PK_final_project-Final.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "# This makes sure that graphs render in your notebook.\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Data Collection: I pulled company data using SQL and pre-processed in Excel to confuscate the data so it's not representative of our company data. Here, I pulled interaction channels (Email, Phone, Chat, SMS) grouped by day and hour, and multiplied all counts by a formula to arrive at an inflated, but non-representative count.  I think passed off to Python!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('interactions_summary_20180112.csv', na_values='0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CX Calls Call Started Date</th>\n",
       "      <th>CX Calls Call Started Hour of Day</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>17767</th>\n",
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       "      <td>669.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1365.0</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "      CX Calls Call Started Date  CX Calls Call Started Hour of Day  calls  \\\n",
       "17767                  1/10/2018                               20.0  669.0   \n",
       "17768                  1/10/2018                               21.0  189.0   \n",
       "17769                  1/10/2018                               22.0   81.0   \n",
       "17770                  1/10/2018                               23.0   17.0   \n",
       "17771                  1/11/2018                                2.0    NaN   \n",
       "\n",
       "       emails  chats   sms  orders  \n",
       "17767   709.0  209.0  13.0  1365.0  \n",
       "17768   553.0   49.0  17.0  1061.0  \n",
       "17769   397.0    NaN   1.0   721.0  \n",
       "17770   301.0    NaN   9.0   377.0  \n",
       "17771    69.0    NaN   NaN     NaN  "
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[:17772]\n",
    "df.tail()\n",
    "#Exploratory data analysis and Data Cleaning.  \"The quality of your data determines the effectiveness of your models\"\n",
    "#Removing footer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'CX Calls Call Started Date', u'CX Calls Call Started Hour of Day',\n",
       "       u'calls', u'emails', u'chats', u'sms', u'orders'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#rename columns\n",
    "newcols={'CX Calls Call Started Date':'date','CX Calls Call Started Hour of Day':'hour_of_day','calls':'calls','emails':'emails','chats':'chats','sms':'sms','orders':'orders'}\n",
    "df = df.rename(columns = newcols)"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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       "      <th>chats</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>605.0</td>\n",
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      "text/plain": [
       "       date  hour_of_day  calls  emails  chats  sms  orders      date2\n",
       "0  1/1/2016         16.0    NaN   877.0    NaN  NaN   725.0 2016-01-01\n",
       "1  1/1/2016          8.0    NaN   361.0    NaN  NaN    81.0 2016-01-01\n",
       "2  1/1/2016          7.0    NaN    65.0    NaN  NaN     9.0 2016-01-01\n",
       "3  1/1/2016         19.0    NaN   501.0    NaN  NaN   357.0 2016-01-01\n",
       "4  1/1/2016         15.0    NaN   889.0    NaN  NaN   605.0 2016-01-01"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change date to datetime, getting rid of crappy excel formatting\n",
    "df['date2'] = pd.to_datetime(df['date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "      <th>emails</th>\n",
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       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>889.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2016-01-01</td>\n",
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      "text/plain": [
       "   hour_of_day  calls  emails  chats  sms  orders       date\n",
       "0         16.0    NaN   877.0    NaN  NaN   725.0 2016-01-01\n",
       "1          8.0    NaN   361.0    NaN  NaN    81.0 2016-01-01\n",
       "2          7.0    NaN    65.0    NaN  NaN     9.0 2016-01-01\n",
       "3         19.0    NaN   501.0    NaN  NaN   357.0 2016-01-01\n",
       "4         15.0    NaN   889.0    NaN  NaN   605.0 2016-01-01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove old date column, rename new date columns\n",
    "df = df.drop(['date'],axis=1)\n",
    "df = df.rename(columns = {'date2':'date'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
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       "      <th>17769</th>\n",
       "      <td>22.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2018-01-10</td>\n",
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       "      <th>17770</th>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>327.0</td>\n",
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       "      <th>17771</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>69.0</td>\n",
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      ],
      "text/plain": [
       "       hour_of_day  calls  emails  chats   sms  orders       date  \\\n",
       "17767         20.0  669.0   709.0  209.0  13.0  1365.0 2018-01-10   \n",
       "17768         21.0  189.0   553.0   49.0  17.0  1061.0 2018-01-10   \n",
       "17769         22.0   81.0   397.0    NaN   1.0   721.0 2018-01-10   \n",
       "17770         23.0   17.0   301.0    NaN   9.0   377.0 2018-01-10   \n",
       "17771          2.0    NaN    69.0    NaN   NaN     NaN 2018-01-11   \n",
       "\n",
       "       interactions  \n",
       "17767        1600.0  \n",
       "17768         808.0  \n",
       "17769         479.0  \n",
       "17770         327.0  \n",
       "17771          69.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I know I'm going to want sum of interactions, so I do a little bit of feature engineering up front\n",
    "df['interactions'] = df[['calls','emails','chats','sms']].sum(axis=1)\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# how to pull 1 week later interactions.  new_columns = ['date', 'interactions', 'orders']\n",
    "# est_interactions_week = interactions at 'date'+7 days. \n",
    "# est_interactions_month = interactions at 'date' + 28 days\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>889.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>605.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>889.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   calls  emails  chats  sms  orders       date  interactions\n",
       "0    NaN   877.0    NaN  NaN   725.0 2016-01-01         877.0\n",
       "1    NaN   361.0    NaN  NaN    81.0 2016-01-01         361.0\n",
       "2    NaN    65.0    NaN  NaN     9.0 2016-01-01          65.0\n",
       "3    NaN   501.0    NaN  NaN   357.0 2016-01-01         501.0\n",
       "4    NaN   889.0    NaN  NaN   605.0 2016-01-01         889.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After doing a lot of Exploratory data analysis (in other notebook), I decide to drop hour_of_day and focus more on weekly trends\n",
    "df2 = df.copy()\n",
    "df2 = df2.drop(['hour_of_day'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>emails</th>\n",
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       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>725.0</td>\n",
       "      <td>877.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0</td>\n",
       "      <td>361.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>65.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>357.0</td>\n",
       "      <td>501.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>889.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>605.0</td>\n",
       "      <td>889.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            calls  emails  chats  sms  orders  interactions\n",
       "date                                                       \n",
       "2016-01-01    NaN   877.0    NaN  NaN   725.0         877.0\n",
       "2016-01-01    NaN   361.0    NaN  NaN    81.0         361.0\n",
       "2016-01-01    NaN    65.0    NaN  NaN     9.0          65.0\n",
       "2016-01-01    NaN   501.0    NaN  NaN   357.0         501.0\n",
       "2016-01-01    NaN   889.0    NaN  NaN   605.0         889.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.set_index('date', inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11152.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6456.0</td>\n",
       "      <td>11166.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16592.0</td>\n",
       "      <td>16961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17544.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>18308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50848.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14444.0</td>\n",
       "      <td>53605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>38604.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14183.0</td>\n",
       "      <td>41188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            calls   emails   chats  sms   orders  interactions\n",
       "date                                                          \n",
       "2016-01-01    0.0  11152.0    14.0  0.0   6456.0       11166.0\n",
       "2016-01-02    0.0  16000.0   961.0  0.0  16592.0       16961.0\n",
       "2016-01-03    0.0  17544.0   764.0  0.0  12735.0       18308.0\n",
       "2016-01-04    0.0  50848.0  2757.0  0.0  14444.0       53605.0\n",
       "2016-01-05    0.0  38604.0  2584.0  0.0  14183.0       41188.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.groupby(by='date').sum()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>week</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-23</th>\n",
       "      <td>10605.0</td>\n",
       "      <td>21616.0</td>\n",
       "      <td>1724.0</td>\n",
       "      <td>4262.0</td>\n",
       "      <td>27064.0</td>\n",
       "      <td>38207.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>357</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>5364.0</td>\n",
       "      <td>11596.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>13132.0</td>\n",
       "      <td>17816.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>358</td>\n",
       "      <td>6</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>783.0</td>\n",
       "      <td>14328.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>4316.0</td>\n",
       "      <td>15887.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>359</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>18572.0</td>\n",
       "      <td>41092.0</td>\n",
       "      <td>2710.0</td>\n",
       "      <td>4329.0</td>\n",
       "      <td>30776.0</td>\n",
       "      <td>66703.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
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       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>21059.0</td>\n",
       "      <td>54500.0</td>\n",
       "      <td>4117.0</td>\n",
       "      <td>8823.0</td>\n",
       "      <td>36016.0</td>\n",
       "      <td>88499.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>20560.0</td>\n",
       "      <td>54056.0</td>\n",
