Electricity_consumption.ipynb

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    "colab": {
      "name": "Electricity_consumption.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sL0q10XV83Ql",
        "colab_type": "text"
      },
      "source": [
        "# Data Set Information:\n",
        "\n",
        "This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). \n",
        "Notes: \n",
        "1. (global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. \n",
        "2. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.\n",
        "\n",
        "\n",
        "# Attribute Information:\n",
        "\n",
        "1. date: Date in format dd/mm/yyyy \n",
        "2. time: time in format hh:mm:ss \n",
        "3. global_active_power: household global minute-averaged active power (in kilowatt) \n",
        "4. global_reactive_power: household global minute-averaged reactive power (in kilowatt) \n",
        "5. voltage: minute-averaged voltage (in volt) \n",
        "6. global_intensity: household global minute-averaged current intensity (in ampere) \n",
        "7. sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). \n",
        "8. sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. \n",
        "9. sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NlVL9sm83Qz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C79sq16GCDpc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "aecc731f-8a8f-45d8-a81d-f56bf6c5f2a1"
      },
      "source": [
        "! git clone https://github.com/prince-tyagi1997/ML_datasets"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'ML_datasets'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects:  33% (1/3)   \u001b[K\rremote: Counting objects:  66% (2/3)   \u001b[K\rremote: Counting objects: 100% (3/3)   \u001b[K\rremote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (3/3), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CieeF1jEFwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "f2510c0f-d894-4462-f091-a620dbb98144"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6kt2DHK83RL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "07cee8d1-4687-4ad0-968e-56e5fa549a62"
      },
      "source": [
        "data=pd.read_csv('gdrive/My Drive/household_power_consumption.txt',sep=';',header=None)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (2,3,4,5,6,7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXt1ZgTX83R1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cea4ec3b-967e-4242-d475-e894b8be2364"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2075260, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtdidIAn83ST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ab632ed0-6f37-4aed-ea8a-f7122c7f8dee"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Date</td>\n",
              "      <td>Time</td>\n",
              "      <td>Global_active_power</td>\n",
              "      <td>Global_reactive_power</td>\n",
              "      <td>Voltage</td>\n",
              "      <td>Global_intensity</td>\n",
              "      <td>Sub_metering_1</td>\n",
              "      <td>Sub_metering_2</td>\n",
              "      <td>Sub_metering_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:24:00</td>\n",
              "      <td>4.216</td>\n",
              "      <td>0.418</td>\n",
              "      <td>234.840</td>\n",
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              "      <td>1.000</td>\n",
              "      <td>17.000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:25:00</td>\n",
              "      <td>5.360</td>\n",
              "      <td>0.436</td>\n",
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              "      <td>23.000</td>\n",
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              "      <td>1.000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:26:00</td>\n",
              "      <td>5.374</td>\n",
              "      <td>0.498</td>\n",
              "      <td>233.290</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>17.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:27:00</td>\n",
              "      <td>5.388</td>\n",
              "      <td>0.502</td>\n",
              "      <td>233.740</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.000</td>\n",
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            "text/plain": [
              "            0         1  ...               7               8\n",
              "0        Date      Time  ...  Sub_metering_2  Sub_metering_3\n",
              "1  16/12/2006  17:24:00  ...           1.000          17.000\n",
              "2  16/12/2006  17:25:00  ...           1.000          16.000\n",
              "3  16/12/2006  17:26:00  ...           2.000          17.000\n",
              "4  16/12/2006  17:27:00  ...           1.000          17.000\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT09CdcX83S2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "e832660e-b7bf-4923-f7d0-e6bde768b426"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2075260</td>\n",
              "      <td>2049281.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>1443</td>\n",
              "      <td>1441</td>\n",
              "      <td>6535</td>\n",
              "      <td>897</td>\n",
