{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#your id here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exam Instruction EE522 Fall 2022:\n",
    "    \n",
    "1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
    "\n",
    "2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
    "\n",
    "3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
    "\n",
    "4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
    "\n",
    "\n",
    "5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
    "\n",
    "6. You have to submit your code on BE-moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 [50 points]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Information:\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
    "\n",
    "Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\t\n",
    "=========================================\n",
    "Dataset characteristics\n",
    "======================\t\n",
    "hour.csv has the following fields:\n",
    "\t\n",
    "\t- instant: record index\n",
    "\t- dteday : date\n",
    "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "\t- yr : year (0: 2011, 1:2012)\n",
    "\t- mnth : month ( 1 to 12)\n",
    "\t- hr : hour (0 to 23)\n",
    "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "\t- weekday : day of the week\n",
    "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\t+ weathersit : \n",
    "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\t- casual: count of casual users\n",
    "\t- registered: count of registered users\n",
    "\t- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.1 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
    "\n",
    "Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
    "\n",
    "\n",
    "[20 Points]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.scatter plot >>CNT is our target value\n",
    "#2.median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: qeds in c:\\users\\putter\\anaconda3\\lib\\site-packages (0.7.0)\n",
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      "Requirement already satisfied: scikit-learn in c:\\users\\putter\\anaconda3\\lib\\site-packages (from qeds) (1.0.2)\n",
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      "Requirement already satisfied: lxml in c:\\users\\putter\\anaconda3\\lib\\site-packages (from pandas-datareader->qeds) (4.8.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from requests->qeds) (3.3)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from requests->qeds) (2021.10.8)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from plotly->qeds) (8.0.1)\n",
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      "Requirement already satisfied: inflection>=0.3.1 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from quandl->qeds) (0.5.1)\n",
      "Requirement already satisfied: sympy in c:\\users\\putter\\anaconda3\\lib\\site-packages (from quantecon->qeds) (1.10.1)\n",
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      "Requirement already satisfied: setuptools in c:\\users\\putter\\anaconda3\\lib\\site-packages (from numba->quantecon->qeds) (61.2.0)\n",
      "Requirement already satisfied: llvmlite<0.39,>=0.38.0rc1 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from numba->quantecon->qeds) (0.38.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from scikit-learn->qeds) (1.1.0)\n",
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      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\putter\\anaconda3\\lib\\site-packages (from sympy->quantecon->qeds) (1.2.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install qeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "# We will import all these here to ensure that they are loaded, but\n",
    "# will usually re-import close to where they are used to make clear\n",
    "# where the functions come from\n",
    "from sklearn import (linear_model, metrics, model_selection) #model selection is very important! \n",
    "\n",
    "df = pd.read_csv(\"hour.csv\") \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =df.copy() #to save data fame in the same place "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12</td>\n",
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       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
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       "      <th>17377</th>\n",
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       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.56</td>\n",
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       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>17379</td>\n",
       "      <td>2012-12-31</td>\n",
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       "      <td>1</td>\n",
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       "      <td>12</td>\n",
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       "      <td>49</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp  \\\n",
       "0           1   0     1   0        0        6           0           1  0.24   \n",
       "1           1   0     1   1        0        6           0           1  0.22   \n",
       "2           1   0     1   2        0        6           0           1  0.22   \n",
       "3           1   0     1   3        0        6           0           1  0.24   \n",
