mercedes-benz-baseline.ipynb

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     "text": [
      "dataset/test.csv\n",
      "dataset/train.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as nSAp # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('dataset/'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "train_df = pd.read_csv('dataset/train.csv')\n",
    "test_df = pd.read_csv('dataset/test.csv')\n"
   ]
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    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.161297Z",
     "iopub.status.busy": "2021-03-18T13:54:48.160262Z",
     "iopub.status.idle": "2021-03-18T13:54:48.170505Z",
     "shell.execute_reply": "2021-03-18T13:54:48.169866Z"
    },
    "papermill": {
     "duration": 0.039854,
     "end_time": "2021-03-18T13:54:48.170663",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.130809",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4209.000000\n",
       "mean      100.669318\n",
       "std        12.679381\n",
       "min        72.110000\n",
       "25%        90.820000\n",
       "50%        99.150000\n",
       "75%       109.010000\n",
       "max       265.320000\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['y'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.224099Z",
     "iopub.status.busy": "2021-03-18T13:54:48.223369Z",
     "iopub.status.idle": "2021-03-18T13:54:48.427921Z",
     "shell.execute_reply": "2021-03-18T13:54:48.427162Z"
    },
    "papermill": {
     "duration": 0.233628,
     "end_time": "2021-03-18T13:54:48.428099",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.194471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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": [
    "train_df['y'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.501250Z",
     "iopub.status.busy": "2021-03-18T13:54:48.500341Z",
     "iopub.status.idle": "2021-03-18T13:54:48.625125Z",
     "shell.execute_reply": "2021-03-18T13:54:48.624012Z"
    },
    "papermill": {
     "duration": 0.171951,
     "end_time": "2021-03-18T13:54:48.625378",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.453427",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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.boxplot(train_df['y']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.703328Z",
     "iopub.status.busy": "2021-03-18T13:54:48.702300Z",
     "iopub.status.idle": "2021-03-18T13:54:48.741628Z",
     "shell.execute_reply": "2021-03-18T13:54:48.740534Z"
    },
    "papermill": {
     "duration": 0.075053,
     "end_time": "2021-03-18T13:54:48.741856",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.666803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#removing of outliars \n",
    "\n",
    "\n",
    "train_df = train_df.drop(train_df[train_df['y'] > 140].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.801192Z",
     "iopub.status.busy": "2021-03-18T13:54:48.800448Z",
     "iopub.status.idle": "2021-03-18T13:54:48.918769Z",
     "shell.execute_reply": "2021-03-18T13:54:48.919370Z"
    },
    "papermill": {
     "duration": 0.15086,
     "end_time": "2021-03-18T13:54:48.919591",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.768731",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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.boxplot(train_df['y']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:48.977491Z",
     "iopub.status.busy": "2021-03-18T13:54:48.976784Z",
     "iopub.status.idle": "2021-03-18T13:54:48.986301Z",
     "shell.execute_reply": "2021-03-18T13:54:48.986803Z"
    },
    "papermill": {
     "duration": 0.040157,
     "end_time": "2021-03-18T13:54:48.987060",
     "exception": false,
     "start_time": "2021-03-18T13:54:48.946903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int64      369\n",
       "object       8\n",
       "float64      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:49.046427Z",
     "iopub.status.busy": "2021-03-18T13:54:49.045683Z",
     "iopub.status.idle": "2021-03-18T13:54:49.066853Z",
     "shell.execute_reply": "2021-03-18T13:54:49.067385Z"
    },
    "papermill": {
     "duration": 0.052725,
     "end_time": "2021-03-18T13:54:49.067632",
