Finalproj.ipynb

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      "source": [
        "**Prediction of future covid19 cases using Linear Regression and Polynomial Regression**"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "dfcovid = pd.read_csv(\"countries-aggregated_csv.csv\")\n",
        "country = dfcovid[ dfcovid.Country.str.contains(\"India\")]\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
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        "outputId": "f5d4b84e-5c6f-406c-b933-65e771a59a8a"
      },
      "source": [
        "dfcovid.head()\n"
      ],
      "execution_count": 2,
      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Country</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Albania</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Angola</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "         Date      Country  Confirmed  Recovered  Deaths\n",
              "0  2020-01-22  Afghanistan          0          0       0\n",
              "1  2020-01-22      Albania          0          0       0\n",
              "2  2020-01-22      Algeria          0          0       0\n",
              "3  2020-01-22      Andorra          0          0       0\n",
              "4  2020-01-22       Angola          0          0       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
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    },
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        "outputId": "5e41ca0e-7974-47a3-b7ed-46899bb03419"
      },
      "source": [
        "country.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Country</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>455</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>643</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>831</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "           Date Country  Confirmed  Recovered  Deaths\n",
              "79   2020-01-22   India          0          0       0\n",
              "267  2020-01-23   India          0          0       0\n",
              "455  2020-01-24   India          0          0       0\n",
              "643  2020-01-25   India          0          0       0\n",
              "831  2020-01-26   India          0          0       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KK9zhbgqGZVc",
        "colab_type": "text"
      },
      "source": [
        "Data Frame showing the rise in covid19 cases in India"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUQ7ADweQaTR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X1 = country.iloc[:, 0].values\n",
        "y = country.iloc[:, 2].values\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aapgygcSWqA",
        "colab_type": "text"
      },
      "source": [
        "We use arrays to store values such as Confirmed cases and Date, for making the DataFrame\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvOw6PRHQmv6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "daily = {'date':X1,'Confirmed':y}\n",
        "df = pd.DataFrame(daily)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUfeneh2Sxen",
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      },
      "source": [
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2020-08-05</td>\n",
              "      <td>1964536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2020-08-06</td>\n",
              "      <td>2027074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>2088611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2020-08-08</td>\n",
              "      <td>2153010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2020-08-09</td>\n",
              "      <td>2215074</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  Confirmed\n",
              "0    2020-01-22          0\n",
              "1    2020-01-23          0\n",
              "2    2020-01-24          0\n",
              "3    2020-01-25          0\n",
              "4    2020-01-26          0\n",
              "..          ...        ...\n",
              "196  2020-08-05    1964536\n",
              "197  2020-08-06    2027074\n",
              "198  2020-08-07    2088611\n",
              "199  2020-08-08    2153010\n",
              "200  2020-08-09    2215074\n",
              "\n",
              "[201 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
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          "height": 542
        },
        "outputId": "e67fcbd7-2453-4dbf-b14b-dc7cee1afdb9"
      },
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.line(df,x= 'date', y='Confirmed',title ='Confirmed cases growth rate')\n",
        "fig.show()"
      ],
      "execution_count": 7,
      "outputs": [
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZPxPHH-aFLz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['date'] = df['date'].str.replace('-', '')\n",
        "df['date'] = df['date'].astype(int)\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d6P0pAxETJy5",
        "colab_type": "text"
      },
      "source": [
        "We clean the data to fit it better into our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQPLhMC7WFsE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "11b42467-c0e5-474d-9118-a4af7427962c"
      },
      "source": [
        "X = df.iloc[:, 0].values\n",
        "y = df.iloc[:, 1].values\n",