       "4           1   0     1   4        0        6           0           1  0.24   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   ...   \n",
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       "\n",
       "        atemp   hum  windspeed  casual  registered  cnt  \n",
       "0      0.2879  0.81     0.0000       3          13   16  \n",
       "1      0.2727  0.80     0.0000       8          32   40  \n",
       "2      0.2727  0.80     0.0000       5          27   32  \n",
       "3      0.2879  0.75     0.0000       3          10   13  \n",
       "4      0.2879  0.75     0.0000       0           1    1  \n",
       "...       ...   ...        ...     ...         ...  ...  \n",
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       "17375  0.2576  0.60     0.1642       8          81   89  \n",
       "17376  0.2576  0.60     0.1642       7          83   90  \n",
       "17377  0.2727  0.56     0.1343      13          48   61  \n",
       "17378  0.2727  0.65     0.1343      12          37   49  \n",
       "\n",
       "[17379 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.drop(['instant','dteday',], axis=1) # 0=row ,1 column\n",
    "df2 #we drop to only correlate with important data and also drop our traget value which is Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.000000</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>0.030284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <td>-0.014524</td>\n",
       "      <td>-0.019157</td>\n",
       "      <td>0.005400</td>\n",
       "      <td>-0.020203</td>\n",
       "      <td>-0.017036</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.044672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>-0.142426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.312025</td>\n",
       "      <td>0.040913</td>\n",
       "      <td>0.201691</td>\n",
       "      <td>0.137603</td>\n",
       "      <td>-0.027340</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.055390</td>\n",
       "      <td>-0.102640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.404772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.319380</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>0.208096</td>\n",
       "      <td>0.133750</td>\n",
       "      <td>-0.030973</td>\n",
       "      <td>-0.008821</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>-0.105563</td>\n",
       "      <td>0.987672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>0.400929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hum</th>\n",
       "      <td>0.150625</td>\n",
       "      <td>-0.083546</td>\n",
       "      <td>0.164411</td>\n",
       "      <td>-0.276498</td>\n",
       "      <td>-0.010588</td>\n",
       "      <td>-0.037158</td>\n",
       "      <td>0.015688</td>\n",
       "      <td>0.418130</td>\n",
       "      <td>-0.069881</td>\n",
       "      <td>-0.051918</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>-0.322911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.149773</td>\n",
       "      <td>-0.008740</td>\n",
       "      <td>-0.135386</td>\n",
       "      <td>0.137252</td>\n",
       "      <td>0.003988</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.011830</td>\n",
       "      <td>0.026226</td>\n",
       "      <td>-0.023125</td>\n",
       "      <td>-0.062336</td>\n",
       "      <td>-0.290105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.093234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.120206</td>\n",
       "      <td>0.142779</td>\n",
       "      <td>0.068457</td>\n",
       "      <td>0.301202</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>0.032721</td>\n",
       "      <td>-0.300942</td>\n",
       "      <td>-0.152628</td>\n",
       "      <td>0.459616</td>\n",
       "      <td>0.454080</td>\n",
       "      <td>-0.347028</td>\n",
       "      <td>0.090287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>0.694564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.174226</td>\n",
       "      <td>0.253684</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>0.374141</td>\n",
       "      <td>-0.047345</td>\n",
       "      <td>0.021578</td>\n",
       "      <td>0.134326</td>\n",
       "      <td>-0.120966</td>\n",
       "      <td>0.335361</td>\n",
       "      <td>0.332559</td>\n",
       "      <td>-0.273933</td>\n",
       "      <td>0.082321</td>\n",
       "      <td>0.506618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <td>0.178056</td>\n",
       "      <td>0.250495</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.394071</td>\n",
       "      <td>-0.030927</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.030284</td>\n",
       "      <td>-0.142426</td>\n",
       "      <td>0.404772</td>\n",
       "      <td>0.400929</td>\n",
       "      <td>-0.322911</td>\n",
       "      <td>0.093234</td>\n",
       "      <td>0.694564</td>\n",
       "      <td>0.972151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              season        yr      mnth        hr   holiday   weekday  \\\n",
       "season      1.000000 -0.010742  0.830386 -0.006117 -0.009585 -0.002335   \n",
       "yr         -0.010742  1.000000 -0.010473 -0.003867  0.006692 -0.004485   \n",
