14: AutoML_auto_ts_electricity_consumption_data.ipynb

{
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  "metadata": {
    "colab": {
      "name": "AutoML_auto_ts_Timeseries_Python.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG9AwKBh1PLF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f133d93-f4aa-487a-cda5-94b75c975ef4"
      },
      "source": [
        "#install auto_ts(auto time series)\n",
        "!pip install auto_ts"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting auto_ts\n",
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            "Collecting fsspec>=0.6.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/d2/d05466997f7751a2c06a7a416b7d1f131d765f7916698d3fdcb3a4d037e5/fsspec-2021.6.0-py3-none-any.whl (114kB)\n",
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            "Collecting threadpoolctl>=2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
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            "Collecting locket\n",
            "  Downloading https://files.pythonhosted.org/packages/50/b8/e789e45b9b9c2db75e9d9e6ceb022c8d1d7e49b2c085ce8c05600f90a96b/locket-0.2.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->auto_ts) (3.7.4.3)\n",
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            "\u001b[31mERROR: pmdarima 1.8.2 has requirement statsmodels!=0.12.0,>=0.11, but you'll have statsmodels 0.10.2 which is incompatible.\u001b[0m\n",
            "Installing collected packages: threadpoolctl, scikit-learn, pmdarima, fsspec, locket, partd, dask, auto-ts\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: dask 2.12.0\n",
            "    Uninstalling dask-2.12.0:\n",
            "      Successfully uninstalled dask-2.12.0\n",
            "Successfully installed auto-ts-0.0.36 dask-2021.6.0 fsspec-2021.6.0 locket-0.2.1 partd-1.2.0 pmdarima-1.8.2 scikit-learn-0.24.2 threadpoolctl-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5G7SE9PLEB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26add759-6e7a-4918-afeb-61565a19740e"
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "import auto_ts as AT\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 6)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imported auto_timeseries version:0.0.36. Call by using:\n",
            "model = auto_timeseries(score_type='rmse',\n",
            "                time_interval='M',\n",
            "                non_seasonal_pdq=None, seasonality=False, seasonal_period=12,\n",
            "                model_type=['best'],\n",
            "                verbose=2)\n",
            "model.fit(traindata, ts_column,target)\n",
            "model.predict(testdata, model='best')\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pc9-9rleb9_Y"
      },
      "source": [
        "def parse(x):\n",
        "\treturn datetime.strptime(x, '%m/%d/%Y')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WSoh7Ze0P-jd"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/electricity_consumption.csv', parse_dates = ['Bill_Date'],date_parser=parse)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZIkHt4_sD4u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a506d816-7d9e-45da-f831-593b2bbcc325"
      },
      "source": [
        "df"
      ],
      "execution_count": 5,
      "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.00</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.20</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.00</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.20</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.60</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>63</td>\n",
              "      <td>245.7</td>\n",
              "      <td>37.80</td>\n",
              "      <td>77.81</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-07-01</td>\n",
              "      <td>110</td>\n",
              "      <td>429.0</td>\n",
              "      <td>66.00</td>\n",
              "      <td>86.67</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-08-01</td>\n",
              "      <td>176</td>\n",
              "      <td>686.4</td>\n",
              "      <td>105.60</td>\n",
              "      <td>122.40</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-09-01</td>\n",
              "      <td>357</td>\n",
              "      <td>1392.3</td>\n",
              "      <td>214.20</td>\n",
              "      <td>223.62</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>235</td>\n",
              "      <td>916.5</td>\n",
              "      <td>141.00</td>\n",
              "      <td>152.54</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2016-11-01</td>\n",
              "      <td>193</td>\n",
              "      <td>752.7</td>\n",
              "      <td>115.80</td>\n",
              "      <td>150.54</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2016-12-01</td>\n",
              "      <td>344</td>\n",
              "      <td>1341.6</td>\n",
              "      <td>206.40</td>\n",
              "      <td>244.37</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>347</td>\n",
              "      <td>1353.3</td>\n",
              "      <td>208.20</td>\n",
              "      <td>263.64</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2017-02-01</td>\n",
              "      <td>330</td>\n",
              "      <td>1287.0</td>\n",
              "      <td>198.00</td>\n",
