Visitors_Time_Series.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "name": "Visitors-Time Series.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "rezY3TMX21pw"
      ],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AdityaSamantaroy/COVID19-prediction/blob/master/Visitors_Time_Series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19w-8ImIzhnx",
        "colab_type": "text"
      },
      "source": [
        "# Imports and path"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bH1l2WNm4gE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "336bcbc6-f57c-4438-97c3-1c545b314558"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import missingno as msno\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1Yy6w9enyUq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9ceefc8b-0c24-400d-e763-8be9e5f4d895"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount = True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpJBUZtF8gnk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a81fcfdc-388e-4184-f241-934747ef0e17"
      },
      "source": [
        "%ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mgdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExLfLG25nwBM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0d8c20e-0f30-40aa-ff89-89cfdf477afd"
      },
      "source": [
        "%cd \"/content/gdrive/My Drive/Machine Learning\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Machine Learning\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT8Di6KLnzvv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7088dd03-9e36-40df-b273-a4911b285972"
      },
      "source": [
        "# %mkdir flipr\n",
        "%cd flipr"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Machine Learning/flipr\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inr_htSrpNJ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7ab3328-ed47-4c54-c9f9-5a7197b597b7"
      },
      "source": [
        "%ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test_dataset.xlsx  Train_dataset.xlsx  Variable_Description.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FUCcXY6y8QU",
        "colab_type": "text"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfDmuj1f0rji",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "db1a2c4f-31cd-4ee4-f98d-5d3ccbb3d492"
      },
      "source": [
        "tdf = pd.read_excel('Test_dataset.xlsx', sheet_name = 1, header=1)\n",
        "tdf.head()"
      ],
      "execution_count": 7,
      "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>Unnamed: 0</th>\n",
              "      <th>April</th>\n",
              "      <th>May</th>\n",
              "      <th>June</th>\n",
              "      <th>July</th>\n",
              "      <th>August</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuensang</td>\n",
              "      <td>1614.0</td>\n",
              "      <td>1946.0</td>\n",
              "      <td>2372.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lakshmeshwar</td>\n",
              "      <td>369179.0</td>\n",
              "      <td>445559.0</td>\n",
              "      <td>541045.0</td>\n",
              "      <td>572860.0</td>\n",
              "      <td>636502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zira</td>\n",
              "      <td>140581.0</td>\n",
              "      <td>169665.0</td>\n",
              "      <td>206030.0</td>\n",
              "      <td>218138.0</td>\n",
              "      <td>242367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yawal</td>\n",
              "      <td>2557179.0</td>\n",
              "      <td>3086249.0</td>\n",
              "      <td>3747597.0</td>\n",
              "      <td>3968032.0</td>\n",
              "      <td>4408916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thana Bhawan</td>\n",
              "      <td>1800363.0</td>\n",
              "      <td>2172850.0</td>\n",
              "      <td>2638469.0</td>\n",
              "      <td>2793662.0</td>\n",
              "      <td>3104060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0      April        May       June       July   August\n",
              "0      Tuensang     1614.0     1946.0     2372.0     2500.0     2769\n",
              "1  Lakshmeshwar   369179.0   445559.0   541045.0   572860.0   636502\n",
              "2          Zira   140581.0   169665.0   206030.0   218138.0   242367\n",
              "3         Yawal  2557179.0  3086249.0  3747597.0  3968032.0  4408916\n",
              "4  Thana Bhawan  1800363.0  2172850.0  2638469.0  2793662.0  3104060"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjZzWyF11bXi",
        "colab_type": "text"
      },
      "source": [
        "## Handling missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gaXeG2l00NM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "outputId": "b7f578d3-be0f-425d-fdfe-b0276e6862fc"
      },
      "source": [
        "tdf.columns = ['city', 'April', 'May', 'June', 'July', 'August']\n",
        "# tdf.columns\n",
        "tdf.head()"
      ],
      "execution_count": 8,
      "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",
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              "\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>city</th>\n",
              "      <th>April</th>\n",
              "      <th>May</th>\n",
              "      <th>June</th>\n",
              "      <th>July</th>\n",
              "      <th>August</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuensang</td>\n",
              "      <td>1614.0</td>\n",
              "      <td>1946.0</td>\n",
              "      <td>2372.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>2769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lakshmeshwar</td>\n",
              "      <td>369179.0</td>\n",
              "      <td>445559.0</td>\n",
              "      <td>541045.0</td>\n",
              "      <td>572860.0</td>\n",
              "      <td>636502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zira</td>\n",
              "      <td>140581.0</td>\n",
              "      <td>169665.0</td>\n",
              "      <td>206030.0</td>\n",
              "      <td>218138.0</td>\n",
              "      <td>242367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yawal</td>\n",
              "      <td>2557179.0</td>\n",
              "      <td>3086249.0</td>\n",
              "      <td>3747597.0</td>\n",
              "      <td>3968032.0</td>\n",
              "      <td>4408916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thana Bhawan</td>\n",
              "      <td>1800363.0</td>\n",
              "      <td>2172850.0</td>\n",
              "      <td>2638469.0</td>\n",
              "      <td>2793662.0</td>\n",
              "      <td>3104060</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           city      April        May       June       July   August\n",
              "0      Tuensang     1614.0     1946.0     2372.0     2500.0     2769\n",
              "1  Lakshmeshwar   369179.0   445559.0   541045.0   572860.0   636502\n",
              "2          Zira   140581.0   169665.0   206030.0   218138.0   242367\n",
              "3         Yawal  2557179.0  3086249.0  3747597.0  3968032.0  4408916\n",
              "4  Thana Bhawan  1800363.0  2172850.0  2638469.0  2793662.0  3104060"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENgpRFkz06-c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "1725953a-c03a-40bc-f3d5-342ef26cc43b"
      },
      "source": [
        "msno.matrix(tdf)\n",
        "\n",
        "for i in range(1,6):\n",
        "  median = tdf.iloc[:,i].median()\n",
        "  tdf.iloc[:,i].fillna(median, inplace=True)\n",
        "\n",
        "msno.matrix(tdf)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fce7d2aecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_OMa5471e9e",
        "colab_type": "text"
      },
      "source": [
        "## Correlation with previous months' data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnnrG75Y1MC7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "5e51c83a-8592-46a1-ac1b-e3647de2b35b"
      },
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(tdf.corr(),annot=True,cmap='viridis')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fce7d2084e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4xZKBmM1R0P",
        "colab_type": "text"
      },
      "source": [