              "      <td>5169</td>\n",
              "      <td>378</td>\n",
              "      <td>154</td>\n",
              "      <td>146</td>\n",
              "      <td>54.0</td>\n",
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              "      <th>top</th>\n",
              "      <td>8/6/2009</td>\n",
              "      <td>18:49:00</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>0.000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1440</td>\n",
              "      <td>1442</td>\n",
              "      <td>25979</td>\n",
              "      <td>472785</td>\n",
              "      <td>25979</td>\n",
              "      <td>169406</td>\n",
              "      <td>1840610</td>\n",
              "      <td>1408273</td>\n",
              "      <td>813979.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "               0         1        2  ...        6        7          8\n",
              "count    2075260   2075260  2075260  ...  2075260  2075260  2049281.0\n",
              "unique      1443      1441     6535  ...      154      146       54.0\n",
              "top     8/6/2009  18:49:00        ?  ...    0.000    0.000        0.0\n",
              "freq        1440      1442    25979  ...  1840610  1408273   813979.0\n",
              "\n",
              "[4 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "Ik8jvjD_83TY",
        "colab_type": "code",
        "colab": {
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          "height": 204
        },
        "outputId": "ff016f3a-95d4-49b0-d3b8-42b82a4051d5"
      },
      "source": [
        "columns=data.iloc[0,:]\n",
        "data.columns=columns\n",
        "data.drop(data.index[0],inplace=True)\n",
        "data.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Global_active_power</th>\n",
              "      <th>Global_reactive_power</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>Sub_metering_1</th>\n",
              "      <th>Sub_metering_2</th>\n",
              "      <th>Sub_metering_3</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:24:00</td>\n",
              "      <td>4.216</td>\n",
              "      <td>0.418</td>\n",
              "      <td>234.840</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:25:00</td>\n",
              "      <td>5.360</td>\n",
              "      <td>0.436</td>\n",
              "      <td>233.630</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>16.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:26:00</td>\n",
              "      <td>5.374</td>\n",
              "      <td>0.498</td>\n",
              "      <td>233.290</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>17.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:27:00</td>\n",
              "      <td>5.388</td>\n",
              "      <td>0.502</td>\n",
              "      <td>233.740</td>\n",
              "      <td>23.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:28:00</td>\n",
              "      <td>3.666</td>\n",
              "      <td>0.528</td>\n",
              "      <td>235.680</td>\n",
              "      <td>15.800</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>17.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0        Date      Time  ... Sub_metering_2 Sub_metering_3\n",
              "1  16/12/2006  17:24:00  ...          1.000         17.000\n",
              "2  16/12/2006  17:25:00  ...          1.000         16.000\n",
              "3  16/12/2006  17:26:00  ...          2.000         17.000\n",
              "4  16/12/2006  17:27:00  ...          1.000         17.000\n",
              "5  16/12/2006  17:28:00  ...          1.000         17.000\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9fmLaVe83T2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.replace('?',0,inplace=True)\n",
        "data[['Global_active_power','Global_reactive_power','Voltage','Global_intensity','Sub_metering_1','Sub_metering_2','Sub_metering_3']]=data[['Global_active_power','Global_reactive_power','Voltage','Global_intensity','Sub_metering_1','Sub_metering_2','Sub_metering_3']].astype(float)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLm9mSeE83UN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "consumption=data['Global_active_power']*(1000/60) - data['Sub_metering_1'] - data['Sub_metering_2'] - data['Sub_metering_3']\n",
        "data['consumption']=consumption"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sQMtZPK83Us",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.drop(columns=['Global_active_power','Global_reactive_power','Sub_metering_1','Sub_metering_2','Sub_metering_3'],inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcyQgQVJ83VC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "14c04648-da9c-4123-d845-f0431b9b4911"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 22,
      "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>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Voltage</th>\n",
              "      <th>Global_intensity</th>\n",
              "      <th>consumption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:24:00</td>\n",
              "      <td>234.84</td>\n",
              "      <td>18.4</td>\n",
