projectfinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import arff\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "#to split data for train and test\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import RANSACRegressor\n",
    "#(RANdom SAmple Consensus) algorithm.~rastgele örneklerin benzerliği üstüne-robust\n",
    "from sklearn.linear_model import TheilSenRegressor\n",
    "#Theil-Sen Estimator: robust multivariate regression model-çok değişkenli regresyon modeli\n",
    "#from sklearn.linear_model import SGDRegressor\n",
    "#SGD's results are terrible. So commented them.\n",
    "import matplotlib.pyplot as plt\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "#another visiualization\n",
    "from sklearn import metrics\n",
    "#to see memory usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#couldnt get it done in loop so did it one by one\n",
    "#i=0\n",
    "#while i<10:\n",
    "#    data[i]=arff.loadarff([i]+'.arff')\n",
    "#    df[i]=pd.DataFrame(data[i][0])\n",
    "#    i+=1\n",
    "\n",
    "data0=arff.loadarff('0.arff')\n",
    "df0=pd.DataFrame(data0[0])\n",
    "data1=arff.loadarff('1.arff')\n",
    "df1=pd.DataFrame(data1[0])\n",
    "data2=arff.loadarff('2.arff')\n",
    "df2=pd.DataFrame(data2[0])\n",
    "data3=arff.loadarff('3.arff')\n",
    "df3=pd.DataFrame(data3[0])\n",
    "data4=arff.loadarff('4.arff')\n",
    "df4=pd.DataFrame(data4[0])\n",
    "data5=arff.loadarff('5.arff')\n",
    "df5=pd.DataFrame(data5[0])\n",
    "data6=arff.loadarff('6.arff')\n",
    "df6=pd.DataFrame(data6[0])\n",
    "data7=arff.loadarff('7.arff')\n",
    "df7=pd.DataFrame(data7[0])\n",
    "data8=arff.loadarff('8.arff')\n",
    "df8=pd.DataFrame(data8[0])\n",
    "data9=arff.loadarff('9.arff')\n",
    "df9=pd.DataFrame(data9[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames=[df1, df2, df3, df4, df5, df6, df7, df8, df9] #geçici dfleri list halinde frame'e atıyoruz\n",
    "df=pd.concat(frames, ignore_index=True) #bu framelerden son olarak bütün dataları içeren df'i oluşturuyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred_Activity</th>\n",
       "      <th>peakCount</th>\n",
       "      <th>Z_breathRate</th>\n",
       "      <th>Z_heartRate</th>\n",
       "      <th>Z_skinTemp</th>\n",
       "      <th>SW_GSR</th>\n",
       "      <th>SW_NearBodyTemp</th>\n",
       "      <th>SW_skinTemp</th>\n",
       "      <th>cosmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'lying'</td>\n",
       "      <td>92.0</td>\n",
       "      <td>16.490182</td>\n",
       "      <td>48.969195</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.097270</td>\n",
       "      <td>10.747776</td>\n",
       "      <td>11.063158</td>\n",
       "      <td>1.803177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'lying'</td>\n",
       "      <td>81.0</td>\n",
       "      <td>16.790604</td>\n",
       "      <td>52.888637</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.097270</td>\n",
       "      <td>10.747776</td>\n",
       "      <td>11.063158</td>\n",
       "      <td>2.121443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'walking'</td>\n",
       "      <td>97.0</td>\n",
       "      <td>16.733168</td>\n",
       "      <td>56.625628</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.097270</td>\n",
       "      <td>10.747776</td>\n",
       "      <td>11.063158</td>\n",
       "      <td>3.184779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'walking'</td>\n",
       "      <td>111.0</td>\n",
       "      <td>15.728140</td>\n",
       "      <td>54.393795</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.097270</td>\n",
       "      <td>10.747776</td>\n",
       "      <td>11.063158</td>\n",
       "      <td>2.327770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'walking'</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.135302</td>\n",
       "      <td>49.127372</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.097270</td>\n",
       "      <td>10.747776</td>\n",
       "      <td>11.063158</td>\n",
       "      <td>2.195212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5317</th>\n",
       "      <td>b'running'</td>\n",
       "      <td>78.0</td>\n",
       "      <td>30.801074</td>\n",
       "      <td>115.630183</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.059786</td>\n",
       "      <td>12.760415</td>\n",
       "      <td>13.764971</td>\n",
       "      <td>8.034513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5318</th>\n",
       "      <td>b'running'</td>\n",
       "      <td>64.0</td>\n",
       "      <td>27.077697</td>\n",
       "      <td>114.928049</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.059786</td>\n",
       "      <td>12.760415</td>\n",
       "      <td>13.764971</td>\n",
       "      <td>8.611814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5319</th>\n",
       "      <td>b'running'</td>\n",
       "      <td>66.0</td>\n",
       "      <td>26.460689</td>\n",
       "      <td>116.620075</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.059786</td>\n",
       "      <td>12.760415</td>\n",