              "      <td>214.32</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2017-03-01</td>\n",
              "      <td>185</td>\n",
              "      <td>1103.0</td>\n",
              "      <td>139.38</td>\n",
              "      <td>151.54</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2017-04-01</td>\n",
              "      <td>179</td>\n",
              "      <td>704.0</td>\n",
              "      <td>109.26</td>\n",
              "      <td>118.79</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2017-05-01</td>\n",
              "      <td>228</td>\n",
              "      <td>829.0</td>\n",
              "      <td>140.89</td>\n",
              "      <td>153.18</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2017-06-01</td>\n",
              "      <td>359</td>\n",
              "      <td>1067.0</td>\n",
              "      <td>195.07</td>\n",
              "      <td>212.08</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2017-07-01</td>\n",
              "      <td>468</td>\n",
              "      <td>1533.0</td>\n",
              "      <td>258.64</td>\n",
              "      <td>281.19</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2017-08-01</td>\n",
              "      <td>400</td>\n",
              "      <td>1252.0</td>\n",
              "      <td>218.87</td>\n",
              "      <td>237.97</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2017-09-01</td>\n",
              "      <td>432</td>\n",
              "      <td>1447.0</td>\n",
              "      <td>261.50</td>\n",
              "      <td>284.30</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2017-10-01</td>\n",
              "      <td>253</td>\n",
              "      <td>777.0</td>\n",
              "      <td>158.83</td>\n",
              "      <td>172.69</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2017-11-01</td>\n",
              "      <td>165</td>\n",
              "      <td>521.0</td>\n",
              "      <td>102.57</td>\n",
              "      <td>111.51</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2017-12-01</td>\n",
              "      <td>186</td>\n",
              "      <td>912.0</td>\n",
              "      <td>139.27</td>\n",
              "      <td>151.42</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>316</td>\n",
              "      <td>1869.0</td>\n",
              "      <td>245.88</td>\n",
              "      <td>267.32</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2018-02-01</td>\n",
              "      <td>256</td>\n",
              "      <td>1229.0</td>\n",
              "      <td>185.20</td>\n",
              "      <td>201.35</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2018-03-01</td>\n",
              "      <td>266</td>\n",
              "      <td>1337.0</td>\n",
              "      <td>194.50</td>\n",
              "      <td>202.91</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>141</td>\n",
              "      <td>812.0</td>\n",
              "      <td>143.44</td>\n",
              "      <td>150.86</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2018-05-01</td>\n",
              "      <td>167</td>\n",
              "      <td>791.0</td>\n",
              "      <td>154.70</td>\n",
              "      <td>163.07</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2018-06-01</td>\n",
              "      <td>257</td>\n",
              "      <td>1051.0</td>\n",
              "      <td>208.55</td>\n",
              "      <td>219.76</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2018-07-01</td>\n",
              "      <td>295</td>\n",
              "      <td>1482.0</td>\n",
              "      <td>269.93</td>\n",
              "      <td>283.98</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2018-08-01</td>\n",
              "      <td>330</td>\n",
              "      <td>1470.0</td>\n",
              "      <td>275.71</td>\n",
              "      <td>290.15</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2018-09-01</td>\n",
              "      <td>261</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>245.48</td>\n",
              "      <td>257.96</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2018-10-01</td>\n",
              "      <td>366</td>\n",
              "      <td>1705.0</td>\n",
              "      <td>311.34</td>\n",
              "      <td>327.19</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2018-11-01</td>\n",
              "      <td>245</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>184.40</td>\n",
              "      <td>193.49</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2018-12-01</td>\n",
              "      <td>125</td>\n",
              "      <td>899.0</td>\n",
              "      <td>145.26</td>\n",
              "      <td>152.34</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1257.0</td>\n",
              "      <td>187.91</td>\n",
              "      <td>196.62</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2019-02-01</td>\n",
              "      <td>121</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>181.85</td>\n",
              "      <td>190.07</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>128</td>\n",
              "      <td>1351.0</td>\n",
              "      <td>190.04</td>\n",
              "      <td>198.57</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>95</td>\n",
