deep_l.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "kaggle1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "O48oshtUCCDa"
      },
      "source": [
        "# Download Data \n",
        "\n",
        "\n",
        "*   Create kaggle folder \n",
        "*   Download data \n",
        "*   Unzip data \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "l14bcWHYCCDc",
        "outputId": "ffd84de5-f7a1-4b79-dd08-cd4fca7c5fec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!rm -dr ~/.kaggle\n",
        "!pip install -U -q kaggle\n",
        "\n",
        "!mkdir -p ~/.kaggle\n",
        "from google.colab import files\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/root/.kaggle': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fXp8ZXnkCTHN",
        "outputId": "91f7f199-f018-499b-f5f8-f5e2dc164b51",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4a1f5f32-ca6b-4744-89ed-d0167f4aef3d\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-4a1f5f32-ca6b-4744-89ed-d0167f4aef3d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"idrissyoussef\",\"key\":\"8a69da9b5e69d5f1ecd4bdbf6d876eb8\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KbqjLuDJBkVK",
        "colab": {}
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lle3EDEdChcg",
        "colab": {}
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "blksYtfaCmSu",
        "outputId": "ec7cb244-6162-4167-f74c-d3d8851f4fa4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "!kaggle competitions download -c ashrae-energy-prediction"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "Downloading test.csv.zip to /content\n",
            " 95% 159M/167M [00:01<00:00, 105MB/s] \n",
            "100% 167M/167M [00:01<00:00, 115MB/s]\n",
            "Downloading weather_train.csv.zip to /content\n",
            "  0% 0.00/1.27M [00:00<?, ?B/s]\n",
            "100% 1.27M/1.27M [00:00<00:00, 183MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            " 95% 114M/120M [00:00<00:00, 174MB/s] \n",
            "100% 120M/120M [00:00<00:00, 175MB/s]\n",
            "Downloading weather_test.csv.zip to /content\n",
            "  0% 0.00/2.53M [00:00<?, ?B/s]\n",
            "100% 2.53M/2.53M [00:00<00:00, 168MB/s]\n",
            "Downloading building_metadata.csv to /content\n",
            "  0% 0.00/44.5k [00:00<?, ?B/s]\n",
            "100% 44.5k/44.5k [00:00<00:00, 35.7MB/s]\n",
            "Downloading sample_submission.csv.zip to /content\n",
            " 86% 76.0M/88.4M [00:00<00:00, 170MB/s]\n",
            "100% 88.4M/88.4M [00:00<00:00, 178MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LWCScicxQEtj",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zXNfUY84QVOj",
        "outputId": "95a31d64-33ca-418d-ba91-97e7128a5382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "!unzip weather_train.csv.zip\n",
        "!unzip weather_test.csv.zip\n",
        "!unzip test.csv.zip\n",
        "!unzip train.csv.zip\n",
        "!unzip sample_submission.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  weather_train.csv.zip\n",
            "  inflating: weather_train.csv       \n",
            "Archive:  weather_test.csv.zip\n",
            "  inflating: weather_test.csv        \n",
            "Archive:  test.csv.zip\n",
            "  inflating: test.csv                \n",
            "Archive:  train.csv.zip\n",
            "  inflating: train.csv               \n",
            "Archive:  sample_submission.csv.zip\n",
            "  inflating: sample_submission.csv   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wEpYZ0uem2qp"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "*   Importing libraries \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9HUoapSnJmq8",
        "outputId": "903667cd-9eaa-4916-eb31-cbb6e915363e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "import gc\n",
        "import os\n",
        "import random\n",
        "\n",
        "import lightgbm as lgb\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split,KFold,GroupKFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import lightgbm as lgb\n",
        "\n",
        "from plotly.offline import init_notebook_mode,iplot,plot\n",
        "import plotly.graph_objects as go\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.figure_factory as ff"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CZoSUz_vOer6"
      },
      "source": [
        "## This method converts the feature type to reduce thr memory usage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Og6T1rez31_P",
        "colab": {}
      },
      "source": [
        "def reduce_mem_usage(df, verbose=True):\n",
        "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "    start_mem = df.memory_usage().sum() / 1024**2    \n",
        "    for col in df.columns:\n",
        "        col_type = df[col].dtypes\n",
        "        if col_type in numerics:\n",
        "            c_min = df[col].min()\n",
        "            c_max = df[col].max()\n",
        "            if str(col_type)[:3] == 'int':\n",
        "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
        "                    df[col] = df[col].astype(np.int8)\n",
        "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
        "                    df[col] = df[col].astype(np.int16)\n",
        "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
        "                    df[col] = df[col].astype(np.int32)\n",
        "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
        "                    df[col] = df[col].astype(np.int64)  \n",
        "            else:\n",
        "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
        "                    df[col] = df[col].astype(np.float16)\n",
        "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
        "                    df[col] = df[col].astype(np.float32)\n",
        "                else:\n",
        "                    df[col] = df[col].astype(np.float64)    \n",
        "    end_mem = df.memory_usage().sum() / 1024**2\n",
        "    if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))\n",
        "    return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Z_sScCuTnpZJ"
      },
      "source": [
        "# Reading the CSV files\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JhLSlM2cFgXT",
        "colab": {}
      },
      "source": [
        "df_train = pd.read_csv(\"data/train.csv\")\n",
        "df_test = pd.read_csv(\"data/test.csv\")\n",
        "df_weather_train = pd.read_csv(\"data/weather_train.csv\")\n",
        "df_weather_test = pd.read_csv(\"data/weather_test.csv\")\n",
