Hackathon Joe Mansour Random Forest Submission .0057 MAE only 13 features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import all the necessary modules\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import pandas_profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"frequency_domain_features_train .csv\")\n",
    "df2 = pd.read_csv(\"heart_rate_non_linear_features_train.csv\")\n",
    "df3 = pd.read_csv(\"time_domain_features_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((369289, 12), (369289, 7), (369289, 20))"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape, df2.shape, df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df1.merge(right=df2,how='inner',on='uuid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(369289, 18)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = df4.merge(right=df3,how='inner',on='uuid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(369289, 37)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.condition.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d9f44d1c0>"
      ]
     },
     "execution_count": 121,
     "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.countplot(df5[\"condition\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    (df5['condition'] == \"no stress\"),\n",
    "    (df5['condition'] == \"interruption condition\"),\n",
    "    (df5['condition'] == \"time pressure\" )\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [1,0,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5['condition2'] = np.select(conditions, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.read_csv(\"frequency_domain_features_test.csv\")\n",
    "df7 = pd.read_csv(\"heart_rate_non_linear_features_test.csv\")\n",
    "df8 = pd.read_csv(\"time_domain_features_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((41033, 12), (41033, 7), (41033, 19))"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.shape, df7.shape, df8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df9 = df6.merge(right=df7,how='inner',on='uuid')\n",
    "df10 = df9.merge(right=df8,how='inner',on='uuid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    (df10['condition'] == \"no stress\"),\n",
    "    (df10['condition'] == \"interruption condition\"),\n",
    "    (df10['condition'] == \"time pressure\" )\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = [1,0,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df10['condition2'] = np.select(conditions, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41033, 37)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make uuid Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df11=train\n",
    "df12=test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df11 = df5.set_index('uuid')\n",
    "df12 = df10.set_index('uuid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features to drop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# newly defined features\n",
    "dftest = df12.drop(['condition','datasetId','SD2','SDRR_RMSSD_REL_RR',\n",
    "                     'SDRR_REL_RR','MEAN_REL_RR','condition2','RMSSD_REL_RR',\n",
    "                     'MEDIAN_REL_RR','RMSSD','LF_PCT','SKEW_REL_RR','HF','LF_NU',\"HF_NU\"\n",
    "                    ,\"SD1\",'VLF','TP',\"LF_HF\",\"MEDIAN_RR\",\"LF\",\"VLF_PCT\",\n",
    "                    \"KURT_REL_RR\"], axis = 1)\n",
    "\n",
    "dftrain = df11.drop(['condition','datasetId','SD2','SDRR_RMSSD_REL_RR',\n",
    "                     'SDRR_REL_RR','MEAN_REL_RR','condition2','RMSSD_REL_RR',\n",
    "                     'MEDIAN_REL_RR','RMSSD','LF_PCT','SKEW_REL_RR','HF','LF_NU',\"HF_NU\"\n",
    "                    ,\"SD1\",'VLF','TP',\"LF_HF\",\"MEDIAN_RR\",\"LF\",\"VLF_PCT\",\n",
    "                    \"KURT_REL_RR\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dftrain.drop(\"HR\",axis=1)\n",
    "y = dftrain[\"HR\"]\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random foresrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2 score:  0.8461282435792761\n",
      "Validation R2 score:  0.847760642532209\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf=RandomForestRegressor(random_state=1, n_estimators=2, max_depth=2)\n",
    "rf.fit(X_train,y_train)\n",
    "#rf.score(X_train,y_train)\n",
    "y_pred_rf_train=rf.predict(X_train)\n",
    "y_pred_rf_val=rf.predict(X_val)\n",
    "print('Train R2 score: ', r2_score(y_train,y_pred_rf_train))\n",
