notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-requierments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('flat.csv', sep='\\t')\n",
    "y = df[['cost']]\n",
    "X = df[['district', 'total_area', 'repair', 'year', 'distance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for LinearRegression - 0.6865328725878797\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import  cross_val_score\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn import linear_model\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "accuracy = r2_score(y_test, predicted)\n",
    "regressor_name = model.__class__.__name__\n",
    "print(f'Accuracy for {regressor_name} - {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'LinearRegression\\nDetermination-0.687')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa8bcf1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "errors = np.round(np.abs(1 - predicted / y_test['cost'].values) * 100, 0)\n",
    "mean_error = np.mean(errors)\n",
    "max_error = errors.max()\n",
    "min_error = errors.min()\n",
    "\n",
    "errors = [x[0] for x in errors]\n",
    "c = Counter([x for x in errors])\n",
    "data = {'error': list(c.keys()), 'count': list(c.values())}\n",
    "df = pd.DataFrame.from_dict(data)\n",
    "sns.lmplot('count', 'error', data=df, fit_reg=False, markers=[\".\"])\n",
    "ax = plt.gca()\n",
    "ax.set_title(f'{regressor_name}\\nDetermination-{round(accuracy,3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More complex model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for AdaBoostRegressor - 0.6788864122735636\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'AdaBoostRegressor\\nDetermination-0.679')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa88a4ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model = AdaBoostRegressor(DecisionTreeRegressor(random_state=0), random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "accuracy = r2_score(y_test, predicted)\n",
    "regressor_name = model.__class__.__name__\n",
    "print(f'Accuracy for {regressor_name} - {accuracy}')\n",
    "\n",
    "errors = np.round(np.abs(1 - predicted / y_test['cost'].values) * 100, 0)\n",
    "mean_error = np.mean(errors)\n",
    "max_error = errors.max()\n",
    "min_error = errors.min()\n",
    "c = Counter([x for x in errors])\n",