        "## Preparing input and output to models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPuTDkFEP03O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4406b967-c545-4aad-8a41-74d9cd308620"
      },
      "source": [
        "x = np.array(tdf.iloc[:,1:5].values)\n",
        "# print(x.shape)\n",
        "y = tdf.iloc[:,-1]\n",
        "\n",
        "x = x[:,:, np.newaxis]\n",
        "y = y[:, np.newaxis]\n",
        "\n",
        "minn = x.min()\n",
        "maxx = x.max()\n",
        "\n",
        "x = (x - minn)/(maxx- minn)\n",
        "# print(x)\n",
        "\n",
        "y = (y - minn)/(maxx - minn)\n",
        "# print(y)\n",
        "\n",
        "print(x.shape,y.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(501, 4, 1) (501, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAgDl1Y-0lao",
        "colab_type": "text"
      },
      "source": [
        "# Time Series Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zko0sY752h6s",
        "colab_type": "text"
      },
      "source": [
        "### LSTM model, mse: 0.0040 - mae: 0.0294"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRvci7QK8fOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ba77fbf7-141a-4606-b5ce-2b77949589a5"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "                  tf.keras.layers.LSTM(2, input_shape = (4, 1)),\n",
        "                  tf.keras.layers.Dense(3, activation=\"relu\"),\n",
        "                  tf.keras.layers.Dense(1, activation=\"relu\")\n",
        "])\n",
        "\n",
        "model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae'])\n",
        "\n",
        "model.fit(x=x, y=y, verbose=1, epochs=100)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2801 - mse: 0.2801 - mae: 0.3633\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2481 - mse: 0.2481 - mae: 0.3340\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.2181 - mse: 0.2181 - mae: 0.3114\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1921 - mse: 0.1921 - mae: 0.2957\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1671 - mse: 0.1671 - mae: 0.2812\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1452 - mse: 0.1452 - mae: 0.2700\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1256 - mse: 0.1256 - mae: 0.2580\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.1081 - mse: 0.1081 - mae: 0.2472\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0920 - mse: 0.0920 - mae: 0.2343\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0781 - mse: 0.0781 - mae: 0.2203\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0651 - mse: 0.0651 - mae: 0.2029\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0529 - mse: 0.0529 - mae: 0.1841\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0415 - mse: 0.0415 - mae: 0.1621\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0317 - mse: 0.0317 - mae: 0.1376\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.0236 - mse: 0.0236 - mae: 0.1155\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0168 - mse: 0.0168 - mae: 0.0941\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0123 - mse: 0.0123 - mae: 0.0749\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0091 - mse: 0.0091 - mae: 0.0630\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0072 - mse: 0.0072 - mae: 0.0535\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0061 - mse: 0.0061 - mae: 0.0464\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0055 - mse: 0.0055 - mae: 0.0414\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0052 - mse: 0.0052 - mae: 0.0396\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0050 - mse: 0.0050 - mae: 0.0393\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0049 - mse: 0.0049 - mae: 0.0396\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0049 - mse: 0.0049 - mae: 0.0398\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0048 - mse: 0.0048 - mae: 0.0397\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0048 - mse: 0.0048 - mae: 0.0385\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0047 - mse: 0.0047 - mae: 0.0375\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0047 - mse: 0.0047 - mae: 0.0382\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0047 - mse: 0.0047 - mae: 0.0373\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0046 - mse: 0.0046 - mae: 0.0375\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0046 - mse: 0.0046 - mae: 0.0375\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0046 - mse: 0.0046 - mae: 0.0379\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0046 - mse: 0.0046 - mae: 0.0375\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0045 - mse: 0.0045 - mae: 0.0373\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0045 - mse: 0.0045 - mae: 0.0378\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0045 - mse: 0.0045 - mae: 0.0369\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0045 - mse: 0.0045 - mae: 0.0371\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0045 - mse: 0.0045 - mae: 0.0379\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0365\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0366\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0369\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0371\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0361\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0369\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0362\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0359\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0044 - mse: 0.0044 - mae: 0.0366\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0357\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0358    \n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0357\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0357\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0350\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0358\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0352    \n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0348\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0349\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0346\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0358\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0347\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0344\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0348\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0342\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0347\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0347\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0043 - mse: 0.0043 - mae: 0.0344\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0344\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0343\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0341\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0333\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0337\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0327\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0332\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0325\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0326\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0325\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0326\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0042 - mse: 0.0042 - mae: 0.0332\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0320\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0332\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0320\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0327\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0331\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0326\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0326\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0322\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0321\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0321\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0041 - mse: 0.0041 - mae: 0.0330\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0317\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0322\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0324\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0320\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0322\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0318    \n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0319\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0319\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0316\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0040 - mse: 0.0040 - mae: 0.0321\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.0039 - mse: 0.0039 - mae: 0.0311\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fce753535f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjPtX91AUHIk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "3adbab02-437d-42ab-a2a4-a9fd01ca716f"