     "exception": false,
     "start_time": "2021-03-18T13:54:49.014907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>X5</th>\n",
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       "      <td>n</td>\n",
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       "    <tr>\n",
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       "      <td>e</td>\n",
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       "    <tr>\n",
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       "      <td>c</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4174 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X0 X1  X2 X3 X4  X5 X6 X8\n",
       "0      k  v  at  a  d   u  j  o\n",
       "1      k  t  av  e  d   y  l  o\n",
       "2     az  w   n  c  d   x  j  x\n",
       "3     az  t   n  f  d   x  l  e\n",
       "4     az  v   n  f  d   h  d  n\n",
       "...   .. ..  .. .. ..  .. .. ..\n",
       "4204  ak  s  as  c  d  aa  d  q\n",
       "4205   j  o   t  d  d  aa  h  h\n",
       "4206  ak  v   r  a  d  aa  g  e\n",
       "4207  al  r   e  f  d  aa  l  u\n",
       "4208   z  r  ae  c  d  aa  g  w\n",
       "\n",
       "[4174 rows x 8 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_feature = train_df.select_dtypes(include='object')\n",
    "cat_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:49.164516Z",
     "iopub.status.busy": "2021-03-18T13:54:49.159776Z",
     "iopub.status.idle": "2021-03-18T13:54:52.095995Z",
     "shell.execute_reply": "2021-03-18T13:54:52.095261Z"
    },
    "papermill": {
     "duration": 3.000343,
     "end_time": "2021-03-18T13:54:52.096170",
     "exception": false,
     "start_time": "2021-03-18T13:54:49.095827",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 10))\n",
    "\n",
    "for index, col in enumerate(cat_feature):\n",
    "    plt.subplot(4, 2, index+1)\n",
    "    sns.countplot(cat_feature[col])\n",
    "   \n",
    "\n",
    "    \n",
    "    \n",
    "plt.tight_layout()    \n",
    "plt.show(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:52.183079Z",
     "iopub.status.busy": "2021-03-18T13:54:52.182279Z",
     "iopub.status.idle": "2021-03-18T13:54:57.790001Z",
     "shell.execute_reply": "2021-03-18T13:54:57.790531Z"
    },
    "papermill": {
     "duration": 5.663189,
     "end_time": "2021-03-18T13:54:57.790758",
     "exception": false,
     "start_time": "2021-03-18T13:54:52.127569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2520 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 35))\n",
    "\n",
    "for index, col in enumerate(cat_feature):\n",
    "    plt.subplot(4, 2, index+1)\n",
    "    sns.boxplot(data=train_df, x=col, y='y')\n",
    "   \n",
    "\n",
    "    \n",
    "    \n",
    "plt.tight_layout()    \n",
    "plt.show(); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:57.878617Z",
     "iopub.status.busy": "2021-03-18T13:54:57.866346Z",
     "iopub.status.idle": "2021-03-18T13:54:57.909523Z",
     "shell.execute_reply": "2021-03-18T13:54:57.908842Z"
    },
    "papermill": {
     "duration": 0.084148,
     "end_time": "2021-03-18T13:54:57.909712",
     "exception": false,
     "start_time": "2021-03-18T13:54:57.825564",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.concat([train_df, test_df])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:57.989978Z",
     "iopub.status.busy": "2021-03-18T13:54:57.987378Z",
     "iopub.status.idle": "2021-03-18T13:54:58.030639Z",
     "shell.execute_reply": "2021-03-18T13:54:58.029723Z"
    },
    "papermill": {
     "duration": 0.086538,
     "end_time": "2021-03-18T13:54:58.030882",
     "exception": false,
     "start_time": "2021-03-18T13:54:57.944344",
     "status": "completed"
    },
    "tags": []
   },
   "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>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>av</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4204</th>\n",
       "      <td>aj</td>\n",
       "      <td>h</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>t</td>\n",
       "      <td>aa</td>\n",
       "      <td>ai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4206</th>\n",
       "      <td>y</td>\n",
