9NNsipAumLkxl/EwH7RsS5LfkFrKXKMugP+X/s3XmUZVV5N+Df7m4mQaFBnBsRUImRiANOOBsnnDAxaEw0RL84xQFpRRNHUOOsxDmfU8eoMYiKomCUCE6fcYqICKKgQQZFaQUU6Aa69/fHPgW3it5AU9V1q+s+z1q1uureU4ddi7POPed33v3udkN6TJI3pbVwubSU8qK0AP2KJM9P8l7VfpNpCMVLWg/I2yXZN8na0ZBzeCjzpCTfHL3x1GZhcvTCqlLKh9PacdS0G85X1lqPHt47PcmRtdaXzOtgWTBKKR9MC9CfVdsaLh9KO5f8NMnLa62fHrbbOe0hzC2THKHVwmTYwMOWuye5UZJt0ipCX5jkp0Pl8VSQfvskH621vmxMw2aMZj6EHULPI9LWczm0E6RPa+3CZCqlbJt2L31akufOOI5G77nvkHYtvCTJ1daQgkmzZNwDgKGi+EojN6U/THJ52hSiPxreWzuEp7ulhWDHJXl8klvM34gZh6G6c10pZashLL9Xkq3SKrim3v9aksckuW1aIHrvIdT6clqo/uu0NkB/OZ6/gjH7aa31l0nunDaD4Qu11ktH3v9QkqOSnCAInRxDaD71fam1rh9uHI5Ncqckj51q0TK1TVpf/b9NayV1JcfNZBg+b9YPn0f3L6U8YvhcSq31KWmh6OOSPK7WenQpZYvhJvQPaZXFTKBSyhPSqvn+dgjQX5Lkr9OC0O2SvKGU8ugkqbX+ptb64bRCAQH6hBgJ0FcleWDaQsO3TysQ2S9tlsueQ9h+WpLnpT2s27+0hYmZMCNB57NKKTvWWo9M8si0h7mHllLuPWz3nbQZmZ9Na+3y2HGNmQXjBmltLi8arnPL1BujAXqt9ZRa6/tqrf8iQAchOmM2EoxuU0rZr5TyjFLKw0spO9S2SOQzkuyf5J+Gm9SSNh36wCQ/SWvbsTytHzqL1BBsTfVA/++0Ni5fT/KnaYs/Znh/yVBx8Zi0BUVXJdlreP+rSV6e5PtpC9ayiJRSti2lPKmUsmNvm5GAc31aVdfew+/unGSfJD+utR5Qaz11kw+YBWEkDN2ilHKTJH9SSlk2BOZHJvlCko+UUp6UZJvh9XukzWy5PG1xWibIUJ019Xn01SQfTPLJJP9VSvlQKeWPaq3H11q/NbSa2zptav3/TbJm+JcJUNrC56P+kKvWbHlqWi/ip9RaD0lyWFo//cOG800SD+Ym0VDosXeSJ9daj0vrQfxXaYvO3jbJu5PcYTgX/STJk9Pa/awe15gZr1LKPyY5uNb62ySptR6b5AzL0loAACAASURBVFFJHpyrB+lvSKtU/2lnd0yOy5JcmKvuleuMwpKHJXlaKeVWYxofLEjauTA2I21bbpgWiN4gbVGui5KsTvLE2hbf+rMk70uydVqrhSuS/DzJvdMuMj+ftlDgF8fwZ7CJTU0nK1f1QL9hWpufFWk90H+d5AW11i8M208dVw9Iqz5/zMw2DDOqj1kESltw+CVprVg+Umv93TVse9u0dlDbpD2M2zKtsvg+AvTJMXJuuWFaz+o7pp1XfpjWyuXwtIX/3ppWAXhy2mJuWya5IMm9a62Xz5xKzeI3tPP5elp7qEOTXJJ27ByRdiwdVGv9dSlleZJ3pc1YWJfkQY6ZxW24VrnjaL/YUspLaq2vH77fKS20+GqSbyZ5Va3196WUm6Y95C9JzkjyiFrr7+f9D2DelFK2S2vP8vFa64+H15alfd7codb6+lLKM5K8LW3m01Fp1zivT3J0WmuOk/RAnxxD+42VSV5Xa7185PWXJbl/rfUhpS1IfPkQiO6X5HNJ/ivJK2qt3xy237rWumYMfwJjcE3XHKWUv0/yjiSH1FrfPPL6jdOuf3dNsv/UAxqgLQwAYzEEnVunVfL9Jq1K+Cdp0+ePSvLJUsq+tdZPlVJOS3tKeoe0J+cfG8KP5yf5XVq4wSIyNaVsaqZCWsue89IW3/rqsM3ZSV6VNgU6tdYvDMfV0lrrCUlOGLa78uJBgL5ovSrJrZK8McmSUsqHNxSkDw9ZflpKeXrajcgeacfV/QXok2U4t9wgbXbLb9JaKpyTNn3+xWmzWZ6b5C/SFvh7WJKL00L29w6/bxHRCTLMiqpprVq2SfL0JP8zhBW7DJt9u9b66ySptf6ulHJ+ktPTetM6Zha/uyR5bynl87XWV5RSPp3k0aWUI2utp9daV5dSdku7pv3gEKCXtArjE9Nm0H1HgL64lVK2SPK9tP/vu5ZSDqu1/myY5fKtJP89PIR7WloF+qdrrZeVUo5OcnCSR6e1AHpEWjUpk+Hxade7tyilPGfks2R52sP9JLli+EwqtdZjSimPSmsH9M/D73xbgD45yvTe5n+T1hJ3p7S2Pt9N8pG0GZZvLG2dhWOSbJ82q/uBSe4rQIfphOjMi5Ebz5nukeTmSf4uyfeGC8Sbp/W6fl+t9VdD6PWjJD8a2d+jSustuV+SB9Raz52HP4N5MEw53KLW+pXh5yVJ/i3JnyU5K+2peJKk1npEKWV92jTo1w+H2X/OfNqu4m/xG84dT0urFH5D0hb4mxmkT1Vs1Vq/PUyZr0m2qbVePN9jZkF4TtrnzXNrrT9MklLKp9Jmuzw+7cHu22qtHymlfGy04m+qrcc4Bs14jFzH7JFk2yRnDGHFE9NmL/xjrfXwodr4vrXWo2qtz5v6fcfMRPh12oO5l5W2wN82aTOdRvuan5e2nsuzSimfSVvs+snDtp+pta6d3yEzBjdIe7i2fZK/TLJTKeV5tdaf11rPS5JSyu5p55oLaq1TQfluSf4nrRr97JHXmQxfSJtl+09JlpZS/n44Bm6dNoslSWq5ag2XOgTpT0zygbT++UyIIX+ZCtA/kTaL//K0z5qnphUtrkxyUNpn1MFpn0Wrk/xvkvvVWhUqwgx6ojNfyrQf2nTFJNkzyc2SfGMIwf4qLTB95TCNcfsk/zBUC0797lZpFYG3TbtJ/UFYFEop90mbIv+wqUr0tJDz/WmrzN8sbVGuKxcDrG0BnVeknc/+tZRyj/keNwvDMLX1wLT+xG9I8pShkisjx1NKKbctpXwhyXNqW0RSgD659kyyZiRA33I4jp6d5MdpC0RusO+dh3OLWyllx1LKnUop/1BKeWYp5XEjb69Jsn2t9YKhym8qQH/9cK75iyQHl1JuPbK/4phZnEopS0spK5Kk1npmklenPfTfM8l/1VpPHB62LB22uTgt0No5LVD/Ytpsl4ME6JOh1nphkm+ktYJ6QZK7J3lXKeU2I5ttkbaGy96llD2HY2z/tNZQ36u1/myeh80YzDi/nJc2W+VlSf4mrT9+0lqLnT9sU2ut64av9aWUG9daP51kRa31F/P/FzAuUw/+SylvTLJvkiemFTDeMm19lgcneWeSJbXWQ9PaGj5keP1RU9fGwHQq0dnkSil/nORvh95av0ryspFKrDPSws87lrZoxb8leenIjejD0k76n02bQp9a69pSysFJblQtorNolFLum9az761JXj0yFbGWUr6U9uR8pySHl1JOr7X+YJilsL7WeuTQ8uVhaVPTmFC19Rs+cPhxqiL936amIg6VXf+UtijtP45lkIzdyEOVS5PsUErZvdZ6xvAwd8vh33cn+XCSPUspp1Z9ZydGKeXOaf2G75RWJbxNkmWllOOSHJLka0l+V0r5blr7joNrrYcPv36HtBvVU5JcGVh0ZuOxmSutR/EbktyslPKJWut/pLUW+27aIuZPLaWcW2t9xdDOZ4vhQd0H0mZYPiDJ2rR2HWds+L/CYjJ17ZpWTf7UJH+S5P+kfd68faQi/cellJemzYx6dFo7se2TPNDD/8mwofPL0BLqg8MmryutB/qNk+xTSrlFWmuXZWkPW7ZOcn4p5bFpD2yYMENB4j3SWvp8Y+Ra9qBSyqVp6yzsn+T9tdaz0h4AA9fAwqJsUkNl8VFp01uXJ5laOOnBQwXXXsP7Fye5fVqA/uYh4Lhd2k3GGUkOHHma2msNw2ZqJEB/V1o136XlqgVCtxl+Xprkvml9i3dIWzD0ByM3I6P7s2jbhCut3+iqtOq+Q9JuTrdL8vYkD02yr1ksDO0Wjkir6npHHelDXEp5cVqrsXt4YDs5Sin7ps1mOS5t6vznktwm7Sbz6WnXMy9Pq9Z6UlpQfp+0itG7JnnLsKt9a+tv7JplkSptUeKvpoXgX0nymqlzyBBs3SKt9cKzkry21vry4b1lSdZPXbs4Rha/mdeqpZSthqKgFyV5ZNoxslfafc8JabMSzhi2fWzaw5aLk/xrrfWn8zx8xmAD55fX1lovGnl/x7Se+S9Pu759f9rC51ekFQgsTStUe5eK4sk1HCc/SHJ0rfXZw2tTD3NTSvlOkt/VWh86xmHCZkWIziYzBOhfTqugeHvajeffpU1z/Uyt9UnDdk9JC7t+nLaI21fSQq6Xp10M3GO4Eb1aWMrmr5Ryr7QbhrentfG5ZCRA3zutVctza63nDC1c7pcWpG+fFqSf5AaUDZkRpL8hrWL0gUnuU2s9cYxDYx6UUv4oyUW11nOuZbt3JHlm2mJdH6+1njGce/4lydlJHu/8MhlGrlveleSNtdZfjry3bVo/0fenVWqtTDu3/FVaUHFR2gJ/q5M8ZJgV44HuIjW0GTwhyYVp7Th+PFyrXhlODNvtMbz/rLRZdq8spWyXdn4ptdYnuYZZ3IYw9EtJjk1bNPaYkff2TnJ8WgHJe0pb7+l9afdCz9eyZTJdw/ll2mdKKeVmaT31X5224PkLxzFeFq5Syo3SigKWJvmLqXPKyL32Z5PsUGu93zjHCZsTITqbxDAV+ntprTlenmTtcKLeKsmH0vqZ37NetdjF05K8NMmN0k7yv0pb0OIxbkQXr1LKDkk+n+ReSW5Xaz196v91KeUuSb6ZdjNxcB0WTxqC9PsmeVtaf/Q/rrWePp6/gIVuCNLfl+QpaVVc91GBvvgN01enWjs94JqC9KFK51Vpi4yemxaGbp3kgrSHuJd7iLv4zbxuqbVeOrx+5f/7YUbUQ5N8PK39xoFDCPbQtJZjpyc5ZvgMW1YtIrpolVIOSet9/9QkJ88MwUspO9dafzN8f5u0ivRnpoWplye5f5I/rbV+a14HzrwazhkfSvLXabNWLktycpLDk/yg1nphKeUVw/sPr7X+rJRyQNp1y5eTHKLyfPJch/PLjWut5w/f7zxs95q0Y+25dVhbwQO6xW94wP/XtdZ/uYZtHpfkyLQitLdMXROX1mr3U2mFjM/M0FJ/048aNm96ojPnhgvGRw8/XjJyIzo1dfFbaf0i71dKWVNr/Wat9QOllC+mheu3SHJa2qI5692ILmpr0irQb57k46WUP6+1njkE6F9NqyI+ZCpAT5LhmPhakn9Im0r/8/kfNpuLIQB9Rlp16CpTWifGpWkPZl+f5DOllP1rrWdvaMPa+uU/r5RyfNpMl22SnJo2BfoKn0GLX++6JWmfOSPfrxs+fz6c5FmllHfWWr+b5MSZ+3PMLHp3TXLBzM+UUsr/SVufZc9Syi+SvKjWekop5Q1pIerj0x7U3avWevJ8D5p5tyytLdSeaW2hXp02K/ftSS4Z2oadmrYo5J2S/CzJZ9LaQx2RZE0p5cnOJxNnY88vH0hr4/KmtOuf5yfW4pgQz0jy5lLKTWqtr+5sc1TazO7DktyulDLVSnf/tDZST1coAtedSnQ2iaHC+B/SKm9eldbHbarq/CtplcRTTku7YPxUkp/UWi8Y2Y/qv0Vu6Bu6X5J3pN1gvibtxuFjSV5Qa71kxvbb1lovHp2dYKYCMNOMc8t5Sa4WpE9VafWqtZxbJsfM65Za62HXsO2D0qZHP7zW+sV5GiILxNDT/D/SCgAeneS3aUUg70lrGXZB2mzKPdNmVj6o1vq/w8OaZUm2Gu1tzOJWStk6ySPSqs/PSOtjfce06vMHJ/nPtJYcX6y1Pnz4nWVJHpXk1FrraeMYN+OxkeeXX6atM/a/pZSbpC1q/aVa66ljGDpjMMy8fGmSFyY5tNZ6aGe7rZMckNbecoe0VkFnJ3lqrfWkeRouLApCdDaZoQfXK5IcnLZg6OtKKZ9Le+L5trSpQ/umrRj9gLQbi8NrrQePZ8SMy9Dm5xFpx8Wt0wL0A2dW3pRSdk2bRn9orfXYeR4msJm5piB9NEBPayn1krQ2Htr9TKgZ1y1Xuxkd6SG6T5JvJXmkz6LJVEq5e5L/TutnfUHa4rLLknwkrdrv90kenuTfk3ys1vp3HspNrpHr3PekBelPrLWeXUr5syR3S1tb4edJ/nJ0LQYm0/U5vwy/p/hsAl3btcuMbfdOW8fl/CR/GGZjAhtBiM4mNZzUX5m2KMpZSdYleVySHw43olMhxt2S7JHkSFMWJ9Nwg/HItEr0NWn98M8eeX+XJB9NskuSPerIwl0APSPhxYaC9CVpD3Lfn6Qm2dtn0GS7DkH60uH9hyV51FRfWiZPKeW+ST6QVtX35STvTvLdqRl0w+KApyT5Wq31yWMbKAvCyEPddyb5Tdr545zhnLJzku2s8cMU5xc2Ru/aZXSmZSnltklem+ScWusLxjZY2Mzpic4mVWu9qJRyaJK1aUH6h6eq/IbqvyVJ1g09Rb87vK7/7AQa+uUfk9YH8h1JjprqY1xKuXVaBfr2GQJ0xwlwXYycW5Krzi2PqrX+KlcF6OuS3HXoga5adIIN1y1TrVxeWUrJjCD9j5M8JMk3BOiTrdb6taGq74a11vNG3xuucfdI639+5XWvHsWTq9Z6WSnl2LRFrN+R5LOllMcOD3V/Nd7RsdA4v7AxetcuIwH67mmtXB6aNrMBuJ6E6Gxyw0n9jWnH2wtLKefUWg8bTupXCyoEo5Or1rpmRtj1qVLKc5O8OcmNktxJgA5srCG8mDq3vDPJ0aWU16Qt8jYVoDu3kGSDN6O11nrYMCPqPcPrL04EF5NuqAqdqgzdsl61EPoOSQ5Ksl2STwzbOk4m3PBQ99gkz81VD3UfW2s9Z8xDYwFyfmFjbODaZX2t9dVDgP6mtAKA+9RaT+zvBbg22rkwb0amGb0gbdGu3grSTLih/cLDk7w1yW2S/CjJXYRcwGwM0+kfkfZgbvckJyXZx7mFDZlx3fLPSe6eZHla25/LzVpgQ0opD07ylCSPSfIA6yww00hrl7elPci9X6313PGOis2B8wvXZkZrl7cnuWXata8AHeaASnTmzcjT0fVJDh0q0j847nGx8AyVOl9Ism2SByV55tBmQcgFXG9DRbpzC9fJyHXLuiQvSlsQ3YwoNmgoADg+rUr0giT3rbWePN5RsRCNzI7aMi3s2mrMQ2KBc37huhq5drkiySFp1zB388AF5oZKdOZdKWX7JE9O8l43oFyT0amLAgtgrji3sDFKKcuT/HmSVR66cE1KKfdMcpckR6ks5toMFelb1lr/MO6xsPA5v7AxSik7JHl2kk/VWn887vHAYiFEZ6zciAIAmwvXLVwbffKBTcX5hY1RSllSa10/7nHAYiJEBwAAAACAjiXjHgAAAAAAACxUExmil1IeX0p5Rynla6WUi0optZTykXGPCwAAAACAhWXZuAcwJi9Lcqckf0hydpI9xzscAAAAAAAWoomsRE/ygiS3S3KjJM8a81gAAAAAAFigJrISvdZ6/NT3pZRxDgUAAAAAgAVsUivRAQAAAADgWgnRAQAAAACgYyLbucyFt7zlAXXcY2DzcMABhydJjjjioDGPhM2B44WN4XjhunKssDEcL2wMxwsbw/HCdeVYYWOtXHnCYu3XPNb88dxzT8m///vfz3o/22yzfZ797KPmYESzMqtjRCU6AAAAAADTXHLJ7+ZkP5deeuGc7GecVKIDAAAAADDNHnvsm5Urj5/VPlatOjA77bTr3AxojFSiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAxkT3RSyn7J9l/+PFmw7/3KqWsGr4/v9b6wnkfGAAAAAAAC8pEhuhJ9k7yNzNe2234SpIzkwjRAQAAAAAm3ES2c6m1vqrWWq7ha9dxjxEAAAAAgPGbyBAdAAAAAACuCyE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAx7JxDwAAAAAAgIWr1pqkjnx/ba9Nf31zJ0QHAAAAAGCaH/7wmHzxi2+a9X5++9tfzMFoxks7FwAAAAAAprnVre6Urbbabtb72WmnXWc/mDFTiQ4AAAAAwDTLl98yz3nO0bPax6pVB2annW49RyMaH5XoAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAEDHsnEPAAAAAACAhWX9+nW54IJzU+v6K7/Wr1+fpP1ba02t64Z/N/z+ZZddOu4/Y04I0QEAAABYUFas2DsrV54w7mHARPvGNz6Yb3/7Y7Pez/bb32wORjNeQnQAAAAAFpSzzjoxRxxx0LiHwWbCA5dNY4cdbjkn+9l66+3nZD/jJEQHAAAAAGCavfbaL3vttd912rbWmqQObVymvmo+8pFnZMmSzX9ZTiE6AAAAAADXWyklScnSpdMD88UQoCfJ4vgrAAAAAABgExCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0LBv3AAAAAAAAWFguvfSiHHfcW7NmzR+uddtSSmqtV3t99eozs80222+K4c0rIToAAAAAANOcfPKx+clPvjLr/Zx99klzMJrxEqIDAAAAADDN3e52QG52s9tn3borZrwzVXFeOq/nyvePPvqVufWt77ZpBjiPhOgAAAAAAExTSsmKFXvPah83vOHOKWVm2L75sbAoAAAAAAB0CNEBAAAAAKBDiA4AAAAAAB16ogMAAAAAMM369evyk598JVdcsfY6bF1y9YVFk9Wrz8zy5SvmfGzzTYgOAAAAAMA03/nOf+TrX3/frPdz+ulfn4PRjJcQHQAAAACAae5858fl4ovPz2WXXTrjnamK89J5PVe+/6MffSG77HLXTTPAeSREBwAAAABgmi233CYPetDzZrWPX/3q1Gy99XZzNKLxsbAoAAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOoToAAAAAADQIUQHAAAAAIAOIToAAAAAAHQI0QEAAAAAoEOIDgAAAAAAHUJ0AAAAAADoEKIDAAAAAECHEB0AAAAAADqE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAA1BTp5QAAGgZJREFUAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdCwb9wAAAAAAAFhYLrvsknzlK+/J2rWXDK/UGVuUq71W61U/l1KyevWZ2XbbnTbpOOeDEB0AAAAAgGlOPPEzOemkz816P7/4xf/MwWjGS4gOAAAAAMA0++zzhCxffstcccXl17ptKWVaFfqUY455bfbY4z6bYnjzSogOAAAAAMA0v/3tL/LZz75y1vv5/e9/PQejGS8LiwIAAAAAMM1xxx0+J/s577zT5mQ/4yREBwAAAABgmoc//MXZbrsbZ+nSLbJ06RZZsmTZyNfSlHLdouWdd95tE49009POBQAAAACAabbf/uZ5xjM+Mat9rFp1YJYvXzFHIxoflegAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgw8KiAAAAAABMs2bNH3L88W/P2rUXX4etS5J6tVdXrz4zN7jB8jkf23wTogMAAAAAMM1JJx2dU0750qz3c9ZZJ87BaMZLiA4AAAAAwDT77POE7Lzz7lm37rLrsPWGK9E/97nDsttu95zzsc03IToAAAAAANOUsiS3uc3dZ7WPHXa4RUrZ/Jfl3Pz/AgAAAAAA2ESE6AAAAAAA0CFEBwAAAACADiE6AAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgQ4gOAAAAAAAdQnQAAAAAAOgQogMAAAAAQIcQHQAAAAAAOpaNewAAAAAAACwsF1/82xx99KuyZs1F12HrkqRe7dXVq8/MlltuO+djm29CdAAAAAAApjnttONzzjk/nPV+fvnLU+ZgNOMlRAcAAAAAYJq73OXPs+uu+2T9+nXXum0pJbVevRL9k598cW5xiztsiuHNKyE6AAAAAABXs+OOu8zq97fa6gZzNJLxsrAoAAAAAAB0CNEBAAAAAKBDiA4AAAAAAB1CdAAAAAAA6BCiAwAAAABAhxAdAAAAAAA6hOgAAAAAANAhRAcAAAAAgA4hOgAAAAAAdAjRAQAAAACgY9m4BwAAAAAAwMKydu3FOeGEd+eyyy6e9nqtNUlSStng61NKKVm9+sxsu+2Om3ag80CIDgAAAADAND/4wWdz8snHzHo/v/jF9+dgNOMlRAcAAAAAYJp99nlCdtxxl6xbd/m1bltKuVolepIcc8xrsvvu994Uw5tXQnQAAAAAAKYpZUn22GPfWe3jm99clVI2/2U5N/+/AAAAAAAANhEhOgAAAAAAdAjRAQAAAACgQ090AAAAAACmOfPM7+XII1846/1ccMG5czCa8VKJDgAAAADANKWUcQ9hwVCJDgAAAADANLvscpesXHn8rPaxatWB2WmnXedmQGMkRAcAAAAA4HqrtSapWb9+fWqd+mo/LwZCdAAAAK6XFSv2TpKsXHnCeAfCZuGss04c9xAA2AinnXZ8Pve5w2a9n4su+tUcjGa8hOgAAABcL1Oh6BFHHDTmkbA5OOCAw8c9BAA2wvLlKxbUfsZJiA4AAAAAwDQ3uckeef7z//PKVi211tS6bqRNy/qrtW+Z+f6nP/3S7LijEB0AAAAAgEXmy19+e77//U/Pej8XXnjuHIxmvJaMewAAAAAAACws226747iHsGCoRAcAAOB6sbAoG8PCogCbl5vc5HZztKcyR/sZHyE6AAAA14uFRdkYFhYF2LxsscVWc7SfredkP+OknQsAAAAAANNcccVlc7IfIToAAAAAAIvO0qVbLKj9jJN2LgAAAAAATLNixd5ZufL4We1j1aoDs9NOu87NgMZIJToAAAAAAHQI0QEAAAAAoEM7FwAAAAAAprnggnPy0Y8+O2vWXDSr/SxbZmFRAAAAAAAWmbPOOnHWAXqSnHfeaXMwmvFSiQ4AAAAAwDR77fXI3PGO+yWpqXXqa31qrSOvrZ/2fa1Jsv7Kn4844gXZeefdx/p3zAUhOgAAAAAAV1NKSVJSyvX7/SVLls7peMZFOxcAAAAAAOgQogMAAAAAQId2LgAAE2DFir2zcuUJ4x4Gm4Gzzjpx3EMAAGAB+PnPv51PferFs97P6tVnzsFoxkuIDgAwAc4668QcccRB4x4Gm4EDDjh83EMAAGAB2GKLredkP1tueYM52c84CdEBAAAAAJjmxjfeLXe4w0Oydu3F016vtf07c7HRqdenlJKcccb/y01vertNOMr5IUQHAAAAAGCak046Oqec8qVZ72cxtAsUogMAAAAAMM0++zwhO++8e9atu+w6bF2SXFWKPlWV/vnPH5bddrvnJhnffBKiAwAAAAAwzamnHpdjj33drPfz059+bQ5GM15Lxj0AAAAAAAAWlpve9PZzsp/ly1fMyX7GSYgOAAAAAMA0V1yxdk72U+u6OdnPOAnRAQAAAACY5sILfzkn+7nool/PyX7GSYgOAAAAAMA0cxWir19/xZzsZ5wsLAoAAAAAwDS77XbPnHba8Tn//J9f732sW3d5/n979x9sd1nfCfz93CQkGCCYC2hWIInRmgV00QUt2G0rO6N1xF0qbHB2aMk6szPrCjvYS9kftStTcKeuuKUttaXd2uu2ayWTdKemWHZ0SAwsjAioiCABjAlCKhggBENIcs+zf9xzIeeSk9x7z8n93ouv18ydk/M93+dz3ufe/9555jmnnHJmH1M1Q4kOAAAAAECHwcFlueSSG3uaMTy8OkcfvahPiZrjOBcAAAAAAOhCiQ4AAAAAAF04zgUAAIApGTvjdGhoY7NBmBUee+zbTUcAYBLuvXddNmy4oec5O3Zs7UOaZinRAQAAmJKxUnTNmisaTsJssGrV9U1HAGASnnrq0aYjzBhKdAAAAKbETnQmw050gNnlueeebDrCjKFEBwAAYErsRGcy7EQHmF22bbun6Qgzhi8WBQAAAACALpToAAAAAAB0WLz41KYjzBhKdAAAAAAAOpxzzqVNR5gxnIkOAAAAAECHlSvPy8qV5/U0Y3h4dQYHl/UnUIPsRAcAAAAAgC6U6AAAAAAA0IUSHQAAAAAAunAmOgAAAAAAHWqtefzx+zMysndSa8aUUrJr11NZvHjpkYg3rZToAAAAAAB0WLv2N7Nt2z09z3n44U19SNMsx7kAAAAAANChHwX6q4USHQAAAACADh/60Kf7MmfOnHl9mdMkJToAAAAAAB02bbqxL3NGRvb1ZU6TlOgAAAAAAHR4wxvOaDrCjKFEBwAAAACgwxlnvL8vc4499qS+zGmSEh0AAAAAgA7f+c6X+zJn164n+zKnSUp0AAAAAAA6PPvsE01HmDGU6AAAAAAAdHjf+67KwoWLMzAwZwI/cw96PUlOPPGNDX+S3s1tOgAAAAAAADPLokVLcv75V2dkZO9h7y2lpNb6iuvr138yxx9/8pGIN62U6AAAAAAAdNi06cbcffdNPc95+OFNfUjTLMe5AAAAAADQ4dFH72g6woyhRAcAAAAAoMN73/ubfZlz7LGv68ucJjnOBQAAgCk55ZQzkyRDQxubDcKs8Nhj3246AgCTcMcdn+/LnF27ftyXOU1SogMAADAlY6XomjVXNJyE2WDVquubjgDAJJx77kdy003/oec5xx33+j6kaZYSHQAAAACADief/NYMDW3oacbw8OoMDi7rT6AGORMdAAAAAAC6UKIDAAAAAEAXjnMBAAAAAKDDk08+kr/8y3/b85x9+/b0IU2z7EQHAAAAAKDD009v68uc5577cV/mNMlOdAAAAAAAOqxceV5Wrjyvpxmvli8WVaIDAAAAANDhxRd/mo0bP5e9e3865Rk7dmzNwoWL+5iqGUp0AAAAAAA6fOc7X87993+l5znbtn2rD2mapUQHAAAAAKDD2WdfnMWLT83IyL72lTrujvKKa7W+/LyUkptvvjYrVrz7iOacDkp0AAAAAAA6vPDCc9m06cbs3v10Xu7Ga7sor+1r9YBrSas18tLrY1qt/dOa+0hQogMAAAAA0OGv//pjefbZJ3qe84Mf3NmHNM0aaDoAAAAAAAAzy8UX/34GBub0PGf58nf1IU2z7EQHAAAAAKDDMceckI9//Gs9zRgeXp158xb0KVFz7EQHAAAAAIAulOgAAAAAANCFEh0AAAAAALpQogMAAAAAQBdKdAAAAAAA6GJu0wEAAAAAAJhZtm69J2vXXtnznOef39GHNM2yEx0AAAAAgCNi7tz5TUfomZ3oAAAAAAB0WLr0n2ZoaENPM4aHV2dwcFl/AjVIiQ4AAAAAQIft2x/MF7/473ues3fvnj6kaZbjXAAAAAAA6PD449/ty5xdu37clzlNUqIDAAAAANBhwYJjm44wYzjOBQAAAACADmec8f6cccb7U2tNra0DHl/+abXGro8c9PV1667KSSe9uemP0jMlOgAAAAAAB1VKSSlzprR2zpx5fU7TDCU6AAAAAAAdtm69J2vXXtnznGeeebwPaZrlTHQAAAAAADqUUvoyZ2Bg9lfQs/8TAAAAAADQV8ccc0IGBno/yOTEE1f0IU2zlOgAAAAAAHTYvv2BtFr7+zDnwT6kaZYz0QEAAAAA6HD66b+SJUtO7yjSa22l1WolaaXWmlarlVpf/mm1RlJrfen1W275dE4++W2NfYZ+UaIDAAAAANDh3nvXZcOGG3qes3nz1/uQplmOcwEAAAAAoMPcuQuajjBjKNEBAAAAAOiwefPGpiPMGI5zAQAAAACgwwUXfCp3331T9u3bM+UZd931xSxf/q4+pmqGEh0AAAAAgA5z5x6Vn//5X+tpxqOP/r/Mmzf7j4VxnAsAAAAAAHShRAcAAAAAgC4c5wIAAAAAQM9qrUlqWq1Wam21n89+SnQAAACm5JRTzkySDA1tbDYIs8Jjj3276QgATMLmzV/P+vVX9zzn6ae39R6mYUp0AAAApmSsFF2z5oqGkzAbrFp1fdMRAJiERYte35c5g4NL+zKnSUp0AAAAAAA6DA4uz/nnfzL79++Z8ozbb//zDA4u61+ohijRAQAAAADosGnTjfnWt/6m5zmbN3+9D2mapUQHAABgSpyJzmQ4Ex1gdulHgf5qoUQHAABgSpyJzmQ4Ex1gdnnLW87LQw/d2nSMGWGg6QAAAAAAAMwsO3c+0XSEGUOJDgAAAABAh3/4h+83HWHGUKIDAAAAAEAXSnQAAAAAADp86EO/25c5AwOz/2s5legAAAAAAHS47bY/7cucVmt/X+Y0afb/NwAAAAAAAH11ySV/moceujX79++dwN0lSU2taT+2kiRf+9rv5U1v+oUjmHJ6KNEBAAAAAOjQao1k584fZ+/e3am11f6pqXUkrVYrSU2rNdK+1ur4GXs9SUZG9jX6OfpBiQ4AAMCUnHLKmUmSoaGNzQZhVnjssW83HQGASfjCFz6SZ599vOc5W7Z8ow9pmqVEBwAAYErGStE1a65oOAmzwapV1zcdAYBJuOCCT2V4eHXPc173up/rPUzDlOgAAAAAAHQYHFyaoaENPc0YHl6dRYuW9ClRcwaaDgAAAAAAADOVEh0AAAAAALpQogMAAAAAQBdKdAAAAAAA6MIXiwIAAAAA0GHr1nuydu2VPc95/vmf9CFNs+xEBwAAAACgQymlL3PmzTu6L3OaZCc6AAAAAAAdTj31HRka2tDTjOHh1RkcXNafQA2yEx0AAAAAALpQogMAAAAAQBdKdAAAAAAA6EKJDgAAAAAAXSjRAQAAAACgi7lNBwAAAAAAYGbZvfvZ3HLL72bPnl0TuLskqa+4umPH1syff2zfs003JToAAAAAAB2+973/my1bvtHznCeeuL8PaZrlOBcAAAAAADq0WiNNR5gx7EQHAAAAAKDD29/+q9m9++ns3bu743qto8e2lFIOen1MKSX33//3Wbr0rCMbdBoo0QEAAAAA6HDUUUfnPe+5rKcZ27c/kPnzF/YpUXOU6AAAAAAAvMKBR7q8vNO8vvTvA6+NXu98vdVqTVfUI0qJDgAAAABAh/vuuzlf/ep1Pc955pnH+pCmWUp0AAAAAAA6nHrq2/Oa17w2L7ywc8ozam3lhBPe2MdUzVCiAwAAAADQ4fjj/1E++tG/6WnG8PDqLF58Sp8SNWeg6QAAAAAAADBTKdEBAAAAAKZZKWVOKeWaUsqWUsqe9uO1pRSnh8ww/iAAAAAAANPvPyb5WJJLk3w3yduSfCHJi0muaTDXS/bufSG1tpLU1Dp6xnmtNbWOtB9bHT+tVufr+/fva/oj9IUSHQAAAABg+p2bZH2tdX37+Q9LKV9O8q4GM73kvvvW56tf/R89z9m584k+pGmW41wAAAAAAKbf7UneU0pZmSSllNOSnJfkK42mStJqjeSJJx5sOsaMYSc6AAAAAMD0+3SSY5M8UEoZyWhX+6la6+eaDNVqjWTduquybdu3mowxoyjRAQAAAACm38VJfj3Jv07yvSRnJvn9UsqWWuufNxVqy5a7sn37g0lqUxFmHCU6AAAAAMD0+0yS62qtX2o//24pZWmS/5yksRL9yScfzr59e/o2b+nSs/o2qylKdAAAAACA6feaJCPjro2k4e+xPOmkN2fevAXZt++Fl67Nm7cgH/jAf82KFedMatbw8OrMn7+w3xGn3YT+IKWUi0opf1hKua2U8lwppZZS/uowa84tpXyllPJ0KWV3KeW+UsoVpZQ5h1hzfillYyllZynl+VLKN0oplx7mfS4tpdzVvn9ne/35h7h/TjvHfaWUF9r5vlJKOffwvwkAAAAAgL5Yn+Q/lVI+UEpZVkr51SS/keT/NBlq+fJ3ZsmSf5x58xYkKZk3b0GWLDkty5e/s8lYfVFKObaUcn0pZWu7G76jlHL24dZNdCf6J5L8kyTPJ/lRkpWHCfMvk6xLsifJTUmeTvLBJL+X5N1J/tVB1lyW5A+T7EjyV0n2JrkoyXAp5a211isPsua6JEPtTH+W5KgkH06yvpRyea31hnH3lyRfas99KMkNSRZn9PyhTaWUC2utfzuB3wcAAAAAQC8uT3JNks8lOSnJ9ox2nL/TZKiBgTm58ML/ni1b7spTTz2SE098U5Yvf2cGBrrujZ5N/meStyW5NKOd8iVJvlZKOa3W+ni3RRMt0T/eHvpIkl9KsqHbjaWU4zL6xx5J8su11rvb1387ya1JLiqlfPiAs35SSlmW5LqMlu1n1Vp/2L7+O0m+mWSolLKu1nrnAWvOzWiB/miSs2utz7SvfybJPUmuK6X83distg9ntEC/I8k/r7Xuaa/5kyS3J/mzUsqttdZdE/y9AAAAAABMWruDvKL9M6MMDMzJihXnTPr4lgO1WiPZu3d3duzYmkcfvbPxIr6UcnSSC5NcWGvd2L58dSnlg0k+mtGN5Ac1oeNcaq0baq0P11on8pWsFyU5McmXxgr09ow9BwT56Lg1H0kyP8kNB5be7WL8v7Wf/rtxa8aef2qsQG+v+WGSP2rP+zfj1oy97yfGCvT2mm9mdMf8ie38AAAAAABMQas1knXrrsquXT/Jjh0/zM03X5N1665KqzX+CPhpNTfJnIyennKgF5L8wqEWHolD6s9rP95ykNc2Jdmd5NxSyvwJrvn7cfdMaU37/c5tv/9tk3gfAAAAAAAmaMuWu7J9+4NJRvdk79v3QrZvfyBbttzVWKb2zv87k3yilPKG9ndnXpLknCRLDrW2TGxz+QELSvnljB7n8r9rrZcc5PVvJjkro8ey3HOQ1+9PcnqS02qtD7avPZXkhCQn1Fp3HGTN80kWJllYa91dSlmY0fPZn6+1HnuQ+09I8lSSJ2utr2tfOz3J/Unur7W+9SBrzsro0TF31VrfNaFfBgAAAAAAHT772ff8dpKr07mJu5Xkk0NDG65tJFSSUsqKJJ9P8osZPY783iSbk7yj1npat3UTPRN9Mha1H3d2eX3s+vGTXLOwfd/uI/ge49cAAAAAADAJQ0Mbrsnol6bOKLXWR5P8UnuT9nG11u2llJuSbDnUuiNxnMvhlPbjZLbAT2XNdL0HAAAAAACzRK31p+0C/bVJ3pfkbw91/5HYiT62o3tRl9ePG3ff2L9PaK95xXEuB6x5boLvcbBd51PJBQAAAADAq0Ap5X0Z3Vj+/SRvSvKZJA8l+YtDrTsSO9Efaj/+3PgXSilzkyxPsj/JDya4ZklGj3L5Ua11dzL6PwVJHk9yTPv18d7cftx8wLVHMnrOzRvbOSayBgAAAACAV4dFSW7IaIn+v5LcnuS9tdZ9h1p0JEr0W9uPv3KQ134xyWuS3FFrfXGCa94/7p4prWm/3x3t9/9nk3gfAAAAAABmuVrrmlrrilrr/FrrklrrZbXWw55MciRK9LVJfpLkw6WUs8YullIWJBn75tU/HrfmL5K8mOSyUsqyA9a8Nsl/aT/9k3Frxp7/Vvu+sTXLknysPW/8Nvyx9722nWdszdlJLk7yVJJ1h/l8AAAAAAD8jCi1Hv57NEspFyS5oP309Rk9bP0HSW5rX/tJrfXKcfevTbInyZeSPJ3kXyR5S/v6qjrujUsplyf5g4yeiX5Tkr1JLkpycpLPHjj/gDWfTfIbSX7UnntURsvwwSSX11pvGHd/SbKmPff7Sda37704yYIkF9ZaD3mIPAAAAAAAPzsmWqJfneSTh7hla6112bg1707yW0nOyWhB/UiSzyf5g1rrSJf3+WCSK5O8I6O75B9IckOt9QuHyHZpksuSnJakleTeJJ+ptf5dl/vnJrk8yUcyenj8niR3Jrm21nrHIT4jAAAAAAA/YyZUogMAAAAAwM+iI3EmOgAAAAAAvCoo0QEAAAAAoAslOgAAAAAAdKFEBwAAAACALpToAAAAAADQhRIdAAAAAAC6UKIDAAAAAEAXSnQAAAAAAOhCiQ4AAAAAAF38f2n521lQbvN8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(data,color=(0.55, 0.55, 0.25));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                     0\n",
       "Employee Code              0\n",
       "  Last Name                0\n",
       "Local Date of Joining      0\n",
       "Designation                8\n",
       "Status                     0\n",
       "Market Unit              141\n",
       "SkillList                  0\n",
       "Location                  13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Location value's are : 13\n",
      "Null Location value's after the preprocessing are : 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Null Location value's are :\",data.Location.isnull().sum())\n",
    "data.Location=data.Location.fillna('bangalore')\n",
    "print(\"Null Location value's after the preprocessing are :\",data.Location.isnull().sum())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "nan vlue in Designation will be replaced with 9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Designation=data.Designation.fillna(9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Deman=pd.read_excel(\"C:\\\\Users\\\\rishi\\\\Desktop\\\\Demandv1.1.xlsx\",sheet_name=\"Demand Trend Last year\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand=pd.DataFrame(Deman)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(Demand,color=(0.55, 0.55, 0.25),figsize=(15,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                     14\n",
       "Employee Code            9459\n",
       "  Last Name              5937\n",
       "Local Date of Joining    2003\n",
       "Designation                13\n",
       "Status                      2\n",
       "Market Unit                12\n",
       "SkillList                1167\n",
       "Location                    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Data preprocessing performing one-hot encoding for region designation status Market unit Location.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "region=data.Region.unique()\n",
    "region_dict=dict(zip(list(region),[i for i in range(0,len(region))]))\n",
    "data.Region=data.Region.map(region_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Location=data.Location.unique()\n",
    "Location_dict=dict(zip(Location,[i for i in range(0,len(Location))]))\n",
    "data.Location=data.Location.map(Location_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "designation_to_Experience_Grade={\n",
    " 'Software associate':'A1',\n",
    "'SOFTWARE ENGINEER':'A2',\n",
    "'SENIOR SOFTWARE ENGINEER':'A3',\n",
    "'Analyst':'A4',\n",
    "'Senior Analyst':'A5',\n",
    "\n",
    "'ASSOCIATE CONSULTANT':'B1',\n",