        "df_metadata=pd.read_csv(\"data/building_metadata.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AgRLN0ra4ZYR",
        "outputId": "863237d2-2889-4177-a04d-73e0f466cb22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "train = df_train.copy()\n",
        "test= df_test.copy() \n",
        "weather_train=df_weather_train.copy()\n",
        "weather_test =df_weather_test.copy()\n",
        "metadata=df_metadata.copy()\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "243"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ze10ylxLzgTt"
      },
      "source": [
        "\n",
        "# Data understanding\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Dn-JcOV5qVyS",
        "outputId": "da84661f-06fc-4abe-b87c-59d77e5cbbd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Distribution of train feature\n",
        "cols = ['air_temperature','cloud_coverage','dew_temperature','precip_depth_1_hr','sea_level_pressure','wind_speed']\n",
        "for ind,col in enumerate(weather_train[cols]):\n",
        "    plt.figure(ind)\n",
        "    sns.distplot(weather_train[col].dropna())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VxPHGTptjOMRZAjDmLLwDPv5l+yHy0+O5+4oCt8MJKgsyEvEpvFvd5nYoZgIsARhzFs/s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\nlgCMMSZMWQIwxpgwZQnAGGPC1P8HD13474vIw7YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xszTgJuC5E9o8B3wiWL4BeDke/eciItKxTrtc3L3FzD4LLCJ62eJD7r7BzL4F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\nLQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3/OHtg5xo7+LWyye7HYqJQHkZSUD03Q0czqLwpcC4kNdjnbLe6hwVkQQgE6g5\nWaOqWuo8N4rIYwSHmv4YZtzGDJumtk4eXnuQq4rymDE6g8fWH3Y7JBNhcp0eQLTdDBZOD2AjMFVE\nJopIIrAYWNmjzkpgibN9I/CKqmpfDYpIgoiMcrZ9wCeA7QMN3pjh8Kd1h6hv6eC2y6e4HYqJUN09\ngGi7GazfHoCqdorIbcAawAs8pKo7RORuoFhVVwIPAstFpAQ4TjBJACAiB4EMIFFErgeuBg4Ba5wP\nfy/wEvC7QX1nxgyC1o4ufv/mAS6eOopZ47LcDsdEqJEpiSR4JOouBQ1nCAhVXQWs6lF2Z8h2K3BT\nH8cW9tHsnPBCNMY9KzYeobqpjVsvP8ftUEwE83i67waOrh6A3QlsTB/aOwP89vV9nFc4gvkTR7od\njolwuRlJVDZGVw/AEoAxfXhq81HK61u59fIpNu2D6Vdeup9K6wEYE/06uwI88No+zirI5FKb89+E\nIS8jiWPWAzAm+j23rZyDNc3cevlk+/ZvwpKX4aeuuYPWjui5G9gSgDE9dHYF+OVLe5kxOp2ri2zB\ndxOe3PTgpaDRdDdwWFcBGRNPntpcyoHqE3xh/nge33ik/wOMIeRmsMZWxo1McTma8FgPwJgQ7Z0B\nfvXyXs4qyOSM/Ay3wzFRJBpvBrMEYEyIvxQf4WhtC9+9epqN/ZsB6U4AFfXRcyLYEoAxjtaOLu57\npYQ5E0bYlT9mwEak+Ej0eqLqSiBLAMY4Hlt/mIqGVvv2b06JiJCX6bcegDHRprm9k9+8VsIFk7O5\nYPIot8MxUWp0RpIlAGOizSNvH6K6qZ3vXj3N7VBMFBudmUxFFE0IZwnAxL3G1g7++419XDY9hzkT\nbM4fM3CPrT/MY+sPU3uindLaFh5dd8jtkMJi9wGYuPe/HttMXXMHM/MzbbEXc1oyk310BpSW9ui4\nG9h6ACauldW18FZJNbPHZVEwwtb6NacnI9kHQH1rh8uRhCesBCAiC0Vkj4iUiMgdvez3i8gKZ/96\nESl0yrNF5FURaRKR+3ocM0dEtjnH/LqvReGNGUr/b80eAK4qynM5EhMLMpOCgyoNLTGSAETEC9wP\nXAsUAZ8VkaIe1W4BalV1CnAvcI9T3gr8CLi9l6YfAP4RmOo8Fp7KGzDmVG0vreevm0u5YPIoRqQk\nuh2OiQHdPYCGlk6XIwlPOD2AeUCJqu5X1XbgcWBRjzqLgEec7SeBK0REVPWEqr5FMBF8QETygQxV\nXeesHfxH4PrTeSPGDISqcvczOxmZmshl0+2mLzM40pN8CLE1BFQAhM6IddQp67WOqnYC9UB2P20e\n7adNAERkqYgUi0hxVVVVGHGHR1QAABRYSURBVOEa07+nt5Sy4eBxvnfNdJJ8XrfDMTHC6xHS/Amx\nMwTkNlVdpqpzVXVuTo59UzOnr7G1g5+u2s2scVncPHec2+GYGJOR7KM+hhJAKRD6VzLWKeu1jogk\nAJlATT9tju2nTWOGxC9f2kt1Uxv/vmgmHo9de2AGV6wlgI3AVBGZKCKJwGJgZY86K4ElzvaNwCvO\n2H6vVLUcaBCRBc7VP18C/jbg6I0ZoPeO1vGHtw+y+LzxnD02y+1wTAzKTE6gIUrOAfR7I5iqdorI\nbcAawAs8pKo7RORuoFhVVwIPAstFpAQ4TjBJACAiB4EMIFFErgeuVtWdwDeBPwDJwPPOw5gh09bZ\nxXf/spWcND93XDvD7XBMjMpI8tHaEaC5vZOUxMi+1zas6FR1FbCqR9mdIdutwE19HFvYR3kxcGa4\ngRrTl97u3v3c/PEfKbv3xb3srWziD185j0zncj1jBlv371ZFfSuTctJcjubkIjs9GTNIig8eZ9kb\n+5g7YQRlda025YMZMhlRlAAi/iogY05XeX0L//ToJsaOSOG6s/LdDsfEuMwkJwFEwayglgBMTGvt\n6GLpH9+lua2T3y+Za9f8myHX3QMoj4J1ASwBmJjV2RXgu3/Zyvayen61+Bym5aW7HZKJA4kJHpJ8\nHo5FQQ/AzgGYmNTeGeBbf97M6h0V/PC6M7jSJnszwygjyRcVPQBLACbmtHcG+PryYl7dU8Wdnyji\nqxdNdDskE2cyk33WAzBmuDW0drD8nUOU1bfw0xvO6vVyUGOGWlaKj31VJ9wOo1+WAEzMqKhv5ZF3\nDtLS3sUX5k8Aer9HwJihlp3qZ+PBWupbOiL6nhM7CWxiQlldC797cz+qytJLJnFGfobbIZk4NjI1\nuL7E4ZpmlyM5OUsAJuqV1rXw4FsH8Cd4WHrJZMZk2dKOxl3ZacEEcLAmsoeBLAGYqLbtaD0PvrUf\nv8/D1y6e9ME3L2PclJ3qB+BQhCcAOwdgotbWI3V88cH1JPu8fO2iSYywD38TIRITPORl+DlQHdlD\nQJYATFTa4nz4Z6X4WHzeeFvT10ScCdmpEd8DsCEgE3U2Ha7li79fz4iURB5fer59+JuIVJidwkE7\nCWzM4Hn3UC1fenADI9MSeXzpAgrshK+JUBOyU6luaqOprdPtUPoUVgIQkYUiskdESkTkjl72+0Vk\nhbN/vYgUhuz7gVO+R0SuCSk/KCLbRGSLiBQPxpsxsW3Njgo+//t1jHI+/O1qHxPJCrNTgcg+Edxv\nAhARL3A/cC1QBHxWRIp6VLsFqFXVKcC9wD3OsUUEVwebCSwEfuO01+1yVZ2tqnNP+52YmKWqLHtj\nH9/407vMGJ3BE9+4gPxM+/A3kW1CdgoAhyJ4GCicHsA8oERV96tqO/A4sKhHnUXAI872k8AVzlq/\ni4DHVbVNVQ8AJU57xoSlub2T76zYwk9X7ea6s/J5fOkCctL9bodlTL8KRwV7AAeqI7cHEM5VQAXA\nkZDXR4H5fdVx1hCuB7Kd8nU9ji1wthV4QUQU+G9VXTbw8E0sO1zTzNLlxew51sjtV08jKyWRv24q\ndTssY8KS5k9gVJo/ooeA3LwM9CJVLRWRXOBFEdmtqm/0rCQiS4GlAOPH28Re8eL/rdnDw28fJBBQ\nlpxfyMhU+9Zvok+kXwkUzhBQKTAu5PVYp6zXOiKSAGQCNSc7VlW7nyuBp+hjaEhVl6nqXFWdm5OT\nE0a4Jtq9s6+G3725H59H+Malk20hFxO1Iv1egHASwEZgqohMFJFEgid1V/aosxJY4mzfCLyiquqU\nL3auEpoITAU2iEiqiKQDiEgqcDWw/fTfjol2q7dXsOThDWQm+/j6pZNtvN9EtcLsFI41tNHcHpmX\ngvY7BOSM6d8GrAG8wEOqukNE7gaKVXUl8CCwXERKgOMEkwROvb8AO4FO4FZV7RKRPOCp4HliEoDH\nVHX1ELw/E0Ue33CYf3tqG7PGZfHxs/JJSbQb1U106z4RfKimOSJnqA3rL0xVVwGrepTdGbLdCtzU\nx7E/AX7So2w/MGugwZrYpKo88Po+frZ6D5dOy+GBL5zL05vL3A7LmNM2NS8NgF3lDRGZAOxOYOOq\nQED5yXO7+NnqPSyaPYbfL5lr3/xNzJiam06aP4F3D9W6HUqv7C/NuKaxtYPP/W4920rrOX9yNucV\njuSJ4qNuh2XMoPF6hHPGZ0VsArAegHHF5sO1fPzXb7G9tJ5rZo7mE2fl4wmeEzImppw7fgR7jjXS\n2NrhdigfYT0AM+j6Wof3c/PHs7uigQde28czW8vIz0xm6SWTmODMmWJMLJozYQSqwSnML54aWZey\nWwIwQ666qY1d5Q38ddNRig/Vkpro5WsXT+LWy6fw3HvlbodnzJCaPT4LEdh0yBKAiRPHGlrZcqSO\nneUNVDW2ATBzTAbfu2Y6n58/niybw9/EiYwkH9Pz0nn3cOSdB7AEYAZNe2eAp7eU8pvXSjha24JH\ngtdBz584kjPyM7j18iluh2iMK86dMIJntpQRCCgeT+Sc67IEYE5bS3sXKzYeZtkb+ymrbyU33c91\nZ+Uze1wWaf6//4r1dW7AmFg3Z/wIHlt/mL2VTUwfHTlTm1gCMKessbWD5esO8eCbB6g50c68wpH8\n9NNnUVrbgtgVPcZ8YM6EEUBwRTtLACaqHT/RzsNrD/C7N/fT2hFgWl4anz53LBNHpVJW12of/sb0\nMCE7hezURN7eV83n5kfOrMaWAExYVJVNh2t5dP1hnnuvnPauAEX5GVw2LZeCEbY6lzEnIyJ84ux8\nHttwmMrGVnLTk9wOCbAEYPrQ0RXgUE0zJZVNvLOvmpd2VVJa10KaP4Gb5o7lyxcUsuFA5F3VYEyk\nWnJBIY+8c4hH1x3mO1dNczscwBJA3Gtu72Rf5QlKqhopqWz64HGoppnOgAKQ5PMwcVQaCyaN5MyC\nTPwJXvvwN6YfvV30MD0vnUfXH+Kbl0/Gn+Dt5ajhZQkgDjy2/jCqyvET7RyqaaasvoWqxjaa27so\nrWv5oJ7XI4xISSQ33c+FU0aRm+4nJ91PXkYSPq/NGmLM6bpgcjYPv32QZ7eW85k5Y90OxxJArOoK\nKLvKGyg+eJwnN5VyqOYEja3BRSl8XiEn3c95hSNZnDOOKblpTMlNY0J2Kk++a5OxGTNUuv/WHlp7\ngOvPKcDr8j0BEly4KzrMnTtXi4uL3Q4jorR2dHGsoZXy+laO1rawp6KBXeWNbDlSR1Nb8AM/K9nH\nhOwUCkelMiE7ldx0v028ZoxL/AkevvvEVm6cM5Z7PnP2sCQBEXlXVef2LA+rByAiC4FfEVwR7Peq\n+p899vuBPwJzCK4FfLOqHnT2/QC4BegCvqWqa8Jp0wRvsKpoaKW8voWK+uCH/Ie3Wzl+ov1Dx/gT\nPEzNS2PR7DHMmziSuYUjeX1PlUvvwBjT02fmjOVIbTO/fGkvAtzzmbNduzu43wQgIl7gfuAq4Ciw\nUURWqurOkGq3ALWqOkVEFgP3ADeLSBHB5SFnAmOAl0Sk+/R3f226RlVp6wzQ1hGgrbOL1o4ArZ1d\ntDnPLe1dNLd30dLRGXxu70I1OIae4BW8HsHn8eBLEBK9XvwJHhITPPi8Hjq6ArR3BoLPXQGa27s4\nfqL9g0fNiXaqGtuoqG+htvmj08emJHrJTPaRkeRjSk4aGeN9ZCX7yEgOPo9ITfzgG8WJti778Dcm\nAn37ymkEFH798l7WHzjOZ84dy5VFuYzNSiEjOWHY7qUJpwcwDyhxlnFERB4HFhFc57fbIuAuZ/tJ\n4D4JvoNFwOOq2gYccNYMnufU66/NQbPkoQ0cqD5BQBVVPvQc0OAHfkCVroDzwd8ZGIowTirR6yHV\n7yXVn0CaP4FpeelkJvuCH/bdz0k+EhPsZKwxseA7V05lam4af95wmF++/D73vvQ+ELzqzp/gJcH5\nQpng8ZDgFdZ8+xKSfIN75VA4CaAAOBLy+igwv686ziLy9UC2U76ux7EFznZ/bQIgIkuBpc7LJhHZ\n06PKKKA6jPcRaaIx7miMGaIz7miMGaIz7mGN+fOneFzyv36kaCBxT+itMOKvAlLVZcCyvvaLSHFv\nJzciXTTGHY0xQ3TGHY0xQ3TGHY0xw+DEHc54QikwLuT1WKes1zoikgBkEjwZ3Nex4bRpjDFmCIWT\nADYCU0VkoogkEjypu7JHnZXAEmf7RuAVDV5fuhJYLCJ+EZkITAU2hNmmMcaYIdTvEJAzpn8bsIbg\nJZsPqeoOEbkbKFbVlcCDwHLnJO9xgh/oOPX+QvDkbidwq6p2AfTW5im+hz6HhyJcNMYdjTFDdMYd\njTFDdMYdjTHDIMQdVTeCGWOMGTx2TaExxsQpSwDGGBOnoj4BiMh3RURFZJTzWkTk1yJSIiLvici5\nbscYSkT+3Ylri4i8ICJjnPKIjVtE/ktEdjtxPSUiWSH7fuDEvEdErnEzzlAicpOI7BCRgIjM7bEv\nImPuJiILndhKROQOt+Ppi4g8JCKVIrI9pGykiLwoInud5xFuxtiTiIwTkVdFZKfz+/HPTnnExi0i\nSSKyQUS2OjH/X6d8ooisd35PVjgX1AyMqkbtg+ClpGuAQ8Aop+w64HlAgAXAerfj7BFzRsj2t4Df\nRnrcwNVAgrN9D3CPs10EbAX8wERgH+B1O14ntjOA6cBrwNyQ8oiN2YnP68Q0CUh0Yi1yO64+Yr0E\nOBfYHlL2M+AOZ/uO7t+VSHkA+cC5znY68L7zOxGxcTufCWnOtg9Y73xG/AVY7JT/FvingbYd7T2A\ne4F/BULPZC8C/qhB64AsEcl3JbpeqGpDyMtU/h57xMatqi+oaqfzch3B+zYgZKoPVT0AhE714SpV\n3aWqPe8ahwiO2fHB1Cuq2g50T5MScVT1DYJX/YVaBDzibD8CXD+sQfVDVctVdZOz3QjsIjg7QcTG\n7XwmNDkvfc5DgY8RnHoHTjHmqE0AIrIIKFXVrT129TZ1RQERRER+IiJHCN4VfqdTHPFxO75KsKcC\n0RNzqEiPOdLj60+eqpY72xVAnpvBnIyIFALnEPxGHdFxi4hXRLYAlcCLBHuJdSFfzE7p9ySip4IQ\nkZeA0b3s+iHwbwSHJiLOyeJW1b+p6g+BHzpTZd8G/J9hDbAX/cXs1Pkhwfs5Hh3O2PoSTszGPaqq\nIhKR15mLSBrwP8C3VbUhdPbNSIxbg/dPzXbOvz0FzBiMdiM6Aajqlb2Vi8hZBMdvtzr/cWOBTSIy\njwiYZqKvuHvxKLCKYAJwNe7+YhaRLwOfAK5QZ9CRCI+5D67/fvQj0uPrzzERyVfVcmcIs9LtgHoS\nER/BD/9HVfWvTnHExw2gqnUi8ipwPsFh4gSnF3BKvydROQSkqttUNVdVC1W1kGD351xVrSA4pcSX\nnKtqFgD1IV0714nI1JCXi4DdznbExi3BxXv+FfiUqjaH7Oprqo9IFukxR/s0KaHTwiwBIqonJsFv\njA8Cu1T1FyG7IjZuEcnpvvJORJIJrqOyC3iV4NQ7cKoxu32Ge5DOkh/k71cBCcHFZvYB2wi5AiQS\nHgS/eWwH3gOeAQoiPW6CJ0qPAFucx29D9v3QiXkPcK3bsYbEdQPBLwZtwDFgTaTHHBLfdQSvTtlH\ncDjL9Zj6iPPPQDnQ4fxb30JwGviXgb3AS8BIt+PsEfNFBE+gvhfy+3xdJMcNnA1sdmLeDtzplE8i\n+OWlBHgC8A+0bZsKwhhj4lRUDgEZY4w5fZYAjDEmTlkCMMaYOGUJwBhj4pQlAGOMiVOWAIwxJk5Z\nAjBRQUTuEpHbB7nNy0TkgsFscyiISKGIfM7tOEzssQRg4tllQEQkABE52bQshcCAE4CIeE85IBMX\nLAGYiCUiPxSR90XkLYJz+yMik0VktYi8KyJvisgMZ6bEA840Glki0iUilzj13+gx/UZ324XAN4Dv\nSHBxnoudW+7/R0Q2Oo8Lnbp3icgjzs87JCKfFpGficg2JxafU+9gSPkGEZnilJ+s3eUishZY7nzT\nf1NENjmP7uT0n8DFTpzfEZEvi8h9Ie/lWRG5zNluEpGfi8hW4HwRmSMirzv/XmskQqYYNxHC7duc\n7WGP3h7AHIJTYqQAGQRvd7+d4O36U50684FXnO3VwEyCE9ZtJDjdgx84cJKfcRdwe8jrx4CLnO3x\nBOeL6a73FsF52GcBzThTSBCcmfF6Z/sgztQNwJeAZ8No910g2XmdAiQ521OBYmf7su62nNdfBu4L\nef0scJmzrcA/ONs+4G0gx3l9M/CQ2/+39oicR0TPBmri2sXAU+pMPiciK4EkgkM2T4RM3+t3nt8k\nuELVROA/gH8EXieYDMJ1JVAU0naGM20wwPOq2iEi2wiu2rXaKd9GcIim259Dnu8No92VqtribPuA\n+0RkNtAFTBtA7N26CM43BcFe05nAi87P9hKcu8cYIMKngzamBw/BRTBm97LvDeCfgDEEF9n5HsFv\nzm8OsP0FqtoaWuh8eLYBqGpARDpUtXsSrQAf/jvSXrZP1u6JkKLvEJy4bpZzzIfqh+jkw8O3SSHb\nrRqcOx6CEwzuUNXz+2jHxDk7B2Ai1RvA9SKSLCLpwCcJDr0cEJGbIDi1r4jMcupvINg7CDgftFuA\nrzvt9KWR4Lqw3V4A/lf3C+eb+EDdHPL8zgDbzQTKVTUAfJHgN/be4jxIcHEQj4iMo+8lLfcAOSJy\nvvNzfSIyc2Bvx8QySwAmImlw3dYVBBdFf56/D+V8HrjFOcm5A2e9XFVtIzhl9Tqn3psEPzS3neTH\nPAPc0H0SGPgWMFdE3hORnQRPEg/UCBF5D/hngt/oGUC7vwGWOO9tBn/vHbwHdInIVhH5DrAWOADs\nBH4NbOqtMQ2uKXwjcI/T5hYi5KonExlsOmhjBomIHCS4jkO127EYEw7rARhjTJyyHoCJeSLyFYJD\nMqHWquqtbsRjTKSwBGCMMXHKhoCMMSZOWQIwxpg4ZQnAGGPilCUAY4yJU/8fuiJ2GB0z/9YAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dQkkjKTqGVfUJVV2vqusrK+2bmYmfqSnlCz84xOKSXH792uVex8k4IsLHNi1j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\nmBUBY4zJYP8f8jXhkipolOIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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47E6gT1UvAP4C+HrWvgOq+iH364sFande+kecma75pFyqwv68btAe7R3BJ9N/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\nWLA3xhgPsGBvjDEeYMHeGGM84P8HyFKZi0e6GL0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6hfBTeERtty_",
        "outputId": "7a5d72ca-b5cc-4d79-b7f2-b3dcd8f9ab42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "cols = ['air_temperature','cloud_coverage','dew_temperature','precip_depth_1_hr','sea_level_pressure','wind_speed']\n",
        "for ind,col in enumerate(weather_train[cols]):\n",
        "    plt.figure(ind)\n",
        "    sns.boxplot(weather_train[col].dropna())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ou6NUvQ18t04/DrxVStkREY/TlP6oh0opPwWIiG/TjO2wcx/rfYdmTJRRZ0fEl4EjgWNo\nxgu4r43HAHBvKeXNOv0p4MP1qBrgfTQ/px+3uU4dQixijUsdC/YTwFmllO31T/MjdrvZG+2uFrit\nlHLlGMt2lFJGX8DYRTMQEqWUXRHR+nu7+4scZT/r/b9SzwtHxBE0R8tDpZQXI2IFez6mUTt591Te\nvh53AItLKQ/sZT3SHjxHrPF6H/C/tYRPo/leswOxo47PC83oVOfXAbVHv3DxA22u75P1ftNpvo7m\ne22sd7RQX41m4PbzW5ZtpfmOuVHP0xxlA/z+PvI8AHxh9DFGxK/V4RGlvbKINV7fBfoj4ingazTf\n+XcgVgEbI2J1KeVJ4Cs0XzOzkeZretr9LsNHaU41bATuKqUMj3e9pZTXgb+hObf8AM14y6P+Hrhl\n9MU64KvAzdF8S++Y77Sovgk8Cfwgmm+WvhX/8tR++PY1TVnRfNvKUCnli9lZpInwiFiSknlErI6I\niEdo3r7V6o9LKY9n5JF6mUUsSck8NSFJySxiSUpmEUtSMotYkpL9Pz3Z+BOrQ6+6AAAAAElFTkSu\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEHCAYAAACQkJyuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRIABmggwQJP/AzPrsEECJYu6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e3AecHRHDEfEB4Lep/uv/XEScA5MffHpi/fyHqI6Od9Wh2wz8ab2dvXmD6vO+2u4GRtp3\n6iPR2fpkx6//Ncvt/jTwYillF3Au1RHrVOOcAFZGxEBErGDvH630DHBERJxa73dBRHxkdtNRPzPA\nmlKpPo/rX6g+jHI9755K+DRwfv0i0xbqz0krpbxNdTnNB+vn3U8VrSf2sZtvAJ9ovwhH9aGqq6L6\nZOinqF6km60PRsTjwCjVES2z2O4XgfPquR3Hu0fHjwM7I+KxiLgYeAB4DngK+Dzw6FQbK9Vnyf0e\ncE29zc10yU99qDt4OUr1jYiYoLru8yvZY5FmwiNgSUriEbDmXET8IdUpgU4PlFIuyBiP1C0MsCQl\n8RSEJCUxwJKUxABLUhIDLElJ/h+EtMs0F5AESQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j4p8g6SxJt8TrS8c4GftbJH3MzKxK5Y5Ubznjfk5UI4TfIump1PLTGvmJ4ilI+o6Z7TOz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\nBgBHhDAAOPp/TOTNu/6NYZ0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5vQ76vnXUr4KYqGxj/fCtyJKUxHfCSVISC1iSkljAkpTEApakJBawJCWxgCUpiQWsCRcR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\nSUry/0my7pmkgT2aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J+uuytp9L12FJ0mcLHF9J2f5pfg3m7wr6RUmbJroWChrf36Vra7uyUD2uenzpuOZnvYjx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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FY-66rUStvqa"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "\n",
        "*   Distribution of sea_level_pressure looks like a normal distribution.\n",
        "*   so many 0 values in precip_depth_1_hr variable.\n",
        "*   Wind_Speed distribution looks like left skewed.\n",
        "*   Dew Temperature looks like a right skewed distribution.\n",
        "*   Cloud_Coverage takes distinct values unlike these other variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mIig6zSgzuHY"
