Linear_Regression_TF.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIdT9iu_Z4Rb"
      },
      "source": [
        "# Basic TensorFlow Regression with Sequential Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rRo8oNqZ-Rj",
        "outputId": "b49e5c9d-deaf-4a73-b9d1-21759e33248f",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "import pathlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn.metrics as skm\n",
        "\n",
        "import sqlite3 as db\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "UrRuxGDtpvx6",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "conn = db.connect(\"data/mock_db_v2.db\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "H3Ou9aJypXHD",
        "outputId": "a1c70b97-236d-4bf7-9f17-49165f1f5053",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
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              "          document.querySelector('#df-3ac4fca9-da0b-4ba0-bb08-b5280f81ce1e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ac4fca9-da0b-4ba0-bb08-b5280f81ce1e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                              ASSETID                         ASSETID:1  \\\n",
              "0    PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ   \n",
              "1    PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ   \n",
              "2    PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ   \n",
              "3    PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ   \n",
              "4    E0D01G0TGSPXF933E41FSG4ATYW0WFQW  E0D01G0TGSPXF933E41FSG4ATYW0WFQW   \n",
              "..                                ...                               ...   \n",
              "871  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE   \n",
              "872  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE   \n",
              "873  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE   \n",
              "874  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE   \n",
              "875  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE   \n",
              "\n",
              "                    ASSETTYPEID       NAME                          ID  \\\n",
              "0    01FX34K9DD3D4X3QKXT3TT69ZE   Facebook  01FX34K9DD3D4X3QKXT3TT69ZE   \n",
              "1    01FX34K9DD3D4X3QKXT3TT69ZE   Facebook  01FX34K9DD3D4X3QKXT3TT69ZE   \n",
              "2    01FX34K9DD3D4X3QKXT3TT69ZE   Facebook  01FX34K9DD3D4X3QKXT3TT69ZE   \n",
              "3    01FX34K9DD3D4X3QKXT3TT69ZE   Facebook  01FX34K9DD3D4X3QKXT3TT69ZE   \n",
              "4    01FX34K9DD3D4X3QKXT3TT69ZE   Facebook  01FX34K9DD3D4X3QKXT3TT69ZE   \n",
              "..                          ...        ...                         ...   \n",
              "871  01FX34K9DDWM39GGYPGW2AF32A  Instagram  01FX34K9DDWM39GGYPGW2AF32A   \n",
              "872  01FX34K9DDWM39GGYPGW2AF32A  Instagram  01FX34K9DDWM39GGYPGW2AF32A   \n",
              "873  01FX34K9DDWM39GGYPGW2AF32A  Instagram  01FX34K9DDWM39GGYPGW2AF32A   \n",
              "874  01FX34K9DDWM39GGYPGW2AF32A  Instagram  01FX34K9DDWM39GGYPGW2AF32A   \n",
              "875  01FX34K9DDWM39GGYPGW2AF32A  Instagram  01FX34K9DDWM39GGYPGW2AF32A   \n",
              "\n",
              "                           ID:1                   CREATEDAT  \\\n",
              "0    01G2YQB5RR17QS3824HQXJE6V4  2022-05-13 12:47:22.159531   \n",
              "1    01G2YQB5RR48751M0R9PACK1V5  2022-05-13 12:47:22.158055   \n",
              "2    01G2YQB5RRQC3C3024HE3V28JK   2022-05-13 12:47:22.16095   \n",
              "3    01G2YQB5RRWG8V1B4JSFHXF4HA  2022-05-13 12:47:22.156237   \n",
              "4    01G2YQB63KFF29W4Y36MZ1NFHG   2022-05-13 12:47:22.40179   \n",
              "..                          ...                         ...   \n",
              "871  01G2YQBK7N0AE2YHQ87YB2ZXNS  2022-05-13 12:47:35.886489   \n",
              "872  01G2YQBK7N33NGDRDV0S996AQ0  2022-05-13 12:47:35.884683   \n",
              "873  01G2YQBK7N6XWJTSSS92JC4AGB  2022-05-13 12:47:35.890981   \n",
              "874  01G2YQBK7NB3NC6JKDQ7APFP08   2022-05-13 12:47:35.88791   \n",
              "875  01G2YQBK7NY505GR43GKKBM9BS  2022-05-13 12:47:35.889542   \n",
              "\n",
              "                      UPDATEDAT                         ASSETID:2       COLOR  \\\n",
              "0    2022-05-13 12:47:22.159531  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ  Light Gray   \n",
              "1    2022-05-13 12:47:22.158055  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ       White   \n",
              "2     2022-05-13 12:47:22.16095  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ       Khaki   \n",
              "3    2022-05-13 12:47:22.156237  PFD01G0TGSPX7VXM9KHWW9DNVR5ASDNÑ       Beige   \n",
              "4     2022-05-13 12:47:22.40179  E0D01G0TGSPXF933E41FSG4ATYW0WFQW        Gray   \n",
              "..                          ...                               ...         ...   \n",
              "871  2022-05-13 12:47:35.886489  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  Steel Blue   \n",
              "872  2022-05-13 12:47:35.884683  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  Light Blue   \n",
              "873  2022-05-13 12:47:35.890981  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE       Green   \n",
              "874   2022-05-13 12:47:35.88791  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE  Light Gray   \n",
              "875  2022-05-13 12:47:35.889542  FAQ01G1RPJFCPYCWF3V4WQ8B5Z6KHIEE       Black   \n",
              "\n",
              "     ...  smScore tvcScore widthPx heightPx                      userId  \\\n",
              "0    ...  71.9500  68.0782    1200      800  01FYEMPG5PQXT3FFTZAK0G9WHG   \n",
              "1    ...  71.9500  68.0782    1200      800  01FYEMPG5PQXT3FFTZAK0G9WHG   \n",
              "2    ...  71.9500  68.0782    1200      800  01FYEMPG5PQXT3FFTZAK0G9WHG   \n",
              "3    ...  71.9500  68.0782    1200      800  01FYEMPG5PQXT3FFTZAK0G9WHG   \n",
              "4    ...  71.9500  76.9291    1200      800  01FYEMPG5PQXT3FFTZAK0G9WHG   \n",
              "..   ...      ...      ...     ...      ...                         ...   \n",
              "871  ...  68.3047  37.6405     720      900  01FX343ZMKRE343QJJY7ETBEJ5   \n",
              "872  ...  68.3047  37.6405     720      900  01FX343ZMKRE343QJJY7ETBEJ5   \n",
              "873  ...  68.3047  37.6405     720      900  01FX343ZMKRE343QJJY7ETBEJ5   \n",
              "874  ...  68.3047  37.6405     720      900  01FX343ZMKRE343QJJY7ETBEJ5   \n",
              "875  ...  68.3047  37.6405     720      900  01FX343ZMKRE343QJJY7ETBEJ5   \n",
              "\n",
              "    deletedAt     status                 parentAssetTypeId  \\\n",
              "0        None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "1        None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "2        None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "3        None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "4        None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "..        ...        ...                               ...   \n",
              "871      None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "872      None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "873      None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "874      None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "875      None  PROCESSED  ÑAP01FXK6THFM2FDX32PB85GKPRNV1RÑ   \n",
              "\n",
              "                    publishedAt  scraped  \n",
              "0    2022-04-17 01:04:36.976424        0  \n",
              "1    2022-04-17 01:04:36.976424        0  \n",
              "2    2022-04-17 01:04:36.976424        0  \n",
              "3    2022-04-17 01:04:36.976424        0  \n",
              "4    2022-04-17 01:04:37.095722        0  \n",
              "..                          ...      ...  \n",
              "871         2022-04-28 00:00:00        0  \n",
              "872         2022-04-28 00:00:00        0  \n",
              "873         2022-04-28 00:00:00        0  \n",
              "874         2022-04-28 00:00:00        0  \n",
              "875         2022-04-28 00:00:00        0  \n",
              "\n",
              "[876 rows x 34 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Query= \"\"\"SELECT *\n",
        "FROM\n",
        "  (SELECT Q1.ASSETID,Q1.ASSETID,\n",
        "             Q1.ASSETTYPEID,\n",
        "             Q1.NAME,\n",
        "             Q1.ID,AC.ID,AC.CREATEDAT,AC.UPDATEDAT,AC.ASSETID,AC.COLOR,AC.COVERAGE\n",
        "   FROM\n",
        "     (SELECT AAT.ASSETID,\n",
        "             AAT.ASSETTYPEID,\n",
        "             AT.NAME,\n",
        "             AT.ID\n",
        "      FROM ASSET_ASSET_TYPES AAT\n",
        "      INNER JOIN ASSET_TYPE AT ON AT.ID=AAT.ASSETTYPEID) Q1\n",
        "   INNER JOIN ASSET_COLOR AC ON AC.ASSETID = Q1.ASSETID) Q2\n",
        "INNER JOIN ASSET A ON A.ID = Q2.ASSETID\"\"\"\n",
        "\n",
        "df=pd.read_sql(Query,con=conn)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "-XgZRJ0jl5DU",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "df=df.loc[:,~df.apply(lambda x: x.duplicated(),axis=1).all()].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LT6wxJg6p7k3",
        "outputId": "8f7421dd-3cac-4d23-b796-330bb25ae334",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        }
      ],
      "source": [
        "df=df.drop(['deletedAt', 'duration', 'frameCount', 'framesPerSecond'],axis=1)\n",
        "\n",
        "df.rename(columns={'NAME':'Asset_Type_Name','ID:1':'ID','name':'Filename','CREATEDAT':'CREATEDAT_COLOR'},inplace=True)\n",
        "\n",
        "df.fillna(value=np.nan, inplace=True)\n",
        "\n",
        "df.ASSETID=df.ASSETID.astype('category')\n",
        "df.ASSETTYPEID=df.ASSETTYPEID.astype('category')\n",
        "df.Asset_Type_Name=df.Asset_Type_Name.astype('category')\n",
        "df.ID=df.ID.astype('category')\n",
        "df.publishedAt=pd.to_datetime(df.publishedAt,format='%Y-%m-%dT%H:%M:%S.%f')\n",
        "df.CREATEDAT_COLOR=pd.to_datetime(df.CREATEDAT_COLOR,format='%Y-%m-%dT%H:%M:%S.%f')\n",
        "df.createdAt=pd.to_datetime(df.createdAt,format='%Y-%m-%dT%H:%M:%S.%f')\n",
        "df.updatedAt=pd.to_datetime(df.updatedAt,format='%Y-%m-%dT%H:%M:%S.%f')\n",
        "df.COLOR=df.COLOR.astype('category')\n",
        "df.Filename=df.Filename.astype('category')\n",
        "df.campaignId=df.campaignId.astype('category')\n",
        "df.sectorId=df.sectorId.astype('category')\n",
        "df.fileType=df.fileType.astype('category')\n",
        "df.mimeType=df.mimeType.astype('category')\n",
        "df.brandId=df.brandId.astype('category')\n",
        "df.parentAssetTypeId=df.parentAssetTypeId.astype('category')\n",
        "\n",
        "data=df[['Asset_Type_Name', 'COLOR', 'COVERAGE', 'fileType', 'brandId','widthPx', 'heightPx', 'publishedAt','oohScore','smScore','tvcScore']]\n",
        "\n",
        "data['weightedCover']=np.array(data['COVERAGE'] )* np.array(data['widthPx']) * np.array(data['heightPx'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CnQV_BN6JeB",
        "outputId": "cca5b270-1de9-42c2-d8c0-69d933498976",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "others                  186\n",
              "Green                   142\n",
              "Light Gray              122\n",
              "Gray                    104\n",
              "Sienna                   96\n",
              "Golden Rod               75\n",
              "Black                    75\n",