       "mnth        0.830386 -0.010473  1.000000 -0.005772  0.018430  0.010400   \n",
       "hr         -0.006117 -0.003867 -0.005772  1.000000  0.000479 -0.003498   \n",
       "holiday    -0.009585  0.006692  0.018430  0.000479  1.000000 -0.102088   \n",
       "weekday    -0.002335 -0.004485  0.010400 -0.003498 -0.102088  1.000000   \n",
       "workingday  0.013743 -0.002196 -0.003477  0.002285 -0.252471  0.035955   \n",
       "weathersit -0.014524 -0.019157  0.005400 -0.020203 -0.017036  0.003311   \n",
       "temp        0.312025  0.040913  0.201691  0.137603 -0.027340 -0.001795   \n",
       "atemp       0.319380  0.039222  0.208096  0.133750 -0.030973 -0.008821   \n",
       "hum         0.150625 -0.083546  0.164411 -0.276498 -0.010588 -0.037158   \n",
       "windspeed  -0.149773 -0.008740 -0.135386  0.137252  0.003988  0.011502   \n",
       "casual      0.120206  0.142779  0.068457  0.301202  0.031564  0.032721   \n",
       "registered  0.174226  0.253684  0.122273  0.374141 -0.047345  0.021578   \n",
       "cnt         0.178056  0.250495  0.120638  0.394071 -0.030927  0.026900   \n",
       "\n",
       "            workingday  weathersit      temp     atemp       hum  windspeed  \\\n",
       "season        0.013743   -0.014524  0.312025  0.319380  0.150625  -0.149773   \n",
       "yr           -0.002196   -0.019157  0.040913  0.039222 -0.083546  -0.008740   \n",
       "mnth         -0.003477    0.005400  0.201691  0.208096  0.164411  -0.135386   \n",
       "hr            0.002285   -0.020203  0.137603  0.133750 -0.276498   0.137252   \n",
       "holiday      -0.252471   -0.017036 -0.027340 -0.030973 -0.010588   0.003988   \n",
       "weekday       0.035955    0.003311 -0.001795 -0.008821 -0.037158   0.011502   \n",
       "workingday    1.000000    0.044672  0.055390  0.054667  0.015688  -0.011830   \n",
       "weathersit    0.044672    1.000000 -0.102640 -0.105563  0.418130   0.026226   \n",
       "temp          0.055390   -0.102640  1.000000  0.987672 -0.069881  -0.023125   \n",
       "atemp         0.054667   -0.105563  0.987672  1.000000 -0.051918  -0.062336   \n",
       "hum           0.015688    0.418130 -0.069881 -0.051918  1.000000  -0.290105   \n",
       "windspeed    -0.011830    0.026226 -0.023125 -0.062336 -0.290105   1.000000   \n",
       "casual       -0.300942   -0.152628  0.459616  0.454080 -0.347028   0.090287   \n",
       "registered    0.134326   -0.120966  0.335361  0.332559 -0.273933   0.082321   \n",
       "cnt           0.030284   -0.142426  0.404772  0.400929 -0.322911   0.093234   \n",
       "\n",
       "              casual  registered       cnt  \n",
       "season      0.120206    0.174226  0.178056  \n",
       "yr          0.142779    0.253684  0.250495  \n",
       "mnth        0.068457    0.122273  0.120638  \n",
       "hr          0.301202    0.374141  0.394071  \n",
       "holiday     0.031564   -0.047345 -0.030927  \n",
       "weekday     0.032721    0.021578  0.026900  \n",
       "workingday -0.300942    0.134326  0.030284  \n",
       "weathersit -0.152628   -0.120966 -0.142426  \n",
       "temp        0.459616    0.335361  0.404772  \n",
       "atemp       0.454080    0.332559  0.400929  \n",
       "hum        -0.347028   -0.273933 -0.322911  \n",
       "windspeed   0.090287    0.082321  0.093234  \n",
       "casual      1.000000    0.506618  0.694564  \n",
       "registered  0.506618    1.000000  0.972151  \n",
       "cnt         0.694564    0.972151  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_data = df2.corr()\n",
    "correlation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(15,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df2.corr(),annot=True,cmap=colormap)\n",
    "#plt.savefig(\"correlation.jpeg\") #provide in current folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "    log_cnt  \n",
       "0  2.772589  \n",
       "1  3.688879  \n",
       "2  3.465736  \n",
       "3  2.564949  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice the log here!\n",
    "y = np.log(df2[\"cnt\"]) #ln(price)\n",
    "df[\"log_cnt\"] = y\n",
    "df.head() #some of data might over and some might under \n",
    "#จะได้เห็นเปอร์เซ็น change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"temp\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"log_cnt\",alpha=0.5, s=10, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "#plt.savefig(\"scatter1.png\")\n",
    "##scatter plot\n",
    "# var scatter =x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      " 17  log_cnt     17379 non-null  float64\n",
      "dtypes: float64(5), int64(12), object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "      <td>4.536082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "      <td>1.486137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</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>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.955827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>5.638355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "      <td>6.884487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt       log_cnt  \n",
       "count  17379.000000  17379.000000  \n",
       "mean     189.463088      4.536082  \n",
       "std      181.387599      1.486137  \n",
       "min        1.000000      0.000000  \n",
       "25%       40.000000      3.688879  \n",
       "50%      142.000000      4.955827  \n",
       "75%      281.000000      5.638355  \n",
       "max      977.000000      6.884487  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Putter\\AppData\\Local\\Temp\\ipykernel_9516\\530051474.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "instant       8690.000000\n",
       "season           3.000000\n",
       "yr               1.000000\n",
       "mnth             7.000000\n",
       "hr              12.000000\n",
       "holiday          0.000000\n",
       "weekday          3.000000\n",
       "workingday       1.000000\n",
       "weathersit       1.000000\n",
       "temp             0.500000\n",
       "atemp            0.484800\n",
       "hum              0.630000\n",
       "windspeed        0.194000\n",
       "casual          17.000000\n",
       "registered     115.000000\n",
       "cnt            142.000000\n",
       "log_cnt          4.955827\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 18 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      " 17  log_cnt     17379 non-null  float64\n",
      "dtypes: float64(5), int64(12), object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  int64  \n",
      " 1   yr          17379 non-null  int64  \n",
      " 2   mnth        17379 non-null  int64  \n",
      " 3   hr          17379 non-null  int64  \n",
      " 4   holiday     17379 non-null  int64  \n",
      " 5   weekday     17379 non-null  int64  \n",
      " 6   workingday  17379 non-null  int64  \n",
      " 7   weathersit  17379 non-null  int64  \n",
      " 8   temp        17379 non-null  float64\n",
      " 9   atemp       17379 non-null  float64\n",
      " 10  hum         17379 non-null  float64\n",
      " 11  windspeed   17379 non-null  float64\n",
      " 12  casual      17379 non-null  int64  \n",
      " 13  registered  17379 non-null  int64  \n",
      " 14  log_cnt     17379 non-null  float64\n",
      "dtypes: float64(5), int64(10)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "# features:\n",
    "X = df.drop([\"instant\", \"dteday\", \"cnt\"], axis=1).copy() # create a matrix capital X  sense)\n",
    "X.info()                                            # you have to exclude price because you have to forecaste price(you will get god R-square if you include price that is not make\n",
    "# ดรอปตัวที่มึงจะ predict ออก"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  float64\n",
      " 1   yr          17379 non-null  float64\n",
      " 2   mnth        17379 non-null  float64\n",
      " 3   hr          17379 non-null  float64\n",
      " 4   holiday     17379 non-null  float64\n",
      " 5   weekday     17379 non-null  float64\n",
      " 6   workingday  17379 non-null  float64\n",
      " 7   weathersit  17379 non-null  float64\n",
      " 8   temp        17379 non-null  float64\n",
      " 9   atemp       17379 non-null  float64\n",
      " 10  hum         17379 non-null  float64\n",
      " 11  windspeed   17379 non-null  float64\n",
      " 12  casual      17379 non-null  float64\n",
      " 13  registered  17379 non-null  float64\n",
      " 14  log_cnt     17379 non-null  float64\n",
      "dtypes: float64(15)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "X = df.drop([\"instant\", \"dteday\", \"cnt\"], axis=1).copy()\n",
    "# convert everything to be a float for later on\n",
    "for col in list(X):\n",
    "    X[col] = X[col].astype(float)\n",
    "X.info()\n",
    "#เปลี่ยน data type ของเราเป็น float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
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       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2.772589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "    log_cnt  \n",
       "0  2.772589  \n",
       "1  3.688879  \n",
       "2  3.465736  \n",
       "3  2.564949  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       const  temp\n",
       "0          1  0.24\n",
       "1          1  0.22\n",
       "2          1  0.22\n",
       "3          1  0.24\n",
       "4          1  0.24\n",
       "...      ...   ...\n",
       "17374      1  0.26\n",
       "17375      1  0.26\n",
       "17376      1  0.26\n",
       "17377      1  0.26\n",
       "17378      1  0.26\n",
       "\n",
       "[17379 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['const'] = 1\n",
    "X1 = ['const', 'temp']\n",
    "df[X1]\n",
    "df[['const', 'temp']]\n",
    "#เอาไว้ทำ Beta 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'hr', 'holiday', 'weekday',\n",
       "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
       "       'casual', 'registered', 'cnt', 'log_cnt', 'const'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add constant term to dataset\n",
    "df['const'] = 1\n",
    "\n",
    "# Create lists of variables to be used in each regression #สร้างตัวแปรที่เราต้องการเพื่อที่จะไป list ใน regression\n",
    "X1 = ['const', 'atemp'] #model1\n",
    "X2 = ['const', 'weathersit'] #modle2\n",
    "X3 = ['const','weathersit', 'atemp'] #model3\n",
    "X4 = ['const','atemp', 'hum','windspeed', 'workingday', 'holiday','weekday','weathersit'] #mdoel 4\n",
    "# Estimate an OLS regression for each set of variables\n",
