fix_Capstone_machine_learning_model_to_predict_vaccination.ipynb

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  "metadata": {
    "colab": {
      "name": "fix Capstone machine learning model to predict vaccination.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eisz_1hVr84z"
      },
      "source": [
        "Importing Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12QiOAkGRNYH"
      },
      "source": [
        "import math\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.utils import Sequence\n",
        "from datetime import timedelta\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from keras.layers.recurrent import LSTM\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import time\n",
        "import plotly.graph_objects as go\n",
        "from keras.layers import Activation, Dense\n",
        "\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RoizEaHNcQH",
        "outputId": "4428a75f-b30a-47b6-c255-29d98e2b3ac5"
      },
      "source": [
        "!python --version"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python 3.7.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WH8a-PBCIvDU"
      },
      "source": [
        "!pip3 freeze > requirements.txt "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APLW3tVXsDPn"
      },
      "source": [
        "import file csv from google drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "np_xxOLgRdJM",
        "outputId": "c633b57c-8911-4df2-d7b4-356eeed23506"
      },
      "source": [
        "!gdown --id 15VXPUzDuYgNyc1NJ6aC7Rnc7tlTfDDEC"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=15VXPUzDuYgNyc1NJ6aC7Rnc7tlTfDDEC\n",
            "To: /content/vaksinasi_jakarta_mau_ke_csv_1.csv\n",
            "\r  0% 0.00/5.17k [00:00<?, ?B/s]\r100% 5.17k/5.17k [00:00<00:00, 9.14MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyQlVjRusLon"
      },
      "source": [
        "Load dataset to dataframe using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "5KwHC-P9Ren-",
        "outputId": "c9e56f6e-9e1a-4fe2-a157-63b92cfd9eea"
      },
      "source": [
        "df = pd.read_csv('vaksinasi_jakarta_mau_ke_csv_1.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>Tanggal</th>\n",
              "      <th>Target Vaksinasi Tenaga Kesehatan</th>\n",
              "      <th>Vaksinasi 1 Tenaga Kesehatan</th>\n",
              "      <th>Vaksinasi 2 Tenaga Kesehatan</th>\n",
              "      <th>Target Vaksinasi Lansia</th>\n",
              "      <th>Vaksinasi 1 Lansia</th>\n",
              "      <th>Vaksinasi 2 Lansia</th>\n",
              "      <th>Target Vaksinasi Pelayan Publik</th>\n",
              "      <th>Vaksinasi 1 Pelayan Publik</th>\n",
              "      <th>Vaksinasi 2 Pelayan Publik</th>\n",
              "      <th>Target Vaksinasi Total</th>\n",
              "      <th>Vaksinasi 1 Total</th>\n",
              "      <th>Vaksinasi 2 Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>05/03/2021</td>\n",
              "      <td>112301</td>\n",
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              "      <td>281997</td>\n",
              "      <td>91078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06/03/2021</td>\n",
              "      <td>112301</td>\n",
              "      <td>144046</td>\n",
              "      <td>91379</td>\n",
              "      <td>911631</td>\n",
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              "      <td>289951</td>\n",
              "      <td>91379</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>07/03/2021</td>\n",
              "      <td>112301</td>\n",
              "      <td>177126</td>\n",
              "      <td>91460</td>\n",
              "      <td>911631</td>\n",
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              "      <td>334506</td>\n",
              "      <td>93974</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>08/03/2021</td>\n",
              "      <td>112301</td>\n",
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              "      <td>911631</td>\n",
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              "      <td>3989</td>\n",
              "      <td>3000689</td>\n",
              "      <td>361710</td>\n",
              "      <td>96978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>09/03/2021</td>\n",
              "      <td>112301</td>\n",
              "      <td>113326</td>\n",
              "      <td>89984</td>\n",
              "      <td>911631</td>\n",
              "      <td>135128</td>\n",
              "      <td>675</td>\n",
              "      <td>1976757</td>\n",
              "      <td>174685</td>\n",
              "      <td>29195</td>\n",
              "      <td>3000689</td>\n",
              "      <td>423139</td>\n",
              "      <td>119854</td>\n",
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            ],
            "text/plain": [
              "      Tanggal  ...  Vaksinasi 2 Total\n",
              "0  05/03/2021  ...              91078\n",
              "1  06/03/2021  ...              91379\n",
              "2  07/03/2021  ...              93974\n",
              "3  08/03/2021  ...              96978\n",
