Nintendo Switch Weekly Sales Forecasting.ipynb

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    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import optimizers\n",
    "from keras.utils import plot_model\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers.convolutional import Conv1D, MaxPooling1D\n",
    "from keras.layers import Dense, LSTM, RepeatVector, TimeDistributed, Flatten\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import datetime\n",
    "from dateutil import rrule\n",
    "\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\")\n",
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    "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Nintendo Switch Sales</th>\n",
       "      <th>PS4 Sales</th>\n",
       "      <th>Xbox One Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/6/2018</td>\n",
       "      <td>71528</td>\n",
       "      <td>59920</td>\n",
       "      <td>49805</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/13/2018</td>\n",
       "      <td>62374</td>\n",
       "      <td>50554</td>\n",
       "      <td>46845</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/20/2018</td>\n",
       "      <td>53734</td>\n",
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       "      <td>44867</td>\n",
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       "    <tr>\n",
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       "      <td>2/3/2018</td>\n",
       "      <td>59834</td>\n",
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       "        Date  Nintendo Switch Sales  PS4 Sales  Xbox One Sales\n",
       "0   1/6/2018                  71528      59920           49805\n",
       "1  1/13/2018                  62374      50554           46845\n",
       "2  1/20/2018                  53734      47961           44867\n",
       "3  1/27/2018                  58457      61473           54336\n",
       "4   2/3/2018                  59834      58728           53374"
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   "source": [
    "weekly_sales = pd.read_csv('Console Sales by Week.csv').dropna()\n",
    "weekly_sales.head()"
   ]
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   "source": [
    "#### Overall Weekly Sales"
   ]
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   "source": [
    "product_weekly_sales_sc = []\n",
    "for product in ['Nintendo Switch Sales', 'PS4 Sales', 'Xbox One Sales']:\n",
    "    current_product_weekly_sales = weekly_sales[product]\n",
    "    product_weekly_sales_sc.append(go.Scatter(x = weekly_sales['Date'], y=current_product_weekly_sales * 0.1, name=(product)))\n",
    "\n",
    "layout = go.Layout(title = 'Store daily sales', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = product_weekly_sales_sc, layout=layout)\n",
    "iplot(fig)"
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  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nintendo Switch Sales"
   ]
  },
  {
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Weekly Sales\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Sales\"}}},\n",
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      ]
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   ],
   "source": [
    "weekly_sales_sc = go.Scatter(x = weekly_sales['Date'], y = weekly_sales['Nintendo Switch Sales'])\n",
    "layout = go.Layout(title='Weekly Sales', xaxis=dict(title='Date'), yaxis=dict(title='Sales'))\n",
    "fig = go.Figure(data=[weekly_sales_sc], layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Train Test Split\n",
    "Since the sales of Nintendo follow similar pattern across different years, it might not be a good idea to randomly split the dataset. We decided to use the sales from 1/6/2018 to 12/28/2019 as the train set and the sales at the first 4 months in 2020 as the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "pycharm": {
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   "source": [
    "weekly_sales['Date'] = pd.DatetimeIndex(weekly_sales['Date']) # convert the data type of Date from String to Datetime\n",
    "weekly_sales['year'] = pd.DatetimeIndex(weekly_sales['Date']).year # extract the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
     "data": {
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       "      <th>Nintendo Switch Sales</th>\n",
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       "      <th>102</th>\n",
       "      <td>2019-12-21</td>\n",
       "      <td>616317</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2019-12-28</td>\n",
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      "text/plain": [
       "          Date  Nintendo Switch Sales\n",
       "99  2019-11-30                 881303\n",
       "100 2019-12-07                 512053\n",
       "101 2019-12-14                 551514\n",
       "102 2019-12-21                 616317\n",
       "103 2019-12-28                 356436"
      ]
     },
     "execution_count": 127,
     "metadata": {},
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    }
   ],
   "source": [
    "train = weekly_sales[['Date', 'Nintendo Switch Sales', 'year']]\n",
    "train = train[train['year'] != 2020].drop(['year'], axis = 1)\n",
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Nintendo Switch Sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>80316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2020-01-11</td>\n",
       "      <td>72995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2020-01-18</td>\n",
       "      <td>65624</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>61031</td>\n",
