sales-forecast-of-using-lstm-rnn-model-on-fred-dataset-retail-sales-clothing-and-clothing-accessories-stores-rsccsn.ipynb

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      "name": "Sales Forecast of using LSTM RNN model on FRED dataset Retail Sales:Clothing and Clothing Accessories Stores(RSCCSN)).ipynb",
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      "source": [
        "LSTM RNN Models for forecast and advance estimation of Sales on FRED RSCCASN-Federal Reserve Economic Research Department, namely Retail Sales: Clothing and Clothing Accessories Stores Number"
      ],
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        "id": "UWx6x8F2MBoY"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ],
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        "df = pd.read_csv('RSCCASN.csv')"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 334 entries, 0 to 333\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   DATE     334 non-null    object\n",
            " 1   RSCCASN  334 non-null    int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 5.3+ KB\n"
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        "\n",
        "#converting to datetime object\n",
        "df['DATE'] = pd.to_datetime(df['DATE'])\n",
        "df.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 334 entries, 0 to 333\n",
            "Data columns (total 2 columns):\n",
            " #   Column   Non-Null Count  Dtype         \n",
            "---  ------   --------------  -----         \n",
            " 0   DATE     334 non-null    datetime64[ns]\n",
            " 1   RSCCASN  334 non-null    int64         \n",
            "dtypes: datetime64[ns](1), int64(1)\n",
            "memory usage: 5.3 KB\n"
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        "\n",
        "#Setting Index of Datframe\n",
        "df.rename(columns={'RSCCASN':'Sales'},inplace=True)\n",
        "df.set_index('DATE',inplace=True)"
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              "            Sales\n",
              "DATE             \n",
              "1992-01-01   6938\n",
              "1992-02-01   7524\n",
              "1992-03-01   8475\n",
              "1992-04-01   9401\n",
              "1992-05-01   9558\n",
              "...           ...\n",
              "2019-06-01  21123\n",
              "2019-07-01  21714\n",
              "2019-08-01  23791\n",
              "2019-09-01  19695\n",
              "2019-10-01  21113\n",
              "\n",
              "[334 rows x 1 columns]"
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        "#Checking dataframe stats\n",
        "df.describe()\n",
        "#334 month records (monthly frequency at every 1st day of month as shown above)\n",
        "#max sales of 34706 goods\n",
        "#mean monthly sales of 16325 goods and median sales 15878\n",
        "#mean and median suggestive of a right skewed distribution"
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              "              Sales\n",
              "count    334.000000\n",
              "mean   16325.095808\n",
              "std     5369.839014\n",
              "min     6938.000000\n",
              "25%    12298.500000\n",
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      "cell_type": "code",
      "source": [
        "#Exploratory Analysis\n",
        "df.plot()\n",
        "#Monthly sales data\n",
        "#periodicity and seasonality in data\n",
        "#A general increasing trend of sales with kink around 2008"
      ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df, bins=25)\n",
        "#Median Sales\n",
        "#Right skewed distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "oP1ktoIQclc8",
