final

Author: Heroif

Energy demand prediction using machine learning.ipynb

@@ -52,15 +52,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Importing libraries\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.model_selection import train_test_split\n",
     "from sklearn.ensemble import RandomForestRegressor\n",
     "from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n",
     "from sklearn.preprocessing import StandardScaler\n",
@@ -69,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +312,7 @@
        "[1000 rows x 10 columns]"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -327,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -391,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +566,7 @@
        "2022-01-01 04:00:00        0          70.811732  "
       ]
      },
-     "execution_count": 76,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -816,7 +815,7 @@
        "[1000 rows x 9 columns]"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -841,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -854,19 +853,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
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+       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "RandomForestRegressor(random_state=42)"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -879,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -889,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -902,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -965,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -994,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {