Gradient Decent Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59fd6aaf",
   "metadata": {},
   "source": [
    "# Gradient Decent Algorithm\n",
    "\n",
    "- Finds the best fit line for a given train DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ae584a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"FuelConsumption.csv\")\n",
    "\n",
    "cdf = df[['ENGINESIZE', 'CYLINDERS', 'FUELCONSUMPTION_COMB', 'CO2EMISSIONS']]\n",
    "\n",
    "msk = np.random.rand(len(cdf)) < 0.8\n",
    "train = cdf[msk]\n",
    "test = cdf[~msk]\n",
    "\n",
    "X_train = np.asanyarray(train[['ENGINESIZE']])\n",
    "y_train = np.asanyarray(train[['CO2EMISSIONS']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "815e3126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m [-1.73511429e+102], b [-4.40696161e+101], cost [2.78674263e+205] iteration 935\n",
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      "m [-1.2746163e+109], b [-3.23735741e+108], cost [1.50383305e+219] iteration 999\n"
     ]
    },
    {
     "data": {
      "image/png": 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AIUQKYADADt5T/pdS+oS7NySEJAN4E8AXABKj2vBdJfBkMpnb+JedrHl5eTPEMtgJLqZMY7Va0dTUBEqpT1Nw+OEBpRQdHR3QarVBadLhJ6ZYPMqSbL29vQAQlCRbKGNsfoPOvHnznEQ+2TXYbDakpKQgOTk5YtRisQnGuLi4GR2UIyMj+Ne//oXu7m6sWbMG69evx+2338695swzz0R3d3egSzwHQCeltMeH16QCSAbwBwATUWv47mrz7jLnQ0ND6OrqcnuyMk/B283EOPslJSWYnJz0WRSCrbuhoQHp6eluhTgDpewKlXL5Sbb29naOjadQKILathtMCBOFNpsNDQ0N0Gq1GBwc5DQIFAqF3yw8f2C32/3yoORyOebOnYs//elP+P73v4+XXnoJHR0d/izhNEJIPYBBAPdSSpsFj18N4HXB7zYRQm4AsB/APdOJPD4mAfwPAAOl9FhUGr6ncVVSqdTpxLfb7WhtbYXVasXy5cvd/sG8ldoopejr68PAwADH2e/q6vJp3YQQ6HQ6dHd3iy4bBgvCJJurtl1mZJ7KVeHuzuNDJpNBLpejuLgYSUlJnLbfwMCAkwYBS9aGap2BlhTZ9z1nzhzMmTPH15cfBFBEKdUTQi4E8BaABexBQogcwCUAfsl7zV8xVaqj0//9I4Cb+G9KKR2f/j2AKEvuianN8//Yer0ejY2NyM/Px9y5cz3eCJ4Mn7Xjstn1/riYLAYfHx9HXV2dqJyAOwSDwCOUw9Lr9TNia5YfiKayE3/j4Wv78Vl4/PmALFEYzGSnv8w9hkBYe5RSHe/n9wghfyGEZFNKWY/4BQAOUkpHeM/jfiaEPA/gXVfvTQhJBVAFwBA1f3FfavOUUvT396O3txeVlZWiGFLuGnUmJibQ2NjIteP6A7Zx2Gw2lJeXB2T0oQC/Xl1UVDSDhMPCBoVCEdFyGuD+tPWkQcCUfFiiMFAyVDBOfH8NnxAyC8DIdFPNcgASABreU66BwM0nhMymlA5N/++lAGaUCwghWQBuBHAuAHPEDZ/fNw94l8Sy2WxcrXj58uWiTytXhJyBgQH09fWhqqoKKSkpfq2feR2FhYWQy+VBMZpQU3aFsTXrcBscHIRWq0VLSwtycnICHrntD8SGGq7INyxRyCdD+aNBEOiJr9fr3d5P11xzDT799FOo1WoUFBTg4Ycfxg9+8INbAYBS+iyAKwDcRgixYSouv5r10E9n5s8D8CPB2/6BEFKDKVe/28XjAHA6gO8CuA5AWkQN3xdJLADQ6XRoamqCVCpFZWWlT5/FP/FtNhuOHDkCQojfrj0ADA8Po6uri/M6JiYmjgvyhhD8DjeLxYKioiIYDAZ0dXVhcnKS47MrFIqobdIRJjsZGYo/DYifKPT0GcGK8V3h9deFOTng5ptvfpb9TCndDGCzq9dSSg0AZqi7UEqvF7GsXAAd02HBSMQM3+FwoK+vDxkZGYiPj/fq2vf29mJwcBBVVVVc77svYDG+Xq9HQ0MDCgsLUVBQ4PV1rk4gxhUwmUxO6ru+nNR6vR5Go9HlaRTJJh1gKj+QkZHBudSs9t7U1MSx11h+INglt2AlFxkZil9qGxsbw9DQEI4ePeqx6hHJGD+EMAGwAAAhJPxZHb5rr1KpkJiY6DExw2rq8fHxTqezrzeIRCKBSqWCRqMRnRdgBsj/HJPJhPr6euTm5rrkCogxWKbTn5qaiq6uLsTFxXGuN7sJI2n4/GtyVXvnn6TBLrmFKsfgToOA367LEoWhPPEjiM8AjBBC8iil4T3xhbV5byw81glXWlrKTW0BxNfkGex2O9RqNZe19yUvwL8JmJ4eowEL4c1gHQ4Hl4hasmQJx1IzmUxcos1gMHDvlZKSEnYFF28bjpC9xkpu/f39HDefGZC/Sc5w5BSEVQ+m/d/W1sa17fqrQWAwGLiyarSAUtoPoB+YSgaGzfBdSWJJpVKXnHVKKY4dOwaVSuWSLsvidTGGbzAY0NDQgMTERMyePdun0hUzZFeiG+6e724j43sK5eXlXBUDmOK3s5ovpRQtLS0wm81oamqCw+HgTtRgT6oNBoQlN6PRiNHRUW54BlP79SXTHu6qAp8VOXfuXOzduxezZ8/mOAS+ahBE44lPCJFQSh2EkHIA14fc8D3V5l3Rb8WIZXjzFBiGhoZw7NgxLF68GBqNxmf3WSKRcOtJTk52Et1wBXcnPvNc+Mq77m5uQgji4+ORkZGBrKwsLkmlUqk4Rh4LC0KRcQ8kxuZz85mIh06nw+joKJdpj8RYMF/Bmm9Y16Sr5hxP9OgojfHZH/U+AO+G1PC9SWIJWXjMlfbGenNXk2dgLrXZbOaSb2NjYz4LSdrtdhw+fBgLFizw2pkHzDR8lpQcGhryKPPl6X2ESarJyUkuvmYZ93DJYvkKvtovAKe2Y6aQG+62Y3/gqjmHT4+Wy+Xc3yAlJcXtie9vZ970Y90AJgDYAdgopUunf6/AVNfePEyV8r7ngq7LRwKA9pAZvphxVcyAWUPL2NiYR1eawdOJz2+BLS8v98rxdwcWs9bW1orWx+cbrN1u54Zz+NLo4w2JiYnIz8/nMu7sRB0YGAhKWBBKyq5wE2N8DH7bscVigclkimjbsTfP0J0GQW9vLzZv3sxpQOTn56O0tJT7Pv3tzONhNY/Bx/ALAB9RSh8lhPxi+v/vc/FadvPvBVAZdMP3pTYvk8mg1+uxb98+KBQKboqsN7g78UdGRtDZ2elyqq1UKuWGIniC3W7HkSNHQClFdna2TzJMbHMxGo2or69HQUGBXyO4xGb1CSFOslg2m21Go04ow4JA4artuL6+/rhpO2bgX8ef//xnXHvttZBKpbj33nvxxz/+EaWlpQCC1pknxDoAq6Z/fhnAp3Bt+CCESCmlfyKEPBxUw/eFdgtM1bL7+/tRXV3t09QZYYjA6uqTk5Nup9qKOfH5BltQUIDGxkaf8gKEEExMTKC3txeLFy