       "      <td>v</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>ak</td>\n",
       "      <td>v</td>\n",
       "      <td>as</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4208</th>\n",
       "      <td>t</td>\n",
       "      <td>aa</td>\n",
       "      <td>ai</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8383 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      X0  X1  X2\n",
       "0      k   v  at\n",
       "1      k   t  av\n",
       "2     az   w   n\n",
       "3     az   t   n\n",
       "4     az   v   n\n",
       "...   ..  ..  ..\n",
       "4204  aj   h  as\n",
       "4205   t  aa  ai\n",
       "4206   y   v  as\n",
       "4207  ak   v  as\n",
       "4208   t  aa  ai\n",
       "\n",
       "[8383 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['X0', 'X1', 'X2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.111086Z",
     "iopub.status.busy": "2021-03-18T13:54:58.110287Z",
     "iopub.status.idle": "2021-03-18T13:54:58.114302Z",
     "shell.execute_reply": "2021-03-18T13:54:58.113631Z"
    },
    "papermill": {
     "duration": 0.047851,
     "end_time": "2021-03-18T13:54:58.114479",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.066628",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def code_сount(dataset, feature):\n",
    "    dict = dataset[feature].value_counts().to_dict()\n",
    "    dataset[feature] = dataset[feature].map(dict)\n",
    "    \n",
    "    return dataset[feature] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.204264Z",
     "iopub.status.busy": "2021-03-18T13:54:58.196188Z",
     "iopub.status.idle": "2021-03-18T13:54:58.214419Z",
     "shell.execute_reply": "2021-03-18T13:54:58.213791Z"
    },
    "papermill": {
     "duration": 0.064556,
     "end_time": "2021-03-18T13:54:58.214617",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.150061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for feature in data[['X0', 'X1', 'X2']]:\n",
    "    code_сount(data, feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.292263Z",
     "iopub.status.busy": "2021-03-18T13:54:58.291491Z",
     "iopub.status.idle": "2021-03-18T13:54:58.295734Z",
     "shell.execute_reply": "2021-03-18T13:54:58.294975Z"
    },
    "papermill": {
     "duration": 0.04574,
     "end_time": "2021-03-18T13:54:58.295920",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.250180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"data = pd.concat([data, pd.get_dummies(data[['X0', 'X1', 'X2']])], axis=1)\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''data = pd.concat([data, pd.get_dummies(data[['X0', 'X1', 'X2']])], axis=1)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.375237Z",
     "iopub.status.busy": "2021-03-18T13:54:58.374380Z",
     "iopub.status.idle": "2021-03-18T13:54:58.404786Z",
     "shell.execute_reply": "2021-03-18T13:54:58.405311Z"
    },
    "papermill": {
     "duration": 0.072957,
     "end_time": "2021-03-18T13:54:58.405545",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.332588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data.drop(columns=['X3', 'X4', 'X5', 'X6', 'X8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.481674Z",
     "iopub.status.busy": "2021-03-18T13:54:58.480945Z",
     "iopub.status.idle": "2021-03-18T13:54:58.485628Z",
     "shell.execute_reply": "2021-03-18T13:54:58.486176Z"
    },
    "papermill": {
     "duration": 0.044789,
     "end_time": "2021-03-18T13:54:58.486404",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.441615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"data.drop(columns=['X0', 'X1', 'X2'], inplace=True)\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''data.drop(columns=['X0', 'X1', 'X2'], inplace=True)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.566552Z",
     "iopub.status.busy": "2021-03-18T13:54:58.565806Z",
     "iopub.status.idle": "2021-03-18T13:54:58.595650Z",
     "shell.execute_reply": "2021-03-18T13:54:58.596466Z"
    },
    "papermill": {
     "duration": 0.073782,
     "end_time": "2021-03-18T13:54:58.596747",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.522965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8383 entries, 0 to 4208\n",
      "Columns: 373 entries, ID to X385\n",