    "data = {'error': list(c.keys()), 'count': list(c.values())}\n",
    "df = pd.DataFrame.from_dict(data)\n",
    "sns.lmplot('count', 'error', data=df, fit_reg=False, markers=[\".\"])\n",
    "ax = plt.gca()\n",
    "ax.set_title(f'{regressor_name}\\nDetermination-{round(accuracy,3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Popular model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for XGBRegressor - 0.6958413104623966\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'XGBRegressor\\nDetermination-0.696')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAF6CAYAAAAnLIKxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAHxxJREFUeJzt3XuUX2V97/H3ZGZywyEJzeCVQrHy7cFWsFDxStIWjHRRUbycLusFNadyCsVal2212CqrWm0RD+pBKGgjVusFxQuK4RQB0VZaFBQKfjmiUj1SO9gkhJBMwmTOH3sPDHEmmSS//Xvmt3/v11pZ87vt/Tx7kvnMk+9+9rMHJicnkSR134LSHZCkfmUAS1IhBrAkFWIAS1IhBrAkFWIAS1IhQ6U7oHaLiF8FrgF+MzNvrF9bCdwAnJWZX4iIVwO/BxwILAS+B5ydmTfUn78WOBTYBAzUn/mHzDynfn8dcCIwVje7AHgEcGFm/nUXDlPaJwPOA1bTIuI1wJ8CvwpsBq4Crs7Mt0XE24Hjgd/NzLvqz/8G8HHgmMz89zqA35eZl9XvLwduA16UmV+rA/jWzDx3Wps/D9xe7+M7XTpUaa84AlbjMvOiiHgm8EHgTmAD8PaIeCTwh8DjM/PuaZ//ckT8EXDALLscqb/es5tmH1d/3QwQEU8H3lnvcyfwlsy8IiIGgb8Bnks1wr4BODIzV9fB/1/ALwHvBy4Fzgd+BRgGrgbekJkPRMRbgecD24GfAqdl5t27ef1ZdbtL6/fOzswvRcRpwKvrfm7KzF/fzTGqxzkCVldExAHAzVTB9cuZeV9EPA94c2Yes4dtr+WhEsQw8ATgY8ArMnNylxLECLAM+CpwfmZeExErgK8DazLzBxHxGKqgfQZwEvASYA1VMH8eWDQtgO/MzFfX/fggcFNmvrcO7nXALcA/ALcCB2fmeES8nuoXzTdmef16qhH8czPzhoh4InAd8GvAKuA84LDMvHdfvtfqHZ6EU7cEVTguB6YCdwB4cAQQESMRcXP957t1eWLKGzLz6Mx8IvBI4DCqssaUd2fm0cCTqMJ2J/CV+r2nAY8GPhMRNwNfrNt9EvBbwKWZuS0ztwMX7dLv66c9Phl4Tb2PbwBPoRoN/z/gW8A3I+Jc4ObM/MxuXj8O+O5UjTsz/w34GrC6bufbhm9/MIDVuPqk26eB19V/PhYRj6Iahf5SRPwcQGZurkP2aODvqU7K/YzM3EA1Aj5+hve2AC8Dng78Uf3yIHD71L7r/T8VWA88QPWLYMrELru8b9rjQaq689Q+jgPOzMydVCPX06jKDO+OiPNne52Zf+4WUI3ud21TLWYAq1H1f9U/AXw+M/8hM/+OKvg+BvyEqqb6yfqk2dQ2P09VHtg1DKfeH6Yajf7LTO/XAf164C8i4rFUI+InRMTx9fZHA/8XeAzwBeClEbEoIoaownK2utx64HURMRARi4DPAWdGxFFUpYbbM/OvgHcDR832et2fiIin1P15ItUvk2tn/UaqlQxgNW3qRNPrp732+8DPAW/PzD8DPgB8JCJuiohbqUbLVwFvnL6fujRxE/BvwF3A22ZrNDM/AtwIvCszx4AX1Pv4FvBh4GX1rIt1VCPxm4B/ojohdv8suz2L6uTYLcC3669/nZnfovolc2NE3Ai8Cnjdbl6/B3gR8N6IuAX4KPDKzLxjN99HtZAn4dTXIuLZVCfJ/r5+fj6wLTP/pGzP1A+chqZ+92/AGyLiDVQ/D98C/mfZLqlfOAKWpEKsAUtSIZYgNGcRcRjVhQS31C8tAHZQXfBw6R62XQsszMwLGurbJcDHMvMf93H7B/sXEacDyzPzHR3q2yDVxRVrqH7mzs3MC2f57O8Da4ElVHONX11fxHEUcAHVRSb3Ul059+V6m18B3lu/NwG8JjO/0Ym+q1kGsPbW1noOLAARcShwdURsycxP7Wa7Z1JNyWpEZq7dz1082L/ZwnE/vIbq6r1fproY5Z8j4puZ+bBpdBFxKvAHVFPwNgKfpJo3/Q7gs8BbM/Pv6jnU10XEKqowvooqqL8YEacAH6G6fFrznAGs/ZKZd0XEn1OdyPo81XoLq6guWriJaurWb1KttXBiRGzNzP8dEX9GNTVsAfAD4Pcz88czrL/wAqqR4G8AB1PNG35k3cYBwIsz85apBXuopp5dTXW123HAQcCfZebH67UnLqq3fxTVVLYXUwXeg/0DRoGVmXlmPUf3fVTT5iapprVdGhGrqabBfY8qWBcBZ2TmNTN8m54P/G1mPgBsiIiPAS/lZ+cxv7ze/38B1CPxhfWFLIdQrUVBZv5HRHwbeA5VUN+ZmV+s9/E54Puz/X1pfrEGrE74FtUluX9KdWXZMZl5FPBj4B2ZeTlVMLy7Dt+X159/Sj2a/iJwybT9bcjMIzPzvfXzwzLzycCpVAF/bWYeC3yJasS4q8OB9Zn5FOBPgKklKX8H+OfMfFr9mfup5gM/rH9TO6kvzPgc8N7MfBLVuhFvj4in1R85jiown0w1l/kts3x/DgF+OO35j3hosaDpjgAOjogv1QH7FmBjPW/4+8Ar6n4dDjyL6vLqI4D/iIgP1HON/w8OrHqGAaxOmKQKs5OBU4Cb6vUSngccOcPnT6a6FPjG+nN/QLVWxJTrd/n8p+uvd9ZfvzTt+UEz7H8HVagDfHPqM5l5PvBP9UprF1CNXB+xm+M6AlicmZ+ut/8x8CmqkSfAXZl5867tzGCmn7OZrvIbplpU6MXAsfX+pi42eS7wwvrCjXOoruDbXm/zW1Qj7GOpasFfrK/U0zznb0p1wq9RnZhbBrw2M68EiIhHAItn+Pwg8M7MfH/9uUXAimnv77oWwvj0J5m5Yw/92V6vwwDVL4eBup13Ui2g80GqReKHefg6ELva05oNW6e9Pr2dm6e9vhb4d6rR6pTHUo2Cd/Vj4PKphXgi4u+BP5/W7nPrMgYRcSXV6Pwg4DvTFvb5bH1C8nCq9ZA1jzkC1n6JiCOANwPvolor4cyIWBgRC4CLgb+qP/oADwXXemBtREwttnMO1eXBTVsD/K/M/DDwn1SjzcEZ+jclge31yTHqZSxfQPXf/FlNX/Qnq7uAfBZ4VUQMRbWY/O8An5lh08uAF0XEkogYoPofxL/W7/1t/XxqbeNfBv4RuBI4LCKOqd87nuqXgXXgHuAIWHtrybQR3k5gG/DGrG4t9GXgXKqTb4NU6/9OrQFxJfC+iICqjvtY4OsRMUk1QjytC30/Bzi3Pmn4ANWawb84Q/+AaqRdr1n8noh4C9XPyzn1GsOr96Ld9wOPp6qVLwQuyszrACLinLqtP6cqixxEddJxkKqsMfX9+z3gkoj4C6r/ITyvXvltS93HC6Jac3kcODUzt+1F/1SIV8JJUiGWICSpEANYkgoxgCWpEANYkgqZ17MgxsY29+wZwhUrlrJhw2w3Vmgnj7k/eMx7b3R0ZMb55o6AGzI0NLjnD7WMx9wfPObOMYAlqZDGShARcRoPTa5fDBwNrKZazeoB4KrMfGtT7UvSfNfYCDgz12Xm6sxcTXVlz1nAhcBLqNZePS4intxU+5I03zVegoiIY4EnAh8DFmXmnZk5SbUewAlNty9J81U3ZkG8CXgrcCDV6v1TNlOt2DSrFSuW9nTBf3R0pHQXus5j7g8ec2c0GsD1yk9RL15yINXtWKaMUK3mP6tenuoyOjrC2Njm0t3oKo+5P3jM+7b9TJouQRxPdXsY6jVOt0fE4+ul9tbwswtvS1LfaLoEEVT3zJpyOtUNAwepZkHc0HD7kjRvNRrAmfk3uzz/OtWtaCSp73khhiQV0toAHt8xwY/G7mN8x0z3PpSk8ub1Yjz7anzHBJdccRv3bNrGymWLWXvykSwa7t3pbJLaqZUj4LGNW7lnU3VLrHs2bWNs49Y9bCFJ3dfKAB5dvoSVy6q7oa9ctpjR5UsK90iSflYrSxCLhgdZe/KRjG3cyujyJZYfJM1LrQxgqEL4caOPKN0NSZpVK0sQktQLDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKsQAlqRCDGBJKmSoyZ1HxBuB5wILgQuA64B1wCRwK3BGZu5ssg+SNF81NgKOiNXA04FnAKuAQ4DzgLMz81nAAHBKU+1L0nzXZAliDXALcDnweeAK4BiqUTDAlcAJDbYvSfNakyWIlcChwMnALwCfAxZk5mT9/mZg2e52sGLFUoaGBhvsYrNGR0dKd6HrPOb+4DF3RpMB/FPgO5m5HciI2EZVhpgyAmzc3Q42bLi/we41a3R0hLGxzaW70VUec3/wmPdt+5k0WYL4KvCciBiIiMcABwBX17VhgJOA6xtsX5LmtcZGwJl5RUQcD/wLVdCfAXwfuDgiFgK3A5c11b4kzXeNTkPLzD+e4eVVTbYpSb3CCzEkqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKGWpy5xHxTeDe+un3gYuA84EHgKsy861Nti9J81ljARwRi4GBzFw97bWbgRcA3wO+EBFPzsybmuqDJM1nTY6AjwKWRsRVdTtvARZl5p0AEbEeOAEwgCX1pSYD+H7gXOAS4AnAlcDGae9vBg7f3Q5WrFjK0NBgYx1s2ujoSOkudJ3H3B885s5oMoDvAL6bmZPAHRGxCTho2vsjPDyQf8aGDfc32L1mjY6OMDa2uXQ3uspj7g8e875tP5MmZ0G8CngXQEQ8BlgKbImIx0fEALAGuL7B9iVpXmtyBPwBYF1EfBWYpArkncBHgEGqWRA3NNi+JM1rjQVwZm4HXjLDW09tqk1J6iVeiCFJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhRjAklSIASxJhQw1ufOIOBj4BnAi8ACwDpgEbgXOyMydTbYvSfNZYyPgiBgGLgK21i+dB5ydmc8CBoBTmmpbknpBkyWIc4ELgR/Xz48BrqsfXwmc0GDbkjTvNVKCiIjTgLHMXB8Rb6xfHsjMyfrxZmDZnvazYsVShoYGm+hiV4yOjpTuQtd5zP3BY+6MpmrArwImI+IE4GjgUuDgae+PABv3tJMNG+5vpnddMDo6wtjY5tLd6CqPuT94zPu2/UwaKUFk5vGZuSozVwM3Ay8HroyI1fVHTgKub6JtSeoVjc6C2MXrgYsjYiFwO3BZF9uWpHmn8QCuR8FTVjXdniT1Ci/EkKRCWhvA4zsm+NHYfYzvmCjdFUmaUTdrwF0zvmOCS664jXs2bWPlssWsPflIFg337nQ2Se3UyhHw2Mat3LNpGwD3bNrG2Mate9hCkrqvlQE8unwJK5ctBmDlssWMLl9SuEeS9LNaWYJYNDzI2pOPZGzjVkaXL7H8IGleamUAQxXCjxt9ROluSNKsWlmCkKReYABLUiEGsCQVMqcAjojTm+6IJPWbuY6Az2y0F5LUh+Y6C+KHEfFl4AYeusUQmXlOI72SpD4w1wD++rTHA010RJL6zZwCODPfGhGjwHH1Nv+cmT9ptGeS1HJzPQm3hurOFq8EXgF8OyJObrJjktR2cy1BvA14ZmZ+HyAiDgc+DVzRVMckqe3mOgtieCp8ATLze3uxrSRpBnMdAf97RPwh8IH6+Vrgrma6JEn9Ya6j2FcDTwO+B3y/fvx7TXVKkvrBXEfAZ2Xmf2+0J5LUZ+Y6Av7tiHD+ryR10FxHwD8FvhMR3+ThV8K9qpFeSVIfmGsAf6jRXkhSH5prAP9uZj670Z5IUp+Zaw14cUQc0mhPJKnPzHUEfDDwg4j4Tx5eAz68kV5JUh+YawA/B/hd4Ejg7cCxwHVNdUqS+sFcSxCnA/8N+FXgh8BpwB801CdJ6gtzDeA1wMuAbZl5L3AicFJjvZKkPjDXAN5Zf52svy6a9pokaR/MNYA/AXwcOKhelOcrwEcb65Uk9YG53hHjnfWi7HcBPw/8RWa6FrAk7Ye5zoIgM9cD6+f6+YgYBC4Ggqp0cTqwDVhXP78VOCMzLWVI6ktNLqr+2wCZ+QzgbKq7apwHnJ2Zz6K6uecpDbYvSfNaYwGcmZ/hoTWDDwU2Asfw0PzhK4ETmmpfkua7OZcg9kVmPhARHwKeD7wQODEzp2ZSbAaW7W77FSuWMjQ02GQXGzU6OlK6C13nMfcHj7kzGg1ggMx8RUT8CXADsGTaWyNUo+JZbdhwf5Nda9To6AhjY5tLd6OrPOb+4DHv2/YzaawEEREvi4g31k/vp5o3fGNErK5fOwm4vqn2SxrfMcFdd9/L+I6J0l2RNI81OQL+NPB3EfEVYBj4Q+B24OKIWFg/vqzB9osY3zHBJVfcxqYt21l2wELWnnwki4Z7t4wiqTmNBXBmbgFePMNbq5pqcz4Y27iVezZtY3hoAfds2sbYxq08bvQRpbslaR5qchpaXxpdvoSVyxYDsHLZYkaXL9nDFpL6VeMn4UoZ3zHB2MatjC5f0tUSwKLhQdaefCQTAwsYnNxp+UHSrFoZwFN12Hs2bWPlssVdr8MuGh7syzPFkvZOK0sQU3VY4ME6rCTNN60MYOuwknpBK0sQi4YHefma4I4fbuSIQ5Zbh5U0L7UygMd3THDp+qxqwLf9xLm4kualVpYgrAFL6gWtDGBrwJJ6QStLEFNzcUvMA5akuWrlCFiSekErR8ClL8SQpLlo5QjYk3CSekErA9iTcJJ6QStLEJ6Ek9QLWhnAUIWw6/BKms9aWYKQpF5gAEtSIQawJBViAEtSIQawJBViAEtSIa0N4PEdE/xo7D7Gd0yU7ookzaiV84BdC0JSL2jlCNi1ICT1glYGsGtBSOoFrSxB9OtaEOM7JvrumKVe1soAhv5bC8K6t9R7WlmC6EfWvaXeYwC3hHVvqfe0tgRR0viOCe66+14GJ3d2rQzQr3VvqZcZwB02VYvdtGU7yw5Y2NVabL/VvaVeZwmiw6zFSpqrRkbAETEMfBA4DFgE/CVwG7AOmARuBc7IzJ1NtF/SVC1205bt1mIl7VZTJYiXAj/NzJdFxEHAzfWfszPz2oi4EDgFuLyh9ovNiZ2qxU4MLOhqDVhS72kqgD8JXFY/HgAeAI4BrqtfuxJ4Ng0FcOk5sYuGBxkdHWFsbHPX2pTUexoJ4My8DyAiRqiC+Gzg3MycrD+yGVi2p/2sWLGUoaG9D8677r6XTVu2Mzy0gE1btjMxsIDR0ZG93s/+KtFmaR5zf/CYO6OxWRARcQjVCPeCzPxoRPz1tLdHgI172seGDffvU9uDkztZdsDCB0fAg5M7uz4a7ccRsMfcHzzmfdt+Jk2dhHskcBVwZmZeXb98U0SszsxrgZOAa5poG6oSwMvXBHf8cCNHHLLcOqykeampEfCbgBXAmyPizfVrrwXeExELgdt5qEbcceM7Jrh0fVYj4Nt+4roIkualpmrAr6UK3F2taqK9Xc00F9cLFCTNN628EMN1EST1glZeiuy6CJJ6QSsDGMqui1BiMR5Jvae1AVxKycV4JPWWVtaAS3IxHklzZQB3mCcAJc2VJYgOczEeSXPlCLgBi4YHOfTRBxq+knbLAJakQgxgSSrEAG7A1Dzg8R0TpbsiaR7zJFyHOQ9Y0lw5Au4w5wFLmisDuMOcByxprixBdFjJecClbkQqad8YwA0ocVPO0jcilbT3LEG0hLVnqfcYwC1h7VnqPZYgWsJF6LvPdZ+1vwzgFim5CH2/cb63OsEShLQPrLmrEwxgaR9Yc1cnWIJoEecBd4/rPqsTDOCWcB5w95WY7612sQTREtYkpd5jALeENUmp97S2BNFv9VDnAUu9p5UB3K/1UOcBS72llSUI66GSekErA9h6qKRe0MoSxKLhQV6+Jrjjhxs54pDlfVF+kNR7WhnA4zsmuHR9VjXg237SNzVgSb2llSUIa8CSekGjI+CIOA54Z2aujohfBNYBk8CtwBmZubOJdqdqwFOzIKwBS5qPGgvgiPhj4GXAlvql84CzM/PaiLgQOAW4vIm2nRMrqRc0WYK4Ezh12vNjgOvqx1cCJzTY9oNzYg1fqXOmFqEf3zFRuiut0NgIODM/FRGHTXtpIDMn68ebgWVNtS2p81yEvvO6OQtier13BNi4pw1WrFjK0FDv/gWPjo6U7kLXecztddfd97Jpy3YANm3ZzsTAgr45dmjm77mbAXxTRKzOzGuBk4Br9rTBhg33N96ppvTjMoUec7sNTu5k2QELHxwBD07u7Jtj39+/59nCu5sB/Hrg4ohYCNwOXNbFttVS/bboUkkuQt95jQZwZv4AeGr9+A5gVZPtqb/066JLJbkIfWe18kIM9QcvuFGvM4DVs1x0Sb2ulWtBqD94wY26YWrucxN1bwNYPc1F6NWkpuc+W4KQpFk0fZ7BAJakWTR9nsEShNRjnPvcPU3PfTaApR7i3Ofua3LusyUIqYc497ldDGCphzj3uV0sQUg9pPTc5ybnxPYjA1jqMaXmPrsecOdZgpA0J9afO88AljQn1p87zxKEOsLaYPu5HnDnGcDab9YG+4frAXeWJQjtN2uD0r4xgLXfrA1K+8YSRIuUWiPA2qC0bwzglii9RoC1QWnvWYJoCeuwUu8xgFvCOqzUeyxBtETpNQJKcW1c9TIDuEX67f5opeve0v6yBKGeZd1bvc4AVs+y7q1eZwlCPatf695qDwNYPa3f6t6luehSZxnAkubERZc6zxqwpDnxpGfnGcCS5sSTnp1nCaIB1snURi661HkGcIdZJ1ObuehSZ1mC6DDrZJLmqqsj4IhYAFwAHAWMA2sz87vd7EPTpupkm7Zst04mabe6XYJ4HrA4M58WEU8F3gWc0kRDLk4uab7rdgnimcCXADLz68CxTTQyVYf9wBdu55IrbmN8x0QTzcxq0fAghz76QMNX0m51ewR8ILBp2vOJiBjKzAdm+vCKFUsZGtr7ELvr7nvZtGU7w0ML2LRlOxMDCxgdHdnHLu+7Em2W