        "outputId": "48b6009a-036e-478c-c5fc-53fa0a94e388"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e48fce090>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "test_ind=18;#one and a half year dataset or 18 months\n",
        "len(df)-test_ind"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjKX4c_DgJFV",
        "outputId": "b6d25014-7071-4d12-a313-c0e7c175b730"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "316"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test train split for testing\n",
        "train_data=df[:-test_ind]\n",
        "test_data=df[-test_ind:]\n",
        "len(train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8l7YGu_iElO",
        "outputId": "18e498dc-f036-49d9-fa22-5c302f4a6e78"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "316"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Preparation\n",
        "#Scaling Data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "scaler.fit(train_data);\n",
        "scaled_train_data = scaler.transform(train_data);\n",
        "scaled_test_data = scaler.transform(test_data)\n",
        "\n",
        "#Sequence Generator\n",
        "seq_length=12;#From time series plot of sales above as the periodicity suggests\n",
        "#import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
        "train_generator=TimeseriesGenerator(scaled_train_data,scaled_train_data,length=seq_length,batch_size=1)\n",
        "validation_generator=TimeseriesGenerator(scaled_test_data,scaled_test_data,length=seq_length,batch_size=1)"
      ],
      "metadata": {
        "id": "iCjL4SdOsD1i"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Model Building\n",
        "n_features=1;#only one feature to predict in the sequence\n",
        "hidden_state=100;# LSTM hidden states\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM,Dense\n",
        "model=Sequential();\n",
        "model.add(LSTM(hidden_state,activation='relu',input_shape=(seq_length,n_features)));\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam',loss='mse')\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB7GIMWcxKHc",
        "outputId": "5e71ee66-77ae-4338-fc7e-04081d2b7990"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 100)               40800     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Early Stopping for regularization to prevent over and underfitting\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop=EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "metadata": {
        "id": "KGDZPwT6xqnM"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Fitting \n",
        "model.fit(train_generator,epochs=20,validation_data=validation_generator,callbacks=[early_stop])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdkPT2SZ1DuH",
        "outputId": "0fc9b1b9-29a8-4cd1-8807-7599eb02111d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "304/304 [==============================] - 4s 8ms/step - loss: 0.0249 - val_loss: 0.0069\n",
            "Epoch 2/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0178 - val_loss: 0.0022\n",
            "Epoch 3/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0103 - val_loss: 0.0021\n",
            "Epoch 4/20\n",
            "304/304 [==============================] - 3s 9ms/step - loss: 0.0048 - val_loss: 0.0160\n",
            "Epoch 5/20\n",
            "304/304 [==============================] - 4s 12ms/step - loss: 0.0051 - val_loss: 5.9322e-04\n",
            "Epoch 6/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0018 - val_loss: 3.0007e-04\n",
            "Epoch 7/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0015 - val_loss: 0.0033\n",
            "Epoch 8/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0013 - val_loss: 2.8709e-04\n",
            "Epoch 9/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0017 - val_loss: 0.0047\n",
            "Epoch 10/20\n",