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       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>61915</td>\n",
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      "text/plain": [
       "          Date  Nintendo Switch Sales\n",
       "104 2020-01-04                  80316\n",
       "105 2020-01-11                  72995\n",
       "106 2020-01-18                  65624\n",
       "107 2020-01-25                  61031\n",
       "108 2020-02-01                  61915"
      ]
     },
     "execution_count": 128,
     "metadata": {},
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   ],
   "source": [
    "test = weekly_sales[['Date', 'Nintendo Switch Sales', 'year']]\n",
    "test = test[test['year'] == 2020].drop(['year'], axis = 1)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify what's the time gap between the last day of train set from the last day of the test set, this will be out lag (the amount of day that need to be forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "lag_size = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Transform The Time Series Dataset to Supervised Problem By Using Shift Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# Define the function for shifting dataset\n",
    "def series_to_supervised(data, window=1, lag=1, dropnan=True):\n",
    "    cols, names = list(), list()\n",
    "    # Input sequence (t-n, ... t-1)\n",
    "    for i in range(window, 0, -1):\n",
    "        cols.append(data.shift(i))\n",
    "        names += [('%s(t-%d)' % (col, i)) for col in data.columns]\n",
    "    # Current timestep (t=0)\n",
    "    cols.append(data)\n",
    "    names += [('%s(t)' % (col)) for col in data.columns]\n",
    "    # Target timestep (t=lag)\n",
    "    cols.append(data.shift(-lag))\n",
    "    names += [('%s(t+%d)' % (col, lag)) for col in data.columns]\n",
    "    # Put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # Drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the current timestep and the last week to forecast 16 weeks ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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",
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       "    }\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>Nintendo Switch Sales(t-2)</th>\n",
       "      <th>Nintendo Switch Sales(t-1)</th>\n",
       "      <th>Nintendo Switch Sales(t)</th>\n",
       "      <th>Nintendo Switch Sales(t+16)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71528.0</td>\n",
       "      <td>62374.0</td>\n",
       "      <td>53734</td>\n",
       "      <td>40765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62374.0</td>\n",
       "      <td>53734.0</td>\n",
       "      <td>58457</td>\n",
       "      <td>41626.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53734.0</td>\n",
       "      <td>58457.0</td>\n",
       "      <td>59834</td>\n",
       "      <td>42223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58457.0</td>\n",
       "      <td>59834.0</td>\n",
       "      <td>64734</td>\n",
       "      <td>41793.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59834.0</td>\n",
       "      <td>64734.0</td>\n",
       "      <td>68945</td>\n",
       "      <td>59457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>64734.0</td>\n",
       "      <td>68945.0</td>\n",
       "      <td>65708</td>\n",
       "      <td>64099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68945.0</td>\n",
       "      <td>65708.0</td>\n",
       "      <td>66374</td>\n",
       "      <td>80980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>65708.0</td>\n",
       "      <td>66374.0</td>\n",
       "      <td>63748</td>\n",
       "      <td>76685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66374.0</td>\n",
       "      <td>63748.0</td>\n",
       "      <td>59834</td>\n",
       "      <td>63339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>63748.0</td>\n",
       "      <td>59834.0</td>\n",
       "      <td>63831</td>\n",
       "      <td>69671.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nintendo Switch Sales(t-2)  Nintendo Switch Sales(t-1)  \\\n",
       "2                      71528.0                     62374.0   \n",
       "3                      62374.0                     53734.0   \n",
       "4                      53734.0                     58457.0   \n",
       "5                      58457.0                     59834.0   \n",
       "6                      59834.0                     64734.0   \n",
       "7                      64734.0                     68945.0   \n",
       "8                      68945.0                     65708.0   \n",
       "9                      65708.0                     66374.0   \n",
       "10                     66374.0                     63748.0   \n",
       "11                     63748.0                     59834.0   \n",
       "\n",
       "    Nintendo Switch Sales(t)  Nintendo Switch Sales(t+16)  \n",
       "2                      53734                      40765.0  \n",
       "3                      58457                      41626.0  \n",
       "4                      59834                      42223.0  \n",
       "5                      64734                      41793.0  \n",
       "6                      68945                      59457.0  \n",
       "7                      65708                      64099.0  \n",
       "8                      66374                      80980.0  \n",
       "9                      63748                      76685.0  \n",
       "10                     59834                      63339.0  \n",
       "11                     63831                      69671.0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window = 2\n",