      "dtypes: float64(1), int64(372)\n",
      "memory usage: 23.9 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.704651Z",
     "iopub.status.busy": "2021-03-18T13:54:58.699179Z",
     "iopub.status.idle": "2021-03-18T13:54:58.710022Z",
     "shell.execute_reply": "2021-03-18T13:54:58.710581Z"
    },
    "papermill": {
     "duration": 0.075373,
     "end_time": "2021-03-18T13:54:58.710809",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.635436",
     "status": "completed"
    },
    "tags": []
   },
   "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>ID</th>\n",
       "      <th>y</th>\n",
       "      <th>X10</th>\n",
       "      <th>X11</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>130.81</td>\n",
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       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>88.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>76.26</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>80.62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>78.02</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 370 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID       y  X10  X11  X12  X13  X14  X15  X16  X17  ...  X375  X376  X377  \\\n",
       "0   0  130.81    0    0    0    1    0    0    0    0  ...     0     0     1   \n",
       "1   6   88.53    0    0    0    0    0    0    0    0  ...     1     0     0   \n",
       "2   7   76.26    0    0    0    0    0    0    0    1  ...     0     0     0   \n",
       "3   9   80.62    0    0    0    0    0    0    0    0  ...     0     0     0   \n",
       "4  13   78.02    0    0    0    0    0    0    0    0  ...     0     0     0   \n",
       "\n",
       "   X378  X379  X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0     0     0  \n",
       "1     0     0     0     0     0     0     0  \n",
       "2     0     0     0     1     0     0     0  \n",
       "3     0     0     0     0     0     0     0  \n",
       "4     0     0     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 370 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features = train_df.select_dtypes(exclude='object').head()\n",
    "num_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.791628Z",
     "iopub.status.busy": "2021-03-18T13:54:58.790617Z",
     "iopub.status.idle": "2021-03-18T13:54:58.798914Z",
     "shell.execute_reply": "2021-03-18T13:54:58.798076Z"
    },
    "papermill": {
     "duration": 0.04851,
     "end_time": "2021-03-18T13:54:58.799122",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.750612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'num_features.describe()'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''num_features.describe()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.879011Z",
     "iopub.status.busy": "2021-03-18T13:54:58.878286Z",
     "iopub.status.idle": "2021-03-18T13:54:58.884188Z",
     "shell.execute_reply": "2021-03-18T13:54:58.883523Z"
    },
    "papermill": {
     "duration": 0.047004,
     "end_time": "2021-03-18T13:54:58.884351",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.837347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'num_nul_var = []\\nfor col in num_features:\\n    if train_df[col].var() == 0:\\n        num_nul_var.append(col)\\n        print(col, train_df[col].var())'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''num_nul_var = []\n",
    "for col in num_features:\n",
    "    if train_df[col].var() == 0:\n",
    "        num_nul_var.append(col)\n",
    "        print(col, train_df[col].var())'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:58.964860Z",
     "iopub.status.busy": "2021-03-18T13:54:58.964139Z",
     "iopub.status.idle": "2021-03-18T13:54:58.970629Z",
     "shell.execute_reply": "2021-03-18T13:54:58.970072Z"
    },
    "papermill": {
     "duration": 0.047786,
     "end_time": "2021-03-18T13:54:58.970789",
     "exception": false,
     "start_time": "2021-03-18T13:54:58.923003",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data.drop(columns=num_nul_var, inplace=True)'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''data.drop(columns=num_nul_var, inplace=True)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:59.055176Z",