       "      <td>3586.0</td>\n",
       "      <td>9295.0</td>\n",
       "      <td>38052.0</td>\n",
       "      <td>87497.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>362</td>\n",
       "      <td>3</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>23062.0</td>\n",
       "      <td>54492.0</td>\n",
       "      <td>3209.0</td>\n",
       "      <td>8450.0</td>\n",
       "      <td>41648.0</td>\n",
       "      <td>89213.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>363</td>\n",
       "      <td>4</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>17461.0</td>\n",
       "      <td>38320.0</td>\n",
       "      <td>2762.0</td>\n",
       "      <td>6943.0</td>\n",
       "      <td>42584.0</td>\n",
       "      <td>65486.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>364</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>14166.0</td>\n",
       "      <td>28452.0</td>\n",
       "      <td>2201.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>32424.0</td>\n",
       "      <td>45197.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>365</td>\n",
       "      <td>6</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>8320.0</td>\n",
       "      <td>25236.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>13164.0</td>\n",
       "      <td>35487.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>22920.0</td>\n",
       "      <td>52476.0</td>\n",
       "      <td>4025.0</td>\n",
       "      <td>4136.0</td>\n",
       "      <td>26624.0</td>\n",
       "      <td>83557.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>17917.0</td>\n",
       "      <td>51640.0</td>\n",
       "      <td>3641.0</td>\n",
       "      <td>8547.0</td>\n",
       "      <td>24884.0</td>\n",
       "      <td>81745.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>16933.0</td>\n",
       "      <td>39624.0</td>\n",
       "      <td>3557.0</td>\n",
       "      <td>9748.0</td>\n",
       "      <td>20916.0</td>\n",
       "      <td>69862.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>18967.0</td>\n",
       "      <td>41340.0</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>11877.0</td>\n",
       "      <td>25216.0</td>\n",
       "      <td>75341.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06</th>\n",
       "      <td>11121.0</td>\n",
       "      <td>21748.0</td>\n",
       "      <td>2069.0</td>\n",
       "      <td>7088.0</td>\n",
       "      <td>32016.0</td>\n",
       "      <td>42026.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-07</th>\n",
       "      <td>6847.0</td>\n",
       "      <td>17608.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>23592.0</td>\n",
       "      <td>29166.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>20112.0</td>\n",
       "      <td>48720.0</td>\n",
       "      <td>3618.0</td>\n",
       "      <td>7974.0</td>\n",
       "      <td>21184.0</td>\n",
       "      <td>80424.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>19138.0</td>\n",
       "      <td>54388.0</td>\n",
       "      <td>4161.0</td>\n",
       "      <td>7324.0</td>\n",
       "      <td>24316.0</td>\n",
       "      <td>85011.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>17505.0</td>\n",
       "      <td>30644.0</td>\n",
       "      <td>3805.0</td>\n",
       "      <td>7194.0</td>\n",
       "      <td>25152.0</td>\n",
       "      <td>59148.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              calls   emails   chats      sms   orders  interactions  year  \\\n",
       "date                                                                         \n",
       "2017-12-23  10605.0  21616.0  1724.0   4262.0  27064.0       38207.0  2017   \n",
       "2017-12-24   5364.0  11596.0   607.0    249.0  13132.0       17816.0  2017   \n",
       "2017-12-25    783.0  14328.0     0.0    776.0   4316.0       15887.0  2017   \n",
       "2017-12-26  18572.0  41092.0  2710.0   4329.0  30776.0       66703.0  2017   \n",
       "2017-12-27  21059.0  54500.0  4117.0   8823.0  36016.0       88499.0  2017   \n",
       "2017-12-28  20560.0  54056.0  3586.0   9295.0  38052.0       87497.0  2017   \n",
       "2017-12-29  23062.0  54492.0  3209.0   8450.0  41648.0       89213.0  2017   \n",
       "2017-12-30  17461.0  38320.0  2762.0   6943.0  42584.0       65486.0  2017   \n",
       "2017-12-31  14166.0  28452.0  2201.0    378.0  32424.0       45197.0  2017   \n",
       "2018-01-01   8320.0  25236.0  1131.0    800.0  13164.0       35487.0  2018   \n",
       "2018-01-02  22920.0  52476.0  4025.0   4136.0  26624.0       83557.0  2018   \n",
       "2018-01-03  17917.0  51640.0  3641.0   8547.0  24884.0       81745.0  2018   \n",
       "2018-01-04  16933.0  39624.0  3557.0   9748.0  20916.0       69862.0  2018   \n",
       "2018-01-05  18967.0  41340.0  3157.0  11877.0  25216.0       75341.0  2018   \n",
       "2018-01-06  11121.0  21748.0  2069.0   7088.0  32016.0       42026.0  2018   \n",
       "2018-01-07   6847.0  17608.0  1852.0   2859.0  23592.0       29166.0  2018   \n",
       "2018-01-08  20112.0  48720.0  3618.0   7974.0  21184.0       80424.0  2018   \n",
       "2018-01-09  19138.0  54388.0  4161.0   7324.0  24316.0       85011.0  2018   \n",
       "2018-01-10  17505.0  30644.0  3805.0   7194.0  25152.0       59148.0  2018   \n",
       "2018-01-11      0.0     69.0     0.0      0.0      0.0          69.0  2018   \n",
       "\n",
       "            month  day_of_year  weekday  week  \n",
       "date                                           \n",
       "2017-12-23     12          357        5    51  \n",
       "2017-12-24     12          358        6    51  \n",
       "2017-12-25     12          359        0    52  \n",
       "2017-12-26     12          360        1    52  \n",
       "2017-12-27     12          361        2    52  \n",
       "2017-12-28     12          362        3    52  \n",
       "2017-12-29     12          363        4    52  \n",
       "2017-12-30     12          364        5    52  \n",
       "2017-12-31     12          365        6    52  \n",
       "2018-01-01      1            1        0     1  \n",
       "2018-01-02      1            2        1     1  \n",
       "2018-01-03      1            3        2     1  \n",
       "2018-01-04      1            4        3     1  \n",
       "2018-01-05      1            5        4     1  \n",
       "2018-01-06      1            6        5     1  \n",
       "2018-01-07      1            7        6     1  \n",
       "2018-01-08      1            8        0     2  \n",
       "2018-01-09      1            9        1     2  \n",
       "2018-01-10      1           10        2     2  \n",
       "2018-01-11      1           11        3     2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Engineering!\n",
    "#Created features based of datetime \n",
    "df2['year'] = df2.index.year\n",
    "df2['month'] = df2.index.month\n",
    "df2['day_of_year'] = df2.index.dayofyear\n",
    "df2['weekday'] = df2.index.weekday\n",
    "df2['week'] = df2.index.week\n",
    "df2.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qLCJBshf80mg0BWK8/hoSHod4DDwWqqYe5Q8W3CnPFvH21YS5Kq2ztj0WjI2mLxgJO9ZO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\nJo3T5Wq9Zhvjy7buvrefRfg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UHNW4MC3lSGrnnctbVLawzfMWj04bRmxt2YyKRVNqrptg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0bf290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='day_of_year', y='interactions', data=df2,\n",
    "          fit_reg=False, hue='year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VuRpBvlQgrkQEYUAcRfSv6lzSvq1kaFpMT+SYsTSebmr8CLt2HaIj4xX9qjF0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\nLcBrgM+69wJeQTofHKSzwLzOrV/t3uO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ZL1qHC2NaQtwwMk1iisUzqc/dC9CWC1DcZA65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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a111d1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot(kind='scatter', x='weekday', y='interactions', c='year', alpha=0.3,  colormap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>date</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11152.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6456.0</td>\n",
       "      <td>11166.0</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16592.0</td>\n",
       "      <td>16961.0</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17544.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>18308.0</td>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50848.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14444.0</td>\n",
       "      <td>53605.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38604.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14183.0</td>\n",
       "      <td>41188.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  calls   emails   chats  sms   orders  interactions  week  \\\n",
       "0 2016-01-01    0.0  11152.0    14.0  0.0   6456.0       11166.0    53   \n",
       "1 2016-01-02    0.0  16000.0   961.0  0.0  16592.0       16961.0    53   \n",
       "2 2016-01-03    0.0  17544.0   764.0  0.0  12735.0       18308.0    53   \n",
       "3 2016-01-04    0.0  50848.0  2757.0  0.0  14444.0       53605.0     1   \n",
       "4 2016-01-05    0.0  38604.0  2584.0  0.0  14183.0       41188.0     1   \n",
       "\n",
       "   day_of_year  year  month  weekday  \n",
       "0            1  2016      1        4  \n",
       "1            2  2016      1        5  \n",
       "2            3  2016      1        6  \n",
       "3            4  2016      1        0  \n",
       "4            5  2016      1        1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.copy()\n",
    "df3 = df3.drop(['hour_of_day'], axis=1).groupby(by='date').sum()\n",
    "df3['week'] = df3.index.week\n",
    "df3.reset_index(level=0, inplace=True)\n",
    "# df3 = df3.set_index('week')\n",
    "df3['day_of_year'] = pd.to_datetime(df3['date']).apply(lambda x: x.dayofyear)\n",
    "df3['year'] = pd.to_datetime(df3['date']).apply(lambda x: x.year)\n",
    "df3['month'] = pd.to_datetime(df3['date']).apply(lambda x: x.month)\n",
    "df3['weekday'] = pd.to_datetime(df3['date']).apply(lambda x: x.dayofweek)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>15188.0</td>\n",
       "      <td>43468.0</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>3648.0</td>\n",
       "      <td>20280.0</td>\n",
       "      <td>65445.0</td>\n",
       "      <td>31</td>\n",
       "      <td>213</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>13740.0</td>\n",
       "      <td>43316.0</td>\n",
       "      <td>2847.0</td>\n",
       "      <td>4530.0</td>\n",
       "      <td>18952.0</td>\n",
       "      <td>64433.0</td>\n",
       "      <td>31</td>\n",
       "      <td>214</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2017-08-03</td>\n",
       "      <td>14669.0</td>\n",
       "      <td>37808.0</td>\n",
       "      <td>2849.0</td>\n",
       "      <td>5910.0</td>\n",
       "      <td>19244.0</td>\n",
       "      <td>61236.0</td>\n",
       "      <td>31</td>\n",
       "      <td>215</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>14796.0</td>\n",
       "      <td>32528.0</td>\n",