      },
      "source": [
        "# Data cleaning + Preparation\n",
        "\n",
        "\n",
        "*   Fill missing values \n",
        "*   Add new date feature\n",
        "*   Merge Metadata building table + train table\n",
        "*   Merge train table +  weather train table\n",
        "*   Remove Rows with null values\n",
        "*   Check the correlated values\n",
        "*   Label encoder\n",
        "*   Fill numeric values with the mean value \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hUBKFUbNqe7G"
      },
      "source": [
        "### Fill missing values \n",
        "### Add new date feature \n",
        "### Reset index for Fast Update\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-icV3a5ouoFW",
        "colab": {}
      },
      "source": [
        "def fill_weather_dataset(weather_df):\n",
        "    \n",
        "    # Find Missing Dates\n",
        "    time_format = \"%Y-%m-%d %H:%M:%S\"\n",
        "\n",
        "    # Add new Features\n",
        "    weather_df[\"datetime\"] = pd.to_datetime(weather_df[\"timestamp\"])\n",
        "    weather_df[\"day\"] = weather_df[\"datetime\"].dt.day\n",
        "    weather_df[\"week\"] = weather_df[\"datetime\"].dt.week\n",
        "    weather_df[\"month\"] = weather_df[\"datetime\"].dt.month\n",
        "    \n",
        "    # Reset Index for Fast Update\n",
        "    weather_df = weather_df.set_index(['site_id','day','month'])\n",
        "\n",
        "    air_temperature_filler = pd.DataFrame(weather_df.groupby(['site_id','day','month'])['air_temperature'].mean(),columns=[\"air_temperature\"])\n",
        "    weather_df.update(air_temperature_filler,overwrite=False)\n",
        "\n",
        "    # Step 1\n",
        "    cloud_coverage_filler = weather_df.groupby(['site_id','day','month'])['cloud_coverage'].mean()\n",
        "    # Step 2\n",
        "    cloud_coverage_filler = pd.DataFrame(cloud_coverage_filler.fillna(method='ffill'),columns=[\"cloud_coverage\"])\n",
        "\n",
        "    weather_df.update(cloud_coverage_filler,overwrite=False)\n",
        "\n",
        "    due_temperature_filler = pd.DataFrame(weather_df.groupby(['site_id','day','month'])['dew_temperature'].mean(),columns=[\"dew_temperature\"])\n",
        "    weather_df.update(due_temperature_filler,overwrite=False)\n",
        "\n",
        "    # Step 1\n",
        "    sea_level_filler = weather_df.groupby(['site_id','day','month'])['sea_level_pressure'].mean()\n",
        "    # Step 2\n",
        "    sea_level_filler = pd.DataFrame(sea_level_filler.fillna(method='ffill'),columns=['sea_level_pressure'])\n",
        "\n",
        "    weather_df.update(sea_level_filler,overwrite=False)\n",
        "\n",
        "    wind_direction_filler =  pd.DataFrame(weather_df.groupby(['site_id','day','month'])['wind_direction'].mean(),columns=['wind_direction'])\n",
        "    weather_df.update(wind_direction_filler,overwrite=False)\n",
        "\n",
        "    wind_speed_filler =  pd.DataFrame(weather_df.groupby(['site_id','day','month'])['wind_speed'].mean(),columns=['wind_speed'])\n",
        "    weather_df.update(wind_speed_filler,overwrite=False)\n",
        "\n",
        "    # Step 1\n",
        "    precip_depth_filler = weather_df.groupby(['site_id','day','month'])['precip_depth_1_hr'].mean()\n",
        "    # Step 2\n",
        "    precip_depth_filler = pd.DataFrame(precip_depth_filler.fillna(method='ffill'),columns=['precip_depth_1_hr'])\n",
        "\n",
        "    weather_df.update(precip_depth_filler,overwrite=False)\n",
        "\n",
        "    weather_df = weather_df.reset_index()\n",
        "    weather_df = weather_df.drop(['datetime','day','week','month'],axis=1)\n",
        "    return weather_df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p0Db7czDu0FU",
        "outputId": "92ae8420-612d-437e-b8ff-2a0689e65e16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        }
      },
      "source": [
        "weather_train = fill_weather_dataset(weather_train)\n",
        "weather_train.columns\n",
        "weather_test = fill_weather_dataset(weather_test)\n",
        "weather_test.columns\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['site_id', 'timestamp', 'air_temperature', 'cloud_coverage',\n",
              "       'dew_temperature', 'precip_depth_1_hr', 'sea_level_pressure',\n",
              "       'wind_direction', 'wind_speed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "UE2srWtDwCl2",
        "colab": {}
      },
      "source": [
        "df= weather_train.copy()\n",
        "df['air_temperature'] = df['air_temperature'].astype('float32')\n",
        "df['cloud_coverage'] = df['cloud_coverage'].astype('float16')\n",
        "df['dew_temperature'] = df['dew_temperature'].astype('float16')\n",
        "df['precip_depth_1_hr'] = df['precip_depth_1_hr'].astype('float32')\n",
        "df['sea_level_pressure'] = df['sea_level_pressure'].astype('float32')\n",
        "df['wind_direction'] = df['wind_direction'].astype('float32')\n",
        "df['wind_speed'] = df['wind_speed'].astype('float16')\n",
        "weather_train= df.copy()\n",
        "df= weather_test.copy()\n",
        "df['air_temperature'] = df['air_temperature'].astype('float32')\n",
        "df['cloud_coverage'] = df['cloud_coverage'].astype('float16')\n",
        "df['dew_temperature'] = df['dew_temperature'].astype('float16')\n",
        "df['precip_depth_1_hr'] = df['precip_depth_1_hr'].astype('float32')\n",
        "df['sea_level_pressure'] = df['sea_level_pressure'].astype('float32')\n",
        "df['wind_direction'] = df['wind_direction'].astype('float32')\n",
        "df['wind_speed'] = df['wind_speed'].astype('float16')\n",
        "weather_test= df.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "reA0BvrcwvgZ",
        "outputId": "42b79268-fb8c-477c-cf4f-4f2d43f8aa4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "source": [
        "print (\"Training Data Shape {}\".format(train.shape))\n",
        "print (\"Testing Data Shape {}\".format(test.shape))\n",
        "train = pd.merge(train,metadata,on='building_id',how='left')\n",
        "test  = pd.merge(test,metadata,on='building_id',how='left')\n",
        "print (\"Training Data Shape {}\".format(train.shape))\n",
        "print (\"Testing Data Shape {}\".format(test.shape))\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Shape (20216100, 4)\n",
            "Testing Data Shape (41697600, 4)\n",
            "Training Data Shape (20216100, 9)\n",
            "Testing Data Shape (41697600, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "t6i--STTrhlQ"
      },