        "y\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       1964536, 2027074, 2088611, 2153010, 2215074])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNqYFOgxaj_e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split \n",
        "X = X.reshape(-1,1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGex0fsYGrgI",
        "colab_type": "text"
      },
      "source": [
        "Splitting the data into training and testing samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYes03Y5bBWg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "e0b5c931-f3f8-4163-970f-b870b82132ef"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(X, y)\n",
        "\n",
        "# Visualizing the Linear Regression results\n",
        "def viz_linear():\n",
        "    plt.plot(X, y, color='red')\n",
        "    plt.plot(X, lin_reg.predict(X), color='blue')\n",
        "    plt.title('Linear Regression')\n",
        "    plt.xlabel('Date')\n",
        "    plt.ylabel('Confirmed Cases')\n",
        "    plt.show()\n",
        "    return\n",
        "viz_linear()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJz1aASAGxob",
        "colab_type": "text"
      },
      "source": [
        "**Fitting the curve with Linear Regression Algorithm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeNBZO69b3bF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "60952cc8-0a31-40c1-b16e-f371c732de2d"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree=2)\n",
        "X_poly = poly_reg.fit_transform(X)\n",
        "pol_reg = LinearRegression()\n",
        "pol_reg.fit(X_poly, y)\n",
        "\n",
        "\n",
        "\n",
        "def viz_polymonial():\n",
        "    plt.plot(X, y, color='red')\n",
        "    plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')\n",
        "    plt.title('Confirmed Cases Prediction')\n",
        "    plt.xlabel('Date')\n",
        "    plt.ylabel('Number of cases')\n",
        "    plt.show()\n",
        "    return\n",
        "viz_polymonial()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ayQ5hQwVG6Jh",
        "colab_type": "text"
      },
      "source": [
        "**Fitting the curve with Poynomial Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDMoVKHk-yzJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e23341bc-734c-4100-a283-58d037273349"
      },
      "source": [
        "s = int(input())\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20202710\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5Rgwf4GVKKm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "8d14516c-6717-4997-cd79-7272c66464e2"
      },
      "source": [
        "print(pol_reg.predict(poly_reg.fit_transform([[s]])))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[46914977.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8swydGvUTF0",
        "colab_type": "text"
      },
      "source": [
        "**Number of cases predicted based on date as input**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ne2-pRhp9oO0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "9225a89e-269d-41ef-bca0-4ee53e36e728"
      },
      "source": [
        "rs = pol_reg.predict(poly_reg.fit_transform(X))\n",
        "rs\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.5248650e+05,  2.4892800e+05,  2.4538650e+05,  2.4186100e+05,\n",
              "        2.3835250e+05,  2.3486100e+05,  2.3138600e+05,  2.2792700e+05,\n",
              "        2.2448550e+05,  2.2106100e+05,  2.2789000e+04,  2.0549000e+04,\n",
              "        1.8325000e+04,  1.6118500e+04,  1.3929000e+04,  1.1756000e+04,\n",
              "        9.6000000e+03,  7.4590000e+03,  5.3365000e+03,  3.2300000e+03,\n",
              "        1.1405000e+03, -9.3350000e+02, -2.9895000e+03, -5.0290000e+03,\n",
              "       -7.0515000e+03, -9.0580000e+03, -1.1048000e+04, -1.3021000e+04,\n",
              "       -1.4977000e+04, -1.6916000e+04, -1.8840000e+04, -2.0745500e+04,\n",
              "       -2.2635000e+04, -2.4507500e+04, -2.6363500e+04, -2.8203500e+04,\n",
              "       -3.0026000e+04, -3.1832000e+04, -3.3621000e+04, -1.1859050e+05,\n",
              "       -1.1916150e+05, -1.1971550e+05, -1.2025350e+05, -1.2077450e+05,\n",
              "       -1.2127800e+05, -1.2176550e+05, -1.2223600e+05, -1.2269100e+05,\n",
              "       -1.2312800e+05, -1.2354850e+05, -1.2395250e+05, -1.2434050e+05,\n",
              "       -1.2471100e+05, -1.2506500e+05, -1.2540200e+05, -1.2572200e+05,\n",
              "       -1.2602700e+05, -1.2631400e+05, -1.2658400e+05, -1.2683800e+05,\n",
              "       -1.2707550e+05, -1.2729600e+05, -1.2749950e+05, -1.2768650e+05,\n",
              "       -1.2785650e+05, -1.2801100e+05, -1.2814750e+05, -1.2826750e+05,\n",
              "       -1.2837100e+05, -1.2845800e+05, -9.3067500e+04, -9.1969500e+04,\n",
              "       -9.0854500e+04, -8.9722500e+04, -8.8575500e+04, -8.7410000e+04,\n",
              "       -8.6228500e+04, -8.5030000e+04, -8.3815000e+04, -8.2584000e+04,\n",
              "       -8.1335500e+04, -8.0070500e+04, -7.8788500e+04, -7.7491000e+04,\n",
              "       -7.6175500e+04, -7.4843500e+04, -7.3495000e+04, -7.2129500e+04,\n",
              "       -7.0748500e+04, -6.9350000e+04, -6.7934500e+04, -6.6502500e+04,\n",
              "       -6.5053500e+04, -6.3589000e+04, -6.2107000e+04, -6.0608000e+04,\n",
              "       -5.9092500e+04, -5.7561000e+04, -5.6012000e+04, -5.4446500e+04,\n",