              "4  09/03/2021  ...             119854\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ukZmuKl4CKX",
        "outputId": "9c04d076-5ebf-4ac0-c938-6bb8e654de74"
      },
      "source": [
        "print(df.info())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 51 entries, 0 to 50\n",
            "Data columns (total 13 columns):\n",
            " #   Column                             Non-Null Count  Dtype \n",
            "---  ------                             --------------  ----- \n",
            " 0   Tanggal                            51 non-null     object\n",
            " 1   Target Vaksinasi Tenaga Kesehatan  51 non-null     int64 \n",
            " 2   Vaksinasi 1 Tenaga Kesehatan       51 non-null     int64 \n",
            " 3   Vaksinasi 2 Tenaga Kesehatan       51 non-null     int64 \n",
            " 4   Target Vaksinasi Lansia            51 non-null     int64 \n",
            " 5   Vaksinasi 1 Lansia                 51 non-null     int64 \n",
            " 6   Vaksinasi 2 Lansia                 51 non-null     int64 \n",
            " 7   Target Vaksinasi Pelayan Publik    51 non-null     int64 \n",
            " 8   Vaksinasi 1 Pelayan Publik         51 non-null     int64 \n",
            " 9   Vaksinasi 2 Pelayan Publik         51 non-null     int64 \n",
            " 10  Target Vaksinasi Total             51 non-null     int64 \n",
            " 11  Vaksinasi 1 Total                  51 non-null     int64 \n",
            " 12  Vaksinasi 2 Total                  51 non-null     int64 \n",
            "dtypes: int64(12), object(1)\n",
            "memory usage: 5.3+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMUXKEoCs36r"
      },
      "source": [
        "using date and vaksinasi 1 total data to be processed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "EEC-AUHnRhnA",
        "outputId": "b091320a-8b16-4aa6-ae26-0e2181c195f0"
      },
      "source": [
        "df['Tanggal']:  pd.to_datetime(df['Tanggal'], format='%d/%m/%Y')\n",
        "df.set_axis(df['Tanggal'], inplace=True)\n",
        "df.drop(columns=['Target Vaksinasi Tenaga Kesehatan','Vaksinasi 1 Tenaga Kesehatan', 'Vaksinasi 2 Tenaga Kesehatan', 'Target Vaksinasi Lansia', 'Vaksinasi 1 Lansia', 'Vaksinasi 2 Lansia', 'Target Vaksinasi Pelayan Publik', 'Vaksinasi 1 Pelayan Publik', 'Vaksinasi 2 Pelayan Publik', 'Target Vaksinasi Total', 'Vaksinasi 2 Total'], inplace=True)\n",
        "print (df.head())\n",
        "\n",
        "trace_1 = go.Scatter(\n",
        "    x = df['Tanggal'],\n",
        "    y = df['Vaksinasi 1 Total'],\n",
        "    mode = 'lines',\n",
        "    name = 'Data'\n",
        ")\n",
        "\n",
        "layout = go.Layout(\n",
        "    title = \"Data vaksinasi\",\n",
        "    xaxis = {'title' : \"Date\"},\n",
        "    yaxis = {'title' : \"Vaksinasi 1 Total\"}\n",
        ")\n",
        "\n",
        "fig = go.Figure(data=[trace_1], layout=layout)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Tanggal  Vaksinasi 1 Total\n",
            "Tanggal                                  \n",
            "05/03/2021  05/03/2021             281997\n",
            "06/03/2021  06/03/2021             289951\n",
            "07/03/2021  07/03/2021             334506\n",
            "08/03/2021  08/03/2021             361710\n",
            "09/03/2021  09/03/2021             423139\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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\"white\", \"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\": \"Data vaksinasi\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Vaksinasi 1 Total\"}}},\n",
              "                        {\"responsive\": true}\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YodTjlhi0-3h",
        "outputId": "4182c02b-ec24-42eb-c809-b4f49d76536e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(51, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvUDbydNibIg"
      },
      "source": [
        "using vaksinasi values to training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jF6Jee2y-YtA",
        "outputId": "ea7a0180-38da-4f08-b6eb-5b0783b54519"
      },
      "source": [
        "Vaksinasi1Total_Data = df['Vaksinasi 1 Total'].values\n",
        "Vaksinasi1Total_Data = Vaksinasi1Total_Data.reshape((-1,1))\n",
        "Vaksinasi1Total_Data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [1770866],\n",
              "       [1770866]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEu0inuiiRa-"
      },
      "source": [
        "function to get difference every one day data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4fsWAAGI66C"
      },
      "source": [
        "def delta_time_series(data):\n",
        "    return data[1:]- data[:-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOKv90eSJDgB",
        "outputId": "12a7df8a-e7ad-4c08-b3a6-5a577e670a15"
      },
      "source": [
        "delta_time_series(Vaksinasi1Total_Data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       [38391],\n",
              "       [24409],\n",
              "       [    0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USguGLQ3iXXy"
      },
      "source": [
        "normalize data and split training and test to 80% and 20% of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg1JZoFNSJ9O",