      "source": [
        "### Merge Metadata building table + train table\n",
        "### Merge Train table + Weather train table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2xGBWOyIzGuN",
        "outputId": "6ce7a910-fc01-4b8b-d997-fef32ba7e4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "train = pd.merge(train,weather_train,on=['site_id','timestamp'],how='left')\n",
        "test  = pd.merge(test,weather_test,on=['site_id','timestamp'],how='left')\n",
        "print (\"Training Data Shape {}\".format(train.shape))\n",
        "print (\"Testing Data Shape {}\".format(test.shape))\n",
        "gc.collect()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Shape (20216100, 16)\n",
            "Testing Data Shape (41697600, 16)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6G7HIPLa6sNz",
        "outputId": "6df39762-0c3a-44bb-d405-045b8875d2aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "d_types = {'building_id': np.int16,\n",
        "          'meter': np.int8,\n",
        "          'site_id': np.int8,\n",
        "          'primary_use': 'category',\n",
        "          'square_feet': np.int32,\n",
        "          'year_built': np.float16,\n",
        "          'floor_count': np.float16,\n",
        "          'air_temperature': np.float32,\n",
        "          # 'cloud_coverage': np.float16,\n",
        "          'dew_temperature': np.float32,\n",
        "          # 'precip_depth_1_hr': np.float16,\n",
        "          'sea_level_pressure': np.float32,\n",
        "          # 'wind_direction': np.float16,\n",
        "          'wind_speed': np.float32}\n",
        "\n",
        "for feature in d_types:\n",
        "    train[feature] = train[feature].astype(d_types[feature])\n",
        "    test[feature] = test[feature].astype(d_types[feature])\n",
        "    \n",
        "train[\"timestamp\"] = pd.to_datetime(train[\"timestamp\"])\n",
        "test[\"timestamp\"] = pd.to_datetime(test[\"timestamp\"])\n",
        "gc.collect();\n",
        "\n",
        "train.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['building_id', 'meter', 'timestamp', 'meter_reading', 'site_id',\n",
              "       'primary_use', 'square_feet', 'year_built', 'floor_count',\n",
              "       'air_temperature', 'cloud_coverage', 'dew_temperature',\n",
              "       'precip_depth_1_hr', 'sea_level_pressure', 'wind_direction',\n",
              "       'wind_speed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6g_zeaqZrvM2"
      },
      "source": [
        "### Remove Rows with null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SMjCFsn4-OfM",
        "outputId": "78106c3b-3f1e-45cc-b429-016bafa5312b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "idx_to_drop = list((train[(train['site_id'] == 0) & (train['timestamp'] < \"2016-05-21 00:00:00\")]).index)\n",
        "print (len(idx_to_drop))\n",
        "train.drop(idx_to_drop,axis='rows',inplace=True)\n",
        "\n",
        "idx_to_drop = list(train[(train['meter'] == \"Electricity\") & (train['meter_reading'] == 0)].index)\n",
        "print(len(idx_to_drop))\n",
        "train.drop(idx_to_drop,axis='rows',inplace=True)\n",
        "idx_to_drop = list((train[(train['building_id']==1099)&(train['meter_reading'] > 30000)&(train['meter'] == \"Steam\")]).index)\n",
        "print (len(idx_to_drop))\n",
        "train.drop(idx_to_drop,axis='rows',inplace=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "392857\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py:1115: FutureWarning:\n",
            "\n",
            "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lFSMeOWF-6a9",
        "colab": {}
      },
      "source": [
        "train['meter_reading'] = np.log1p(train['meter_reading'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fYD0E8Ers5bX"
      },
      "source": [
        "### Label encoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6U1TKX5h_lEc",
        "colab": {}
      },
      "source": [
        "\n",
        "le = LabelEncoder()\n",
        "\n",
        "train['meter']= le.fit_transform(train['meter']).astype(\"uint8\")\n",
        "test['meter']= le.fit_transform(test['meter']).astype(\"uint8\")\n",
        "train['primary_use']= le.fit_transform(train['primary_use']).astype(\"uint8\")\n",
        "test['primary_use']= le.fit_transform(test['primary_use']).astype(\"uint8\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YHRjqmM4sqIe"
      },
      "source": [
        "### Check the correlated values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7gj8Q6wJBEUV",
        "outputId": "3cc1071f-1980-4ce4-fc30-b4e8a9036299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "source": [
        "# Let's check the correlation between the variables and eliminate the one's that have high correlation\n",
        "# Threshold for removing correlated variables\n",
        "threshold = 0.9\n",
        "\n",
        "# Absolute value correlation matrix\n",
        "corr_matrix = train.corr().abs()\n",
        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
        "upper"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building_id</th>\n",
              "      <th>meter</th>\n",
              "      <th>meter_reading</th>\n",
              "      <th>site_id</th>\n",
              "      <th>primary_use</th>\n",
              "      <th>square_feet</th>\n",
              "      <th>year_built</th>\n",
              "      <th>floor_count</th>\n",
              "      <th>air_temperature</th>\n",
              "      <th>cloud_coverage</th>\n",
              "      <th>dew_temperature</th>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <th>wind_direction</th>\n",
              "      <th>wind_speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>building_id</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.209164</td>\n",
              "      <td>0.076379</td>\n",
              "      <td>0.980292</td>\n",
              "      <td>0.068045</td>\n",
              "      <td>0.084392</td>\n",
              "      <td>0.206786</td>\n",
              "      <td>0.348353</td>\n",
              "      <td>0.313025</td>\n",
              "      <td>0.143564</td>\n",
              "      <td>0.096365</td>\n",
              "      <td>0.088082</td>\n",
              "      <td>0.089607</td>\n",
              "      <td>0.046686</td>\n",
              "      <td>0.008138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meter</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.041417</td>\n",
              "      <td>0.242600</td>\n",
              "      <td>0.079594</td>\n",