       "      <td>2428.0</td>\n",
       "      <td>4619.0</td>\n",
       "      <td>20220.0</td>\n",
       "      <td>54371.0</td>\n",
       "      <td>31</td>\n",
       "      <td>216</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2017-08-05</td>\n",
       "      <td>8350.0</td>\n",
       "      <td>16748.0</td>\n",
       "      <td>1773.0</td>\n",
       "      <td>4347.0</td>\n",
       "      <td>24028.0</td>\n",
       "      <td>31218.0</td>\n",
       "      <td>31</td>\n",
       "      <td>217</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>2017-08-06</td>\n",
       "      <td>4899.0</td>\n",
       "      <td>13212.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>5360.0</td>\n",
       "      <td>19568.0</td>\n",
       "      <td>24827.0</td>\n",
       "      <td>31</td>\n",
       "      <td>218</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>2017-08-07</td>\n",
       "      <td>17177.0</td>\n",
       "      <td>54216.0</td>\n",
       "      <td>3033.0</td>\n",
       "      <td>6298.0</td>\n",
       "      <td>19336.0</td>\n",
       "      <td>80724.0</td>\n",
       "      <td>32</td>\n",
       "      <td>219</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>2017-08-08</td>\n",
       "      <td>15977.0</td>\n",
       "      <td>44644.0</td>\n",
       "      <td>3384.0</td>\n",
       "      <td>3287.0</td>\n",
       "      <td>20992.0</td>\n",
       "      <td>67292.0</td>\n",
       "      <td>32</td>\n",
       "      <td>220</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2017-08-09</td>\n",
       "      <td>15237.0</td>\n",
       "      <td>41068.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>4993.0</td>\n",
       "      <td>19972.0</td>\n",
       "      <td>64348.0</td>\n",
       "      <td>32</td>\n",
       "      <td>221</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>14947.0</td>\n",
       "      <td>35848.0</td>\n",
       "      <td>2712.0</td>\n",
       "      <td>3238.0</td>\n",
       "      <td>19200.0</td>\n",
       "      <td>56745.0</td>\n",
       "      <td>32</td>\n",
       "      <td>222</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>16557.0</td>\n",
       "      <td>34332.0</td>\n",
       "      <td>2753.0</td>\n",
       "      <td>3016.0</td>\n",
       "      <td>20988.0</td>\n",
       "      <td>56658.0</td>\n",
       "      <td>32</td>\n",
       "      <td>223</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>10025.0</td>\n",
       "      <td>19320.0</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>23952.0</td>\n",
       "      <td>34353.0</td>\n",
       "      <td>32</td>\n",
       "      <td>224</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>2017-08-13</td>\n",
       "      <td>8289.0</td>\n",
       "      <td>14292.0</td>\n",
       "      <td>1677.0</td>\n",
       "      <td>2226.0</td>\n",
       "      <td>19251.0</td>\n",
       "      <td>26484.0</td>\n",
       "      <td>32</td>\n",
       "      <td>225</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2017-08-14</td>\n",
       "      <td>29127.0</td>\n",
       "      <td>52160.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>3938.0</td>\n",
       "      <td>21040.0</td>\n",
       "      <td>88765.0</td>\n",
       "      <td>33</td>\n",
       "      <td>226</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>2017-08-15</td>\n",
       "      <td>37250.0</td>\n",
       "      <td>45148.0</td>\n",
       "      <td>3901.0</td>\n",
       "      <td>3857.0</td>\n",
       "      <td>21816.0</td>\n",
       "      <td>90156.0</td>\n",
       "      <td>33</td>\n",
       "      <td>227</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2017-08-19</td>\n",
       "      <td>60892.0</td>\n",
       "      <td>17592.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>2697.0</td>\n",
       "      <td>26248.0</td>\n",
       "      <td>83513.0</td>\n",
       "      <td>33</td>\n",
       "      <td>231</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>62831.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>2324.0</td>\n",
       "      <td>20872.0</td>\n",
       "      <td>82288.0</td>\n",
       "      <td>33</td>\n",
       "      <td>232</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>18349.0</td>\n",
       "      <td>48420.0</td>\n",
       "      <td>3758.0</td>\n",
       "      <td>3958.0</td>\n",
       "      <td>20744.0</td>\n",
       "      <td>74485.0</td>\n",
       "      <td>34</td>\n",
       "      <td>234</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2017-08-23</td>\n",
       "      <td>16886.0</td>\n",
       "      <td>41740.0</td>\n",
       "      <td>3769.0</td>\n",
       "      <td>3388.0</td>\n",
       "      <td>20812.0</td>\n",
       "      <td>65783.0</td>\n",
       "      <td>34</td>\n",
       "      <td>235</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>2017-08-24</td>\n",
       "      <td>14420.0</td>\n",
       "      <td>40384.0</td>\n",
       "      <td>3405.0</td>\n",
       "      <td>3697.0</td>\n",
       "      <td>19736.0</td>\n",
       "      <td>61906.0</td>\n",
       "      <td>34</td>\n",
       "      <td>236</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2017-08-25</td>\n",
       "      <td>14556.0</td>\n",
       "      <td>34976.0</td>\n",
       "      <td>3033.0</td>\n",
       "      <td>3520.0</td>\n",
       "      <td>21312.0</td>\n",
       "      <td>56085.0</td>\n",
       "      <td>34</td>\n",
       "      <td>237</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2017-08-26</td>\n",
       "      <td>8146.0</td>\n",
       "      <td>19524.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>3013.0</td>\n",
       "      <td>24352.0</td>\n",
       "      <td>32796.0</td>\n",
       "      <td>34</td>\n",
       "      <td>238</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2017-08-27</td>\n",
       "      <td>4681.0</td>\n",
       "      <td>14996.0</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>2408.0</td>\n",
       "      <td>19080.0</td>\n",
       "      <td>23918.0</td>\n",
       "      <td>34</td>\n",
       "      <td>239</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>18240.0</td>\n",
       "      <td>56780.0</td>\n",
       "      <td>3528.0</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>20024.0</td>\n",
       "      <td>82211.0</td>\n",
       "      <td>35</td>\n",
       "      <td>240</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>16298.0</td>\n",
       "      <td>46088.0</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>4006.0</td>\n",
       "      <td>19360.0</td>\n",
       "      <td>69793.0</td>\n",
       "      <td>35</td>\n",
       "      <td>241</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>2017-08-30</td>\n",
       "      <td>14104.0</td>\n",
       "      <td>40288.0</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>19656.0</td>\n",
       "      <td>60957.0</td>\n",
       "      <td>35</td>\n",
       "      <td>242</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>14107.0</td>\n",
       "      <td>38436.0</td>\n",
       "      <td>2973.0</td>\n",
       "      <td>3294.0</td>\n",
       "      <td>18624.0</td>\n",
       "      <td>58810.0</td>\n",
       "      <td>35</td>\n",
       "      <td>243</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date    calls   emails   chats     sms   orders  interactions  week  \\\n",
       "578 2017-08-01  15188.0  43468.0  3141.0  3648.0  20280.0       65445.0    31   \n",
       "579 2017-08-02  13740.0  43316.0  2847.0  4530.0  18952.0       64433.0    31   \n",
       "580 2017-08-03  14669.0  37808.0  2849.0  5910.0  19244.0       61236.0    31   \n",
       "581 2017-08-04  14796.0  32528.0  2428.0  4619.0  20220.0       54371.0    31   \n",
       "582 2017-08-05   8350.0  16748.0  1773.0  4347.0  24028.0       31218.0    31   \n",
       "583 2017-08-06   4899.0  13212.0  1356.0  5360.0  19568.0       24827.0    31   \n",
       "584 2017-08-07  17177.0  54216.0  3033.0  6298.0  19336.0       80724.0    32   \n",
       "585 2017-08-08  15977.0  44644.0  3384.0  3287.0  20992.0       67292.0    32   \n",
       "586 2017-08-09  15237.0  41068.0  3050.0  4993.0  19972.0       64348.0    32   \n",
       "587 2017-08-10  14947.0  35848.0  2712.0  3238.0  19200.0       56745.0    32   \n",
       "588 2017-08-11  16557.0  34332.0  2753.0  3016.0  20988.0       56658.0    32   \n",
       "589 2017-08-12  10025.0  19320.0  2308.0  2700.0  23952.0       34353.0    32   \n",
       "590 2017-08-13   8289.0  14292.0  1677.0  2226.0  19251.0       26484.0    32   \n",
       "591 2017-08-14  29127.0  52160.0  3540.0  3938.0  21040.0       88765.0    33   \n",
       "592 2017-08-15  37250.0  45148.0  3901.0  3857.0  21816.0       90156.0    33   \n",
       "596 2017-08-19  60892.0  17592.0  2332.0  2697.0  26248.0       83513.0    33   \n",
       "597 2017-08-20  62831.0  16500.0   633.0  2324.0  20872.0       82288.0    33   \n",
       "599 2017-08-22  18349.0  48420.0  3758.0  3958.0  20744.0       74485.0    34   \n",
       "600 2017-08-23  16886.0  41740.0  3769.0  3388.0  20812.0       65783.0    34   \n",
       "601 2017-08-24  14420.0  40384.0  3405.0  3697.0  19736.0       61906.0    34   \n",
       "602 2017-08-25  14556.0  34976.0  3033.0  3520.0  21312.0       56085.0    34   \n",
       "603 2017-08-26   8146.0  19524.0  2113.0  3013.0  24352.0       32796.0    34   \n",
       "604 2017-08-27   4681.0  14996.0  1833.0  2408.0  19080.0       23918.0    34   \n",
       "605 2017-08-28  18240.0  56780.0  3528.0  3663.0  20024.0       82211.0    35   \n",
       "606 2017-08-29  16298.0  46088.0  3401.0  4006.0  19360.0       69793.0    35   \n",
       "607 2017-08-30  14104.0  40288.0  3249.0  3316.0  19656.0       60957.0    35   \n",
       "608 2017-08-31  14107.0  38436.0  2973.0  3294.0  18624.0       58810.0    35   \n",
       "\n",
       "     day_of_year  year  month  weekday  \n",
       "578          213  2017      8        1  \n",
       "579          214  2017      8        2  \n",
       "580          215  2017      8        3  \n",
       "581          216  2017      8        4  \n",
       "582          217  2017      8        5  \n",
       "583          218  2017      8        6  \n",
       "584          219  2017      8        0  \n",
       "585          220  2017      8        1  \n",
       "586          221  2017      8        2  \n",
       "587          222  2017      8        3  \n",
       "588          223  2017      8        4  \n",
       "589          224  2017      8        5  \n",
       "590          225  2017      8        6  \n",
       "591          226  2017      8        0  \n",
       "592          227  2017      8        1  \n",
       "596          231  2017      8        5  \n",
       "597          232  2017      8        6  \n",
       "599          234  2017      8        1  \n",
       "600          235  2017      8        2  \n",
       "601          236  2017      8        3  \n",
       "602          237  2017      8        4  \n",
       "603          238  2017      8        5  \n",
       "604          239  2017      8        6  \n",
       "605          240  2017      8        0  \n",
       "606          241  2017      8        1  \n",
       "607          242  2017      8        2  \n",
       "608          243  2017      8        3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding and removing outliers caused by the eclipse\n",
    "df3 = df3.drop([593,594,595,598])\n",
    "df3[(df3['month']==8) & (df3['year']==2017)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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></th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11152.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6456.0</td>\n",
       "      <td>11166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16592.0</td>\n",
       "      <td>16961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>17544.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>18308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50848.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14444.0</td>\n",
       "      <td>53605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>38604.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14183.0</td>\n",
       "      <td>41188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  calls   emails   chats  sms   orders  interactions\n",
       "year day_of_year                                                    \n",
       "2016 1              0.0  11152.0    14.0  0.0   6456.0       11166.0\n",
       "     2              0.0  16000.0   961.0  0.0  16592.0       16961.0\n",
       "     3              0.0  17544.0   764.0  0.0  12735.0       18308.0\n",
       "     4              0.0  50848.0  2757.0  0.0  14444.0       53605.0\n",
       "     5              0.0  38604.0  2584.0  0.0  14183.0       41188.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearday = df3.drop(['date','month','weekday','week'],axis=1).groupby(by=['year','day_of_year']).sum()\n",
    "dfyearday.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>day_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11152.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6456.0</td>\n",
       "      <td>11166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16592.0</td>\n",
       "      <td>16961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17544.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12735.0</td>\n",
       "      <td>18308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50848.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14444.0</td>\n",
       "      <td>53605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38604.0</td>\n",
       "      <td>2584.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14183.0</td>\n",
       "      <td>41188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_of_year  year  calls   emails   chats  sms   orders  interactions\n",
       "0            1  2016    0.0  11152.0    14.0  0.0   6456.0       11166.0\n",
       "1            2  2016    0.0  16000.0   961.0  0.0  16592.0       16961.0\n",
       "2            3  2016    0.0  17544.0   764.0  0.0  12735.0       18308.0\n",
       "3            4  2016    0.0  50848.0  2757.0  0.0  14444.0       53605.0\n",
       "4            5  2016    0.0  38604.0  2584.0  0.0  14183.0       41188.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearday.reset_index(level=0, inplace=True)\n",
    "dfyearday.reset_index(level=0, inplace=True)\n",
    "dfyearday.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yqqZR2kqzEj4Itya6QmFh+fxIJPqj/4mQsvz9LHN+txQTdsU0o5VMPDW3bsu2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\njKIoilI3VMkoiqIodeP/Az1UysG9BlOoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19c88e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='day_of_year', y='interactions', data=dfyearday,\n",
    "          fit_reg=False, hue='year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j5wzDMExQkPHL9YqJS7lPl/GqWLhJl3K2cDONi9rCTdSnrwWXcttvVxq/bBeb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1LqRICSr0u4FILCSHU2aIkDNUOkY68PICfqNNd96O1S7th+Zckua80d3w3qfWu4mTTf1l4ptzyr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3G/1oAC2zlPffvLyIC0tN66S3ooDjx4/ba7wVDbhfH9qCRx99tPD59nk7gM3HCKpYJmS4w6ih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\n2LaZwZIkmWWWBUCYuGGQUKlKkCSJTKQyKIgodN1fudF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2CznhEm5JugcymkBSbo4XGiMl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XX37uksuiiUcavlKeX2/T8u2cUnm5Yso27/bI8RUMgve0Jqsa1z3nnnCdHoL++etNXkpqEL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\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sg0u/Zn5wouhc021PVLEpVo2BgVZ9bCqew8qwq5pv95zGzO3X8o9hh5gfbyW6Ivht/yrH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\n3BKJRCKRSCQSiUQikRwE/j+dSiZDinkTVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19e364d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfyearday[['calls', 'emails','chats','sms','orders']].plot(figsize=(15,7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TAKylAD98KJINyTMqJPdj5GKBabnWPb12rXtpWzrFZZy7PxNsJ96SNJLmmjAi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a14b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfyearmonth = df3.drop(['date','day_of_year','weekday','week'],axis=1).groupby(by=['year','month']).sum()\n",
    "\n",
    "dfyearmonth.reset_index(level=0, inplace=True)\n",
    "dfyearmonth.reset_index(level=0, inplace=True)\n",
    "\n",
    "sns.lmplot(x='month', y='interactions', data=dfyearmonth,\n",
    "          fit_reg=False, hue='year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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></th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>226788.0</td>\n",
       "      <td>13474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100215.0</td>\n",
       "      <td>240262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3705.0</td>\n",
       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90457.0</td>\n",
       "      <td>252286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             calls    emails    chats  sms    orders  interactions\n",
       "year week                                                         \n",
       "2016 1         0.0  226788.0  13474.0  0.0  100215.0      240262.0\n",
       "     2      3705.0  219524.0  14359.0  0.0   99338.0      237588.0\n",
       "     3     35242.0  202812.0  14232.0  0.0   90457.0      252286.0\n",
       "     4     35589.0  223544.0  17148.0  0.0  102816.0      276281.0\n",
       "     5     71026.0  218936.0  16596.0  0.0   97688.0      306558.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Daily became way to hectic, monthly was too sparse, so let's try Weekly!\n",
    "dfyearweek = df3.drop(['date','month','weekday','day_of_year'],axis=1).groupby(by=['year','week']).sum()\n",
    "dfyearweek.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226788.0</td>\n",
       "      <td>13474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100215.0</td>\n",
       "      <td>240262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>3705.0</td>\n",
       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90457.0</td>\n",
       "      <td>252286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  year    calls    emails    chats  sms    orders  interactions\n",
       "0     1  2016      0.0  226788.0  13474.0  0.0  100215.0      240262.0\n",
       "1     2  2016   3705.0  219524.0  14359.0  0.0   99338.0      237588.0\n",
       "2     3  2016  35242.0  202812.0  14232.0  0.0   90457.0      252286.0\n",
       "3     4  2016  35589.0  223544.0  17148.0  0.0  102816.0      276281.0\n",
       "4     5  2016  71026.0  218936.0  16596.0  0.0   97688.0      306558.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearweek.reset_index(level=0, inplace=True)\n",
    "dfyearweek.reset_index(level=0, inplace=True)\n",
    "dfyearweek.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>50</td>\n",
       "      <td>2017</td>\n",
       "      <td>105533.0</td>\n",
       "      <td>276124.0</td>\n",
       "      <td>20929.0</td>\n",
       "      <td>42088.0</td>\n",
       "      <td>185100.0</td>\n",
       "      <td>444674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>51</td>\n",
       "      <td>2017</td>\n",
       "      <td>105065.0</td>\n",
       "      <td>257024.0</td>\n",
       "      <td>18599.0</td>\n",
       "      <td>35586.0</td>\n",
       "      <td>181468.0</td>\n",
       "      <td>416274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>52</td>\n",
       "      <td>2017</td>\n",
       "      <td>122680.0</td>\n",
       "      <td>298180.0</td>\n",
       "      <td>18585.0</td>\n",
       "      <td>39050.0</td>\n",
       "      <td>235464.0</td>\n",
       "      <td>478495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>103025.0</td>\n",
       "      <td>249672.0</td>\n",
       "      <td>19432.0</td>\n",
       "      <td>45055.0</td>\n",
       "      <td>166412.0</td>\n",
       "      <td>417184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>133821.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>22492.0</td>\n",
       "      <td>70652.0</td>\n",
       "      <td>224652.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  year     calls    emails    chats      sms    orders  interactions\n",
       "102    50  2017  105533.0  276124.0  20929.0  42088.0  185100.0      444674.0\n",
       "103    51  2017  105065.0  257024.0  18599.0  35586.0  181468.0      416274.0\n",
       "104    52  2017  122680.0  298180.0  18585.0  39050.0  235464.0      478495.0\n",
       "105     1  2018  103025.0  249672.0  19432.0  45055.0  166412.0      417184.0\n",
       "106     2  2018   56755.0  133821.0  11584.0  22492.0   70652.0      224652.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearweek.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27Ys33njj25l3WTGzUEcuffIZlXxpgPYLCtCPmhH29SuZb9yTp9anHY9dxzt5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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a107490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='week', y='interactions', data=dfyearweek,\n",
    "          fit_reg=False, hue='year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G4cOHMW/ePLz22mu2c+h0OuzatQubNm3CvffeiwMHDuDo0aOYPn06/vznPwMA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eYfQii/HTgN/utkt6XPpDYRfHQ5YAHO39dYlagReXJ3NmRjiyct9ME/867MTe2aah3Rviqf0O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a107690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfyearweek[['calls', 'emails','chats','sms', 'interactions', 'orders']].plot(figsize=(15,7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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92bp1a5II5MvAMHUAAAAOs3PnzuzatSubN2/OunXrsnnz5uzatSs7d+4cdWmrgjAOAADA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19bcf250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfyearweek[['calls', 'emails','chats','sms','interactions', 'orders']].boxplot(figsize=(15,7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KfvVq4OKyv60at30PmAb+rEyvASbdp07ro43AM8A5PfvSn7hPLe1nlK+8P3E6c2b+Gjh+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\ng0WSVJXBIkmq6v8AIWUm4QM9uK4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a0b1110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfyearday.interactions.hist(bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#THIS SECTION NOW DATA MODELING\n",