    "'CONSULTANT':'B2',\n",
    "\n",
    "'SENIOR CONSULTANT':'C1',\n",
    "'MANAGER':'C2', \n",
    "\n",
    "'SENIOR MANAGER' :'D1',\n",
    "'PORTFOLIO MANAGER':'D2',\n",
    "\n",
    "'EXECUTIVE VICE PRESIDENT':'E1',\n",
    "'VICE PRESIDENT':'E2',\n",
    "'DIRECTOR':'E3',\n",
    "'SENIOR DIRECTOR':'E4'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Experience_grade_toNumber=dict(zip(list(designation_to_Experience_Grade.values()),[i for i in range(0,15)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Designation=data.Designation.map(designation_to_Experience_Grade)\n",
    "data.Designation=data.Designation.map(Experience_grade_toNumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Designation.isnull().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "filling null value with 9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Designation.fillna(9999,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Designation=[int(i) for i in data.Designation]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Designation.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.Status=data.Status.map({'Billable': 0,'Bench': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Market_Unit=data['Market Unit'].unique()\n",
    "Market_Unit_dict=dict(zip(Market_Unit,[i for i in range(0,len(Market_Unit))]))\n",
    "data['Market Unit']=data['Market Unit'].map(Market_Unit_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "SkillList=data['SkillList'].unique()\n",
    "SkillList_dict=dict(zip(SkillList,[i for i in range(0,len(SkillList))]))\n",
    "data['SkillList']=data['SkillList'].map(SkillList_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.DataFrame({'grade':list(Experience_grade_toNumber.keys()), 'Numeric':list(Experience_grade_toNumber.values())})\n",
    "b=pd.DataFrame({'Post':list(designation_to_Experience_Grade.keys()), 'Numeric':list(Experience_grade_toNumber.values())})\n",
    "designation_to_Experience_Grade_df=pd.merge(a,b,on='Numeric')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Region</th>\n",
       "      <th>Employee Code</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Local Date of Joining</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Status</th>\n",
       "      <th>Market Unit</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45149</td>\n",
       "      <td>AALLURI</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>47875</td>\n",
       "      <td>AARATTUKULAM</td>\n",
       "      <td>2014-11-20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>90386</td>\n",
       "      <td>AASHIK</td>\n",
       "      <td>2014-12-15</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>41161</td>\n",
       "      <td>Aastha</td>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>92293</td>\n",
       "      <td>ABBAN</td>\n",
       "      <td>2013-12-02</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Employee Code     Last Name Local Date of Joining  Designation  \\\n",
       "0       0          45149       AALLURI            2016-10-31            6   \n",
       "1       1          47875  AARATTUKULAM            2014-11-20            2   \n",
       "2       1          90386        AASHIK            2014-12-15            5   \n",
       "3       1          41161        Aastha            2015-02-26            5   \n",
       "4       1          92293         ABBAN            2013-12-02            6   \n",
       "\n",
       "   Status  Market Unit  SkillList  Location  \n",
       "0       0            0          0         0  \n",
       "1       0            0          1         1  \n",
       "2       0            0          1         2  \n",
       "3       0            0          2         1  \n",
       "4       0            0          3         3  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Preprocessing for Demand "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "making Experience Grade same for the both table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand[\"Experience Grade\"]=Demand[\"Experience Grade\"].map(Experience_grade_toNumber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.Location=Demand.Location.map(Location_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_dict=dict((v,k) for k,v in enumerate(calendar.month_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand['Month DD Raised']=Demand['Month DD Raised'].map(month_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Demand.Location=Demand['Location'].map(Location_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>grade</th>\n",
       "      <th>Numeric</th>\n",
       "      <th>Post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>0</td>\n",
       "      <td>Software associate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>1</td>\n",
       "      <td>SOFTWARE ENGINEER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>2</td>\n",
       "      <td>SENIOR SOFTWARE ENGINEER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>3</td>\n",
       "      <td>Analyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>4</td>\n",
       "      <td>Senior Analyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B1</td>\n",
       "      <td>5</td>\n",
       "      <td>ASSOCIATE CONSULTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B2</td>\n",
       "      <td>6</td>\n",
       "      <td>CONSULTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C1</td>\n",
       "      <td>7</td>\n",
       "      <td>SENIOR CONSULTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C2</td>\n",
       "      <td>8</td>\n",
       "      <td>MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D1</td>\n",
       "      <td>9</td>\n",
       "      <td>SENIOR MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D2</td>\n",
       "      <td>10</td>\n",
       "      <td>PORTFOLIO MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>E1</td>\n",
       "      <td>11</td>\n",
       "      <td>EXECUTIVE VICE PRESIDENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E2</td>\n",
       "      <td>12</td>\n",
       "      <td>VICE PRESIDENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E3</td>\n",
       "      <td>13</td>\n",
       "      <td>DIRECTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>E4</td>\n",
       "      <td>14</td>\n",
       "      <td>SENIOR DIRECTOR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grade  Numeric                      Post\n",
       "0     A1        0        Software associate\n",
       "1     A2        1         SOFTWARE ENGINEER\n",
       "2     A3        2  SENIOR SOFTWARE ENGINEER\n",
       "3     A4        3                   Analyst\n",
       "4     A5        4            Senior Analyst\n",
       "5     B1        5      ASSOCIATE CONSULTANT\n",
       "6     B2        6                CONSULTANT\n",
       "7     C1        7         SENIOR CONSULTANT\n",
       "8     C2        8                   MANAGER\n",
       "9     D1        9            SENIOR MANAGER\n",
       "10    D2       10         PORTFOLIO MANAGER\n",
       "11    E1       11  EXECUTIVE VICE PRESIDENT\n",
       "12    E2       12            VICE PRESIDENT\n",
       "13    E3       13                  DIRECTOR\n",
       "14    E4       14           SENIOR DIRECTOR"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "designation_to_Experience_Grade_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Demand Practice == data Skillset \n",
    "skilllist is same for the data frame \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, Data(headcount) table contain all the elemement from Demand table\n"
     ]
    }
   ],
   "source": [
    "# checking, are we hiring the similar peolple or new people\n",
    "result =  all(elem in dat.SkillList.unique()  for elem in Deman.SkillList.unique())\n",
    "if result:\n",
    "    print(\"Yes, Data(headcount) table contain all the elemement from Demand table\")    \n",
    "else :\n",
    "    print(\"No, Data(headcount) table doesn't contain all the elemement from Demand table\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "that is we can relocate some of our employee to other location if they are on bench, and we can also give them pormotion if they are eligible, to fulllfill our current need of the employee without hiring new people. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.SkillList=Demand.SkillList.map(SkillList_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data (headcount has total Market Unit) 12\n",
      "Demand  11\n"
     ]
    }
   ],
   "source": [
    "print('data (headcount has total Market Unit) ' + str(dat[\"Market Unit\"].nunique()))\n",
    "print('Demand  ' + str(Demand.Practice.nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Market unit and practise are the kind of thigs \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Market_Unit_dict.update({k: v for k, v in zip(['M&FT','TEST','ADM','CFS','DCX','Cards','I&D'],range(13,20))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.Practice=Demand.Practice.map(Market_Unit_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month DD Raised               0\n",
       "No. of FTE Request Raised     0\n",
       "SkillList                     0\n",
       "Location                      0\n",
       "Experience Grade              0\n",
       "Practice                      0\n",
       "Skill Group                  42\n",
       "Demand Source                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demand.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>13</td>\n",
       "      <td>(F) IT Operations</td>\n",
       "      <td>Account</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>(D) Banking Sector</td>\n",
       "      <td>Proactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month DD Raised  No. of FTE Request Raised  SkillList  Location  \\\n",
       "0                5                          4          0         0   \n",
       "1               10                          2          1         1   \n",
       "2                3                          2          1         2   \n",
       "3               10                          1          2         1   \n",
       "4                6                          2          3         3   \n",
       "\n",
       "   Experience Grade  Practice         Skill Group Demand Source  \n",
