5_13_2020_Covid_LSTM.ipynb

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    "colab": {
      "name": " 5-13-2020 Covid_LSTM.ipynb",
      "provenance": [],
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      "machine_shape": "hm",
      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning-Driven-Approach-for-2019-nCoV-Warning-System/blob/master/Implementation/5_13_2020_Covid_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfcjS4CKTyAP"
      },
      "source": [
        "# **1- Introduction** \n",
        "\n",
        "Predicting confirmed cases of Convid-19 with LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTYReJPf9UFm",
        "outputId": "ccf747f0-ee30-4185-91e5-de0485b17e52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/hussain0048/Machine-Learning-Driven-Approach-for-2019-nCoV-Warning-System"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Machine-Learning-Driven-Approach-for-2019-nCoV-Warning-System'...\n",
            "fatal: could not read Username for 'https://github.com': No such device or address\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85cNlo5f1-Z3"
      },
      "source": [
        "# **2- Import Nessary library**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxdLWHIJTyAU"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from fbprophet import Prophet\n",
        "import matplotlib.pyplot as plt\n",
        " \n",
        "plt.rcParams['figure.figsize']=(20,10)\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NeSSztObv-7"
      },
      "source": [
        "plt.rcParams['figure.figsize']=(20,10)\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhadCtyRcFFY"
      },
      "source": [
        "pd.plotting.register_matplotlib_converters()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vcGxjHQGL1c"
      },
      "source": [
        "#[3]\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d17uQ_kpKn5w"
      },
      "source": [
        "from fbprophet import Prophet\n",
        "\n",
        "import logging\n",
        "logging.getLogger().setLevel(logging.ERROR)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ia255xaIs1iD"
      },
      "source": [
        "# LSTM\n",
        "import math\n",
        "import pandas_datareader as web\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,LSTM\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwsfXTJk2R0M",
        "outputId": "e03f8c93-a8da-420e-b702-f430e02607b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5p8oL_STyAg"
      },
      "source": [
        "# **3-Import dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDKPEa7keRWE"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/Datasets/Covid-19/data_cases1.csv\", usecols=['Date','Confirmed'],index_col='Date', parse_dates=True )\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_qRu1k2Dyt2O"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-E75RQRTyAx"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8BthJfITyBS"
      },
      "source": [
        "#**4-Data cleaning and feature engineering**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ns2tk6CCGzDo"
      },
      "source": [
        "df1 = df.reset_index()"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGxkU_bZ_eoq"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wiEfm8xA9Cb4"
      },
      "source": [
        "#we get rid of possible duplicates and missing values in the data [3]\n",
        "df1 = df1[['Date', 'Confirmed']].dropna().drop_duplicates()"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqj24WXrFwbV"
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8oKf0uV2GG02"
      },
      "source": [
        "#we need to convert Date to the datetime format because by default pandas treats this field as string-valued[3]\n",
        "df1['Date'] = pd.to_datetime(df1['Date'])"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijc8tkqbGqpE"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cnFzmAAHIWq"
      },
      "source": [
        "#Let's sort the dataframe by time and take a look at what we've got [3]\n",
        "df1.sort_values(by=['Date']).head(n=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vegTUwkq9kw8"
      },
      "source": [
        "#We will just trim our time series to keep only those rows that fall onto the period from March 10, 2020 to March 31, 2020[3]\n",
        "df1 = df1[(df1['Date'] > '2020-03-09') & (df1['Date'] < '2020-04-01')].sort_values(by=['Date'])\n",
        "df1.head(n=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rahdPkyK-81A"
      },
      "source": [
        "#As we are going to predict the number of confirm Cases of Covid-19, we will aggregate and count unique Cases at each given point in time. We will name the corresponding new column Confirmed_Cases:\n",
        "aggr_df = df1.groupby('Date')[['Confirmed']].count()\n",
        "aggr_df.columns = ['Confirmed_Cases']"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-av4_rg_pfa"
      },
      "source": [
        "aggr_df.head(n=3)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WPfYYLU_6dD"
      },
      "source": [
        "#In this practice, we are interested in the number of confirmed cases a day. But at this moment all our data is divided into irregular time intervals that are less than a day. \n",
        "#This is called a sub-daily time series.To fix this, we need to aggregate the Cases counts by \"bins\" of a date size. \n",