              "      <td>52.266667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:25:00</td>\n",
              "      <td>233.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>72.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:26:00</td>\n",
              "      <td>233.29</td>\n",
              "      <td>23.0</td>\n",
              "      <td>70.566667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:27:00</td>\n",
              "      <td>233.74</td>\n",
              "      <td>23.0</td>\n",
              "      <td>71.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>16/12/2006</td>\n",
              "      <td>17:28:00</td>\n",
              "      <td>235.68</td>\n",
              "      <td>15.8</td>\n",
              "      <td>43.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "0        Date      Time  Voltage  Global_intensity  consumption\n",
              "1  16/12/2006  17:24:00   234.84              18.4    52.266667\n",
              "2  16/12/2006  17:25:00   233.63              23.0    72.333333\n",
              "3  16/12/2006  17:26:00   233.29              23.0    70.566667\n",
              "4  16/12/2006  17:27:00   233.74              23.0    71.800000\n",
              "5  16/12/2006  17:28:00   235.68              15.8    43.100000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODvfWKGh83Vh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data['year'], data['month'] = data['Date'].dt.year, data['Date'].dt.month\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRLT9AZz83V6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "year=data[['year','consumption']]\n",
        "yr_sum=[]\n",
        "for yr in set(year['year']) :\n",
        "    y=[]\n",
        "    y=[yr,(year[year['year']==yr]['consumption'].sum())]\n",
        "    yr_sum.append(y)\n",
        "yr_sum=pd.DataFrame(yr_sum,columns=['year','consumption'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yV0u7SA683WF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEWgASD383WT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c85e090e-65fb-4ac0-a288-584c46e35863"
      },
      "source": [
        "sns.barplot(x='year',y='consumption',data=yr_sum)\n",
        "plt.title('Year-Wise Electricity COnsumption')\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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odkP08PfiIWC8pNdI2h14L6/8p/UtSp190Z56/nb6lINQYZJeC9wAnBkR66v7\nIl0/dzaHfgDwHuBzpMvvNwKf6P2WNl4v9EVrOUOBt5P+H2yL1NO+kLQ38BbSU0GGAUdIek+DmttQ\nPe2LiLgNuAX4DemDyT3Ai41pbWP11t/I5sRBqCBJ25B+oX4YET/NyU+0Di3lnytzenuPHFoKPJgv\nrzcCPwMOYAvTS33R6gTgxohoOxSxReilvvgwMCcPzz5Nulfyzr5of2/qrd+LiLgoIsZExFGke0b/\n1Rft701d7Iv2bHaPLnMQKiTPVLoCWBQRl1R2zQAm5/XJwE2V9El5BtA4YF2+DJ9LGvtufVrtEcDC\nhp9AL+rFvmh1ElvoUFwv9sWfgMMkDchvXoeR7iNsMXqrLyT1l7RbLnNfYF/gtj45iV7Sjb5oz+b3\n6LKSsyKaeQHeTbp0fhh4MC8TgN2A2cBi4JfArjm/SF/I9xjwCDC2UtZRuZxHgCuBbUufX8G+GEn6\nZNev9HmV7AvSLKh/IwWehcAlpc+tYF9sn/tgITAHGFP63PqgL15HGiVZDzyV13fO+yaQrgQfA/53\n6XPzY3vMzKwYD8eZmVkxDkJmZlaMg5CZmRXjIGRmZsU4CJmZWTEOQmZmVoyDkFkT2JKfJ2hbNwch\ns82MpPMlnVnZvkjSGZI+L2lu/q6cL1b2/0zS/Pw9M1Mq6U9LuljSQ2yBj+yx5uAgZLb5mQZMApDU\nj/RolRXAKNKj+McAB0o6NOf/ZEQcCIwF/mfrI2pI351zb0TsFxF39eUJmNVrQOkGmNkrRcQfJK2W\ntD/p0fwPkJ6QfnReh/TlhaOAO0mB58M5fUROX016UvQNfdl2s65yEDLbPF1O+kqO15GujI4EvhQR\n/1bNJOlw4H3AOyPiWUl3kJ6VBvBcRGyRX1lgzcPDcWabpxtJ3/75DtL3Is0EPpm/TwZJwyTtAewC\nrM0BaB/S11qbbTF8JWS2GYqIFyTdDjyVr2Zuk/QW4J70VH+eBj4O/AL4R0mLgEdJT4k222L4Kdpm\nm6E8IeF+4KMRsbh0e8waxcNxZpsZSaOBFmC2A5Bt7XwlZGZmxfhKyMzMinEQMjOzYhyEzMysGAch\nMzMrxkHIzMyK+W8jlNy7oRjfFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37xHi_Sn83Wz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "month=data[['month','consumption']]\n",
        "mon_sum=[]\n",
        "for mon in set(month['month']) :\n",
        "    y=[]\n",
        "    y=[mon,(month[month['month']==mon]['consumption'].sum())]\n",
        "    mon_sum.append(y)\n",
        "mon_sum=pd.DataFrame(mon_sum,columns=['month','consumption'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXSuhcw783W-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4e41dfb4-4260-4cb4-d23e-2c99fd5b0155"
      },
      "source": [
        "sns.barplot(x='month',y='consumption',data=mon_sum)\n",
        "plt.title('Month-Wise Electricity COnsumption')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y3aJ8rbrS8ufT+q3VtQ5J4yRNlzR96dKlnXuWZmbWru5OQuOBdwBDgYXA97s5fkMi4tKI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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrCW_vrQ83Xa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['Hour']=data['Time'].str.slice(0,2)\n",
        "data['hourminute']=data['Time'].str.slice(0,2)+data['Time'].str.slice(3,5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmU-Ai4583Xo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hour=data[['Hour','consumption']]\n",
        "hour_sum=[]\n",
        "for ho in set(hour['Hour']) :\n",
        "    y=[]\n",
        "    y=[ho,(hour[hour['Hour']==ho]['consumption'].sum())]\n",
        "    hour_sum.append(y)\n",
        "hour_sum=pd.DataFrame(hour_sum,columns=['Hour','consumption'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlIGcUX483X5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "13db2246-f5c7-4994-a900-601e395ad59b"
      },
      "source": [