       "0                 3         0   (F) IT Operations       Account  \n",
       "1                 6        13   (F) IT Operations       Account  \n",
       "2                 2        14                 NaN       Support  \n",
       "3                 6        13   (F) IT Operations       Account  \n",
       "4                 4        19  (D) Banking Sector     Proactive  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                   396\n",
       "Employee Code            396\n",
       "  Last Name              396\n",
       "Local Date of Joining    396\n",
       "Designation              396\n",
       "Status                   396\n",
       "Market Unit              396\n",
       "SkillList                396\n",
       "Location                 396\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Status==1].count()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "these 396 people can be assignd to a project accoeding to their the skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "465465"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Status==1]\n",
    "465465"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
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       "      <td>(D) Banking Sector</td>\n",
       "      <td>Proactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month DD Raised  No. of FTE Request Raised  SkillList  Location  \\\n",
       "0                5                          4          0         0   \n",
       "1               10                          2          1         1   \n",
       "2                3                          2          1         2   \n",
       "3               10                          1          2         1   \n",
       "4                6                          2          3         3   \n",
       "\n",
       "   Experience Grade  Practice         Skill Group Demand Source  \n",
       "0                 3         0   (F) IT Operations       Account  \n",
       "1                 6        13   (F) IT Operations       Account  \n",
       "2                 2        14                 NaN       Support  \n",
       "3                 6        13   (F) IT Operations       Account  \n",
       "4                 4        19  (D) Banking Sector     Proactive  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demand.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.sort_values(by='Month DD Raised',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "del Demand['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BANK': 0,\n",
       " 'INS': 1,\n",
       " 'ANZ': 2,\n",
       " 'OTHER': 3,\n",
       " 'APAC': 4,\n",
       " 'GP': 5,\n",
       " 'FS': 6,\n",
       " 'Insights & Data': 7,\n",
       " 'ERP': 8,\n",
       " 'Finance': 9,\n",
       " 'HR': 10,\n",
       " 'Mktg. & Grp. Sales': 11,\n",
       " nan: 12,\n",
       " 'M&FT': 13,\n",
       " 'TEST': 14,\n",
       " 'ADM': 15,\n",
       " 'CFS': 16,\n",
       " 'DCX': 17,\n",
       " 'Cards': 18,\n",
       " 'I&D': 19}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Market_Unit_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Demand.sort_values(by=['Month DD Raised','SkillList','Location'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEMAND=Demand[['Month DD Raised','SkillList', 'Location','Experience Grade', 'Practice','No. of FTE Request Raised']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEMAND.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "del DEMAND['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month DD Raised  SkillList  Location  Experience Grade  Practice  \\\n",
       "0                1          1         6                 0        17   \n",
       "1                1          1         6                 0        17   \n",
       "2                1          2         6                 0        17   \n",
       "3                1          2         6                 0        17   \n",
       "4                1          2         6                 0        17   \n",
       "\n",
       "   No. of FTE Request Raised  \n",
       "0                          1  \n",
       "1                          4  \n",
       "2                          4  \n",
       "3                          3  \n",
       "4                          4  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEMAND.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3781: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  return super(DataFrame, self).rename(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "DEMAND.rename(columns={'No. of FTE Request Raised':'Request'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "processedDemand=pd.DataFrame(columns=['Month DD Raised','SkillList','Location','Experience Grade','Practice','Request'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,k in DEMAND.groupby(['Month DD Raised','SkillList','Location','Experience Grade','Practice','Request']):\n",
    "    processedDemand=pd.concat([processedDemand,k],ignore_index=True,join='inner')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>4</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DD Raised SkillList Location Experience Grade Practice Request\n",
       "0               1         1        6                0       17       1\n",
       "1               1         1        6                0       17       4\n",
       "2               1         2        6                0       17       2\n",
       "3               1         2        6                0       17       3\n",
       "4               1         2        6                0       17       4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processedDemand.head()\n",
    "#this is the processed data that contain clear information about the reuesrt for employee.\n",
    "#this can be feed to make prediction about the demand. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region                   396\n",
       "Employee Code            396\n",
       "  Last Name              396\n",
       "Local Date of Joining    396\n",
       "Designation              396\n",
       "Status                   396\n",
       "Market Unit              396\n",
       "SkillList                396\n",
       "Location                 396\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Status==1].count()\n",
    "#these 396 people can be assigned to work. \n",
    "#status 1 means that Employee is on bench."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>3</td>\n",
       "      <td>Analyst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B2</td>\n",
       "      <td>6</td>\n",
       "      <td>CONSULTANT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C1</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C2</td>\n",
       "      <td>8</td>\n",
       "      <td>MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D1</td>\n",
       "      <td>9</td>\n",
       "      <td>SENIOR MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>D2</td>\n",
       "      <td>10</td>\n",
       "      <td>PORTFOLIO MANAGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>E1</td>\n",
       "      <td>11</td>\n",
       "      <td>EXECUTIVE VICE PRESIDENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E2</td>\n",
       "      <td>12</td>\n",
       "      <td>VICE PRESIDENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E3</td>\n",
       "      <td>13</td>\n",
       "      <td>DIRECTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>E4</td>\n",
       "      <td>14</td>\n",
       "      <td>SENIOR DIRECTOR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grade  Numeric                      Post\n",
       "0     A1        0        Software associate\n",
       "1     A2        1         SOFTWARE ENGINEER\n",
       "2     A3        2  SENIOR SOFTWARE ENGINEER\n",
       "3     A4        3                   Analyst\n",
       "4     A5        4            Senior Analyst\n",
       "5     B1        5      ASSOCIATE CONSULTANT\n",
       "6     B2        6                CONSULTANT\n",
       "7     C1        7         SENIOR CONSULTANT\n",
       "8     C2        8                   MANAGER\n",
       "9     D1        9            SENIOR MANAGER\n",
       "10    D2       10         PORTFOLIO MANAGER\n",
       "11    E1       11  EXECUTIVE VICE PRESIDENT\n",
       "12    E2       12            VICE PRESIDENT\n",
       "13    E3       13                  DIRECTOR\n",
       "14    E4       14           SENIOR DIRECTOR"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "designation_to_Experience_Grade_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month DD Raised</th>\n",
       "      <th>No. of FTE Request Raised</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Experience Grade</th>\n",
       "      <th>Practice</th>\n",
       "      <th>Skill Group</th>\n",
       "      <th>Demand Source</th>\n",
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       "  <tbody>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Month DD Raised, No. of FTE Request Raised, SkillList, Location, Experience Grade, Practice, Skill Group, Demand Source]\n",
       "Index: []"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#there is no demand for the people with experience grade is greator than 7 ie. C1\t7\tSENIOR CONSULTANT\n",
    "#we will only hire below the post of senior consultant. \n",
    "Demand[Demand['Experience Grade']>7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DD Raised SkillList Location Experience Grade Practice Request\n",
       "0               1         1        6                0       17       1\n",
       "1               1         1        6                0       17       4\n",
       "2               1         2        6                0       17       2\n",
       "3               1         2        6                0       17       3\n",
       "4               1         2        6                0       17       4"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processedDemand.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "'''Now I am passing this processed data to make Prediction about the demand of the Employee, so there'll be neither \n",
    "shortage or employee nor abundance of employee on bench. '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "MonthlyRequest_df=pd.DataFrame(columns=processedDemand.columns.tolist())\n",