            "304/304 [==============================] - 2s 7ms/step - loss: 0.0015 - val_loss: 2.0255e-04\n",
            "Epoch 11/20\n",
            "304/304 [==============================] - 2s 8ms/step - loss: 0.0014 - val_loss: 0.0020\n",
            "Epoch 12/20\n",
            "304/304 [==============================] - 2s 7ms/step - loss: 0.0012 - val_loss: 0.0025\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7e48e786d0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Loss plot\n",
        "losses=pd.DataFrame(model.history.history)\n",
        "losses.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3ocTop42FGW",
        "outputId": "5bcfe17c-fd48-4105-9eb1-ca639fbf5be7"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loss', 'val_loss'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "rbOUfY3U2ip3",
        "outputId": "905a7999-c804-419a-91ad-645608a6bba9"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e48bb2f50>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Sikq7w1FKqVppAvCRW0aksO9wKe98t8vuUJRSqlaaAHzk7NRY+nZpy8wlW6iqCpxZ15RSrYcmAB8REaaNSGFLbhGfbthrdzhKKXUCTQA+dGG/TiS2D2fmkq12h6KUUifQBOBDTkcQU4f1YOX2A6zYtt/ucJRS6jiaAHzsqvQk2kcEM1OHh1BK+RlNAD4WHuJg0pnd+XTDPjbv1QljlFL+QxNAM5g4tDthwUF6L0Ap5Vc0ATSDDpEhTBjUlXdW57D7kA4VrZTyD5oAmsmUs5OpMvDil9vsDqVpKsuhSns3K9USaAJoJkkdIrj4tM68+s0ODh0J4EHiZl8K7/3K7iiUUl6gCaAZ3Ty8B4WlFcz5ZrvdoTRO8X7YsRw2fwJGezcrFeg0ATSjvl2iGdYzlhe/3EZJeQCWUbZ/BRg4vBsOZdsdjVKqiTQBNLNbR6SQe7iUBd/l2B1Kw21bdmw5+1v74lBKeYUmgGY2NCWGtIRonl2ylcpAGyRu2zLodjY4w2GnJgClAp1HCUBERonIRhHJEpF7ank/VETmut7/RkS6u9bHiMhiESkUkadrfOZz1z5Xux4dvXFA/s4aJK4HW/OK+OSHPXaH47ni/bB3HaScAwkDNAEo1QLUmwBExAFMB0YDfYCrRaRPjc2mAAeMManAE8DfXOtLgAeA39ax+2uNMf1dj32NOYBANLpfZ7p2iGDGF1sxgXIz1V3/7z4MkjJgz/dQrn0alApknlwBZABZxpitxpgy4HVgTI1txgCzXcvzgfNERIwxRcaYZViJQLk4goSpw3uwZudBvv0pQAaJ27bMKv10GQCJGVBVAbu+szsqpVQTeJIAEoCd1V5nu9bVuo0xpgI4BMR4sO8XXeWfB0REPNi+xbhyYCIxkSE8EyiDxG1bBl0HgzPEugIA2PmNvTEppZrEzpvA1xpj0oBhrsf1tW0kIjeLSKaIZObm5jZrgL4UFuxg8pndWbwxlx/3FNgdzsm56//dz7ZeR8ZChx6wc4W9cSmlmsSTBJADJFV7nehaV+s2IuIEooH8k+3UGJPjej4MvIpVaqptu1nGmHRjTHpcXJwH4QaO64d2IyLEwSx/HySuev3fLWmwdQUQKPcwlFIn8CQBrAB6ikiyiIQAE4CFNbZZCExyLY8DFpmT3N0UEaeIxLqWg4GLgXUNDT7QtYuwBolbuHoXOQf9+IZq9fq/W+IgKM6DAz/ZF5dSqknqTQCumv4dwEfABmCeMWa9iDwkIpe6NnseiBGRLOA3wNGmoiKyDXgcmCwi2a4WRKHARyLyPbAa6wriWe8dVuCYMiwZA7ywzI9PpNXr/25Jg61nbQ6qVMByerKRMeYD4IMa6/5YbbkEuLKOz3avY7cDPQuxZUtoF86lp3fhtW93cOe5qbSLCKn/Q83JXf8/977j13fsDSFtrARw+gR7YlNKNYn2BPYD00b0oLiskv9+7YeDxNVW/wcIckDiQL0CUCqAaQLwA706teWcU+N46Ss/HCSutvq/W2IG7FsPpTrVpVKBSBOAn7hlRAp5hWXMX+lno2zWVv93SxoMpgpyVjZ/XEqpJtME4CcGJ3fg9KR2PLvUjwaJq9n+v6ZE120c7Q+gVEDSBOAnRIRbhvdge34xH633k0Hi6qr/u4W3h7he2iNYqQClCcCPXNC3E8mxkTzzxRb/GCTuZPV/t6QMyF4BVVXNF5dSyis0AfgRR5AwdVgPvs8+xPKtJ+1I3TxOVv93S8yAkoOQv7n54lJKeYUmAD9z+YAEYqNCmfmFzcND1Ff/d9MOYUoFLE0AfiYs2MENZ3Xni025/LDLxkHi6qv/u8WkQlg7vQ+gVADSBOCHrhvcjcgQB7OW2DhUtCf1f4CgoGP3AZRSAUUTgB+KjgjmmsFdeff73ezcX2xPEJ7U/90SMyD3RzhywPdxKaW8RhOAn7rx7GQEeN6OQeI8rf+7uSeIydYOYUoFEk0AfqpzdDhj+icwd8VODhSVNe+Xe1r/d0sYCBKk9wGUCjCaAPzYtBE9OFJeycvLm3mQOE/r/26hURDfF7K1JZBSgUQTgB87Jb4N5/XqyOzl2zhS1oyDxDWk/u+WmAHZmVDlZ4PZKaXqpAnAz00bkcL+ojLmr9zZPF/Y0Pq/W9JgKCuEfRt8E5dSyus0Afi5Qd3bM6BrO2Yt3UpFZTMMt9DQ+r9b0iDrWe8DKBUwNAH4ORFh2ogUdu4/wofrmmGQuIbW/93aJ0NknPYHUCqAaAIIAOf3jqdHXDMNEteY+j+AiHUfQK8AlAoYmgACQFCQMG14D9bvKuDLLB8OEtfY+r9bUgbs3wpFed6NSynlE5oAAsRlZyTQsU0oM305PIS7/t+tCQkAdGA4pQKEJoAAEep0cOPZySzdnMe6nEO++RJ3/T+hgfV/ty5nQJBT+wMoFSA0AQSQawZ3JSrUycwlPhoqetsy61e8M7Rxnw8Oh06n6RWAUgFCE0AAaRsWzLWDu/L+97u8P0jc0fp/A5t/1pQ0GHJWQWW5d+JSSvmMJoAAc+PZyTiChGeXevkq4Gj7/0bW/92SBkHFEdiz1ithKaV8RxNAgIlvG8bYMxKYl7mT/MJS7+24qfV/N/cMYdofQCm/pwkgAN08PIWS8irvDhLX1Pq/W3QitOmi/QGUCgCaAAJQascozu8Tz+zl2yguq2j6Dr1V/3dLyoCdegWglL/TBBCgbhnRg4PF5cxb4YVB4rxV/3dLyoBDO6Bgt3f2p5TyCU0AAWpgtw4M6t6eZ5f+RHlTB4nzVv3f7eh9AG0OqpQ/0wQQwKYNTyHn4BE+WNvEX9reqv+7dToNHKHaH0ApP+dRAhCRUSKyUUSyROSeWt4PFZG5rve/EZHurvUxIrJYRApF5OkanxkoImtdn3lSRMQbB9SanNurIz07RjHj8y1UVTVykDhv1//BGkiuyxl6I1gpP1dvAhARBzAdGA30Aa4WkT41NpsCHDDGpAJPAH9zrS8BHgB+W8uuZwBTgZ6ux6jGHEBrFhQk3HFuKj/uOcxrK3Y0biferv+7JQ2C3WugvMS7+1VKeY0nVwAZQJYxZqsxpgx4HRhTY5sxwGzX8nzgPBERY0yRMWYZViI4SkQ6A22NMV8ba3zjl4HLmnIgrdWlp3dhaI8YHvvwR/YdbsTJ1tv1f7ekwVBZZiUBpZRf8iQBJADVm5pku9bVuo0xpgI4BMTUs8/sevapPCAiPDy2H6XlVfzlvUZMx+jt+r9bomtkUL0RrJTf8vubwCJys4hkikhmbm6u3eH4pZS4KG4bmcK7a3bxxaYG/Bn5ov7v1iYe2nXT+wBK+TFPEkAOkFTtdaJrXa3biIgTiAZONnNJjms/J9snAMaYWcaYdGNMelxcnAfhtk63npNCj9hI7n97LUfKKj37kK/q/25Jg62WQL6exUwp1SieJIAVQE8RSRaREGACsLDGNguBSa7lccAic5K5C40xu4ECERniav0zEXinwdGro0KdDh4Zm8bO/Ud4atFmzz7kq/q/W1IGFO6Fg428Qa2U8ql6E4Crpn8H8BGwAZhnjFkvIg+JyKWuzZ4HYkQkC/gNcLSpqIhsAx4HJotIdrUWRLcBzwFZwBbgQ+8cUus1NCWGcQMTmbVkKxv3HK7/A76q/7u5ZwjTgeGU8ktOTzYyxnwAfFBj3R+rLZcAV9bx2e51rM8E+nkaqPLMvRf25rMNe7l3wVremDaUoKA6ule46/8j7/NdMB37QnCkdR8gbZzvvkcp1Sh+fxNYNUyHyBDuu6gPK7cf4PWTjRPk6/o/gMNplZe0R7BSfkkTQAt0xYAEhvTowGMfbqi7b4Cv6/9uSRnW5DBlRb79HqVUg2kCaIFEhEfGplFSXsXDdfUN8HX93y1pMJhK2PWdb79HKdVgmgBaqJS4KG49J4WFtfUN8GX7/5oSB1nP2h9AKb+jCaAFc/cNeODtdZSUV+sb0Bz1f7eIDhDTUyeIUcoPaQJowcKCHTw8th879hcf3zeguer/bkkZ1pAQ2iFMKb+iCaCFOzMllisGJDLzi61s2uvqG9Bc9X+3pAwozof9W5vn+5RSHtEE0Arcd1Fv2oQ5ufettVQVNWP93809MJzeB1DKr2gCaAU6RIZw74W9ydx+gGWfvUOz1f/d4npBaFvtD6CUn9EE0EqMG5jI4OQO7Fj1MaY56/8AQUGQmK4JQCk/owmglXD3DRhgfmBTSO/mq/+7JQ2GfT9ASUHzfq9Sqk6aAFqR1Kgyest23j2UwpKGzBvgDYmDAAM5mc37vUqpOmkCaE12LEcw7Gg7gPtr9g3wtcR0QLQ/gFJ+RBNAa+Jq/3/12DEn9g3wtbBo6NhbWwIp5Uc0AbQm25ZCUgZDT0ng8gEJzFpSrW9Ac0jKgOxMqKpqvu9UStVJE0BrUbwf9hxr/3/fhb2JDHVy34K1VFU1Uw/dxAwoPQR5G5vn+5RSJ6UJoLXYsZzq7f9jokK598LerNh2gHmZJ5k3wJuSBlvP2hxUKb+gCaC1qGX8nysHJpKR3IG/frCB3MOlvo8hJgXCO2gCUMpPtPwEUFUFy56A7+fZHYm9XPX/6u3/RYS/jk3jSHklj7z/g+9jEDk2MJxSynYtPwFgYPMn8O6vIC/L7mDsUaP+X11qxyhuPSeVt1fvYunmZugbkDgI8jZZMSmlbNXyE0CQA654zvrlO38ylNcxRWJLVqP+X9Nt56SQHBvZPH0D3PcBsrVDmFJ2a/kJAKBtFxj7jDU37cf32x1N86tn/P+wYAePXNaP7fnFPL3Ix1dJCQNAHNofQCk/0DoSAMApP4ehd8CKZ+GHhXZH07xqqf/XdGZqLJcPSGDmki1s9mXfgJBI6NRP7wMo5QdaTwIAOO9P0GUALLwDDmy3O5rmcZL6f03uvgH3+rpvQGIGZK+EygrffYdSql6tKwE4Q2DcC9bUhG9OgcpyuyPyvXrq/9XFRIVy72irb8AbK33YNyBpMJQXWaODKqVs07oSAECHZLjk35C9AhY9bHc0vtfA+X+vTHf3DfiRvEIf9Q1IGmQ9630ApWzV+hIAQL/LYeAN8OW/IOtTu6PxLQ/q/9VZfQP6UVxWwSPvb/BNTO26QVS8lYSVUrZpnQkAYNSj0LEvvDUNDu+xOxrfaED9v7rUjm24dUQKC77LYdnmPO/H5e4QplcAStmq9SaA4HC48kUoL4Y3b4KqZhwbv7k0oP5f020jU+keE8H9b6/1Td+AxAw4sA0K93l/30opj7TeBAAQdypc+A+rTLL0/+yOxvsaWP+vLizYwSNj09iWX8z0xT7oG6ADwyllu9adAAD6XwtpV8Hnj8K2L+2OxrsaWP+v6azUWC4/I4FnvvBB34DOp0NQsPYHUMpGHiUAERklIhtFJEtE7qnl/VARmet6/xsR6V7tvT+41m8UkZ9XW79NRNaKyGoRsW9cABG4+HFon2yVgorybQvFqxpZ/6/pvot81DcgOAy69NcrAKVsVG8CEBEHMB0YDfQBrhaRPjU2mwIcMMakAk8Af3N9tg8wAegLjAL+49qf20hjTH9jTHqTj6QpQttY9wOK8+Cd26x+AoGuCfX/6nzaNyAxA3Z9BxVl3t2vUsojTg+2yQCyjDFbAUTkdWAMUL0XzxjgQdfyfOBpERHX+teNMaXATyKS5drfcu+E70WdT4cLHoEP74av/wNDb7c7oqZpQv2/pivTE5m/Mpu/fvAj5/WOJzaqcSWl6owxFHUcQFTFdL7PXMpPYb3IKywj93ApeYXWI/dwKUfKK7nr/FO56LTOTf5OpdTxPEkACUD1n37ZwOC6tjHGVIjIISDGtf7rGp9NcC0b4GMRMcBMY8yshofvZRlT4acv4JM/QdchkDDQ7ogar4n1/+pEhEfG9uPCJ5fy1/c38Pj4/rVuZ4zh0JFy8gpL2Xe4lLzCMvIOl5JbWEqe68RuLZeRV1hKTNURvgmDBe8u4MXK0QA4g4TYqFDi2oTSsU0ouw+VcOdrqygqO42r0pOafCxKqWM8SQC+crYxJkdEOgKfiMiPxpglNTcSkZuBmwG6du3q24hEYMzT8MwweOMGuGUphEX79jt9wV3/H3mf13bZM74Nt4xI4alFWSS0D6fKGNev9eN/tZdXnlg+cwYJMVEhxLUJJTYqlF6d2rqWe1P8ZX99mMoAABimSURBVGdu75bPNRcOJ65NKNHhwVgXj65DKatg2isr+d387zlSVsmkM7t77ZiUau08SQA5QPWfXomudbVtky0iTiAayD/ZZ40x7ud9IrIAqzR0QgJwXRnMAkhPT/d9cT68PVzxPLw4Gt79JYx70UoMgcRL9f+abh+Zyvtrd/PUoiwcQUJsVAixUdZJ/dRObVzL1ok+LiqUWNdzdHgwQUF1/BnuHkrEzm+JjW9T69sRIU6em5TOHa9+x58WrqeorILbzkn16nEpHziUY/077HdF4P3/aUU8SQArgJ4ikox18p4AXFNjm4XAJKza/jhgkTHGiMhC4FUReRzoAvQEvhWRSCDIGHPYtXwB8JBXjsgbug6Gc++Hz/4MySMg/Qa7I2oYL9b/qwsLdvD+ncM4Ul5Ju5Od1BsiaTCsfwsOZUN0Yq2bhDod/OfaAfz2jTX8/X8bKSqt4LcXnHrclYLyI6WH4ZWxkLcRjhywSqvKL9WbAFw1/TuAjwAH8IIxZr2IPARkGmMWAs8Dr7hu8u7HShK4tpuHdcO4ArjdGFMpIvHAAtd/YCfwqjHmfz44vsY761dWHf1/91i19Pi+dkfkOS/W/2sKD3EQHuKof0NPHR0Y7ts6EwBAsCOIx6/qT3iwg+mLt1BUWskfL+7jnSSkvMcYePs2yN8MnfvD//5gPbv/npVfERNATR7T09NNZmYzdhko3AfPnA1h7eDmxdZkJv6ueD/8vYdV/x9xt93R1K+yHB5Nsq6yRj1a7+bGGB5+fwPPL/uJq9ITefTy03BoEvAfy56ATx+E8/8CA66HmSOgqgKmLYHIWLuja7VEZGVtze21J/DJRHWEy2dZk5h/+Du7o/GMj+r/PuMIhi5neDwwnIhw/0W9+cV5PZmXmc0vX/+O8soqHwepPLJlEXz2EPQdC2fead1PG/8KFOVZ82+0xPG2ApwmgPr0OAeG/xa++y98P8/uaOrno/q/TyVlwO7vofyIR5uLCL85/xT+MLoX732/m1v/u9L3k9mrkzuwHebfCLGnwqVPH7vx2/l0uOj/YOvnsPgRW0NUJ9IE4IkR90DXM+G9X0P+FrujObltS616qw/q/z6TlAFV5bBrdYM+Nm1ECn+5rB+fbtjHlNkrKCrVKSZtUX4E5l4HVVUwYQ6ERh3//oDrYcBEa8DFjR/aE6OqlSYATziccMVzVrnijclQ4aOZsprKS+P/NLvEDOu5EQPDXT+kG/935eks35LPxBe+5dCRVjDNpz8xxvphtOd7q1wak1L7dqP/YV0NvDUN9m9t3hhVnTQBeCo6AS6bYf1D//gBu6OpXaDV/92i4qzB+Bo5MNwVAxOZfs0Avs8+yDXPfk2+r6ayVCda8Rysec26Sj51VN3bBYfBVS9bpaG5Ez0u9ynf0gTQEKeOhiG3wbczYcN7dkdzom3LwBkWmENYJA22EkAjW6WNTuvMrInpZO0rZPysr9lbUOLlANUJdnxtNZM+ZRSM+H3927fvbl1J710H79/VMgZdDHCaABrqZw9a7ZrfuQ0O7rA7muP5sP2/zyUNgqJ91ixhjTTy1I68dEMGuw8e4cpnlrNzf7H34lPHK9gN8yZCu64wdiYEeXgq6Xk+jPgdrJ4DK1/yaYiqfpoAGsoZag0dXVUF86dY7dj9QaDW/93cM4Q1caL4oSkx/PemwRwsLuOqmcvZklvoheB8wBj4ekZgtCyrqaIM3phk9fgdPwfC2zXs8yN+DynnWU2rc1b5JkblEU0AjdGhB1zyL+umpb80bQvU+r9bxz4QEuWVieLP6Nqe128eSllFFeNnLmfD7gIvBOhFFaXw1lSrfPLWVPj0z9YPikDx0R+sv6cx0yG+5tQgHghyWKWgqHiYN8n68aJsoQmgsdLGwYBJVs/HrM/sjiaw6/9gnRQSBnpthrA+Xdoy75ahOIOCmDDra1bvPOiV/TZZ8X5rnJy1b1jjTQ28AZY9biUCf21dVt13c6wbv2feCf0ub/x+IjrAVbOhcI81E592ErOFJoCmGPUYxPWGBdPg8F57Ywnk+r9bUoZ1g7DUO2WblLgo3rhlKNHhwVz77Nd8vdXm6T73/wTPX2CVua54HobfDRc/Yd1XWjffSgz+/Gt413dWk8/k4XDeg03fX8JAGP132PIZfPH3pu9PNZgmgKYIiYArX7JOWG9Nte9XTKDX/92SBoOpgl3eqwsndYjgjVuG0rldOJNe+JbPN+7z2r4bJHslPPczKMqFie9YV5BgNYs8+9dWQsheAS/8vEk3wn2mKB/mXg+RcdYQ6Q4vTSUycDKcfg188TfY/Il39qk8pgmgqTr2ggv/bs0ktuxxe2II9Pq/W6JrrCov3AeoLr5tGHNvHkJqxyimvpzJh2t3e3X/9drwLrx0kdVD9qZPoduZJ26TNg6uf9sagPC5n0HOyuaN8WQqK2D+DVZs41/x7qBuItZQEfH9rFLQge3e27eqlyYAbzjjeug3Dhb/FbZ/1fzfH+j1f7fw9tZYMjub1hKoNjFRobw6dQhpCdHc/uoq3lqV7fXvqNXy/1i/nOP7wpRPIbZn3dt2PwumfALBEfDSxfDjB80TY30WPWT9wLn4cd+MMRUSAeNftlpGzZsI5dqHo7loAvAGEauW266b9Sumueu4LaH+75Y0yGpd5YNOQtHhwbwyZTBDesTwm3lreOVrH/7arKqED39vtZjpdRFMetfq8VyfuFOsq4S4XjD3Wvj2Wd/F6In1C+DLf0P6jXDGdb77ng494PKZsHt14Iy8620VZdZ9oq1fWINPLv4rLLgFXrwInjzDJyVmO+cEblnC2lr9A547H96+FS78JzhCwBkCjlDr5BzkxYlU3I7O/3uv9/dth6TB1j/+/KyT/1pupMhQJy9MHsTtc1bxwNvrKC6tYNqIOsavaayyIuuHwMYPYOgdcP5DDfu7j+oIk9+z9vHBb617Auf/xfPOVt6ybwO8fTskDrIaPPjaqaNh2F3WoHFJGb5NOHaoKLVmvju4HQ7utDqSHtwBh1zLBbuA6j98BNp2sTrbJQ6yhs+oOdBeE2kC8KYuZ8AFf7Had2+qZYIzCXIlA1dSOC5BhFivj3s/2Eocx70f4lrnWj60kxZR/3dzDwy38xufJACwprZ85vqB/Gruah798EeKyir59c96emeKycJ98Op465fs6H/A4Jsbt5+QSBj/X2tGreVPW3/PY2dCcHjTY/REySF4/Vorjqtebr6ry5H3QXamNVREpzRrALlAUX7k2In9kOvkXv1EX7jn+O3FAW0TrBN88ghol2Qtt+sK0UnWe84Qn4asCcDbBt8CMT3h8C4r41eWWY+KMqgsPX65wvXe0eVSq2dxScGxZfc+KlyvK0td7cWr/VKIiA38+r9b7CkQFm31B/DhL8BgRxBPTjiDyBAHT362maLSCu6/qHfTkkDuRpgzzpoAZfwc6HVh04IMcsDov0H7bvDRfXB4D0x4DSJjmrbf+lRVWaN2Htxula7advHt91UX5IBxL8DM4da9k2lfWPeG/ElZMax705oo6lC1E3xR7vHbBQVbg0i26wqpPzt2cnef6Nt08V5rqkbSBOBtItDzZ779DmOseqA7GQSHt4z6P1hljsQMr3UIOxlHkPDY5acREeLk+WU/UVxWwcOXpTVuismfllhj4jtCYfL73rtZKgJDb7d+Db51Mzz/M7h2ft3DLnvD0n/Cpg+tNvq1tVjytchYuHI2vDjaSkRXv9785a/aVJbDqpetPguFe6wr8GjXyfzU0a6Te7dj69p08k3Z14s0AQQiEeuXg8MZGPMUN1RSBmR9CkcONnycmQYKChL+dEkfIkOtyeZ3HSwhvVt7YqJCiYkKITYqhNioUGKiQokMcdR+hbBmLrxzu3Uj89o3rF/s3tb3MmjTGV6bAM+fb50UkzK8/z2bPrZuPp42HjIaWb7yhqRB1hzRH/wWlv2f1WnOLlVV1i/+xY/AgZ8gaYh1ldJ1qH8kpibQBKD8T1IGYCAn07p09jER4e6f96JtWDD/+XwLX2zKrXW7UGcQsVGhxEaFWAkiIphLC+YwLHsWubGD2TxyBu1L2hNzuIQOESE4HV4+OXQdbLUQmjMOZl9iTcDSZ4z39p+/Bd66CTr1g4v/dWxax5MwxlBwpII9BSXsLShhT0EJ+wpK2FtQSkFJOREhTtqEOYkMcRIV5iQq1EFUaDCRoQ5rfaiTqFAnbVzrjvszG3STdSW46BGrxJlyrveO1RPGWPfyPvsL7FsP8WlwzRvWiKbeuF/kB8QE0Jjc6enpJjMz0+4wlK+VHobHusLw38HIPzT/11dUsr+ojPzCMvIKS8krLCO/sJT8omOvDx4uYuqhJ7mkahFvVp7NPeU3U17t95QItAsPdl09WAkjNvLY1USM68oiOtw68UWGWidJj8pPRXnw2tVWz+GfP2LNUdHUE1JZkdWC7fAuuPlzaN+dI2WV7D3uxF563LL7pF9aceJAdu0igmkbFkxxWSWFpeWUlHs22F1YcBBRrqQQGeokJriCv+7/Fe2q9jP9lBeoapN4NGlEhVpJxf06IsRBWLCDsOAgwpzWcqgziKDGlPS2LbMmuN/5jXVlN/I+6Ht5wP7iF5GVxpj0E9ZrAlB+acbZVi144tt2R3KikkNWh6Wtn2NG/J6CjN+SV2wljPzC0mNJo6j0aBJxPxeUnHze4vBgx3EJISrUSYTrdVSI+2TnoI2zggs2/pFuez9le+r1ZGc8QGR4KFGubSNcn60toVRUVpFbWMreglL2HCphX8ERBq28m1PzP+UfcY/waWk/9haU1BprWHAQndqGEe96dIoOo2ObUDpFu163DSOuTShhwY4TvrOotJLCsgoKSyooLLUeRaXW68PuZfejxHp9uLSC6OLtPHHo12yjCxMq/kRhRcPq6iHOIEKdQSckh7DgIFeSOLacXL6ZUXtmkVLwDYUhcXzXYxrbu44lNCTU9Zlj24bXWA4PcRDmdDQu4fhYXQlAS0DKPyUNgrXzrZvd/nQj7eBOePUqqwXIZTOQ/tcQDURHhpDiQT+v6lcXuYWlFBwpp6i0kuKyaifE0kqKSiuOrssvLGNHfjFFZRUUlVZSVFaBMfBXJnOvM4SpWa+wceMPTCm/gxKObwzg/kUdEeIkPNjB/mIrEVX/3TfF8T4Tgz9hhuNaltOfHnGhDE2JOXaSbxtGfNtQ4qPDaBPqbFRLKacjiOiIIKIjghv8WRgKGyLpN/c61g35nPLR/7SSQ0kFRWXHEkhJWSUlFZWUlFdRUl7tuaKS0qPrKimtOPZ+YWkFeYVldCzdztiSOZxX9RUHTRR/rbyG2SUXULo6BFZvbFC0Ic6go8kh3JU03MkhPMRKFqGu944mDvd21T8X4jj2+WAHqR2jGtdA4ST0CkD5pzWvW6Os3vqVNYyCP9i12mrjX15sjYnT4xxbwqiqMhwpt5JEUVkloSufpfPyByno0I/lGdM5ENT+6K9p9zZFpRUcKaukfUTI0ZN5fJswUopW0f2Da+HU0cj4//p3bfvjB+CrJ63+EKdP8M4+D2XD54/B6let4VSG3g5n3oEJbUt5pXElFCuBlFZLLkfKay5XcqTMWnfc6wrr2b3O/b61beXRz1d5cBr+8S+jTriy8pReAajAkjjIet75rX8kgE0fwRs3WOPYX/9R4yZC8ZKgILFKRKGu/74//xV060n0/CmM+vp6uO5NzzrRHcqGmXdYTUovm+HfJ3+A8/5kDUn97q+sweM69Wv8voryYOnj1twGGKvF07C7jg7XIUCIUwhxBtE2rDFXLZ4zxlBWWXUsoVRLGMeSSRWhTu/ff9ArAOWfjIF/pELPC2DsDHtjWfEcfHC31TP1mnlW+25/lL0SXhtvtVe/+rWTt+EvL7Ha2edthqmLrDGIAkHhPnhmmDWA3M2fW50GG6KkAJZPt3pXlxdD/2usKSrbdfVFtH6jriuAwLylrVo+Eas5aLbvO4TVqarKKju8fxekng+TP/Dfkz9A4kBrNNHIOHh5jHUPpS4f3m3NuzB2RuCc/MEaJ+mq2VbP27dv83zQwPIS+Opp+Pfp8MVjkHoe3Oaa1rKFn/xPRhOA8l9JGdagcEU2zORVfsQaA/+rJyF9Ckx41esDcflEh2SY8jEkpMObU6wpS2ueJDNftHq0DrsLel9iT5xN0XUIXPAw/PieNVLpyVRWwMrZ8NQA+Pg+6NIfpi62xjcKpMTnI3oPQPkv98BwM4ZaE4hHxrkesa6H63VEtdchEU3/3qJ8eP1qqw34BQ9bI3r6e328uogOcP0CeOc2+PRBa5KVC/9p9RzfucIqZ6WcZ7VtD1SDb7HuD332Z2vYjeThx79fVQU/vA2LHob9W6x7SmNnQnKAz5rnZZoAlP/qOgRG3m91vy/KswbbyttsPVccqf0zwZHWYGm1JYfIuBPfqznaYv4Wq6dtwS5rPJq+l/n+OH0hOAwuf84al+bLf0FBjjWw3LyJ1uBuVzznX81rG0oELn0K9q6H+TfCtCXWcRljDSPy2UOw53vo2McaQO/U0YGVxJuJRzeBRWQU8G/AATxnjHmsxvuhwMvAQCAfGG+M2eZ67w/AFKAS+IUx5iNP9lkbvQmsjiorshJBUb7r2fUorv4671jiqCqvfT+h0dUSRKw1o5uI78basUPmC9Z9DHFAkBNu+sS6od0S5G6CZ0daJ/rz/miNY7TjK2tQtpH3WVNtBnKi85JG9wQWEQewCTgfyAZWAFcbY36ots1twGnGmFtEZAIw1hgzXkT6AK8BGUAX4FPAXXg76T5rowlANYoxUFpwLBlUfy7Oq5Yw8iG0DVz2H9+OtmmHTR/De7+G8/98bEL6lmL9AnhjsrUcFW8NHDdgks/H0g8kTekHkAFkGWO2unb0OjAGqH6yHgM86FqeDzwtVnfBMcDrxphS4CcRyXLtDw/2qZR3iFjNBcOiW96J3VOnXAC/WW93FL7Rd6yVzMuPwKApLXOEXB/xJAEkADurvc4GBte1jTGmQkQOATGu9V/X+GyCa7m+fSqllGcyptodQUDy+2agInKziGSKSGZubu3D9CqllGo4TxJADpBU7XWia12t24iIE4jGuhlc12c92ScAxphZxph0Y0x6XJwHo20ppZTyiCcJYAXQU0SSRSQEmAAsrLHNQmCSa3kcsMhYd5cXAhNEJFREkoGewLce7lMppZQP1XsPwFXTvwP4CKvJ5gvGmPUi8hCQaYxZCDwPvOK6ybsf64SOa7t5WDd3K4DbjTGVALXt0/uHp5RSqi46GJxSSrVwOhicUkqp42gCUEqpVkoTgFJKtVIBdQ9ARHKB7Y38eCyQ58Vw/ElLPjZo2cenxxa4Aun4uhljTmhHH1AJoClEJLO2myAtQUs+NmjZx6fHFrhawvFpCUgppVopTQBKKdVKtaYEMMvuAHyoJR8btOzj02MLXAF/fK3mHoBSSqnjtaYrAKWUUtVoAlBKqVaqxScAERklIhtFJEtE7rE7Hm8SkSQRWSwiP4jIehH5pd0xeZuIOETkOxF5z+5YvElE2onIfBH5UUQ2iMhQu2PyJhH5tevf5DoReU1EwuyOqbFE5AUR2Sci66qt6yAin4jIZtdzeztjbKwWnQBc8xlPB0YDfYCrXfMUtxQVwF3GmD7AEOD2FnZ8AL8ENtgdhA/8G/ifMaYXcDot6BhFJAH4BZBujOmHNeLvBHujapKXgFE11t0DfGaM6Ql85nodcFp0AqDafMbGmDLAPfdwi2CM2W2MWeVaPox1Ekk4+acCh4gkAhcBz9kdizeJSDQwHGsYdYwxZcaYg/ZG5XVOINw1QVQEsMvmeBrNGLMEa5j76sYAs13Ls4HLmjUoL2npCaC2+YxbzAmyOhHpDpwBfGNvJF71L+B3QJXdgXhZMpALvOgqbz0nIi1mJnNjTA7wT2AHsBs4ZIz52N6ovC7eGLPbtbwHiLczmMZq6QmgVRCRKOBN4FfGmAK74/EGEbkY2GeMWWl3LD7gBAYAM4wxZwBFBGgJoTauevgYrETXBYgUkevsjcp3XLMfBmR7+paeADyeezhQiUgw1sl/jjHmLbvj8aKzgEtFZBtW6e5cEfmvvSF5TTaQbYxxX63Nx0oILcXPgJ+MMbnGmHLgLeBMm2Pytr0i0hnA9bzP5ngapaUngBY997CICFYdeYMx5nG74/EmY8wfjDGJxpjuWH9vi4wxLeJXpDFmD7BTRE51rToPa9rUlmIHMEREIlz/Rs+jBd3kdqk+D/ok4B0bY2m0eucEDmR1zWdsc1jedBZwPbBWRFa71t1rjPnAxpiUZ+4E5rh+mGwFbrA5Hq8xxnwjIvOBVVgt1b4jgIdNEJHXgHOAWBHJBv4EPAbME5EpWEPUX2VfhI2nQ0EopVQr1dJLQEoppeqgCUAppVopTQBKKdVKaQJQSqlWShOAUkq1UpoAlFKqldIEoJRSrdT/A2Ik+T6c4LhzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Prediction\n",