              "Rosy Brown               40\n",
              "Bordeaux/ Fire Brick     36\n",
              "Name: COLOR, dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Top80Color = data.COLOR.value_counts().loc[data.COLOR.value_counts().cumsum().apply(lambda x: x / data.COLOR.count()) <= 0.8].index.tolist()\n",
        "data.COLOR = data.COLOR.apply(lambda x: x if (x in Top80Color) else('others'))\n",
        "data.COLOR.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbXueHKn1AEi"
      },
      "source": [
        "Let's change the approach by changing Year & month as integer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcMPje5CeR0a",
        "outputId": "22141e74-3c95-442b-8538-52f0562ab8a5",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "data['year']=data.publishedAt.dt.year.astype('int')\n",
        "data['month']=data.publishedAt.dt.month.astype('int')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "de3VIKHt1Jbw"
      },
      "source": [
        "create the dummies variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "vwCgiqUngOZC",
        "outputId": "338aad31-bf89-4fdd-a146-412c2df25ade",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f7e169b5-eaad-452d-9d16-5fbf7787f7c9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>publishedAt</th>\n",
              "      <th>oohScore</th>\n",
              "      <th>smScore</th>\n",
              "      <th>tvcScore</th>\n",
              "      <th>weightedCover</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>COLORBordeaux/ Fire Brick</th>\n",
              "      <th>COLORGolden Rod</th>\n",
              "      <th>COLORGray</th>\n",
              "      <th>COLORGreen</th>\n",
              "      <th>COLORLight Gray</th>\n",
              "      <th>COLORRosy Brown</th>\n",
              "      <th>COLORSienna</th>\n",
              "      <th>COLORothers</th>\n",
              "      <th>Asset_Type_NameInstagram</th>\n",
              "      <th>fileTypeVIDEO</th>\n",
              "      <th>brandIdARE01G1181F9W2HYK675JR5GMQDN610E</th>\n",
              "      <th>brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3</th>\n",
              "      <th>brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-04-17 01:04:36.976424</td>\n",
              "      <td>83.7600</td>\n",
              "      <td>71.9500</td>\n",
              "      <td>68.0782</td>\n",
              "      <td>161400.000</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-04-17 01:04:36.976424</td>\n",
              "      <td>83.7600</td>\n",
              "      <td>71.9500</td>\n",
              "      <td>68.0782</td>\n",
              "      <td>306450.240</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-04-17 01:04:36.976424</td>\n",
              "      <td>83.7600</td>\n",
              "      <td>71.9500</td>\n",
              "      <td>68.0782</td>\n",
              "      <td>99749.760</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-04-17 01:04:36.976424</td>\n",
              "      <td>83.7600</td>\n",
              "      <td>71.9500</td>\n",
              "      <td>68.0782</td>\n",
              "      <td>120450.240</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-04-17 01:04:37.095722</td>\n",
              "      <td>82.2400</td>\n",
              "      <td>71.9500</td>\n",
              "      <td>76.9291</td>\n",
              "      <td>124949.760</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>871</th>\n",
              "      <td>2022-04-28 00:00:00.000000</td>\n",
              "      <td>72.2284</td>\n",
              "      <td>68.3047</td>\n",
              "      <td>37.6405</td>\n",
              "      <td>111754.080</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>872</th>\n",
              "      <td>2022-04-28 00:00:00.000000</td>\n",
              "      <td>72.2284</td>\n",
              "      <td>68.3047</td>\n",
              "      <td>37.6405</td>\n",
              "      <td>212863.464</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>873</th>\n",
              "      <td>2022-04-28 00:00:00.000000</td>\n",
              "      <td>72.2284</td>\n",
              "      <td>68.3047</td>\n",
              "      <td>37.6405</td>\n",
              "      <td>70999.416</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>2022-04-28 00:00:00.000000</td>\n",
              "      <td>72.2284</td>\n",
              "      <td>68.3047</td>\n",
              "      <td>37.6405</td>\n",
              "      <td>107795.448</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>875</th>\n",
              "      <td>2022-04-28 00:00:00.000000</td>\n",
              "      <td>72.2284</td>\n",
              "      <td>68.3047</td>\n",
              "      <td>37.6405</td>\n",
              "      <td>82469.664</td>\n",
              "      <td>2022</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
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              "<p>876 rows × 20 columns</p>\n",
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            ],
            "text/plain": [
              "                   publishedAt  oohScore  smScore  tvcScore  weightedCover  \\\n",
              "0   2022-04-17 01:04:36.976424   83.7600  71.9500   68.0782     161400.000   \n",
              "1   2022-04-17 01:04:36.976424   83.7600  71.9500   68.0782     306450.240   \n",
              "2   2022-04-17 01:04:36.976424   83.7600  71.9500   68.0782      99749.760   \n",
              "3   2022-04-17 01:04:36.976424   83.7600  71.9500   68.0782     120450.240   \n",
              "4   2022-04-17 01:04:37.095722   82.2400  71.9500   76.9291     124949.760   \n",
              "..                         ...       ...      ...       ...            ...   \n",
              "871 2022-04-28 00:00:00.000000   72.2284  68.3047   37.6405     111754.080   \n",
              "872 2022-04-28 00:00:00.000000   72.2284  68.3047   37.6405     212863.464   \n",
              "873 2022-04-28 00:00:00.000000   72.2284  68.3047   37.6405      70999.416   \n",
              "874 2022-04-28 00:00:00.000000   72.2284  68.3047   37.6405     107795.448   \n",
              "875 2022-04-28 00:00:00.000000   72.2284  68.3047   37.6405      82469.664   \n",
              "\n",
              "     year  month  COLORBordeaux/ Fire Brick  COLORGolden Rod  COLORGray  \\\n",
              "0    2022      4                          0                0          0   \n",
              "1    2022      4                          0                0          0   \n",
              "2    2022      4                          0                0          0   \n",
              "3    2022      4                          0                0          0   \n",
              "4    2022      4                          0                0          1   \n",
              "..    ...    ...                        ...              ...        ...   \n",
              "871  2022      4                          0                0          0   \n",
              "872  2022      4                          0                0          0   \n",
              "873  2022      4                          0                0          0   \n",
              "874  2022      4                          0                0          0   \n",
              "875  2022      4                          0                0          0   \n",
              "\n",
              "     COLORGreen  COLORLight Gray  COLORRosy Brown  COLORSienna  COLORothers  \\\n",
              "0             0                1                0            0            0   \n",
              "1             0                0                0            0            1   \n",
              "2             0                0                0            0            1   \n",
              "3             0                0                0            0            1   \n",
              "4             0                0                0            0            0   \n",
              "..          ...              ...              ...          ...          ...   \n",
              "871           0                0                0            0            1   \n",
              "872           0                0                0            0            1   \n",
              "873           1                0                0            0            0   \n",
              "874           0                1                0            0            0   \n",
              "875           0                0                0            0            0   \n",
              "\n",
              "     Asset_Type_NameInstagram  fileTypeVIDEO  \\\n",
              "0                           0              0   \n",
              "1                           0              0   \n",
              "2                           0              0   \n",
              "3                           0              0   \n",
              "4                           0              0   \n",
              "..                        ...            ...   \n",
              "871                         1              1   \n",
              "872                         1              1   \n",
              "873                         1              1   \n",
              "874                         1              1   \n",
              "875                         1              1   \n",
              "\n",
              "     brandIdARE01G1181F9W2HYK675JR5GMQDN610E  \\\n",
              "0                                          0   \n",
              "1                                          0   \n",
              "2                                          0   \n",
              "3                                          0   \n",
              "4                                          0   \n",
              "..                                       ...   \n",
              "871                                        1   \n",
              "872                                        1   \n",
              "873                                        1   \n",
              "874                                        1   \n",
              "875                                        1   \n",
              "\n",
              "     brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3  \\\n",
              "0                                          0   \n",
              "1                                          0   \n",
              "2                                          0   \n",
              "3                                          0   \n",
              "4                                          0   \n",
              "..                                       ...   \n",
              "871                                        0   \n",
              "872                                        0   \n",
              "873                                        0   \n",
              "874                                        0   \n",
              "875                                        0   \n",
              "\n",
              "     brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132  \n",
              "0                                          0  \n",
              "1                                          0  \n",
              "2                                          0  \n",
              "3                                          0  \n",
              "4                                          0  \n",
              "..                                       ...  \n",
              "871                                        0  \n",
              "872                                        0  \n",