    "print('Validation R2 score: ', r2_score(y_val,y_pred_rf_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "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>Method</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Adj_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Validation</td>\n",
       "      <td>4.016635</td>\n",
       "      <td>16.133357</td>\n",
       "      <td>3.233378</td>\n",
       "      <td>0.847761</td>\n",
       "      <td>0.847707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Method      RMSE        MSE       MAE        R2    Adj_R2\n",
       "0  Random Forest Validation  4.016635  16.133357  3.233378  0.847761  0.847707"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_rf = np.sqrt(mean_squared_error(y_val, y_pred_rf_val))\n",
    "mse_rf = mean_squared_error(y_val, y_pred_rf_val)\n",
    "mae_rf = mean_absolute_error(y_val, y_pred_rf_val)\n",
    "r2_rf = r2_score(y_val, y_pred_rf_val)\n",
    "adjr2_rf = 1-(1-r2_rf)*(len(y_val)-1)/(len(y_val)-len(X_val.columns)-1)\n",
    "\n",
    "resultsDf1 = pd.DataFrame({'Method':['Random Forest Validation'], 'RMSE': rmse_rf , 'MSE': mse_rf , 'MAE': mae_rf ,\"R2\": r2_rf, 'Adj_R2': adjr2_rf})\n",
    "resultsDf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Method</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>R2</th>\n",
       "      <th>Adj_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest Train</td>\n",
       "      <td>4.056885</td>\n",
       "      <td>16.458312</td>\n",
       "      <td>3.260154</td>\n",
       "      <td>0.846128</td>\n",
       "      <td>0.846122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Method      RMSE        MSE       MAE        R2    Adj_R2\n",
       "0  Random Forest Train  4.056885  16.458312  3.260154  0.846128  0.846122"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_rf = np.sqrt(mean_squared_error(y_train, y_pred_rf_train))\n",
    "mse_rf = mean_squared_error(y_train, y_pred_rf_train)\n",
    "mae_rf = mean_absolute_error(y_train, y_pred_rf_train)\n",
    "r2_rf = r2_score(y_train, y_pred_rf_train)\n",
    "adjr2_rf = 1-(1-r2_rf)*(len(y_train)-1)/(len(y_train)-len(X_train.columns)-1)\n",
    "\n",
    "resultsDf1 = pd.DataFrame({'Method':['Random Forest Train'], 'RMSE': rmse_rf , 'MSE': mse_rf , 'MAE': mae_rf ,\"R2\": r2_rf, 'Adj_R2': adjr2_rf})\n",
    "resultsDf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe of predicted vs actual HR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "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>HR</th>\n",
       "      <th>predictedHR_RF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uuid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80ffbdb6-9cee-44b7-91b3-cf26ac25e5d5</th>\n",
       "      <td>67.923866</td>\n",
       "      <td>69.346895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eb61e4c8-c28f-4087-82b8-b427a5a52bef</th>\n",
       "      <td>76.960355</td>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80f5f9e2-a85d-4a51-8b89-39505b206ccf</th>\n",
       "      <td>76.086628</td>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c81a3c50-ee66-459a-9d55-a8261093c724</th>\n",
       "      <td>74.739285</td>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21eef6fc-3b7d-4ec7-a07e-202ca0075a4d</th>\n",
       "      <td>92.082130</td>\n",
       "      <td>96.580064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9d09efd1-8c82-4d48-9615-ba7d980635a2</th>\n",
       "      <td>56.797168</td>\n",
       "      <td>60.246170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f5f36380-eeeb-40c6-9d23-c23d253a3266</th>\n",
       "      <td>70.558553</td>\n",
       "      <td>69.346895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d219e619-cca3-4617-b297-ea2a06fac092</th>\n",
       "      <td>79.060016</td>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aad5f511-3ebd-469a-a156-708ae4a9097a</th>\n",
       "      <td>71.649756</td>\n",
       "      <td>69.346895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14182c6b-60c8-4827-9f45-dc4486eda1e9</th>\n",
       "      <td>78.675666</td>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36929 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             HR  predictedHR_RF\n",
       "uuid                                                           \n",
       "80ffbdb6-9cee-44b7-91b3-cf26ac25e5d5  67.923866       69.346895\n",