        "#In time series analysis, this process is referred to as resampling. And if we reduce the sampling rate of data it is often called downsampling.\n",
        "#Luckily, pandas has a built-in functionality for this task. We will resample our time index down to 1-day bins [3]\n",
        "daily_df = aggr_df.resample('D').apply(sum)\n",
        "daily_df.head(n=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9n0GTxkvAeq5"
      },
      "source": [
        "weekly_df = df1.resample('W').apply(sum)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUR6GRnOJp5T",
        "outputId": "de7058f9-8c63-4574-9f99-7d16f1ad130a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        }
      },
      "source": [
        "weekly_df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-ce1d036ecd55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mweekly_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'weekly_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVCbk-vsAz-n",
        "outputId": "10a05488-8331-4da1-9360-b60fd39a21cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "source": [
        "daily_df = daily_df.loc[daily_df.index >= '2020-03-10']\n",
        "daily_df.head(n=3)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-21cb9bcf1ba2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdaily_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdaily_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdaily_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m'2020-03-10'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdaily_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'daily_df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQLlg3g4OvWh"
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Daily Confirmed Cases of COVID-19 (World)')\n",
        "plt.plot(daily_df['Confirmed_Cases'])\n",
        "plt.xlabel('Date', fontsize=18)\n",
        "plt.ylabel('Confirmed Cases', fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8ivtMFNTyEs"
      },
      "source": [
        "#**5- Modelling**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVFBqHaJBM2J"
      },
      "source": [
        "#Lstm\n",
        "data=aggr_df.filter(['Confirmed'])"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uucawvui-7iT"
      },
      "source": [
        "dataset=aggr_df.values"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMJiPPce_C3A"
      },
      "source": [
        "training_data_len=math.ceil(len(dataset)*.8)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqoNjyXg_qjc"
      },
      "source": [
        "training_data_len"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgLle6F7_0DK"
      },
      "source": [
        "scaler=MinMaxScaler(feature_range=(0,1))"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUPaY-3DAE9v"
      },
      "source": [
        "scaled_data=scaler.fit_transform(dataset)"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQgo0uoxAZT9"
      },
      "source": [
        "scaled_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TtxrK2kBS8F",
        "outputId": "87e86f76-7a27-476d-c702-a8b717dcb985",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# create the training dataset\n",
        "# create the scaled training dataset\n",
        "train_data=scaled_data[0:training_data_len,:]\n",
        "# split the data into x_train and y_train dataset\n",
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range (12, len(train_data)):\n",
        "  x_train.append(train_data[i-12:i,0])\n",
        "  y_train.append(train_data[i,0])\n",
        "  if i<= 12:\n",
        "    print(x_train)\n",
        "    print(y_train)\n",
        "    print()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([0.        , 0.04458599, 0.07006369, 0.08280255, 0.12738854,\n",
            "       0.12101911, 0.15923567, 0.15286624, 0.15923567, 0.17197452,\n",
            "       0.1910828 , 0.21656051])]\n",
            "[0.624203821656051]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZl7H0pBChBy",
        "outputId": "1e2b2a96-c815-4174-f808-9f137cddfc95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Convert the x_train and y_train to numpy arrays\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "x_train"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.04458599, 0.07006369, 0.08280255, 0.12738854,\n",
              "        0.12101911, 0.15923567, 0.15286624, 0.15923567, 0.17197452,\n",
              "        0.1910828 , 0.21656051],\n",
              "       [0.04458599, 0.07006369, 0.08280255, 0.12738854, 0.12101911,\n",
              "        0.15923567, 0.15286624, 0.15923567, 0.17197452, 0.1910828 ,\n",
              "        0.21656051, 0.62420382]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "901KPDDeCpu5",
        "outputId": "380d8dd1-a08a-4a7c-e397-7a5832ac87d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Reshap the data\n",
        "x_train=np.reshape(x_train, (x_train.shape[0], x_train.shape[1],1))\n",
        "x_train.shape"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 12, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo5bwUE-Cz9g"
      },
      "source": [
        "# build the LSTM model\n",
        "model=Sequential()\n",
        "model.add(LSTM(40, return_sequences=True,input_shape=(x_train.shape[1],1)))\n",