              "        9.9358500e+04,  1.0212500e+05,  1.0490900e+05,  1.0770950e+05,\n",
              "        1.1052700e+05,  1.1336000e+05,  1.1621100e+05,  1.1907850e+05,\n",
              "        1.2196250e+05,  1.2486350e+05,  1.2778000e+05,  1.3071400e+05,\n",
              "        1.3366500e+05,  1.3663250e+05,  1.3961600e+05,  1.4261700e+05,\n",
              "        1.4563450e+05,  1.4866900e+05,  1.5172000e+05,  1.5478700e+05,\n",
              "        1.5787150e+05,  1.6097250e+05,  1.6409050e+05,  1.6722500e+05,\n",
              "        1.7037500e+05,  1.7354300e+05,  1.7672750e+05,  1.7992850e+05,\n",
              "        1.8314550e+05,  1.8638050e+05,  1.8963150e+05,  4.5868600e+05,\n",
              "        4.6312250e+05,  4.6757450e+05,  4.7204450e+05,  4.7653050e+05,\n",
              "        4.8103400e+05,  4.8555250e+05,  4.9008900e+05,  4.9464250e+05,\n",
              "        4.9921200e+05,  5.0379850e+05,  5.0840100e+05,  5.1302100e+05,\n",
              "        5.1765750e+05,  5.2231100e+05,  5.2698100e+05,  5.3166650e+05,\n",
              "        5.3637000e+05,  5.4109000e+05,  5.4582700e+05,  5.5057950e+05,\n",
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              "        1.0529910e+06,  1.0592790e+06,  1.0655845e+06,  1.0719070e+06,\n",
              "        1.0782460e+06,  1.0846020e+06,  1.0909735e+06,  1.0973625e+06,\n",
              "        1.1037680e+06,  1.1101905e+06,  1.1166290e+06,  1.1230845e+06,\n",
              "        1.1295575e+06,  1.1360465e+06,  1.1425525e+06,  1.1490740e+06,\n",
              "        1.1556135e+06,  1.1621695e+06,  1.1687420e+06,  1.1753305e+06,\n",
              "        1.6780480e+06,  1.6858225e+06,  1.6936135e+06,  1.7014215e+06,\n",
              "        1.7092450e+06,  1.7170860e+06,  1.7249440e+06,  1.7328185e+06,\n",
              "        1.7407095e+06])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26nHIH1M85R7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "80eb162f-5ae9-4c64-f310-03a2e4d48fa1"
      },
      "source": [
        "xDf  = {'Predict': rs}\n",
        "fin = pd.DataFrame(xDf)\n",
        "fin['date'] = X1\n",
        "xDf"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Predict': array([ 2.5248650e+05,  2.4892800e+05,  2.4538650e+05,  2.4186100e+05,\n",
              "         2.3835250e+05,  2.3486100e+05,  2.3138600e+05,  2.2792700e+05,\n",
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              "         5.1765750e+05,  5.2231100e+05,  5.2698100e+05,  5.3166650e+05,\n",
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              "         5.7459600e+05,  5.7944950e+05,  5.8432000e+05,  5.8920700e+05,\n",
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              "         1.0782460e+06,  1.0846020e+06,  1.0909735e+06,  1.0973625e+06,\n",
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              "         1.7092450e+06,  1.7170860e+06,  1.7249440e+06,  1.7328185e+06,\n",
              "         1.7407095e+06])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwzBoy9u_k2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "573eeee3-ec69-4137-fbd3-dd3278965d71"
      },
      "source": [
        "fin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Predict</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>252486.5</td>\n",
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              "      <td>248928.0</td>\n",
              "      <td>2020-01-23</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>245386.5</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>241861.0</td>\n",
              "      <td>2020-01-25</td>\n",
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              "      <th>4</th>\n",
              "      <td>238352.5</td>\n",
              "      <td>2020-01-26</td>\n",
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              "    <tr>\n",
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              "      <th>196</th>\n",
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              "      <td>1717086.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Predict        date\n",
              "0     252486.5  2020-01-22\n",
              "1     248928.0  2020-01-23\n",
              "2     245386.5  2020-01-24\n",
              "3     241861.0  2020-01-25\n",
              "4     238352.5  2020-01-26\n",
              "..         ...         ...\n",
              "196  1709245.0  2020-08-05\n",
              "197  1717086.0  2020-08-06\n",
              "198  1724944.0  2020-08-07\n",
              "199  1732818.5  2020-08-08\n",
              "200  1740709.5  2020-08-09\n",
              "\n",
              "[201 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlGPpeEdVxQI",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABL8k3KtDMN-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "date = country.iloc[:,0].values\n",
        "Confirmed =df.iloc[:,1].values\n",
        "predict = fin.iloc[:,0].values\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeHX9Ko9Cd-J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "7aec4b27-aa49-490d-b822-d53948ce481d"
      },
      "source": [
        "\n",
        "finald = { 'date' :date ,'Confirmed':Confirmed ,'predicted':predict}\n",
        "finaldataframe = pd.DataFrame(finald)\n",