              "873                                        0  \n",
              "874                                        0  \n",
              "875                                        0  \n",
              "\n",
              "[876 rows x 20 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data_4_corr=data.copy()\n",
        "data_4_corr = pd.get_dummies(data=data_4_corr , columns=['COLOR'], drop_first=True, prefix='COLOR', prefix_sep='')\n",
        "data_4_corr = pd.get_dummies(data=data_4_corr , columns=['Asset_Type_Name'], drop_first=True, prefix='Asset_Type_Name', prefix_sep='')\n",
        "data_4_corr = pd.get_dummies(data=data_4_corr , columns=['fileType'], drop_first=True, prefix='fileType', prefix_sep='')\n",
        "data_4_corr = pd.get_dummies(data=data_4_corr , columns=['brandId'], drop_first=True, prefix='brandId', prefix_sep='')\n",
        "data_4_corr = data_4_corr.drop(['widthPx', 'heightPx','COVERAGE'],axis=1)\n",
        "data_4_corr\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cuym4yvk76vU"
      },
      "source": [
        "### Split Data into Train and Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "F7duGf0jgnAl",
        "outputId": "53d1c98c-78c2-4de5-acc9-48f6d2496927",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
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              "      <th>632</th>\n",
              "      <td>2019-07-23 16:02:04</td>\n",
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              "      <th>377</th>\n",
              "      <td>2022-03-11 18:22:05</td>\n",
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              "      <th>615</th>\n",
              "      <td>2019-05-03 17:30:02</td>\n",
              "      <td>151951.302000</td>\n",
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              "      <th>625</th>\n",
              "      <td>2019-07-04 10:30:43</td>\n",
              "      <td>120466.958400</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>2022-03-11 18:22:05</td>\n",
              "      <td>329264.352000</td>\n",
              "      <td>2022</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>780</th>\n",
              "      <td>2021-07-30 13:05:59</td>\n",
              "      <td>918217.036800</td>\n",
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              "      <td>68.1750</td>\n",
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              "      <th>152</th>\n",
              "      <td>2021-09-26 09:20:08</td>\n",
              "      <td>390434.349822</td>\n",
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              "      <th>837</th>\n",
              "      <td>2022-01-24 11:47:20</td>\n",
              "      <td>216293.857221</td>\n",
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              "      <th>195</th>\n",
              "      <td>2021-10-08 10:30:15</td>\n",
              "      <td>73779.263336</td>\n",
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              "<p>613 rows × 18 columns</p>\n",
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              "            publishedAt  weightedCover  year  month  \\\n",
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              "615 2019-05-03 17:30:02  151951.302000  2019      5   \n",
              "501 2021-04-27 12:44:31  140697.000000  2021      4   \n",
              "625 2019-07-04 10:30:43  120466.958400  2019      7   \n",
              "..                  ...            ...   ...    ...   \n",
              "378 2022-03-11 18:22:05  329264.352000  2022      3   \n",
              "780 2021-07-30 13:05:59  918217.036800  2021      7   \n",
              "152 2021-09-26 09:20:08  390434.349822  2021      9   \n",
              "837 2022-01-24 11:47:20  216293.857221  2022      1   \n",
              "195 2021-10-08 10:30:15   73779.263336  2021     10   \n",
              "\n",
              "     COLORBordeaux/ Fire Brick  COLORGolden Rod  COLORGray  COLORGreen  \\\n",
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              "     Asset_Type_NameInstagram  fileTypeVIDEO  \\\n",
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              "..                        ...            ...   \n",
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              "632                                        0   \n",
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              "..                                       ...   \n",
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              "632                                        0   \n",
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              "..                                       ...   \n",
              "378                                        1   \n",
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              "\n",
              "     brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132  smScore  \n",
              "632                                        1  45.7125  \n",
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              "..                                       ...      ...  \n",
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              "\n",
              "[613 rows x 18 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_4_corr.drop(columns=['smScore','tvcScore','oohScore'],axis=1), data_4_corr.smScore,test_size=0.3)\n",
        "data_train=pd.concat([X_train,y_train],axis=1)\n",
        "data_train\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-IY8ZVb1hgr"
      },
      "source": [
        "Descriptive Statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "mYblyuDakzoH",
        "outputId": "cd461824-f0b9-454e-ffbd-ed289d73b430",
        "vscode": {
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      "outputs": [
        {
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              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLORRosy Brown</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.047308</td>\n",
              "      <td>0.212471</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLORSienna</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.114192</td>\n",
              "      <td>0.318305</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLORothers</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.195759</td>\n",
              "      <td>0.397108</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Asset_Type_NameInstagram</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.949429</td>\n",
              "      <td>0.219299</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fileTypeVIDEO</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.008157</td>\n",
              "      <td>0.090018</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brandIdARE01G1181F9W2HYK675JR5GMQDN610E</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.161501</td>\n",
              "      <td>0.368293</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.477977</td>\n",
              "      <td>0.499923</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132</th>\n",
              "      <td>613.0</td>\n",
              "      <td>0.309951</td>\n",
              "      <td>0.462851</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ae6378a-3615-48c1-8cf4-7ce345db08a6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ae6378a-3615-48c1-8cf4-7ce345db08a6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ae6378a-3615-48c1-8cf4-7ce345db08a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                         count           mean            std  \\\n",
              "weightedCover                            613.0  285285.425890  242479.724152   \n",
              "year                                     613.0    2021.081566       0.919933   \n",
              "month                                    613.0       5.993475       3.262977   \n",
              "COLORBordeaux/ Fire Brick                613.0       0.042414       0.201697   \n",
              "COLORGolden Rod                          613.0       0.091354       0.288347   \n",
              "COLORGray                                613.0       0.117455       0.322225   \n",
              "COLORGreen                               613.0       0.156607       0.363726   \n",
              "COLORLight Gray                          613.0       0.145188       0.352578   \n",
              "COLORRosy Brown                          613.0       0.047308       0.212471   \n",
              "COLORSienna                              613.0       0.114192       0.318305   \n",
              "COLORothers                              613.0       0.195759       0.397108   \n",
              "Asset_Type_NameInstagram                 613.0       0.949429       0.219299   \n",
              "fileTypeVIDEO                            613.0       0.008157       0.090018   \n",
              "brandIdARE01G1181F9W2HYK675JR5GMQDN610E  613.0       0.161501       0.368293   \n",
              "brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3  613.0       0.477977       0.499923   \n",
              "brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132  613.0       0.309951       0.462851   \n",
              "\n",
              "                                                  min           25%  \\\n",
              "weightedCover                            20631.033856  150752.94264   \n",
              "year                                      2016.000000    2021.00000   \n",
              "month                                        1.000000       3.00000   \n",
              "COLORBordeaux/ Fire Brick                    0.000000       0.00000   \n",
              "COLORGolden Rod                              0.000000       0.00000   \n",
              "COLORGray                                    0.000000       0.00000   \n",
              "COLORGreen                                   0.000000       0.00000   \n",
              "COLORLight Gray                              0.000000       0.00000   \n",
              "COLORRosy Brown                              0.000000       0.00000   \n",
              "COLORSienna                                  0.000000       0.00000   \n",
              "COLORothers                                  0.000000       0.00000   \n",
              "Asset_Type_NameInstagram                     0.000000       1.00000   \n",
              "fileTypeVIDEO                                0.000000       0.00000   \n",
              "brandIdARE01G1181F9W2HYK675JR5GMQDN610E      0.000000       0.00000   \n",
              "brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3      0.000000       0.00000   \n",
              "brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132      0.000000       0.00000   \n",
              "\n",
              "                                                  50%          75%        max  \n",
              "weightedCover                            207093.66336  324769.3056  2181168.0  \n",
              "year                                       2021.00000    2022.0000     2022.0  \n",
              "month                                         5.00000       9.0000       12.0  \n",
              "COLORBordeaux/ Fire Brick                     0.00000       0.0000        1.0  \n",
              "COLORGolden Rod                               0.00000       0.0000        1.0  \n",
              "COLORGray                                     0.00000       0.0000        1.0  \n",
              "COLORGreen                                    0.00000       0.0000        1.0  \n",
              "COLORLight Gray                               0.00000       0.0000        1.0  \n",
              "COLORRosy Brown                               0.00000       0.0000        1.0  \n",
              "COLORSienna                                   0.00000       0.0000        1.0  \n",
              "COLORothers                                   0.00000       0.0000        1.0  \n",
              "Asset_Type_NameInstagram                      1.00000       1.0000        1.0  \n",
              "fileTypeVIDEO                                 0.00000       0.0000        1.0  \n",
              "brandIdARE01G1181F9W2HYK675JR5GMQDN610E       0.00000       0.0000        1.0  \n",
              "brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3       0.00000       1.0000        1.0  \n",
              "brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132       0.00000       1.0000        1.0  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_stats_ = data_train.describe()\n",
        "train_stats_.pop('smScore')\n",
        "train_stats_ = train_stats_.transpose()\n",
        "train_stats_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Db7Auq1yXUvh"
      },
      "source": [
        "### Split the train & test labels \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "l3bknJTMlht2",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "train_labels_ =  data_train.pop('smScore')\n",
        "test_labels_ = y_test.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRklxK5s388r"
      },
      "source": [
        "### Normalize Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "qErYesFGnB4m",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "data_train=data_train.drop('publishedAt',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "aQNwVV8CnJo6",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "X_test=X_test.drop('publishedAt',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "JlC5ooJrgjQF",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats_['mean']) / train_stats_['std']\n",
        "normed_train_data_ = norm(data_train)\n",
        "normed_test_data_= norm(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWsQC1zp5sSJ",
        "outputId": "8e0fea15-1ff2-4b6d-8f80-9ea912f60e82",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "weightedCover                              0\n",
              "year                                       0\n",
              "month                                      0\n",
              "COLORBordeaux/ Fire Brick                  0\n",
              "COLORGolden Rod                            0\n",
              "COLORGray                                  0\n",
              "COLORGreen                                 0\n",
              "COLORLight Gray                            0\n",
              "COLORRosy Brown                            0\n",
              "COLORSienna                                0\n",
              "COLORothers                                0\n",
              "Asset_Type_NameInstagram                   0\n",
              "fileTypeVIDEO                              0\n",
              "brandIdARE01G1181F9W2HYK675JR5GMQDN610E    0\n",
              "brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3    0\n",
              "brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check for null values\n",
        "normed_train_data_.isnull().sum().sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuiClDk45eS4"
      },
      "source": [
        "Normalized data are going to be the input for the Model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmjdzxKzEu1-"
      },
      "source": [
        "## Model\n",
        "\n",
        "the model is set up with 2 dense layer and an output layer that delivers a single continuous value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmWrVIX_opiw",
        "outputId": "537134b1-72f6-4f1d-dd86-53818e0d4c04",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['weightedCover', 'year', 'month', 'COLORBordeaux/ Fire Brick',\n",
              "       'COLORGolden Rod', 'COLORGray', 'COLORGreen', 'COLORLight Gray',\n",
              "       'COLORRosy Brown', 'COLORSienna', 'COLORothers',\n",
              "       'Asset_Type_NameInstagram', 'fileTypeVIDEO',\n",
              "       'brandIdARE01G1181F9W2HYK675JR5GMQDN610E',\n",
              "       'brandIdARÑ01G1183KRNFW4MH52KVBVY2KX3PD3',\n",
              "       'brandIdDDN01G118PW1JKFN915J6FY9B2GXQ132'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# check the Keys for training data\n",
        "data_train.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "c26juK7ZG8j-",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "def build_model():\n",
        "  model = keras.Sequential([\n",
        "    layers.Dense(64, activation='relu', input_shape=[len(data_train.keys())]),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "  model.compile(loss='mse',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mae', 'mse'])\n",
        "  return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "cGbPb-PHGbhs",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [],
      "source": [
        "model = build_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sj49Og4YGULr"
      },
      "source": [
        "### Inspect the model\n",
        "\n",
        "let's check a simple descriptive characteristics of the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReAD0n6MsFK-",
        "outputId": "b6503a7a-2661-4eb1-c304-de18ed6e945d",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 64)                1088      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,313\n",
            "Trainable params: 5,313\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vt6W50qGsJAL"
      },
      "source": [
        "Let's check the model initial predictions with the first 10 records"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-d-gBaVtGTSC",
        "outputId": "de20a530-92e5-4535-b5e7-a616f3536d9e",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 0.02019587],\n",
              "       [-0.00476295],\n",
              "       [ 0.0769046 ],\n",
              "       [-0.37341204],\n",
              "       [-0.43606198],\n",
              "       [-0.2918655 ],\n",
              "       [ 0.09849226],\n",
              "       [-0.21730417],\n",
              "       [ 0.01549529],\n",
              "       [ 0.12277706]], dtype=float32)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example_batch = normed_train_data_[:10]\n",
        "example_result = model.predict(example_batch)\n",
        "example_result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-qWCsh6DlyH"
      },
      "source": [
        "### Model Training \n",
        "\n",
        "Let's train the model with 1000 epochs "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sD7qHCmNIOY0",
        "outputId": "a90a209f-96f5-4f91-aff3-c48541d18a07",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "...................................................................................................."
          ]
        }
      ],
      "source": [
        "# Display training progress by printing a single dot for each completed epoch\n",
        "class PrintDot(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs):\n",
        "    if epoch % 100 == 0: print('')\n",
        "    print('.', end='')\n",
        "\n",
        "EPOCHS = 1000\n",
        "\n",
        "history = model.fit(\n",
        "  normed_train_data_, train_labels_,\n",
        "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
        "  callbacks=[PrintDot()])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQm3pc0FYPQB"
      },
      "source": [
        "let's check the progress of the last epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4Xj91b-dymEy",
        "outputId": "2605268b-4c50-49c7-8cbc-64dad3fe64e6",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d74829c2-5da6-4389-868a-e3666eaf197d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>mse</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mae</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>97.739769</td>\n",
              "      <td>6.795467</td>\n",
              "      <td>97.739769</td>\n",
              "      <td>274.838593</td>\n",
              "      <td>13.340646</td>\n",
              "      <td>274.838593</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>98.220985</td>\n",
              "      <td>6.791252</td>\n",
              "      <td>98.220985</td>\n",
              "      <td>274.190033</td>\n",
              "      <td>13.154826</td>\n",
              "      <td>274.190033</td>\n",
              "      <td>996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>98.095543</td>\n",
              "      <td>6.785890</td>\n",
              "      <td>98.095543</td>\n",
              "      <td>271.047394</td>\n",
              "      <td>13.187753</td>\n",
              "      <td>271.047394</td>\n",
              "      <td>997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>97.746925</td>\n",
              "      <td>6.780339</td>\n",
              "      <td>97.746925</td>\n",
              "      <td>270.606293</td>\n",
              "      <td>13.107995</td>\n",
              "      <td>270.606293</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>99.321938</td>\n",
              "      <td>6.838481</td>\n",
              "      <td>99.321938</td>\n",
              "      <td>271.853424</td>\n",
              "      <td>13.215747</td>\n",
              "      <td>271.853424</td>\n",
              "      <td>999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d74829c2-5da6-4389-868a-e3666eaf197d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d74829c2-5da6-4389-868a-e3666eaf197d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d74829c2-5da6-4389-868a-e3666eaf197d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          loss       mae        mse    val_loss    val_mae     val_mse  epoch\n",
              "995  97.739769  6.795467  97.739769  274.838593  13.340646  274.838593    995\n",
              "996  98.220985  6.791252  98.220985  274.190033  13.154826  274.190033    996\n",
              "997  98.095543  6.785890  98.095543  271.047394  13.187753  271.047394    997\n",
              "998  97.746925  6.780339  97.746925  270.606293  13.107995  270.606293    998\n",
              "999  99.321938  6.838481  99.321938  271.853424  13.215747  271.853424    999"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "B6XriGbVPh2t",
        "outputId": "053b5f0c-d531-4718-caf8-7502293f8032",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_history(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Abs Error [MPG]')\n",
        "  plt.plot(hist['epoch'], hist['mae'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mae'],\n",
        "           label = 'Val Error')\n",
        "  plt.ylim([0,70])\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
        "  plt.plot(hist['epoch'], hist['mse'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mse'],\n",
        "           label = 'Val Error')\n",
        "  plt.ylim([0,400])\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AqsuANc11FYv"
      },
      "source": [
        "the graph showed low improvement, even getting worse in latter epochs. \n",
        "\n",
        "Let's use EarlyStopping to set a condition for training for each epoch, therefore after *n* number of epochs without improvement training must be stopped."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "fdMZuhUgzMZ4",
        "outputId": "a106ed77-be66-46c3-ac91-b2e410e55725",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "........................................................................."