    "lag = lag_size\n",
    "series = series_to_supervised(train.drop('Date', axis=1), window=window, lag=lag)\n",
    "series.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "# Label\n",
    "labels_col = 'Nintendo Switch Sales(t+%d)' % lag_size\n",
    "labels = series[labels_col]\n",
    "series = series.drop(labels_col, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set shape (51, 3)\n",
      "Validation set shape (35, 3)\n"
     ]
    },
    {
     "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>Nintendo Switch Sales(t-2)</th>\n",
       "      <th>Nintendo Switch Sales(t-1)</th>\n",
       "      <th>Nintendo Switch Sales(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>246756.0</td>\n",
       "      <td>82734.0</td>\n",
       "      <td>68800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>51024.0</td>\n",
       "      <td>54337.0</td>\n",
       "      <td>58848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>76542.0</td>\n",
       "      <td>78293.0</td>\n",
       "      <td>107933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>65730.0</td>\n",
       "      <td>66535.0</td>\n",
       "      <td>78855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59834.0</td>\n",
       "      <td>64734.0</td>\n",
       "      <td>68945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nintendo Switch Sales(t-2)  Nintendo Switch Sales(t-1)  \\\n",
       "53                    246756.0                     82734.0   \n",
       "73                     51024.0                     54337.0   \n",
       "77                     76542.0                     78293.0   \n",
       "57                     65730.0                     66535.0   \n",
       "6                      59834.0                     64734.0   \n",
       "\n",
       "    Nintendo Switch Sales(t)  \n",
       "53                     68800  \n",
       "73                     58848  \n",
       "77                    107933  \n",
       "57                     78855  \n",
       "6                      68945  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_valid, Y_train, Y_valid = train_test_split(series, labels.values, test_size=0.4, random_state=0)\n",
    "print('Train set shape', X_train.shape)\n",
    "print('Validation set shape', X_valid.shape)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Predictive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MLP for Time Series Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "epochs = 60\n",
    "batch = 32\n",
    "lr = 0.0005\n",
    "adam = optimizers.Adam(lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_10 (Dense)             (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 501\n",
      "Trainable params: 501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_mlp = Sequential()\n",
    "model_mlp.add(Dense(100, activation='relu', input_dim=X_train.shape[1]))\n",
    "model_mlp.add(Dense(1))\n",
    "model_mlp.compile(loss='mae', optimizer=adam)\n",
    "model_mlp.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 51 samples, validate on 35 samples\n",
      "Epoch 1/60\n",
      " - 0s - loss: 130879.1867 - val_loss: 77566.5420\n",
      "Epoch 2/60\n",
      " - 0s - loss: 130171.2912 - val_loss: 76924.4703\n",
      "Epoch 3/60\n",
      " - 0s - loss: 129428.5461 - val_loss: 76361.7185\n",
      "Epoch 4/60\n",
      " - 0s - loss: 128865.4616 - val_loss: 75819.2833\n",
      "Epoch 5/60\n",
      " - 0s - loss: 128522.4378 - val_loss: 75271.9431\n",
      "Epoch 6/60\n",
      " - 0s - loss: 127815.4395 - val_loss: 74764.1473\n",
      "Epoch 7/60\n",
      " - 0s - loss: 127276.1158 - val_loss: 74243.7681\n",
      "Epoch 8/60\n",
      " - 0s - loss: 126720.2521 - val_loss: 73718.9368\n",
      "Epoch 9/60\n",
      " - 0s - loss: 126118.4021 - val_loss: 73208.8217\n",
      "Epoch 10/60\n",
      " - 0s - loss: 125684.8701 - val_loss: 72679.8306\n",
      "Epoch 11/60\n",
      " - 0s - loss: 125236.6838 - val_loss: 72157.2109\n",
      "Epoch 12/60\n",
      " - 0s - loss: 124791.8658 - val_loss: 71648.3819\n",
      "Epoch 13/60\n",
      " - 0s - loss: 124329.4139 - val_loss: 71144.3658\n",
      "Epoch 14/60\n",
      " - 0s - loss: 124040.7742 - val_loss: 70629.5962\n",
      "Epoch 15/60\n",
      " - 0s - loss: 123567.2595 - val_loss: 70151.6018\n",
      "Epoch 16/60\n",
      " - 0s - loss: 123127.3566 - val_loss: 69698.1270\n",
      "Epoch 17/60\n",
      " - 0s - loss: 122676.9142 - val_loss: 69250.7391\n",
      "Epoch 18/60\n",
      " - 0s - loss: 122333.5827 - val_loss: 68783.7563\n",
      "Epoch 19/60\n",
      " - 0s - loss: 121923.3100 - val_loss: 68343.6467\n",
      "Epoch 20/60\n",
      " - 0s - loss: 121573.9251 - val_loss: 67938.5092\n",
      "Epoch 21/60\n",
      " - 0s - loss: 121238.1575 - val_loss: 67538.3821\n",
      "Epoch 22/60\n",
      " - 0s - loss: 120979.5699 - val_loss: 67140.3176\n",
      "Epoch 23/60\n",
      " - 0s - loss: 120588.9168 - val_loss: 66824.0676\n",
      "Epoch 24/60\n",
      " - 0s - loss: 120463.7071 - val_loss: 66524.0232\n",
      "Epoch 25/60\n",
      " - 0s - loss: 120090.2780 - val_loss: 66290.5333\n",
      "Epoch 26/60\n",
      " - 0s - loss: 119834.6644 - val_loss: 66089.9998\n",
      "Epoch 27/60\n",
      " - 0s - loss: 119656.6854 - val_loss: 65898.2571\n",
      "Epoch 28/60\n",
      " - 0s - loss: 119432.6751 - val_loss: 65709.4667\n",
      "Epoch 29/60\n",
      " - 0s - loss: 119253.6339 - val_loss: 65513.7788\n",
      "Epoch 30/60\n",
      " - 0s - loss: 119164.9458 - val_loss: 65339.3609\n",
      "Epoch 31/60\n",
      " - 0s - loss: 118886.5119 - val_loss: 65257.8127\n",