    "totalRequest=[]\n",
    "for i ,k in processedDemand.groupby(['Month DD Raised','SkillList','Location','Experience Grade','Practice']):\n",
    "    totalRequest.append(k.Request.sum())\n",
    "    ki=pd.DataFrame(k.iloc[0].T)  \n",
    "    #print(ki.T)\n",
    "\n",
    "    MonthlyRequest_df=pd.concat([MonthlyRequest_df,ki.T],ignore_index=True)\n",
    "MonthlyRequest_df['MonthlyRequest']=totalRequest\n",
    "del MonthlyRequest_df['Request']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Month DD Raised</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Experience Grade</th>\n",
       "      <th>Practice</th>\n",
       "      <th>MonthlyRequest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month DD Raised SkillList Location Experience Grade Practice  MonthlyRequest\n",
       "0               1         1        6                0       17               5\n",
       "1               1         2        6                0       17              13\n",
       "2               1         5        6                0       17               6\n",
       "3               1         8        6                0       17              23\n",
       "4               1        12        6                0       17               7"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MonthlyRequest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=MonthlyRequest_df[['Month DD Raised', 'SkillList', 'Location', 'Experience Grade','Practice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=MonthlyRequest_df[ 'MonthlyRequest']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,Y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month DD Raised     512\n",
       "SkillList           512\n",
       "Location            512\n",
       "Experience Grade    512\n",
       "Practice            512\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month DD Raised     129\n",
       "SkillList           129\n",
       "Location            129\n",
       "Experience Grade    129\n",
       "Practice            129\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Modelling Algo</th>\n",
       "      <th>RMSLE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.579941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>0.597868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoostRegressor</td>\n",
       "      <td>0.699918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>0.591720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.577136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Modelling Algo     RMSLE\n",
       "0       LinearRegression  0.579941\n",
       "1  RandomForestRegressor  0.597868\n",
       "2      AdaBoostRegressor  0.699918\n",
       "3       BaggingRegressor  0.591720\n",
       "4    KNeighborsRegressor  0.577136"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models=[LinearRegression(),RandomForestRegressor(),AdaBoostRegressor(),BaggingRegressor(),KNeighborsRegressor()]\n",
    "model_names=['LinearRegression','RandomForestRegressor','AdaBoostRegressor','BaggingRegressor','KNeighborsRegressor']\n",
    "rmsle=[]\n",
    "d={}\n",
    "for model in range (len(models)):\n",
    "    clf=models[model]\n",
    "    clf.fit(X_train,y_train)\n",
    "    test_pred=clf.predict(X_test)\n",
    "    rmsle.append(np.sqrt(mean_squared_log_error(test_pred,y_test)))\n",
    "    d={'Modelling Algo':model_names,'RMSLE':rmsle}   \n",
    "pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random forest and knn regressor is giving the lowest rmsle that why I am using the these two for final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "max_features = ['auto', 'sqrt']\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "bootstrap = [True, False]\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "print(random_grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   26.7s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 357 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 640 tasks      | elapsed:  6.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 10.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor()\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, \n",
    "                               cv = 10, verbose=2, random_state=42, n_jobs = -1)\n",
    "rf_random.fit(X_test,y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 800,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 100,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg=RandomForestRegressor(n_estimators= 400,min_samples_split=10,min_samples_leaf= 4,max_features='auto',\n",
    "                          max_depth= 70,bootstrap= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=70,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=4, min_samples_split=10,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.40009538145802"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1822209188999091"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_log_error(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41125955054611674"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "and I have also implemented the knn regressor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# knn regressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_range = list(range(1, 31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = dict(n_neighbors=k_range)\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(KNeighborsRegressor(), param_grid,scoring='neg_mean_squared_log_error', cv=10,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "          weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_log_error', verbose=0)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[mean: -0.51535, std: 0.13929, params: {'n_neighbors': 1},\n",
       " mean: -0.42382, std: 0.09797, params: {'n_neighbors': 2},\n",
       " mean: -0.37489, std: 0.07316, params: {'n_neighbors': 3},\n",
       " mean: -0.34077, std: 0.06301, params: {'n_neighbors': 4},\n",
       " mean: -0.34169, std: 0.07148, params: {'n_neighbors': 5},\n",
       " mean: -0.35063, std: 0.07490, params: {'n_neighbors': 6},\n",
       " mean: -0.34507, std: 0.07506, params: {'n_neighbors': 7},\n",
       " mean: -0.34469, std: 0.07702, params: {'n_neighbors': 8},\n",
       " mean: -0.34044, std: 0.07471, params: {'n_neighbors': 9},\n",
       " mean: -0.33603, std: 0.07525, params: {'n_neighbors': 10},\n",
       " mean: -0.33073, std: 0.08294, params: {'n_neighbors': 11},\n",
       " mean: -0.33009, std: 0.08612, params: {'n_neighbors': 12},\n",
       " mean: -0.33567, std: 0.08918, params: {'n_neighbors': 13},\n",
       " mean: -0.33104, std: 0.08303, params: {'n_neighbors': 14},\n",
       " mean: -0.33103, std: 0.08539, params: {'n_neighbors': 15},\n",
       " mean: -0.33290, std: 0.08936, params: {'n_neighbors': 16},\n",
       " mean: -0.33170, std: 0.08891, params: {'n_neighbors': 17},\n",
       " mean: -0.33151, std: 0.09020, params: {'n_neighbors': 18},\n",
       " mean: -0.33294, std: 0.08871, params: {'n_neighbors': 19},\n",
       " mean: -0.33284, std: 0.09038, params: {'n_neighbors': 20},\n",
       " mean: -0.33386, std: 0.08860, params: {'n_neighbors': 21},\n",
       " mean: -0.33392, std: 0.08594, params: {'n_neighbors': 22},\n",
       " mean: -0.33262, std: 0.08823, params: {'n_neighbors': 23},\n",
       " mean: -0.33263, std: 0.08748, params: {'n_neighbors': 24},\n",
       " mean: -0.33311, std: 0.08882, params: {'n_neighbors': 25},\n",
       " mean: -0.33131, std: 0.08870, params: {'n_neighbors': 26},\n",
       " mean: -0.33111, std: 0.08909, params: {'n_neighbors': 27},\n",
       " mean: -0.32940, std: 0.08973, params: {'n_neighbors': 28},\n",
       " mean: -0.32897, std: 0.08766, params: {'n_neighbors': 29},\n",
       " mean: -0.32725, std: 0.08619, params: {'n_neighbors': 30}]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters\n",
      "{'n_neighbors': 1}\n",
      "\n",
      "CV Validation Score\n",
      "[-0.49722933 -0.39744364 -0.44855543 -0.85760585 -0.60192997 -0.60020536\n",
      " -0.45027746 -0.54234274 -0.34835421 -0.41222107]\n",
      "\n",
      "Mean Validation Score\n",
      "-0.5153497869543124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "print('Parameters')\n",
    "print(grid.grid_scores_[0].parameters)\n",
    "print('')\n",
    "print('CV Validation Score')\n",
    "print(grid.grid_scores_[0].cv_validation_scores)\n",
    "print('')\n",
    "print('Mean Validation Score')\n",
    "print(grid.grid_scores_[0].mean_validation_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]\n",
    "plt.plot(k_range, grid_mean_scores)\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-Validated Accuracy');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.3272523633751552\n",
      "{'n_neighbors': 30}\n",
      "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "          metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
      "          weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
    "          metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
    "          weights='uniform')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "knnPred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3309498328743857"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the result of the knn regressor\n",
    "mean_squared_log_error(y_test,knnPred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1822209188999091"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this is the result of random forest \n",
    "mean_squared_log_error(y_test,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# so for now I'll use random forest regressor for making the prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# for making the prediction I am using the only Month DD Raised, SkillList, Location, Experience Grade, Practice, MonthlyRequest. \n",
    "# when someone is using algo. for making the prediction in the background it automatically making predictions for 2 months in advance. e.g. you make a prediction for January then it will automatically give you result for Feb and March. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# and I am using the random forest algorithm for making prediction for making prediction because it has low root mean Square log error."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "each numer is representing to a specific  that is stored in the form of dictionary."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "as we get to know the exact demand of the employee with the desired skill, location and other information \n",