        "model.add(LSTM(40, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ph3rquB-DZsX",
        "outputId": "a754b201-7b40-4edc-81b9-41b542a57d36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# train the model\n",
        "model.fit(x_train,y_train,batch_size=1,epochs=1)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.4434\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f754031fef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mxp-MSYPDOzz"
      },
      "source": [
        "# compile the model\n",
        "model.compile(optimizer='adam',loss='mean_squared_error')"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8b3sFhSEDiL"
      },
      "source": [
        "test_data=scaled_data[training_data_len -12:,:]\n",
        "x_test= []\n",
        "y_test = dataset[training_data_len:,:]\n",
        "for i in range(12, len(test_data)):\n",
        "  x_test.append(test_data[i -12:i,0])"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RuY3KUkGEOYA"
      },
      "source": [
        "x_test=np.array(x_test)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IrrrGaW1EQuf",
        "outputId": "8bc16ae1-9e25-4d7e-d53b-bed388093491",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x_test"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.07006369, 0.08280255, 0.12738854, 0.12101911, 0.15923567,\n",
              "        0.15286624, 0.15923567, 0.17197452, 0.1910828 , 0.21656051,\n",
              "        0.62420382, 0.72611465],\n",
              "       [0.08280255, 0.12738854, 0.12101911, 0.15923567, 0.15286624,\n",
              "        0.15923567, 0.17197452, 0.1910828 , 0.21656051, 0.62420382,\n",
              "        0.72611465, 0.78343949],\n",
              "       [0.12738854, 0.12101911, 0.15923567, 0.15286624, 0.15923567,\n",
              "        0.17197452, 0.1910828 , 0.21656051, 0.62420382, 0.72611465,\n",
              "        0.78343949, 0.89808917]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLKwuQ2mEUY-"
      },
      "source": [
        "x_test=np.reshape(x_test,(x_test.shape[0], x_test.shape[1],1))"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2GibjoyEco3"
      },
      "source": [
        "predictions=model.predict(x_test)\n",
        "predictions=scaler.inverse_transform(predictions)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCknKvIiDSvC"
      },
      "source": [
        "rmse=np.sqrt(np.mean(predictions - y_test)**2)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UAIiiUzDStS",
        "outputId": "3446c8d6-c16c-49fb-eecf-bb3d06d0f811",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "rmse"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130.11727078755698"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "auMST9VMFXil"
      },
      "source": [
        "train=aggr_df[:training_data_len]\n",
        "valid=aggr_df[training_data_len:]"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9U1h6R3DGG_4"
      },
      "source": [
        "valid['Predictions']=predictions"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0bIwuB0J-nn",
        "outputId": "1ac7531b-a6d0-4236-a2c7-61c51c05eaeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "valid.head()"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed_Cases</th>\n",
              "      <th>Predictions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-24</th>\n",
              "      <td>140</td>\n",
              "      <td>26.088554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>158</td>\n",
              "      <td>27.168270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>174</td>\n",
              "      <td>28.391363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Confirmed_Cases  Predictions\n",
              "Date                                    \n",
              "2020-03-24              140    26.088554\n",
              "2020-03-25              158    27.168270\n",
              "2020-03-26              174    28.391363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9o1cWAPG85L",
        "outputId": "e85abd2f-70b0-4105-ad29-aed877108e7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        }
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Confirmed Cases',fontsize=18)\n",
        "plt.plot(train['Confirmed_Cases'])\n",
        "plt.plot(valid[['Confirmed_Cases','Predictions']])\n",
        "plt.legend(['Train','Val','Predictions'],loc='lower right')"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f753f352be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QmrbiUI8sjn8"
      },
      "source": [
        "# **References**\n",
        "\n",
        "[1]Forecasting Time Series Data With Prophet I & II\n",
        "https://nextjournal.com/eric-brown/forecasting-with-prophet\n",
        "\n",
        "[2]Forecasting Time Series Data With Prophet IV\n",
        "https://nextjournal.com/eric-brown/forecasting-with-prophet-part-4\n",
        "\n",
        "[3]Time series analysis in Python\n",
        "https://mlcourse.ai/articles/topic9-part2-prophet/\n",
        "\n",
        "[4] https://www.kaggle.com/kashnitsky/topic-9-part-2-time-series-with-facebook-prophet\n",
        "\n",
        "[5] How To Predict Coronavirus (COVID-19) Cases Using Deep Learning in Python\n",
        "https://laconicml.com/predict-coronavirus-cases-deep-learning/"
      ]
    }
  ]
}