     "iopub.status.busy": "2021-03-18T13:54:59.054505Z",
     "iopub.status.idle": "2021-03-18T13:54:59.057695Z",
     "shell.execute_reply": "2021-03-18T13:54:59.058295Z"
    },
    "papermill": {
     "duration": 0.048951,
     "end_time": "2021-03-18T13:54:59.058468",
     "exception": false,
     "start_time": "2021-03-18T13:54:59.009517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = data[:train_df.shape[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:59.141417Z",
     "iopub.status.busy": "2021-03-18T13:54:59.140723Z",
     "iopub.status.idle": "2021-03-18T13:54:59.144863Z",
     "shell.execute_reply": "2021-03-18T13:54:59.144181Z"
    },
    "papermill": {
     "duration": 0.048377,
     "end_time": "2021-03-18T13:54:59.145031",
     "exception": false,
     "start_time": "2021-03-18T13:54:59.096654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_df =  data[train_df.shape[0]:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:59.227943Z",
     "iopub.status.busy": "2021-03-18T13:54:59.227204Z",
     "iopub.status.idle": "2021-03-18T13:54:59.244359Z",
     "shell.execute_reply": "2021-03-18T13:54:59.244879Z"
    },
    "papermill": {
     "duration": 0.06063,
     "end_time": "2021-03-18T13:54:59.245106",
     "exception": false,
     "start_time": "2021-03-18T13:54:59.184476",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train_df.drop(columns=['ID', 'y'])\n",
    "y = train_df['y']\n",
    "X_test = test_df.drop(columns=['ID', 'y'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:59.328207Z",
     "iopub.status.busy": "2021-03-18T13:54:59.327500Z",
     "iopub.status.idle": "2021-03-18T13:54:59.342400Z",
     "shell.execute_reply": "2021-03-18T13:54:59.342962Z"
    },
    "papermill": {
     "duration": 0.058669,
     "end_time": "2021-03-18T13:54:59.343204",
     "exception": false,
     "start_time": "2021-03-18T13:54:59.284535",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:54:59.434014Z",
     "iopub.status.busy": "2021-03-18T13:54:59.433292Z",
     "iopub.status.idle": "2021-03-18T13:55:00.833490Z",
     "shell.execute_reply": "2021-03-18T13:55:00.832795Z"
    },
    "papermill": {
     "duration": 1.450867,
     "end_time": "2021-03-18T13:55:00.833688",
     "exception": false,
     "start_time": "2021-03-18T13:54:59.382821",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6126267480366778"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(max_depth=5, min_samples_leaf=3, n_estimators=145,\n",
    "                      n_jobs=-1, random_state=1)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_val)\n",
    "\n",
    "r2_score(y_val, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:55:00.920782Z",
     "iopub.status.busy": "2021-03-18T13:55:00.920086Z",
     "iopub.status.idle": "2021-03-18T13:55:00.923625Z",
     "shell.execute_reply": "2021-03-18T13:55:00.924116Z"
    },
    "papermill": {
     "duration": 0.050072,
     "end_time": "2021-03-18T13:55:00.924346",
     "exception": false,
     "start_time": "2021-03-18T13:55:00.874274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"params= {'max_depth': list(range(5,21, 1)), \\n              'min_samples_leaf': list(range(1,6, 1)),\\n              'n_estimators': [145], 'n_jobs': [-1], 'random_state': [1]}\\n\\n\\ncv_rf = GridSearchCV(RandomForestRegressor(), params)\\n\\ncv_rf.fit(X_train, y_train)\\ny_pred = cv_rf.predict(X_val)\\n\\nr2_score(y_val, y_pred), cv_rf.best_estimator_\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''params= {'max_depth': list(range(5,21, 1)), \n",
    "              'min_samples_leaf': list(range(1,6, 1)),\n",
    "              'n_estimators': [145], 'n_jobs': [-1], 'random_state': [1]}\n",
    "\n",
    "\n",
    "cv_rf = GridSearchCV(RandomForestRegressor(), params)\n",
    "\n",
    "cv_rf.fit(X_train, y_train)\n",
    "y_pred = cv_rf.predict(X_val)\n",
    "\n",
    "r2_score(y_val, y_pred), cv_rf.best_estimator_'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:55:01.010831Z",
     "iopub.status.busy": "2021-03-18T13:55:01.010144Z",
     "iopub.status.idle": "2021-03-18T13:55:02.613018Z",
     "shell.execute_reply": "2021-03-18T13:55:02.612259Z"
    },
    "papermill": {