    "reg1 = sm.OLS(df['log_cnt'], df[X1], missing='drop').fit()\n",
    "reg2 = sm.OLS(df['log_cnt'], df[X2], missing='drop').fit()\n",
    "reg3 = sm.OLS(df['log_cnt'], df[X3], missing='drop').fit()\n",
    "reg4 = sm.OLS(df['cnt'], df[X4], missing='drop').fit()\n",
    "#เอาไว้สร้าง ANOVA หรือ OLS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Table 1 - OLS Regressions\n",
      "=====================================================================\n",
      "                   Model 1     Model 2       Model3       Model 4    \n",
      "---------------------------------------------------------------------\n",
      "const            2.961819*** 4.933113***  3.262901***  149.884447*** \n",
      "                 (0.030660)  (0.027346)   (0.040317)   (6.895546)    \n",
      "atemp            3.308839***              3.235491***  410.199932*** \n",
      "                 (0.060611)               (0.060725)   (6.983004)    \n",
      "hum                                                    -286.165600***\n",
      "                                                       (7.196474)    \n",
      "windspeed                                              42.248033***  \n",
      "                                                       (10.322185)   \n",
      "workingday                                             3.074589      \n",
      "                                                       (2.644903)    \n",
      "holiday                                                -19.680575*** \n",
      "                                                       (7.382383)    \n",
      "weekday                                                1.489800**    \n",
      "                                                       (0.596170)    \n",
      "weathersit                   -0.278563*** -0.186759*** 6.923563***   \n",
      "                             (0.017506)   (0.016322)   (2.085591)    \n",
      "R-squared        0.146397    0.014362     0.152781     0.254524      \n",
      "R-squared Adj.   0.146348    0.014305     0.152684     0.254223      \n",
      "Adj-R-squared    0.15        0.01         0.15         0.25          \n",
      "No. observations 17379       17379        17379        17379         \n",
      "=====================================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "info_dict={'Adj-R-squared' : lambda x: f\"{x.rsquared_adj:.2f}\",\n",
    "           'No. observations' : lambda x: f\"{int(x.nobs):d}\"}\n",
    "\n",
    "results_table = summary_col(results=[reg1,reg2,reg3,reg4],\n",
    "                            float_format='%0.6f',\n",
    "                            stars = True,\n",
    "                            model_names=['Model 1','Model 2','Model3','Model 4'],\n",
    "                            info_dict=info_dict,\n",
    "                            regressor_order=['const','atemp', 'hum','windspeed', 'workingday', 'holiday','weekday','weathersit'])\n",
    "\n",
    "results_table.add_title('Table 1 - OLS Regressions')\n",
    "\n",
    "print(results_table)\n",
    "#ไว้ ดูr sqaure ยิ่ง model ไหนมี rsqaure ที่สูง model นั้นยิ่งเป็น model ที่มีประทธิภาพ\n",
    "# model 4 is the best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.LinearRegression() #computer understamd that you want to use regression model #บอกให้ computer รู้ว่าเราจะใช้ linearregression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "atemp_lr_model=linear_model.LinearRegression() #change name, in oder to compare the result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'yr', 'mnth', 'hr', 'holiday', 'weekday', 'workingday',\n",
       "       'weathersit', 'temp', 'atemp', 'hum', 'windspeed', 'casual',\n",
       "       'registered', 'log_cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = atemp_lr_model.fit(X[[\"atemp\",\"weathersit\"]], y) #you want to regress it by fit comand , y is target variables\n",
    "                                                # 1 square backet= series\n",
    "    # ช่องนี้ประมาณกำหนดตัวแปร x คือ sqft_living และ y คือ target variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2629008198227805"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.intercept_ #หาB0 ส่าคือเท่าไหร่"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.235491310904151"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.coef_[0] #หา  B1\n",
    "#when atemp is increase by 1 celcius ,the count of renatl bike increase by 3.2345%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.18675929750005135"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.coef_[1] #หา  B2\n",
    "#when weather sit increase by 1 condition  the count of rental decreased by -0.186% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: log(cnt) = 3.2629 + 3.2355 atemp +(-0.1868) weathersit\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "from sklearn import linear_model\n",
    "\n",
    "# construct the model instance\n",
    "atemp_lr_model = linear_model.LinearRegression()\n",
    "\n",
    "# fit the model\n",
    "model1 = atemp_lr_model.fit(X[[\"atemp\",\"weathersit\"]], y) # be careful\n",
    "\n",
    "# print the coefficients\n",
    "beta_0 = atemp_lr_model.intercept_\n",
    "beta_1 = atemp_lr_model.coef_[0]\n",
    "beat_2 = atemp_lr_model.coef_[1]\n",
    "\n",