5jH3B4+5M7odwPcC049iwWzhC7Bhw/371Mjg5E6WHbDwwWUKByd3dv2sbT+eKfaY+4PHvG/bz6TbAfw14LeBT9Q14FuaaMRFWiT1gm4H8OXAiRHxT8AA8MqmGnKRFknzXVcDODN3Aqd3s01Jmq+8EEOSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSCjGAJakQA1iSChmYnJws3QdJ6kuOgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgrp9h0xWi0ihoEPAocBi4C/zMzPFe1Ul0TEwcA3gBMz8zul+9O0iHgj8FxgIXBBZn6gcJcaVf/b/hDVv+0J4H+0/e85Io4D3pmZqyPiF4F1wCRwK3BGfYOJ/eIIuLNeCvw0M58FPAd4X+H+dEX9w3kRsLV0X7ohIlYDTweeAawCDinaoe74LWAoM58OnAO8rXB/GhURfwxcAiyuXzoPOLv+2R4ATulEOwZwZ30SeHP9eACY9Y7PLXMucCHw49Id6ZI1VDeUvRz4PHBF2e50xR3AUEQsAA4EdhTuT9PuBE6d9vwY4Lr68ZXACZ1oxADuoMy8LzM3R8QIcBlwduk+NS0iTgPGMnN96b500UrgWOBFVPc4/EhEDJTtUuPuoyo/fAe4GHhP0d40LDM/xcN/yQxk5tRlw5uBZZ1oxwDusIg4BLgG+HBmfrR0f7rgVVR3ur4WOBq4NCIeVbZLjfspsD4zt2dmAtuA0cJ9atrrqI75COAo4EMRsXgP27TJ9HrvCLCxEzv1JFwHRcQjgauAMzPz6tL96YbMPH7qcR3Cp2fmf5TrUVd8FXhtRJwHPBo4gCqU22wDD40I/wsYBgbLdafrboqI1Zl5LXAS1SBrvxnAnfUmYAXw5oiYqgWflJl9cXKqX2TmFRFxPPAvVP+LPCMzJwp3q2nvBj4YEddTzfx4U2ZuKdynbno9cHFELARupyox7jdXQ5OkQqwBS1IhBrAkFWIAS1IhBrAkFWIAS1IhBrA0BxHxCxHR6gV31H0GsDQ3hwKPL90JtYvzgNUa9XoM7wCeT7UQ0kVUC6f8LXAQsAU4KzP/NSLWAddm5rp628nMHIiItwCPBZ5AFbqXZObbIuLbwOHAhzLzjK4emFrLEbDa5IVUS0T+CvAU4JVUK5W9JzOfRLWewWURsWgP+3kS8GzgOOBPI2I5cBZwo+GrTjKA1SargE9k5nhm3gc8E1iZmZ8GyMyvU61jEHvYzzX1Qjv/WX++IytfSbsygNUmu65RezjVuszTDVCtgTI59V69oPx026Y9fvBzUqcZwGqTrwCnRsRwRCwFPgFMRsSpABHxVOBRVLeUuQd4Yr3d8+aw7wdw8Sp1mAGs1sjMy4GvAd8E/hU4n+rWQWdFxC1Ut4g6NTO3A+8HVtUn154B3L2H3d8OLI+IDzfVf/UfZ0FIUiGOgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgoxgCWpEANYkgr5/26HIemftBSjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa88a43f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "model = xgb.XGBRegressor(random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "accuracy = r2_score(y_test, predicted)\n",
    "regressor_name = model.__class__.__name__\n",
    "print(f'Accuracy for {regressor_name} - {accuracy}')\n",
    "errors = np.round(np.abs(1 - predicted / y_test['cost'].values) * 100, 0)\n",
    "mean_error = np.mean(errors)\n",
    "max_error = errors.max()\n",
    "min_error = errors.min()\n",
    "c = Counter([x for x in errors])\n",
    "data = {'error': list(c.keys()), 'count': list(c.values())}\n",
    "df = pd.DataFrame.from_dict(data)\n",
    "sns.lmplot('count', 'error', data=df, fit_reg=False, markers=[\".\"])\n",
    "ax = plt.gca()\n",