     "duration": 1.647227,
     "end_time": "2021-03-18T13:55:02.613181",
     "exception": false,
     "start_time": "2021-03-18T13:55:00.965954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 77.88237674  93.84218364  77.43634294 ...  93.76928174 111.49805528\n",
      "  93.80689513]\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(max_depth=5, min_samples_leaf=3, n_estimators=145,\n",
    "                      n_jobs=-1, random_state=1)\n",
    "\n",
    "model.fit(X, y)\n",
    "predict = model.predict(X_test)\n",
    "print(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T13:55:02.702143Z",
     "iopub.status.busy": "2021-03-18T13:55:02.701355Z",
     "iopub.status.idle": "2021-03-18T13:55:02.725734Z",
     "shell.execute_reply": "2021-03-18T13:55:02.725089Z"
    },
    "papermill": {
     "duration": 0.072595,
     "end_time": "2021-03-18T13:55:02.725894",
     "exception": false,
     "start_time": "2021-03-18T13:55:02.653299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'ID': test_df.ID, 'y': predict})\n",
    "output.to_csv('mercedes_benz_baseline.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID           y\n",
      "0        1   77.882377\n",
      "1        2   93.842184\n",
      "2        3   77.436343\n",
      "3        4   78.014133\n",
      "4        5  112.151172\n",
      "...    ...         ...\n",
      "4204  8410  102.994275\n",
      "4205  8411   94.233831\n",
      "4206  8413   93.769282\n",
      "4207  8414  111.498055\n",
      "4208  8416   93.806895\n",
      "\n",
      "[4209 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "results = pd.read_csv('mercedes_benz_baseline.csv')\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        77.882377\n",
      "1        93.842184\n",
      "2        77.436343\n",
      "3        78.014133\n",
      "4       112.151172\n",
      "5        93.749262\n",
      "6       111.587836\n",
      "7        93.818794\n",
      "8       116.589011\n",
      "9        93.847504\n",
      "10      116.589011\n",
      "11      110.148810\n",
      "12       95.790964\n",
      "13       93.846421\n",
      "14      104.490001\n",
      "15       95.790964\n",
      "16      117.344099\n",
      "17       95.790964\n",
      "18       94.135646\n",
      "19       94.337788\n",
      "20       95.790964\n",
      "21       95.790964\n",
      "22       94.120901\n",
      "23       95.790964\n",
      "24       93.841601\n",
      "25      116.124245\n",
      "26      102.304054\n",
      "27      103.413481\n",
      "28       93.879517\n",
      "29       77.446324\n",
      "30      112.353327\n",
      "31       93.827675\n",
      "32      104.046898\n",
      "33       94.141130\n",
      "34      103.471145\n",
      "35      111.532533\n",
      "36       93.867250\n",
      "37      105.163110\n",
      "38       94.101436\n",
      "39       94.311731\n",
      "40      103.649601\n",
      "41       93.892610\n",
      "42      103.461979\n",
      "43      103.860784\n",
      "44      114.864825\n",
      "45       93.892610\n",
      "46      105.163110\n",
      "47       93.915981\n",
      "48      103.047468\n",
      "49      111.613740\n",
      "50       95.790964\n",
      "51      111.618432\n",
      "52       93.836964\n",
      "53       94.152638\n",
      "54      103.256275\n",
      "55       93.553660\n",
      "56      111.541484\n",
      "57       93.760087\n",
      "58      111.599397\n",
      "59       93.890635\n",
      "60      103.282344\n",
      "61      111.618432\n",
      "62       93.671929\n",
      "63      111.575561\n",
      "64       94.150965\n",
      "65      111.647434\n",
      "66      111.505277\n",
      "67       93.683186\n",
      "68      112.777275\n",
      "69      111.618432\n",
      "70       93.882672\n",
      "71      111.705249\n",
      "72       93.780631\n",
      "73      112.501626\n",
      "74      112.324083\n",
      "75      111.632063\n",
      "76      103.675067\n",
      "77      104.086800\n",
      "78       93.826593\n",
      "79       93.827675\n",
      "80      112.834370\n",
      "81      112.116357\n",
      "82       93.769806\n",
      "83      111.620678\n",