        "sns.barplot(x='Hour',y='consumption',data=hour_sum)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Px610a29JVwK/7U2HkrakOIoZAbwE/BwY05tttUtETAGmAIwePXqVI0Bbtxx9XfXd69KD\nf9XGkZitm5b++KYetd/mxP0rt606MaHWSKC3J1H/Dng6IpZFxF8orj/3CWCApO6iOARYnJYXA0MB\n0votgBfK8ZqcRvEXmvRhZmYZVCpC3fcN6n5QTNU+tZd9/h7YU9Jm6b2dfYBHgNsobh0OMBG4IS3P\nSs9J62+NiEjxCWn23AiKwng3MB8YmWbCbUwxeWFWymnUh5mZZdDn9xOKiLskzQTuBVYA91Gc+vol\nMEPSd1NsakqZClyeJh4spygqRMTCNLPukbSdEyLiLQBJJ1Lc+XVDoCMiFqZtndqgDzMzy6Dq7LhP\nAPdHxH9L+jKwC3BBRDzbm04j4gzgjJrwU7w7u63c9nXgkAbbORs4u058NjC7TrxuH2ZmlkfV94Qu\nBl6T9BHgFOB3FBc1NTMz67WqRWhFek9lHPDjiLgI8C2/zcxstVT9nNCrkk4Hvgx8StIGwHvaNywz\nM+sPqhahwyiuNDApIpZIGgac175h2frs3y7fr3Wj5KtHzGnjSMwst6qz45YAPyw9/z1+T8jMzFZT\n1c8JfUHSk5JeTp8VejV9XsjMzKzXqp6O+x7w+Yh4tJ2DMTOz/qXq7LjnXYDMzGxNq3oktEDSVcD1\n+M6qZma2hlQtQpsDr1HcWbVbUFx81MzMrFf6/M6qZmZm3arOjhsi6TpJS9PjGklD2j04MzNbv1Wd\nmHApxa0T3p8eN6aYmZlZr1UtQoMi4tKIWJEelwGD2jguMzPrB6pOTHgh3cLhyvT8cIo7lZqZtdVJ\n1y2q3PbCg4e2bmRrlapHQscAhwJLgOco7k56VJvGZGZm/UTVI6EzgYkR8SKApK2A71MUpx6TNAC4\nBNiJYqr3McDjwFXAcOAZ4NCIeDHdAvwCYCzFNPGjIuLetJ2JwD+mzX43Iqal+K7AZcCmFDe3Ozki\nIo17lT568xrM1hYHzLyicttfjP9SG0di1nNVj4R2Lv+xjojlwMdWo98LgF9FxIeAjwCPAqcBt0TE\nSOCW9Bxgf2BkekymuMFedyE8A9iD4m6pZ0jaMuVcDBxbyhuT4o36MDOzDKoWoQ1Kf+C7C0DVo6iV\nSNoC+BQwFSAi3oyIlyhumDctNZsGHJSWxwHTozAPGCBpe2A/YG5ELE8Fci4wJq3bPCLmpRvxTa/Z\nVr0+zMwsg6qF5AfAnZJ+np4fApzdyz5HAMuAS9Ptwu8BTga2jYjnUpslwLZpeTBQfmeyK8Waxbvq\nxGnSR0vLLv73qk0ZdNyXK7e1tdfY60+p3Hb2QT9o40jM1l9Vr5gwXdICYO8U+kJEPLIafe4CfC0i\n7pJ0ATWnxdL7N9HL7VfSrA9JkylO/TFs2LB2DsPsHZ+7Zkrltr/84uTV7u/zM6+v3PbG8T5pYO1R\n+ZRaKjq9LTxlXUBXRNyVns+kKELPS9o+Ip5Lp9SWpvWLgfK8yyEpthjYqyZ+e4oPqdOeJn2sJCKm\nAFMARo8e3dZiaOufz11X/abDvzz4H9o4ErO1X6/e11kd6fbgiyT9dUQ8DuxDUdweASYC56SvN6SU\nWcCJkmZQTEJ4ORWROcD/Lb1XtS9wekQsTzfe2xO4CzgS+FFpW/X6MLM2G3/NvZXbzvziLm0cia1N\n+rwIJV8DrpC0MfAUcDTFJImrJU0CnqX4XBIUU6zHAp0UU7SPhmKGnqSzgPmp3Zlp1h7A8bw7Rfum\n9ICi+NTrw8wqOmjmLZXbXj9+nzaOxNYHWYpQRNwPjK6zapU9Ns1wO6HBdjqAjjrxBRSfQaqNv1Cv\nDzMzy6PqFG0zM7M1zkXIzMyycREyM7NsXITMzCwbFyEzM8vGRcjMzLLJ9TmhfuP5i6t/en7b4/zp\neTPrX3wkZGZm2bgImZlZNi5CZmaWjYuQmZll44kJa6HfXzi+R+2HnTSzTSMxM2svHwmZmVk2PhJa\nj8z/t8/3qP1uX72xTSMxM6vGR0JmZpaNj4SMOVPH9qj9fpNmt2kkZtbfZCtCkjYEFgCLI+IASSOA\nGcDWwD3AERHxpqRNgOnArsALwGER8UzaxunAJOAt4KSImJPiY4ALgA2BSyLinBSv20cfveT1zlWX\njulR+8OO/lWbRmJm66qcp+NOBh4tPT8XOD8idgBepCgupK8vpvj5qR2SRgETgA8DY4CfSNowFbeL\ngP2BUcDhqW2zPszMLIMsRUjSEOBzwCXpuYC9ge65xtOAg9LyuPSctH6f1H4cMCMi3oiIp4FOYPf0\n6IyIp9JRzgxgXIs+zMwsg1xHQv8KfAt4Oz3fGngpIlak513A4LQ8GFgEkNa/nNq/E6/JaRRv1oeZ\nmWXQ50VI0gHA0oi4p6/7rkrSZEkLJC1YtmxZ7uGYma23chwJfQI4UNIzFKfK9qaYRDBAUvdEiSHA\n4rS8GBgKkNZvQTFB4Z14TU6j+AtN+lhJREyJiNERMXrQoEG9f6VmZtZUnxehiDg9IoZExHCKiQW3\nRsSXgNuA7uvVTARuSMuz0nPS+lsjIlJ8gqRN0qy3kcDdwHxgpKQRkjZOfcxKOY36MDOzDNamD6ue\nCnxTUifF+zdTU3wqsHWKfxM4DSAiFgJXA48AvwJOiIi30ns+JwJzKGbfXZ3aNuvDzMwyyPph1Yi4\nHbg9LT9FMbOtts3rwCEN8s8Gzq4Tnw2s8onKRn2YmVkea9ORkJmZ9TMuQmZmlo2LkJmZZeMiZGZm\n2bgImZlZNi5CZmaWjYuQmZll4yJkZmbZuAiZmVk2LkJmZpaNi5CZmWXjImRmZtm4CJmZWTYuQmZm\nlo2LkJmZZeMiZGZm2bgImZlZNn1ehCQNlXSbpEckLZR0copvJWmupCfT1y1TXJIulNQp6UFJu5S2\nNTG1f1LSxFJ8V0kPpZwLJalZH2ZmlkeOI6EVwCkRMQrYEzhB0ijgNOCWiBgJ3JKeA+wPjEyPycDF\nUBQU4AxgD4pbdp9RKioXA8eW8sakeKM+zMwsgz4vQhHxXETcm5ZfBR4FBgPjgGmp2TTgoLQ8Dpge\nhXnAAEnbA/sBcyNieUS8CMwFxqR1m0fEvIgIYHrNtur1YWZmGWR9T0jScOBjwF3AthHxXFq1BNg2\nLQ8GFpXSulKsWbyrTpwmfdSOa7KkBZIWLFu2rOcvzMzMKslWhCS9D7gG+HpEvFJel45gop39N+sj\nIqZExOiIGD1o0KB2DsPMrF/LUoQkvYeiAF0REdem8PPpVBrp69IUXwwMLaUPSbFm8SF14s36MDOz\nDHLMjhMwFXg0In5YWjUL6J7hNhG4oRQ/Ms2S2xN4OZ1SmwPsK2nLNCFhX2BOWveKpD1TX0fWbKte\nH2ZmlsFGGfr8BHAE8JCk+1Ps28A5wNWSJgHPAoemdbOBsUAn8BpwNEBELJd0FjA/tTszIpan5eOB\ny4BNgZvSgyZ9mJlZBn1ehCLit4AarN6nTvsATmiwrQ6go058AbBTnfgL9fowM7M8fMUEMzPLxkXI\nzMyycREyM7NsXITMzCwbFyEzM8vGRcjMzLJxETIzs2xchMzMLBsXITMzy8ZFyMzMsnERMjOzbFyE\nzMwsGxchMzPLxkXIzMyycREyM7NsXITMzCybflmEJI2R9LikTkmn5R6PmVl/1e+KkKQNgYuA/YFR\nwOGSRuUdlZlZ/9TvihCwO9AZEU9FxJvADGBc5jGZmfVL/bEIDQYWlZ53pZiZmfUxRUTuMfQpSeOB\nMRHxlfT8CGCPiDix1GYyMDk9/Wvg8QabGwj8sRfDcJ7z1re8dWGMzuvbvP8ZEYNabiEi+tUD+Dgw\np/T8dOD0Xm5rgfOc57x1Y4zOW3vyyo/+eDpuPjBS0ghJGwMTgFmZx2Rm1i9tlHsAfS0iVkg6EZgD\nbAh0RMTCzMMyM+uX+l0RAoiI2cDsNbCpKc5znvP6vC/nrft57+h3ExPMzGzt0R/fEzIzs7WEi1AP\n1LvcT5rgcFeKXZUmO7TKOTE9D0kDe9DXFSn2sKQOSe+pmDdV0gOSHpQ0U9L7quSV1l0o6U89GOdl\nkp6WdH96fLRiniSdLekJSY9KOqli3m9Kff1B0vUV8/aRdG/K+62kHSrm7Z3yHpY0TdJGNTkdkpZK\nergU20rSXElPpq9b1umrXt4hkhZKelvS6AY/g3p550l6LP3Mr5M0oGLeWSnnfkk3S3p/lbzSulMa\n7dcN+vtnSYtLP7+xVfuT9LX0GhdK+l7F/q4q9fWMpPsr5n1U0ryUt0DS7hXzPiLpTkkPSbpR0uZ1\n8oZKuk3SI+m1nJziDfeZJjlN95cmeU33lyZ5LfeXllZ3el1/eVBMYvgd8AFgY+ABisv+XA1MSG1+\nChxXIedjwHDgGWBgD/oaCyg9riz31SJv81KbHwKnVclL60YDlwN/6sE4LwPG9+J7eTQwHdggtdum\n6jhLba4BjqzY3xPA36Q2xwOXVcxbBOyY2pwJTKrJ+xSwC/BwKfa97u87cBpwbp3vS728v6H4rNrt\nwOgG3896efsCG6Xlc3vQX3lfOQn4aZW8FB9KMeHnWerv1/X6+2fg/7T43auX9xng18Am9faVZuMs\nrf8B8E8V+7sZ2D8tjwVur5g3H/h0Wj4GOKtO3vbALmn5r9J+OarZPtMkp+n+0iSv6f7SJK/l/tLq\n4SOh6hpd7mdvYGZqMw04qFVORNwXEc/0tK+ImB0JcDcwpGLeK1AcaQCbArVvBNbNU3GdvfOAb/Xw\ne9JKo7zjgDMj4m2AiFjak/7Sf5l7A7VHQo3yAuj+z3QL4A8V8r4IvBkRT6Q2c1PsHRFxB7C8Zlvj\nKPYPWHU/aZgXEY9GRKMPSzfLuzkiVqSn81h1X2mU90rp6f9g1X2l0esDOJ9iX6n7RnOTvKYa5B0H\nnBMRb6Q2tftK0/7S78KhFP/MVclrta80ytsRuCMtr7KvpLznIuLetPwq8CjFVVwa7jONclrtL03y\nmu4vTfJa7i+tuAhV1+hyPy+Vfni1lwDq7SWCmuapOA13BPCrqnmSLgWWAB8CflQx70RgVkQ814tx\nnp0O08+XtEnFvA8Ch6XTHTdJGtmD/qD4Jb2l5hejWd5XgNmSuii+n+dUyNsO2Kh0qmM8xRFAK9uW\nvo9LgG0r5KwpxwA3VW2s4pToIuBLwD9VzBkHLI6IB3oxvhPTvtKhOqcpG9gR+KSKU+H/KWm3Hvb5\nSeD5iHiyYvuvA+el78v3KT7kXsVC3v1H6RBa7CuShlOcKbmLivtMTU5lTfKa7i+1eb3ZX8pchNZN\nPwHuiIjfVE2IiKOB91P8B3NYhZTNKH5pagtWFadTFLvdgK2AUyvmbQK8HhGjgZ8BHT3s93Dq/Gfb\nxDeAsRExBLiU4lRlK0HxAefzJd0NvAq81ZNBpiPZPpmWKuk7wArgiqo5EfGdiBiack5s1V7SZsC3\n6cUfIOBiin8+Pgo8R3GKrIqNKPatPYF/AK5ORzdV9XRfOQ74Rvq+fAOYWjHvGOB4SfdQnMZ6s1FD\nFe/VXgN8vfYfqUb7TLOcZhrltdpf6uX1dH+p5SJU3WJW/i9mSIoN0LtvTHfHWuX0ti8knQEMAr7Z\nkzyAiHiLd08ptcr7HbAD0CnpGWAzSZ1V+kuH7pFOlVxKcVqryji7gGtT7Dpg56qvL70RvjvwS1ZV\nL+954CMR0f1f4FXA/6r4+u6MiE9GxO4Up1qeoLXnJW2fxro9sMrpozVN0lHAAcCX0h+xnrqCOqeP\n6vggMAJ4IO0rQ4B7JW3XKjEino+It9Ip2J+x6r7SSBdwbdrP7gbepriOWUvp9/ULFD/zqiby7r75\n86rjjIjHImLfiNiVouj9rsGY3kPxx/2KiOjup+k+0yCnpUZ5rfaXCv1V3V9W4iJUXaPL/dxGcUoG\nih31hgo5vepL0leA/YDDu983qZi3A7xzHvxA4LEKeddHxHYRMTwihgOvRUTt7LFG/XX/4ojiFFnt\nLKpG35frKd5wBvg0q/5xb/b9HA/8IiJer/p9AbaQtGNq81mKo8Qqr2+b9Po2oTjK+2mdPmvNotg/\nYNX9ZI2TNIbi/ZkDI+K1HuSVT4GOY9V9ZRUR8VBEbFPaV7oo3sReUqG/7UtPD2bVfaWRd/aV9DPc\nmOoX4Pw74LGI6KrYHor3gD6dlvcGKp3GK+0rGwD/SJ19Jf2eTAUejYjy0XjDfaZJTqvx1M1rtb80\nyevx/rKK6OFMhv78oJgV8wTFfzPfSbEPUEwS6KT4D2mTCjknUfyirqDYuS+p2NeK9Pz+9Kg3s2el\nPIp/NP4f8BDFL/gVlGa0NOvgxiDGAAACFklEQVSvZv0qs+OajPPWUn//DryvYt4AiiOZh4A7KY5U\nKo2TYjbQmB7+7A5OfT2Q8j9QMe88ioL1OMVpidqcKylOLf0l/ZwnAVsDt1D88fo1sFXFvIPT8hsU\nR29zKuZ1Uryf1b2v1JvlVi/vmvRzexC4keLN55Z5Neufof7suHr9XZ5+Bg9S/NHdvmLexmnfehi4\nF9i76jgpZm/+fZN9pV5/fwvck/aVu4BdK+adnPafJyjec1SdvL+lONX2YOnnNbbZPtMkp+n+0iSv\n6f7SJK/l/tLq4SsmmJlZNj4dZ2Zm2bgImZlZNi5CZmaWjYuQmZll4yJkZmbZuAiZrWVUc8VySUdJ\n+nGu8Zi1k4uQWT+hmltOmK0NXITM1iGShku6NV3w8xZJw1L8MknjS+3+lL7upeJ+S7OARzIN26wh\n/2dktvbZVCvfbG0r3r080Y+AaRExTdIxwIXUuS1EjV2AnSLi6TU/VLPV4yJktvb5c0S8czfadGHJ\n7ltHfJzi4ptQXPJmlTuK1nG3C5CtrXw6zmz9sIL0+5wullm+zfx/ZxmRWQUuQmbrlv+iuJo3FDcR\n676n1DPArmn5QOA9fTsss95xETJbt3wNOFrSgxR3gz05xX8GfFrSAxSn7Hz0Y+sEX0XbzMyy8ZGQ\nmZll4yJkZmbZuAiZmVk2LkJmZpaNi5CZmWXjImRmZtm4CJmZWTYuQmZmls3/B/amtO5bwzr/AAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwPjvBIp83Yd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "07fc9bde-d83d-4043-e9ca-d7465c7e25d9"
      },
      "source": [
        "''''morning=5-11(till 11:59:59)\n",
        "afternoon=12-16(till 16:59:59)\n",
        "evening=17-20( till 20:59:59)\n",
        "night=21-4(till 4:59:59 )\n",
        "'''\n",
        "timesplit={\n",
        "'morning':['05','06','07','08','9','10','11'],\n",
        "'afternoon':['12','13','14','15','16'],\n",
        "'evening':['17','18','19','20'],\n",
        "'night':['21','22','23','00','01','02','03','04']\n",
        "}\n",
        "timesplit.keys()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['morning', 'afternoon', 'evening', 'night'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sO5iKat83Y5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "timeofday=[]\n",
        "for key in timesplit.keys():\n",
        "    tm=[]\n",
        "    tm=[key,data[data['Hour'].isin(timesplit[key])]['consumption'].sum()]\n",
        "    timeofday.append(tm)\n",
        "timeofday=pd.DataFrame(timeofday,columns=['timeofday','consumption'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1m1rzqlo83ZI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a12d5480-9c2e-44da-acaa-1f3d8b1ef117"
      },
      "source": [
        "sns.barplot(x='timeofday',y='consumption',data=timeofday)\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UDJuEHk7yuiQ7ttfrmOCJpJIkTcWwSegNwCuBB4D76Z5KevKI+iRJmiWGHaJ9DrC0qh4B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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2nZDCUu83Zd",
        "colab_type": "text"
      },
      "source": [
        "# Forecasting Electrical Consumption in a Household\n",
        "I am going to train a Neural Network based on these two features i.e. time of day(hour) and month of the year.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_519NLlRQQT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "d77f7d5c-3c9e-4e70-d12d-05c0aaa275e9"
      },
      "source": [
        "def clean_dataset(df):\n",
        "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
        "    df.dropna(inplace=True)\n",
        "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
        "    return df[indices_to_keep].astype(np.float64)\n",
        "df=clean_dataset(data[['Hour','month','consumption']])"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eDbTVcr83Zg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df[['Hour','month']]\n",
        "Y=df['consumption']\n",
        "Y=np.array(Y,dtype=float)\n",
        "X=np.array(X,dtype=float)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OVEGwySK83Zr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4834307f-1e21-4c00-a9ad-dbf6713d8833"
      },
      "source": [
        "len(X),len(Y)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2049280, 2049280)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQWgylTy83aD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)\n",
        "from keras.models import Sequential\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Dropout\n",
        "from keras.layers.core import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Input\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWPVSkq183aY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import Sequential\n",