    "\n",
    "#I have lots of other failed attempts you can see in the PK_final_project_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TRY TESTING OUT USING THIS WEEK TO PREDICT NEXT WEEK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226788.0</td>\n",
       "      <td>13474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100215.0</td>\n",
       "      <td>240262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>3705.0</td>\n",
       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90457.0</td>\n",
       "      <td>252286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  year    calls    emails    chats  sms    orders  interactions\n",
       "0     1  2016      0.0  226788.0  13474.0  0.0  100215.0      240262.0\n",
       "1     2  2016   3705.0  219524.0  14359.0  0.0   99338.0      237588.0\n",
       "2     3  2016  35242.0  202812.0  14232.0  0.0   90457.0      252286.0\n",
       "3     4  2016  35589.0  223544.0  17148.0  0.0  102816.0      276281.0\n",
       "4     5  2016  71026.0  218936.0  16596.0  0.0   97688.0      306558.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearweek.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing outliers caused by week 53\n",
    "dfyearweek[dfyearweek['week'] == 53]\n",
    "dfyearweek = dfyearweek.drop([52])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dURBpZ0Qy4dev7htz3apdMypD5mMqLbK1M1Z53apdM4CNIw0yvZqbm6tefvnl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\n/wE6YH14jBXRigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a271090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='week', y='interactions', data=dfyearweek,\n",
    "          fit_reg=False, hue='year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>50</td>\n",
       "      <td>2017</td>\n",
       "      <td>105533.0</td>\n",
       "      <td>276124.0</td>\n",
       "      <td>20929.0</td>\n",
       "      <td>42088.0</td>\n",
       "      <td>185100.0</td>\n",
       "      <td>444674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>51</td>\n",
       "      <td>2017</td>\n",
       "      <td>105065.0</td>\n",
       "      <td>257024.0</td>\n",
       "      <td>18599.0</td>\n",
       "      <td>35586.0</td>\n",
       "      <td>181468.0</td>\n",
       "      <td>416274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>52</td>\n",
       "      <td>2017</td>\n",
       "      <td>122680.0</td>\n",
       "      <td>298180.0</td>\n",
       "      <td>18585.0</td>\n",
       "      <td>39050.0</td>\n",
       "      <td>235464.0</td>\n",
       "      <td>478495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>103025.0</td>\n",
       "      <td>249672.0</td>\n",
       "      <td>19432.0</td>\n",
       "      <td>45055.0</td>\n",
       "      <td>166412.0</td>\n",
       "      <td>417184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>133821.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>22492.0</td>\n",
       "      <td>70652.0</td>\n",
       "      <td>224652.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  year     calls    emails    chats      sms    orders  interactions\n",
       "102    50  2017  105533.0  276124.0  20929.0  42088.0  185100.0      444674.0\n",
       "103    51  2017  105065.0  257024.0  18599.0  35586.0  181468.0      416274.0\n",
       "104    52  2017  122680.0  298180.0  18585.0  39050.0  235464.0      478495.0\n",
       "105     1  2018  103025.0  249672.0  19432.0  45055.0  166412.0      417184.0\n",
       "106     2  2018   56755.0  133821.0  11584.0  22492.0   70652.0      224652.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearweek.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>next_week_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226788.0</td>\n",
       "      <td>13474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100215.0</td>\n",
       "      <td>240262.0</td>\n",
       "      <td>237588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>3705.0</td>\n",
       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "      <td>252286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90457.0</td>\n",
       "      <td>252286.0</td>\n",
       "      <td>276281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "      <td>306558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
       "      <td>306909.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  year    calls    emails    chats  sms    orders  interactions  \\\n",
       "0     1  2016      0.0  226788.0  13474.0  0.0  100215.0      240262.0   \n",
       "1     2  2016   3705.0  219524.0  14359.0  0.0   99338.0      237588.0   \n",
       "2     3  2016  35242.0  202812.0  14232.0  0.0   90457.0      252286.0   \n",
       "3     4  2016  35589.0  223544.0  17148.0  0.0  102816.0      276281.0   \n",
       "4     5  2016  71026.0  218936.0  16596.0  0.0   97688.0      306558.0   \n",
       "\n",
       "   next_week_interactions  \n",
       "0                237588.0  \n",
       "1                252286.0  \n",
       "2                276281.0  \n",
       "3                306558.0  \n",
       "4                306909.0  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding a new column for next_week_interactions which pulls the next week's interactions\n",
    "#I am now going to use this as my target variable\n",
    "dfyearweek['next_week_interactions'] = dfyearweek['interactions'].drop([0]).reset_index().drop(['index'],axis=1)\n",
    "dfyearweek.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>next_week_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>50</td>\n",
       "      <td>2017</td>\n",
       "      <td>105533.0</td>\n",
       "      <td>276124.0</td>\n",
       "      <td>20929.0</td>\n",
       "      <td>42088.0</td>\n",
       "      <td>185100.0</td>\n",
       "      <td>444674.0</td>\n",
       "      <td>478495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>51</td>\n",
       "      <td>2017</td>\n",
       "      <td>105065.0</td>\n",
       "      <td>257024.0</td>\n",
       "      <td>18599.0</td>\n",
       "      <td>35586.0</td>\n",
       "      <td>181468.0</td>\n",
       "      <td>416274.0</td>\n",
       "      <td>417184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>52</td>\n",
       "      <td>2017</td>\n",
       "      <td>122680.0</td>\n",
       "      <td>298180.0</td>\n",
       "      <td>18585.0</td>\n",
       "      <td>39050.0</td>\n",
       "      <td>235464.0</td>\n",
       "      <td>478495.0</td>\n",
       "      <td>224652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>103025.0</td>\n",
       "      <td>249672.0</td>\n",
       "      <td>19432.0</td>\n",
       "      <td>45055.0</td>\n",
       "      <td>166412.0</td>\n",
       "      <td>417184.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>56755.0</td>\n",
       "      <td>133821.0</td>\n",
       "      <td>11584.0</td>\n",
       "      <td>22492.0</td>\n",
       "      <td>70652.0</td>\n",
       "      <td>224652.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  year     calls    emails    chats      sms    orders  interactions  \\\n",
       "102    50  2017  105533.0  276124.0  20929.0  42088.0  185100.0      444674.0   \n",
       "103    51  2017  105065.0  257024.0  18599.0  35586.0  181468.0      416274.0   \n",
       "104    52  2017  122680.0  298180.0  18585.0  39050.0  235464.0      478495.0   \n",
       "105     1  2018  103025.0  249672.0  19432.0  45055.0  166412.0      417184.0   \n",
       "106     2  2018   56755.0  133821.0  11584.0  22492.0   70652.0      224652.0   \n",
       "\n",
       "     next_week_interactions  \n",
       "102                478495.0  \n",
       "103                417184.0  \n",
       "104                224652.0  \n",
       "105                     NaN  \n",
       "106                     NaN  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfyearweek.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>next_week_interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>48</td>\n",
       "      <td>2017</td>\n",
       "      <td>94240.0</td>\n",
       "      <td>267192.0</td>\n",
       "      <td>20482.0</td>\n",
       "      <td>45821.0</td>\n",
       "      <td>158896.0</td>\n",
       "      <td>427735.0</td>\n",
       "      <td>444674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>49</td>\n",
       "      <td>2017</td>\n",
       "      <td>95048.0</td>\n",
       "      <td>259592.0</td>\n",
       "      <td>20355.0</td>\n",
       "      <td>40308.0</td>\n",
       "      <td>165016.0</td>\n",
       "      <td>415303.0</td>\n",
       "      <td>416274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>50</td>\n",
       "      <td>2017</td>\n",
       "      <td>105533.0</td>\n",
       "      <td>276124.0</td>\n",
       "      <td>20929.0</td>\n",
       "      <td>42088.0</td>\n",
       "      <td>185100.0</td>\n",
       "      <td>444674.0</td>\n",
       "      <td>478495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>51</td>\n",
       "      <td>2017</td>\n",
       "      <td>105065.0</td>\n",
       "      <td>257024.0</td>\n",
       "      <td>18599.0</td>\n",
       "      <td>35586.0</td>\n",
       "      <td>181468.0</td>\n",
       "      <td>416274.0</td>\n",
       "      <td>417184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>52</td>\n",
       "      <td>2017</td>\n",
       "      <td>122680.0</td>\n",
       "      <td>298180.0</td>\n",
       "      <td>18585.0</td>\n",
       "      <td>39050.0</td>\n",
       "      <td>235464.0</td>\n",
       "      <td>478495.0</td>\n",
       "      <td>224652.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     week  year     calls    emails    chats      sms    orders  interactions  \\\n",
       "100    48  2017   94240.0  267192.0  20482.0  45821.0  158896.0      427735.0   \n",
       "101    49  2017   95048.0  259592.0  20355.0  40308.0  165016.0      415303.0   \n",
       "102    50  2017  105533.0  276124.0  20929.0  42088.0  185100.0      444674.0   \n",
       "103    51  2017  105065.0  257024.0  18599.0  35586.0  181468.0      416274.0   \n",
       "104    52  2017  122680.0  298180.0  18585.0  39050.0  235464.0      478495.0   \n",
       "\n",
       "     next_week_interactions  \n",
       "100                444674.0  \n",
       "101                416274.0  \n",
       "102                478495.0  \n",
       "103                417184.0  \n",
       "104                224652.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the two weeks from 2018\n",
    "dfyearweek = dfyearweek.drop([105,106])\n",
    "dfyearweek.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a10a8a4d0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5sv76179q+fLl2rFjh0JDQ7V8+XJlZ2dr//79OnjwoPr166fFixdr7NixWr58+XWP/dVX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19e9ef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "\n",
    "# Compute the correlation matrix\n",
    "corr = dfyearweek.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>week</th>\n",
       "      <th>year</th>\n",
       "      <th>calls</th>\n",
       "      <th>emails</th>\n",