      "Epoch 32/60\n",
      " - 0s - loss: 118724.8444 - val_loss: 65201.9446\n",
      "Epoch 33/60\n",
      " - 0s - loss: 118543.4462 - val_loss: 65142.2783\n",
      "Epoch 34/60\n",
      " - 0s - loss: 118415.9017 - val_loss: 65097.9913\n",
      "Epoch 35/60\n",
      " - 0s - loss: 118227.2282 - val_loss: 65089.0982\n",
      "Epoch 36/60\n",
      " - 0s - loss: 118135.2610 - val_loss: 65080.1866\n",
      "Epoch 37/60\n",
      " - 0s - loss: 118030.4655 - val_loss: 65070.0342\n",
      "Epoch 38/60\n",
      " - 0s - loss: 117951.2993 - val_loss: 65059.0002\n",
      "Epoch 39/60\n",
      " - 0s - loss: 117867.8943 - val_loss: 65057.5821\n",
      "Epoch 40/60\n",
      " - 0s - loss: 117807.4966 - val_loss: 65059.2554\n",
      "Epoch 41/60\n",
      " - 0s - loss: 117695.2201 - val_loss: 65045.7491\n",
      "Epoch 42/60\n",
      " - 0s - loss: 117571.3750 - val_loss: 65034.0384\n",
      "Epoch 43/60\n",
      " - 0s - loss: 117495.9199 - val_loss: 65022.4634\n",
      "Epoch 44/60\n",
      " - 0s - loss: 117371.5165 - val_loss: 65014.3009\n",
      "Epoch 45/60\n",
      " - 0s - loss: 117314.0864 - val_loss: 65005.1998\n",
      "Epoch 46/60\n",
      " - 0s - loss: 117174.1140 - val_loss: 64996.6587\n",
      "Epoch 47/60\n",
      " - 0s - loss: 117049.0132 - val_loss: 64983.5217\n",
      "Epoch 48/60\n",
      " - 0s - loss: 116971.3934 - val_loss: 64974.5286\n",
      "Epoch 49/60\n",
      " - 0s - loss: 116853.1676 - val_loss: 64960.0232\n",
      "Epoch 50/60\n",
      " - 0s - loss: 116736.7393 - val_loss: 64944.2125\n",
      "Epoch 51/60\n",
      " - 0s - loss: 116644.8275 - val_loss: 64919.8775\n",
      "Epoch 52/60\n",
      " - 0s - loss: 116530.8312 - val_loss: 64891.6681\n",
      "Epoch 53/60\n",
      " - 0s - loss: 116405.5000 - val_loss: 64875.0766\n",
      "Epoch 54/60\n",
      " - 0s - loss: 116315.9796 - val_loss: 64864.1212\n",
      "Epoch 55/60\n",
      " - 0s - loss: 116217.1838 - val_loss: 64849.1087\n",
      "Epoch 56/60\n",
      " - 0s - loss: 116101.5938 - val_loss: 64840.7873\n",
      "Epoch 57/60\n",
      " - 0s - loss: 115994.5259 - val_loss: 64834.1893\n",
      "Epoch 58/60\n",
      " - 0s - loss: 115860.7379 - val_loss: 64824.0967\n",
      "Epoch 59/60\n",
      " - 0s - loss: 115763.9338 - val_loss: 64818.6694\n",
      "Epoch 60/60\n",
      " - 0s - loss: 115673.3294 - val_loss: 64818.6306\n"
     ]
    }
   ],
   "source": [
    "mlp_history = model_mlp.fit(X_train.values, Y_train, validation_data=(X_valid.values, Y_valid), epochs=epochs, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RNN (LSTM) for Times Series Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set shape (51, 3, 1)\n",
      "Validation set shape (35, 3, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_series = X_train.values.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "X_valid_series = X_valid.values.reshape((X_valid.shape[0], X_valid.shape[1], 1))\n",
    "print('Train set shape', X_train_series.shape)\n",
    "print('Validation set shape', X_valid_series.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_4 (LSTM)                (None, 100)               40800     \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 40,901\n",
      "Trainable params: 40,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_lstm = Sequential()\n",
    "model_lstm.add(LSTM(100, activation='relu', input_shape=(X_train_series.shape[1], X_train_series.shape[2])))\n",
    "model_lstm.add(Dense(1))\n",
    "model_lstm.compile(loss='mae', optimizer=adam)\n",
    "model_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "pycharm": {
     "is_executing": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 51 samples, validate on 35 samples\n",
      "Epoch 1/60\n",
      " - 0s - loss: 159337.2338 - val_loss: 105265.3828\n",
      "Epoch 2/60\n",
      " - 0s - loss: 158027.7160 - val_loss: 103835.5560\n",
      "Epoch 3/60\n",
      " - 0s - loss: 156719.7178 - val_loss: 102623.2462\n",
      "Epoch 4/60\n",
      " - 0s - loss: 155436.7485 - val_loss: 100946.4462\n",
      "Epoch 5/60\n",
      " - 0s - loss: 154363.1566 - val_loss: 100339.1132\n",
      "Epoch 6/60\n",
      " - 0s - loss: 153200.4709 - val_loss: 99057.1812\n",
      "Epoch 7/60\n",
      " - 0s - loss: 152003.4638 - val_loss: 97883.3426\n",
      "Epoch 8/60\n",
      " - 0s - loss: 150750.9170 - val_loss: 96187.8402\n",
      "Epoch 9/60\n",
      " - 0s - loss: 148969.3822 - val_loss: 94484.7589\n",
      "Epoch 10/60\n",
      " - 0s - loss: 147736.1131 - val_loss: 94062.0042\n",
      "Epoch 11/60\n",
      " - 0s - loss: 146613.3447 - val_loss: 92684.8661\n",
      "Epoch 12/60\n",
      " - 0s - loss: 145465.6841 - val_loss: 91311.7967\n",
      "Epoch 13/60\n",
      " - 0s - loss: 143883.2169 - val_loss: 89931.9391\n",
      "Epoch 14/60\n",
      " - 0s - loss: 142496.1621 - val_loss: 88539.8511\n",
      "Epoch 15/60\n",
      " - 0s - loss: 141276.8487 - val_loss: 87217.2717\n",
      "Epoch 16/60\n",
      " - 0s - loss: 140020.4773 - val_loss: 85818.6368\n",
      "Epoch 17/60\n",
      " - 0s - loss: 138980.4424 - val_loss: 84588.3806\n",
      "Epoch 18/60\n",
      " - 0s - loss: 138054.4839 - val_loss: 83364.9069\n",
      "Epoch 19/60\n",
      " - 0s - loss: 137331.0968 - val_loss: 82555.3359\n",
      "Epoch 20/60\n",
      " - 0s - loss: 136827.1504 - val_loss: 81769.0100\n",
      "Epoch 21/60\n",
      " - 0s - loss: 136112.6569 - val_loss: 80967.9279\n",
      "Epoch 22/60\n",
      " - 0s - loss: 135648.0332 - val_loss: 80438.0083\n",
      "Epoch 23/60\n",
      " - 0s - loss: 135142.8667 - val_loss: 79880.0011\n",
      "Epoch 24/60\n",