        "outputId": "27595ae1-adc1-47b0-ea82-9b3c3a1ecbd4"
      },
      "source": [
        "dataset = Vaksinasi1Total_Data\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "dataset = scaler.fit_transform(dataset)\n",
        "\n",
        "split_percent = 0.80\n",
        "split = int(split_percent*len(dataset))\n",
        "\n",
        "Vaksinasi1Total_train = dataset[:split]\n",
        "Vaksinasi1Total_test = dataset[split:]\n",
        "\n",
        "date_train = df['Tanggal'][:split]\n",
        "date_test = df['Tanggal'][split:]\n",
        "\n",
        "print(len(Vaksinasi1Total_train))\n",
        "print(len(Vaksinasi1Total_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40\n",
            "11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4nALLFvtwjs"
      },
      "source": [
        "training a machine learning model requires the data to be in {features,target} format. so, this will need to convert the given data into this format, using consept of a look back.\n",
        "\n",
        "Look back is the number of previous days data to use, to predict the value for the next day."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dwDmYvbSNSk"
      },
      "source": [
        "look_back = 5\n",
        "\n",
        "train_generator = TimeseriesGenerator(Vaksinasi1Total_train, Vaksinasi1Total_train, length=look_back, sampling_rate=1,stride=1,batch_size=5)     \n",
        "test_generator = TimeseriesGenerator(Vaksinasi1Total_test, Vaksinasi1Total_test, length=look_back, sampling_rate=1,stride=1, batch_size=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhnocUB6v0X3"
      },
      "source": [
        "using architecture of LSTM units trained using sgd optimizer and Mean Squared Loss function for 100 epochs. using model.fit_generator() because already created a data generator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XfpUO1-SrFu",
        "outputId": "d859dd80-7440-424d-e4da-44be769cb9bf"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(4, input_shape=(look_back, 1)))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='sgd')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_3 (LSTM)                (None, 4)                 96        \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 101\n",
            "Trainable params: 101\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NhIQDzQj9QI",
        "outputId": "c21cd1e7-b7da-4a08-dd4b-18ddec773d04"
      },
      "source": [
        "Start_Train = model.fit_generator(train_generator, epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning:\n",
            "\n",
            "`Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "7/7 [==============================] - 4s 33ms/step - loss: 0.1001\n",
            "Epoch 2/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0759\n",
            "Epoch 3/100\n",
            "7/7 [==============================] - 0s 43ms/step - loss: 0.0504\n",
            "Epoch 4/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0311\n",
            "Epoch 5/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0154\n",
            "Epoch 6/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0247\n",
            "Epoch 7/100\n",
            "7/7 [==============================] - 0s 39ms/step - loss: 0.0159\n",
            "Epoch 8/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0173\n",
            "Epoch 9/100\n",
            "7/7 [==============================] - 0s 39ms/step - loss: 0.0215\n",
            "Epoch 10/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 0.0141\n",
            "Epoch 11/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0161\n",
            "Epoch 12/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0161\n",
            "Epoch 13/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0140\n",
            "Epoch 14/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0200\n",
            "Epoch 15/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0123\n",
            "Epoch 16/100\n",
            "7/7 [==============================] - 0s 41ms/step - loss: 0.0116\n",
            "Epoch 17/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0142\n",
            "Epoch 18/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0104\n",
            "Epoch 19/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0103\n",
            "Epoch 20/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0104\n",
            "Epoch 21/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0107\n",
            "Epoch 22/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0101\n",
            "Epoch 23/100\n",
            "7/7 [==============================] - 0s 39ms/step - loss: 0.0103\n",
            "Epoch 24/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0151\n",
            "Epoch 25/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0122\n",
            "Epoch 26/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0146\n",
            "Epoch 27/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0073\n",
            "Epoch 28/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0140\n",