    "ax.set_title(f'{regressor_name}\\nDetermination-{round(accuracy,3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# From Russia with Love"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for CatBoostRegressor - 0.6990870875803248\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'CatBoostRegressor\\nDetermination-0.699')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffa834b4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from catboost import CatBoostRegressor\n",
    "model = CatBoostRegressor(verbose=False, random_state=0)\n",
    "model.fit(x_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "accuracy = r2_score(y_test, predicted)\n",
    "regressor_name = model.__class__.__name__\n",
    "print(f'Accuracy for {regressor_name} - {accuracy}')\n",
    "errors = np.round(np.abs(1 - predicted / y_test['cost'].values) * 100, 0)\n",
    "mean_error = np.mean(errors)\n",
    "max_error = errors.max()\n",
    "min_error = errors.min()\n",
    "c = Counter([x for x in errors])\n",
    "data = {'error': list(c.keys()), 'count': list(c.values())}\n",
    "df = pd.DataFrame.from_dict(data)\n",
    "sns.lmplot('count', 'error', data=df, fit_reg=False, markers=[\".\"])\n",
    "ax = plt.gca()\n",
    "ax.set_title(f'{regressor_name}\\nDetermination-{round(accuracy,3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for LinearRegression - 0.6939594465172012\n",
      "Accuracy for CatBoostRegressor - 0.7439838443789393\n",
      "Accuracy for XGBRegressor - 0.7212568562820845\n",
      "Accuracy for AdaBoostRegressor - 0.6924071663923113\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import  KFold\n",
    "cat = CatBoostRegressor(verbose=False, random_state=0)\n",
    "xgbtree = xgb.XGBRegressor(random_state=0)\n",
    "regr = linear_model.LinearRegression()\n",
    "adaboost = AdaBoostRegressor(DecisionTreeRegressor(random_state=0), random_state=0)\n",
    "for model in [regr, cat, xgbtree, adaboost]:\n",
    "    regressor_name = model.__class__.__name__\n",
    "    fold = KFold(n_splits=10, shuffle=True, random_state=0)\n",
    "    scores_on_this_split = cross_val_score(estimator=model, X=x_train, y=y_train, cv=fold, scoring='r2')\n",
    "    accuracy = scores_on_this_split.mean()\n",
    "    print(f'Accuracy for {regressor_name} - {accuracy}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}