        "finaldataframe"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Confirmed</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0</td>\n",
              "      <td>252486.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0</td>\n",
              "      <td>248928.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0</td>\n",
              "      <td>245386.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0</td>\n",
              "      <td>241861.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0</td>\n",
              "      <td>238352.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2020-08-05</td>\n",
              "      <td>1964536</td>\n",
              "      <td>1709245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2020-08-06</td>\n",
              "      <td>2027074</td>\n",
              "      <td>1717086.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>2088611</td>\n",
              "      <td>1724944.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>2020-08-08</td>\n",
              "      <td>2153010</td>\n",
              "      <td>1732818.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>2020-08-09</td>\n",
              "      <td>2215074</td>\n",
              "      <td>1740709.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  Confirmed  predicted\n",
              "0    2020-01-22          0   252486.5\n",
              "1    2020-01-23          0   248928.0\n",
              "2    2020-01-24          0   245386.5\n",
              "3    2020-01-25          0   241861.0\n",
              "4    2020-01-26          0   238352.5\n",
              "..          ...        ...        ...\n",
              "196  2020-08-05    1964536  1709245.0\n",
              "197  2020-08-06    2027074  1717086.0\n",
              "198  2020-08-07    2088611  1724944.0\n",
              "199  2020-08-08    2153010  1732818.5\n",
              "200  2020-08-09    2215074  1740709.5\n",
              "\n",
              "[201 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZLT1BOE67TN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "84113908-18ab-4e20-de6d-9867f6f9f060"
      },
      "source": [
        "#filtering data \n",
        "s=0\n",
        "n = list(finaldataframe.iloc[:,2].values)\n",
        "for i in range(len(n)):\n",
        "  s = s+1\n",
        "  if n[i]== 9.9358500e+04:\n",
        "    break\n",
        "  else:\n",
        "    n[i] = 0\n",
        "\n",
        "\n",
        "n\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {
            "tags": []
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          "execution_count": 22
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      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksNeJ3sTWdRY",
        "colab_type": "text"
      },
      "source": [
        "We solve underfitting by filtering the data\n"
      ]
    },
    {
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      "metadata": {
        "id": "uItZMbgY3w1h",
        "colab_type": "code",
        "colab": {
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          "height": 406
        },
        "outputId": "344004ef-0e5c-4779-fd03-87a0276801ef"
      },
      "source": [
        "finaldataframe['predicted'] = n\n",
        "finaldataframe"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>date</th>\n",
              "      <th>Confirmed</th>\n",
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              "           date  Confirmed  predicted\n",
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              "4    2020-01-26          0        0.0\n",
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              "196  2020-08-05    1964536  1709245.0\n",
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              "198  2020-08-07    2088611  1724944.0\n",
              "199  2020-08-08    2153010  1732818.5\n",
              "200  2020-08-09    2215074  1740709.5\n",
              "\n",
              "[201 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a7d53db3-7e88-4c82-cb49-363371fe7154"
      },
      "source": [
        "import plotly.express as px\n",
        "def ploti():\n",
        "\n",
        "  s = px.line(finaldataframe,x='date', y='predicted',title ='predicted cases growth rate')\n",
        "  t = px.line(finaldataframe,x ='date',y='Confirmed',title=\"confirmed \")\n",
        "  s.show()\n",
        "  t.show()\n",
        "ploti()"
      ],
      "execution_count": 24,
      "outputs": [
        {
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\"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"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\": \"confirmed \"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Confirmed\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('95690ff9-4729-4bbe-a7df-d44a486ce3fd');\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": "code",
      "metadata": {
        "id": "3H5qeWv-61g9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "7bd36f51-85ae-4f3a-d5f6-0b509c283965"
      },
      "source": [
        "fig, ax = plt.subplots() \n",
        "finaldataframe.plot(x = 'date', y = 'predicted', ax = ax) \n",
        "finaldataframe.plot(x = 'date', y = 'Confirmed', ax = ax, secondary_y = True) "
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e907524a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8m5exKAXPYI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}