    "so now we can make the search for the employee with these particular specifications if we found out someone with desired skills at the bench then we can change the status of that employee to billable and assign that particular person to that particular project where we need them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "employeeOnBench_df=data[data.Status==1][['Employee Code', 'Local Date of Joining','Designation',\n",
    "                                         'Status', 'Market Unit', 'SkillList', 'Location']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthOnBench=[i.month for i in  employeeOnBench_df['Local Date of Joining']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "employeeOnBench_df['MonthOnBench']=monthOnBench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupbyEmployee_df=pd.DataFrame(columns=['Employee Code','MonthOnBench','SkillList','Location','Designation','Market Unit'])\n",
    "for i,k in employeeOnBench_df.groupby(['MonthOnBench','SkillList','Location','Designation','Market Unit']):\n",
    "    groupbyEmployee_df=pd.concat([groupbyEmployee_df,k[['Employee Code','MonthOnBench','SkillList','Location','Designation','Market Unit']]],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Employee Code</th>\n",
       "      <th>MonthOnBench</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Market Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52594</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee Code MonthOnBench SkillList Location Designation Market Unit\n",
       "0         52594            1         5        4          10           0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbyEmployee_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Employee Code', 'MonthOnBench', 'SkillList', 'Location', 'Designation',\n",
       "       'Market Unit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbyEmployee_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checking_For_The_Employee_From_Bench(Month,SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location):\n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    print('\\n\\t\\tchecking For the Employee From Bench\\n')\n",
    "    df=groupbyEmployee_df[(groupbyEmployee_df.MonthOnBench==Month)&(groupbyEmployee_df.SkillList==SkillList)&\n",
    "              (groupbyEmployee_df.Designation==ExperienceGrade)&(groupbyEmployee_df['Market Unit']==Practice)]\n",
    "    if df.count()[0]==0:\n",
    "        print(\"Right now We don't have any Employee on Bench for desired skillset\")\n",
    "        print(\"we need to hire {} employee\".format(requiement_for_next_month))\n",
    "        print('checking The Employee From Bench who just below require Designation')\n",
    "    else:\n",
    "        print('These Employee can be assigned to project')\n",
    "        print(df)\n",
    "        requiement_for_next_month=requiement_for_next_month-df.count()[0]\n",
    "        print('we do need to hire {}'.format(requiement_for_next_month))\n",
    "        vacantDesignation.extend(df.Designation)\n",
    "        vacantLocation.extend(df.Location)\n",
    "        vacantMarketUnit.extend(df['Market Unit'])\n",
    "        vacantSkilllist.extend(df.SkillList)\n",
    "    print('These employee can be promoted to fulfill the reuiqrement \\n')\n",
    "    if requiement_for_next_month>0:\n",
    "        checking_For_The_Employee_From_Bench_who_just_below_require_Designation(SkillList=SkillList,\n",
    "                                                                             ExperienceGrade=ExperienceGrade,\n",
    "                                                                             Practice=Practice,\n",
    "                                                                        requiement_for_next_month=requiement_for_next_month,request_Location=request_Location)\n",
    "    else: \n",
    "        print('Requirement Fulfilled')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "as we get to know the about the demand of the employee to fulfill the requirement, so we will now search \n",
    "for these employees in the dataset that contain the information about the employee who are on the bench and \n",
    "tries to make them billable and thus we will increase the profit of the company. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "if we are not having the emppoyee on the bench then I am searching the employee for same skill set\\\n",
    "but with low experience grade so we can give the training to the employee and promote the empoyee to fullfill our need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Employee Code</th>\n",
       "      <th>MonthOnBench</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Market Unit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52594</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee Code MonthOnBench SkillList Location Designation Market Unit\n",
       "0         52594            1         5        4          10           0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupbyEmployee_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we do need to start working from here "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checking_For_The_Employee_From_Bench_who_just_below_require_Designation(SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location):\n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    print(\"\\n\\tchecking For The Employee From Bench who are just below require Designation Method \\n\")\n",
    "\n",
    "    _=groupbyEmployee_df[(groupbyEmployee_df.SkillList==SkillList)&(groupbyEmployee_df.Designation==ExperienceGrade-1)\n",
    "                              &(groupbyEmployee_df['Market Unit']==Practice)][['Employee Code','Location','SkillList','Designation','Market Unit']]\n",
    "    if _.count()[0]!=0:\n",
    "        print(_)\n",
    "    else:\n",
    "        print('Searching the Employees those can be promoted from Location from where request is genrated')\n",
    "    requiement_for_next_month=requiement_for_next_month-  _.count()[0]\n",
    "    print('\\nStill we do need to hire {} Employees to fulfill the requireent'.format( requiement_for_next_month))\n",
    "    #print(' with these skills',SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location)\n",
    "    #_[['SkillList','Location','Designation','Market Unit']]\n",
    "    vacantDesignation.extend(_.Designation)\n",
    "    vacantLocation.extend(_.Location)\n",
    "    vacantMarketUnit.extend(_['Market Unit'])\n",
    "    vacantSkilllist.extend(_.SkillList)\n",
    "    if requiement_for_next_month>0:\n",
    "        promotionFromSameLocation(SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location)\n",
    "    else:\n",
    "        print('Requirement Fulfilled')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Month DD Raised</th>\n",
       "      <th>No. of FTE Request Raised</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Experience Grade</th>\n",
       "      <th>Practice</th>\n",
       "      <th>Skill Group</th>\n",
       "      <th>Demand Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>(T) Web Development Technologies</td>\n",
       "      <td>Proactive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month DD Raised  No. of FTE Request Raised  SkillList  Location  \\\n",
       "4                1                          1          1         6   \n",
       "\n",
       "   Experience Grade  Practice                       Skill Group Demand Source  \n",
       "4                 0        17  (T) Web Development Technologies     Proactive  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Demand.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "vacantDesignation=[]\n",
    "vacantSkilllist=[]\n",
    "vacantLocation=[]\n",
    "vacantMarketUnit=[]\n",
    "def prediction(Month,SkillList,Location,ExperienceGrade,Practice):\n",
    "\n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "\n",
    "    if Month==12:\n",
    "        Month1=1\n",
    "        Month2=2\n",
    "    else:\n",
    "        Month1=Month+1\n",
    "        Month2=Month+2\n",
    "    for i in list([Month,Month1,Month2]):\n",
    "        print('Month of:',list(month_dict.keys())[list(month_dict.values()).index(i)] )\n",
    "    print('Required Skillset is : {}'.format(list(SkillList_dict.keys())[list(SkillList_dict.values()).index(SkillList)]))\n",
    "    print(\"Location is : {}\".format(list(Location_dict.keys())[list(Location_dict.values()).index(Location)]))\n",
    "    \n",
    "    print('Designation is :',list(designation_to_Experience_Grade.keys())[list(designation_to_Experience_Grade.values()).index(list(Experience_grade_toNumber.keys())[list(Experience_grade_toNumber.values()).index(ExperienceGrade)])])\n",
    "    \n",
    "    print('Practice in :',list(Market_Unit_dict.keys())[list(Market_Unit_dict.values()).index(Practice)])\n",
    "\n",
    "    \n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    print(\"\\n\\t\\t\\t Prediction Method\\n\")\n",
    "    req=[]\n",
    "    req.append(list(reg.predict([[Month,SkillList,Location,ExperienceGrade,Practice]]))[0])\n",
    "    req.append(list(reg.predict(list([[Month1,SkillList,Location,ExperienceGrade,Practice]])))[0])\n",
    "    req.append(list(reg.predict(list([[Month2,SkillList,Location,ExperienceGrade,Practice]])))[0])\n",
    "    print('We do need these empoyees in upcoming two months \\n')\n",
    "    print(pd.DataFrame({'Month':[Month,Month1,Month2],'SkillList':[SkillList,SkillList,SkillList],\n",
    "                        'Location':[Location,Location,Location],\n",
    "                        'ExperienceGrade':[ExperienceGrade,ExperienceGrade,ExperienceGrade],\n",
    "                        'Practice':[Practice,Practice,Practice],'Request':req}),'\\n')\n",
    "    requiement_for_next_month = math.ceil(sum(req))\n",
    "    print('Company need {} emoplyee to fulfill the Demand'.format(requiement_for_next_month),'\\n')\n",
    "\n",
    "    print(\"checking for the required employee in the bench dataset \\n\")\n",