    "print(f\"Fit model: log(cnt) = {beta_0:.4f} + {beta_1:.4f} atemp +({beat_2:.4f}) weathersit\") #.4 decimals you have to use 0.0004 multipier by 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Putter\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "X has 1 features, but LinearRegression is expecting 15 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [119]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#prediction when atemp equal to 30 celcius and weather sit is 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m logp_30 \u001b[38;5;241m=\u001b[39m \u001b[43matemp_lr_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m      4\u001b[0m np\u001b[38;5;241m.\u001b[39mexp(logp_30[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:362\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    349\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    350\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[0;32m    351\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    360\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[0;32m    361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:345\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decision_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    343\u001b[0m     check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 345\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:585\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    582\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 585\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_n_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:400\u001b[0m, in \u001b[0;36mBaseEstimator._check_n_features\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_features \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_:\n\u001b[1;32m--> 400\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    401\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_features\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features, but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    402\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis expecting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features as input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    403\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: X has 1 features, but LinearRegression is expecting 15 features as input."
     ]
    }
   ],
   "source": [
    "#prediction when atemp equal to 30 celcius and weather sit is 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\n",
    "logp_30 = atemp_lr_model.predict([[30],[4]])[0]\n",
    "np.exp(logp_30[0]) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 19 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      " 17  log_cnt     17379 non-null  float64\n",
      " 18  const       17379 non-null  int64  \n",
      "dtypes: float64(5), int64(13), object(1)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.633161120788967e-15 MAE_test\n",
      "4.655991952786404e-15 MAE_train\n"
     ]
    }
   ],
   "source": [
    "#model1\n",
    "model1 = atemp_lr_model.fit(X_train,y_train)\n",
    "\n",
    "#Evaluation of the model\n",
    "MAE_test = metrics.mean_absolute_error(y_test,model1.predict(X_test))\n",
    "\n",
    "MAE_train= metrics.mean_absolute_error(y_train,model1.predict(X_train))\n",
    "print(MAE_test,'MAE_test')\n",
    "\n",
    "print(MAE_train,'MAE_train') #MAEtrain>test becasue the model is underfitting MAE train should be smaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.170550672411329e-15"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(metrics.mean_squared_error(y, atemp_lr_model.predict(X)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.3 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last excercise, using all features which are\n",
    "- holiday : weather day is holiday or not \n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_all = atemp_lr_model.fit(X[[\"holiday\",\"weekday\",\"workingday\",\"weathersit\",\"temp\",\"atemp\",\"hum\",\"windspeed\"]], y) #you want to regress it by fit comand , y is target variables\n",
    "                                                # 1 square backet= series\n",
    "    # ช่องนี้ประมาณกำหนดตัวแปร x คือ sqft_living และ y คือ target variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import\n",
    "from sklearn import linear_model\n",
    "\n",
    "# construct the model instance\n",
    "atemp_lr_model = linear_model.LinearRegression()\n",
    "\n",
    "# fit the model\n",
    "model_all = atemp_lr_model.fit(X[[\"holiday\",\"weekday\",\"workingday\",\"weathersit\",\"temp\",\"atemp\",\"hum\",\"windspeed\"]], y) # be careful\n",
    "\n",
    "# print the coefficients\n",
    "beta_0 = atemp_lr_model.intercept_\n",
    "beta_1 = atemp_lr_model.coef_[0]\n",
    "beat_2 = atemp_lr_model.coef_[1]\n",
    "beta_3 = atemp_lr_model.coef_[2]\n",
    "beat_4 = atemp_lr_model.coef_[3]\n",
    "beta_5 = atemp_lr_model.coef_[4]\n",
    "beat_6 = atemp_lr_model.coef_[5]\n",
    "beta_7 = atemp_lr_model.coef_[6]\n",
    "beat_8 = atemp_lr_model.coef_[7]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Putter\\anaconda3\\lib\\site-packages\\sklearn\\base.py:493: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names unseen at fit time:\n",