              "      <td>683.0</td>\n",
              "      <td>111.62</td>\n",
              "      <td>115.15</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>162</td>\n",
              "      <td>783.0</td>\n",
              "      <td>147.83</td>\n",
              "      <td>153.18</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>131</td>\n",
              "      <td>722.0</td>\n",
              "      <td>127.51</td>\n",
              "      <td>138.53</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2019-07-01</td>\n",
              "      <td>276</td>\n",
              "      <td>1254.0</td>\n",
              "      <td>219.52</td>\n",
              "      <td>238.48</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2019-08-01</td>\n",
              "      <td>376</td>\n",
              "      <td>1636.0</td>\n",
              "      <td>284.90</td>\n",
              "      <td>309.65</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2019-09-01</td>\n",
              "      <td>501</td>\n",
              "      <td>2139.0</td>\n",
              "      <td>371.61</td>\n",
              "      <td>403.89</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2019-10-01</td>\n",
              "      <td>279</td>\n",
              "      <td>1309.0</td>\n",
              "      <td>224.52</td>\n",
              "      <td>244.03</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>171</td>\n",
              "      <td>843.0</td>\n",
              "      <td>133.84</td>\n",
              "      <td>145.46</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2019-12-01</td>\n",
              "      <td>99</td>\n",
              "      <td>927.0</td>\n",
              "      <td>128.12</td>\n",
              "      <td>139.26</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>211</td>\n",
              "      <td>1761.0</td>\n",
              "      <td>231.71</td>\n",
              "      <td>251.85</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>190</td>\n",
              "      <td>1635.0</td>\n",
              "      <td>210.86</td>\n",
              "      <td>229.18</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1179.0</td>\n",
              "      <td>156.56</td>\n",
              "      <td>170.16</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>75</td>\n",
              "      <td>625.0</td>\n",
              "      <td>87.27</td>\n",
              "      <td>94.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>147</td>\n",
              "      <td>686.0</td>\n",
              "      <td>124.39</td>\n",
              "      <td>135.19</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Bill_Date  On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "0  2016-01-01      365    1423.5        219.00         247.73            31\n",
              "1  2016-02-01      292    1138.8        175.20         234.11            31\n",
              "2  2016-03-01      130     507.0         78.00         123.85            29\n",
              "3  2016-04-01      117     456.3         70.20         111.22            29\n",
              "4  2016-05-01      136     530.4         81.60         118.37            29\n",
              "5  2016-06-01       63     245.7         37.80          77.81            32\n",
              "6  2016-07-01      110     429.0         66.00          86.67            30\n",
              "7  2016-08-01      176     686.4        105.60         122.40            29\n",
              "8  2016-09-01      357    1392.3        214.20         223.62            33\n",
              "9  2016-10-01      235     916.5        141.00         152.54            29\n",
              "10 2016-11-01      193     752.7        115.80         150.54            32\n",
              "11 2016-12-01      344    1341.6        206.40         244.37            31\n",
              "12 2017-01-01      347    1353.3        208.20         263.64            33\n",
              "13 2017-02-01      330    1287.0        198.00         214.32            27\n",
              "14 2017-03-01      185    1103.0        139.38         151.54            31\n",
              "15 2017-04-01      179     704.0        109.26         118.79            29\n",
              "16 2017-05-01      228     829.0        140.89         153.18            31\n",
              "17 2017-06-01      359    1067.0        195.07         212.08            31\n",
              "18 2017-07-01      468    1533.0        258.64         281.19            32\n",
              "19 2017-08-01      400    1252.0        218.87         237.97            30\n",
              "20 2017-09-01      432    1447.0        261.50         284.30            30\n",
              "21 2017-10-01      253     777.0        158.83         172.69            31\n",
              "22 2017-11-01      165     521.0        102.57         111.51            29\n",
              "23 2017-12-01      186     912.0        139.27         151.42            31\n",
              "24 2018-01-01      316    1869.0        245.88         267.32            33\n",
              "25 2018-02-01      256    1229.0        185.20         201.35            29\n",
              "26 2018-03-01      266    1337.0        194.50         202.91            26\n",
              "27 2018-04-01      141     812.0        143.44         150.86            34\n",
              "28 2018-05-01      167     791.0        154.70         163.07            29\n",
              "29 2018-06-01      257    1051.0        208.55         219.76            30\n",
              "30 2018-07-01      295    1482.0        269.93         283.98            33\n",
              "31 2018-08-01      330    1470.0        275.71         290.15            29\n",
              "32 2018-09-01      261    1375.0        245.48         257.96            26\n",
              "33 2018-10-01      366    1705.0        311.34         327.19            25\n",
              "34 2018-11-01      245    1036.0        184.40         193.49            30\n",
              "35 2018-12-01      125     899.0        145.26         152.34            32\n",
              "36 2019-01-01      151    1257.0        187.91         196.62            31\n",
              "37 2019-02-01      121    1281.0        181.85         190.07            31\n",
              "38 2019-03-01      128    1351.0        190.04         198.57            27\n",
              "39 2019-04-01       95     683.0        111.62         115.15            31\n",
              "40 2019-05-01      162     783.0        147.83         153.18            29\n",
              "41 2019-06-01      131     722.0        127.51         138.53            31\n",
              "42 2019-07-01      276    1254.0        219.52         238.48            31\n",
              "43 2019-08-01      376    1636.0        284.90         309.65            30\n",
              "44 2019-09-01      501    2139.0        371.61         403.89            34\n",
              "45 2019-10-01      279    1309.0        224.52         244.03            27\n",
              "46 2019-11-01      171     843.0        133.84         145.46            31\n",
              "47 2019-12-01       99     927.0        128.12         139.26            32\n",
              "48 2020-01-01      211    1761.0        231.71         251.85            31\n",
              "49 2020-02-01      190    1635.0        210.86         229.18            32\n",
              "50 2020-03-01      151    1179.0        156.56         170.16            29\n",
              "51 2020-04-01       75     625.0         87.27          94.85            29\n",
              "52 2020-05-01      147     686.0        124.39         135.19            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHXUmVGyJLtF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11a05692-36e8-4848-de23-41bee4a0b658"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 53 entries, 0 to 52\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Bill_Date      53 non-null     datetime64[ns]\n",
            " 1   On_peak        53 non-null     int64         \n",
            " 2   Off_peak       53 non-null     float64       \n",
            " 3   Usage_charge   53 non-null     float64       \n",
            " 4   Billed_amount  53 non-null     float64       \n",
            " 5   Billing_days   53 non-null     int64         \n",
            "dtypes: datetime64[ns](1), float64(3), int64(2)\n",
            "memory usage: 2.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekRbR-YqwEtA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdf1d9aa-491e-4c29-b361-89d60c13a181"
      },
      "source": [
        "print (\"Rows     : \" ,df.shape[0])\n",
        "print (\"Columns  : \" ,df.shape[1])\n",
        "print (\"\\nFeatures : \\n\" ,df.columns.tolist())\n",
        "print (\"\\nMissing values :  \", df.isnull().any())\n",
        "print (\"\\nUnique values :  \\n\",df.nunique())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  53\n",
            "Columns  :  6\n",
            "\n",
            "Features : \n",
            " ['Bill_Date', 'On_peak', 'Off_peak', 'Usage_charge', 'Billed_amount', 'Billing_days']\n",
            "\n",
            "Missing values :   Bill_Date        False\n",
            "On_peak          False\n",
            "Off_peak         False\n",
            "Usage_charge     False\n",
            "Billed_amount    False\n",
            "Billing_days     False\n",
            "dtype: bool\n",
            "\n",
            "Unique values :  \n",
            " Bill_Date        53\n",
            "On_peak          51\n",
            "Off_peak         53\n",
            "Usage_charge     53\n",
            "Billed_amount    52\n",
            "Billing_days      9\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oKlNM0Vfogp"
      },
      "source": [
        "ec_df = df.set_index('Bill_Date')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BnU-vnefvyt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "47ae852a-84c9-4e82-d82a-e96733f6dea5"
      },
      "source": [
        "ec_df.head()"
      ],
      "execution_count": 9,
      "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>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bill_Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.0</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-01</th>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.2</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-01</th>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01</th>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.2</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-05-01</th>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.6</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "Bill_Date                                                               \n",