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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+h6xQwE9lWj+3YcnCGJNEMX3Qq+orItIH9yG/DPjQO/Ql4Pvx3FBVlwN7TUqoqmtx7Rd77m8GLornHoeTQGYuja3ZZNqUH8aYJIp5KlNV/RMwEvdNvx7YDlygqk92U2wG1yOqIq2flSyMMUkV6+JHs4Bf4pLLC8C1qlrXnYEZJy8zwNbGYobYWAtjTBLFWrL4Ia4hehywAfjvbovI7CY/M8jGtkJXsugd7frGmBQUa7KoVdWlqlquqj+kk7YF0z1yMwOsivSHcD1UrU92OMaYXirWZDHAGyF9mogUEWd3WXPg8jMD/DM8wm2ULUxuMMaYXivWZPGfwDHAj4FVwNEi8qKI/EREZnZbdIb8zCAfR0vQYBZsXJDscIwxvVSsXWfndNwWkRJc8hgPnANYj6hukpsRIIqfluKJhMosWRhjkiPW3lDTgeWquh1AVctwk/291I2xGVzJAqCmYAKh5Q9CuAGCWUmOyhjT28Q6+nouUC4ibbgBeR8Ay73nj1S1pZvi6/Xys1zz0Lbc8fTTKGxaAsNPTXJUxpjeJtY2i28Cm4H7gP8CVuJmov0lriut6Sa5Ga5kUZZ9tNthVVHGmCSIKVmo6gPAybhFiu4BWoHrVXWaqvbvxvh6vfxMV7Iob82EgtHWyG2MSYp4pvtoUtWfAdOAUcACETm+2yIzgGvgBqhqbIXBU1z3WRucZ4zpYbE2cJ+GG709DjgCKAbq6GTFOpNYaX4fOaE0appaoeQ4WPYE7FgLBSOTHZoxpheJtYH7dWAZ8BRwn6qu766AzN7yM4NUNYZhsFeQ27jAkoUxpkfFWg11DfAOcC7wnoisEJE/isgPROT87gvPgGu3qGpshaJxkN4HNr6X7JCMMb1MrIPyfttxe49BeV8C/pr40Ey73MwgNY1h8Plg0GSb9sMY0+PiXeUOsEF5PS0/M8D6iga3MXgKvPkLaK6FUJ/kBmaM6TViqoYSkSWJOMccmJ1tFuCShbbBpsXJDcoY06vE2mZxhIgs38/jA6AwlguJyGARme+1e3wkItd7+28XkU0issx7nNPhPbeKyBoRWSUi/xb/j3loy88MUtccoTEcgUGlbqdVRRljelCs1VDjYjgnGuO1IsC3VXWJiOQAi0VkrnfsblW9q+PJInIk8GXgKGAg8KqIjFHVWO93yJswJA+ABet2MHVssWvotsF5xpgeFGsDd8Km9FDVLcAW73WdiHwMDNrPW2YAT3nzT60TkTW4xZf+laiYUt2UYX0J+n28vbrCJYvBU2DF81C3Fda/DevegI0L4YzvwxFfSHa4xpjDUMwjuLuDiAwDJgLtfUGv86q1HhaRfG/fIKDjAtRldJJcvMWZFonIou3bt3dj1D0vI+indFg+b6+pcDtKpkBzNfxyLPz5KljxHDRsh7m3QTSS3GCNMYelmJOFOIMTdWMRyQb+DNygqrXAg8BIYAKu5PHLeK6nqnNUtVRVS4uKihIVZso4ZXQhK7fWUV7XDOPOhWNnwuduh6/Ph5vWwRfucSO7P3o22aEaYw5D8cwNpcCLibipiARwieIJVf2Ld/1tqhpV1Tbgd+xa53sT0DFJlXj7epVTR7kE+M6aCsjsCxf8D5xyIwyaBD4/jD0Xio+Et+6CtrYkR2uMOdzEWw21RESOO5gbiogADwEfq+qvOuwf0OG0C3DrZgA8D3xZRNJFZDgwGuh1rbtHDexDfmaAt1ZXdH6Czwenfhu2r4SPn+/Z4Iwxh714B+UdD1wqIhuABkBwhY7xcVzjZOA/gA9EZJm373vATBGZgJsGfT3wDdzFPxKRp4EVuJ5U1/amnlDtfD7hpFGFvL26AlXF5dw9HHUBvP4TePMuOHIGdHaOMcYcgHiTxUGPcVDVt3FJZk/7rOJS1TuBOw/23oe6U0cV8vflW1hdXs+Yfjl7n+Dzu9LFX6+BT16GsWf3fJDGmMNSXNVQXhfaPOAL3iMvkd1qzf6dMtqNe3x7X1VRAMdcBHlD4Y2f27oXxpiEiStZeKOtn8CtZ1EM/K+IfLM7AjN7K8nPZHhh1q4utJ3xB1zD9+Yl8OlrPRecMeawFm8D91XA8ap6m6reBpwAfD3xYZl9OWVUIe+urSQc2U+PpwlfgT4l8MKNUL1x3+cZY0yM4k0Wwu7TekTpvP3BdJNTRhfSGI6y9LOqfZ+Ulg4XPw5N1fDouVD9Wc8FaIw5LMWbLB7BLX50u4jcDryL6wZresiJIwvw+2T/VVEAJZPhsr+6kd6PngtV1rRkjDlwcY3gBv4EXAHs8B5XqOo93RSb6USfUIBjS3L3Pd6io0GT4LLn3NoXj54L5Suhpgy2rYDP3oV1b7okYoP4jDFdiLnrrKqqiLyoqscAtnZFEp0yuoj7X1tNTWMruZmB/Z88cCLMeh4enwG/Ob7zc9JC0HckFI2BKbNh6EmJD9oYc0iLd5zFEhE5TlVtMYUkOmNcMffNW82fFm/ka6eO6PoNA46Fq151Yy/Sc9wKe+l9wJcGVeugYrV7rH/bzS01/svw+R9DdnH3/zDGmENCMkZwm4M0YXAep44u5IH5a7jkuMHkhLooXQAUjoLC6zo5cPqul+EGeOuX8M59sOolOOMHcOwlEMh0XXKNMb2WaIwDt7w2i1OBvVpKU21gXmlpqS5atCjZYXSr5WXVfPH+d7h++mhuPHNMYi9esRpe/A6sfX3XPl8apGW4Ukl2P8jp754Lx8DkWRDMSmwMxpgeJyKLVbW002OxJgvvQh94bRYprTckC4D/88Ri3li1nTdvmkZBdnpiL64Kq+dCxSfQ2gSRJvfcVA31W6Fum3turHRjOs76iVt4yeajMuaQtb9kYW0Wh7BvnTmWlz/cygPzP+W2LxyZ2IuLwJjPu8f+rH8HXvwuPP0fMHI6nP1zV+XVmUgYPvuXK50MnJjYeI0x3epA2iy+KiLrsTaLpBtVnM2Fk0v433c3cNWpwxmUl9HzQQw7Gb7xJiz8Hbx2p+tx1f8YGDjJJYT+R7suu5+8BGteg3Cde9/w09ykh8NPj680ogqLH4VQrptlt7P3NlXBmnkgPvAH3SOUCyXHuancjTFxi7caamhn+63NInk2VTcx7a7XOX/CQH5+4bHJDaZuK7z3WyhbCFveh5baXcey+8OYf4MxZ7kV/f75a1eNNagUpt4Co8/s+vrRVvj7t2DJ4257+Olw7q92lWSirbDwIXjjpy5h7GnyFXDe3VZVZsw+HHSbhYjcpKo/915fpKp/6nDsv1X1ewmLNgF6U7IA+PELK3jknXW8dP1pjO3fydTlydDW5pLC1uWQPwwGTNj9W31rMyx7At6+B2o+g3HnuSqs3L2WV3da6uFPl8OauXDqd1wD+7wfQaQZTvmWK8G8ejtUroERU2Hqra40EQ27JPLhn+Hd37geXqd9t9t/fGMORYlIFktUddKerzvbTgW9LVlU1rcw/Vdv4Bfhwa9OZsrwvskOKXbRVvjX/fD6z1yPq+k/hOO+5tbmaFe3Df5wEWz9wJUkSq/Ytf8f34cPvO8uhWPg83e6UsqepQdVePZqWP4UzPgNTLy0Z36+VLR6riv9Tb3VSllmN4lIFktVdeKerzvbTgW9LVkArCmvZ/bji9hY1ciPZhzNzClDkh1SfHasc1VMn74GRUe4AYGtTdDa6KYoiYbhokddVdae1r/tqsCOnLH/8SCRMPzhYjfNyVeehtGf67Yfp1OtTe7nLD7i4D6kIy1ussgDsfR/4flvgrbB+Q+6GYqN8ewvWcTa2qf7eN3ZtkmCUcXZPHvtyZw0spBb//IBtz33Ia3RQ2jOp77D4at/gS895EaZR1rc2I28oa6d44oXO08UAMNOgWMu7HrgYFoQLvk99DsKnr4MPvyLqw7rbq1N8K/fwL3HwoMnwlOXQkMMc3vtqb4c/vBl+OkQePd/9j2nV7gR2jpZefide+G5a101XckU+McPoHFH/HGYXinWkkWUXb2fMoDG9kNASFVjHt4rIoOBx4F+uEQzR1XvFZG+wB+BYbg1uC9W1SpvMOC9wDnefS9X1f3OTdUbSxbtom3Kz15eyZw313LSyAIevHRy1/NH9TZ12+DRc1z7RnouHHEeHP0l12Du308Hwfb/K7GWChoqXbXX2/dAQzkMOxWGnOA+tEN5MOOBvbsmR1rAF9i719bKF12JoKXOTd9StgBGTIPzfwN9BrpzKlbDP++D959y07mMPdu1BY043a3N/s69cNS/wwW/hcrV8NvT4NgvuziMIYGD8hIUzABggKouEZEcYDFwPnA5sENVfyoitwD5qnqziJwDfBOXLI4H7lXVfcyI5/TmZNHumcVl3PqX5Qzum8kjlx/H0AIbYb2baATWveEavj/+m+u55QtA3mDXIJ8/zH3g1m5yC0hVfwZ1W9itIC0+18ur7wgoGOEmYww3uEb9LcuhbrM7b/jprsdX+wSNWz+Ev8yG8o9g0izoM8i93rYCdnwKwWw3Y/CgUigphZV/h6W/d12S//13UDQOFj8Cr