      "- casual\n",
      "- hr\n",
      "- log_cnt\n",
      "- mnth\n",
      "- registered\n",
      "- ...\n",
      "Feature names must be in the same order as they were in fit.\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "X has 15 features, but LinearRegression is expecting 8 features as input.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [125]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m model_all \u001b[38;5;241m=\u001b[39m atemp_lr_model\u001b[38;5;241m.\u001b[39mfit(X_train,y_train)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m#Evaluation of the model\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m MAE_test \u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mmean_absolute_error(y_test,\u001b[43mmodel1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m      7\u001b[0m MAE_train\u001b[38;5;241m=\u001b[39m metrics\u001b[38;5;241m.\u001b[39mmean_absolute_error(y_train,model1\u001b[38;5;241m.\u001b[39mpredict(X_train))\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(MAE_test,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMAE_test\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:362\u001b[0m, in \u001b[0;36mLinearModel.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    349\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    350\u001b[0m \u001b[38;5;124;03m    Predict using the linear model.\u001b[39;00m\n\u001b[0;32m    351\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    360\u001b[0m \u001b[38;5;124;03m        Returns predicted values.\u001b[39;00m\n\u001b[0;32m    361\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 362\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_decision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:345\u001b[0m, in \u001b[0;36mLinearModel._decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    342\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_decision_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    343\u001b[0m     check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m--> 345\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:585\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    582\u001b[0m     out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[0;32m    584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 585\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_n_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:400\u001b[0m, in \u001b[0;36mBaseEstimator._check_n_features\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_features \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_:\n\u001b[1;32m--> 400\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    401\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_features\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features, but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    402\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis expecting \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_features_in_\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features as input.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    403\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: X has 15 features, but LinearRegression is expecting 8 features as input."
     ]
    }
   ],
   "source": [
    "#model1\n",
    "model_all = atemp_lr_model.fit(X_train,y_train)\n",
    "\n",
    "#Evaluation of the model\n",
    "MAE_test = metrics.mean_absolute_error(y_test,model1.predict(X_test))\n",
    "\n",
    "MAE_train= metrics.mean_absolute_error(y_train,model1.predict(X_train))\n",
    "print(MAE_test,'MAE_test')\n",
    "\n",
    "print(MAE_train,'MAE_train') #MAEtrain>test becasue the model is underfitting MAE train should be smaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.170550672411329e-15"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(metrics.mean_squared_error(y, model_all.predict(X)))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.4 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#ANS we should use only two feature in this model because if we use all model. it will be over fitting our models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.5 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The umemployment rate in Apr is 14.8\n",
      "The umemployment rate in Apr is 13.3\n",
      "The umemployment rate in Apr is 11.1\n",
      "The umemployment rate in Apr is 10.2\n",
      "The umemployment rate in Apr is 8.4\n"
     ]
    }
   ],
   "source": [
    "for i in unemp:\n",
    "    if i > 8 : \n",
    "        print(\"The umemployment rate in Apr is\", i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The answer is 1307.3320269308394\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "summation = 0\n",
    "log1 = range(2,501,2)\n",
    "sumation = 0\n",
    "for i in log1:\n",
    "    summation = summation + np.log(i)\n",
    "    if i > 500:\n",
    "        break\n",
    "\n",
    "print(\"The answer is\",summation ) #use break\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1307.3320269308392"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.log(range(2,501,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here is the space for your codes"
   ]
  }
 ],
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