        "test_pred=[]#to store predictions\n",
        "first_batch= scaled_train_data[-seq_length:];\n",
        "current_batch = first_batch.reshape((1,seq_length,n_features))\n",
        "for i in range(test_ind):\n",
        "    current_pred = model.predict(current_batch);\n",
        "    test_pred.append(current_pred[0])\n",
        "    current_batch=np.append(current_batch[:,1:,:],[[current_pred[0]]],axis=1)\n",
        "#Data Predictions\n",
        "predictions=scaler.inverse_transform(test_pred)"
      ],
      "metadata": {
        "id": "S1StVTw72nfb"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data['Predictions']=predictions;\n",
        "test_data.plot()\n",
        "#Sales Predictions remain a bit erroneous over the actual"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "CrZ5Qthv8KJb",
        "outputId": "ed63452b-fccb-43c0-8fc9-e77f356bde3a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e48baad50>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSE\n",
        "mse=(1/len(test_data))*np.sum((test_data['Sales']-test_data['Predictions'])**2)\n",
        "RMSE=np.sqrt(mse)\n",
        "print('RMSE for predictions=',RMSE,'units \\nRange of sales data between 6938 and 34706 units min and max sales')\n",
        "print('Percentage RMSE error with respect to minimum sales\\n:',100*RMSE/6938,'%')\n",
        "#Error seems more or less tolerable under 15 % limit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5E10pWdIYyf",
        "outputId": "2287b61f-c728-4cff-b792-4a20766aa8cf"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE for predictions= 956.1928168743965 units \n",
            "Range of sales data between 6938 and 34706 units min and max sales\n",
            "Percentage RMSE error with respect to minimum sales\n",
            ": 13.78196622765057 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Forecasting Sales"
      ],
      "metadata": {
        "id": "drSP2EKpBlXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Sales Forecast\n",
        "fperiod = 12;\n",
        "#Scaling and Normalization \n",
        "full_scaler=MinMaxScaler()\n",
        "full_scaler.fit(df)\n",
        "scaled_df=full_scaler.transform(df)\n",
        "#forecast sequence generator using full data\n",
        "fgenerator=TimeseriesGenerator(scaled_df,scaled_df,seq_length,batch_size=1)\n",
        "#Forecast Model Build\n",
        "hidden_state=100;\n",
        "fmodel=Sequential()\n",
        "fmodel.add(LSTM(hidden_state,activation='relu',input_shape=(seq_length,n_features)))\n",
        "fmodel.add(Dense(1))\n",
        "fmodel.compile(optimizer='adam',loss='mse')\n",
        "fmodel.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQKUWAk3_6B2",
        "outputId": "bcf7ce30-8235-456e-cd6b-a9c92e7b0c3b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_3 (LSTM)               (None, 100)               40800     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fmodel.fit_generator(fgenerator,epochs=8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqdQQlRBE81i",
        "outputId": "e67981ff-6aae-480a-c507-7cc9b1218e3f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "322/322 [==============================] - 4s 8ms/step - loss: 0.0269\n",
            "Epoch 2/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0172\n",
            "Epoch 3/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0100\n",
            "Epoch 4/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0044\n",
