linear_machine.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, r2_score\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>SPEC(High, Mid, Low)</th>\n",
       "      <th>CPU NAME</th>\n",
       "      <th>CPU BenchMark</th>\n",
       "      <th>CPU Core</th>\n",
       "      <th>CPU Thread</th>\n",
       "      <th>CPU ClockSpeed GHZ</th>\n",
       "      <th>CPU ClockSpeed Turbo GHz</th>\n",
       "      <th>GPU NAME</th>\n",
       "      <th>GPU BenchMark</th>\n",
       "      <th>GPU Memory GB</th>\n",
       "      <th>GPU Clock MHz</th>\n",
       "      <th>GPU Clock Boost MHz</th>\n",
       "      <th>GAME NAME</th>\n",
       "      <th>RESOLUTION</th>\n",
       "      <th>GAME SETTING</th>\n",
       "      <th>GAME MIN FRAME</th>\n",
       "      <th>GAME AVG FRAME</th>\n",
       "      <th>GAME MAX FRAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>Intel Core i9-12900</td>\n",
       "      <td>38803</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>GeForce RTX 3090</td>\n",
       "      <td>26303</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1560</td>\n",
       "      <td>1860</td>\n",
       "      <td>1</td>\n",
       "      <td>QHD</td>\n",
       "      <td>ULTRA</td>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High</td>\n",
       "      <td>Intel Core i9-12900</td>\n",
       "      <td>38803</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>GeForce RTX 3090</td>\n",
       "      <td>26303</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1560</td>\n",
       "      <td>1860</td>\n",
       "      <td>1</td>\n",
       "      <td>QHD</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>96</td>\n",
       "      <td>117</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Intel Core i9-12900</td>\n",
       "      <td>38803</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>GeForce RTX 3090</td>\n",
       "      <td>26303</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1560</td>\n",
       "      <td>1860</td>\n",
       "      <td>1</td>\n",
       "      <td>QHD</td>\n",
       "      <td>MEDIUM</td>\n",
       "      <td>125</td>\n",
       "      <td>152</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Intel Core i9-12900</td>\n",
       "      <td>38803</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>GeForce RTX 3080 Ti</td>\n",
       "      <td>26619</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1365</td>\n",
       "      <td>1665</td>\n",
       "      <td>1</td>\n",
       "      <td>QHD</td>\n",
       "      <td>ULTRA</td>\n",
       "      <td>65</td>\n",
       "      <td>80</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Intel Core i9-12900</td>\n",
       "      <td>38803</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>GeForce RTX 3080 Ti</td>\n",
       "      <td>26619</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1365</td>\n",
       "      <td>1665</td>\n",
       "      <td>1</td>\n",
       "      <td>QHD</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>88</td>\n",
       "      <td>108</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  SPEC(High, Mid, Low)             CPU NAME  CPU BenchMark  CPU Core  \\\n",
       "0                 High  Intel Core i9-12900          38803        16   \n",
       "1                 High  Intel Core i9-12900          38803        16   \n",
       "2                 High  Intel Core i9-12900          38803        16   \n",
       "3                 High  Intel Core i9-12900          38803        16   \n",
       "4                 High  Intel Core i9-12900          38803        16   \n",
       "\n",
       "   CPU Thread  CPU ClockSpeed GHZ  CPU ClockSpeed Turbo GHz  \\\n",
       "0          24                 2.4                       5.1   \n",
       "1          24                 2.4                       5.1   \n",
       "2          24                 2.4                       5.1   \n",
       "3          24                 2.4                       5.1   \n",
       "4          24                 2.4                       5.1   \n",
       "\n",
       "              GPU NAME  GPU BenchMark  GPU Memory GB  GPU Clock MHz  \\\n",
       "0     GeForce RTX 3090          26303           24.0           1560   \n",
       "1     GeForce RTX 3090          26303           24.0           1560   \n",
       "2     GeForce RTX 3090          26303           24.0           1560   \n",
       "3  GeForce RTX 3080 Ti          26619           12.0           1365   \n",
