New Year - Forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resolution Trend - Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>diet: (Worldwide)</th>\n",
       "      <th>gym: (Worldwide)</th>\n",
       "      <th>finance: (Worldwide)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-03</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-04</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-05</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  diet: (Worldwide)  gym: (Worldwide)  finance: (Worldwide)\n",
       "0  2004-01                100                31                    48\n",
       "1  2004-02                 75                26                    49\n",
       "2  2004-03                 67                24                    47\n",
       "3  2004-04                 70                22                    48\n",
       "4  2004-05                 72                22                    43"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('multiTimeline.csv', skiprows=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the columns name to - Month, Diet, Gym, Finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Diets</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-03</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-04</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-05</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Diets  Gym  Finance\n",
       "0  2004-01    100   31       48\n",
       "1  2004-02     75   26       49\n",
       "2  2004-03     67   24       47\n",
       "3  2004-04     70   22       48\n",
       "4  2004-05     72   22       43"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns = ['Month', 'Diets', 'Gym', 'Finance']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-03</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-04</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-05</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Diet  Gym  Finance\n",
       "0  2004-01   100   31       48\n",
       "1  2004-02    75   26       49\n",
       "2  2004-03    67   24       47\n",
       "3  2004-04    70   22       48\n",
       "4  2004-05    72   22       43"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(columns = {'Diets': 'Diet'}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the Month column to datetime in pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168 entries, 0 to 167\n",
      "Data columns (total 4 columns):\n",
      "Month      168 non-null datetime64[ns]\n",
      "Diet       168 non-null int64\n",
      "Gym        168 non-null int64\n",
      "Finance    168 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3)\n",
      "memory usage: 5.3 KB\n"
     ]
    }
   ],
   "source": [
    "data['Month'] = pd.to_datetime(data['Month'])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-01</th>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-01</th>\n",
       "      <td>2004-03-01</td>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-01</th>\n",
       "      <td>2004-04-01</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05-01</th>\n",
       "      <td>2004-05-01</td>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                Month  Diet  Gym  Finance\n",
       "Month                                    \n",
       "2004-01-01 2004-01-01   100   31       48\n",
       "2004-02-01 2004-02-01    75   26       49\n",
       "2004-03-01 2004-03-01    67   24       47\n",
       "2004-04-01 2004-04-01    70   22       48\n",
       "2004-05-01 2004-05-01    72   22       43"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = data['Month']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the month column from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-02-01</th>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-03-01</th>\n",
       "      <td>67</td>\n",
       "      <td>24</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-01</th>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05-01</th>\n",
       "      <td>72</td>\n",
       "      <td>22</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Diet  Gym  Finance\n",
       "Month                         \n",
       "2004-01-01   100   31       48\n",
       "2004-02-01    75   26       49\n",
       "2004-03-01    67   24       47\n",
       "2004-04-01    70   22       48\n",
       "2004-05-01    72   22       43"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('Month', axis=1, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the trend in all the different categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of persons')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "data.Diet.plot()\n",
    "data.Gym.plot()\n",
    "data.Finance.plot()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of persons')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show a smoothe trend for Gym."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "data.Gym.rolling(12).mean().plot()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of persons')\n",