       "      <td>13.764971</td>\n",
       "      <td>8.115253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5320</th>\n",
       "      <td>b'running'</td>\n",
       "      <td>60.0</td>\n",
       "      <td>26.375527</td>\n",
       "      <td>111.890192</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.059786</td>\n",
       "      <td>12.760415</td>\n",
       "      <td>13.764971</td>\n",
       "      <td>8.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5321</th>\n",
       "      <td>b'running'</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.280017</td>\n",
       "      <td>106.973655</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.059786</td>\n",
       "      <td>12.760415</td>\n",
       "      <td>13.764971</td>\n",
       "      <td>8.794428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5322 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pred_Activity  peakCount  Z_breathRate  Z_heartRate  Z_skinTemp  \\\n",
       "0         b'lying'       92.0     16.490182    48.969195        10.0   \n",
       "1         b'lying'       81.0     16.790604    52.888637        10.0   \n",
       "2       b'walking'       97.0     16.733168    56.625628        10.0   \n",
       "3       b'walking'      111.0     15.728140    54.393795        10.0   \n",
       "4       b'walking'      100.0     12.135302    49.127372        10.0   \n",
       "...            ...        ...           ...          ...         ...   \n",
       "5317    b'running'       78.0     30.801074   115.630183         8.0   \n",
       "5318    b'running'       64.0     27.077697   114.928049         8.0   \n",
       "5319    b'running'       66.0     26.460689   116.620075         9.0   \n",
       "5320    b'running'       60.0     26.375527   111.890192         9.0   \n",
       "5321    b'running'       74.0     24.280017   106.973655         9.0   \n",
       "\n",
       "         SW_GSR  SW_NearBodyTemp  SW_skinTemp    cosmed  \n",
       "0     10.097270        10.747776    11.063158  1.803177  \n",
       "1     10.097270        10.747776    11.063158  2.121443  \n",
       "2     10.097270        10.747776    11.063158  3.184779  \n",
       "3     10.097270        10.747776    11.063158  2.327770  \n",
       "4     10.097270        10.747776    11.063158  2.195212  \n",
       "...         ...              ...          ...       ...  \n",
       "5317  12.059786        12.760415    13.764971  8.034513  \n",
       "5318  12.059786        12.760415    13.764971  8.611814  \n",
       "5319  12.059786        12.760415    13.764971  8.115253  \n",
       "5320  12.059786        12.760415    13.764971  8.331000  \n",
       "5321  12.059786        12.760415    13.764971  8.794428  \n",
       "\n",
       "[5322 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['pred_Activity'] = df['pred_Activity'].str.decode('utf-8')\n",
    "#no error for multilanguage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.252066"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cosmed.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lying               1141\n",
       "sitting              821\n",
       "walking              803\n",
       "standing             710\n",
       "cycling              489\n",
       "kneeling             373\n",
       "allfours             371\n",
       "standing_leaning     220\n",
       "running              208\n",
       "transition           186\n",
       "Name: pred_Activity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pred_Activity.value_counts()#spesifik kolunda neyden kaç tane var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(df.pred_Activity, drop_first=True)\n",
    "#one hot encoding, drop_first true ile gereksiz 1 sütundan kurtuluyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "      cycling  kneeling  lying  running  sitting  standing  standing_leaning  \\\n",
       "0           0         0      1        0        0         0                 0   \n",
       "1           0         0      1        0        0         0                 0   \n",
       "2           0         0      0        0        0         0                 0   \n",
       "3           0         0      0        0        0         0                 0   \n",
       "4           0         0      0        0        0         0                 0   \n",
       "...       ...       ...    ...      ...      ...       ...               ...   \n",
       "5317        0         0      0        1        0         0                 0   \n",
       "5318        0         0      0        1        0         0                 0   \n",
       "5319        0         0      0        1        0         0                 0   \n",
       "5320        0         0      0        1        0         0                 0   \n",
       "5321        0         0      0        1        0         0                 0   \n",
       "\n",
       "      transition  walking  \n",
       "0              0        0  \n",
       "1              0        0  \n",
       "2              0        1  \n",
       "3              0        1  \n",
       "4              0        1  \n",