        "from keras.layers import Dense\n",
        "def build_regressor():\n",
        "    regressor = Sequential()\n",
        "    regressor.add(Dense(units=13, input_dim=2,activation=\"relu\"))\n",
        "    regressor.add(Dense(units=8,activation=\"relu\"))\n",
        "    regressor.add(Dense(units=4,activation=\"relu\"))\n",
        "    regressor.add(Dense(units=4,activation=\"relu\"))\n",
        "    regressor.add(Dense(units=1,activation=\"linear\"))\n",
        "    opt = Adam(lr=1e-3, decay=1e-3 / 200)\n",
        "    regressor.compile(loss=\"mean_absolute_percentage_error\", optimizer=opt)\n",
        "    regressor.compile(optimizer='adam', loss='mean_squared_error',  metrics=['mae','accuracy'])\n",
        "    return regressor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0YauOsm83bB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "regressor = KerasRegressor(build_fn=build_regressor, batch_size=32,epochs=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jxtn6M4b83bg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "88a3a228-03fa-44d2-e32f-9fedc9a276f7"
      },
      "source": [
        "results=regressor.fit(X_train,y_train)\n",
        "y_pred= regressor.predict(X_test)\n",
        "\n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 80.8225 - mean_absolute_error: 5.9277 - acc: 0.0019\n",
            "Epoch 2/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 77.1586 - mean_absolute_error: 5.7247 - acc: 0.0023\n",
            "Epoch 3/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 76.3168 - mean_absolute_error: 5.6806 - acc: 0.0025\n",
            "Epoch 4/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 76.2015 - mean_absolute_error: 5.6818 - acc: 0.0024\n",
            "Epoch 5/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 76.0324 - mean_absolute_error: 5.6801 - acc: 0.0024\n",
            "Epoch 6/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 75.1438 - mean_absolute_error: 5.6369 - acc: 0.0025\n",
            "Epoch 7/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 73.8198 - mean_absolute_error: 5.5901 - acc: 0.0025\n",
            "Epoch 8/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 72.6441 - mean_absolute_error: 5.5549 - acc: 0.0025\n",
            "Epoch 9/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 72.1361 - mean_absolute_error: 5.5239 - acc: 0.0025\n",
            "Epoch 10/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 71.5568 - mean_absolute_error: 5.4758 - acc: 0.0025\n",
            "Epoch 11/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 71.2373 - mean_absolute_error: 5.4493 - acc: 0.0026\n",
            "Epoch 12/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 71.0954 - mean_absolute_error: 5.4345 - acc: 0.0026\n",
            "Epoch 13/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.9828 - mean_absolute_error: 5.4224 - acc: 0.0026\n",
            "Epoch 14/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 70.9145 - mean_absolute_error: 5.4110 - acc: 0.0026\n",
            "Epoch 15/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 70.8349 - mean_absolute_error: 5.4016 - acc: 0.0026\n",
            "Epoch 16/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 70.7563 - mean_absolute_error: 5.3937 - acc: 0.0026\n",
            "Epoch 17/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 70.7111 - mean_absolute_error: 5.3865 - acc: 0.0026\n",
            "Epoch 18/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 70.6565 - mean_absolute_error: 5.3803 - acc: 0.0026\n",
            "Epoch 19/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.6056 - mean_absolute_error: 5.3740 - acc: 0.0026\n",
            "Epoch 20/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.5649 - mean_absolute_error: 5.3701 - acc: 0.0026\n",
            "Epoch 21/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.5424 - mean_absolute_error: 5.3676 - acc: 0.0026\n",
            "Epoch 22/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.5137 - mean_absolute_error: 5.3640 - acc: 0.0026\n",
            "Epoch 23/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.4877 - mean_absolute_error: 5.3603 - acc: 0.0026\n",
            "Epoch 24/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.4567 - mean_absolute_error: 5.3577 - acc: 0.0025\n",
            "Epoch 25/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.4284 - mean_absolute_error: 5.3522 - acc: 0.0026\n",
            "Epoch 26/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.3918 - mean_absolute_error: 5.3474 - acc: 0.0026\n",
            "Epoch 27/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.3447 - mean_absolute_error: 5.3422 - acc: 0.0026\n",
            "Epoch 28/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.3298 - mean_absolute_error: 5.3398 - acc: 0.0026\n",
            "Epoch 29/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.3166 - mean_absolute_error: 5.3384 - acc: 0.0026\n",
            "Epoch 30/100\n",
            "1434496/1434496 [==============================] - 48s 34us/step - loss: 70.3037 - mean_absolute_error: 5.3372 - acc: 0.0026\n",
            "Epoch 31/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.2794 - mean_absolute_error: 5.3352 - acc: 0.0026\n",