    "plt.title('Gym-Trend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Naive Based approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split the data into training and validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Diet  Gym  Finance\n",
       "Month                         \n",
       "2017-12-01    39   45       56"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>45</td>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>39</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>36</td>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>56</td>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01</th>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>44</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>41</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Diet  Gym  Finance\n",
       "Month                         \n",
       "2016-02-01    48   46       46\n",
       "2016-03-01    48   47       44\n",
       "2016-04-01    48   44       43\n",
       "2016-05-01    47   46       42\n",
       "2016-06-01    44   46       45\n",
       "2016-07-01    43   58       41\n",
       "2016-08-01    45   53       41\n",
       "2016-09-01    43   51       44\n",
       "2016-10-01    40   45       41\n",
       "2016-11-01    39   44       43\n",
       "2016-12-01    36   44       39\n",
       "2017-01-01    55   56       43\n",
       "2017-02-01    56   51       44\n",
       "2017-03-01    50   51       44\n",
       "2017-04-01    49   48       42\n",
       "2017-05-01    48   48       43\n",
       "2017-06-01    48   49       41\n",
       "2017-07-01    52   52       43\n",
       "2017-08-01    46   52       43\n",
       "2017-09-01    44   50       47\n",
       "2017-10-01    44   47       45\n",
       "2017-11-01    41   47       47\n",
       "2017-12-01    39   45       56"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data = data.loc[:'2016-02-01']                  #splitting into 70-30\n",
    "validation_data = data.loc['2016-02-01':]                   #say 100 records,multiply by 0.7, utne training data mai, baaki validaytion mai\n",
    "training_data.tail()\n",
    "validation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19f7aa3f4a8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_data.Gym.plot()\n",
    "validation_data.Gym.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "2016-02-01    46\n",
       "2016-03-01    47\n",
       "2016-04-01    44\n",
       "2016-05-01    46\n",
       "2016-06-01    46\n",
       "2016-07-01    58\n",
       "2016-08-01    53\n",
       "2016-09-01    51\n",
       "2016-10-01    45\n",
       "2016-11-01    44\n",
       "2016-12-01    44\n",
       "2017-01-01    56\n",
       "2017-02-01    51\n",
       "2017-03-01    51\n",
       "2017-04-01    48\n",
       "2017-05-01    48\n",
       "2017-06-01    49\n",
       "2017-07-01    52\n",
       "2017-08-01    52\n",
       "2017-09-01    50\n",
       "2017-10-01    47\n",
       "2017-11-01    47\n",
       "2017-12-01    45\n",
       "Name: Gym, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_value = training_data.Gym.iloc[-1]\n",
    "naive_list = [naive_value for i in range(0, validation_data.shape[0])]\n",
    "naive_approach = validation_data.copy()\n",
    "naive_approach.Gym\n",
    "# print(validation_data.Gym)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "2016-02-01    46\n",
       "2016-03-01    47\n",
       "2016-04-01    44\n",
       "2016-05-01    46\n",
       "2016-06-01    46\n",
       "2016-07-01    58\n",
       "2016-08-01    53\n",
       "2016-09-01    51\n",
       "2016-10-01    45\n",
       "2016-11-01    44\n",
       "2016-12-01    44\n",
       "2017-01-01    56\n",
       "2017-02-01    51\n",
       "2017-03-01    51\n",
       "2017-04-01    48\n",
       "2017-05-01    48\n",
       "2017-06-01    49\n",
       "2017-07-01    52\n",
       "2017-08-01    52\n",
       "2017-09-01    50\n",
       "2017-10-01    47\n",
       "2017-11-01    47\n",
       "2017-12-01    45\n",
       "Name: Gym, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_approach['Naive'] = naive_list\n",
    "validation_data.Gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "2016-02-01    46\n",
       "2016-03-01    46\n",
       "2016-04-01    46\n",
       "2016-05-01    46\n",
       "2016-06-01    46\n",
       "2016-07-01    46\n",
       "2016-08-01    46\n",
       "2016-09-01    46\n",
       "2016-10-01    46\n",
       "2016-11-01    46\n",
       "2016-12-01    46\n",
       "2017-01-01    46\n",
       "2017-02-01    46\n",
       "2017-03-01    46\n",
       "2017-04-01    46\n",
       "2017-05-01    46\n",
       "2017-06-01    46\n",
       "2017-07-01    46\n",
       "2017-08-01    46\n",
       "2017-09-01    46\n",
       "2017-10-01    46\n",
       "2017-11-01    46\n",
       "2017-12-01    46\n",
       "Name: Naive, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "naive_approach.Naive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19f7aac4cc0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_data.Gym.plot()\n",
    "validation_data.Gym.plot()\n",