              "2016-01-01      365    1423.5         219.0         247.73            31\n",
              "2016-02-01      292    1138.8         175.2         234.11            31\n",
              "2016-03-01      130     507.0          78.0         123.85            29\n",
              "2016-04-01      117     456.3          70.2         111.22            29\n",
              "2016-05-01      136     530.4          81.6         118.37            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sTtaV86a76l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c35d285e-fb7d-4a94-95d8-ae5983d661e8"
      },
      "source": [
        "ec_data = ec_df['Billed_amount']\n",
        "ec_data.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bill_Date\n",
              "2016-01-01    247.73\n",
              "2016-02-01    234.11\n",
              "2016-03-01    123.85\n",
              "2016-04-01    111.22\n",
              "2016-05-01    118.37\n",
              "Name: Billed_amount, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McpB6CtVcjMN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "fdbb229b-4905-4b49-f1ea-7d48d9c728d0"
      },
      "source": [
        "ec_data.plot(grid=True)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea5f831d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBCZn1zUPWRJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "59311184-904a-4494-b205-80ba7d88fd4c"
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019['Billed_amount']\n",
        "ec_data_2019.plot(grid=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea5e83fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYOMJac8RKUD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "c6a64415-f22c-4630-cfbe-0f6490cf22de"
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019['Billed_amount']\n",
        "ec_data_2019.plot.bar()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ea5956dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUMprmLoH2RD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "65161946-c386-4635-ef9b-69f8ce7f362d"
      },
      "source": [
        "ec_df_2019=ec_df.loc['2019']\n",
        "ec_data_2019=ec_df_2019[['Billed_amount','On_peak','Off_peak','Billing_days']]\n",
        "ec_data_2019.plot(subplots=True)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f0ea58e6b10>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ea5892f50>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ea5e0fdd0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f0ea5806590>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsRF5qBxoWnq"
      },
      "source": [
        "final_df = df.copy()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJHsRdAl5C6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "1285a5bc-8245-4241-dfb1-887b4ccc0e71"
      },
      "source": [
        "final_df.head(10)"
      ],
      "execution_count": 16,
      "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Usage_charge</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>219.0</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>175.2</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>70.2</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>81.6</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016-06-01</td>\n",
              "      <td>63</td>\n",
              "      <td>245.7</td>\n",
              "      <td>37.8</td>\n",
              "      <td>77.81</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2016-07-01</td>\n",
              "      <td>110</td>\n",
              "      <td>429.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>86.67</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2016-08-01</td>\n",
              "      <td>176</td>\n",
              "      <td>686.4</td>\n",
              "      <td>105.6</td>\n",
              "      <td>122.40</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2016-09-01</td>\n",
              "      <td>357</td>\n",
              "      <td>1392.3</td>\n",
              "      <td>214.2</td>\n",
              "      <td>223.62</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>235</td>\n",
              "      <td>916.5</td>\n",
              "      <td>141.0</td>\n",
              "      <td>152.54</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Bill_Date  On_peak  Off_peak  Usage_charge  Billed_amount  Billing_days\n",
              "0 2016-01-01      365    1423.5         219.0         247.73            31\n",
              "1 2016-02-01      292    1138.8         175.2         234.11            31\n",
              "2 2016-03-01      130     507.0          78.0         123.85            29\n",
              "3 2016-04-01      117     456.3          70.2         111.22            29\n",
              "4 2016-05-01      136     530.4          81.6         118.37            29\n",
              "5 2016-06-01       63     245.7          37.8          77.81            32\n",
              "6 2016-07-01      110     429.0          66.0          86.67            30\n",
              "7 2016-08-01      176     686.4         105.6         122.40            29\n",
              "8 2016-09-01      357    1392.3         214.2         223.62            33\n",