      " - 0s - loss: 134560.6256 - val_loss: 79323.0750\n",
      "Epoch 25/60\n",
      " - 0s - loss: 134206.1587 - val_loss: 78797.6071\n",
      "Epoch 26/60\n",
      " - 0s - loss: 133501.7172 - val_loss: 78581.2402\n",
      "Epoch 27/60\n",
      " - 0s - loss: 133021.8067 - val_loss: 78220.1846\n",
      "Epoch 28/60\n",
      " - 0s - loss: 132576.2773 - val_loss: 77792.0069\n",
      "Epoch 29/60\n",
      " - 0s - loss: 132249.6198 - val_loss: 77806.4446\n",
      "Epoch 30/60\n",
      " - 0s - loss: 131766.5476 - val_loss: 77455.5913\n",
      "Epoch 31/60\n",
      " - 0s - loss: 131308.9733 - val_loss: 77109.9114\n",
      "Epoch 32/60\n",
      " - 0s - loss: 130949.1287 - val_loss: 76698.4368\n",
      "Epoch 33/60\n",
      " - 0s - loss: 130561.4499 - val_loss: 76388.0480\n",
      "Epoch 34/60\n",
      " - 0s - loss: 130327.6313 - val_loss: 75960.6259\n",
      "Epoch 35/60\n",
      " - 0s - loss: 129771.4789 - val_loss: 75748.1911\n",
      "Epoch 36/60\n",
      " - 0s - loss: 129432.7788 - val_loss: 75300.3402\n",
      "Epoch 37/60\n",
      " - 0s - loss: 129040.1360 - val_loss: 74865.8029\n",
      "Epoch 38/60\n",
      " - 0s - loss: 128672.2031 - val_loss: 74330.8694\n",
      "Epoch 39/60\n",
      " - 0s - loss: 128370.2884 - val_loss: 73866.1234\n",
      "Epoch 40/60\n",
      " - 0s - loss: 127899.7025 - val_loss: 73240.7342\n",
      "Epoch 41/60\n",
      " - 0s - loss: 127383.0463 - val_loss: 72723.7252\n",
      "Epoch 42/60\n",
      " - 0s - loss: 126876.4972 - val_loss: 72386.6868\n",
      "Epoch 43/60\n",
      " - 0s - loss: 126674.4289 - val_loss: 71906.9304\n",
      "Epoch 44/60\n",
      " - 0s - loss: 126038.9669 - val_loss: 71359.2643\n",
      "Epoch 45/60\n",
      " - 0s - loss: 125552.4764 - val_loss: 70755.9536\n",
      "Epoch 46/60\n",
      " - 0s - loss: 125072.8468 - val_loss: 70137.2431\n",
      "Epoch 47/60\n",
      " - 0s - loss: 124663.0934 - val_loss: 69427.7234\n",
      "Epoch 48/60\n",
      " - 0s - loss: 124372.6873 - val_loss: 68568.3261\n",
      "Epoch 49/60\n",
      " - 0s - loss: 123113.3496 - val_loss: 67879.1875\n",
      "Epoch 50/60\n",
      " - 0s - loss: 122099.1446 - val_loss: 67165.4286\n",
      "Epoch 51/60\n",
      " - 0s - loss: 121966.5046 - val_loss: 66942.7759\n",
      "Epoch 52/60\n",
      " - 0s - loss: 121393.7976 - val_loss: 66341.7179\n",
      "Epoch 53/60\n",
      " - 0s - loss: 120795.4773 - val_loss: 65812.6279\n",
      "Epoch 54/60\n",
      " - 0s - loss: 120401.3156 - val_loss: 65046.7092\n",
      "Epoch 55/60\n",
      " - 0s - loss: 119800.1898 - val_loss: 65018.4464\n",
      "Epoch 56/60\n",
      " - 0s - loss: 119784.2408 - val_loss: 64531.4482\n",
      "Epoch 57/60\n",
      " - 0s - loss: 119240.9286 - val_loss: 63935.4176\n",
      "Epoch 58/60\n",
      " - 0s - loss: 119007.9350 - val_loss: 62845.1297\n",
      "Epoch 59/60\n",
      " - 0s - loss: 118677.3667 - val_loss: 62541.0185\n",
      "Epoch 60/60\n",
      " - 0s - loss: 118281.2405 - val_loss: 62408.8654\n"
     ]
    }
   ],
   "source": [
    "lstm_history = model_lstm.fit(X_train_series, Y_train, validation_data=(X_valid_series, Y_valid), epochs=epochs, verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Sales Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "X_pred = pd.DataFrame({'Nintendo Switch Sales(t-2)}': [72995],\n",
    "                      'Nintendo Switch Sales(t-1)}': [65624],\n",
    "                      'Nintendo Switch Sales(t)}': [61031]})\n",
    "\n",
    "X_predict = X_pred.values.reshape((X_pred.shape[0], X_pred.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55560.254]], dtype=float32)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales predicted by LSTM = 64075.83 (since the LSTM model has random process, it may produce different values in different trials)\n",
    "model_lstm.predict(X_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[57400.406]], dtype=float32)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales predicted by MLP = 57486.945\n",
    "model_mlp.predict(X_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Data Visulization of Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "MLP_prediction = pd.DataFrame(model_mlp.predict(series))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train = train.drop([0,1])\n",
    "train = train.reset_index(drop = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MLP Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "MLP_df = pd.concat([train, MLP_prediction], axis=1)\n",
    "MLP_df.rename(columns={'Nintendo Switch Sales':'actual sales',0:'predicted sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "MLP_df = MLP_df[MLP_df.index <= 114]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'MLP_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-b2f306d433ca>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mMLP_sc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'actual sales'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'predicted sales'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcurrent_sales\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMLP_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mMLP_sc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mScatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMLP_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurrent_sales\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'MLP_df' is not defined"
     ]
    }
   ],
   "source": [
    "MLP_sc = []\n",
    "for values in ['actual sales', 'predicted sales']:\n",
    "    current_sales = MLP_df[values]\n",