              "      <td>0.132254</td>\n",
              "      <td>0.017342</td>\n",
              "      <td>0.176926</td>\n",
              "      <td>0.007746</td>\n",
              "      <td>0.134420</td>\n",
              "      <td>0.075971</td>\n",
              "      <td>0.018892</td>\n",
              "      <td>0.039250</td>\n",
              "      <td>0.025739</td>\n",
              "      <td>0.080740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meter_reading</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.055871</td>\n",
              "      <td>0.376335</td>\n",
              "      <td>0.197650</td>\n",
              "      <td>0.342052</td>\n",
              "      <td>0.004755</td>\n",
              "      <td>0.014194</td>\n",
              "      <td>0.023413</td>\n",
              "      <td>0.013098</td>\n",
              "      <td>0.011381</td>\n",
              "      <td>0.016991</td>\n",
              "      <td>0.029325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>site_id</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.086547</td>\n",
              "      <td>0.088052</td>\n",
              "      <td>0.175358</td>\n",
              "      <td>0.336626</td>\n",
              "      <td>0.305147</td>\n",
              "      <td>0.109223</td>\n",
              "      <td>0.123464</td>\n",
              "      <td>0.086733</td>\n",
              "      <td>0.070980</td>\n",
              "      <td>0.049433</td>\n",
              "      <td>0.015841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary_use</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.090409</td>\n",
              "      <td>0.052822</td>\n",
              "      <td>0.213936</td>\n",
              "      <td>0.045337</td>\n",
              "      <td>0.131355</td>\n",
              "      <td>0.015790</td>\n",
              "      <td>0.019882</td>\n",
              "      <td>0.037005</td>\n",
              "      <td>0.025193</td>\n",
              "      <td>0.052997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>square_feet</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.147862</td>\n",
              "      <td>0.584495</td>\n",
              "      <td>0.014549</td>\n",
              "      <td>0.038775</td>\n",
              "      <td>0.021906</td>\n",
              "      <td>0.036118</td>\n",
              "      <td>0.012909</td>\n",
              "      <td>0.017812</td>\n",
              "      <td>0.035241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year_built</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.147381</td>\n",
              "      <td>0.167201</td>\n",
              "      <td>0.077240</td>\n",
              "      <td>0.095199</td>\n",
              "      <td>0.021107</td>\n",
              "      <td>0.028277</td>\n",
              "      <td>0.063277</td>\n",
              "      <td>0.049114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>floor_count</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.184021</td>\n",
              "      <td>0.214326</td>\n",
              "      <td>0.174789</td>\n",
              "      <td>0.140502</td>\n",
              "      <td>0.003962</td>\n",
              "      <td>0.074262</td>\n",
              "      <td>0.020472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>air_temperature</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001965</td>\n",
              "      <td>0.750891</td>\n",
              "      <td>0.004255</td>\n",
              "      <td>0.304421</td>\n",
              "      <td>0.106697</td>\n",
              "      <td>0.086288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cloud_coverage</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.072797</td>\n",
              "      <td>0.045830</td>\n",
              "      <td>0.110430</td>\n",
              "      <td>0.013562</td>\n",
              "      <td>0.176253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_temperature</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.075263</td>\n",
              "      <td>0.220381</td>\n",
              "      <td>0.179202</td>\n",
              "      <td>0.144413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>precip_depth_1_hr</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.038579</td>\n",
              "      <td>0.005985</td>\n",
              "      <td>0.026457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sea_level_pressure</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.100383</td>\n",
              "      <td>0.208343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wind_direction</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.431636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wind_speed</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    building_id     meter  ...  wind_direction  wind_speed\n",
              "building_id                 NaN  0.209164  ...        0.046686    0.008138\n",
              "meter                       NaN       NaN  ...        0.025739    0.080740\n",
              "meter_reading               NaN       NaN  ...        0.016991    0.029325\n",
              "site_id                     NaN       NaN  ...        0.049433    0.015841\n",
              "primary_use                 NaN       NaN  ...        0.025193    0.052997\n",
              "square_feet                 NaN       NaN  ...        0.017812    0.035241\n",
              "year_built                  NaN       NaN  ...        0.063277    0.049114\n",
              "floor_count                 NaN       NaN  ...        0.074262    0.020472\n",
              "air_temperature             NaN       NaN  ...        0.106697    0.086288\n",
              "cloud_coverage              NaN       NaN  ...        0.013562    0.176253\n",
              "dew_temperature             NaN       NaN  ...        0.179202    0.144413\n",
              "precip_depth_1_hr           NaN       NaN  ...        0.005985    0.026457\n",
              "sea_level_pressure          NaN       NaN  ...        0.100383    0.208343\n",
              "wind_direction              NaN       NaN  ...             NaN    0.431636\n",
              "wind_speed                  NaN       NaN  ...             NaN         NaN\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QEwpXiWMBEPc",
        "outputId": "482ec77b-1acd-441f-a72f-d5b748281ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Select columns with correlations above threshold\n",
        "to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
        "\n",
        "print('There are %d columns to remove.' % (len(to_drop)))\n",