            "Epoch 32/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 70.2559 - mean_absolute_error: 5.3330 - acc: 0.0026\n",
            "Epoch 33/100\n",
            "1434496/1434496 [==============================] - 48s 34us/step - loss: 70.2446 - mean_absolute_error: 5.3318 - acc: 0.0026\n",
            "Epoch 34/100\n",
            "1434496/1434496 [==============================] - 48s 34us/step - loss: 70.2213 - mean_absolute_error: 5.3291 - acc: 0.0026\n",
            "Epoch 35/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 70.2031 - mean_absolute_error: 5.3272 - acc: 0.0026\n",
            "Epoch 36/100\n",
            "1434496/1434496 [==============================] - 48s 34us/step - loss: 70.2097 - mean_absolute_error: 5.3272 - acc: 0.0026\n",
            "Epoch 37/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.1881 - mean_absolute_error: 5.3267 - acc: 0.0026\n",
            "Epoch 38/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.1871 - mean_absolute_error: 5.3255 - acc: 0.0026\n",
            "Epoch 39/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.1692 - mean_absolute_error: 5.3244 - acc: 0.0026\n",
            "Epoch 40/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 70.1166 - mean_absolute_error: 5.3218 - acc: 0.0025\n",
            "Epoch 41/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 70.0489 - mean_absolute_error: 5.3146 - acc: 0.0026\n",
            "Epoch 42/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 70.0044 - mean_absolute_error: 5.3124 - acc: 0.0025\n",
            "Epoch 43/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.9818 - mean_absolute_error: 5.3110 - acc: 0.0025\n",
            "Epoch 44/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.9709 - mean_absolute_error: 5.3111 - acc: 0.0025\n",
            "Epoch 45/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.9456 - mean_absolute_error: 5.3103 - acc: 0.0025\n",
            "Epoch 46/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.9410 - mean_absolute_error: 5.3092 - acc: 0.0026\n",
            "Epoch 47/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.9333 - mean_absolute_error: 5.3088 - acc: 0.0026\n",
            "Epoch 48/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.9079 - mean_absolute_error: 5.3080 - acc: 0.0025\n",
            "Epoch 49/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.9048 - mean_absolute_error: 5.3072 - acc: 0.0026\n",
            "Epoch 50/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.8720 - mean_absolute_error: 5.3056 - acc: 0.0026\n",
            "Epoch 51/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.8715 - mean_absolute_error: 5.3052 - acc: 0.0026\n",
            "Epoch 52/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.8755 - mean_absolute_error: 5.3042 - acc: 0.0026\n",
            "Epoch 53/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.8445 - mean_absolute_error: 5.3020 - acc: 0.0026\n",
            "Epoch 54/100\n",
            "1434496/1434496 [==============================] - 44s 31us/step - loss: 69.8084 - mean_absolute_error: 5.2995 - acc: 0.0026\n",
            "Epoch 55/100\n",
            "1434496/1434496 [==============================] - 44s 30us/step - loss: 69.7966 - mean_absolute_error: 5.2987 - acc: 0.0026\n",
            "Epoch 56/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7449 - mean_absolute_error: 5.2960 - acc: 0.0026\n",
            "Epoch 57/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.7401 - mean_absolute_error: 5.2936 - acc: 0.0026\n",
            "Epoch 58/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7470 - mean_absolute_error: 5.2943 - acc: 0.0026\n",
            "Epoch 59/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7393 - mean_absolute_error: 5.2919 - acc: 0.0026\n",
            "Epoch 60/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.7358 - mean_absolute_error: 5.2932 - acc: 0.0026\n",
            "Epoch 61/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.7272 - mean_absolute_error: 5.2911 - acc: 0.0027\n",
            "Epoch 62/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.7225 - mean_absolute_error: 5.2911 - acc: 0.0026\n",
            "Epoch 63/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7265 - mean_absolute_error: 5.2920 - acc: 0.0026\n",
            "Epoch 64/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.7226 - mean_absolute_error: 5.2913 - acc: 0.0026\n",
            "Epoch 65/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.6903 - mean_absolute_error: 5.2900 - acc: 0.0026\n",
            "Epoch 66/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7098 - mean_absolute_error: 5.2900 - acc: 0.0026\n",
            "Epoch 67/100\n",
            "1434496/1434496 [==============================] - 45s 31us/step - loss: 69.7164 - mean_absolute_error: 5.2900 - acc: 0.0026\n",
            "Epoch 68/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.6948 - mean_absolute_error: 5.2884 - acc: 0.0026\n",
            "Epoch 69/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6957 - mean_absolute_error: 5.2894 - acc: 0.0026\n",
            "Epoch 70/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6947 - mean_absolute_error: 5.2884 - acc: 0.0026\n",
