random_forest copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import make_regression,fetch_california_housing\n",
    "from sklearn.metrics import r2_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GradBoost(model, X_train, y_train, X_test, boosting_rounds, lr:float=0.1):\n",
    "    y_hat_train = np.repeat(np.mean(y_train),len(y_train))\n",
    "    y_hat_test = np.repeat(np.mean(y_train),len(X_test))\n",
    "    residuals = y_train-y_hat_train\n",
    "    for i in range(0, boosting_rounds):\n",
    "        model = model.fit(X_train,residuals)\n",
    "        y_hat_train += lr*model.predict(X_train)\n",
    "        y_hat_test += lr*model.predict(X_test)\n",
    "        residuals = y_train - y_hat_train\n",
    "    return y_hat_train,y_hat_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_regression(n_samples = 1000, n_features= 20, n_informative=15, n_targets = 1, bias = 0.0, noise= 20, shuffle= True)\n",
    "X_train = X[:800]\n",
    "y_train = y[:800]\n",
    "X_test = X[800:]\n",
    "y_test = y[800:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18e81e07c70>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DecisionTreeRegressor(max_depth=100,max_features='sqrt',min_samples_split=2)\n",
    "y_hat_train,y_hat_test = GradBoost(model,X_train,y_train,X_test,boosting_rounds=100,lr=0.1)\n",
    "plt.scatter(y_train,y_hat_train)\n",
    "plt.plot(y_train,y_train)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18e832777c0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,y = fetch_california_housing(return_X_y=True)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X, y)\n",
    "y_hat_train,y_hat_test = GradBoost(model,X_train,y_train,X_test,boosting_rounds=50,lr=0.1)\n",
    "plt.scatter(y_train,y_hat_train)\n",
    "plt.plot(y_train,y_train)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18e82f95d00>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VK/5Pk64o7cSMPYehFyYBmBf9//hatbmB83s3MYAFzCzlNGxSs0XNpA5d4kFQkTYVUb/kGd14tq7p8/Vdm4yhlYpvAtXuJ01t2qUUe04EKh0qw/Gc3+uVkL6P/pSUJmChZlJHkM4oOiEMsgHo59rQjcdP4E3HMLixH6PvXaqrLheEqzdmm3hXPTkr05IrhMjMio0K928YRiVBAlLTLZ5EBN/uwMCAGB0dDf24DONEl9UnM+lMU7N1NR5Ur3Wyas9JrbB6dRYp5Ky6UqJD21b7+lTTwGObVyh95laWQk9lD4r7twqjLnW/44asK4Ll10Hm3UPbjc9HRGNCiAHVc2xRM22Ll0UbZAPQpMaDynL3stq8Lt5S2a65KmR8b9rpL+RwcHAdBu5aiv3Hp2rj781b2PfgWu2NMS7cPmTnKqiZcelcW0MvTAJ0s8ZKXNEg6UhkZ5gm8BLjICnmfskzugzJrWv6YGWTja+NA3nTkjerK2Ub/YUcjuzcgPEv3V/z47szRZNGpp4H/YXkprUqVd6eE4msHtiiZiInqrrROotWAJi5MQsrQ3V1Ibx2772SZ3SW++lz01i8oCe2jbwMVZNc4mbHpsYO3G5/e6t+9lbxSkAK6q+eEwKDG/sjL+EaBLaoGSOaTaH2qtfRKl5W3OUZG6CqP7jVFHMvyz1IdEF/IYfevBX4/JIkRBoATp+bNkr6OX1uOrFQQa8EpKDWfoYII+PFVBX4Yos6QdqlQ0krKdRR1o3280PaFYHFC3vqWks5cX7/svHqlbLd8Ft4xVEDZnUgtty9FM/9u3+OkfEidg9PtFXss1dij3zOtAt5lBRLZWw5dKrhegpq7VeE0NbiTgq2qBMiSkvT9PymFnIrKdRR143280Pqqqi5v//LM9UNPtVv4VWpzzQR4szbl7Dl0CkAwKObV7TcMitOvMRtSc6qfZdpQHc9BbX2nbW4k85KBFioEyPJcopBbxKtiG1cdaO1xyNg6MXJus869OIkdg9PeFpKZbuC/censOXQKewensAiK9OyG0VGeBwbuwCBm4V+clb6L0OdyBGZZxDGjfN6asYwuFgq1wyBIzs3NGwcW1nCkZ0bwhiqL+mfIR1Kkh1Kgt4kWhHbuOpG6/yQQjS2q7Irwsi6KpXtOov7+uxcrXi+0+0SFJlYImtQX59NX80NU0ozdiq76kjk2JoxDBre4540MfqvWKgTIskOJUFvEq2IbVx1o+V5oixHKetFr9xzslbXuFW/bNmuJLZJGAbLC7nI5mxv3mrZ7SDHpprDVoZqm7vuWeOe34dffquhs0yrnWSCwJuJCZFkI02/zTE3QVK6de+PsqC/c0MwrgSEyzM2njg6Ecu50opMCnlp8oOG58IoFvXxtVlsv2dZ0xt6zuvJbw77bewn3aOTU8gTJKmoD6/U6zRGnegII03YCc2ry/JCDjM3ZlOf0p0kvXlLK6K9eSvwd6erFeJM4w6yepEZk2HNZ68yA/kFGfz9h1cbnvvJH1uMbzzxGeNzcAp5SomrQ4nqvEDzFnJaCH0TSwDvHNqOkfEi9h+fCu+4HYDbQr5mz+Hkmx8ov//8gh7kF/QEcwtp7EW5oSeLXPmFNsraG/kF4Uqb1wr4yaOTyvd8f3omtPOzUCdIknHUqptEGOOJ8zOFvezMEGHvyFkMv37et9N1t+H+Nsp2RXuTLJbKOLJzg/Fqp98jHt3pjtPFQxOAT9+9FG+8f6WldmleeBk3ulotYbrhWKgTIow+fGkbj98xwhbxQhNLbKAaDnfNnmsQn5stqphWOfzyW/jkiiV49fuXPQXL6Uc22bORhaGClpx108xcTGoFDLBQJ0aUGXtJjccv7C/sG9M1H2vNyhIgUGcdWxnC7JxZeF6z9IdQC7ndKZbKvt9BIWdh/0P1fmTTbjnux3V1OVSrrrQZSSawUCdE0rvIpuf1Go/bKtFdmBdL5dBvTCPjRc9C93ITavS9S3j+tfOoCIEsERb0ZHD1RnTJGVkinNlzn3bziam6Kh7dvAIHB9fVPR7UYnXOP13vQ1UkU9qMJBNYqBMiaIhc1AQdj8oq0YVkLS/kQr8xecWvyiLyI+NFHBsr1i7gihCRirQ8B9BcM9tuQQB4afIDnD43HdgNJsXZPd9UIq0Ld02bkWQCJ7wkRFwZe1GNZ//xqQYREtAnDoSd4ON1UTm7cMQtlHJjLI4EnHbGmfVpWufGWfoAUBsFWSLfxKokk82ahS3qhEhbiFzQPoW6