    "    MLP_sc.append(go.Scatter(x = MLP_df['Date'], y=current_sales, name=(values)))\n",
    "\n",
    "layout = go.Layout(title = 'MLP Prediction', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = MLP_sc, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LSTM Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "series = series.values.reshape((series.shape[0], series.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "LSTM_prediction = pd.DataFrame(model_lstm.predict(series))\n",
    "LSTM_df = pd.concat([train, LSTM_prediction], axis=1)\n",
    "LSTM_df.rename(columns={'Nintendo Switch Sales':'actual sales',0:'predicted sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
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       "data": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LSTM_sc = []\n",
    "for values in ['actual sales', 'predicted sales']:\n",
    "    current_sales = LSTM_df[values]\n",
    "    LSTM_sc.append(go.Scatter(x = LSTM_df['Date'], y=current_sales, name=(values)))\n",
    "\n",
    "layout = go.Layout(title = 'LSTM Prediction', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = LSTM_sc, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "### 7. Deep Learning Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MLP: Prediction = 57486.945; MAE = 64821\n",
    "<br>\n",
    "LSTM: Prediction = 64075.83; MAE = 61376"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from xgboost import plot_importance\n",
    "import lightgbm as lgb\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import preprocessing\n",
    "\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "# Set seeds to make the experiment more reproducible.\n",
    "from tensorflow import set_random_seed\n",
    "from numpy.random import seed\n",
    "set_random_seed(1)\n",
    "seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load And Modify Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Nintendo Switch Sales</th>\n",
       "      <th>PS4 Sales</th>\n",
       "      <th>Xbox One Sales</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71528</td>\n",
       "      <td>59920</td>\n",
       "      <td>49805</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62374</td>\n",
       "      <td>50554</td>\n",
       "      <td>46845</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53734</td>\n",
       "      <td>47961</td>\n",
       "      <td>44867</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58457</td>\n",
       "      <td>61473</td>\n",
       "      <td>54336</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59834</td>\n",
       "      <td>58728</td>\n",
       "      <td>53374</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nintendo Switch Sales  PS4 Sales  Xbox One Sales  year  month  week  day\n",
       "0                  71528      59920           49805  2018      1     1    6\n",
       "1                  62374      50554           46845  2018      1     2   13\n",
       "2                  53734      47961           44867  2018      1     3   20\n",
       "3                  58457      61473           54336  2018      1     4   27\n",
       "4                  59834      58728           53374  2018      2     5    3"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_sales = pd.read_csv('Console Sales by Week.csv').dropna()\n",
    "weekly_sales = pd.read_csv('Console Sales by Week.csv').dropna()\n",
    "weekly_sales['Date'] = pd.DatetimeIndex(weekly_sales['Date']) # convert the data type of Date from String to Datetime\n",
    "weekly_sales['year'] = pd.DatetimeIndex(weekly_sales['Date']).year \n",
    "weekly_sales['month'] = pd.DatetimeIndex(weekly_sales['Date']).month \n",
    "weekly_sales['week'] = pd.DatetimeIndex(weekly_sales['Date']).week\n",
    "weekly_sales['day'] = pd.DatetimeIndex(weekly_sales['Date']).day\n",
    "weekly_sales = weekly_sales.drop(['Date'], axis=1)\n",
    "weekly_sales.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data Preprocessing and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = weekly_sales.drop(['Nintendo Switch Sales', 'PS4 Sales', 'Xbox One Sales'], axis=1)\n",
    "y = weekly_sales['Nintendo Switch Sales']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Predictive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest and XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressors = [\n",
    "    RandomForestRegressor(max_depth=10, random_state=1),\n",
    "    XGBRegressor(max_depth=10, learning_rate=0.05, random_state=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:51:40] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[47193.78952380952, 52083.1259765625]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE = []\n",
    "for regressor in regressors:\n",
    "    fit = regressor.fit(X_train, y_train)\n",
    "    pred = fit.predict(X_test)\n",
    "    mae = mean_absolute_error( y_test,pred)\n",
    "    MAE.append(mae)\n",
    "MAE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'regression',\n",
    "    'metric': {'l2', 'l1'},\n",
    "    'num_leaves': 10,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.9,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'verbose': 0\n",
    "}\n",
    "\n",
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's l1: 85753.2\tvalid_0's l2: 3.25048e+10\n",
      "Training until validation scores don't improve for 300 rounds\n",
      "[2]\tvalid_0's l1: 85670.2\tvalid_0's l2: 3.21243e+10\n",
      "[3]\tvalid_0's l1: 85591.4\tvalid_0's l2: 3.178e+10\n",
      "[4]\tvalid_0's l1: 85632.8\tvalid_0's l2: 3.14687e+10\n",
      "[5]\tvalid_0's l1: 85737.6\tvalid_0's l2: 3.1187e+10\n",
      "[6]\tvalid_0's l1: 85575.3\tvalid_0's l2: 3.09221e+10\n",