3zfdQueeiusf8udl5buEkBLHXzyD9dV2R901XilV8I5d+1qD5p7m0sgV7y0/8kjG3e4do78oS7ZmdT1yT9g1OcOuIt4SiWLvQIQeQ6433tMVdUtXkJ5XVXHishvvddPeuevaj9vX9e0ZOG8t7aSb/zvYgSYc1kpxw07hBq+e1Jrs+tltWmxm7K9ar17tNS6b+15QyFvCOQMcI3wqCtlaBRqN0Plp67nV2OFSyCFY6D/eBgw3n0ID5rc+T1f+7Fr3Edccup3lEsETTtcA/S2Fe4eCJxyA0z9nqtKa1f5qUs6mxa5ksqUr8OUb0B2kTseaXFJZNXLUDjazSjcsVQUboAHToBABlz99u7Xrt8OK/8GK56DdW95ceDahc78sUscJrW8cx/M/SF84T43Bc8BSNlkISLDgDeBo4HPVDXP2y9AlarmicgLwE9V9W3v2DzgZlVdtMe1ZgOzAYYMGTJ5w4aUGvqRNOsqGrjy0YVsqmrivy44mosmlyDWAyY2qvE1RDfXuNJJMDP299Rtg/TszufWCjfA5mWQkQ/99jFCPxpxVVL9x7vrxOuTV1yj/7Tvw8gzXAlizVzYtARQV2o68nwYd67rfPD23a495KRvurXeY7mnqps2ZsM7bnqYaMTr0hx2bVXjL3HtVN1BFcpXuMS7/WM3QLRqHRSMhCEnukf/8fuvfuwOkRb3+8gbCvnDD36w6Lv/Ay/f7KoZ//13B/zzpGSyEJFs4A3gTlX9i4hUtycL73iVqubHmiw6spLF7qobw3zj94t5b90OJg3J49ZzjrBShtnlj/8BHz/vbYir+hp1pksQ/Y7aPWHWbHLjWT542q2D0u9oGDjBjaMpHAPRFpfkwg0uMXz2ruut1lC++z19AVdKizS56r6JX3Ulo74xTLkPLgk07oD6bZBVtKs01dG6N+HVO1zJC9z9+o50pbiKVa70CBDIcj9nwSg3wLNgtCsN7mvMTyxqytzvJ6tw72Ofvevanyo+cdvBHDdOaMCxLjEPOSG+LykLH3I9Cced53oMHsQM0SmXLEQkALwAvKKqv/L27axesmqoxItE23hmcRm/mvsJ5XUtfP7Iftx01jhGFR/At1FzeKkvd20XAye60kVmDF8kNi6EFX+FzUtd+0z7NC57yu4Pw091jfvDTnHVeb60XR+GZYvgvf9x66i0Rd03/axC126Tnu3aYFrq3ASWzTVumeD6cvdoa/VuIm4ql7Fnw9hzXHfreT+CtfNde9Cp34KhJ7tE0bGqrXaz++D+7F1X+qhY7WYVaL/mqM+5MT2j/23/39Rbm6FmI2xc4EoL699ybVzidz34Jn4VRn/eDSCd9yNY8DvILYHP3e56ym15f1c7V6TJJeApX4djLnIlTlU3I0HNRnd+VpH7HaX38bpCX+d6DF78+91/vgOQyFlnBbgUGKGqPxKRIUB/VV0Q5zUewzVm39Bh/y+Ayg4N3H1V9SYRORe4jl0N3Pep6pT93cOSxb41hiM89NY6fvvmWppao1xcOpgbPzea4j6hZIdmDlVtba5hfsc61/4RzPI+7HPcSPtYviXXboFFD8Gn8yFc75VO6t0HcXq2a5PJyHOj8rOKIaefmyI/uxgq1ri5xzYv3XW9jL5w2neg9CoIxLN91REAABdUSURBVPFvu6XOJY1VL7lOBXVbIGegN6Oyz32YR1pcbLWbXeeHjqWmzALXTjX0FPfe95/cVfrxB917jv8GnPHDvavwwg1ugOmC38G2D11PvdxBLvGE6/eO1R90g1pHngEznzzwsTcdJDJZPAi0AWeo6hEikg/8Q1WPi+MapwBvAR941wL4HvAe8DQwBLdmxsWqusNLLvcDZ+G6zl6xvyoosGQRi4r6Fn49bzVPvPcZAb+Pr506nNmnjYhtISVjUlHtZvchH2mGif/hZjc+GNGIW11y8SOuKs0fdFVLgZBbECxngOs9lzvElRQGHOs6KHRsf4hGYM2rLvE0VsLn7oAh++3M6UoSn73rJsxsqfPuMdg9B7LcdRq2u0daums7CiRmEtFEJoslqjppjxHd76tqkmew250li9htqGzgF6+s4oXlW+ibFWT2aSP46glDyU7v4QY/Y0zSJWIEd7tWEfHjdTYXkSJ2lQ7MIWhoQRb3f2USz193MkcN7MNPX1rJyT99jXte/YTqxnCywzPGpIh4SxaXApcAk3DtDhcCP+g4C20qsJLFgXt/YzX3z1/D3BXbyAr6mTqumCMH9GFc/xzGDejDwNyQdb015jCVkGoor+2gBMgCpuOm+pinqh8nKtBEsWRx8FZurWXOm2tZtL6Kz3Y07tzfv0+IGRMGcsGkQYzrf5B1wsaYlGJrcJuDUtfcyifb6lixpY7XV5bzxifbibQpRw7ow9lH92dQfgYF2ekUZgcpzglRmB200ocxh6BEJovHgPtTfQ1uSxbdq7K+hb+9v5lnl27i/bKavY7nhNIYXZzN6OIcRvfLZlSxewzMzcDnsyRiTKpKZLJYCYzCdW1N2TW4LVn0nLrmVirqw1TUt1BZ38K22hbWlNfzybY61pTXU9mwq5E8M+hnRFEW4/r3YXxJLuNL8hjXP4dQwL+fOxhjesr+kkW8/SP3saCA6a1yQgFyQgGGF3Y+q21lvUsea7bXu+fyeuavLOeZxWUApPmEUcXZDC/MYlhhFsMKMhncN5OCrHTyMwPkZQYJph3kvDnGmIMWV7JQ1Q3eQLzRQMdhkTZrn+lUQXY6BdnpHD+iYOc+VWVzTTMflFXzflkNK7fUsmprHXNXbCPStndJNyvopygnneKcEMV93HO/Pun0zw3Rr0+I/n1CDMgLkZ5mJRRjuktcyUJEvgZcj+sVtQw4AfgXcEbiQzOHKxFhUF4Gg/IyOOvoATv3R6JtbKlpZuOORnY0hqlqbKW6IcyOxjAV9WG21Tbz0eZaXqstpzG8+0pw6Wk+pgzvyymjCjlldCFH9O9DS6SNivoWtte3UN0YpiArnYF5GdYAb8wBiLca6nrgOOBdVZ0mIuOA/058WKY3SvP7GNzXVUN1pa65lW21zWypaWZrTTMrttTy9uoKfvLSSngJgn4f4X0sK5ue5mNQXgbZoTTSfEKaz0eaX+gTCjC0IJMhBZkM7ZvFwLwQ2aE0ctIDhAI+SzCmV4s3WTSrarOIICLpqrpSRMZ2S2TG7Ed7W8mo4t3XQdhW28zbqytYta2OvMwAhdnpFGWnk5sZoLI+zKaqRjZVN7G5upmGcIRomxKJKq3RNlaX1/HaqnLCkb2TjE8gKz2NjICfzKCfUMBPVnoaY/plc9ywvkwZ3peSfJfkwpE2PtvRwJryBloiUY4tyWNoQaYlG3NIizdZlIlIHvBXYK6IVGHtFSaF9OsT4kuTSw74/W1tytbaZjZUNrK1ton6ligNLREaWiLUt0RoCkdpao3SGI5S19zKC8u38OSCjQAMzA0RCvjZsKOR6B5tLwVZQSYOyeOIAX1ojSqNYXe9xpYordE2Im1KpK2NSFTpnxvi6IG5HD0ol6MG9aHPPiZ3VFW3YB/gty7Jppsd8HoWInI6kAu8pKqtXZ3fk6zrrOkp0TZl1dY6Fq7fwYL1O2hrU0YVZzOyyD38PmHpxiqWbKhm6WdVrK1oIOj3kZnuJyuYRmbQTzDNR5rfR5pP8IuwsaqRLTXNO++RmxGgrU2JtClRVdq8547/dUvyMxjTz41rGV2cQ0F2kIyAKwFlBPw7S1kdk0pza5QPN9WweEMVa7c3MKo4m2NKXJKyiSR7p0SOs7its/2q+qMDjK1bWLIwqSoSbSPN33VX4Ir6Fj7aXMuHm2oor23G7/Ph94HPSyh+n+AT94i2tbG2ooE15fWs3d6wz7Yav08oznG9yNoUVmyuoTXq/v/nZwaoanTf+URgRGEWoYCflkgbYe/hEwgF/KQH/KSn+cgJpe3sodYvJ52inBA5oTTvESAnlIbfJwiuU4MA9S0Rqhtb2dEYproxTCSqXrtQGtkhV83X/jP6RPD7hdyMAFlBv1Xj9YBEjrNo6PA6BJwHpNzcUMakqlgSBUBhdjqnjyni9DGdLBe6H5FoG5/taKSmqZWm1ijNrVGawm1UNYbZWuN1CKhtIhJVrjxlOJOH5DNpaD6F2elU1LfwQVkN75dVs2JzLdE2JZjmIz3NRzDNR5u60khLpI3m1ii1zRE+La+gvK6l0y7PiRT0+8jLDJCfGaRNtcPPFiU7lMbQAjdGZ2hBFv36hAj4hYDfR8DvQ1XZ7g0YLa9tpqK+haz0NPpmBSnMTqdvVpCi7HSX9PqEKMgK4vcJ9S0RqhpcYmtoiRBM8xFK8xMK+MgI+inOCXU6Bqi+JcLa7fVE25RB+RkUZqUfFjMXHNSyqiKSjlsadWrCIkoAK1kY03Pa2pSqxjDb61uoa45Q3xyhtrmV+pYIbW1Km3rtK0BWMI38rCD5mQHys4L4xX0o13vtQo3hKG2q7tEGrdE2appaXTfqxjBVjWH8PtlZvRYK+KlpamVDZQMbKhspr2vZZ5wiLgkXZAVpao1SWR+mviWy13k+caWw9lLX/q7XLyfEoPwMBuSGqGoM82l5A1trm3c7L5jmY2BuiAG5GRTmuPsX5aSTmxGguTVKQ0uUhnCExnCE7PQAhdkuiRVmp1Pf0sraigbWVzSwrqKBxnCUAbkZDMpz9+3XJ+SqM9P9ZHrVmgPzMg64GjGRJYs9ZeLGXBhjeimfT3YOvky2hpYIFfUttEZ3dRhQhaIcN9HlniW75tYoOxrCbK9rYVttM+V1rvQRjioFWUHys4L0zQqQFUwjHG2judWVqhrDETZXN7OpuolNVU18sKmGvIwAJ40sYGRxNiOLskjz+dhc445vqm5iqzcQtaKTJBUK+MgMplHX3NppkirKSWd4QRZFOels3NHIu2srO010APd/ZSLnjR+YuF+qJ95BeR/gLXwE+IEiIKXaK4wxvVdWehpZcXyrDgXcN/GBeYlZljRWza1RappaXRfsoH9nElNVapsibK9vcdVlwTSGFWZ2utxxbXMr5bXNNIZd6aSp1ZXMJg3J75aY4y1ZnNfhdQTYpqqdp7d9EJGHveuUq+rR3r7bga8D273TvqeqL3rHbgWuAqLA/1XVV+KM2RhjUkrIq0Lbk4iQmxkgNzPAqOLs/V6jTyiwz27V3SHuuaEScM9HgfuBx/fYf7eq3tVxh4gcCXwZOAoYCLwqImNUNYoxxpgeE2811Lf2d1xVf9XVNVT1TREZFuMtZwBPqWoLsE5E1gBTcPNRGWOM6SHxzv1cClwDDPIeV+PW487xHgfjOhFZLiIPezPb4t1jY4dzyrx9exGR2SKySEQWbd++vbNTjDHGHKB4k0UJMElVv62q3wYmA0NU9Q5VveMg4ngQGAlMALYAv4z3Aqo6R1VLVbW0qCi+vunGGGP2L95k0Q8Id9gOe/sOiqpuU9WoqrYBv8NVNQFsAgZ3OLXE22eMMaYHxdsb6nFggYg8i1tS9Xxcg/VBEZEBqrrF27wA+NB7/TzwBxH5Fa6BezSw4GDvZ4wxJj7x9oa6U0ReAk7Fjbe4XFWXxnMNEXkSmAoUikgZ8J/AVBGZ4F1zPfAN734ficjTwApcV91rrSeUMcb0vJiShYgcB2xU1a2qukREjgG+BIwSkQ2quiPWG6rqzE52P7Sf8+8E7oz1+sYYYxIv1jaL3+K1VYjIacBPgMeAGmBO94RmjDEmVcRaDeXvUHq4BJijqn8G/iwiy7onNGOMMaki1pKFX0TaE8t04LUOx2yVFGOMOczF+kH/JPCGiFQATcBbACIyClcVZYwx5jAWU7LwekHNAwYA/9Bdi2D4gG92V3DGGGNSQ8xVSKr6bif7PklsOMYYY1JRvCO4jTHG9EKWLIwxxnTJkoUxxpguxbueRTpu5Pawju9VVVta1RhjDmPxjpF4DtdVdjHQkvhwjDHGpKJ4k0WJqp7VLZEYY4xJWfG2WfzTm0TQGGNMLxJvyeIU4HIRWYerhhJAVXV8wiMzxhiTMuJNFmd3SxTGGGNSWryLH20QkXzcinWhDoc2JDQqY4wxKSXerrNfA67HrYW9DDgB+BdwRuJDM8YYkyribeC+HjgO2KCq04CJQHXCozLGGJNS4k0WzaraDG6AnqquBMbGcwEReVhEykXkww77+orIXBFZ7T3ne/tFRO4TkTUislxEJsUZrzHGmASIN1mUiUge8Fdgrog8R/ztFY8Ce47VuAWYp6qjgXneNrgG9dHeYzbwYJz3MsYYkwDxNnBf4L28XUTmA7nAy3Fe400RGbbH7hnAVO/1Y8DrwM3e/se99TPeFZE8ERmgqlviuacxxpiDE1fJwqsW+qqI3Kaqb+AauSckII5+HRLAVqCf93oQsLHDeWXevs5imy0ii0Rk0fbt2xMQkjHGmHbxVkP9BjgRmOlt1wEPJDIgrxShXZ649/vmqGqpqpYWFRUlMiRjjOn14k0Wx6vqtUAzgKpWAcEExLFNRAYAeM/l3v5NwOAO55V4+4wxxvSgeJNFq4j48b75i0gR0JaAOJ4HZnmvZ+Fmt23ff5lX/XUCUGPtFcYY0/Pine7jPuBZoJ+I3AlcCPwwnguIyJO4xuxCESkD/hP4KfC0iFyF6111sXf6i8A5wBqgEbgizniNMcYkQLy9oZ4QkcXAdG/XDG+sRTzXmLmPQ9P33OG1X1wbz/WNMcYkXkzJQkSe33OX9/xvIoKqfjGxYRljjEklsZYsTsR1YX0SeI9dycIYY0wvEGuy6A+ciesy+xXg78CTqvpRdwVmjDEmdcTUG0pVo6r6sqrOws00uwZ4XUSu69bojDHGpISYG7hFJB04F1e6GMaunlHGGGMOc7E2cD8OHI3rynqHqn7YxVuMMcYcRmItWXwVaMCtZ/F/RXa2b7evwd2nG2IzxhiTImJKFqoa70hvY4wxhxFLAsYYY7pkycIYY0yXLFkYY4zpkiULY4wxXbJkYYwxpkuWLIwxxnTJkoUxxpguWbIwxhjTJUsWxhhjumTJwhhjTJfiXYO7W4nIeqAOiAIRVS0Vkb7AH3Ez3a4HLlbVqmTFaIwxvVEqliymqeoEVS31tm8B5qnqaGCet22MMaYHpWKy2NMM4DHv9WPA+UmMxRhjeqVUSxYK/ENEFovIbG9fP1Xd4r3eCvRLTmjGGNN7pVSbBXCKqm4SkWJgrois7HhQVVVEtLM3esllNsCQIUO6P1JjjOlFUqpkoaqbvOdy3JKtU4BtIjIAwHsu38d756hqqaqWFhUV9VTIxhjTK6RMshCRLBHJaX8NfB74EHgemOWdNgt4LjkRGmNM75VK1VD9gGe9JVvTgD+o6ssishB4WkSuAjYAFycxRmOM6ZVSJlmo6lrg2E72VwLTez4iY4wx7VKmGsoYY0zqsmRhjDGmS5YsjDHGdMmShTHGmC5ZsjDGGNMlSxbGGGO6ZMnCGGNMlyxZGGOM6ZIlC2OMMV2yZGGMMaZLliyMMcZ0yZKFMcaYLlmyMMYY0yVLFsYYY7pkycIYY0yXLFkYY4zpkiULY4wxXbJkYYwxpkuWLIwxxnTpkEgWInKWiKwSkTUickuy4zHGmN4m5ZOFiPiBB4CzgSOBmSJyZHKjMsaY3iXlkwUwBVijqmtVNQw8BcxIckzGGNOrpCU7gBgMAjZ22C4Djt/zJBGZDcz2NutFZNUB3q8QqDjA9/akQyVOOHRitTgT71CJ1eJ0hu7rwKGQLGKiqnOAOQd7HRFZpKqlCQipWx0qccKhE6vFmXiHSqwWZ9cOhWqoTcDgDtsl3j5jjDE95FBIFguB0SIyXESCwJeB55MckzHG9CopXw2lqhERuQ54BfADD6vqR914y4Ouyuohh0qccOjEanEm3qESq8XZBVHVZN3bGGPMIeJQqIYyxhiTZJYsjDHGdMmSRQepOq2IiDwsIuUi8mGHfX1FZK6IrPae85MZoxfTYBGZLyIrROQjEbk+hWMNicgCEXnfi/UOb/9wEXnP+zfwR69TRbJj9YvIUhF5IVVjBBCR9SLygYgsE5FF3r5U/NvnicgzIrJSRD4WkRNTNM6x3u+y/VErIjckK1ZLFp4Un1bkUeCsPfbdAsxT1dHAPG872SLAt1X1SOAE4Frvd5iKsbYAZ6jqscAE4CwROQH4GXC3qo4CqoCrkhhju+uBjztsp2KM7aap6oQOYwFS8W9/L/Cyqo4DjsX9blMuTlVd5f0uJwCTgUbgWZIVq6rawzXynwi80mH7VuDWZMfVIZ5hwIcdtlcBA7zXA4BVyY6xk5ifA85M9ViBTGAJbmaACiCts38TSYqtBPeBcAbwAiCpFmOHWNcDhXvsS6m/PZALrMPr3JOqcXYS9+eBd5IZq5UsdulsWpFBSYolFv1UdYv3eivQL5nB7ElEhgETgfdI0Vi96p1lQDkwF/gUqFbViHdKKvwbuAe4CWjztgtIvRjbKfAPEVnsTb8Dqfe3Hw5sBx7xqvb+n4hkkXpx7unLwJPe66TEasniMKDuK0bK9IEWkWzgz8ANqlrb8VgqxaqqUXVF/BLchJXjkhzSbkTkPKBcVRcnO5YYnaKqk3BVudeKyGkdD6bI3z4NmAQ8qKoTgQb2qMZJkTh38tqkvgj8ac9jPRmrJYtdDrVpRbaJyAAA77k8yfEAICIBXKJ4QlX/4u1OyVjbqWo1MB9XpZMnIu2DVZP9b+Bk4Isish432/IZuPr2VIpxJ1Xd5D2X4+rWp5B6f/syoExV3/O2n8Elj1SLs6OzgSWqus3bTkqslix2OdSmFXkemOW9noVrH0gqERHgIeBjVf1Vh0OpGGuRiOR5rzNwbSsf45LGhd5pSY1VVW9V1RJVHYb79/iaql5KCsXYTkSyRCSn/TWujv1DUuxvr6pbgY0iMtbbNR1YQYrFuYeZ7KqCgmTFmuyGm1R6AOcAn+Dqrr+f7Hg6xPUksAVoxX0zugpXdz0PWA28CvRNgThPwRWJlwPLvMc5KRrreGCpF+uHwG3e/hHAAmANrtifnuxYvbimAi+kaoxeTO97j4/a//+k6N9+ArDI+9v/FchPxTi9WLOASiC3w76kxGrTfRhjjOmSVUMZY4zpkiULY4wxXbJkYYwxpkuWLIwxxnTJkoUxxpguWbIw5gCISHSPGUETNpmbiAzrOMOwMakg5ZdVNSZFNambKsSYXsFKFsYkkLemw8+9dR0WiMgob/8wEXlNRJaLyDwRGeLt7yciz3rrarwvIid5l/KLyO+8tTb+4Y0yNyZpLFkYc2Ay9qiGuqTDsRpVPQa4HzdrLMCvgcdUdTzwBHCft/8+4A1162pMwo1+BhgNPKCqRwHVwJe6+ecxZr9sBLcxB0BE6lU1u5P963GLKq31JlXcqqoFIlKBW4Og1du/RVULRWQ7UKKqLR2uMQyYq25xG0TkZiCgqv/V/T+ZMZ2zkoUxiaf7eB2Plg6vo1j7okkySxbGJN4lHZ7/5b3+J27mWIBLgbe81/OAa2DnYky5PRWkMfGwbyvGHJgMb5W9di+ranv32XwRWY4rHcz09n0Ttzrbd3ErtV3h7b8emCMiV+FKENfgZhg2JqVYm4UxCeS1WZSqakWyYzEmkawayhhjTJesZGGMMaZLVrIwxhjTJUsWxhhjumTJwhhjTJcsWRhjjOmSJQtjjDFd+v/pxQdKvNX3eAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = build_model()\n",