            "Epoch 29/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0095\n",
            "Epoch 30/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0083\n",
            "Epoch 31/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0098\n",
            "Epoch 32/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0083\n",
            "Epoch 33/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0084\n",
            "Epoch 34/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0059\n",
            "Epoch 35/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0065\n",
            "Epoch 36/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0082\n",
            "Epoch 37/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0104\n",
            "Epoch 38/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 0.0059\n",
            "Epoch 39/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0097\n",
            "Epoch 40/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0048\n",
            "Epoch 41/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0076\n",
            "Epoch 42/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0098\n",
            "Epoch 43/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0050\n",
            "Epoch 44/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0063\n",
            "Epoch 45/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0050\n",
            "Epoch 46/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0044\n",
            "Epoch 47/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0074\n",
            "Epoch 48/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0037\n",
            "Epoch 49/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0052\n",
            "Epoch 50/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0048\n",
            "Epoch 51/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0067\n",
            "Epoch 52/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0026\n",
            "Epoch 53/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0052\n",
            "Epoch 54/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0029\n",
            "Epoch 55/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0058\n",
            "Epoch 56/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0027\n",
            "Epoch 57/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0036\n",
            "Epoch 58/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0031\n",
            "Epoch 59/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0037\n",
            "Epoch 60/100\n",
            "7/7 [==============================] - 0s 43ms/step - loss: 0.0039\n",
            "Epoch 61/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0029\n",
            "Epoch 62/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0023\n",
            "Epoch 63/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0026\n",
            "Epoch 64/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 0.0031\n",
            "Epoch 65/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0026\n",
            "Epoch 66/100\n",
            "7/7 [==============================] - 0s 39ms/step - loss: 0.0024\n",
            "Epoch 67/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0042\n",
            "Epoch 68/100\n",
            "7/7 [==============================] - 0s 40ms/step - loss: 0.0024\n",
            "Epoch 69/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0026\n",
            "Epoch 70/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0027\n",
            "Epoch 71/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0019\n",
            "Epoch 72/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0015\n",
            "Epoch 73/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 0.0030\n",
            "Epoch 74/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0016\n",
            "Epoch 75/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0015\n",
            "Epoch 76/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0031\n",
            "Epoch 77/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0016\n",
            "Epoch 78/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0022\n",
            "Epoch 79/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0029\n",
            "Epoch 80/100\n",
            "7/7 [==============================] - 0s 34ms/step - loss: 0.0029\n",
            "Epoch 81/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0014\n",
            "Epoch 82/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 0.0017\n",
            "Epoch 83/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0013\n",
            "Epoch 84/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 9.4797e-04\n",
            "Epoch 85/100\n",
            "7/7 [==============================] - 0s 39ms/step - loss: 0.0016\n",
            "Epoch 86/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 9.1552e-04\n",
            "Epoch 87/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0010\n",
            "Epoch 88/100\n",
            "7/7 [==============================] - 0s 37ms/step - loss: 0.0023\n",
            "Epoch 89/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0024\n",