    "    if len(req)>0:\n",
    "        checking_For_The_Employee_From_Bench(Month,SkillList,ExperienceGrade,Practice,requiement_for_next_month,Location)\n",
    "    else:\n",
    "        print(\"Requirement fulfilled\")\n",
    "    print('with: {}'.format(list(SkillList_dict.keys())[list(SkillList_dict.values()).index(SkillList)]),'Skill set')\n",
    "    print(\"Location is : {}\".format(list(Location_dict.keys())[list(Location_dict.values()).index(Location)]))\n",
    "    \n",
    "    print('Designation is :',list(designation_to_Experience_Grade.keys())[list(designation_to_Experience_Grade.values()).index(list(Experience_grade_toNumber.keys())[list(Experience_grade_toNumber.values()).index(ExperienceGrade)])])\n",
    "    \n",
    "    print('Practice in :',list(Market_Unit_dict.keys())[list(Market_Unit_dict.values()).index(Practice)])\n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    \n",
    "    def locator(Designation,skillist,marketunit,location,k):\n",
    "        desig=list(designation_to_Experience_Grade.keys())[list(designation_to_Experience_Grade.values()).index(list(Experience_grade_toNumber.keys())[list(Experience_grade_toNumber.values()).index(ExperienceGrade)])]\n",
    "        skill=list(SkillList_dict.keys())[list(SkillList_dict.values()).index(skillist)]\n",
    "        pract=list(Market_Unit_dict.keys())[list(Market_Unit_dict.values()).index(marketunit)]\n",
    "        locata=list(Location_dict.keys())[list(Location_dict.values()).index(Location)]\n",
    "        print('Due to promotion,',k,'seat will beacome vacant at',locata,'with these skill',skill,'and postition is ',desig)\n",
    "        \n",
    "\n",
    "    \n",
    "    if(len(vacantDesignation)>0):\n",
    "        awe=pd.DataFrame({'vacantDesignation':vacantDesignation,\"vacantSkilllist\":vacantSkilllist,'vacantMarketUnit':vacantMarketUnit,'vacantLocation':vacantLocation})\n",
    "        print('\\t\\tNew vacant seats due to promotion\\n')\n",
    "        print(awe)\n",
    "        vacantDesignation.clear()\n",
    "        vacantLocation.clear()\n",
    "        vacantMarketUnit.clear()\n",
    "        vacantSkilllist.clear()\n",
    "        vacantSeat=[]\n",
    "        aww=pd.DataFrame({'vacantDesignation':vacantDesignation,\"vacantSkilllist\":vacantSkilllist,'vacantMarketUnit':vacantMarketUnit,'vacantLocation':vacantLocation})\n",
    "        for i,k in awe.groupby(['vacantDesignation','vacantLocation','vacantMarketUnit','vacantSkilllist']):\n",
    "            ind=aww.index.max()\n",
    "            aww.at[ind+1]=k.iloc[0].values\n",
    "            oooo=aww.at[ind+1]=k.iloc[0].values\n",
    "            locator(oooo[0],oooo[1],oooo[2],oooo[3], k.count()[0])\n",
    "            \n",
    "    print('--------------------------------------------------------------------------------------------\\n')\n",
    "\n",
    "            \n",
    "    print('\\t\\t\\t\\t####################')\n",
    "    print('Experience is counted in year and sorted in descending order throughout the code')\n",
    "    \n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "            \n",
    "            \n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# till now I am  trying to make our employee billable as many possible  from Bench Employee."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now I am searching the potential candidates from the headcount employee dataset those can get the promotion (these employee are still billable working on a project) so we can hire employee internally.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# employee having more than 3 years only will get the promotion I am just assuming this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "billable_employe_df=data[data.Status==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=[i.year for i in billable_employe_df['Local Date of Joining']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_year=2018 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "experience_Y=[current_year -i for i in year]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rishi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "billable_employe_df['Experience_Y']=experience_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Region</th>\n",
       "      <th>Employee Code</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Local Date of Joining</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Status</th>\n",
       "      <th>Market Unit</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Experience_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45149</td>\n",
       "      <td>AALLURI</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Employee Code   Last Name Local Date of Joining  Designation  \\\n",
       "0       0          45149     AALLURI            2016-10-31            6   \n",
       "\n",
       "   Status  Market Unit  SkillList  Location  Experience_Y  \n",
       "0       0            0          0         0             2  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billable_employe_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Region', 'Employee Code', '  Last Name', 'Local Date of Joining',\n",
       "       'Designation', 'Status', 'Market Unit', 'SkillList', 'Location',\n",
       "       'Experience_Y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billable_employe_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Region</th>\n",
       "      <th>Employee Code</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Local Date of Joining</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Status</th>\n",
       "      <th>Market Unit</th>\n",
       "      <th>SkillList</th>\n",
       "      <th>Location</th>\n",
       "      <th>Experience_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>45149</td>\n",
       "      <td>AALLURI</td>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Employee Code   Last Name Local Date of Joining  Designation  \\\n",
       "0       0          45149     AALLURI            2016-10-31            6   \n",
       "\n",
       "   Status  Market Unit  SkillList  Location  Experience_Y  \n",
       "0       0            0          0         0             2  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billable_employe_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def promotionFromSameLocation(SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location):\n",
    "    \n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    print('\\n\\t\\t\\tpromotion From Same Location\\n')\n",
    "    print('\\nExperience is in year & employee are sorted in descending order of experience')\n",
    "    print('\\n these employee can be promoted from same location to fulfill the demand \\n')\n",
    "    same_Location_employee=billable_employe_df[(billable_employe_df.SkillList==SkillList)&\n",
    "                              (billable_employe_df.Designation==ExperienceGrade-1)&\n",
    "                              (billable_employe_df.Location==request_Location)&(billable_employe_df.Experience_Y>2)][['Employee Code', '  Last Name',\n",
    "                               'Designation',  'SkillList','Experience_Y','Location','Market Unit']].sort_values(by='Experience_Y',ascending=False)\n",
    "    requiement_for_next_month=requiement_for_next_month-same_Location_employee.count()[0]\n",
    "    if same_Location_employee.count()[0]==0:\n",
    "        print(\"Right Now at {} this office no one for promotion\".format(list(Location_dict.keys())[list(Location_dict.values()).index(request_Location)]))\n",
    "    \n",
    "    else:\n",
    "        print(same_Location_employee)\n",
    "    print(\"\\nStill we do need {} more employees\".format(requiement_for_next_month))\n",
    "    if requiement_for_next_month>0:\n",
    "        promotionFromDifferentLocation(SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location)\n",
    "    else: \n",
    "        print(\"Requirement Fulfilled\")\n",
    "    \n",
    "    vacantDesignation.extend(same_Location_employee.Designation)\n",
    "    vacantMarketUnit.extend(same_Location_employee['Market Unit'])\n",
    "    vacantSkilllist.extend(same_Location_employee.SkillList)\n",
    "    for i in range(0,same_Location_employee.count()[0]):\n",
    "        vacantLocation.append(request_Location)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def promotionFromDifferentLocation(SkillList,ExperienceGrade,Practice,requiement_for_next_month,request_Location):\n",
    "    print('--------------------------------------------------------------------------------------------')\n",
    "    print(\"\\n\\t\\t\\tPromotion From Different Location\\n\")\n",
    "    print('\\n these employee can be relocated from location other location to fulfill the demand with a promotion  \\n')\n",
    "    employee_for_desired_location_from_different_Location=billable_employe_df[(billable_employe_df.SkillList==SkillList)&\n",
    "                              (billable_employe_df.Designation==ExperienceGrade-1)&(billable_employe_df.Experience_Y>2)][['Employee Code', '  Last Name',\n",
    "                               'Designation',  'SkillList','Experience_Y','Location']].sort_values(by='Experience_Y',ascending=False)\n",
    "    if employee_for_desired_location_from_different_Location.count()[0]==0:\n",
    "        print('\\t\\tNo match found')\n",
    "        \n",
    "    else:\n",
    "        print(employee_for_desired_location_from_different_Location)\n",
    "    \n",
    "    requiement_for_next_month=requiement_for_next_month-employee_for_desired_location_from_different_Location.count()[0]\n",
    "    print(\"\\nStill we need to hire :\",requiement_for_next_month,\"Employee\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------------\n",
      "Month of: July\n",
      "Month of: August\n",
      "Month of: September\n",
      "Required Skillset is : Angular.js\n",
      "Location is : bangalore\n",
      "Designation is : PORTFOLIO MANAGER\n",
      "Practice in : INS\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\t Prediction Method\n",
      "\n",
      "We do need these empoyees in upcoming two months \n",
      "\n",
      "   Month  SkillList  Location  ExperienceGrade  Practice   Request\n",
      "0      7          5         8               10         1  9.389035\n",