            "Epoch 5/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0027\n",
            "Epoch 6/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0022\n",
            "Epoch 7/8\n",
            "322/322 [==============================] - 2s 7ms/step - loss: 0.0018\n",
            "Epoch 8/8\n",
            "322/322 [==============================] - 3s 8ms/step - loss: 0.0014\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7e445dd5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Prediction\n",
        "test_pred=[]#to store predictions\n",
        "first_batch= scaled_df[-seq_length:];\n",
        "current_batch = first_batch.reshape((1,seq_length,n_features))\n",
        "for i in range(fperiod):#iteration over forecast period\n",
        "    current_pred = model.predict(current_batch);\n",
        "    test_pred.append(current_pred[0])\n",
        "    current_batch=np.append(current_batch[:,1:,:],[[current_pred[0]]],axis=1)\n",
        "#Data Predictions\n",
        "forecast=scaler.inverse_transform(test_pred)"
      ],
      "metadata": {
        "id": "GXiQlcrPF4ys"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast.transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bguj-su9HS5e",
        "outputId": "826189e3-676d-43fa-a78d-d1c57deb3dfe"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[26787.04886484, 36463.76668072, 17168.54094887, 19136.95855761,\n",
              "        22893.642869  , 22490.21884251, 24095.01666164, 22137.30326509,\n",
              "        22736.74243116, 24644.91436958, 20920.95835114, 22219.41376638]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[-1:]\n",
        "#Date of last update required for forecast period index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "zqDSPZL8Hvmj",
        "outputId": "4b5ee5e3-b308-48d0-9e69-e531e1d84872"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Sales\n",
              "DATE             \n",
              "2019-10-01  21113"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb178fe9-993f-49a8-9820-9821e55bb301\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>21113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb178fe9-993f-49a8-9820-9821e55bb301')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb178fe9-993f-49a8-9820-9821e55bb301 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-eb178fe9-993f-49a8-9820-9821e55bb301');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_period=pd.date_range(start='2019-11-01',periods=fperiod,freq='MS')\n",
        "forecast_period"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QN5if7vTH_8h",
        "outputId": "4d2e4e24-1083-482c-ab89-5d2b2794e86a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01',\n",
              "               '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01',\n",
              "               '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01'],\n",
              "              dtype='datetime64[ns]', freq='MS')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_df=pd.DataFrame(data=forecast,index=forecast_period,columns=['Sales Forecast'])\n",
        "forecast_df.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "gWR8YoBvIyYK",
        "outputId": "e4414d4d-d18b-4a76-8865-af840f9464df"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e4745d6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=df.plot()\n",
        "forecast_df.plot(ax=ax)\n",
        "#probable general trend identification thus, taking the 2008 kink into account"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "H8l2bQgdLGdf",
        "outputId": "5628e5fd-188d-4ccc-84be-ee609d779276"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7e47488850>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mp-94F3-MAC0"
      },
      "execution_count": 61,
      "outputs": []
    }
  ]
}