              "9 2016-10-01      235     916.5         141.0         152.54            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04OS1JBzKkCy"
      },
      "source": [
        "final_df=final_df[['Bill_Date','On_peak','Off_peak','Billed_amount','Billing_days']]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZriIFhrF4i7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "079d12f3-0147-45b8-d0f0-83a2098d17cf"
      },
      "source": [
        "train = final_df[:50]\n",
        "test = final_df[50:]\n",
        "print(train.shape, test.shape)\n",
        "train.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50, 5) (3, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-01</td>\n",
              "      <td>365</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>247.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-02-01</td>\n",
              "      <td>292</td>\n",
              "      <td>1138.8</td>\n",
              "      <td>234.11</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-03-01</td>\n",
              "      <td>130</td>\n",
              "      <td>507.0</td>\n",
              "      <td>123.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-04-01</td>\n",
              "      <td>117</td>\n",
              "      <td>456.3</td>\n",
              "      <td>111.22</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-05-01</td>\n",
              "      <td>136</td>\n",
              "      <td>530.4</td>\n",
              "      <td>118.37</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Bill_Date  On_peak  Off_peak  Billed_amount  Billing_days\n",
              "0 2016-01-01      365    1423.5         247.73            31\n",
              "1 2016-02-01      292    1138.8         234.11            31\n",
              "2 2016-03-01      130     507.0         123.85            29\n",
              "3 2016-04-01      117     456.3         111.22            29\n",
              "4 2016-05-01      136     530.4         118.37            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwhAwDnHF4ex"
      },
      "source": [
        "ts_column = 'Bill_Date'\n",
        "sep = ','\n",
        "target = 'Billed_amount'"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy-GmW13YPx3"
      },
      "source": [
        "Use Verbose=1 for more detailed charts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_liVTTyoF4UT"
      },
      "source": [
        "ml_dict = AT.auto_timeseries(train, ts_column,\n",
        "                            target, sep,  score_type='rmse', forecast_period=6,\n",
        "                            time_interval='Months', non_seasonal_pdq=None, seasonality=True,\n",
        "                            seasonal_period=12,seasonal_PDQ=None, model_type=['best'],\n",
        "                            verbose=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NH9LotVba8TG"
      },
      "source": [
        "ml_dict = AT.auto_timeseries(score_type='rmse', non_seasonal_pdq=(2,1,2), seasonality=True, model_type=['best'], verbose=2)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkO8GJ0YbP99",
        "outputId": "accb6970-1bce-4475-ea88-f793b94d566c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "ml_dict"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<auto_ts.auto_timeseries at 0x7f0ea10561d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZjqRzbGbUDf"
      },
      "source": [
        "ml_dict.fit(traindata=train[['Bill_Date','Billed_amount']], ts_column='Bill_Date', target='Billed_amount', cv=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6HLitikbT2N"
      },
      "source": [
        "ml_dict.get_leaderboard()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QpZNPtnbTzV"
      },
      "source": [
        "ml_dict.get_cv_scores()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9VdEjmw5CwA"
      },
      "source": [
        "results_dict = ml_dict.get_ml_dict()\n",
        "results_dict"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PjxYhuDJpy6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e44d5411-6501-4e30-c5fe-b4eb8ea8614a"
      },
      "source": [
        "results_dict['auto_SARIMAX']['forecast']"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([160.80677993, 215.30496206, 195.14222643, 224.17910818,\n",
              "        207.06634403]),\n",
              " array([143.8689546 , 178.47062915, 194.86562228, 223.21325042,\n",
              "        202.46918096]),\n",
              " array([405.68847567, 302.68749245, 250.12853014, 221.46948323,\n",
              "        220.7903006 ])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J2ODKcflcPCu",
        "outputId": "832673f5-5a09-487b-df2c-92e9d9bfdaaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "future_predictions = ml_dict.predict(testdata=test[['Bill_Date','Billed_amount']], model='auto_SARIMAX')  \n",
        "future_predictions"
      ],
      "execution_count": 35,
      "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>Billed_amount</th>\n",
              "      <th>yhat</th>\n",
              "      <th>mean_se</th>\n",
              "      <th>mean_ci_lower</th>\n",
              "      <th>mean_ci_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>210.305594</td>\n",
              "      <td>52.050154</td>\n",
              "      <td>108.289166</td>\n",
              "      <td>312.322022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>175.975253</td>\n",
