Power Plant Energy Prediction AI Challenge.ipynb

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  "metadata": {
    "colab": {
      "name": "powerPlant.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyODVmMd4nFllewMAmkzg0O3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aman5898/LRC-Content-Hot-Dev/blob/main/Power%20Plant%20Energy%20Prediction%20AI%20Challenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N7dLKGLbZkk",
        "outputId": "2687b4ba-d632-4382-a039-6a29366a86d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-20 10:41:08--  https://dockship-job-models.s3.ap-south-1.amazonaws.com/4b4d5e9d7624d0bce302d236fb1dd255?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIDOPTEUZ2LEOQEGQ%2F20211220%2Fap-south-1%2Fs3%2Faws4_request&X-Amz-Date=20211220T103803Z&X-Amz-Expires=1800&X-Amz-Signature=9b5135dfa08c15ba3573f514e4d4603d88432f692d1d1dfa2dce9128e9b75f5e&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%3D%22power_plant_energy_prediction_ai_challenge-dataset.zip%22\n",
            "Resolving dockship-job-models.s3.ap-south-1.amazonaws.com (dockship-job-models.s3.ap-south-1.amazonaws.com)... 52.219.158.78\n",
            "Connecting to dockship-job-models.s3.ap-south-1.amazonaws.com (dockship-job-models.s3.ap-south-1.amazonaws.com)|52.219.158.78|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 111746 (109K) [binary/octet-stream]\n",
            "Saving to: ‘power_plant_energy_prediction_ai_challenge-dataset.zip’\n",
            "\n",
            "power_plant_energy_ 100%[===================>] 109.13K   172KB/s    in 0.6s    \n",
            "\n",
            "2021-12-20 10:41:09 (172 KB/s) - ‘power_plant_energy_prediction_ai_challenge-dataset.zip’ saved [111746/111746]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O \"power_plant_energy_prediction_ai_challenge-dataset.zip\" \"https://dockship-job-models.s3.ap-south-1.amazonaws.com/4b4d5e9d7624d0bce302d236fb1dd255?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIDOPTEUZ2LEOQEGQ%2F20211220%2Fap-south-1%2Fs3%2Faws4_request&X-Amz-Date=20211220T103803Z&X-Amz-Expires=1800&X-Amz-Signature=9b5135dfa08c15ba3573f514e4d4603d88432f692d1d1dfa2dce9128e9b75f5e&X-Amz-SignedHeaders=host&response-content-disposition=attachment%3B%20filename%3D%22power_plant_energy_prediction_ai_challenge-dataset.zip%22\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_6IIA1Rbl2N",
        "outputId": "f7179ebc-5f3d-400c-f92d-8268f9ee22bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "power_plant_energy_prediction_ai_challenge-dataset.zip\tsample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/power_plant_energy_prediction_ai_challenge-dataset.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiSpcoWkbl5A",
        "outputId": "a37e9c8f-ecc0-4347-c490-9c8bf18a7395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/power_plant_energy_prediction_ai_challenge-dataset.zip\n",
            "  inflating: SampleSubmission.csv    \n",
            "  inflating: TEST.csv                \n",
            "  inflating: TRAIN.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "sVDQ0iXfbl9O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "traindf = pd.read_csv('/content/TRAIN.csv')"
      ],
      "metadata": {
        "id": "stOrWKwGcImD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "testdf = pd.read_csv(\"/content/TEST.csv\")"
      ],
      "metadata": {
        "id": "y2R7fsPMcJH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "traindf\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "8jDSUP84cJR5",
        "outputId": "eb0c705d-7952-41f5-91fc-54a3f460f60d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "         AT      V       AP     RH      PE\n",
              "0     22.85  60.29  1017.29  61.92  448.58\n",
              "1     11.75  35.76  1018.99  57.92  473.01\n",
              "2     12.24  44.90  1020.31  82.22  464.25\n",
              "3     13.43  40.10  1015.82  87.90  471.77\n",
              "4      4.65  35.19  1018.23  94.78  489.36\n",
              "...     ...    ...      ...    ...     ...\n",
              "7995  18.74  45.09  1014.24  41.54  456.25\n",
              "7996  15.43  40.89  1010.63  73.03  468.35\n",
              "7997  12.36  52.75  1026.04  68.71  465.14\n",
              "7998  25.98  73.18  1012.28  85.78  439.73\n",
              "7999   9.08  42.49  1010.67  85.35  481.46\n",
              "\n",
              "[8000 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler"
      ],
      "metadata": {
        "id": "aJkk_jZNcJTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VPqKBi0DcJWg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "D1GO49v2cJZi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}