        "\n",
        "# The patience parameter is the amount of epochs to check for improvement\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "history = model.fit(normed_train_data_, train_labels_, epochs=EPOCHS,\n",
        "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
        "\n",
        "plot_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "sagW3HxbBNK1",
        "outputId": "a36cd653-90ce-4fa6-ccd8-00b3e3660176",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-46dbf00e-bfe9-4ef1-af19-d7468f835bbe\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedAE</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>R2S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>model1</th>\n",
              "      <td>8.906454</td>\n",
              "      <td>11.203411</td>\n",
              "      <td>15.508314</td>\n",
              "      <td>-0.358515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model2</th>\n",
              "      <td>9.202757</td>\n",
              "      <td>10.360238</td>\n",
              "      <td>12.613939</td>\n",
              "      <td>0.151068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sequential</th>\n",
              "      <td>10.321890</td>\n",
              "      <td>11.539916</td>\n",
              "      <td>13.733385</td>\n",
              "      <td>-0.166698</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46dbf00e-bfe9-4ef1-af19-d7468f835bbe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46dbf00e-bfe9-4ef1-af19-d7468f835bbe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46dbf00e-bfe9-4ef1-af19-d7468f835bbe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                MedAE        MAE        MSE       R2S\n",
              "model1       8.906454  11.203411  15.508314 -0.358515\n",
              "model2       9.202757  10.360238  12.613939  0.151068\n",
              "Sequential  10.321890  11.539916  13.733385 -0.166698"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Error_m=pd.DataFrame(columns=['MedAE','MAE','MSE','R2S'],\n",
        "                     data=[[8.906454,\t11.203411,\t15.508314,\t-0.358515],\n",
        "                      \t[9.202757,\t10.360238,\t12.613939,\t0.151068]],index=['model1','model2'])\n",
        "Error_m.loc['Sequential','MedAE']=skm.median_absolute_error(y_test, test_predictions)\n",
        "Error_m.loc['Sequential','MAE']=skm.mean_absolute_error(y_test, test_predictions)\n",
        "Error_m.loc['Sequential','MSE']=np.sqrt(skm.mean_squared_error(y_test, test_predictions))\n",
        "Error_m.loc['Sequential','R2S']=skm.r2_score(y_test, test_predictions)\n",
        "Error_m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Xe7RXH3N3CWU",
        "outputId": "48ac0edd-d290-46ca-88cb-9fd66f29d321",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_predictions = model.predict(normed_test_data_).flatten()\n",
        "\n",
        "plt.scatter(test_labels_, test_predictions)\n",
        "plt.xlabel('True Values [smScore]')\n",
        "plt.ylabel('Predictions [smScore]')\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([0,plt.xlim()[1]])\n",
        "plt.ylim([0,plt.ylim()[1]])\n",
        "_ = plt.plot([-100, 100], [-100, 100])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19wyogbOSU5t"
      },
      "source": [
        "Parece que nuestro modelo predice razonablemente bien. Echemos un vistazo a la distribución de errores."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "f-OHX4DiXd8x",
        "outputId": "45d66d2e-19e2-4546-be2f-d31b005c5f10",
        "vscode": {
          "languageId": "python"
        }
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "error = test_predictions - test_labels_\n",
        "plt.hist(error, bins = 25)\n",
        "plt.xlabel(\"Prediction Error [smScore]\")\n",
        "_ = plt.ylabel(\"Count\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0CB5tBjSU5w"
      },
      "source": [
        "The residuals distribution is kinda Normal, but shows a good shape."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}