       "eb61e4c8-c28f-4087-82b8-b427a5a52bef  76.960355       79.549592\n",
       "80f5f9e2-a85d-4a51-8b89-39505b206ccf  76.086628       79.549592\n",
       "c81a3c50-ee66-459a-9d55-a8261093c724  74.739285       79.549592\n",
       "21eef6fc-3b7d-4ec7-a07e-202ca0075a4d  92.082130       96.580064\n",
       "...                                         ...             ...\n",
       "9d09efd1-8c82-4d48-9615-ba7d980635a2  56.797168       60.246170\n",
       "f5f36380-eeeb-40c6-9d23-c23d253a3266  70.558553       69.346895\n",
       "d219e619-cca3-4617-b297-ea2a06fac092  79.060016       79.549592\n",
       "aad5f511-3ebd-469a-a156-708ae4a9097a  71.649756       69.346895\n",
       "14182c6b-60c8-4827-9f45-dc4486eda1e9  78.675666       79.549592\n",
       "\n",
       "[36929 rows x 2 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_vs_actual = pd.DataFrame(y_val)\n",
    "pred_vs_actual['predictedHR_RF'] = y_pred_rf_val\n",
    "pred_vs_actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HF_PCT', 'HF_LF', 'sampen', 'higuci', 'MEAN_RR', 'SDRR', 'SDSD',\n",
       "       'SDRR_RMSSD', 'HR', 'pNN25', 'pNN50', 'KURT', 'SKEW', 'SDSD_REL_RR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HF_PCT', 'HF_LF', 'sampen', 'higuci', 'MEAN_RR', 'SDRR', 'SDSD',\n",
       "       'SDRR_RMSSD', 'pNN25', 'pNN50', 'KURT', 'SKEW', 'SDSD_REL_RR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preform on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_test=rf.predict(dftest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = pd.DataFrame(dftest)\n",
    "pred_test['HR'] = y_pred_rf_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HF_PCT', 'HF_LF', 'sampen', 'higuci', 'MEAN_RR', 'SDRR', 'SDSD',\n",
       "       'SDRR_RMSSD', 'pNN25', 'pNN50', 'KURT', 'SKEW', 'SDSD_REL_RR', 'HR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission_yhats = pred_test.drop(['HF_PCT', 'HF_LF', 'sampen', 'higuci', 'MEAN_RR', 'SDRR', 'SDSD',\n",
    "       'SDRR_RMSSD', 'pNN25', 'pNN50', 'KURT', 'SKEW', 'SDSD_REL_RR'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uuid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62b75db5-bc40-4c8f-9166-daf0efcab4c2</th>\n",
       "      <td>69.346895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a99549ad-3eb6-4413-bc90-9053e7f7e684</th>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cb573d3a-c767-4556-b32e-ad8c08ded214</th>\n",
       "      <td>69.346895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47a0c6de-2aef-4ac3-997d-252fa6fd07f1</th>\n",
       "      <td>60.246170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>de3fd54f-c74e-4fe8-bf2a-7a127f68b312</th>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11253232-cf0a-4c40-abfb-ac2795effd9b</th>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38052c36-d08d-4305-905b-830f600ce2b9</th>\n",
       "      <td>96.580064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8ef3781b-512f-42ce-bcfd-47f5c1070bb0</th>\n",
       "      <td>60.246170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2bf689f3-af22-426d-91cf-e9e173ad3a54</th>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0b654db0-e4d3-4ba3-b9ce-9c9291c8271e</th>\n",
       "      <td>79.549592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41033 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             HR\n",
       "uuid                                           \n",
       "62b75db5-bc40-4c8f-9166-daf0efcab4c2  69.346895\n",
       "a99549ad-3eb6-4413-bc90-9053e7f7e684  79.549592\n",
       "cb573d3a-c767-4556-b32e-ad8c08ded214  69.346895\n",
       "47a0c6de-2aef-4ac3-997d-252fa6fd07f1  60.246170\n",
       "de3fd54f-c74e-4fe8-bf2a-7a127f68b312  79.549592\n",
       "...                                         ...\n",
       "11253232-cf0a-4c40-abfb-ac2795effd9b  79.549592\n",
       "38052c36-d08d-4305-905b-830f600ce2b9  96.580064\n",
       "8ef3781b-512f-42ce-bcfd-47f5c1070bb0  60.246170\n",
       "2bf689f3-af22-426d-91cf-e9e173ad3a54  79.549592\n",
       "0b654db0-e4d3-4ba3-b9ce-9c9291c8271e  79.549592\n",
       "\n",
       "[41033 rows x 1 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Submission_yhats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "Submission_yhats.to_csv ('submission19.csv', index = True, header=True)"
   ]
  }
 ],
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