      "1      8          5         8               10         1  9.391431\n",
      "2      9          5         8               10         1  9.391431 \n",
      "\n",
      "Company need 29 emoplyee to fulfill the Demand \n",
      "\n",
      "checking for the required employee in the bench dataset \n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\tchecking For the Employee From Bench\n",
      "\n",
      "Right now We don't have any Employee on Bench for desired skillset\n",
      "we need to hire 29 employee\n",
      "checking The Employee From Bench who just below require Designation\n",
      "These employee can be promoted to fulfill the reuiqrement \n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\tchecking For The Employee From Bench who are just below require Designation Method \n",
      "\n",
      "Searching the Employees those can be promoted from Location from where request is genrated\n",
      "\n",
      "Still we do need to hire 29 Employees to fulfill the requireent\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\tpromotion From Same Location\n",
      "\n",
      "\n",
      "Experience is in year & employee are sorted in descending order of experience\n",
      "\n",
      " these employee can be promoted from same location to fulfill the demand \n",
      "\n",
      "Right Now at bangalore this office no one for promotion\n",
      "\n",
      "Still we do need 29 more employees\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\tPromotion From Different Location\n",
      "\n",
      "\n",
      " these employee can be relocated from location other location to fulfill the demand with a promotion  \n",
      "\n",
      "      Employee Code     Last Name  Designation  SkillList  Experience_Y  \\\n",
      "8869          34853  SURESH KUMAR            9          5            12   \n",
      "2110          67573         DUTTA            9          5             8   \n",
      "8554          89843         SINHA            9          5             6   \n",
      "8             77180     ABBARAJUL            9          5             5   \n",
      "8560          20652         SINHA            9          5             5   \n",
      "\n",
      "      Location  \n",
      "8869         2  \n",
      "2110         2  \n",
      "8554         2  \n",
      "8            2  \n",
      "8560         1  \n",
      "\n",
      "Still we need to hire : 24 Employee\n",
      "with: Angular.js Skill set\n",
      "Location is : bangalore\n",
      "Designation is : PORTFOLIO MANAGER\n",
      "Practice in : INS\n",
      "--------------------------------------------------------------------------------------------\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\t\t####################\n",
      "Experience is counted in year and sorted in descending order throughout the code\n",
      "--------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "prediction(Month=7,SkillList=5,Location=8,ExperienceGrade=10,Practice=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now I am making some changes in the dataset to demonstrate when it'll be deployed to original data or if it has found the desired match then how it is going to perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupbyEmployee_df.at[199,'Location']=8\n",
    "groupbyEmployee_df.at[199,'Designation']=10\n",
    "groupbyEmployee_df.at[199,'SkillList']=5\n",
    "groupbyEmployee_df.at[210,'Location']=1\n",
    "groupbyEmployee_df['Market Unit'].at[210]=1\n",
    "groupbyEmployee_df.at[210,'Designation']=9\n",
    "groupbyEmployee_df.at[210,'SkillList']=5\n",
    "groupbyEmployee_df.at[250,'Location']=6\n",
    "groupbyEmployee_df.at[250,'Designation']=9\n",
    "groupbyEmployee_df.at[250,'SkillList']=5\n",
    "billable_employe_df.at[8,'Location']=8\n",
    "billable_employe_df.at[2110,'Location']=8\n",
    "billable_employe_df.at[8869,'Location']=8\n",
    "billable_employe_df.at[2110,'Market Unit']=1\n",
    "billable_employe_df.at[8869,'Market Unit']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------------------\n",
      "Month of: July\n",
      "Month of: August\n",
      "Month of: September\n",
      "Required Skillset is : Angular.js\n",
      "Location is : bangalore\n",
      "Designation is : PORTFOLIO MANAGER\n",
      "Practice in : INS\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\t Prediction Method\n",
      "\n",
      "We do need these empoyees in upcoming two months \n",
      "\n",
      "   Month  SkillList  Location  ExperienceGrade  Practice   Request\n",
      "0      7          5         8               10         1  9.389035\n",
      "1      8          5         8               10         1  9.391431\n",
      "2      9          5         8               10         1  9.391431 \n",
      "\n",
      "Company need 29 emoplyee to fulfill the Demand \n",
      "\n",
      "checking for the required employee in the bench dataset \n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\tchecking For the Employee From Bench\n",
      "\n",
      "These Employee can be assigned to project\n",
      "    Employee Code MonthOnBench SkillList Location Designation Market Unit\n",
      "199         19368            7         5        8          10           1\n",
      "we do need to hire 28\n",
      "These employee can be promoted to fulfill the reuiqrement \n",
      "\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\tchecking For The Employee From Bench who are just below require Designation Method \n",
      "\n",
      "    Employee Code Location SkillList Designation Market Unit\n",
      "210         63628        1         5           9           1\n",
      "250         48956        6         5           9           1\n",
      "\n",
      "Still we do need to hire 26 Employees to fulfill the requireent\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\tpromotion From Same Location\n",
      "\n",
      "\n",
      "Experience is in year & employee are sorted in descending order of experience\n",
      "\n",
      " these employee can be promoted from same location to fulfill the demand \n",
      "\n",
      "      Employee Code     Last Name  Designation  SkillList  Experience_Y  \\\n",
      "8869          34853  SURESH KUMAR            9          5            12   \n",
      "2110          67573         DUTTA            9          5             8   \n",
      "8             77180     ABBARAJUL            9          5             5   \n",
      "\n",
      "      Location  Market Unit  \n",
      "8869         8            1  \n",
      "2110         8            1  \n",
      "8            8            1  \n",
      "\n",
      "Still we do need 23 more employees\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\tPromotion From Different Location\n",
      "\n",
      "\n",
      " these employee can be relocated from location other location to fulfill the demand with a promotion  \n",
      "\n",
      "      Employee Code     Last Name  Designation  SkillList  Experience_Y  \\\n",
      "8869          34853  SURESH KUMAR            9          5            12   \n",
      "2110          67573         DUTTA            9          5             8   \n",
      "8554          89843         SINHA            9          5             6   \n",
      "8             77180     ABBARAJUL            9          5             5   \n",
      "8560          20652         SINHA            9          5             5   \n",
      "\n",
      "      Location  \n",
      "8869         8  \n",
      "2110         8  \n",
      "8554         2  \n",
      "8            8  \n",
      "8560         1  \n",
      "\n",
      "Still we need to hire : 18 Employee\n",
      "with: Angular.js Skill set\n",
      "Location is : bangalore\n",
      "Designation is : PORTFOLIO MANAGER\n",
      "Practice in : INS\n",
      "--------------------------------------------------------------------------------------------\n",
      "\t\tNew vacant seats due to promotion\n",
      "\n",
      "   vacantDesignation  vacantSkilllist  vacantMarketUnit  vacantLocation\n",
      "0                 10                5                 1               8\n",
      "1                  9                5                 1               1\n",
      "2                  9                5                 1               6\n",
      "3                  9                5                 1               8\n",
      "4                  9                5                 1               8\n",
      "5                  9                5                 1               8\n",
      "Due to promotion, 1 seat will beacome vacant at bangalore with these skill Angular.js and postition is  PORTFOLIO MANAGER\n",
      "Due to promotion, 1 seat will beacome vacant at bangalore with these skill Angular.js and postition is  PORTFOLIO MANAGER\n",
      "Due to promotion, 3 seat will beacome vacant at bangalore with these skill Angular.js and postition is  PORTFOLIO MANAGER\n",
      "Due to promotion, 1 seat will beacome vacant at bangalore with these skill Angular.js and postition is  PORTFOLIO MANAGER\n",
      "--------------------------------------------------------------------------------------------\n",
      "\n",
      "\t\t\t\t####################\n",
      "Experience is counted in year and sorted in descending order throughout the code\n",
      "--------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "prediction(Month=7,SkillList=5,Location=8,ExperienceGrade=10,Practice=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction for some more cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction(Month=2,SkillList=5,Location=5,ExperienceGrade=10,Practice=10)\n",
    "#prediction(Month=12,SkillList=45,Location=1,ExperienceGrade=10,Practice=1)\n",
    "#prediction(Month=10,SkillList=11,Location=2,ExperienceGrade=10,Practice=1)\n",
    "#prediction(Month=4,SkillList=11,Location=2,ExperienceGrade=10,Practice=1)\n",
    "#prediction(Month=2,SkillList=5,Location=5,ExperienceGrade=10,Practice=10)\n",
    "#prediction(Month=12,SkillList=45,Location=5,ExperienceGrade=10,Practice=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                         thank you !!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\rishi'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  }
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