        "print (\"Following columns can be dropped {}\".format(to_drop))\n",
        "\n",
        "train.drop(to_drop,axis=1,inplace=True)\n",
        "test.drop(to_drop,axis=1,inplace=True)\n",
        "#test_row_id= test[\"row_id\"]\n",
        "#test.drop(\"row_id\",axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 1 columns to remove.\n",
            "Following columns can be dropped ['site_id']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ru4kjJueJUUi",
        "outputId": "276717fd-345e-4119-f97a-1b3a6c31b594",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "print (train.isnull().sum())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "building_id                  0\n",
            "meter                        0\n",
            "timestamp                    0\n",
            "meter_reading                0\n",
            "primary_use                  0\n",
            "square_feet                  0\n",
            "year_built            12127645\n",
            "floor_count           16316310\n",
            "air_temperature          90495\n",
            "cloud_coverage           90495\n",
            "dew_temperature          90495\n",
            "precip_depth_1_hr        90495\n",
            "sea_level_pressure       90495\n",
            "wind_direction           90495\n",
            "wind_speed               90495\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "IQgMAQ2ytx3k"
      },
      "source": [
        "###  Fill numeric values with the mean value "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wsB2tMp7DK-S",
        "outputId": "67ad8224-5644-4a1b-f3bc-d9a3e64d1f4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        }
      },
      "source": [
        "train['air_temperature'].fillna(train['air_temperature'].mean(), inplace=True)\n",
        "train['dew_temperature'].fillna(train['dew_temperature'].mean(), inplace=True)\n",
        "train['wind_speed'].fillna(train['wind_speed'].mean(), inplace=True)\n",
        "train['precip_depth_1_hr'].fillna(method='bfill', inplace=True)\n",
        "train['wind_direction'].fillna(method='ffill', inplace=True)\n",
        "train['sea_level_pressure'].fillna(method='ffill', inplace=True)\n",
        "train['month'] = train['timestamp'].dt.month\n",
        "train['day'] = train['timestamp'].dt.weekday\n",
        "train['hour'] = train['timestamp'].dt.hour\n",
        "train['weekend'] = np.where((train['day'] == 5) | (train['day'] == 6), 1, 0)\n",
        "\n",
        "gc.collect()\n",
        "print (train.isnull().sum())\n",
        "\n",
        "train.dtypes\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "building_id                  0\n",
            "meter                        0\n",
            "timestamp                    0\n",
            "meter_reading                0\n",
            "primary_use                  0\n",
            "square_feet                  0\n",
            "year_built            12127645\n",
            "floor_count           16316310\n",
            "air_temperature              0\n",
            "cloud_coverage           90495\n",
            "dew_temperature              0\n",
            "precip_depth_1_hr            0\n",
            "sea_level_pressure           0\n",
            "wind_direction               0\n",
            "wind_speed                   0\n",
            "month                        0\n",
            "day                          0\n",
            "hour                         0\n",
            "weekend                      0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "building_id                    int16\n",
              "meter                          uint8\n",
              "timestamp             datetime64[ns]\n",
              "meter_reading                float64\n",
              "primary_use                    uint8\n",
              "square_feet                    int32\n",
              "year_built                   float16\n",
              "floor_count                  float16\n",
              "air_temperature              float32\n",
              "cloud_coverage               float16\n",
              "dew_temperature              float32\n",
              "precip_depth_1_hr            float32\n",
              "sea_level_pressure           float32\n",
              "wind_direction               float32\n",
              "wind_speed                   float32\n",
              "month                          int64\n",
              "day                            int64\n",
              "hour                           int64\n",
              "weekend                        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ISr1-lN9BfrT",
        "colab": {}
      },
      "source": [
        "y = train['meter_reading']\n",
        "train.drop(['meter_reading','floor_count'],axis=1,inplace=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QE5ddH93DCry",
        "outputId": "a7e66acc-c329-43a6-8ff1-de489ac9f3aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "source": [
        "train.columns\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['building_id', 'meter', 'timestamp', 'primary_use', 'square_feet',\n",
              "       'year_built', 'air_temperature', 'cloud_coverage', 'dew_temperature',\n",
              "       'precip_depth_1_hr', 'sea_level_pressure', 'wind_direction',\n",
              "       'wind_speed', 'month', 'day', 'hour', 'weekend'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L8eHc1OYCvSF",
        "outputId": "72f7e073-70b9-4bec-ab5d-ba63de2d41cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "print(train.shape, df_train.shape)\n",
        "print(test.shape, df_test.shape)\n",
        "print( y.shape)\n",
        "submission=pd.read_csv(\"data/sample_submission.csv\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(19823243, 17) (20216100, 4)\n",
            "(41697600, 15) (41697600, 4)\n",
            "(19823243,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XS5c0BsFnd6a",
        "outputId": "a3a9a574-4af5-4acd-ba25-8b75dc31145d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(submission.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(41697600, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PGRxQJEJnzTu",
        "outputId": "ef5354f9-7528-4bd7-8ae3-e5607307da72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "print(test.columns)\n",
        "\n",
        "print(train.columns)\n",
        "\n",
        "\n",
        "categoricals = [\"building_id\",\"primary_use\", \"meter\", \"month\", \"day\", \"hour\", \"weekend\"]\n",
        "\n",
        "\n",