       "      <th>chats</th>\n",
       "      <th>sms</th>\n",
       "      <th>orders</th>\n",
       "      <th>interactions</th>\n",
       "      <th>next_week_interactions</th>\n",
       "      <th>week_2</th>\n",
       "      <th>...</th>\n",
       "      <th>week_43</th>\n",
       "      <th>week_44</th>\n",
       "      <th>week_45</th>\n",
       "      <th>week_46</th>\n",
       "      <th>week_47</th>\n",
       "      <th>week_48</th>\n",
       "      <th>week_49</th>\n",
       "      <th>week_50</th>\n",
       "      <th>week_51</th>\n",
       "      <th>week_52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226788.0</td>\n",
       "      <td>13474.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100215.0</td>\n",
       "      <td>240262.0</td>\n",
       "      <td>237588.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>3705.0</td>\n",
       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "      <td>252286.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90457.0</td>\n",
       "      <td>252286.0</td>\n",
       "      <td>276281.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "      <td>306558.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
       "      <td>306909.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  year    calls    emails    chats  sms    orders  interactions  \\\n",
       "0     1  2016      0.0  226788.0  13474.0  0.0  100215.0      240262.0   \n",
       "1     2  2016   3705.0  219524.0  14359.0  0.0   99338.0      237588.0   \n",
       "2     3  2016  35242.0  202812.0  14232.0  0.0   90457.0      252286.0   \n",
       "3     4  2016  35589.0  223544.0  17148.0  0.0  102816.0      276281.0   \n",
       "4     5  2016  71026.0  218936.0  16596.0  0.0   97688.0      306558.0   \n",
       "\n",
       "   next_week_interactions  week_2   ...     week_43  week_44  week_45  \\\n",
       "0                237588.0       0   ...           0        0        0   \n",
       "1                252286.0       1   ...           0        0        0   \n",
       "2                276281.0       0   ...           0        0        0   \n",
       "3                306558.0       0   ...           0        0        0   \n",
       "4                306909.0       0   ...           0        0        0   \n",
       "\n",
       "   week_46  week_47  week_48  week_49  week_50  week_51  week_52  \n",
       "0        0        0        0        0        0        0        0  \n",
       "1        0        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        0  \n",
       "3        0        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        0        0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dummies for weeks\n",
    "dummies_week = pd.get_dummies(dfyearweek.week, prefix='week')\n",
    "dummies_week.drop(dummies_week.columns[0], axis=1, inplace=True)\n",
    "\n",
    "# Concatenate the original DataFrame and the dummy DataFrame (axis=0 means rows, axis=1 means columns).\n",
    "dfyearweekdummies = pd.concat([dfyearweek, dummies_week], axis=1)\n",
    "\n",
    "# Print 5 random rows.\n",
    "dfyearweekdummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>year</th>\n",
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       "      <th>week_51</th>\n",
       "      <th>week_52</th>\n",
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       "      <th>0</th>\n",
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       "      <td>219524.0</td>\n",
       "      <td>14359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99338.0</td>\n",
       "      <td>237588.0</td>\n",
       "      <td>252286.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>35242.0</td>\n",
       "      <td>202812.0</td>\n",
       "      <td>14232.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>35589.0</td>\n",
       "      <td>223544.0</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102816.0</td>\n",
       "      <td>276281.0</td>\n",
       "      <td>306558.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>71026.0</td>\n",
       "      <td>218936.0</td>\n",
       "      <td>16596.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97688.0</td>\n",
       "      <td>306558.0</td>\n",
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       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   week  year    calls    emails    chats  sms    orders  interactions  \\\n",
       "0     1  2016      0.0  226788.0  13474.0  0.0  100215.0      240262.0   \n",
       "1     2  2016   3705.0  219524.0  14359.0  0.0   99338.0      237588.0   \n",
       "2     3  2016  35242.0  202812.0  14232.0  0.0   90457.0      252286.0   \n",
       "3     4  2016  35589.0  223544.0  17148.0  0.0  102816.0      276281.0   \n",
       "4     5  2016  71026.0  218936.0  16596.0  0.0   97688.0      306558.0   \n",
       "\n",
       "   next_week_interactions  week_2    ...      week_44  week_45  week_46  \\\n",
       "0                237588.0       0    ...            0        0        0   \n",
       "1                252286.0       1    ...            0        0        0   \n",
       "2                276281.0       0    ...            0        0        0   \n",
       "3                306558.0       0    ...            0        0        0   \n",
       "4                306909.0       0    ...            0        0        0   \n",
       "\n",
       "   week_47  week_48  week_49  week_50  week_51  week_52  year_2017  \n",
       "0        0        0        0        0        0        0          0  \n",
       "1        0        0        0        0        0        0          0  \n",
       "2        0        0        0        0        0        0          0  \n",
       "3        0        0        0        0        0        0          0  \n",
       "4        0        0        0        0        0        0          0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_year = pd.get_dummies(dfyearweek.year, prefix='year')\n",
    "dummies_year.drop(dummies_year.columns[0], axis=1, inplace=True)\n",
    "dfyearweekdummies = pd.concat([dfyearweekdummies, dummies_year], axis=1)\n",
    "dfyearweekdummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create feature matrix (X).\n",
    "feature_cols = ['year', 'orders', 'calls','emails','chats','sms', 'interactions','week', 'week_2', 'week_3', 'week_4', 'week_5', 'week_6','week_7', 'week_8', 'week_9', 'week_10', 'week_11', 'week_12', 'week_13', 'week_14', 'week_15', 'week_16', 'week_17', 'week_18', 'week_19', 'week_20', 'week_21', 'week_22', 'week_23', 'week_24', 'week_25', 'week_26', 'week_27', 'week_28', 'week_29', 'week_30', 'week_31', 'week_32', 'week_33', 'week_34', 'week_35', 'week_36', 'week_37', 'week_38', 'week_39', 'week_40', 'week_41', 'week_42', 'week_43', 'week_44', 'week_45', 'week_46', 'week_47','week_48','week_49','week_50','week_51','week_52']\n",
    "X = dfyearweekdummies[feature_cols]\n",
    "\n",
    "# Create response vector (y).\n",
    "y = dfyearweekdummies.next_week_interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>week_8</td>\n",
       "      <td>0.000355</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>week_22</td>\n",
       "      <td>0.000373</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>week_12</td>\n",
       "      <td>0.000392</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>week_29</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>week_11</td>\n",
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       "      <th>24</th>\n",
       "      <td>week_18</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>week_9</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>week_2</td>\n",
       "      <td>0.000631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>week_7</td>\n",
       "      <td>0.000728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>week_26</td>\n",
       "      <td>0.000737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>week_23</td>\n",
       "      <td>0.000811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>week_31</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>week_28</td>\n",
       "      <td>0.000845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>week_34</td>\n",
       "      <td>0.000856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>week_19</td>\n",
       "      <td>0.000918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>week_30</td>\n",
       "      <td>0.000918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>week_24</td>\n",
       "      <td>0.000949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>week_43</td>\n",
       "      <td>0.000980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>week_10</td>\n",
       "      <td>0.001036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>week_6</td>\n",
       "      <td>0.001036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>week_38</td>\n",
       "      <td>0.001093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>week_42</td>\n",
       "      <td>0.001129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>week_5</td>\n",
       "      <td>0.001243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>week_37</td>\n",
       "      <td>0.001269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>week_3</td>\n",
       "      <td>0.001275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>week_27</td>\n",
       "      <td>0.001286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>week_20</td>\n",
       "      <td>0.001306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>week_44</td>\n",
       "      <td>0.001358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>week_25</td>\n",
       "      <td>0.001440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>week_41</td>\n",
       "      <td>0.001447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>week_39</td>\n",
       "      <td>0.001492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>week_4</td>\n",
       "      <td>0.001605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>week_40</td>\n",
       "      <td>0.001708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>week_16</td>\n",
       "      <td>0.001709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>week_49</td>\n",
       "      <td>0.002148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>week_14</td>\n",
       "      <td>0.002210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>week_15</td>\n",
       "      <td>0.002373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>week_33</td>\n",
       "      <td>0.002507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>week_32</td>\n",
       "      <td>0.002719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>week_48</td>\n",
       "      <td>0.002889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>week_51</td>\n",
       "      <td>0.002977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>week_13</td>\n",
       "      <td>0.002978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>week_17</td>\n",
       "      <td>0.003192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>week_46</td>\n",
       "      <td>0.003782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>week_45</td>\n",
       "      <td>0.003899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>week_35</td>\n",
       "      <td>0.004718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>week_21</td>\n",
       "      <td>0.005044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>week_36</td>\n",
       "      <td>0.005135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>week_50</td>\n",
       "      <td>0.006639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>week_52</td>\n",
       "      <td>0.007084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>week_47</td>\n",
       "      <td>0.010274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>week</td>\n",
       "      <td>0.045466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sms</td>\n",
       "      <td>0.096968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>year</td>\n",
       "      <td>0.111272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>emails</td>\n",
       "      <td>0.119298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chats</td>\n",
       "      <td>0.123108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>calls</td>\n",
       "      <td>0.123348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>interactions</td>\n",
       "      <td>0.136817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orders</td>\n",
       "      <td>0.139179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  importance\n",
       "14        week_8    0.000355\n",
       "28       week_22    0.000373\n",
       "18       week_12    0.000392\n",
       "35       week_29    0.000474\n",
       "17       week_11    0.000528\n",
       "24       week_18    0.000588\n",
       "15        week_9    0.000624\n",
       "8         week_2    0.000631\n",
       "13        week_7    0.000728\n",
       "32       week_26    0.000737\n",
       "29       week_23    0.000811\n",
       "37       week_31    0.000841\n",
       "34       week_28    0.000845\n",
       "40       week_34    0.000856\n",
       "25       week_19    0.000918\n",
       "36       week_30    0.000918\n",
       "30       week_24    0.000949\n",
       "49       week_43    0.000980\n",
       "16       week_10    0.001036\n",
       "12        week_6    0.001036\n",
       "44       week_38    0.001093\n",
       "48       week_42    0.001129\n",
       "11        week_5    0.001243\n",
       "43       week_37    0.001269\n",
       "9         week_3    0.001275\n",
       "33       week_27    0.001286\n",
       "26       week_20    0.001306\n",
       "50       week_44    0.001358\n",
       "31       week_25    0.001440\n",
       "47       week_41    0.001447\n",
       "45       week_39    0.001492\n",
       "10        week_4    0.001605\n",
       "46       week_40    0.001708\n",
       "22       week_16    0.001709\n",
       "55       week_49    0.002148\n",
       "20       week_14    0.002210\n",
       "21       week_15    0.002373\n",
       "39       week_33    0.002507\n",
       "38       week_32    0.002719\n",
       "54       week_48    0.002889\n",
       "57       week_51    0.002977\n",
       "19       week_13    0.002978\n",
       "23       week_17    0.003192\n",
       "52       week_46    0.003782\n",
       "51       week_45    0.003899\n",
       "41       week_35    0.004718\n",
       "27       week_21    0.005044\n",
       "42       week_36    0.005135\n",
       "56       week_50    0.006639\n",
       "58       week_52    0.007084\n",
       "53       week_47    0.010274\n",
       "7           week    0.045466\n",
       "5            sms    0.096968\n",
       "0           year    0.111272\n",
       "3         emails    0.119298\n",
       "4          chats    0.123108\n",
       "2          calls    0.123348\n",
       "6   interactions    0.136817\n",
       "1         orders    0.139179"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# max_features=5 is best and n_estimators=150 is sufficiently large.\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=5, oob_score=True, random_state=1)\n",
    "rfreg.fit(X, y)\n",
    "\n",
    "# Compute feature importances.\n",
    "pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values(by='importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.723317834338\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31382.776512933749"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the out-of-bag R-squared score.\n",
    "print((rfreg.oob_score_))\n",
    "\n",
    "# Find the average RMSE.\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rfreg, X, y, cv=10, scoring='neg_mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(78, 8)\n",
      "(78, 30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state = 89)\n",
    "# Fit the model on only the train data\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=5, oob_score=True, random_state=1)\n",
    "rfreg.fit(X_train, y_train)\n",
    "\n",
    "# Set a threshold for which features to include.\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "print(SelectFromModel(rfreg, threshold='mean', prefit=True).transform(X_train).shape)\n",
    "print(SelectFromModel(rfreg, threshold='median', prefit=True).transform(X_train).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26720.968428305699"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new feature matrix that only includes important features.\n",
    "\n",
    "X_important =  SelectFromModel(rfreg, threshold='mean', prefit=True).transform(X_test)\n",
    "\n",
    "# Check the RMSE for a random forest that only includes important features.\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=3, random_state=1)\n",
    "\n",
    "scores = cross_val_score(rfreg, X_important, y_test, cv=10, scoring='neg_mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "######Optional: Tuning Individual Parameters\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfreg = RandomForestRegressor()\n",
    "\n",
    "\n",
    "# List of values to try for n_estimators:\n",
    "estimator_range = list(range(70, 125, 1))\n",
    "\n",
    "# List to store the average RMSE for each value of n_estimators:\n",
    "RMSE_scores = []\n",
    "\n",
    "# Use five-fold cross-validation with each value of n_estimators (Warning: Slow!).\n",
    "for estimator in estimator_range:\n",
    "    rfreg = RandomForestRegressor(n_estimators=estimator, random_state=1)\n",
    "    MSE_scores = cross_val_score(rfreg, X, y, cv=5, scoring='neg_mean_squared_error')\n",
    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a8alTp4iOjsbV1dX0+HHjxpGeng7Arl27SElJYdKkSfz444/o9XpKS0vR6/Wy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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19e75710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE (y-axis) versus n_estimators (x-axis).\n",
    "plt.plot(estimator_range, RMSE_scores);\n",
    "\n",
    "plt.xlabel('n_estimators');\n",
    "plt.ylabel('RMSE (lower is better)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35460.916937965994, 91)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the best RMSE and the corresponding max_depth.\n",
    "sorted(zip(RMSE_scores, estimator_range))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Tuning max_features\n",
    "# List of values to try for max_features:\n",
    "feature_range = list(range(1, len(feature_cols)+1))\n",
    "\n",
    "# List to store the average RMSE for each value of max_features:\n",
    "RMSE_scores = []\n",
    "\n",
    "# Use 10-fold cross-validation with each value of max_features (Warning: Super slow!).\n",
    "for feature in feature_range:\n",
    "    rfreg = RandomForestRegressor(n_estimators=91, max_features=feature, random_state=1)\n",
    "    MSE_scores = cross_val_score(rfreg, X, y, cv=10, scoring='neg_mean_squared_error')\n",
    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GtsyYdHQn6alz5LqDBDchhEfKzG8W3Er0V/WMfV/nolQq+NW91zN0UAApp4tdHrx5td77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\naI4ENyGEEJojwU0IIYTm/D9EKwryeoGmnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a397410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot max_features (x-axis) versus RMSE (y-axis).\n",
    "\n",
    "plt.plot(feature_range, RMSE_scores);\n",
    "\n",
    "plt.xlabel('max_features');\n",
    "plt.ylabel('RMSE (lower is better)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31054.919308661843, 11)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the best RMSE and the corresponding max_features.\n",
    "sorted(zip(RMSE_scores, feature_range))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26876.024840238177"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create feature matrix (X).\n",
    "feature_cols = ['year', 'orders', 'calls','emails','chats','sms', 'interactions','week', 'week_47','week_50','week_52']\n",
    "X = dfyearweekdummies[feature_cols]\n",
    "\n",
    "# Create response vector (y).\n",
    "y = dfyearweekdummies.next_week_interactions\n",
    "###\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# # # max_features=5 is best and n_estimators=150 is sufficiently large.\n",
    "# rfreg = RandomForestRegressor(n_estimators=91, max_features=11, oob_score=True, random_state=1)\n",
    "# rfreg.fit(X, y)\n",
    "\n",
    "# # # Compute feature importances.\n",
    "# # pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values(by='importance')\n",
    "\n",
    "#####\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state = 89)\n",
    "# Fit the model on only the train data\n",
    "rfreg = RandomForestRegressor(n_estimators=91, max_features=11, oob_score=True, random_state=1)\n",
    "rfreg.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "scores = cross_val_score(rfreg, X_test, y_test, cv=10, scoring='neg_mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26522.242034155606"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create feature matrix (X).\n",
    "feature_cols = ['year', 'orders', 'calls','emails','chats','sms', 'interactions', 'week','week_2', 'week_3', 'week_4', 'week_5', 'week_6','week_7', 'week_8', 'week_9', 'week_10', 'week_11', 'week_12', 'week_13', 'week_14', 'week_15', 'week_16', 'week_17', 'week_18', 'week_19', 'week_20', 'week_21', 'week_22', 'week_23', 'week_24', 'week_25', 'week_26', 'week_27', 'week_28', 'week_29', 'week_30', 'week_31', 'week_32', 'week_33', 'week_34', 'week_35', 'week_36', 'week_37', 'week_38', 'week_39', 'week_40', 'week_41', 'week_42', 'week_43', 'week_44', 'week_45', 'week_46', 'week_47','week_48','week_49','week_50','week_51','week_52']\n",
    "X = dfyearweekdummies[feature_cols]\n",
    "\n",
    "# Create response vector (y).\n",
    "y = dfyearweekdummies.next_week_interactions\n",
    "###\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state = 89)\n",
    "# Fit the model on only the train data\n",
    "rfreg = RandomForestRegressor(n_estimators=91, max_features=11, oob_score=True, random_state=1)\n",
    "rfreg.fit(X_train, y_train)\n",
    "\n",
    "scores = cross_val_score(rfreg, X_test, y_test, cv=10, scoring='neg_mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>week_40</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>week_39</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>week_15</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>week_25</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>week_6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>week_32</td>\n",