            "Epoch 90/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0014\n",
            "Epoch 91/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 0.0012\n",
            "Epoch 92/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0011\n",
            "Epoch 93/100\n",
            "7/7 [==============================] - 0s 41ms/step - loss: 0.0012\n",
            "Epoch 94/100\n",
            "7/7 [==============================] - 0s 36ms/step - loss: 0.0010\n",
            "Epoch 95/100\n",
            "7/7 [==============================] - 0s 38ms/step - loss: 8.5900e-04\n",
            "Epoch 96/100\n",
            "7/7 [==============================] - 0s 35ms/step - loss: 0.0013\n",
            "Epoch 97/100\n",
            "7/7 [==============================] - 0s 32ms/step - loss: 7.9049e-04\n",
            "Epoch 98/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0018\n",
            "Epoch 99/100\n",
            "7/7 [==============================] - 0s 33ms/step - loss: 0.0013\n",
            "Epoch 100/100\n",
            "7/7 [==============================] - 0s 45ms/step - loss: 0.0017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GO31pbjkixMp"
      },
      "source": [
        "evaluate the model based on loss value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZmltNQBG-YQ",
        "outputId": "da62c928-c778-40b0-87fa-08b846f0bfb1"
      },
      "source": [
        "model.evaluate_generator(test_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1948: UserWarning:\n",
            "\n",
            "`Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01662379503250122"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z__K7Hx4yZBq"
      },
      "source": [
        "completed training, let see if it performed well. testing the model on testing data and see if the prediction and the actual values overlap, then plot it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "h9JibG9RS1Qc",
        "outputId": "35a5c9d0-a0a1-46ed-a18e-568beea7e14e"
      },
      "source": [
        "prediction = model.predict(test_generator)\n",
        "\n",
        "Vaksinasi1Total_train = Vaksinasi1Total_train.reshape((-1))\n",
        "Vaksinasi1Total_test = Vaksinasi1Total_test.reshape((-1))\n",
        "prediction = prediction.reshape((-1))\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x = date_train,\n",
        "    y = Vaksinasi1Total_train *1000000,\n",
        "    mode = 'lines',\n",
        "    name = 'Real Data use to Training'\n",
        ")\n",
        "trace2 = go.Scatter(\n",
        "    x = date_test,\n",
        "    y = prediction*1000000,\n",
        "    mode = 'lines',\n",
        "    name = 'Prediction after Training'\n",
        ")\n",
        "trace3 = go.Scatter(\n",
        "    x = date_test,\n",
        "    y = Vaksinasi1Total_test*1000000,\n",
        "    mode='lines',\n",
        "    name = 'Real Data use to Test as Ground Truth'\n",
        ")\n",
        "layout = go.Layout(\n",
        "    title = \"Predict vaksinasi\",\n",
        "    xaxis = {'title' : \"Date\"},\n",
        "    yaxis = {'title' : \"Vaksinasi 1 Total\"}\n",
        ")\n",
        "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"white\", \"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\": \"Predict vaksinasi\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Vaksinasi 1 Total\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c179232b-9bf4-4e7f-b3d9-41f7514d2f03');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzEA_cNkSecN"
      },
      "source": [
        "save as json"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HIsdErEPPRGm",
        "outputId": "74de8634-aa1b-4bea-b5f5-929b573ba247"
      },
      "source": [
        "pred= []\n",
        "datelist= []\n",
        "\n",
        "prediction[0]\n",
        "for x in range(prediction.size):\n",
        "  datelist.append(date_test[x])\n",
        "  pred.append(int(prediction[x]*1000000))\n",
        "\n",
        "prediksi = {'Tanggal': datelist, 'prediction': pred}\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_jason_prediction = pd.DataFrame(prediksi, columns = ['Tanggal', 'prediction'])\n",
        "print(df_save_jason_prediction)\n",
        "\n",
        "#save as json\n",
        "df_save_jason_prediction.to_json(r'Data Hasil Prediksi ada tanggal fix.json')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Tanggal  prediction\n",
            "0  14/04/2021      832452\n",
            "1  15/04/2021      835823\n",
            "2  16/04/2021      839434\n",
            "3  17/04/2021      843132\n",
            "4  18/04/2021      847838\n",
            "5  19/04/2021      853766\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61-yqiBFQIHn",
        "outputId": "119624d4-2615-4c43-aa23-a0250e3f5243"
      },
      "source": [
        "train= []\n",
        "datelist= []\n",
        "\n",
        "Vaksinasi1Total_train[0]\n",
        "for x in range(Vaksinasi1Total_train.size):\n",
        "  datelist.append(date_train[x])\n",
        "  train.append(int(Vaksinasi1Total_train[x]*1000000))\n",
        "\n",