        "\n",
        "numericals = [\"square_feet\", \"air_temperature\", \"dew_temperature\",\n",
        "              \"precip_depth_1_hr\", \"sea_level_pressure\", \"wind_direction\", \"wind_speed\"]\n",
        "\n",
        "features = categoricals + numericals\n",
        "\n",
        "for f in features:\n",
        "  print (f, train[f].isna().sum())\n",
        "X = train[features].values\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['row_id', 'building_id', 'meter', 'timestamp', 'primary_use',\n",
            "       'square_feet', 'year_built', 'floor_count', 'air_temperature',\n",
            "       'cloud_coverage', 'dew_temperature', 'precip_depth_1_hr',\n",
            "       'sea_level_pressure', 'wind_direction', 'wind_speed'],\n",
            "      dtype='object')\n",
            "Index(['building_id', 'meter', 'timestamp', 'primary_use', 'square_feet',\n",
            "       'year_built', 'air_temperature', 'cloud_coverage', 'dew_temperature',\n",
            "       'precip_depth_1_hr', 'sea_level_pressure', 'wind_direction',\n",
            "       'wind_speed', 'month', 'day', 'hour', 'weekend'],\n",
            "      dtype='object')\n",
            "building_id 0\n",
            "primary_use 0\n",
            "meter 0\n",
            "month 0\n",
            "day 0\n",
            "hour 0\n",
            "weekend 0\n",
            "square_feet 0\n",
            "air_temperature 0\n",
            "dew_temperature 0\n",
            "precip_depth_1_hr 0\n",
            "sea_level_pressure 0\n",
            "wind_direction 0\n",
            "wind_speed 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8vYPWHA4wZ8r",
        "outputId": "58e9d25d-944c-40e0-fac6-06be743638e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(y.shape)\n",
        "print(submission.shape, submission.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(19823243,)\n",
            "(41697600, 2) Index(['row_id', 'meter_reading'], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5Q9Tcxly35Tu",
        "outputId": "9da040f7-ab38-47f5-d976-885004a15aa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "train =reduce_mem_usage(train)\n",
        "test =reduce_mem_usage(test)\n",
        "df_test= reduce_mem_usage( df_test)\n",
        "\n",
        "submission= reduce_mem_usage(submission)\n",
        "df_train= reduce_mem_usage( df_train)\n",
        "\n",
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mem. usage decreased to 831.82 Mb (47.6% reduction)\n",
            "Mem. usage decreased to 1829.23 Mb (25.8% reduction)\n",
            "Mem. usage decreased to 596.49 Mb (53.1% reduction)\n",
            "Mem. usage decreased to 198.83 Mb (68.7% reduction)\n",
            "Mem. usage decreased to 289.19 Mb (53.1% reduction)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "fAmc92J2967x",
        "outputId": "a78b0a0f-e824-4b4f-dd7e-759c657a6822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 61
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
        "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras import backend as K"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ZTCzJOm2u8_F"
      },
      "source": [
        "# Model design \n",
        "\n",
        "* Split data test and train data \n",
        "* Create lmodel \n",
        "* Run model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gRLAHhZf9_3m",
        "outputId": "b370ef0d-282c-45fa-eaa6-11a300565c36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "print(X_test.shape, X_train.shape, y_test.shape, y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4955811, 14) (14867432, 14) (4955811,) (14867432,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NgBRlha4-LAy",
        "colab": {}
      },
      "source": [
        "def root_mean_squared_error(y_true, y_pred):\n",
        "    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=0))\n",
        "\n",
        "def create_model(dense_layer_sizes, optimizer=\"adam\", dropout=0.1, init='uniform', nbr_features=14, dense_nparams=256, lr=0.001):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(dense_nparams, activation='relu', input_shape=(nbr_features,), kernel_initializer=init,)) \n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Dropout(dropout), )\n",
        "    for layer_size in dense_layer_sizes:\n",
        "        model.add(Dense(layer_size, activation='relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(dropout), )\n",
        "    model.add(Dense(1, activation='relu'))\n",
        "    opt = Adam(lr)\n",
        "    model.compile(loss='mse', optimizer=opt, metrics=[root_mean_squared_error])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JWjtPQHI-Qvp",
        "outputId": "775a9996-7c2f-4cb3-b924-5ed8ee6338d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "model = create_model(dense_layer_sizes=[4,8], dropout=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_nj0GNXx-aWV",
        "outputId": "c6c81e87-3f7b-476f-856c-997902e9d6ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "hist = model.fit(X_train, y_train, epochs=8, batch_size=8192)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 14867432 samples\n",
            "Epoch 1/8\n",
            "14867432/14867432 [==============================] - 362s 24us/sample - loss: 2.2324 - root_mean_squared_error: 1.4939\n",
            "Epoch 2/8\n",
            "14867432/14867432 [==============================] - 356s 24us/sample - loss: 2.2154 - root_mean_squared_error: 1.4882\n",
            "Epoch 3/8\n",
            " 2359296/14867432 [===>..........................] - ETA: 5:04 - loss: 2.2072 - root_mean_squared_error: 1.4854"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "08Wt7hm7XM2q",
        "outputId": "a05f204e-8389-43f4-b575-f209e23d253f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "gc.collect()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "336"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-KN5mJXgXMHP",
        "outputId": "db8f6a90-4fc6-46d2-d61a-90add85e4802",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "from keras.models import load_model\n",
        "model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'\n",
        "model.load_weights(\"my_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "omfWRbSEjX-6",
        "colab": {}
      },
      "source": [
        "print (submission)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ubLRaF-ciyOF",
        "colab": {}
      },
      "source": [
        "pred_train= model.predict(x_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "tMUQDvpGwZux"
      },
      "source": [
        ""
      ]
    }
  ]
}