       "      <td>0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>week_29</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>week_8</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>week_2</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>week_30</td>\n",
       "      <td>0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>week_12</td>\n",
       "      <td>0.000156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>week_26</td>\n",
       "      <td>0.000227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>week_10</td>\n",
       "      <td>0.000252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>week_11</td>\n",
       "      <td>0.000276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>week_28</td>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>week_22</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>week_18</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>week_3</td>\n",
       "      <td>0.000317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>week_24</td>\n",
       "      <td>0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>week_38</td>\n",
       "      <td>0.000353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>week_7</td>\n",
       "      <td>0.000362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>week_27</td>\n",
       "      <td>0.000374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>week_43</td>\n",
       "      <td>0.000393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>week_34</td>\n",
       "      <td>0.000420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>week_41</td>\n",
       "      <td>0.000428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>week_44</td>\n",
       "      <td>0.000448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>week_42</td>\n",
       "      <td>0.000495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>week_23</td>\n",
       "      <td>0.000519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>week_9</td>\n",
       "      <td>0.000525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>week_50</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>week_17</td>\n",
       "      <td>0.000717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>week_37</td>\n",
       "      <td>0.000753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>week_31</td>\n",
       "      <td>0.000865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>week_46</td>\n",
       "      <td>0.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>week_20</td>\n",
       "      <td>0.000930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>week_19</td>\n",
       "      <td>0.000988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>week_14</td>\n",
       "      <td>0.001140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>week_49</td>\n",
       "      <td>0.001246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>week_5</td>\n",
       "      <td>0.001265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>week_35</td>\n",
       "      <td>0.001439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>week_51</td>\n",
       "      <td>0.001474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>week_4</td>\n",
       "      <td>0.001514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>week_36</td>\n",
       "      <td>0.001677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>week_16</td>\n",
       "      <td>0.001725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>week_33</td>\n",
       "      <td>0.001879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>week_13</td>\n",
       "      <td>0.002336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>week_45</td>\n",
       "      <td>0.003422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>week_48</td>\n",
       "      <td>0.004921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>week_52</td>\n",
       "      <td>0.005379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>week_21</td>\n",
       "      <td>0.005476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>week_47</td>\n",
       "      <td>0.011451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>week</td>\n",
       "      <td>0.055869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>year</td>\n",
       "      <td>0.074757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chats</td>\n",
       "      <td>0.076915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>emails</td>\n",
       "      <td>0.102542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sms</td>\n",
       "      <td>0.114580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orders</td>\n",
       "      <td>0.150888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>calls</td>\n",
       "      <td>0.175104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>interactions</td>\n",
       "      <td>0.189999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  importance\n",
       "46       week_40    0.000000\n",
       "45       week_39    0.000000\n",
       "21       week_15    0.000000\n",
       "31       week_25    0.000000\n",
       "12        week_6    0.000000\n",
       "38       week_32    0.000041\n",
       "35       week_29    0.000042\n",
       "14        week_8    0.000080\n",
       "8         week_2    0.000091\n",
       "36       week_30    0.000123\n",
       "18       week_12    0.000156\n",
       "32       week_26    0.000227\n",
       "16       week_10    0.000252\n",
       "17       week_11    0.000276\n",
       "34       week_28    0.000300\n",
       "28       week_22    0.000303\n",
       "24       week_18    0.000315\n",
       "9         week_3    0.000317\n",
       "30       week_24    0.000324\n",
       "44       week_38    0.000353\n",
       "13        week_7    0.000362\n",
       "33       week_27    0.000374\n",
       "49       week_43    0.000393\n",
       "40       week_34    0.000420\n",
       "47       week_41    0.000428\n",
       "50       week_44    0.000448\n",
       "48       week_42    0.000495\n",
       "29       week_23    0.000519\n",
       "15        week_9    0.000525\n",
       "56       week_50    0.000690\n",
       "23       week_17    0.000717\n",
       "43       week_37    0.000753\n",
       "37       week_31    0.000865\n",
       "52       week_46    0.000897\n",
       "26       week_20    0.000930\n",
       "25       week_19    0.000988\n",
       "20       week_14    0.001140\n",
       "55       week_49    0.001246\n",
       "11        week_5    0.001265\n",
       "41       week_35    0.001439\n",
       "57       week_51    0.001474\n",
       "10        week_4    0.001514\n",
       "42       week_36    0.001677\n",
       "22       week_16    0.001725\n",
       "39       week_33    0.001879\n",
       "19       week_13    0.002336\n",
       "51       week_45    0.003422\n",
       "54       week_48    0.004921\n",
       "58       week_52    0.005379\n",
       "27       week_21    0.005476\n",
       "53       week_47    0.011451\n",
       "7           week    0.055869\n",
       "0           year    0.074757\n",
       "4          chats    0.076915\n",
       "3         emails    0.102542\n",
       "5            sms    0.114580\n",
       "1         orders    0.150888\n",
       "2          calls    0.175104\n",
       "6   interactions    0.189999"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute feature importances.\n",
    "pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values(by='importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27Wnfvn1BmioiIrexAoXaiBEjzG6HiIjI31bgCxpfunSJDRs2sHbtWi5cuHDVgSMiIiL/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\n/w//OHllKoxDNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aff1590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values(by='importance', ascending = False)\n",
    "\n",
    "features = features[:12]\n",
    "sns.barplot(x='feature', y='importance', data=features, palette='Blues_r')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.718601059\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31054.919308661843"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "predicted = rfreg.predict(X_test)\n",
    "# accuracy = accuracy_score(y_test, predicted)\n",
    "accuracy = accuracy_score(y_test, predicted.round(), normalize=False)\n",
    "\n",
    "\n",
    "# Compute the out-of-bag R-squared score.\n",
    "print((rfreg.oob_score_))\n",
    "\n",
    "# #compute the mean accuracy score\n",
    "print accuracy\n",
    "\n",
    "# Find the average RMSE.\n",
    "scores = cross_val_score(rfreg, X, y, cv=10, scoring='neg_mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-bag R-2 score estimate:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.71860105900039928"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "predicted_train = rfreg.predict(X_train)\n",
    "predicted_test = rfreg.predict(X_test)\n",
    "test_score = r2_score(y_test, predicted_test)\n",
    "spearman = spearmanr(y_test, predicted_test)\n",
    "pearson = pearsonr(y_test, predicted_test)\n",
    "print('Out-of-bag R-2 score estimate:') \n",
    "rfreg.oob_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data R-2 score:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.84509102580223594"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test data R-2 score:') \n",
    "test_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Spearman correlation:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.94051282051282048"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test data Spearman correlation:') \n",
    "spearman[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data Pearson correlation:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.92072581662943953"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearson = pearsonr(y_test, predicted_test)\n",
    "print('Test data Pearson correlation:') \n",
    "pearson[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84509102580223594"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfreg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AeyzAVhSVpG+u8M8DZ7ldUknvHo4smqbn0YC+VleSTIjOQJKfEBZ2NruQuMTT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\nJ1sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19ddf850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rfreg.predict(X_test)\n",
    "plt.scatter(x=y_test, y=y_pred)\n",
    "plt.xlim([225000,450000])\n",
    "plt.ylim([225000,450000])\n",
    "plt.plot([225000,450000],[225000,450000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#The model fits the test set reasonably well with an R2 of 0.845"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Conclusion\n",
    "#This worked quite well and likely much better than our current method of forecasting by either order volume or interaction volume.\n",
    "#I will go back and re-run these steps with actual data, where I'll be able to identify outliers a bit better (because the data will make sense)\n",
    "#Hopefully we can operationalize this and use it for demand forecasting, as well as staffing utilization, to staff the appropriate headcount the week before\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}