      "[7]\tvalid_0's l1: 85417.2\tvalid_0's l2: 3.0682e+10\n",
      "[8]\tvalid_0's l1: 85273.8\tvalid_0's l2: 3.04627e+10\n",
      "[9]\tvalid_0's l1: 85134\tvalid_0's l2: 3.02636e+10\n",
      "[10]\tvalid_0's l1: 85007.4\tvalid_0's l2: 3.00818e+10\n",
      "[11]\tvalid_0's l1: 85258\tvalid_0's l2: 2.98727e+10\n",
      "[12]\tvalid_0's l1: 85511.3\tvalid_0's l2: 2.96869e+10\n",
      "[13]\tvalid_0's l1: 85740.3\tvalid_0's l2: 2.95201e+10\n",
      "[14]\tvalid_0's l1: 85972.4\tvalid_0's l2: 2.93725e+10\n",
      "[15]\tvalid_0's l1: 86182\tvalid_0's l2: 2.92397e+10\n",
      "[16]\tvalid_0's l1: 85721.1\tvalid_0's l2: 2.92095e+10\n",
      "[17]\tvalid_0's l1: 85289.8\tvalid_0's l2: 2.91831e+10\n",
      "[18]\tvalid_0's l1: 84880.1\tvalid_0's l2: 2.916e+10\n",
      "[19]\tvalid_0's l1: 84490.8\tvalid_0's l2: 2.91398e+10\n",
      "[20]\tvalid_0's l1: 84121.1\tvalid_0's l2: 2.91223e+10\n",
      "[21]\tvalid_0's l1: 83813.3\tvalid_0's l2: 2.90323e+10\n",
      "[22]\tvalid_0's l1: 83520.8\tvalid_0's l2: 2.89487e+10\n",
      "[23]\tvalid_0's l1: 83207.6\tvalid_0's l2: 2.88722e+10\n",
      "[24]\tvalid_0's l1: 82944.4\tvalid_0's l2: 2.88e+10\n",
      "[25]\tvalid_0's l1: 82785.1\tvalid_0's l2: 2.88638e+10\n",
      "[26]\tvalid_0's l1: 82640.1\tvalid_0's l2: 2.88177e+10\n",
      "[27]\tvalid_0's l1: 82502.5\tvalid_0's l2: 2.87749e+10\n",
      "[28]\tvalid_0's l1: 82371.7\tvalid_0's l2: 2.87353e+10\n",
      "[29]\tvalid_0's l1: 82435\tvalid_0's l2: 2.86962e+10\n",
      "[30]\tvalid_0's l1: 82349.7\tvalid_0's l2: 2.86595e+10\n",
      "[31]\tvalid_0's l1: 82338.8\tvalid_0's l2: 2.85804e+10\n",
      "[32]\tvalid_0's l1: 82403.5\tvalid_0's l2: 2.85655e+10\n",
      "[33]\tvalid_0's l1: 82384.7\tvalid_0's l2: 2.84922e+10\n",
      "[34]\tvalid_0's l1: 82367.5\tvalid_0's l2: 2.84239e+10\n",
      "[35]\tvalid_0's l1: 82455.8\tvalid_0's l2: 2.84119e+10\n",
      "[36]\tvalid_0's l1: 82329.1\tvalid_0's l2: 2.84655e+10\n",
      "[37]\tvalid_0's l1: 82237.7\tvalid_0's l2: 2.85173e+10\n",
      "[38]\tvalid_0's l1: 82155.7\tvalid_0's l2: 2.85672e+10\n",
      "[39]\tvalid_0's l1: 82082.8\tvalid_0's l2: 2.86153e+10\n",
      "[40]\tvalid_0's l1: 82013.6\tvalid_0's l2: 2.86616e+10\n",
      "[41]\tvalid_0's l1: 81953.9\tvalid_0's l2: 2.85702e+10\n",
      "[42]\tvalid_0's l1: 82188.2\tvalid_0's l2: 2.85205e+10\n",
      "[43]\tvalid_0's l1: 82147.4\tvalid_0's l2: 2.84362e+10\n",
      "[44]\tvalid_0's l1: 82363.6\tvalid_0's l2: 2.83916e+10\n",
      "[45]\tvalid_0's l1: 82319\tvalid_0's l2: 2.83137e+10\n",
      "[46]\tvalid_0's l1: 82556.2\tvalid_0's l2: 2.82298e+10\n",
      "[47]\tvalid_0's l1: 82833.4\tvalid_0's l2: 2.8154e+10\n",
      "[48]\tvalid_0's l1: 83141.9\tvalid_0's l2: 2.80854e+10\n",
      "[49]\tvalid_0's l1: 83447.8\tvalid_0's l2: 2.80228e+10\n",
      "[50]\tvalid_0's l1: 83728.1\tvalid_0's l2: 2.79667e+10\n",
      "[51]\tvalid_0's l1: 83670\tvalid_0's l2: 2.79625e+10\n",
      "[52]\tvalid_0's l1: 83614.9\tvalid_0's l2: 2.79592e+10\n",
      "[53]\tvalid_0's l1: 83562.5\tvalid_0's l2: 2.79566e+10\n",
      "[54]\tvalid_0's l1: 83527.7\tvalid_0's l2: 2.79546e+10\n",
      "[55]\tvalid_0's l1: 83529.1\tvalid_0's l2: 2.79531e+10\n",
      "[56]\tvalid_0's l1: 83775\tvalid_0's l2: 2.80447e+10\n",
      "[57]\tvalid_0's l1: 84024.1\tvalid_0's l2: 2.81337e+10\n",
      "[58]\tvalid_0's l1: 84260.8\tvalid_0's l2: 2.822e+10\n",
      "[59]\tvalid_0's l1: 84485.7\tvalid_0's l2: 2.83037e+10\n",
      "[60]\tvalid_0's l1: 84699.3\tvalid_0's l2: 2.83846e+10\n",
      "[61]\tvalid_0's l1: 85136.2\tvalid_0's l2: 2.83272e+10\n",
      "[62]\tvalid_0's l1: 85551.2\tvalid_0's l2: 2.82752e+10\n",
      "[63]\tvalid_0's l1: 85945.5\tvalid_0's l2: 2.8228e+10\n",
      "[64]\tvalid_0's l1: 86320.1\tvalid_0's l2: 2.81851e+10\n",
      "[65]\tvalid_0's l1: 86675.9\tvalid_0's l2: 2.81463e+10\n",
      "[66]\tvalid_0's l1: 86658.6\tvalid_0's l2: 2.81177e+10\n",
      "[67]\tvalid_0's l1: 86642.2\tvalid_0's l2: 2.8091e+10\n",
      "[68]\tvalid_0's l1: 86643.9\tvalid_0's l2: 2.80662e+10\n",
      "[69]\tvalid_0's l1: 86659\tvalid_0's l2: 2.8043e+10\n",
      "[70]\tvalid_0's l1: 86673.4\tvalid_0's l2: 2.80214e+10\n",
      "[71]\tvalid_0's l1: 86384\tvalid_0's l2: 2.80439e+10\n",
      "[72]\tvalid_0's l1: 86109\tvalid_0's l2: 2.8066e+10\n",
      "[73]\tvalid_0's l1: 85847.8\tvalid_0's l2: 2.80874e+10\n",
      "[74]\tvalid_0's l1: 85774.2\tvalid_0's l2: 2.80881e+10\n",
      "[75]\tvalid_0's l1: 85534\tvalid_0's l2: 2.81088e+10\n",
      "[76]\tvalid_0's l1: 85359.8\tvalid_0's l2: 2.81414e+10\n",
      "[77]\tvalid_0's l1: 85197.4\tvalid_0's l2: 2.8173e+10\n",
      "[78]\tvalid_0's l1: 85055.1\tvalid_0's l2: 2.82035e+10\n",
      "[79]\tvalid_0's l1: 84924.7\tvalid_0's l2: 2.82329e+10\n",
      "[80]\tvalid_0's l1: 84824.3\tvalid_0's l2: 2.82613e+10\n",
      "[81]\tvalid_0's l1: 84950.7\tvalid_0's l2: 2.82043e+10\n",
      "[82]\tvalid_0's l1: 85111.3\tvalid_0's l2: 2.81652e+10\n",
      "[83]\tvalid_0's l1: 85259.2\tvalid_0's l2: 2.81145e+10\n",
      "[84]\tvalid_0's l1: 85404.7\tvalid_0's l2: 2.80799e+10\n",
      "[85]\tvalid_0's l1: 85542.1\tvalid_0's l2: 2.80349e+10\n",
      "[86]\tvalid_0's l1: 84822.6\tvalid_0's l2: 2.80943e+10\n",
      "[87]\tvalid_0's l1: 84139.1\tvalid_0's l2: 2.81559e+10\n",
      "[88]\tvalid_0's l1: 83489.8\tvalid_0's l2: 2.82191e+10\n",
      "[89]\tvalid_0's l1: 82872.9\tvalid_0's l2: 2.82833e+10\n",
      "[90]\tvalid_0's l1: 82297.9\tvalid_0's l2: 2.83481e+10\n",
      "[91]\tvalid_0's l1: 82668.1\tvalid_0's l2: 2.84414e+10\n",
      "[92]\tvalid_0's l1: 83019.7\tvalid_0's l2: 2.85318e+10\n",
      "[93]\tvalid_0's l1: 83353.8\tvalid_0's l2: 2.86193e+10\n",