       "...          ...      ...  \n",
       "5317           0        0  \n",
       "5318           0        0  \n",
       "5319           0        0  \n",
       "5320           0        0  \n",
       "5321           0        0  \n",
       "\n",
       "[5322 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('pred_Activity',axis=1,inplace=True)\n",
    "#dismiss the non number values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.join(dummies)\n",
    "#use these numeric values instead of those not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5322 entries, 0 to 5321\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   peakCount         5322 non-null   float64\n",
      " 1   Z_breathRate      5322 non-null   float64\n",
      " 2   Z_heartRate       5322 non-null   float64\n",
      " 3   Z_skinTemp        5322 non-null   float64\n",
      " 4   SW_GSR            5322 non-null   float64\n",
      " 5   SW_NearBodyTemp   5322 non-null   float64\n",
      " 6   SW_skinTemp       5322 non-null   float64\n",
      " 7   cosmed            5322 non-null   float64\n",
      " 8   cycling           5322 non-null   uint8  \n",
      " 9   kneeling          5322 non-null   uint8  \n",
      " 10  lying             5322 non-null   uint8  \n",
      " 11  running           5322 non-null   uint8  \n",
      " 12  sitting           5322 non-null   uint8  \n",
      " 13  standing          5322 non-null   uint8  \n",
      " 14  standing_leaning  5322 non-null   uint8  \n",
      " 15  transition        5322 non-null   uint8  \n",
      " 16  walking           5322 non-null   uint8  \n",
      "dtypes: float64(8), uint8(9)\n",
      "memory usage: 379.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peakCount</th>\n",
       "      <th>Z_breathRate</th>\n",
       "      <th>Z_heartRate</th>\n",
       "      <th>Z_skinTemp</th>\n",
       "      <th>SW_GSR</th>\n",
       "      <th>SW_NearBodyTemp</th>\n",
       "      <th>SW_skinTemp</th>\n",
       "      <th>cosmed</th>\n",
       "      <th>cycling</th>\n",
       "      <th>kneeling</th>\n",
       "      <th>lying</th>\n",
       "      <th>running</th>\n",
       "      <th>sitting</th>\n",
       "      <th>standing</th>\n",
       "      <th>standing_leaning</th>\n",
       "      <th>transition</th>\n",
       "      <th>walking</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
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       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "      <td>5322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.191281</td>\n",
       "      <td>16.184407</td>\n",
       "      <td>52.714605</td>\n",
       "      <td>10.460376</td>\n",
       "      <td>10.236605</td>\n",
       "      <td>10.362160</td>\n",
       "      <td>10.802736</td>\n",
       "      <td>2.871644</td>\n",
       "      <td>0.091883</td>\n",
       "      <td>0.070086</td>\n",
       "      <td>0.214393</td>\n",
       "      <td>0.039083</td>\n",
       "      <td>0.154265</td>\n",
       "      <td>0.133408</td>\n",
       "      <td>0.041338</td>\n",
       "      <td>0.034949</td>\n",
       "      <td>0.150883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.610145</td>\n",
       "      <td>6.950643</td>\n",
       "      <td>24.589749</td>\n",
       "      <td>0.781704</td>\n",
       "      <td>0.454538</td>\n",
       "      <td>0.988507</td>\n",
       "      <td>0.911089</td>\n",
       "      <td>2.120833</td>\n",
       "      <td>0.288888</td>\n",
       "      <td>0.255317</td>\n",
       "      <td>0.410439</td>\n",
       "      <td>0.193811</td>\n",
       "      <td>0.361237</td>\n",
       "      <td>0.340048</td>\n",
       "      <td>0.199089</td>\n",
       "      <td>0.183669</td>\n",
       "      <td>0.357969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.978526</td>\n",
       "      <td>16.470771</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.909891</td>\n",
       "      <td>7.291151</td>\n",
       "      <td>9.178588</td>\n",
       "      <td>0.252066</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>10.931264</td>\n",
       "      <td>33.892015</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.997848</td>\n",
       "      <td>9.635023</td>\n",
       "      <td>10.127249</td>\n",
       "      <td>1.268516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>14.842876</td>\n",
       "      <td>47.273752</td>\n",
       "      <td>10.206243</td>\n",
       "      <td>10.066470</td>\n",
       "      <td>10.454214</td>\n",
       "      <td>10.639629</td>\n",
       "      <td>2.043793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>20.427898</td>\n",
       "      <td>65.945047</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.233255</td>\n",
       "      <td>10.891589</td>\n",
       "      <td>11.260191</td>\n",
       "      <td>3.851824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>126.000000</td>\n",
       "      <td>37.824698</td>\n",
       "      <td>131.489297</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.371005</td>\n",
       "      <td>12.825198</td>\n",
       "      <td>14.018766</td>\n",