GswC1YtEdYwwE3x0K4DevFU7X9wWkpWhugL7F0tlLOmwxrNRYbLxZ+JKmhMC7/i0v0oy2axZWKgTJMldZBWm4zFxO6iOLd+ruxEE2YnXXWz7Hlxb+zuMBqdByGSq1rNzXN0s0tkMYUGWjJvmqn6roElNJlZx2owkE1iomcCYuB10oqu7GILuxJtcbEPbVmPoxcnYWiddn53DgRONLqFug4CGjvDO30mX9UmozgN3FIjp92llCDM3ZrFyz8la4ku/RoTdc1GWwk2rcLNQM4Hxczs0E/4U2U58zHkr3Z52rlpRqURRlWEogIbf29R9VchZ+NH1mzcAubloMvfaIVyPhZoJjMrtQAC237MMQHOi67cTL9O6pSvBHWEiL67R9y7Vogl0IVtuwiggxASrqKjL5pNdWqRF65fU1Ju3MP6l+7Hxy6+goskm9Zt77RCux0LNBEaVyisAHBsrYuCupU2FP3k1ql2556TycTdlu1I3JtMUXhbp4OSsLHZs6sdLkx/Ubp6LAjRA8EoKct50Sx4i7dyT8FvJmLQTC/KeuOHwPCYQ0pf3rKL/nLRCmgl/CtqAVAeLbvTI0LeBu5bWNT24PGNj93y9br/2blvX9Hmeo2xXlHPMSZBqj15zz2u+mrSs03XoCbNzD1vUjDEmO/AXS2U8rSjI47cs9mtUyySPdDNIthw6pYylB/z9vKfPTbc0FhmvLjcAvdxXfnNPF0G0dU2fke9aFykfZgQ9W9SMMSY78MsLuaa7uvg1qmWSxe2G8HMNeLV3a8Wt4BRRmTSjE+nFC7JY2JPB7uGJusbGTisZgHK+nj43bdSybkYTfqh7vBnYok6QJMucNoPJxeXsqNHsZ4k7/pkxw+0iMPmddHOm2cqHWSI89fA6rdGQJcKcEFheyGHrmj4cG7uZnCUbGzsLdUkr+amH1zVEqwQp9BQ1bFEnRNBO4GnAJJlg1/AENhx4panPIS0dFul0cvX6bN3varKvIOeM24r1q3yoggB85QvrMbixXyuWMjPxzJ77lBaxXRENtcZ1lr9uvmeIYr9OfYWaiBYR0etENElEU0R0II6BdTpBO4GnAdMNv1LZxhPDEw2T2WtjZu/IWewenmCRTjGlsl1nTJjUM9m6pk9plJhmKzr59N1La6s0kw3rIJav6rW6+V4RInajysSivg7gPiHEegAbAHyWiDZHOqouQDeJZBxpGi1rp+8ZgOcFOgfUtbPyWkGMjBcbunZIevNWKNEgTDiU7QqePDpZJ9ZzHmGQz792HruGJ5rK1nTPrjfev1I7r0kRsSBFlpbkrAYjwutGFLdR5eujFtXutx/P/2nN/9cVUVBR+pC9/HtxZEY1+9ncvudVe05qJ4OzzoVuBbH/+BQWL+zRHqObM/0KKS3oJC1KoDofvApPNduOKqtIVnImoZiUENi6pk/ZsSdDgNP7YWUIV2/M1vmynZ8vDb5qo81EIsoCGAPwTwH8gRDiNcVrHgfwOACsWLEizDEmQtRppUPbVmPohUltb74wM6Pcorx1TR+Gv3O+VgOjtskC/8/mPpbpppBuUpfKdirFKA2k+XuRljUw3709RHJW1rMf46o9J1HIWxCi+h1liVAslXHgxBT2H5/ClbKN5YUcrl6fVR7jtkUWFi/s8aw9Ij/f7uGJQE0JosJIqIUQFQAbiKgA4OtE9DNCiO+6XvMMgGcAYGBgoO0t7ljSSn0muMkd288yVt1wVFaGXRE4cGLKsx6CM4VbHsuL3rxV+7fXCkJlPTHpR1rWYRWhchZz8oqnF6hfacm543zMa25eKduY2HczHnyVIvPVedwgTQmiIlB4nhCiRETfBPBZAN/1eXlbE3Va6eGX3/Kt6uZ3xzax+oNUH5MT3S3KixdkcWN2LlBn7gwB1+1KLf3bK0uLRTqdZInwyL131lqZqSjblVButKpiTlF1yGkmzBCoD/2LO5TWJOqjb96SBhHlAPwCgHMRjytxou4C4Sf4Jndsk8iRoDeWkfEihl6YrLOcr96oBBJpoOoDdAb8B9nl56Yo0WIak1sRAgcH1+ErX1jvuaFbEaLaSLhJVHPdnTQVFqpzmUYzOUP/4s53MPnNlgE4TURvAvgOgG8IIV6KdljJE3WrLC/BN83kM7H6g9xYCjmraukHFOUgmFx0hRxHekTFkZ0b8Ps7N6CQs/xfjKpb4PDLb2HHpn5tlE9/IYfFC8wW5/2FHB7bvMIoa1Vmqr5zaHst0qgZ/M7lvinoPmeSrbpMoj7eBLAxhrGkiqi7QOjqCwQtNOPXkFZ1HitLqFQEnDaulSHsf2itdoc7LGSrLq+lZmnGxqfvXoozb1+KdCzdyK7hiUCiJ0Mpj40V8ci9d+LYWFFZw8Vk3sjXNnMNtdIsWNVxyI0zkkRV08bLSNPVGQlzJcAp5B5E2SorjBuBSe833Xl05466KBJBHzblhEU6Opr5fct2BafPTdfSt5uZN85IChl9FKQT+cKeTG2uL16QhZXN1KI+dD7yZpoLB702devPMNelJCLYyBkYGBCjo6OhH5dpJOxYb+mjjtL9wVEe7QkBdY1jnXOvkLfw8bXZluaNbkWps3Cdr907clZ5839s8wocHFzX9JhMuPuLf6Gcz1kivP3U54yPQ0RjQogB1XNsUbc5YVv98ljuULwwYZFOJ3IJr7uROl1qbvG8PGPDyhIKOQtXyrZxdx0nuvBXk1BZKcYyQkVGrEQt0oB+Poc5z1momQak+HtlHTKdx9M7Nyh7XgI3G8eu2nOylkyiKni0eGEPJvbdH7h7uES1QW4aKntwcF0swuxGt+fSygaoGxbqLqBZ90gY5UbdWWYZAOFV6e1cskS4dVFPrNmJck64fbRLchauOrL3vOaEFE/3MUwtbFVkhcmmOZBc2WDdnotfF5sgsFDHSBT+ZHdquHtzBkDTqfCt7LRLCAJEgLxGWaTNmBMC+x9aG1nShxt3uJ7Tpbbl0CnjG4ZTPP0iKdzoIitMNs2T7CT+0uQH2sfDsvC5HnVMhF1/WnW8Z199v+H4B05MNV1O1V0tT7WBnrOyeGzzCmQz6t31GXsO7JIOjgBw4MQU4qh/JkMzdZgmTXmFsKm6/gSJp/brGJRk2WDdTSzM1RBb1DERdu0Qk9Twsl3RvsZ58UnLvFgq1zaS+h0Wv3N8e0fO1m3Y7NjUj4OD6+q6UTPhEGXlwN68hdKMXStutGt4Ak8enWz47QG966E3byG/oMd4hdjKxrffe9uhk3grsFDHRNgTqdUJ6Oy84VwySj9isVTG7uGJWoKEtJSOjRXritUMv34eJ99kkW438gt6sO/Btdrf3uk20Plgt9+zLJHNOxWmfux2hV0fMRF27RDT96kcEs4lqpdl7uwovWtetBt2+udEV9eMbleKpTL2H290i0lkrXBA3zG81U7iYRJ1yQcvdOn4pmn6JrBQx0TYE8m0kIzbw9mbt+r8e52yNGSC47cKKpVtjIwXjVeDXq3WosbEjx0VD6xfFujxZuDMxBiJM+pDFw7l9itevT7LbgtGi9xI1sUJyzoaJtmDncjIeBG7hyeUW76FnFVX99oPr8xEFmoNScVktopzY9AEK0uAQKQp40x7o2oJ5hZhXfd4VZ3pTsEk5PBdR8q9H15Cza4PBWGH0sWFc9ym2BWBWxb1GDWsZcLlyM4NoWavNUPeynj+5oRGF4nbfQaEs1mepOukGYI05WgVFmoFScZktkKzE0duBhKA23I9sDQx0Ux49BdyGNzYb7zX0Oq5Htu8Qvm72nPCM2NQ9Ux+QU/D6rLVzfJ2NI7i3N9hoVbQrjGZzY6PgNoFcnnGZjdIyLjl0b2JvLAnustQnuvg4DrcsqgxGteuiMCrKNU8091wZm7MGoltOxpHfjehMBenLNQKom7DJQl7qdfs+FiWo0Wg6i5wRyNIKzLKzVyn2JU0YZQVIZQRSbrwMtU8k1EX7vdcnrE9LWN5DejcdWk2jvxWQ2Fu/7FQK4gjJjOMpZ5b6Leu6eMfNKV8fH0WT+/cUNdvLy4fpxQ73Y1c3jzcoW37H1ob6DoY3NiPxQsbrXadZWyyp5LmhBV5c9IZzmHGUXNmooKo23DJY7eSUq4qQnNsrMjWcUqxKwJPHJ2odTcZ2rY6dGtR1xJKip1XcSOvFO0g10EQt6HfjSquhJVWGNzYjwMnppRJX2G6PlioNUTZhgsINqFHxot1k0HeqVVCz6QX6fovlsoYenESSxRhb63w6OYVGP7OediVm3JtZakmds0YIEGvgyCp3F43Kne9kTSjcynpHm8GFuqECFJjd+jFybqLjxNU2h+7InClbMPKUCibt/2FHAbuWorh18/XP+E6tInwtpJDYFKSVKK7Btot9jqOOiMdK9RhJKz4HSPIOVRZhLqOzk4Ov/xWnUgznYNAtT63TCjRuS78yFlZbF3TV6t+58SeE4EqNLZa1zmI1R5E1NNMHJ+jIzMTw0hn9TtGkHOoXksAPn33Urz7UblhQjtFvZlfx8oANlfobxt68xbGv3R/g4vLBCLg0XtXNNz0616D+qa0XsSdYWhi7LRDlnAYY+y6FPIwJpvfMYKcQ/daws0+dUD1x46yqSyTXt5VdPd21gd3dslxImu3eEVOBJn3uj6ZQcQ+TEbGixh6YbLOPWRlCId/eX3qxLpVuq4LeRgJK37HCHIO3WsFUFuWNtsMlOk8VH7kVXtOKl9bmrE9N60IwXr3+flb47Zu9x+favDh23MC+49PdZxQe9GRYbdhJKz4HSPIObzOK0U8zroBTPzIhBcVznhbXRKU13zzml8C1WYPpvH5XjkESaR5x9Hmqh3oSKEOI2HF7xhBzjG0bbX2IpUXmV8hpf5CDu8e2h6oiE/SBX+6EUtzRW2/Zxme3rlBWW+DqCrQj/7h32DX8ESdEA69OFld/nvMN78MuSCp2F51ndsxzbtT6EjXRxgJK37HCHKOwY39GH3vEp579f06/59T2LOa+tGSD39YxsYvvxJoo2lo22rsGp4wfj3TOrOaTdzT56Zrbavc+xCXZ2w8MTyh7NBuVwQOnJjC+JeqdY295pvXRmQQt58uhC/uGjgj40Vk6Gb8uZPefHhZf2EQtUuoI4UaCCdhJcykl4OD6zBw11Ltj+kl0kA1iiNoy6snjk40O1ymSXS/ohQzaZm6l+5eQTryd/eaj/K4ujkSRkxvnH0JpZtFJdJWlrDvQX3X9LhpNaTRhI4V6qhp5sdxX2jSH3nRsbsfJlwELz04xSwqC9TruGHE9MYZ96zbs8kS4fDn0xXx0Wo5CBNYqJskyI+jWhYBUHaAZjoPt5jpLFMdpsV9dMct5KxQBCOOGjgS3U1nTgjjpLKwx6Y7vu63DPIb++Er1ER0J4A/A/BPUF2hPSOE+C+hjSBFBPmhdROpWCrXrGRVBqK0vBf2ZDjKowtQ1axQWaYZ6N0fD6xfZjQ3dRbv/ofCcRPEGZoX1M0StfvB6/i61XCY3ZJMLOpZAE8KId4golsBjBHRN4QQfxvaKBLAL6Xb74fWTSRZhF8ew72BCFQtbxbpzocAZaKJzjL9na+9iRlFSulLkx8Yzc0oLd44/LBOgrpZonY/eB1ftxoOc5XsK9RCiA8AfDD/7x8R0fcA9ANoW6FWTTqdoOp+aNVEUtVqYIdG9+K0/lTWqFvEd2sidFQxw2W7giePTtaVTZV7IFEIZxx+WCdBbzpRR6R4Hb/fo7hUWATyURPRSgAbAbwW2ggSQDXp/Hbr3cgJ4wyJYlFmJISbG3im1mhQ37W02JzHA6KxqKMQQj9XSpCbTtQRKV7Hj2OT1TjhhYhuAXAMwC4hxA8Vzz9ORKNENDo9PR3aAKMgyORaotnICVJAR9Uzj+lcCNXa0F6dXFSJIrqkFpOY4bJdwYETU5FlDobdni7sLMeouzJ5Hd8rSSgsjISaiCxURfo5IcTXVK8RQjwjhBgQQgz09ZnXFkiCIJNLtR+wd+Qsdg9PGIl0zsri0c0r6n7EHZvSE1rEhM/TOzfUklsAc2tUd8Hve7CxJZaKyzN2ZJmDYQth2FmOUYtlHGLshW/1PCIiAP8TwCUhxC6TgyZdPc+PoAWQCKgLq9s9POHr5nC+x/lj7h05q/SHM+klb2VQtudQyFsQArhStpHR7PQHqZ6YJcKcEEYuipHxYktZpmF0TAkz6iNtVfpaIYyyykDr1fO2APhVAGeJaGL+sd8RQvyF8QhShmqjYubGrNZCdi7NFlkZX5HNEuHtpz7X8PjIeJFFuo2QLgyndSzRXZyyeJFfkwhA7WP2akzRm7c8V3G5+RuKijCiNMLcqIwzyzFq4tho9XV9CCG+LYQgIcQ9QogN8/+1rUhLBjf248ye+/DOoe04s+c+o+Vl2a4YuTtUltbIeBFPHp1kkW4jBICTb36gfE63FAbQ4Hs9NlbEjk39tdeq4mvdy36VD/fja7Oe411kZUMrzhQ1UfuU4ySOGiicmTiP28puRVDdYTnyouPsw/bj8oyNkfGittiW+/Eth04pravT56ZrLhFdbWnnha2y0vx6K16esdGbt7CwJ6MtA+onHnEltcSZ5Rg13DMxQXTZRoWcheuzc1r/dgaNdRW41nT68aq14reENWmd5hRIkwu7WWvs8oytLanrPoebuJNaoor5jptUheelFV2h9WZeM/TiZG2pqbtoH1i/rLbEVUI3J7U8b5g5/0z4bLl7KW5dpLdZvETT7aLQ4RRIk2W/TlB785ayprUT3Tj8xIPrTTdHHBEhbW1Rm1gAplbCgRNTRt2+ZV1h3Q78nKgubfMLsrh6g63oduDM25c8ny94xDGbrJbcAmmy7NdZabK8Z9DemiZRH3HXm+4kol4dtLVQm+y2mu7ImtZ6lpPWa6ksABbpDuLja7NaP7WfiGUI2LGp8SL2u7BNG1eYrNh0NUck0nWjM1PaMRKj02hr14eJBeBV5a6ZLCg5aTf/RG/g9zLtiT0ntMt/PxGbE8Dwd843NdfckUm6mjN+NdpM/NI6sW/XSIxOo62F2iSt1WuSypRV04vIylJt0r77ES8HuwndDd+vXyFQbacVlZ93cGO/b4TSyk/orwEv103c2XeMnrZ2fZjstqpeIwm6UbJ4QU9t0vIGYXehu+E7XRRecyJKP6+uepvk1e9f1j6nG5efu4SpJ+qwxra2qE12W+VrdFwslY0volLZrkWPMO2JSdSE+2m/5b90UXiVtYzSz+tn1XvF74ddbKkbGRkvYuiFybrkpKEXJkMphiVpa6E2ZXBjv/YiWl7IGU9K2RSA01aSJW9ltBl+XsioicO/vL52c+/NW7VWV/J4ty2y0Ju3AodaDW1brbwJOF1mUeBnjHh9T52UIZgU+49PNSQj2XMC+49PhXaOtnZ9BAnQ37qmD8+++n7DMbau6cPAXUt9Cy2pmgIwyXB9VuCdQ9sDFdfqzVvY9+DahqgJoHEelco2clYWT+/cEGj5Kl/rDJ1znzcqBjf244XR95Whhl4b352UIZgUujDJIOGTfrS1UAcphnL6nLpGtoyL1k1ywN8HyMSLXMoPbuzH6HuX8Pxr57XL+5yVwVMP3+MpPGEW1fEKu4vaj6nb4Pbb+O6UDMFOpq1dH0EC9P1eq5vMsmxlmG11mNaQ3oWR8SKOjRU9fbDXNNXknMSR6BF2oXwVcXTDZhrJW2oZ1T3eDG0t1EE2Qvxe6xVvvWrPSVy9PgsrG15XYaYFRFX49h+f8nV7CMA3sieODbU40rN1vugwu2EzjSzUbOTqHm+GthFqVb2OIBshfq/1uigFqv4mkxRzJnrmECyF2s8yjmNDLQ6rPY5u2EwjJU1Ws+7xZmgLodYtGwEYF0PxC+UzSVxg0kOQjRo/yziOojpxWO069xy77aJF11dV93gztMVmoteyUZdaq8Jr08Q0cYGJl1ajbUwt46g31OIohRnHOZhGdJ6lMD1ObWFRx1nVa+aGdxcNJjr6Czkc2bmhzrJ9dPMKpVvCK2dF+mRVlrFJydsokFZ7wWFlLQpxs8l5jqQasHYrcbg+2sKijqODQtCGt0y4SMtPZdkO3LW0IazNq9Grql8lEH9hfBXXZ29GoVyesUM/P4faxU8c+tQWFnWYmz06i4q7sMRLhm4uDbNEylKgEncVOfkeFV7+2KQL4yd9fiYa4tiMbguLOqzsKZVFNfTiZOAi7EzzEKqF+D++NltLu60IgWNjRQzctdT3N/XqP+l3cXiFYG45dCryzLy44rU5yzBe4sjubAuhBsJZ0h040Rh3a1cEi3RMyOShLYdONTRqcGcC6gRHt/LJEvn6Y3VLVFnDBYjWHRL1ElkWB5I3QFkcCIjPtdOtRO1yagvXRxiMjBeNu7gw4WNlbhYm8sug88ri01mfc0L4XiiqJaoqqiQqd0TUS+Q4igMxydA2FnWrsB8wHnJWFjs29ePkmx/UboyFnIX9D90sTKRrY0akby0lxbMVq1S1RNXdNKKIKIp6iRxHcSAmGTpWqN1LZ46Njgfpfjg4qC+7qe01KbzrUhRLZRzZuaGlWGH3ElV3Y4iqHjNHZTDN0JFCrdo0ZMLBypJnKr1KhNw3zd681ZQbKksUulXaSUkiuu+116OLOtMedKRQc6hddBz+/HrP2t0yeqKQtyBEddnt9AMXS2VYGfIVfBXO8qZhxh0DnVGPed+DazH04mTd92plCfseXJvgqJgw6EihjrI/XTdTyFm1GtCqJgzZDNVWL07Lzi3H9pxAIWdh8cKemjhevT7r60uNqgpcp7gjOummw9TTkULNPunwsTKE/Q9VLTPpf5YF+7NEWGRlcPWG+SqmVLYxse/+2t8mmaFcBc6fTrnpMPWQiGDyDwwMiNHR0dCPawqng4eDdFn0G1hmq/acDFw8qTdvoTRj1yw/wLsolozDZphOhIjGhBADqufaMo7ar7COszgN0zxSpE0qFDYTJXF5xm4oW3tmz304snMDN1xlGAdt5/rwKqwDNPrnALB13QKm/n5V9EQQnJmJSftaOQ2bSRu+rg8i+hMADwD4UAjxMyYHbcb1YXpx6OJeCzkL12fnGsKsnnp4HUbfu4TnXn2fu4g3QRB3g/M3zC/IBvJZA1VXyzuHtjcxyvBQuc3kPGKxZqLEy/VhYlH/KYCvAvizMAflJEj5SZ2Fp4oYcKYCs0gHJ6i7wWkNj4wXceDEVKB46aiSTIIQZkdyhgkLXx+1EOJbAC5FOYgg5R+DXszFUpkjQAxQFe1v1oqUN16nSFtZQiFnVavn5ayGRsFp8UHH2aSCYUwJzUdNRI8DeBwAVqxYEei9QS4OlS+01XZNTPW7Diu0S3XjtSsCixf21ELy0uoHjqMIPMMEJTShFkI8A+AZoOqjDvLeIBdHkMI6jDlhCpHJjTet8b6dlFLOdA6piPoIenGYFtZhGunNW7hmN266hilE7WyVJh1xwjAqUiHUrV4crYaGdQs5K1ur+xClELW7VZpWa5/pXkzC854H8BkAtwP4fwD2CSH+2Os9SWQmPvqHf4Mzb0e655lKdLWdnc/PCVErknSlbMdiJabVB80wacUrPK/tU8ilIHSj68MZ37tyz0nlawjA05oazhwbzDDpoeNSyCXOlk3dhjt8Tpcuv7yQ4+7XDNPmtLVQd2Ldab9KnjkriyM7NzTU39i6pg/ut0q/MMcGM0x709ZC3QlCkyWqJZgc2bkB7zy1HYWcuiMHAUp3xch4EcfGinWx5ARgx6bqppgu2qIdojAYhklJ1EeztHsMNQH4yhfWNwivzqou5C2lT1m1shAATp+bBtD+URgM0+20tUU9tG11QznMduLRzSuUwlvS1MfQPe7n2nCWfW01PZxhmPhpa4taFX998UoZ7dIIRNepO2jCiMnrOTaYYdqXtraogaoADW1bXRXpUvuIdJaooeGBRLVS8HJVBH09wzDtRdvEUesSKEbGixh6YRL2XJsotAMrQ7hlUU9dOypnmdAgCSOcYMIw7U3bJ7x4FXPff3zKt3t1HIRRwY+TUBime2n7hBevhI0kRdr55QkAGU20hqV7wgUnoTAMo6IthDqNCRtWBphzPTYnqkXxe/M346ALOQuHf3m9caPdTogNZxgmXNoi6sMrqmHmxmygdk+t0j/v/909PKF8/krZ1vb9M6nwx0koDMO4aQuL2iuqYd+DaxvaOkWFs9FrRpOVohNadyxzmttRMQyTLtrCojapV/3k0UnPcp+tQqjeMOTGpupcfkLrjmXmSA2GYUxoi6gPE0bGi9ilcUe0CqGaRXhwcJ22m0yWSJkOzjAMY0LbR32YEJZAEoDHNq+oS7d+eueGWhahbrNvTggWaYZhIqEtXB+mPLZ5BZ599f2WjiEtZx3t3A+QYZj2pGMsagBYu3yJ72sIQM7K1P4t6c1bOOKwnHVwujbDMHHTERb1lRkb67/8Su3vn13Zi4ulayiWyrWegv0hbdZxl2qGYeKm7YX6q6f+Hr/3yt/V/v6/v70Vdy7NR3pOrkTHMEyctK1Q/+OVa