      "[94]\tvalid_0's l1: 83696.4\tvalid_0's l2: 2.87038e+10\n",
      "[95]\tvalid_0's l1: 84036.5\tvalid_0's l2: 2.87854e+10\n",
      "[96]\tvalid_0's l1: 84417.4\tvalid_0's l2: 2.87353e+10\n",
      "[97]\tvalid_0's l1: 84779.3\tvalid_0's l2: 2.86899e+10\n",
      "[98]\tvalid_0's l1: 85123.1\tvalid_0's l2: 2.86487e+10\n",
      "[99]\tvalid_0's l1: 85345.4\tvalid_0's l2: 2.85807e+10\n",
      "[100]\tvalid_0's l1: 85732.6\tvalid_0's l2: 2.8544e+10\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[41]\tvalid_0's l1: 81953.9\tvalid_0's l2: 2.85702e+10\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(params,\n",
    "                train_set=lgb_train,\n",
    "                num_boost_round=100,\n",
    "                valid_sets=lgb_eval,\n",
    "                early_stopping_rounds=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[47193.78952380952, 52083.1259765625, 81953.85695471083]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)\n",
    "\n",
    "MAE.append(mean_absolute_error(y_test, y_pred))\n",
    "MAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Sales Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = datetime.datetime(2020, 5, 9)\n",
    "X_pred = pd.DataFrame({'year': [x.year], 'month':[x.month], \n",
    "                       'week':[x.isocalendar()[1]], 'day':[x.day]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([65338.12124415])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales predicted by LightGBM = 65338.12124415\n",
    "gbm.predict(X_pred, num_iteration=gbm.best_iteration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51551.793], dtype=float32)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales predicted by XGBoost = 51551.793\n",
    "fit.predict(X_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51409.96666667])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales predicted by RandomForest = 51409.96666667\n",
    "rf = RandomForestRegressor(max_depth=10, random_state=1)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "rf.predict(X_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Data Visualization of Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LightGBM Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_sales = pd.read_csv('Console Sales by Week.csv').dropna()\n",
    "gbm_prediction = pd.DataFrame(gbm.predict(X, num_iteration=gbm.best_iteration))\n",
    "gbm_df = pd.concat([weekly_sales, gbm_prediction], axis=1)\n",
    "gbm_df.rename(columns={'Nintendo Switch Sales':'actual sales',0:'predicted sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_sc = []\n",
    "for values in ['actual sales', 'predicted sales']:\n",
    "    current_sales = gbm_df[values]\n",
    "    gbm_sc.append(go.Scatter(x = gbm_df['Date'], y=current_sales, name=(values)))\n",
    "\n",
    "layout = go.Layout(title = 'LightGBM Prediction', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = gbm_sc, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RandomForest Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_prediction = pd.DataFrame(rf.predict(X))\n",
    "rf_df = pd.concat([weekly_sales, rf_prediction], axis=1)\n",
    "rf_df.rename(columns={'Nintendo Switch Sales':'actual sales',0:'predicted sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "rf_sc = []\n",
    "for values in ['actual sales', 'predicted sales']:\n",
    "    current_sales = rf_df[values]\n",
    "    rf_sc.append(go.Scatter(x = rf_df['Date'], y=current_sales, name=(values)))\n",
    "\n",
    "layout = go.Layout(title = 'Random Forest Prediction', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = rf_sc, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGB Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "XGB_prediction = pd.DataFrame(fit.predict(X))\n",
    "XGB_df = pd.concat([weekly_sales, XGB_prediction], axis=1)\n",
    "XGB_df.rename(columns={'Nintendo Switch Sales':'actual sales',0:'predicted sales'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "XGB_sc = []\n",
    "for values in ['actual sales', 'predicted sales']:\n",
    "    current_sales = XGB_df[values]\n",
    "    XGB_sc.append(go.Scatter(x = XGB_df['Date'], y=current_sales, name=(values)))\n",
    "\n",
    "layout = go.Layout(title = 'XGB Prediction', xaxis = dict(title = 'Date'), yaxis = dict(title = 'Sales'))\n",
    "fig = go.Figure(data = XGB_sc, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Machine Learning Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForest: Prediction = 51409.96666667; MAE = 47193\n",
    "<br>\n",
    "XGBoost: Prediction = 51551.793; MAE = 52083"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Final Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Collect Predicted sales of RandomForest, XGBoost, MLP, and LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_Pred = 51410\n",
    "XGB_Pred = 51552\n",
    "MLP_Pred = 57487\n",
    "LSTM_Pred = 64076"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Calculate Weights for RandomForest, XGBoost, MLP, and LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAE = [47193, 52083, 64821, 61376]\n",
    "Weights = [float(i)/sum(MAE) for i in MAE]\n",
    "RF_Weight = Weights[0]\n",
    "XGB_Weight = Weights[1]\n",
    "MLP_Weight = Weights[2]\n",
    "LSTM_Weight = Weights[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Calculate Final Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56637.68322149437"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_Sales = RF_Pred * RF_Weight + XGB_Pred * XGB_Weight + MLP_Pred * MLP_Weight + LSTM_Pred * LSTM_Weight\n",
    "Predicted_Sales"
   ]
  }
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