       "      <td>10.732107</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         peakCount  Z_breathRate  Z_heartRate   Z_skinTemp       SW_GSR  \\\n",
       "count  5322.000000   5322.000000  5322.000000  5322.000000  5322.000000   \n",
       "mean     67.191281     16.184407    52.714605    10.460376    10.236605   \n",
       "std      39.610145      6.950643    24.589749     0.781704     0.454538   \n",
       "min       0.000000      2.978526    16.470771     8.000000     9.909891   \n",
       "25%      39.000000     10.931264    33.892015    10.000000     9.997848   \n",
       "50%      84.000000     14.842876    47.273752    10.206243    10.066470   \n",
       "75%      99.000000     20.427898    65.945047    11.000000    10.233255   \n",
       "max     126.000000     37.824698   131.489297    12.000000    12.371005   \n",
       "\n",
       "       SW_NearBodyTemp  SW_skinTemp       cosmed      cycling     kneeling  \\\n",
       "count      5322.000000  5322.000000  5322.000000  5322.000000  5322.000000   \n",
       "mean         10.362160    10.802736     2.871644     0.091883     0.070086   \n",
       "std           0.988507     0.911089     2.120833     0.288888     0.255317   \n",
       "min           7.291151     9.178588     0.252066     0.000000     0.000000   \n",
       "25%           9.635023    10.127249     1.268516     0.000000     0.000000   \n",
       "50%          10.454214    10.639629     2.043793     0.000000     0.000000   \n",
       "75%          10.891589    11.260191     3.851824     0.000000     0.000000   \n",
       "max          12.825198    14.018766    10.732107     1.000000     1.000000   \n",
       "\n",
       "             lying      running      sitting     standing  standing_leaning  \\\n",
       "count  5322.000000  5322.000000  5322.000000  5322.000000       5322.000000   \n",
       "mean      0.214393     0.039083     0.154265     0.133408          0.041338   \n",
       "std       0.410439     0.193811     0.361237     0.340048          0.199089   \n",
       "min       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000          0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000          1.000000   \n",
       "\n",
       "        transition      walking  \n",
       "count  5322.000000  5322.000000  \n",
       "mean      0.034949     0.150883  \n",
       "std       0.183669     0.357969  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     0.000000  \n",
       "50%       0.000000     0.000000  \n",
       "75%       0.000000     0.000000  \n",
       "max       1.000000     1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df[[\"peakCount\",\"Z_breathRate\",\"Z_heartRate\",\"Z_skinTemp\",\"cycling\",\"kneeling\",\"lying\",\"running\",\"sitting\",\"standing\",\"standing_leaning\",\"transition\",\"walking\"]]\n",
    "#taking datas except the one we will get with regression, cosmed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(a, df[\"cosmed\"], test_size=0.33)\n",
    "#we can change test_size due to project requirements, also random_state=(int) can be added for multiwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3565 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      peakCount  Z_breathRate  Z_heartRate  Z_skinTemp  cycling  kneeling  \\\n",
       "122       113.0     13.147652    61.527237   11.000000        0         0   \n",
       "2082       42.0     16.206171    41.236200   10.721663        0         0   \n",
       "1826      101.0     18.473745    34.833753   11.721663        0         1   \n",
       "1332        8.0      9.843646    18.000000   10.000000        0         0   \n",
       "3318       84.0     17.388034    39.489433   10.000000        0         0   \n",
       "...         ...           ...          ...         ...      ...       ...   \n",
       "3079        0.0     10.219275    28.644618   11.000000        0         0   \n",
       "3192      102.0     12.035416    31.872090   10.002198        0         1   \n",
       "1663        0.0     10.748121    29.857516    9.721663        0         0   \n",
       "685        96.0     11.767304    42.717837   10.206243        0         0   \n",
       "4363       49.0     11.911165    54.343659   10.000000        0         0   \n",
       "\n",
       "      lying  running  sitting  standing  standing_leaning  transition  walking  \n",
       "122       0        0        1         0                 0           0        0  \n",
       "2082      1        0        0         0                 0           0        0  \n",
       "1826      0        0        0         0                 0           0        0  \n",
       "1332      1        0        0         0                 0           0        0  \n",
       "3318      0        0        0         1                 0           0        0  \n",
       "...     ...      ...      ...       ...               ...         ...      ...  \n",
       "3079      1        0        0         0                 0           0        0  \n",
       "3192      0        0        0         0                 0           0        0  \n",