9j81P+u/f3vP3M3/uNn1yQ4IoZhmGhoS6Hef3wKf/rX79b+Ht37C7j9loXJDYhhGCZC2kqo357+GP/yK/+n9vd/euCn8Ws/tyrBETEMw0RPWwi1EAK//twb+Mvv/mPtse8e2IZbFrbF8BmGYVoi9Ur35oUSHvrqmdrfT+9cj1/aeEeCI2IYhomX1Ar13JzA5//7X+ON90sAgNtvWYAze+7Dwp72bWbLMAzTDKkU6m///Q/w2B+/Vvv7f/ybn8XWNT+W4IgYhmGSI1VCfWN2Dp85fBoXr1wDAKxdfhuO/+bPIWvYIYVhGKYTSZVQ/7O9f1n799d+/dP45IreBEfDMAyTDlIl1Hu3/xQmL1zBf/2VDSBNYX6GYZhuI1VC/W//xU8kPQSGYZjU0VHV8xiGYToRI6Emos8S0VtE9A9EtCfqQTEMwzA38RVqIsoC+AMAvwjgpwE8QkQ/HfXAGIZhmComFvWnAPyDEOL7QogbAP4cwL+OdlgMwzCMxESo+wGcd/x9Yf6xOojocSIaJaLR6enpsMbHMAzT9ZgItSpOrqF1uRDiGSHEgBBioK+vr/WRMQzDMADMhPoCgDsdf98B4GI0w2EYhmHcmAj1dwD8JBGtIqIFAH4FwPFoh8UwDMNISIgGL0bji4g+B+AIgCyAPxFC/K7P66cBvOd6+HYAP2humG0Pf/bupFs/e7d+bqC1z36XEELpNzYS6jAgolEhxEAsJ0sZ/Nn5s3cT3fq5geg+O2cmMgzDpBwWaoZhmJQTp1A/E+O50gZ/9u6kWz97t35uIKLPHpuPmmEYhmkOdn0wDMOkHBZqhmGYlBOLUHdrmVQi+hMi+pCIvpv0WOKEiO4kotNE9D0imiKi30p6THFBRIuI6HUimpz/7AeSHlPcEFGWiMaJ6KWkxxInRPQuEZ0logkiGg312FH7qOfLpP4dgH+Fajr6dwA8IoT420hPnAKI6OcBfAzgz4QQP5P0eOKCiJYBWCaEeIOIbgUwBmCwS35zArBYCPExEVkAvg3gt4QQryY8tNggoicADAC4TQjxQNLjiQsiehfAgBAi9GSfOCzqri2TKoT4FoBLSY8jboQQHwgh3pj/948AfA+KioudiKjy8fyf1vx/XbNjT0R3ANgO4I+SHksnEYdQG5VJZToTIloJYCOA1xIeSmzML/0nAHwI4BtCiK757KiWmvhtAHMJjyMJBIBXiGiMiB4P88BxCLVRmVSm8yCiWwAcA7BLCPHDpMcTF0KIihBiA6qVJj9FRF3h9iKiBwB8KIQYS3osCbFFCPFJVLth/ca86zMU4hBqLpPahcz7Z48BeE4I8bWkx5MEQogSgG8C+GyyI4mNLQAemvfV/jmA+4jo2WSHFB9CiIvz//8QwNdRdfuGQhxCzWVSu4z5DbU/BvA9IcTvJz2eOCGiPiIqzP87B+AXAJxLdFAxIYT4ohDiDiHESlSv81NCiMcSHlYsENHi+Y1zENFiAPcDCC3aK3KhFkLMAvhNAC+juql0VAgxFfV50wARPQ/gbwCsJqILRPRrSY8pJrYA+FVULaqJ+f8+l/SgYmIZgNNE9CaqRso3hBBdFabWpfw4gG8T0SSA1wGcFEL8VVgH5xRyhmGYlMOZiQzDMCmHhZphGCblsFAzDMOkHBZqhmGYlMNCzTAMk3JYqBmGYVIOCzXDMEzK+f+aHytEY1UoCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_hat_test)\n",
    "plt.plot(y_test,y_test)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8171414810143506"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_hat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9df1b3d7a578ae7f9767aeeed95c1791e173583b81c588f90ca906e4d0abdec3"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}