       "4  GeForce RTX 3080 Ti          26619           12.0           1365   \n",
       "\n",
       "   GPU Clock Boost MHz  GAME NAME RESOLUTION GAME SETTING  GAME MIN FRAME  \\\n",
       "0                 1860          1        QHD        ULTRA              71   \n",
       "1                 1860          1        QHD         HIGH              96   \n",
       "2                 1860          1        QHD       MEDIUM             125   \n",
       "3                 1665          1        QHD        ULTRA              65   \n",
       "4                 1665          1        QHD         HIGH              88   \n",
       "\n",
       "   GAME AVG FRAME  GAME MAX FRAME  \n",
       "0              87             133  \n",
       "1             117             179  \n",
       "2             152             234  \n",
       "3              80             122  \n",
       "4             108             162  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('FPS_DATA.xlsx')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 330 entries, 0 to 329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   SPEC(High, Mid, Low)      330 non-null    object \n",
      " 1   CPU NAME                  330 non-null    object \n",
      " 2   CPU BenchMark             330 non-null    int64  \n",
      " 3   CPU Core                  330 non-null    int64  \n",
      " 4   CPU Thread                330 non-null    int64  \n",
      " 5   CPU ClockSpeed GHZ        330 non-null    float64\n",
      " 6   CPU ClockSpeed Turbo GHz  330 non-null    float64\n",
      " 7   GPU NAME                  330 non-null    object \n",
      " 8   GPU BenchMark             330 non-null    int64  \n",
      " 9   GPU Memory GB             330 non-null    float64\n",
      " 10  GPU Clock MHz             330 non-null    int64  \n",
      " 11  GPU Clock Boost MHz       330 non-null    int64  \n",
      " 12  GAME NAME                 330 non-null    object \n",
      " 13  RESOLUTION                330 non-null    object \n",
      " 14  GAME SETTING              330 non-null    object \n",
      " 15  GAME MIN FRAME            330 non-null    int64  \n",
      " 16  GAME AVG FRAME            330 non-null    int64  \n",
      " 17  GAME MAX FRAME            330 non-null    int64  \n",
      "dtypes: float64(3), int64(9), object(6)\n",
      "memory usage: 46.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x1ee697b7640>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig  = plt.figure(figsize=(9,6))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "x = data['CPU BenchMark']\n",
    "y = data['GPU BenchMark']\n",
    "z = data['GAME AVG FRAME']\n",
    "\n",
    "ax.scatter(x,z,y, color='r', alpha = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scale_cols = ['CPU BenchMark', 'GPU BenchMark', 'CPU Core', 'CPU Thread', 'GPU Memory GB', 'GPU Clock Boost MHz', 'GAME AVG FRAME']\n",
    "\n",
    "df_scaled = data[scale_cols]\n",
    "\n",
    "df_scaled = pd.DataFrame(df_scaled)\n",
    "df_scaled.columns = scale_cols\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(330, 6)\n"
     ]
    },
    {
     "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>CPU BenchMark</th>\n",
       "      <th>GPU BenchMark</th>\n",
       "      <th>CPU Core</th>\n",
       "      <th>CPU Thread</th>\n",
       "      <th>GPU Memory GB</th>\n",
       "      <th>GPU Clock Boost MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.987409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.987409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.987409</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.537402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.537402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CPU BenchMark  GPU BenchMark  CPU Core  CPU Thread  GPU Memory GB  \\\n",
       "0            1.0       0.987409       1.0         1.0       1.000000   \n",
       "1            1.0       0.987409       1.0         1.0       1.000000   \n",
       "2            1.0       0.987409       1.0         1.0       1.000000   \n",
       "3            1.0       1.000000       1.0         1.0       0.489362   \n",
       "4            1.0       1.000000       1.0         1.0       0.489362   \n",
       "\n",
       "   GPU Clock Boost MHz  \n",
       "0             0.921260  \n",
       "1             0.921260  \n",
       "2             0.921260  \n",
       "3             0.537402  \n",