       "1663      1        0        0         0                 0           0        0  \n",
       "685       0        0        0         1                 0           0        0  \n",
       "4363      0        0        0         0                 0           1        0  \n",
       "\n",
       "[3565 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>peakCount</th>\n",
       "      <th>Z_breathRate</th>\n",
       "      <th>Z_heartRate</th>\n",
       "      <th>Z_skinTemp</th>\n",
       "      <th>cycling</th>\n",
       "      <th>kneeling</th>\n",
       "      <th>lying</th>\n",
       "      <th>running</th>\n",
       "      <th>sitting</th>\n",
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       "      <th>standing_leaning</th>\n",
       "      <th>transition</th>\n",
       "      <th>walking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>98.0</td>\n",
       "      <td>19.460120</td>\n",
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       "      <th>2561</th>\n",
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       "      <th>535</th>\n",
       "      <td>19.0</td>\n",
       "      <td>11.494352</td>\n",
       "      <td>26.447051</td>\n",
       "      <td>9.206243</td>\n",
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       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>95.0</td>\n",
       "      <td>22.893257</td>\n",
       "      <td>53.274799</td>\n",
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       "      <th>1742</th>\n",
       "      <td>93.0</td>\n",
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       "      <td>34.138179</td>\n",
       "      <td>10.721663</td>\n",
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       "      <th>3963</th>\n",
       "      <td>83.0</td>\n",
       "      <td>17.247295</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>12.000000</td>\n",
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       "      <th>699</th>\n",
       "      <td>97.0</td>\n",
       "      <td>13.233108</td>\n",
       "      <td>42.390713</td>\n",
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       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>81.0</td>\n",
       "      <td>19.491228</td>\n",
       "      <td>72.715344</td>\n",
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       "      <th>3592</th>\n",
       "      <td>73.0</td>\n",
       "      <td>23.726055</td>\n",
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       "<p>1757 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      peakCount  Z_breathRate  Z_heartRate  Z_skinTemp  cycling  kneeling  \\\n",
       "5103       98.0     19.460120    56.314732   10.000000        0         0   \n",
       "2561        0.0      9.378079    40.459292   11.000000        0         0   \n",
       "535        19.0     11.494352    26.447051    9.206243        0         0   \n",
       "2215       95.0     22.893257    53.274799   10.721663        0         0   \n",
       "1742       93.0      8.535356    34.138179   10.721663        0         0   \n",
       "...         ...           ...          ...         ...      ...       ...   \n",
       "3963       83.0     17.247295    18.000000   12.000000        0         0   \n",
       "699        97.0     13.233108    42.390713   10.206243        0         0   \n",
       "1048       81.0     19.491228    72.715344    9.206243        1         0   \n",
       "3592       73.0     23.726055    54.139327   10.000000        0         0   \n",
       "586        10.0     12.301863    34.532109   10.206243        0         0   \n",
       "\n",
       "      lying  running  sitting  standing  standing_leaning  transition  walking  \n",
       "5103      0        0        0         0                 0           0        1  \n",
       "2561      0        0        0         0                 0           0        0  \n",
       "535       1        0        0         0                 0           0        0  \n",
       "2215      0        0        0         0                 0           0        1  \n",
       "1742      0        0        1         0                 0           0        0  \n",
       "...     ...      ...      ...       ...               ...         ...      ...  \n",
       "3963      0        0        1         0                 0           0        0  \n",
       "699       0        0        0         1                 0           0        0  \n",
       "1048      0        0        0         0                 0           0        0  \n",
       "3592      0        0        0         0                 0           0        1  \n",
       "586       1        0        0         0                 0           0        0  \n",
       "\n",
       "[1757 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm=LinearRegression()\n",
    "ran=RANSACRegressor()\n",
    "theil=TheilSenRegressor()\n",
    "#sgd=SGDRegressor()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RANSACRegressor()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TheilSenRegressor(max_subpopulation=10000)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theil.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sgd.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictlm=lm.predict(x_test)\n",