            "Epoch 71/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6898 - mean_absolute_error: 5.2867 - acc: 0.0026\n",
            "Epoch 72/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6813 - mean_absolute_error: 5.2882 - acc: 0.0026\n",
            "Epoch 73/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6854 - mean_absolute_error: 5.2883 - acc: 0.0027\n",
            "Epoch 74/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6876 - mean_absolute_error: 5.2876 - acc: 0.0026\n",
            "Epoch 75/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6670 - mean_absolute_error: 5.2878 - acc: 0.0026\n",
            "Epoch 76/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6736 - mean_absolute_error: 5.2869 - acc: 0.0026\n",
            "Epoch 77/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6748 - mean_absolute_error: 5.2877 - acc: 0.0027\n",
            "Epoch 78/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6776 - mean_absolute_error: 5.2877 - acc: 0.0026\n",
            "Epoch 79/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6619 - mean_absolute_error: 5.2864 - acc: 0.0026\n",
            "Epoch 80/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6549 - mean_absolute_error: 5.2868 - acc: 0.0026\n",
            "Epoch 81/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.6557 - mean_absolute_error: 5.2865 - acc: 0.0026\n",
            "Epoch 82/100\n",
            "1434496/1434496 [==============================] - 45s 32us/step - loss: 69.6598 - mean_absolute_error: 5.2868 - acc: 0.0026\n",
            "Epoch 83/100\n",
            "1434496/1434496 [==============================] - 47s 32us/step - loss: 69.6570 - mean_absolute_error: 5.2859 - acc: 0.0025\n",
            "Epoch 84/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 69.6555 - mean_absolute_error: 5.2856 - acc: 0.0026\n",
            "Epoch 85/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 69.6588 - mean_absolute_error: 5.2865 - acc: 0.0026\n",
            "Epoch 86/100\n",
            "1434496/1434496 [==============================] - 48s 33us/step - loss: 69.6404 - mean_absolute_error: 5.2851 - acc: 0.0026\n",
            "Epoch 87/100\n",
            "1434496/1434496 [==============================] - 48s 34us/step - loss: 69.6468 - mean_absolute_error: 5.2856 - acc: 0.0026\n",
            "Epoch 88/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6449 - mean_absolute_error: 5.2857 - acc: 0.0026\n",
            "Epoch 89/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6445 - mean_absolute_error: 5.2844 - acc: 0.0025\n",
            "Epoch 90/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6385 - mean_absolute_error: 5.2838 - acc: 0.0026\n",
            "Epoch 91/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6462 - mean_absolute_error: 5.2851 - acc: 0.0027\n",
            "Epoch 92/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6380 - mean_absolute_error: 5.2841 - acc: 0.0026\n",
            "Epoch 93/100\n",
            "1434496/1434496 [==============================] - 47s 33us/step - loss: 69.6388 - mean_absolute_error: 5.2847 - acc: 0.0026\n",
            "Epoch 94/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6230 - mean_absolute_error: 5.2836 - acc: 0.0026\n",
            "Epoch 95/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6359 - mean_absolute_error: 5.2844 - acc: 0.0025\n",
            "Epoch 96/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6368 - mean_absolute_error: 5.2855 - acc: 0.0026\n",
            "Epoch 97/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6266 - mean_absolute_error: 5.2832 - acc: 0.0026\n",
            "Epoch 98/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6289 - mean_absolute_error: 5.2853 - acc: 0.0026\n",
            "Epoch 99/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6242 - mean_absolute_error: 5.2841 - acc: 0.0025\n",
            "Epoch 100/100\n",
            "1434496/1434496 [==============================] - 46s 32us/step - loss: 69.6205 - mean_absolute_error: 5.2828 - acc: 0.0026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_RR3HnHIEfm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0d024cf6-7111-4b9f-d800-64ce1be3a4d6"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(y_test, y_pred)\n",
        "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)\n",
        "ax.set_xlabel('Measured')\n",
        "ax.set_ylabel('Predicted')\n",
        "plt.show()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vpKYdI1aELFWSEhNoe37Nk7HmHNu1pa0XXlZJJHdNTE5O1iVLlgTkuSw5nJJ9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\nMcaRJQhjjDGOLEEYY4xxZAnCGGOMo/8PRbSh0D46kyAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATMpc_hYKL6K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "77e9a9fd-045f-4fe8-9a94-2286c6692be7"
      },
      "source": [
        "y_test[:5],y_pred[:5]"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 6.16666667,  8.93333333,  3.53333333, 22.63333333,  1.4       ]),\n",
              " array([ 7.460497 , 13.983513 ,  4.482464 , 13.983513 ,  3.9012299],\n",
              "       dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2RwdGcrM2eq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}