/2e6S2v5DJZE6a8+7CAoVCEXWGxK9iVFdXh6zt2BsC9XgkEgmsVituu+025Ofn+/MW7jrzKIAPCSEUwHOU0r9N/z6PR9cdBuAyJp1m/9mnf/5N0AyfxfNihBTYrPjR0VHMmjXL51FTfCPmj6AW1tWFr/GUF2CnJN9gfQkPKKVQqVQwGAxYsWJFQPF2sOr4/LCAr5bb39/PJdqEYUEku/PY5wORazsOxvUHoLDrqTPvDErpACEkF8B/CCGtlNLP+S+e5ve7vHGm8wdpAPQATEEzfHbCe/vShGIZw8PDPn8WO/HZTSBmBLU7I3Y4HGhvb4der58hmCHW8JkmH6UUBQUFUZdkA2aq5QrDgoSEBCgUCi7nEm0IVtuxNwTK2mNr84fD4Kkzj1I6MP17JSFkB4DlmJLLHmFNOoSQ2QDcacy9BqAVgBXBVuDxZijDw8NcDJ6RkQGdTueXmiwhBAMDAwAgWt3G1drY6CuFQoG6uroZm5aYk5d195WUlHBioIEiHMw9YVjAtPFMJhMOHDgQEdlvX07bULUdB0Pe2xdyGR/uOvOmm3MklNKJ6Z/XAPjt9Mt2Avg+gEen//u2m7cfBXAnpbSBECIJC4GHiWVYLBYnsQxhrC4GJpMJ/f39SElJQW1tregbRWj4rLa+cOFCLsvs7TVCMJ4A6+4bHh4Oyuy5SFB2Wcvr0NAQampqYDAYoNFo0NfXBwCcWx2swZSu4C9d15e2Y28NOoGe+J7+bq4686bzAbd66swjhOQB2DH93cgAvEYp/WD6bR8F8CYh5GZMjcf6npuPvxnA+PQaHSE3fE9iGb4aPhOuzMvLQ0JCgs9cfYfDAUqnpuOOjIx4ra17Cg/a2towOTnpRAEWa7CUUrS1tUGj0SAzMxNZWVlRw8qj04KmLCwAvp1QOzIygra2Ni4sECuZ7ctnB+M78NR2zJR8+LRi/vqDceK7C3lddeYBwK233vos4L4zj1LaBaDa1WsppRpMafB5BKV0D///g2r4wovlD49IS0ub8Xyxhs/q/OPj41i6dClGR0cxOTnp09okEglsNhvq6+shl8u9jtBi1yM0ZH54sHDhwhkNLd4Mn6/HV1NTg/Hx8RlxttVqjaqsu3BCbTBoua4QqsQiv0GHTiv5CKsdzCMIxokfjTkSIUJy4rN+dwAem2LEGL7JZEJjYyMyMzOxZMkSUc09rjA5OQmNRoNFixaJVtoRnvhMY99deODN8Fk+YP78+cjOzobVanUitDCDUqvVsNls0Ol0nEGFUx7Lm/GxsKCgoGAGLRfwPywI5fgsBsJT8hFKfjc1NcFisUAqlUKj0fjVdmyxWIIygivUCPrdxMQyioqKvNYxvbWxMgovn+POXudLiDA4OIiuri6kpqb6JK/FX19fXx/6+/s9aux7uh4m2sHyAa42LnZDAt/eoBqNBj09PZBIJFAoFMjKyooqequQlsvKbmxePT8s8JbpjkQpUdh2rFQqoVQq/W47ZgzEaEdQDX94eBgdHR0BSVkBUzdAZ2enWwqv2BOf3wZbV1eH5mahSrFnEDI18YZ1yXlT63HVnUcpRXt7OyYmJtySi9y9F7+ObTabOTKLXq93IuNEU/mQX3bju9UsLPDExgvHiS8GKSkpmDdvHgDf246jsTPPFYJq+JmZmQFJWQHfduelp6djyZIlLm8EMSc+I/bk5eWhvLwcdrvd5/DAZrOhv78fxcXFXlV8gZmuvtVqRUNDA9LS0lyWC92dcK5Chvj4eCd6K989ZRz9rKyskGbdfYXQreaHBYyNxzav1NTUiJOHgJlZfV/bjj115gUwPmsugFcwxcqjAP5GKX1q+jUPQcT4LCGCavgJCQku++vFYnR0FC0tLV6787yd+Gq1GkePHnUi9vjapMNKWXl5eSgsLBT1Gr7Bsni+tLTUbWdfIO2vfPeUkXGYe52YmOiUdY8WuAsLWLY9Li4Odrsdk5OTEVu3J6/DW9txX18f3nnnHZhMJpjN5hmxfgBNOjZMDck4SKYksg8QQv5DKT0y/fj/Ug/js1whpFl9sXA4HOju7oZKpRLVnefuxOeHCEuXLnX64sXKYjERELVajeLiYp82C/YZrFnIU8jj6bvyJOjhCnwyDv9UYjPfmUZeZmZmVPXAC8MClUqF3t5etLe3O4UF4Uxu2u120aGTcCObP38+Wltb8d577+H000/H9ddfjzvvvJN7vr9NOtNc/KHpnycIIS0A8gEc8fhCD4i4vDYAHDx40KPwhhCuTnxWJktLS3M7tsobbDYbGhsbkZCQgKVLl0KpVHJSWGKh1WqddADCDeGpxMgsGo0Gx44dg0wmQ1ZWVtSN1mICJGlpaTjllFOcmnR6enpACHEKC0JJIvL3vdPT07F48WIQQvC73/2Oa0v3ER7HZxFC5gGoBcCvy3sbnzUDETX8sbExGAwGn4dZCE/88fFxNDU1YcGCBX5rnTH1Xf5axHoJwJTb2traCkqpy3jeFwSTuSckswj1/ViMGg0de3zmnqsmndHRUS4sCFU4E0yFXT++T2/js1IAbMMU9Zbx+r2Oz3KFiBg+pRTd3d1QKpWclpkvYPV/Oj3vbnBwUPQoa1dgrrlQfVesy802jTlz5kCr1UbNKeoKQn0/FqP29/cDmEqujo+Ph/RUdQdPlN24uDgnEo4wnAlWWBBoZcHTMA1v8NSkQwiJw5TR/4tSup33PFHjs4QIe4zPOtkSExOxbNkyNDY2+szXZwbZ2NgIiUSCZcuW+T3vzlOpTUxCUKlUoqOjA5WVlZBKpRgb8+pleUW4uPpCIQ+r1Yp9+/ZhcHAQOp2Ok5xWKBRhUcQRS9l1lWTjhwXMW8jKyvJZ8jvQE99oNPo9is1Dkw4B8AKAFkrpnwSv8To+yxXCeuIzl5wvl+1Po47RaITBYEBRUZGooRiuwHICGRkZbl1zTwbIEolarRZLly6FXC6HyWQSbbAGgwEDAwMcwy0avIS4uDjExcWhvLycU8RhlRabzRbSQRqA/wQeYVjAuPkDAwNOG1hmZqbXsCDQE99oNLot5wXQpHMGgOsBNBJCDk+/HSvbiRmfNQNhMXzKE50UuuS+Gj5jwCUmJvpl9My9FZMTcHfisznuycnJHI0YEH9Ss3LjnDlzMDg4iNbWViQnJ3NJt0h05wnBFHFSUlJQWFjIKc1qNBpuCAhbb7BkvYJF4BFy8/lhARuu6a5lN5Rz8wJo0vkS36rkCh8TMz5rBkLu6lutVjQ3N3ONMcIvVazhs444o9GIZcuWYd++fX6tr7+/H/39/aipqfFKrXSV3GO6cMXFxZg9e/aM9/cUGrDOQKVSyW0YTDSTtcEeOXIEJpMJ8fHxSE9PjxpCjlBpVtjowk8S+htjh4LA4yksYC27/GrBCTpFZwaCfuLzTyt2sroyEgaxjToNDQ3Izs6e0REnFg6HAyaTCWq1GsuWLRN1cwoNmbUFV1ZWuuw29HRSOxwONDc3gxDClRuZ+Cf/dC0qKsLQ0BBGR0edCDnM6KKlAUQo68Vn5LHSm68xdjgou67CApbcnJiYgMVigVKpRE5Ojl95DaPReHIaPvDt/HompOnpZPVm+CzGFDbqsM8Rc1OxKTZSqRSLFi3yeYQWnZ6kMzo66lHxx53hs8+fPXs2R/315MrLZDIkJSWhuLiYc1WZN2Cz2aKuh59PZCkpKZlhTPwwxtPGFQnKrlwud6Lk7t27l+vxsFqtPiv5nLQnPmtqkUqlonj77gyfMejcsfmY8Xi7UdjGUV5ejmPHjvmsmsuGaiQmJrrtHWBwlRNgCU1XG5fYNTBXtbCwkBtKwXr4WT07Kysrolr0fAiNSSiLxbrdMjIynL5Ph8MR1vZjIZiARlFRkVslH35Y4OreC6ScF04E/Vuur69HXl6eaGlhqVQKs9ns9DtW8ktKSvI4Rstut7s1RL7SDts4enp6fKLCss4ssT38wpN8cHAQPT09PnMMPHkE/KEU/MQVO6HY6C2hUUUKQlks/jSdjo4OxMfHc95ANDTp8OFKyUfoyQjLnUaj0aXCboBTdM4H8BQAKYC/U0ofnf59MYA3AGQBOADgekqp9+ERCIHh19XV+fR84YnP8gIlJSVOE3Ldvc4VO8pms6G5uRkymcxp4/ClUUelUuHo0aNITk4WXZdlNy2T1jIYDKLzCf7AFT2Xb1SsFz4rKytqmnVcjQTTaDTo6OiATqdDSkoKpFJp2MVHGDxtPEJPhpU72ab74YcfwmazubzH/G3QIYRIAfwZwHmYmna7jxCyc7pB5zFMNei8QQh5FlO6en8Vc51B/2Z97YLjG35/fz/6+vq85gU8fQ6bjjt37twZ5T4xffz8Jp26ujqXu7O31x88eBBpaWk+iYHy4W85T5h5F5axQl2H9wesLFtQUICuri4QQqDT6aJafARwXe4cGRnBtm3bcO655yI7Oxu7du3i1hzAFJ3lADqmdfdACHkDwLrpRp2zAVw7/byXATyESBm+r2CjsllfudgT0lVuwNVQDD68bUpsHfHx8Vi6dCkopT5tYgaDAQaDwYmgFEnwe+GZN8BKcHK53KkOHw2glCItLY3zBiwWCzQaDSc+kpKSwq05msRHgKn78aKLLsJjjz2GPXv2+EvddtWgkw+gj/ecfgArMOXeaymlNt7vRY/uCUk5zxdYrVaMjIxg/vz5osQuGPhGzGfRecu6u6sgsPFZhYWFXH7CF+FERspJTEwM2OhDQeBxVYdnLrbJZOJyA5EkDgkpu3K53Kv4SCADQoUIljQ6U0/yER4bdIKNiJ74SqUSR48eRWpqqmixCwZ24jOVm9TUVCcWnbvXuPrjMm0/oacgxgD5pJylS5fiwIEDPl1HpMB3sR0OB8fKMxqNOHz4cNBZeWLgKbnnTnyEr1DM1uxvPsPfQRj89fu7cbpr0AEwAIA/ebVg+ncaABmEENn0qc9+LwoR685rb2+HTqdDTU0N2trafH4PiUQCvV6P1tZWjyo3wtfwDZ91CapUqhnCHWIgJOX4qiir0WiQlpY2w0MJN2WXX6bSarUoKyvjqLmTk5NIT09HVlZWyIU8fCHwCMVHmAcTiPhIoAQik8nk96bjrkEHgBbAgukM/gCAqwFcO/28TzDF738DnqfozEDYXX2LxYL6+npOLttms/k1RstoNGJ4eBhLliwRXTflG77dbkdTUxPi4uJ8NlpgqtR3+PBhzJo1C4WFhT6diuw7kMvl6OnpAQAnplukkZCQ4MTKcyXkkZWVFXRvwN9yHuFp+zFaLstn+CI+Egyevq8NOgBw6623Am4adADYCCGbAPwbU+W8f/DEOe4D8AYh5HcADmGqg08Uwnris7FVfE09X5t02EhslkTzhSzBDH9ychKHDx92mfkXg0BIOax3v7S0FBkZGSCEwGq1QqPRcPVh5nlEw2ANIcXVbDY7eQNpaWmcNxBo+S1YlF2+B8NfMxMfSU1N5TYC/vcbSp6+uwYdBncNOtOPvQdghoDmdKZ/uc8LRRi783p6ejA8PDxjbJUvKjdmsxn19fXIzs72y2AlEgkmJibQ3d3NDe70Ff6ScoBvE4BMi4/t+HFxcU714aGhIQwMDKC+vh4SiYQ7YaNBKis+Pp4T8mAcfab9zwgv/q41VAQe/ppdiY8wCjSAgNV3oqVC4g0hd/VZiUwul2P58uV+76jMW2CnbH9/v09ZWEopxsbGMD4+juXLl/tMbw2ElMPakoeHh7lcgsPhgNVq5TLZ7B8j5aSlpWHhwoVcSYudVvx429Ma1EY1enQ9KEorQnaS66GggUIoNsm0//myXuxkFfN9hWuSjlB8hCn9jo2NgRCCwcFBv8RHjheePhDiE9+Vjp2v4Mtr8b0FiUQiWszQbrejubkZZrMZ+fn5Pv9B2cw9f0g5DocDLS0tHEeBlRQdDgfi4uLgcDjgcDhgt9u5kIc1BgHOJS3+Cdvd3e023t7SsgWb/rMJcZI4WB1W/HnNn3FF2RU+XbM/4Gv/OxwOTExMcHV4MWScSFB2+Uq/arUaKpUKNpvNiQItlvQUM3x8O0JaqGPnC5jBEkJm9PKLnaYzOTmJ+vp6zJkzB3K53GfVXIfDgb1793psLRaC3cAsiZednc1NZmGGTgiBRCLhrokZOyPasNifeQHMI2AnbGlpKUwmEzQaDTo7O7laPJKBTf/ZhEnbJCYxNVj0xx/+GKsKV4Xs5HcFiUTCnaz8jj3+JCBhnC1WeitUoJQiISEBhYWFTuIjjPQUFxfHbV6uEpuehmlEG4Ju+JRSHDlyBGaz2ePATFev43+RjFDDas3CL1nMNB3WmccGayiVSp/CA7VaDaPRiBUrVrhkAroCWyfzdubPn8/pxvON3tX1MCLS5OQkFi1aBABOHgEwteEx4xBm37VaLT7v/BwS6mw8cZI49Oh6wmr4Qgh57swbYHG2QqHg9AkiBWFWX0h6YgrF/Cm7LOyKi4tz25nnrUGHgRCyDMB/MZXR30oIWQ3gf3lPKZt+7C1CyEsAzsL0zHsAGymlh8Vea9AN32KxIDk5GeXl5T6z8NiXzpJgnhJwnqoBLDwYGhpyaukV20fA7+xLTk72yWMhhECtVqOtrQ1VVVXcaCimCuzK6IGpm66xsREpKSmoqqpyeo4wHGDXLfQGFAoFzkg4A47/Ol+j2WaGXW3HqHzUY9deuLgDfDIOi7M1Gg1MJhMOHToUsbmA3nIMfIVifijT19eHAwcO4Msvv8T8+fNnbCBiGnSmm3EeA/Ah+x2l9BMANdOPKwB08B8H8DNK6VZ/rjXohp+QkICioiKfXsNvsWWCF94INe6M2OFw4MiRI6CUYunSpT6HB3xSDpP48sUgzGYzOjs7ufVTSrmxYu5uKqYwVFBQ4DIXwgxbJpNxIQHfC7Db7dwGkJ2UjT+v+TN+/OGPuRj/mXOfQWleKde1F22KPqyqwaYRMxmyxsZGAN/O1wu1KKndbvdJpIWFMgBQUFCAxsZGfPPNN6itrcVTTz2F1atXAxDdoPMTTMlnL3Pz+BUA3qeUGkUt0Asi3qQDfNuT39zcLErwgr1GeOIzpRt3pBpv4YErUg6T3/KW2GFJPLvdjtraWsjlci6J5+6UB6bakJubm1FWViaK382+F6lUyiUHmTfBvIFLSi7ByhtXom+iD/My5nEuPuPiCxV9oqUDjsX4Qm9gdHQUAwMDaG1tRUpKCrfeYHsDdrvd740wLy8Pc+bMwdq1a3H55Zf7NEOSEJKPKWns1XBv+FcD+JPgd48QQn4N4CMAv6CUmme+zDUi3qQDTBlNfX09SktLRSfQhCc+K/eVl5dzxA1vr+HDHSlHTHjAT+KxGM9TPM+gVCrR1dWF6upqv+u/zBtgn8k2AEWCAoqEqe/BZrPNKBfyFX3YwM3x8XGYzWYMDQ2FxLDEQPhdCQdp6PV6aDQarlGHbQLB8AaCMUyDKfP4SLx6EsB9lFKHq2sghMwGUIkp9h7DLwEMA5AD+BumWHy/FfuBITnxfeGasxtu0aJFoo0ecD7x+/r6MDAwMIMcJIQ7I2YVCFekHG8EI2ESb3R0lAtb3Bk9yyGMjo5iyZIlQWPn8b0BAC69AUoppFIpFxrwOe92ux0HDhyAxWJxMqzs7GyfB1OEAnw1H9aow8ZqCSXK/Tm5g6Gp72c5bymmqLcAkA3gQkKIjVL61vTj3wOwg1LK1a/pt0M0zISQFwHc68sHRszVdzgcaG9vh8FgwKxZs3z+QzG3vbm5GXa7XdQ0HVdNOu3t7dDr9W4rEMzVdwU+E48l8eLi4tDa2orc3FxkZ2fPMGoWEhBCUFNTE9LylTtvgF0P3xPgbxpMc46RWxiV2B3VNVKQyWRO03b5EuV2u51j5ImVKA/m3DxfQCktZj9PZ+vf5Rk9AFyDqRMevOfNppQOkandYj1ETtBhiIjhM9dYoVCgtrYW7e3tPjfq2Gw2jI+PIzc3F0VFRaJOI77hs6EYKSkpHkk57rwERkEWJvHKyspgMBigUqlw6NChqYRbdjZycnIQFxeHxsZGZGdn+9zYEyjceQOuqgXs5BOOsRaW4FiCMNK5AWCmRDk/hBErUe5Jw1EMWB+AECIadDxd1zxMteV+JnjoX4SQHEwN2jgMwPMbCRB2V5/F0oE06mi1WjQ3NyM+Pp4jxogBM2JPQzG8XQuTXrbZbJyeHz+Jx09OlZaWwmw2Q61Wo7W1FVqtFgqFAikpKREXlhR6A3a7Hf39/UhMTITNZuOuhf9fftJNSMjxlZ4bagjbdsVIlAfaj++OueetQYcPSulGwf93w4WyDqX0bJ8XyENY/0JMU8/VGC2xWVCm119XV4dDhw759PkSiYTL3LuT53L1GnbiW61WHD582ImJ5y1zHx8fj6SkJFgsFixduhQ2m40bzJGUlMQJT0ayrEYIQWdnJxwOB8e/YMlJh8PBbQR88pCQkMOoxL29vU7El3AKeXi6Pk8S5UzEw2q1Bmz40dBWLQZhMXxWW3c4HC619sXW1xl/2p/puIzUYzKZcOaZZ4o2NKGXIJaJxzA4OMjVpxmRiF9WU6lUaGxshMPhQFZWFnJycsKaSGO6BCkpKSgpKeE+lx8SeCMP8RtfSkpKZrTuhkvIQyyEEuVMxGNiYoLTivBHopyNPjseEHLDZ1z52bNnu41rXWnr88HacXNycnxiBDKwjQeYEqD05Y9DCIFWq8Xg4KBPTDxKKTo6OmA0GrFkyZIZNzz/FJo3bx7HXuvt7cXExATS09ORnZ0dUtfZarVyvAd3bc6+kIeYkQhbd/lCHmzYJvMGIg2+iIdSqURlZSUmJib8liiPhlkGYhCyGB/4VsuOceXdLkImc9s8w3ICCxcu5NRXfYGQlPPf//7Xp9ezuvGyZctEM/HYKZqUlDSDfusOwp788fFxqFQqzlhYgjBY+viTk5NoaGhASUkJl2vxBjHkIWG5UCjkwYZtCkU+xfZChBKsY9KdRDmT9GLeAH8zj6RIqT8I2ey8Y8eOcVNhvbXBukvuDQwMoLe31y/RCyAwpRwWWpjNZixYsADx8fGimHhmsxkNDQ2YM2eO6GlCQhBCnPrcJycnuQSh2WxGVlYWsrOz/VaXnZiYQFNTExYtWhSQwXkrF7ryBvjDNvmjt1mH4cDAQETHgQn/rt4kyvnegLv7wt8mnenf2QE0Tj+ll1J6yfTv/Z6iA4TI8I8dO4bJyUm346+EEJbMmLyW2Wz2KHrBklCuPsMTKccbWBIvKyuLY4yJMfqJiQk0NzfjlFNOccse9AeJiYmYO3cuNy1ndHQUQ0NDHIWVxatiauvs9KqqqgpqC6mYciF7Hr8lmZ2ujDzEz+VE23BQdxLlR48exe233w6TyYRdu3Zh9erVTvecv00605iklNa4eInfU3SAEBl+cXGxT+2vMpmMc59ZjT8rKwtlZWUe3WS2YfBvCjGkHE8QJvE6Ozu5G5fdtK6gUqnQ2dmJysrKkPZkS6VSpzKVXq+HSqXC4cOHAYALCVxJXw0PD3MeVKiTUGLIQ/yQgLnZ/A2OL58dbY1FgLNE+c6dO3HZZZfh008/xb///W88/fTT3POC1KTDYZq04/cUHSBEhi+2/ZWBufo6nQ6NjY1ONX4xr2PGLZaU4w6umHhJSUno6OjA+Pg4cnJyZmR6WbWATfUNJ6ONT2FlYhdqtRqdnZ0wGo3IzMxEdnY2MjMz0d/fD41Gg7q6urDX2T15A+xni8Xi5MFJpdIZw0GFrLzs7OyQd+yJhcPhQG5uLh5//HGfX+ulSSeBELIfgA3Ao9OMvoCm6ABR0p0nkUhgMBjQ3NyMmpoa0Scmf4NhgyDEkHJckWd6e3sxNDTkxMSz2+1cZn1sbAwjIyM4evQoUlJSkJOTg8zMTHR1dcHhcKC2tjbi7qhcLnfKpmu1WqhUKjQ1NUEikaC4uBg2my3iBBtX5KGhoSGP5CFhHZ7P0Q/GaK1Ak3MBqu88CfdNOkWU0gFCSAmAjwkhjfhWfMNvhDSrLwYOhwNdXV0wm80466yzfLop2YnvbhKOKwhFP/hMPNa/L2TiSSQSJ/d6YmICIyMjaGlp4dxTk8kUFeUpBibTNTAwgPz8fMyePRsajYbrbWAJwkifmIQQdHV1wW63Y+HChQC+5Q6wTUGYIBRy9Fnlhd+/z2YUiL22YHTmBaC357ZJh1I6AExJaRNCPgVQi6mQIIP4OUUHiPCJz+fsJyQk+HwSEUIwMDAArVYrehIO3/D5Sbzi4qk+CW9JPNZyOTo6ymXFWYjAMu45OTlIT0+PqEExgdCcnBxuPBnjsbMe976+PkxMTHCDKrOyssLqDTCZtri4OCd+hi8JQmHHHrs2fmMRC3c8hWGBGn4gJ767Jh1CSCYAI6XUTKbGaX0HwB8CnaIDRNDwdTodmpqauCTayMiIT69nxJCkpCTR1QNgJhOPjd8Sy8TTarWcjh9ffYWVezQaDQYHB9HS0oLU1FTk5OSE3aCYok9RUZHL0WLCHvfx8XGo1WpOuTcnJwfZ2dkh9WAcDgfHGCwuLnb5fftTLhReGyPjMKVfdzMKgtGZ5+7ED6BJpxzAc4QQB6ZGaj1KKT0y/ZjfU3SACLn6rNRWXV3t1y7JSDnx8fEoKiryaaeWSCQci6yyshJpaWmimHhs3azXwFWdWSqVOrmgOp0OKpUK3d3dISHhuILBYEBjYyMWLlwoStGHzxmYP38+TCaTkwfD+vF9pa96gt1uR0NDA7KyskQPS/WmNcCqQvxKAb+xiCU/3c0oCOWJ72+TDqX0a0wJcLh6nt9TdIAwn/j8oRT+lNoAZybf2NiYT9UDYOo07O7unpHE80a/7erqwsTEhOisOJ+/Pn/+fI6E09LSAqvVyoUEwYyxtVotl+vwN95MSEhw8mBGR0e5pGZycjKXafc3icZCkNmzZ/s9awHwT2vA3YwCNr+Q9fT701gUYIwfdoTN8C0WCxoaGpCRkeFXqQ341lNgmf/x8XHRhs8nhtTU1HDTbPhxoyvY7XYcOXIEcrkc1dXVfhspn4Rjs9k4dVYWY7OQwF93U6lUct9NsFhvrjgDarUa9fX1AMBtAmL78S0WCw4fPuw2BPEX/pCHWPKTsSNVKhX6+/u5xiJ31Fx3CEB9JyIIi6s/MTGBxsZGLp53B3c96oyUMzEx4eQpiO3jZ80oTK1VLBOPJR9nz57t16w+d5DJZDNibJVKha6uLsTHx3MhgVgD7uvrg1KpRF1dXch4BPwkGuvHZyGTwWBARkYGV/p0ZShMCLW0tNSvngtfICY3wN8EgKl7KTU1FfPnz+dKoazDUC6Xc7kBd2GawWAQ3fMQDQj5iT88PIyuri5uUKQ7CMk4DHxSTl1dnZORelPNBTAjiccIIN6MXq/Xo6mpCQsWLPCZ5+8L+DH2ggULYDQaoVarncpu7lp12QAOo9EYdh6B0G3WarUceUi4eRmNRjQ0NIjOOwQTfMOmlDptAnyPj/EH2Gv403YZNZc16rhq23WX3AtgmEYNpph4aQDsAB6hlP7f9HNfQgDDNIAQG35bWxsmJiawbNkyryeRK8Nn03SKiopcxoOsJOcOrL7PT+LJ5XJ0dHRg9uzZyMnJcRmrspbMQGJlf5GUlMSNcGKtuj09PdDr9UhPT0dOTg4UCgUIIWhpaYFMJkNlZWXE1Xz4hsLfvMxmMywWCxYsWODXdOJggm30fG+A/dNqtYiPj58xtgxwpuayRh1+2+74+DjGxsZc3isB8PSNAG6glLYTQuYAOEAI+TelVDv9uN/DNIAQducdPHgQqampM05pdxC67WJIOVKpFCaTyeVj7ph4hYWFyMnJgUqlQn19PQghTvz2vr4+jIyMoK6uLiLy0nzwW3VZ+VKlUqGjowMWi4XLxEcDZZUPtnllZGSgqakJxcXF0Gq16O3t5erqWVlZERfsZCFBV1cXrFYr5s+fDwAetQb4jTqMSrxt2zZ8+umn+OKLL7B+/Xrcc889nJfoL0+fUtrG+3mQEKIEkANAG4xrD1mMX1ZW5lMdmBk+dTFS2h1c9QS4YuLxe+ilUiknylhcXAyz2cxJYY2Pj0Mul6O8vDziN6UQrK89OTkZWq2Wm1bE1HvY5hUNwpfA1JyDo0ePora2louLWYmT1dX5fPxIDZvs6uqCwWDA4sWLnbgArrQGgJnkoeTkZNxwww34/PPPcdddd2FkZMSnJqKBgQHAyzANQshyTOnnd/J+7fcwDSCErn5KSorPjTpWqxXNzc2glIoi5Qi9BJbEy8zMRHl5OQBxmnizZs2CSqVCQUEBUlNTOQ4437WOBskoFivz8w6MraZWq7lEG2tgUSgUEekfYLG+sMLAL3Gyab9qtRrt7e0wmUxOTUXhWDcz+oqKihmf5