    "naive_approach.Naive.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the validation of our Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error is -  4.719399037242694\n"
     ]
    }
   ],
   "source": [
    "naive_error = sqrt(mean_squared_error(validation_data.Gym, naive_approach.Naive))\n",
    "print('Error is - ', naive_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecasting based on Moving approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "moving_approach = validation_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.666666666666664"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_value = training_data.Gym.rolling(6).mean().iloc[-1]\n",
    "moving_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_approach.shape[0]"
   ]
  },
  {
   "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>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "      <th>Moving</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>44.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "      <td>44.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>44.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>44.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>45</td>\n",
       "      <td>44.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Diet  Gym  Finance     Moving\n",
       "Month                                    \n",
       "2016-02-01    48   46       46  44.666667\n",
       "2016-03-01    48   47       44  44.666667\n",
       "2016-04-01    48   44       43  44.666667\n",
       "2016-05-01    47   46       42  44.666667\n",
       "2016-06-01    44   46       45  44.666667"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moving_list = [moving_value for i in range(moving_approach.shape[0])]\n",
    "moving_approach['Moving'] = moving_list\n",
    "moving_approach.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "training_data.Gym.plot()\n",
    "validation_data.Gym.plot()\n",
    "moving_approach.Moving.plot()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of persons')\n",
    "plt.title('Gym-Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation result for moving approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.501866174396302\n"
     ]
    }
   ],
   "source": [
    "rmse_moving = sqrt(mean_squared_error(moving_approach.Moving, validation_data.Gym))\n",
    "print(rmse_moving)\n",
    "# validation_data.Gym.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Exponential Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Diet</th>\n",
       "      <th>Gym</th>\n",
       "      <th>Finance</th>\n",
       "      <th>SES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46.805421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "      <td>46.805421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>46.805421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>46.805421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>44</td>\n",
       "      <td>46</td>\n",
       "      <td>45</td>\n",
       "      <td>46.805421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Diet  Gym  Finance        SES\n",
       "Month                                    \n",
       "2016-02-01    48   46       46  46.805421\n",
       "2016-03-01    48   47       44  46.805421\n",
       "2016-04-01    48   44       43  46.805421\n",
       "2016-05-01    47   46       42  46.805421\n",
       "2016-06-01    44   46       45  46.805421"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_approach = validation_data.copy()\n",
    "fit_data = SimpleExpSmoothing(np.asarray(training_data['Gym'])).fit(smoothing_level = 0.6, optimized=False)\n",
    "ses_approach['SES'] = fit_data.forecast(len(validation_data))\n",
    "ses_approach.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "2016-02-01    46.805421\n",
       "2016-03-01    46.805421\n",
       "2016-04-01    46.805421\n",
       "2016-05-01    46.805421\n",
       "2016-06-01    46.805421\n",
       "2016-07-01    46.805421\n",
       "2016-08-01    46.805421\n",
       "2016-09-01    46.805421\n",
       "2016-10-01    46.805421\n",
       "2016-11-01    46.805421\n",
       "2016-12-01    46.805421\n",
       "2017-01-01    46.805421\n",
       "2017-02-01    46.805421\n",
       "2017-03-01    46.805421\n",
       "2017-04-01    46.805421\n",
       "2017-05-01    46.805421\n",
       "2017-06-01    46.805421\n",
       "2017-07-01    46.805421\n",
       "2017-08-01    46.805421\n",
       "2017-09-01    46.805421\n",
       "2017-10-01    46.805421\n",
       "2017-11-01    46.805421\n",
       "2017-12-01    46.805421\n",
       "Name: SES, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ses_approach.SES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "training_data.Gym.plot()\n",
    "validation_data.Gym.plot()\n",
    "ses_approach.SES.plot()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Number of persons')\n",
    "plt.title('Gym-Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.196520260591098\n"
     ]
    }
   ],
   "source": [
    "rmse_moving = sqrt(mean_squared_error(ses_approach.SES, validation_data.Gym))\n",
    "print(rmse_moving)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}