    "predictran=ran.predict(x_test)\n",
    "predicttheil=theil.predict(x_test)\n",
    "#predictsgd=sgd.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1aea72228b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictlm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1aea2235520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1aea822adc0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predicttheil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(y_test,predictsgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BahaD\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cosmed', ylabel='Density'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predictlm))\n",
    "#shows that predict is very good but its not a perf metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BahaD\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cosmed', ylabel='Density'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predictran))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\BahaD\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cosmed', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot((y_test-predicttheil))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.distplot((y_test-predictsgd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(30,12))\n",
    "corr_map = sns.heatmap(df.corr(),annot=True)\n",
    "\n",
    "#df.corr()\n",
    "#corr matrix. +1 show that they are parallel datas but might not show causality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                 128\n",
       "peakCount           42576\n",
       "Z_breathRate        42576\n",
       "Z_heartRate         42576\n",
       "Z_skinTemp          42576\n",
       "SW_GSR              42576\n",
       "SW_NearBodyTemp     42576\n",
       "SW_skinTemp         42576\n",
       "cosmed              42576\n",
       "cycling              5322\n",
       "kneeling             5322\n",
       "lying                5322\n",
       "running              5322\n",
       "sitting              5322\n",
       "standing             5322\n",
       "standing_leaning     5322\n",
       "transition           5322\n",
       "walking              5322\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.memory_usage(deep=True)\n",
    "#be sure that there is not and wont be any memory problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6973734879688431"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test,predictlm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76636384402989"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test,predictran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.747424590620317"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_absolute_error(y_test,predicttheil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics.mean_absolute_error(y_test,predictsgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5322.000000\n",
       "mean        2.871644\n",
       "std         2.120833\n",
       "min         0.252066\n",
       "25%         1.268516\n",
       "50%         2.043793\n",
       "75%         3.851824\n",
       "max        10.732107\n",
       "Name: cosmed, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cosmed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8695443514724247"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, predictlm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1190319237548496"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, predictran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0048814501291858"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_test, predicttheil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.932493620070628"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test,predictlm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.057843052515282"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test,predictran))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0024377537429374"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test,predicttheil))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8120326698098572"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, predictlm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7581015358796119"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, predictran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7827771717238599"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y_test, predicttheil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#metrics.r2_score(y_test, predictsgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5103    2.857101\n",
       "2561    1.741393\n",
       "535     1.555140\n",
       "2215    2.050550\n",
       "1742    1.379332\n",
       "          ...   \n",
       "3963    0.949089\n",
       "699     1.816757\n",
       "1048    4.991076\n",
       "3592    4.108389\n",
       "586     1.603498\n",
       "Name: cosmed, Length: 1757, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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