yt5yGAwIC8vD6eeeqpPa7jzzjsB78M0XgXwfUopM6iAhmkAUdKkw9DS0oL8/Hy/5LL9ZeIxJZqioiLMmjULALjXsyaXzs5OJCYmcoy2SIQA4+PjOHLkCBYvXjxD0DEuLs4p0TY2NsYZVFJSUljXPTIygp6eHm6MmCcIOQNjY2NOQqShXDf/pPd2r3kjD/EVin3F/v37ATc8fUJIGoBdAH5FKf2GvSbQYRpAlBi+TqfDyMgICgsLfZLLFjbp8JN4YoyeGVN5efmMxBMhhJOMYsQOd3mBUIMlkmpqarzeXHxaqnDdgOd+/UAxODiIoaEhv1p/hW24BoMBarUaDQ0NXCgTrIk+vhi9Kwi9gTfeeANms9kv/sSxY8cAYB4wg6cvB7ADwCvCJB4JcJgGABAv7Yh+9yrabDZRNXZW7lMoFEhPT/dpjBYbXyWVSjlSjlj67cjICLq7u1FVVeXzTs3yAiqVKuSNOQMDAxgcHER1dXXAJx/r11epVDAajRwdNxiudW9vLzQaDaqqqoIeFrFQRq1Wc9UNliD09bM6OzsxOTmJioqKoPyttm3bhr///e949913vQ7TyMvLc8fTJ4CT4W8lhFwH4EUAzby320gpPUwI+RhTiT5umAalVO/LuiNm+EyFVqfTobq6GgMDA5BKpaKJMg7H1DhrtVqNM888c0YSz92NzPQAx8fHUVlZGXDzDPM4VCoVdDpd0PICbJ06nQ6VlZVBNyaHw4HR0VGo1WqMjY0hOTmZc619SWyyder1eqcEWajAqhtqtRoajQZyuZxbt7cNPNhGv3PnTmzevBnvvvtuoKXKsGdjI2L4jJSTnJyMU045BYQQ9Pf3w263c9lqT2BJvNTUVOh0OixdulSUa89ktmUyGU455ZSg36T8vMDo6KjfeQFKKVpbWwHAq/xYMMCX8FKr1ZBIJKJCGcaotFqtWLRoUUSqCZOTk9y6LRaLWyHSYBv9+++/jyeeeAK7du0Khr7iiW/47kg5Q0NDmJycRElJicf35SfxsrKycODAAc7wvdFvGxoakJubK7ojLBDw42u1Wi06L2C329HY2MiNqoqEMbGxXyqVCiaTCQqFggtlmDFRStHS0gKpVMpt3pEGU+ZRq9UYHx/nlJJ0Oh0sFkvQjH737t343e9+h/feey9Y9OMTx/D5rZIMnkg5SqUS4+PjWLBggdv3FCbx7HY7vvrqK9TV1SEhIcHtH5W1qs6fPz/kPHF3EJMXYL0BgUhzBxusQ0+lUnHGlJ2djZGREU7rLxqMXgjWi9/W1ga9Xu8UygSS2Pzss8/w4IMPYteuXcFsNDoxDZ9PyqmurnaZ/WRxcllZmcv3Y0w8YRKvv78fIyMjoJQiOzsbubm5TieqRqNBe3t7ROi37uAqL5Ceno6+vr6Ibk7ewEIZxrXgl9yiSXYM+LaVmrn3LLGpVqs5IVKmmyg25Pvqq69w3333YdeuXT4loUXgxDN8FldTSl0SJRi0Wi0GBgZQUVHh9Ht+O+3ixYvdJvHYH1apVMJkMnEEl7GxMVRXV0eNJLMQlFIMDg6ivb0dcXFxnHsaKb6AJ7Be+lmzZiE/Px8mk4kLZaJJdowZvclkcpl74HMdhLkYd/fJnj17cPfdd2Pnzp2YO3dusJd84hi+w+GAXq9HfX29qBn2ExMTOHbsGKqqqrjf8Zl4zKUU01lns9nQ1NSEiYkJSKVSZGZmIjc3N2yMMF/APBJWVvQnLxAOsDBk7ty5HNGJD6EXEynZMdaxaDabRSUcGd+efedseClfiPTAgQP4yU9+grfeessnnokPOHEMX6fTYf/+/aJn3hmNRo7bDfjPxGNGn5KSgtLSUs49VSqVXAdVbm5u2G9IVxgaGkJ/f7/bGn04+QKewKTOxM7Z48uOaTQaxMXFiS65BQJfjd4VWEekWq3G7t278fnnn6OtrQ3bt29HTU1N8Bc9hRPH8C0WC5dUEQOz2YzGxkYsXbrULRPP2zQbJjI5d+5clzEY/4ZUq9WIj49Hbm6u2/bcUIFSip6eHoyNjYnmEoSKL+ANbNpxIGPB+CW3UMmOBcPohaivr8ddd92F6upqHDx4EL/97W9xwQUXBGG1M3DiGD6lFBaL6Bl+sNlsOHDgAGbPnu0yieeNiafT6dDc3IyysjLRYg/MrVapVCCEICcnB7m5uSE/ldra2mCz2VBeXu5X6BEsvoA3GAwGNDQ0BDxckw8mO6ZSqZzGgQciOxYKo29tbcXGjRvx+uuvc3kndwpRQcDJa/h2ux2fffYZsrKynJJ4Yoye6c1VVlb6nV1mbrVSqYTVauUqBMFsc7Xb7WhubkZSUhJKS0uD8r7+8gW8gW2klZWVIauG8GXHNBoN4uPjuQ1MLO89FEbf3t6O66+/Hq+++iqqq6sDfj8RODkNnyXxJiYmsGrVKtFJPOYyj46OorKyMmg99DabjasQGAwGjsASiMS01WrlCEQhyApzCEZegKn1VlVVhbVMx0+yeZMdA76lfVutVqeBHIGgu7sb11xzDV588UXU1dUF/H4iceIYPjB1E3oDS+KVlJSgq6sLp512mmj6bUtLCyf6EapsPeO0M4KRP4q4TGiyuLjYo9hosOFPXkCj0aCjo8Mt3yJcYEk2lUoFvV4/Q8wzFEbf19eHq666Cn/729+wfLnfkvX+4OQyfGES76uvvsKpp57q1ejZ6ZmdnY3CwsKwZbiZa6pUKp1i65ycHLfehl6vR2Njo8vW33BCmBdISEhAbm6uU15AqVSiu7sbNTU1UcUh4A8AHRsbQ0JCAjdW29/WWiEGBwdx5ZVX4plnnsEZZ5wRhFX7hBPL8C0Wi9sppH19fVy7aUJCAiil+Prrr1FQUICcnBy3RAqmQlNSUhLW01MIFlsrlUqo1WpOgz43N5c7KZn8VDSxBgHXeQG5XI7JycmQSnQHA6xHYGJigvPyWEjgbz5meHgYV155Jf74xz9i1apVQV6xKJz4hu9wOHD06FFYLJYZSTwW46lUKm5CrdCQGNffVe9zJGEymaBUKqFSqWC325GYmAi9Xi9KPCPSOHbsGDemOpoYeEK4cu+Zvr9KpeJkx3yh4qpUKlx22WV49NFHcd5554XhKlzixDZ8X5h4jA6qVCrhcDgQHx8Pg8Hgdm5dNOF4MSQATj3/bE5BJPgC3iAmpmdUXBYSeJPv0mg0uPzyy/HQQw/hwgsvDMdluMOJZfhWq5XTxDMajRz7a9asWaKZeJRSHD16FFqtFnK5PGSltmCA3Zwmk4nrS3BlSLm5uRETwhSu1WKxuOUTiMkLhGut7e3tsNvtovUJhOEMAKfuvPHxcVx22WX45S9/iXXr1oX6ErzhxDR8YTuuWKO32+1oampCUlISpx/PL7Ux+ajc3NyIn6asGUkul2PBggVuTyS+ISUnJ3OGFG4+e2trKwghWLhwoV+GFK4+An+M3hVYE9fw8DBuvfVW2O12XHXVVfjVr34VDTmNE8/we3p6MDAwwEktiyXlmM1mNDQ0eOxNZ73iSqUSOp2Oa8YJ5khnMWCKQllZWaIUhIBv+8WZIcXFxXH04VB2EjLJssTExIBIROHoIwiW0fOh1+txxRVX4PTTT8fExAQMBgNeeumlwBcbGE4sw2fZV1+ZeBMTE9wobLH8cBbfKZVKaLVapKWlcS51KGNTs9mM+vp6FBYWuuxaEwt+YpNSyiU2g0mgYeo+GRkZQe0yC0VeIBRGbzAYcNVVV2Hjxo0eR1pFACeW4ev1eshkMhBCuB56TwYPgNOxr6ys9NuF5NfbNRpNyFxqpuwTSAOLK1gsFi6xaTabuZxGINLSrJc+Ly8vqJN/hQhGXoD1MzgcjqAZ/eTkJK6++mpcddVV+MEPfuDXe2zZsgUPPfQQWlpasHfvXixdunTGc/r6+nDDDTdgZGQEhBD88Ic/xB133AEA+NnPfoZ33nkHcrkcpaWlePHFF5GRkQFCSDGAFgBHp9/mG0rprTPePIgIqeHbbDZOjENMEq+3txdqtRpVVVVBi7uYkCSrt8vlcuTl5Xkk3YiBpwEXwQRralEqldDr9X6FM1arFYcPH0ZBQUGwlWM8wp+8QCiM3mw2Y8OGDVi7di1uu+02v9+zpaUFEokEP/rRj/DEE0+4NPyhoSFutsDExASWLFmCt956C4sWLcKHH36Is88+GzKZDPfddx8A4LHHHmOG/y6ldHEg1+kLQp5REku/PXr0KBwOR9DHPRPy7Vz30tJSjnRz6NAhSKVS5ObmIjc316e4ms2yD0eNXiaTIS8vD3l5eVw4MzIygqNHjyI1NZXTFnDnUrNQpLi4OOzz2wkhbucUusoLMKOnlAbN6C0WC77//e9jzZo1ARk9AG4smyewiUYAkJqaivLycgwMDGDRokVYs2YN97xTTz0VW7d6H3ZLCCHUy+nsD0Jq+A899BAKCgpw0UUXuZ0xb7Va0djYiMzMTMybNy/kmfnk5GQUFxejuLiYI900NjaKjqv7+/sxPDwcEYabcEqOTqeDUqlEV1eXS5c6GL30wUR8fLzTyCyNRoOBgQG0tLQgLS0NVquVG1oajPvAarXi5ptvxhlnnIE77rgj7FWf7u5uHDp0CCtWrJjx2D/+8Q9cddVV/F8VE0IOAdABeIBS+gUAhMLogTAk97Zu3Yp3330XaWlpuOSSS3DxxRcjJycHhBAYjUY0NjZi3rx5wVQs9QsWiwVKpZJry2WbAFNkZTpubHBENAzR5INPH5ZIJEhPT4dSqURFRUXQeulDBYfDgcbGRkxOToJSisTExID5AjabDT/84Q9RUVGBBx54QLTRn3vuuRgeHp7x+0ceeYSr969atcqtq8+g1+tx1lln4Ve/+hUuu+yyGe+1f/9+bN++nXnCCQBSKKUaQsgSAG8B2IKppN9eAF2U0j2iLkAkQmr43JtM90xv3boVO3fuRHx8PGpra9HY2IhXXnkl6m5MNrJJqVRicnISCoUCRqMR8fHxYRlwESg0Gg2am5s5hmO0jdDmg+/eL1y4EAAC5gvY7XbcfvvtmDdvHn77298G/Zq9Gb7VasXatWvx3e9+F3fffbfTYy+99BKee+45fPTRR3zP0mmBhJBhAAcA/AXAQgBnA9hMKf0gWNcQFsN3ekNK8dRTT+HJJ59EcXExrFYrLr74Yqxfvx4FBQVRd2NaLBYcPHgQwNTa+cKd0bZWYGYvPX8TMxqNXFw9nU2O6FqFRu9qPb7yBRwOB+644w5kZWXh0UcfDQmfw5PhU0rx/e9/HwqFAk8++aTTYx988AHuvvtufPbZZ075FkJILoBRSqmdEFIN4L8ACiilo4SQRZjaACYBPEMpfS8Y1xARw3/sscewadMmJCcnY2hoCNu2bcP27dthMpmwdu1arFu3LmJTZPhgyrL5+fmYM2eOU3JtfHw8aui3DEyxVziXnkFIeIokD59RsQkhoifxeOMLOBwO3HvvvUhISMCf/vSnoP9NduzYgZ/85CdQqVTIyMhATU0N/v3vf2NwcBA/+MEP8N577+HLL7/EypUrud4HAPj973+PCy+8EPPnz+c2L2Aqwffss8+CEHIFpubbWwE4pv99QyndRAjJA/AnTG0G6ZTSR4JxLWE3fLcfRCmUSiV27NiB7du3Y2xsDBdddBHWrVsXkRFNrP3X3YALvnrv6Ogop96bnZ0dkfjf11564fr9HZrpD/wxelfvwfgCGo0GmzdvhsPhgEKhwAsvvBAVG7EPIISQ0wHEUUo/I4SUAXgYwBxMJeD/DuATAP8H4LuU0tGAPzBaDF8IjUaDt956C9u2bcPIyAjOP/98rF+/3m+BSl/A9OYqKiqQlpbm9fmMfsuSa/zkVDgy/0ymu6amxq/P43MdNBoNZDIZJzAS7E7IYBi9EHa7Hffddx8aGhq4nMCOHTuCsNrwgBDyOqbGXucA+HT633sASgFIKKXN0897G8BtlNLBgD8zWg2fD61Wi507d2L79u3o6enBueeei0svvRRVVVVB3wT4Ay78pcuyybMqlQoymSykHPy+vj6oVCpUV1cHzdNgcthKpdLtaDJ/EAqjp5Ti97//Pfr6+vDiiy9CKpVCr9dHlfCJJ7S1tWHhwoX/oZSuIYTIAdwOoATAHgBvUkqthBAFgFcBDFFK/aMdCnBcGD4fOp0Ou3btwvbt23H06FGcc845WL9+PZYsWRLwJjA4OIiBgQG3Ay78weTkJFcmDLaEt7CXPhRwNZosNzfXZ038UBn9E088gdbWVrz66qt+0bEDpeE+9NBDeP7557lkHYvnAeB//ud/8MILL0AqleLpp5/Gd7/73Rnv3dvbi6Kiog4A11JK9xFC4gFcB6ACwKuU0kOEkMsBLKGU3u/zBbrBcWf4fBiNRrz//vvYtm0bGhoasGrVKqxfvx4rVqzw6fSjlKK7uxtarRZVVVUhi9H5Et42m82JK+ALWC89k5QOVzzLkmtKpRITExPIyMgQNZosVEb/zDPPYP/+/Xj99df9DqkCpeE+9NBDSElJwb333uv0miNHjuCaa67B3r17MTg4iHPPPRdtbW0u7y1CyJ0A8gC8RCltmzb+JwAkUEpv8evCvCCyM6QCRFJSEi6//HJcfvnlMJlM+M9//oNXXnkFd9xxB8444wxceumlOP300z2eBOymdDgcqK6uDqkR8ZlrVqsVKpUK7e3tMJlMohtx2HoppUGb9y4WfIoz0xZQKpVoa2tzO5qM9f5LpVK3OgW+glKK5557Dl9//TW2bt0aUB4lUBquO7z99tu4+uqrER8fj+LiYsyfPx979+7Faaedhs8//xzz5s1DYWEhe/rXAC4FcBUh5C1KaSMh5EEAzxNCMiilWr8v0A2Oq9SnJyQkJODiiy/GK6+8gkOHDuHSSy/F1q1bcdppp+EnP/kJdu/ePUPn3263o6GhAXFxcWFJGvIRFxeHOXPmoKamBsuWLUNqaip6enrwzTff4OjRoxgbG3OpV9jc3AypVBpxIpFEIoFCoUBZWRlOPfVUFBUVYWJiAvv378ehQ4cwMDAAs9kcEqN/4YUXsHv3brz55pthVwN2RcPdvHkzqqqqcNNNN2FsbAwAMDAw4DQ/oaCgAAMDA/jNb36D1atX4//+7//Q0dHBrmkfgPcBSAE8RAj5AaYy+dZQGD1wnLv6YmCz2fDZZ59hy5Yt+PLLL1FXV4d169ahsrIS27dvx2WXXRbSNlVfIdTxT09PR15eHtLT09Hc3Iy0tDQUFxdHepkewejDvb29IISgsLAQeXl5QclrvPLKK9i+fTvefvtt0e8XShruyMgIsrOzQQjBgw8+iKGhIfzjH//Apk2bcOqpp+K6664DANx888049dRTUV9fj4qKCjQ1NaGwsBDr16/HwoULCQAQQgoALAJwMaYIPb/x4avxCce1qy8GMpkM55xzDs455xzY7XZ8+eWXeOmll3DLLbdg5cqVmDdvHhQKRVgnxniCRCJBdnY2srOzOXd6ZGQE9fX1SE5OxuzZs2G326OuV4CPpKQkmEwmzJ49G4WFhVCr1WhpaQlYL/H111/Hm2++iXfeecenTWT37t2+XsIMWK1WXH755diwYYMT957fY3LLLbdg7dq1AID8/Hz09fVxj/X396OsrAznnHMOSkpKUF9fjyeffBI7duzAL3/5y8WU0iZKaT+AfkLIJ5RSa8CL9oATxtUXA6lUitNPPx2tra1499138Ytf/AL79u3D2Wefjeuvvx7bt2+HXq+P9DI5SCQSpKamQq/Xo6ysDGVlZRgfH8e+fftQX1+PoaEhWK0hvT98BtO9l8lkWLBgARISElBQUIC6ujosWbIEycnJOHbsGBfSjI6OcoKsnrB161a88sor2LlzZ0g1/lyBUoqbb74Z5eXlM7j3Q0ND3M87duzA4sVTLfWXXHIJ3njjDZjNZnR2dqK9vR2nn346J81WXV2Nu+++G62trQBwISFkFiHkMULIvFAbPXASuPquYDabnWrqDocDhw4dwpYtW/DBBx+gqKgIl1xyCS688MKINhBZLBYcPnwY8+bNcxoewgQumI4/0+vLzc2N6AQcZvRxcXGcOKo7+DKabOfOndi8eTN27doV9L9HoDTc66+/HocPHwYhBPPmzcNzzz3HJQIfeeQR/OMf/4BMJsOTTz6JqqoqUEqdQsuuri6UlpZuxlQjjoFSGpbZXSel4XuCw+FAU1MTtmzZgvfeew95eXlYt24dLrroorD2tJtMJhw+fBgLFixwq2XAYDQauU2AEMJtAuGcP+CL0bt6rXA0WUZGBpKSkrB//3488cQT2LVrV1RoCviLn/3sZ+jr64PRaMTixYvx+9//nnuMEPIuAAWAlZRS7+5PEBAzfA9gNzPTFMjIyHDSFAgVWJ9AWVmZz/P2+INI7Ha731wBXxCI0bt6L4PBgEOHDuGOO+6ARqPBvffeiw0bNkRVElYMWC7mmWeewRdffIF//vOfuPrqqzFr1iz85S9/AQCMjo4iKyvrDwAepJSKnysfIGKGLxKMNLN161a88847XPlw3bp1yMvLC1ppjSkMB0PLj7HuRkZGYLFYQjKIJJhGz8dnn32GBx98EM8//zy++uorEELw4x//OCjvHWp0dXWhpKSE+/833ngD5eXleO2119DX14fXXnuNGzBz+umnAxFQ2Q1qcu/BBx9EVVUVampqsGbNGgwOuu8l0Ol0KCgowKZNm4K5hJCBEIIFCxbgl7/8Jb766iv84x//gMPhwA033IDzzz8fmzdvRn9/v9shoWIwPj6OpqYmVFVVBUXAUy6XY86cOaitrZ2RWGtra4NWqw1ovaEy+i+//BIPPPAA3nnnHdTW1mLTpk0+Gf2WLVu4SUb79+93+Zy+vj6sXr0aixYtQkVFBZ566inusauuugo1NTWoqanBvHnzUFNTA2Cqhp+YmMg9duutM4Vwt23bhqqqKtxzzz3c71QqFb7zne9gYGAAr732GoApkc0tW7aISmyGAkE98XU6HdfN9vTTT+PIkSN49tlnXT73jjvugEqlgkKhwObNm335mKgCpRSDg4PYtm0bduzYAbPZzGkK+KIhODo6ira2NlRXV4dcwFPYly+WessHM3omFR0so9+zZw/uvvtuvPPOO3679oHScPm45557kJ6ejl//+tfo7u7G2rVr0dTU5PJzR0dHsX79elx99dV4//33UVJSwm0od9xxBz7//HM89dRT2LVrF7744gvs2rULmZmZwPF+4vNbWA0Gg9ub4cCBAxgZGXFSHT1eQQhBfn4+fvrTn+Ljjz/Gjh07kJmZiTvvvBOrV6/G448/zqnMuINKpUJHRwdqa2vDMlmXjfSuqKjAihUrkJeXB5VKhT179qC5uZmb+OsOlFJuXFgwjf7AgQO466678NZbbwUUz5eXl3MyXu4we/Zs1NXVAXCm4fJBKcWbb76Ja665RtTnKhQKPP/887j55pvxzDPPoLW1lfMKnnrqKVxxxRXYtWsXDAYDdu7cyYw+Igh6jP+rX/2K09H75JNPZiTBHA4Hzj77bPzzn//E7t27sX///uP6xPcEtVrNaQqoVCpccMEFWLdunZOK7PDwMPr6+vzupQ8mxAwiYUYfHx8fVKOvr6/Hrbfeim3btmH+/PlBeU8xbDxgyoU/88wz0dTU5HR4ff7557j77ru5cKG7uxsVFRU45ZRTkJaWht/97ndYuXKl2/ft7+/HLbfcgoKCAjz//POor69HRkaGqzFr0T9JRwz9EZhqSTSZTHj44Yednrd582YYjUb8/Oc/x0svvXRCGz4fY2NjnKZAb28vzjvvPNjtdmRkZOCOO+4I69BMMXA1iCQ3N5crtwXT6Jubm3HzzTdjy5YtXk9qhnCo4d52222YP38+F6+bzWbo9XpkZWXhwIEDWL9+PUejdofBwUHcc8892L9/P+Li4vDFF1+4Ks9Gv+GLRW9vLy688MIZ8dCGDRvwxRdfQCKRQK/Xw2Kx4Pbbb8ejjz7q70cdd9DpdLjtttvw9ddfIyMjA6tXr8b69etRV1cXtZJRbFyYxWLhPIFgKPS0trZi48aNeP3111FRURGk1U4hEDVcm82G/Px8HDhwwG3YIdajePTRR7F582Z89tlnKC0tdfWUsBt+UI+Z9vZ2LFiwAMBUW2JZWdmM5/zrX//ifmYn/slk9AA4hZi2tjZYrVa89957+Otf/4qmpiasWrUK69at81lTIJRgegU5OTkoKSmB2WyGUqlEc3MzHA6H3wM+29vbsXHjRvzzn/8MutF7gycaLjDF7y8rK3MyepaMlkql6OrqQnt7u1PZzhU0Gg12796N3bt3uzP6iCCoJ/7ll1+Oo0ePQiKRoKioCM8++yzy8/Oxf/9+PPvss/j73//u9PyTydUXA5PJhA8//BBbtmzBwYMHsXLlSqxfv96rpkAowWL6hIQElJSUzHDv+QM+GVcgLy+PG0TiDt3d3bjmmmvw4osvckm2YCFQGi4AbNy4EaeeeqpTyW7btm349a9/jbi4OEgkEjz88MO4+OKLva7HZDJ584xOHFc/hsBgsVjw0UcfYevWrfjmm29w2mmnYd26dTjzzDPDlgSklKK5uZmL6b1BOIjEnUxXX18frrrqKvztb3/D8uVhoaZHO2KGH8NMWK1WfPbZZ9i6dSu+/PJLLFmyBOvWrcPq1atDIuAJ+G70QghlujIzM6HX65GTk4Nrr70Wmzdvxne+850QrPy4RMzwY/AMu92OL774Atu2bcMnn3yCyspKrF+/Hueee27QOACBGr0QbBDJ/fffjw8++ADLly/Hj3/8Y6xZsybqqhkRQnQSeJiKjc1mC/V6AoIYynBPTw/q6upQU1ODiooKt8zCaIVUKsWqVavwzDPPoL6+Hps2bcKePXuwevVq3HDDDdixY0dAmgLBNnpgSlfAbrfjyJEjePXVV3H//ffj008/jRhdNQZM/aE9/KOUUtre3k7PPvtsmpSURLds2UKjFePj49zPTz31FP3Rj3404zlms5maTCZKKaUTExO0qKiIDgwMhG2NoYLdbqf79u2jP//5z2lVVRVdt24dfeGFF+jQ0BA1GAyi/un1erpnzx7a0NAg+jVi/vX29tJly5bRXbt2BXSNb775Jl20aBElhNB9+/a5fM7k5CRdtmwZraqqoosWLaK//vWvuce6urro8uXLaWlpKf3e975HzWYzpZRSk8lEv/e979HS0lK6fPlyeuzYsYDW6Qe82WHQ/4k68fPy8rBr1y4sXryYE4RwOByglEbVri2GMiyXy7m42Gw2R9X6A4FEIsHSpUvx2GOP4dChQ3j44YfR1dWFCy+8EFdeeSVeffVVTgjSFej0SZ+UlBTUstPY2BiuvPJKPPDAA1zG3F8sXrwY27dvx5lnnun2OfHx8fj4449RX1+Pw4cP44MPPsA333wDALjvvvtw1113oaOjA5mZmXjhhRcAAC+88AIyMzPR0dGBu+66C/fdd19A6zwu4GVn4PDf//6X1tbWut2yHA5HIDte0HD//ffTgoICWlFRQZVKpcvn9Pb20srKSpqYmEg3b94c5hWGFw6HgzY1NdGHHnqILlu2jK5Zs4Zu3ryZdnd3zzjpGxsbg3rSDw0N0dNPP51u3bo1qNd01llnuT3x+TAYDLS2tpZ+88031OFw0KysLGq1WimllH799dd0zZo1lFJK16xZQ7/++mtKKaVWq5VmZWWF+36OzhMfAN5//32OoaTX6/HNN9/gnnvuwaeffgoA3Om6d+9e3HTTTXjyySdhMpmCvlGde+65WLx48Yx/b7/9NoApymZfXx82bNjglh8wd+5cNDQ0oKOjAy+//DJGRkaCvs5oASEEFRUV+M1vfoM9e/bgmWeewfj4OK6++mqsXbsWf/3rX3HNNddgYmLCKxnFF+j1enzve9/Dpk2bcPnllwftfcXAbrejpqYGubm5OO+887BixQpoNBpkZGRwyUQmdw04S2HLZDKkp6dDo9GEdc1hh5edgdrtdmo0GumZZ55J33//fUoppT/96U/pY489Rt9880160UUXcXGUyWSi3d3d9Be/+AXNzs6mH374YYg2SO/o6emhFRUVXp934403RnXeIlRwOBy0ra2N1tbW0qqqKrpy5Ur6hz/8gba1tVG9Xh/QSa9UKunq1avpyy+/7PO6zjnnHFpRUTHj31tvvcU9R+yJPzY2RletWkUbGxupSqWipaWl3GO9vb3c/VFRUUH7+vq4x0pKSqhKpfJ57QEg+k58iUSC4eFh6HQ6rFmzBhaLBW+88Qa2bNkCs9mMJ554AseOHUN7ezvi4+NRVFSEFStW4MYbb+RPCsHY2Bhef/11PPLIIx4FOgJBe3s797M7ynB/fz8mJye5NX355ZeiG0NOJBBC0NHRgSuuuAKHDx/Ga6+9BrlcjltuuQXnnXcennrqKXR3d/ss1DE5OYlrr70W1157LW644Qaf17V79240NTXN+MdvABML1gfxwQcfICsrC1qtlqtM9ff3Iz8/H4CzFLbNZsP4+LhXncPjHR4Nn/UT/+xnP0NtbS0kEgk++eQTFBQUYMuWLfjmm2+wceNG7N6922nIw+eff+40dsjhcODOO+/Ehx9+iPb2dtx6660zep+DgV/84hdYvHgxqqqq8OGHH3IiCPv378cPfjA1ZLSlpQUrVqxAdXU1zjrrLNx7772orKwM+lqOB1xwwQW4//77QQhBQUEB7rjjDnzyySfYvn070tPT8dOf/pTTFGhvb/e6CZjNZlx33XW49NJLcfPNN4fpKpyhUqmg1WoBTG1C//nPf7ipQ6tXr8bWrVsBAC+//DK3mVxyySV4+eWXAUzJeJ999tkRnVIUFnhyB3Q6Hf3jH/9ITz/9dJqQkECff/55+sILL9ANGzY4+SlDQ0NOP1933XX0vffe43738ssv040bN1K1Wk0ppXTlypVcaWfv3r305z//OX3ggQdoR0cHpTR6EoUxUKpUKunf/vY3ev7559MlS5bQBx54gO7bt29GODA2NkYvvvhi+uSTT4bs77d9+3aan59P5XI5zc3N5ZJzAwMD9IILLqCUUlpfX09rampoZWUlraiooA8//DD3+s7OTrps2TJaWlpKr7jiCq6sOzk5Sa+44gpaWlpKly1bRjs7O0Oyfg8Iu6svmrnHRiLV1NTgxhtvhNlsxrXXXov58+djwYIFkMvlIITgzTffxMcff4yf/OQnqKioQG9vL/76179i3rx5+NGPfgS9Xo+77roL1157LVavXo3c3Fz84Q9/QHNzMz766CO89NJLqKqqCv2OF4PPGB0d5TQF+vr6sGbNGlx66aUoKyvjRkTde++9J/5pGXxEJ3MPAHJzc7F06VLIZDL85S9/wQUXXIAXX3wRO3bsQFxcHCYmJgAAHR0dWLJkCZch7u7uhtls5touOzo6kJycDLvdjvb2dqSmpmLjxo14/PHHcfbZZ+PLL78MwWXGEAwoFAps3LgRO3fuxKeffoqqqio8/vjjOOWUU1BSUhIz+uMIfhGlU1NTceONN+LGG2+E3W6HRCLBV199hauvvhp2ux3XXXcdrr32WgBTN8vg4CAnp9TQ0ACJRILKykpYLBZUVlbirLPOwsqVK6HVarlZZJTS4/ImevDBB/H2229DIpEgNzcXL730EubMmeP0nMOHD+O2226DTqeDVCrFr371K1x11VURWrF/SE9Px4YNG7BhwwYMDw8jJyfnuPx7nbTwEgv4BKPRSN988026evVqumnTJkoppU1NTbSqqop7ztVXX01feOEF2tfXR88++2y6Y8cO+txzz9G6ujq6Z88eXz8y6iCGNnz06FHa1tZGKZ2KT2fNmkXHxsbCtcQYog/RV87zBYmJibjyyivx8ccf45lnngEAzJkzB3V1dfjOd76DjRs3wmw246abbsLHH3+MlJQUrF+/Hj/84Q+xbt26E8LNF0MbPuWUUzilojlz5iA3NxcqlSpsa4wWiNG/N5lMWL58OaqrqzkiEsOGDRuwcOFCLF68GDfddBM3QPTTTz9Feno6p3//29/+NizXczwh5D2RmZmZePHFF7FlyxZIJBKcddZZAKZECFmt1Gg0QiaTcSU+epy6+QxCpWFP2Lt3LywWS1TJMoULjHv/ox/9yO1zGPc+JSUFVqsVZ5xxBi644AKceuqp2LBhA/75z38CAK699lr8/e9/x2233QYAWLlyJd59992wXMfxiLApO1555ZW4/PLLkZ2dDWBKpqu9vR1r1qzBTTfdhN7eXlxxxRXhWk5ACAZtGJga6nD99dfjxRdfjFqRzVBCjP49IQQpKSkApgRJrFYrdyhceOGFIISAEILly5ejv78/5Gs+YeAlFgg5PvnkE/r0009TnU4Xjo8LKzzRhsfHx2ltbe1JSRcWwhsF12az0erqapqcnEx//vOfz3jcYrHQ2tpa+vnnn1NKp+4phUJBq6qq6Pnnn0+bmppCtvYgIewxfsTlT1atWoVVq1ZFehlBgxilYYvFgksvvRQ33HDDcePl+Auxcxg8QSqV4vDhw9Bqtbj00ku5oaIMt99+O84880xuuEVdXR16enqQkpKC9957D+vXr3eic8eAyJ/4Jxouu+wyWlFRQSsrK+natWtpf38/pZTSffv20ZtvvplSSumrr75KZTIZra6u5v4dOnQogquOLMQ23VBK6cMPP0wff/xx7v8feughum7dOmq3292+pqioKNxNN74i7Cd+zPBjiDg8Gb5SqeRKnUajkZ5xxhn0nXfeoZRS+vzzz9PTTjuNGo1Gp9cMDQ1xtOE9e/bQuXPnRjsN/Pgu58UQgy/YsWMHCgoK8N///hcXXXQRvvvd7wKYqvgwtZ6hoSGsXr0aVVVVWLZsGc477zysXbsWAHDrrbdiZGQEp512mlPZbuvWrVi8eDGqq6vx05/+FG+88cZxXSUKBWIquzHEEHlEL1c/huMLYhSHAeD8889HRkYGd4rGcHIgduKfoNDpdByL8Omnn8aRI0dcSol/9NFHMBqNeO6552KEl8gh6gZqxHACgBDySwCFlNLb3Dy+CsC9lNLYsX+SIOJ1/BhCB0LIIwBuADAOYHWElxNDFCEW4x/HIITsJoQ0ufi3DgAopb+ilM4F8C8AmyK72hiiCbET/zgGpfRckU/9F4D3APzG2xNjODkQO/FPUBBCFvD+dx2A1kitJYboQyy5d4KCELINwEIADgA9AG6llA4QQpZO//yD6ed9AaAMQAoADYCbKaX/jtCyYwgTYoYfQwwnIWKufgwxnISIGX4MMZyEiBl+DDGchIgZfgwxnISIGX4MMZyEiBl+DDGchIgZfgwxnISIGX4MMZyE+P8Lc2f7UOcfGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gradient_descent(x,y):\n",
    "    m_curr = b_curr = 0\n",
    "    iterations = 1000\n",
    "    n = len(x)\n",
    "    learning_rate = 0.08\n",
    "\n",
    "    for i in range(iterations):\n",
    "        y_predicted = m_curr * x + b_curr\n",
    "        cost = (1/n) * sum([val**2 for val in (y-y_predicted)])\n",
    "        md = -(2/n)*sum(x*(y-y_predicted))\n",
    "        bd = -(2/n)*sum(y-y_predicted)\n",
    "        m_curr = m_curr - learning_rate * md\n",
    "        b_curr = b_curr - learning_rate * bd\n",
    "        print (\"m {}, b {}, cost {} iteration {}\".format(m_curr,b_curr,cost, i))\n",
    "\n",
    "    ax = plt.axes(projection =\"3d\")\n",
    "    ax.scatter3D(b_curr, m_curr, cost, color = \"green\")\n",
    "    \n",
    "gradient_descent(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c848b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46355692",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad7893f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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