      },
      "source": [
        "model.evaluate(x,y) #*(maxx - minn) + minn"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16/16 [==============================] - 0s 1ms/step - loss: 0.0039 - mse: 0.0039 - mae: 0.0316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00393439456820488, 0.00393439456820488, 0.031572237610816956]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoNCFg-ySbbH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# preds = model.predict(x)*(maxx - minn) + minn"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCvb3bWB2nsc",
        "colab_type": "text"
      },
      "source": [
        "### Baseline, mse: 0.0102 rse: 0.0488"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnpiavREdeHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "last = x[:,-1,:]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqm0e_lZdunh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db902dac-9d7d-42e9-bba3-35bff0d62e6d"
      },
      "source": [
        "print(mean_squared_error(last,y), mean_absolute_error(last,y))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.010203777677728435 0.048848464072459255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rezY3TMX21pw",
        "colab_type": "text"
      },
      "source": [
        "### MLP, mse: 0.0200 - mae: 0.0861"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsLrbrtzei_A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "33b2c453-e87f-458c-8af0-ecce4df187ec"
      },
      "source": [
        "mlp = tf.keras.models.Sequential([\n",
        "                  tf.keras.layers.Dense(5,activation=\"relu\"),\n",
        "                  tf.keras.layers.Dense(1, activation=\"relu\")\n",
        "])\n",
        "\n",
        "mlp.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae'])\n",
        "\n",
        "mlp.fit(x=x,y=y, epochs=100, verbose=1)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 2/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 3/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 4/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 5/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 6/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 7/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 8/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 9/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 10/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 11/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 12/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 13/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 14/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 15/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 16/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 17/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 18/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 19/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 20/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 21/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 22/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 23/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 24/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 25/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 26/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 27/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 28/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 29/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 30/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 31/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 32/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 33/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 34/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 35/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 36/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 37/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 38/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 39/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 40/100\n",
            "16/16 [==============================] - 0s 2ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 41/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 42/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 43/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 44/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 45/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 46/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 47/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 48/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 49/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 50/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 51/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 52/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 53/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 54/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 55/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 56/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 57/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 58/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 59/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 60/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 61/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 62/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 63/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 64/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 65/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 66/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 67/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 68/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 69/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 70/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 71/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 72/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 73/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 74/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 75/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 76/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 77/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 78/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 79/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 80/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 81/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 82/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 83/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 84/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 85/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 86/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 87/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 88/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 89/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 90/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 91/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 92/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 93/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 94/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 95/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 96/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 97/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 98/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 99/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n",
            "Epoch 100/100\n",
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fce72138828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Dw9qQY3fYJ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f0cf03dc-8ab6-4509-a4e2-281efe073976"
      },
      "source": [
        "mlp.evaluate(x,y) #*(maxx - minn) + minn"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16/16 [==============================] - 0s 1ms/step - loss: 0.3202 - mse: 0.3202 - mae: 0.3952\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3202061355113983, 0.3202061355113983, 0.39516425132751465]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLJx6VShrrhA",
        "colab_type": "text"
      },
      "source": [
        "# Task2 data output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbtLTZwO55CQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5f53e82-ede5-4066-a764-96a0bb92286b"
      },
      "source": [
        "x_test = np.array(tdf.iloc[:,2:].values)\n",
        "# print(x_test.shape)\n",
        "# y = tdf.iloc[:,-1]\n",
        "\n",
        "x_test = x_test[:,:, np.newaxis]\n",
        "# y = y[:, np.newaxis]\n",
        "\n",
        "# minn = x.min()\n",
        "# maxx = x.max()\n",
        "\n",
        "x_test = (x_test - minn)/(maxx- minn)\n",
        "# print(x)\n",
        "\n",
        "# y = (y - minn)/(maxx - minn)\n",
        "# print(y)\n",
        "\n",
        "print(x_test.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(501, 4, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-F1wc9k60YY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = model.predict(x_test)*(maxx - minn) + minn"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHd4EpsC5j78",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "33cbae61-93f4-4a66-85c4-952d99625fde"
      },
      "source": [
        "output_df = pd.read_excel('Test_dataset.xlsx', sheet_name = 0, header=0)\n",
        "output_df.head()"
      ],
      "execution_count": 25,
      "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>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Type</th>\n",
              "      <th>Population [2011]</th>\n",
              "      <th>Popuation [2001]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th>Female Population</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuensang</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>T.C</td>\n",
              "      <td>36774.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>931.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>94.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>0.253390</td>\n",
              "      <td>34237.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lakshmeshwar</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>T.M.C</td>\n",
              "      <td>36754.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>934.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>62.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.192555</td>\n",
              "      <td>34328.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>636502.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zira</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>M.Cl.</td>\n",
              "      <td>36732.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>883.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>63.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>0.887882</td>\n",
              "      <td>32434.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>242367.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yawal</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>M.Cl</td>\n",
              "      <td>36706.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>887.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>60.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.407838</td>\n",
              "      <td>32558.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4408916.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thana Bhawan</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>N.P.</td>\n",
              "      <td>36669.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>877.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>92.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>0.324456</td>\n",
              "      <td>32159.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3104060.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           City          State  ... # of hospitals  Foreign Visitors\n",
              "0      Tuensang       Nagaland  ...           17.0            2769.0\n",
              "1  Lakshmeshwar      Karnataka  ...           13.0          636502.0\n",
              "2          Zira         Punjab  ...           17.0          242367.0\n",
              "3         Yawal    Maharashtra  ...           11.0         4408916.0\n",
              "4  Thana Bhawan  Uttar Pradesh  ...           23.0         3104060.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeHowy_Qrs6a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dbf2b48b-c8b4-4a93-e104-a23ee5d74312"
      },
      "source": [
        "output_df.shape, preds.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((501, 15), (501, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqW1STCp7czE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output_df['Foreign Visitors'] = preds"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tsDTyrS9dui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "3d10bde0-7f19-4f18-c390-db3638389807"
      },
      "source": [
        "output_df.head()"
      ],
      "execution_count": 28,
      "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>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Type</th>\n",
              "      <th>Population [2011]</th>\n",
              "      <th>Popuation [2001]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th>Female Population</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuensang</td>\n",
              "      <td>Nagaland</td>\n",
              "      <td>T.C</td>\n",
              "      <td>36774.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>931.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>94.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>0.253390</td>\n",
              "      <td>34237.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1.066507e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lakshmeshwar</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>T.M.C</td>\n",
              "      <td>36754.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>934.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>62.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.192555</td>\n",
              "      <td>34328.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>6.629805e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zira</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>M.Cl.</td>\n",
              "      <td>36732.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>883.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>63.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>0.887882</td>\n",
              "      <td>32434.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2.331244e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yawal</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>M.Cl</td>\n",
              "      <td>36706.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>887.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>60.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.407838</td>\n",
              "      <td>32558.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4.743584e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thana Bhawan</td>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>N.P.</td>\n",
              "      <td>36669.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>877.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>92.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>0.324456</td>\n",
              "      <td>32159.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3.853626e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           City          State  ... # of hospitals  Foreign Visitors\n",
              "0      Tuensang       Nagaland  ...           17.0      1.066507e+04\n",
              "1  Lakshmeshwar      Karnataka  ...           13.0      6.629805e+05\n",
              "2          Zira         Punjab  ...           17.0      2.331244e+05\n",
              "3         Yawal    Maharashtra  ...           11.0      4.743584e+06\n",
              "4  Thana Bhawan  Uttar Pradesh  ...           23.0      3.853626e+06\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxzbjJsv9hLa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "output_df.to_excel('T2Test_dataset.xlsx')"
      ],
      "execution_count": 29,
      "outputs": []
    }
  ]
}