       "4             0.537402  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_scaled.shape)\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Python\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1ee639c1580>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2160x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df_scaled, x_vars=['CPU BenchMark', 'GPU BenchMark', 'CPU Core', 'CPU Thread', 'GPU Memory GB', 'GPU Clock Boost MHz'], y_vars=['GAME AVG FRAME'], size=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_scaled[['GAME AVG FRAME']].to_numpy()\n",
    "\n",
    "df_scaled = df_scaled.drop(columns=['GAME AVG FRAME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df_scaled.values\n",
    "columns = df_scaled.columns\n",
    "\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "tmp = scaler.fit_transform(x)\n",
    "df_scaled = pd.DataFrame(tmp)\n",
    "df_scaled.columns = columns\n",
    "\n",
    "x = df_scaled.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(df, delta):\n",
    "    y = df[['GAME AVG FRAME']].to_numpy()\n",
    "    X = df_scaled.values\n",
    "    return np.squeeze(LinearRegression().fit(X, y).predict(delta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_predictions(df, frame):\n",
    "\n",
    "    df['date_delta'] = df.date.sub(mn).div(day)\n",
    "\n",
    "    dd = (date - mn) / day\n",
    "\n",
    "    return df.groupby('group').apply(model, delta=frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.85, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(normalize=True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression(fit_intercept=True, normalize=True, copy_X=True)\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data Accuracy:  0.08580513294493863\n"
     ]
    }
   ],
   "source": [
    "print(\"Train data Accuracy: \", format(lr.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict  =lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Accuracy :  0.032076748193837434\n",
      "Test data Accuracy :  0.032076748193837434\n"
     ]
    }
   ],
   "source": [
    "print(\"Test data Accuracy : \", format(lr.score(x_test, y_test)))\n",
    "print(\"Test data Accuracy : \", format(r2_score(y_test, lr.predict(x_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.464301659748"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1,\n",
       " 'max_depth': 1,\n",
       " 'min_samples_leaf': 15,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Gradient Boosting Regressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "num_estimators = [100, 500, 1000]\n",
    "learn_rates = [0.1, 0.5, 1]\n",
    "max_depths = [1, 2, 3, 4]\n",
    "min_samples_leaf = [5, 10, 15]\n",
    "min_samples_split = [2, 5, 10]\n",
    "\n",
    "param_grid = {'n_estimators' : num_estimators,\n",
    "            'learning_rate' : learn_rates,\n",
    "            'max_depth' : max_depths,\n",
    "            'min_samples_leaf' : min_samples_leaf,\n",
    "            'min_samples_split' : min_samples_split}\n",
    "y_train = np.ravel(y_train, order='C')\n",
    "grid_search = GridSearchCV(GradientBoostingRegressor(loss='huber'), param_grid, cv=3, return_train_score=True)\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.5, min_samples_leaf=10,\n",
       "                          min_samples_split=5, n_estimators=1000)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "gb = GradientBoostingRegressor(min_samples_leaf = 10, min_samples_split = 5, learning_rate = 0.5, max_depth = 3, n_estimators = 1000)\n",
    "gb.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=50, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### random forest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfr = RandomForestRegressor(n_estimators=50, random_state = 42)\n",
    "rfr.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data Accuracy :  0.11232595791529654\n"
     ]
    }
   ],
   "source": [
    "print(\"Train data Accuracy : \", format(rfr.score(x_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = rfr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Accuracy :  -0.3610544056714562\n",
      "Test data Accuracy :  -0.3610544056714562\n"
     ]
    }
   ],
   "source": [
    "print(\"Test data Accuracy : \", format(rfr.score(x_test, y_test)))\n",
    "print(\"Test data Accuracy : \", format(r2_score(y_test, rfr.predict(x_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170.02762440024028"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test, y_predict)"
   ]
  }
 ],
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