        "prediksi = {'Tanggal': datelist, 'training': train}\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_jason_train = pd.DataFrame(prediksi, columns = ['Tanggal', 'training'])\n",
        "print(df_save_jason_train)\n",
        "\n",
        "#save as json\n",
        "df_save_jason_train.to_json(r'Data yang digunakan untuk Training ada tanggal fix.json')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Tanggal  training\n",
            "0   05/03/2021         0\n",
            "1   06/03/2021      5342\n",
            "2   07/03/2021     35267\n",
            "3   08/03/2021     53539\n",
            "4   09/03/2021     94798\n",
            "5   10/03/2021    128619\n",
            "6   11/03/2021    135334\n",
            "7   12/03/2021    159569\n",
            "8   13/03/2021    171921\n",
            "9   14/03/2021    180063\n",
            "10  15/03/2021    213382\n",
            "11  16/03/2021    245797\n",
            "12  17/03/2021    275623\n",
            "13  18/03/2021    303829\n",
            "14  19/03/2021    355037\n",
            "15  20/03/2021    364418\n",
            "16  21/03/2021    379107\n",
            "17  22/03/2021    401131\n",
            "18  23/03/2021    432133\n",
            "19  24/03/2021    480722\n",
            "20  25/03/2021    513467\n",
            "21  26/03/2021    544499\n",
            "22  27/03/2021    564010\n",
            "23  28/03/2021    576538\n",
            "24  29/03/2021    601964\n",
            "25  30/03/2021    630424\n",
            "26  31/03/2021    661831\n",
            "27  01/04/2021    684668\n",
            "28  02/04/2021    700951\n",
            "29  03/04/2021    710253\n",
            "30  04/04/2021    718897\n",
            "31  05/04/2021    742302\n",
            "32  06/04/2021    761078\n",
            "33  07/04/2021    775532\n",
            "34  08/04/2021    801744\n",
            "35  09/04/2021    822576\n",
            "36  10/04/2021    837025\n",
            "37  11/04/2021    843183\n",
            "38  12/04/2021    864429\n",
            "39  13/04/2021    874534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNszKFbZSbvd",
        "outputId": "f98cfb21-fbcc-44ea-a07a-425364b9ade0"
      },
      "source": [
        "test= []\n",
        "datelist= []\n",
        "\n",
        "Vaksinasi1Total_test[0]\n",
        "for x in range(Vaksinasi1Total_test.size):\n",
        "  datelist.append(date_test[x])\n",
        "  test.append(int(Vaksinasi1Total_test[x]*1000000))\n",
        "\n",
        "prediksi = {'Tanggal': datelist, 'test': test}\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_jason_test = pd.DataFrame(prediksi, columns = ['Tanggal', 'test'])\n",
        "print(df_save_jason_test)\n",
        "\n",
        "#save as json\n",
        "df_save_jason_test.to_json(r'Data yang digunakan untuk test ada tanggal fix.json')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       Tanggal     test\n",
            "0   14/04/2021   884123\n",
            "1   15/04/2021   895619\n",
            "2   16/04/2021   907702\n",
            "3   17/04/2021   913401\n",
            "4   18/04/2021   916331\n",
            "5   19/04/2021   931288\n",
            "6   20/04/2021   943939\n",
            "7   21/04/2021   957820\n",
            "8   22/04/2021   983605\n",
            "9   23/04/2021  1000000\n",
            "10  24/04/2021  1000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNQ_YIebi2eI"
      },
      "source": [
        "save as excel file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3jaWQsjTdjPt"
      },
      "source": [
        "Vaksinasi1Total_train\n",
        "reshaped_Train = Vaksinasi1Total_train.reshape((-1,1))\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_exel_Train = pd.DataFrame (reshaped_Train)\n",
        "\n",
        "## save to xlsx file\n",
        "\n",
        "filepath = 'Data yang digunakan untuk Training.xlsx'\n",
        "\n",
        "df_save_exel_Train.to_excel(filepath, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VWKyX8EdlaW"
      },
      "source": [
        "prediction\n",
        "reshaped_prediction = prediction.reshape((-1,1))\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_exel_prediction = pd.DataFrame (reshaped_prediction)\n",
        "\n",
        "## save to xlsx file\n",
        "\n",
        "filepath = 'Data Hasil Prediksi.xlsx'\n",
        "\n",
        "df_save_exel_prediction.to_excel(filepath, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1g-S3RMedo-w"
      },
      "source": [
        "Vaksinasi1Total_test\n",
        "reshaped_Test = Vaksinasi1Total_test.reshape((-1,1))\n",
        "\n",
        "## convert your array into a dataframe\n",
        "df_save_exel_Test = pd.DataFrame (reshaped_Test)\n",
        "\n",
        "## save to xlsx file\n",
        "\n",
        "filepath = 'Data yang digunakan untuk Test hasil Training.xlsx'\n",
        "\n",
        "df_save_exel_Test.to_excel(filepath, index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F40m5GzMi5V8"
      },
      "source": [
        "save model to h5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Jf-YAudiCQY"
      },
      "source": [
        "model.save(\"Predict_vakccination.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}