      "84       93.545978\n",
      "85      111.613829\n",
      "86       93.760087\n",
      "87      111.649334\n",
      "88       94.388155\n",
      "89       93.667357\n",
      "90      116.124245\n",
      "91      111.624319\n",
      "92       93.806313\n",
      "93      111.509570\n",
      "94       93.866375\n",
      "95      111.618432\n",
      "96      111.618432\n",
      "97      111.512959\n",
      "98       93.892610\n",
      "99       93.859689\n",
      "100      93.714259\n",
      "101     113.082462\n",
      "102      93.854157\n",
      "103     111.416967\n",
      "104      93.673360\n",
      "105     111.584454\n",
      "106      94.122665\n",
      "107     112.151120\n",
      "108     104.059547\n",
      "109      93.826593\n",
      "110     110.148810\n",
      "111      93.590209\n",
      "112      93.854529\n",
      "113     103.282344\n",
      "114     111.981481\n",
      "115     116.589011\n",
      "116      93.713983\n",
      "117     111.509570\n",
      "118     111.509570\n",
      "119      93.786090\n",
      "120     111.623799\n",
      "121     111.623709\n",
      "122      93.760087\n",
      "123     117.344099\n",
      "124      93.825292\n",
      "125     111.647837\n",
      "126     103.081221\n",
      "127      93.836964\n",
      "128      79.030111\n",
      "129     111.446829\n",
      "130     112.626666\n",
      "131      93.778678\n",
      "132      93.839647\n",
      "133     111.781431\n",
      "134      93.825292\n",
      "135     117.344099\n",
      "136      93.884730\n",
      "137      93.867903\n",
      "138      93.769806\n",
      "139     113.046273\n",
      "140      93.890635\n",
      "141      80.763301\n",
      "142      93.709173\n",
      "143     111.623851\n",
      "144     103.212947\n",
      "145     111.618432\n",
      "146      93.826857\n",
      "147     112.413598\n",
      "148      94.275286\n",
      "149     105.163110\n",
      "150      93.768959\n",
      "151     111.623799\n",
      "152     111.618432\n",
      "153      94.287067\n",
      "154     104.059547\n",
      "155      93.867622\n",
      "156     111.476912\n",
      "157     111.469364\n",
      "158      93.780631\n",
      "159     111.569296\n",
      "160     114.864825\n",
      "161      93.892610\n",
      "162      93.692632\n",
      "163      80.161906\n",
      "164      93.834044\n",
      "165     111.509570\n",
      "166     107.327916\n",
      "167     111.509570\n",
      "168      93.676082\n",
      "169      93.547356\n",
      "170      93.547356\n",
      "171      94.287067\n",
      "172      93.672804\n",
      "173     111.416967\n",
      "174     103.853642\n",
      "175      93.876490\n",
      "176     103.990729\n",
      "177      93.867250\n",
      "178      93.804359\n",
      "179     116.589011\n",
      "180      93.651589\n",
      "181     111.599724\n",
      "182     111.339655\n",
      "183      93.683186\n",
      "184     111.691352\n",
      "185      93.601672\n",
      "186      78.150693\n",
      "187      93.902907\n",
      "188     112.546762\n",
      "189     111.589339\n",
      "190     114.893951\n",
      "191     103.228738\n",
      "192      93.836118\n",
      "193     111.651357\n",
      "194      93.816193\n",
      "195      77.214159\n",
      "196      94.161674\n",
      "197      93.662404\n",
      "198     111.474194\n",
      "199     103.461979\n",
      "200     111.623799\n",
      "201      93.553660\n",
      "202     111.769553\n",
      "203     112.808051\n",
      "204      93.872781\n",
      "205     103.768206\n",
      "206      77.747046\n",
      "207     111.509570\n",
      "208     111.509570\n",
      "209      93.671929\n",
      "210      77.041396\n",
      "211     112.157795\n",
      "212      93.749262\n",
      "213      93.553660\n",
      "214     112.105841\n",
      "215      93.749262\n",
      "216     106.064410\n",
      "217      93.882239\n",
      "218      93.835880\n",
      "219      93.827675\n",
      "220      93.795487\n",
      "221     112.140752\n",
      "222      93.867622\n",
      "223     111.698345\n",
      "224      93.846421\n",