              "      <td>58.824392</td>\n",
              "      <td>60.681563</td>\n",
              "      <td>291.268944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>192.464518</td>\n",
              "      <td>58.828917</td>\n",
              "      <td>77.161959</td>\n",
              "      <td>307.767077</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Billed_amount        yhat    mean_se  mean_ci_lower  mean_ci_upper\n",
              "2020-03-01     210.305594  52.050154     108.289166     312.322022\n",
              "2020-04-01     175.975253  58.824392      60.681563     291.268944\n",
              "2020-05-01     192.464518  58.828917      77.161959     307.767077"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "RWxut7qWcrny",
        "outputId": "9741f562-a82b-4468-9a41-ef02c1f480a6"
      },
      "source": [
        "test"
      ],
      "execution_count": 36,
      "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>Bill_Date</th>\n",
              "      <th>On_peak</th>\n",
              "      <th>Off_peak</th>\n",
              "      <th>Billed_amount</th>\n",
              "      <th>Billing_days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>151</td>\n",
              "      <td>1179.0</td>\n",
              "      <td>170.16</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>75</td>\n",
              "      <td>625.0</td>\n",
              "      <td>94.85</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>147</td>\n",
              "      <td>686.0</td>\n",
              "      <td>135.19</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Bill_Date  On_peak  Off_peak  Billed_amount  Billing_days\n",
              "50 2020-03-01      151    1179.0         170.16            29\n",
              "51 2020-04-01       75     625.0          94.85            29\n",
              "52 2020-05-01      147     686.0         135.19            29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mm1o6J5Cc1jP",
        "outputId": "65a84a63-8b54-4080-a704-87d16b63bbd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "\n",
        "future_predictions = ml_dict.predict(testdata=test[['Bill_Date','Billed_amount']], model='VAR')  \n",
        "future_predictions"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(Error) Model of type 'VAR' does not exist. No predictions will be made.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XvK0TDjc-ZH",
        "outputId": "45ff70c9-2495-4e1d-a022-31680e83402d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "future_predictions = ml_dict.predict(testdata=test[['Bill_Date','Billed_amount']], model='Prophet')  \n",
        "future_predictions"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building Forecast dataframe. Forecast Period = 3\n"
          ],
          "name": "stdout"
        },
        {
          "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",
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              "    }\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>ds</th>\n",
              "      <th>trend</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "      <th>trend_lower</th>\n",
              "      <th>trend_upper</th>\n",
              "      <th>additive_terms</th>\n",
              "      <th>additive_terms_lower</th>\n",
              "      <th>additive_terms_upper</th>\n",
              "      <th>yearly</th>\n",
              "      <th>yearly_lower</th>\n",
              "      <th>yearly_upper</th>\n",
              "      <th>multiplicative_terms</th>\n",
              "      <th>multiplicative_terms_lower</th>\n",
              "      <th>multiplicative_terms_upper</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>236.509372</td>\n",
              "      <td>224.574221</td>\n",
              "      <td>326.911223</td>\n",
              "      <td>236.509371</td>\n",
              "      <td>236.509373</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>38.754600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>275.263972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2020-03-31</td>\n",
              "      <td>237.943874</td>\n",
              "      <td>85.088043</td>\n",
              "      <td>190.006012</td>\n",
              "      <td>237.943870</td>\n",
              "      <td>237.943877</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>-99.361344</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>138.582529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2020-04-30</td>\n",
              "      <td>239.332101</td>\n",
              "      <td>93.904143</td>\n",
              "      <td>202.063184</td>\n",
              "      <td>239.332094</td>\n",
              "      <td>239.332109</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>-90.347428</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>148.984673</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds       trend  ...  multiplicative_terms_upper        yhat\n",
              "50 2020-02-29  236.509372  ...                         0.0  275.263972\n",
              "51 2020-03-31  237.943874  ...                         0.0  138.582529\n",
              "52 2020-04-30  239.332101  ...                         0.0  148.984673\n",
              "\n",
              "[3 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ls12EewegK8E"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}