      "225      93.851055\n",
      "226      93.867250\n",
      "227     103.012774\n",
      "228     104.050381\n",
      "229      93.863398\n",
      "230     113.338404\n",
      "231     103.193336\n",
      "232     103.003285\n",
      "233     103.212743\n",
      "234     116.589011\n",
      "235     115.315123\n",
      "236     111.698048\n",
      "237      77.081150\n",
      "238      93.662636\n",
      "239      93.826593\n",
      "240      94.112266\n",
      "241     111.528882\n",
      "242     111.509570\n",
      "243     103.282344\n",
      "244      77.436343\n",
      "245      93.902907\n",
      "246      93.607002\n",
      "247     111.640707\n",
      "248      94.100357\n",
      "249     103.443369\n",
      "250      93.863398\n",
      "251     111.623762\n",
      "252      77.551056\n",
      "253     111.439026\n",
      "254      93.484689\n",
      "255      93.672804\n",
      "256      93.830776\n",
      "257      93.653532\n",
      "258     111.599724\n",
      "259      93.651163\n",
      "260     111.904183\n",
      "261     112.512020\n",
      "262      93.607254\n",
      "263     111.653519\n",
      "264      93.882425\n",
      "265     112.192837\n",
      "266     104.800312\n",
      "267      93.892610\n",
      "268     103.282344\n",
      "269      93.826593\n",
      "270      93.859689\n",
      "271     112.120477\n",
      "272      93.825292\n",
      "273      78.258073\n",
      "274      93.883079\n",
      "275     103.190908\n",
      "276     103.432937\n",
      "277      94.176541\n",
      "278     111.651357\n",
      "279      93.878448\n",
      "280     110.093382\n",
      "281     110.148810\n",
      "282      94.109882\n",
      "283     111.640707\n",
      "284      93.827675\n",
      "285     105.163110\n",
      "286      93.892610\n",
      "287     116.589011\n",
      "288     112.651501\n",
      "289      93.672786\n",
      "290      93.749688\n",
      "291     111.555327\n",
      "292     111.632063\n",
      "293     111.509570\n",
      "294     102.378754\n",
      "295      93.857222\n",
      "296     103.206352\n",
      "297      93.869992\n",
      "298      93.883079\n",
      "299     112.114034\n",
      "300     111.609649\n",
      "301     112.466401\n",
      "302      93.836118\n",
      "303     103.282344\n",
      "304      93.879517\n",
      "305     111.599724\n",
      "306      94.126774\n",
      "307     102.203761\n",
      "308     103.038939\n",
      "309     111.579167\n",
      "310      93.769806\n",
      "311     110.976455\n",
      "312     111.663400\n",
      "313     111.639361\n",
      "314      93.692058\n",
      "315     111.623709\n",
      "316      93.848268\n",
      "317     103.694533\n",
      "318      94.022534\n",
      "319      94.112266\n",
      "320     116.099406\n",
      "321      94.352104\n",
      "322      94.109882\n",
      "323      93.827675\n",
      "324     111.640707\n",
      "325     112.274120\n",
      "326      93.647896\n",
      "327      93.529197\n",
      "328     102.962564\n",
      "329      93.779103\n",
      "330     112.282932\n",
      "331     103.327263\n",
      "332     111.559327\n",
      "333      93.573358\n",
      "334      94.167016\n",
      "335      93.830776\n",
      "336      93.825292\n",
      "337     111.446829\n",
      "338     103.677076\n",
      "339      94.111740\n",
      "340     103.213946\n",
      "341      93.826593\n",
      "342      93.524442\n",
      "343     111.641819\n",
      "344      94.287067\n",
      "345      94.091485\n",
      "346      94.006075\n",
      "347     103.042536\n",
      "348     116.482861\n",
      "349     111.810672\n",
      "350      93.847790\n",
      "351     103.670982\n",
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      "Name: ID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', results.shape[0]+1)\n",
    "print(results.y, results.ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 24.177328,
   "end_time": "2021-03-18T13:55:03.860468",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-03-18T13:54:39.683140",
   "version": "2.2.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}