diff --git a/Predicting Energy Efficiency of Buildings/Predicting Energy Efficiency of Buildings.ipynb b/Predicting Energy Efficiency of Buildings/Predicting Energy Efficiency of Buildings.ipynb
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@@ -0,0 +1,1683 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "504041c1",
+   "metadata": {},
+   "source": [
+    "# #Stage B Data Science Internship"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73974946",
+   "metadata": {},
+   "source": [
+    "### Graded Quiz Sol'n "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6489cf1",
+   "metadata": {},
+   "source": [
+    "### Yamini Vijaywargiya \n",
+    "\n",
+    "#### Machine Learninig: Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f7b64448",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Appliances</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>T1</th>\n",
+       "      <th>RH_1</th>\n",
+       "      <th>T2</th>\n",
+       "      <th>RH_2</th>\n",
+       "      <th>T3</th>\n",
+       "      <th>RH_3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>...</th>\n",
+       "      <th>T9</th>\n",
+       "      <th>RH_9</th>\n",
+       "      <th>T_out</th>\n",
+       "      <th>Press_mm_hg</th>\n",
+       "      <th>RH_out</th>\n",
+       "      <th>Windspeed</th>\n",
+       "      <th>Visibility</th>\n",
+       "      <th>Tdewpoint</th>\n",
+       "      <th>rv1</th>\n",
+       "      <th>rv2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-01-11 17:00:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>47.596667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.730000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.033333</td>\n",
+       "      <td>45.53</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>733.5</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>13.275433</td>\n",
+       "      <td>13.275433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-01-11 17:10:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.693333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.722500</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.066667</td>\n",
+       "      <td>45.56</td>\n",
+       "      <td>6.483333</td>\n",
+       "      <td>733.6</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.666667</td>\n",
+       "      <td>59.166667</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>18.606195</td>\n",
+       "      <td>18.606195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-01-11 17:20:00</td>\n",
+       "      <td>50</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.300000</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.626667</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.933333</td>\n",
+       "      <td>18.926667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>6.366667</td>\n",
+       "      <td>733.7</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.333333</td>\n",
+       "      <td>55.333333</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>28.642668</td>\n",
+       "      <td>28.642668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-01-11 17:30:00</td>\n",
+       "      <td>50</td>\n",
+       "      <td>40</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.066667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.590000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.250000</td>\n",
+       "      <td>733.8</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>51.500000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>45.410389</td>\n",
+       "      <td>45.410389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-01-11 17:40:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>40</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.333333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.530000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.133333</td>\n",
+       "      <td>733.9</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>5.666667</td>\n",
+       "      <td>47.666667</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>10.084097</td>\n",
+       "      <td>10.084097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
+       "0  2016-01-11 17:00:00          60      30  19.89  47.596667  19.2  44.790000   \n",
+       "1  2016-01-11 17:10:00          60      30  19.89  46.693333  19.2  44.722500   \n",
+       "2  2016-01-11 17:20:00          50      30  19.89  46.300000  19.2  44.626667   \n",
+       "3  2016-01-11 17:30:00          50      40  19.89  46.066667  19.2  44.590000   \n",
+       "4  2016-01-11 17:40:00          60      40  19.89  46.333333  19.2  44.530000   \n",
+       "\n",
+       "      T3       RH_3         T4  ...         T9   RH_9     T_out  Press_mm_hg  \\\n",
+       "0  19.79  44.730000  19.000000  ...  17.033333  45.53  6.600000        733.5   \n",
+       "1  19.79  44.790000  19.000000  ...  17.066667  45.56  6.483333        733.6   \n",
+       "2  19.79  44.933333  18.926667  ...  17.000000  45.50  6.366667        733.7   \n",
+       "3  19.79  45.000000  18.890000  ...  17.000000  45.40  6.250000        733.8   \n",
+       "4  19.79  45.000000  18.890000  ...  17.000000  45.40  6.133333        733.9   \n",
+       "\n",
+       "   RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
+       "0    92.0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
+       "1    92.0   6.666667   59.166667        5.2  18.606195  18.606195  \n",
+       "2    92.0   6.333333   55.333333        5.1  28.642668  28.642668  \n",
+       "3    92.0   6.000000   51.500000        5.0  45.410389  45.410389  \n",
+       "4    92.0   5.666667   47.666667        4.9  10.084097  10.084097  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv'\n",
+    "df = pd.read_csv(url, error_bad_lines= False)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7a4f79af",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Appliances</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>T1</th>\n",
+       "      <th>RH_1</th>\n",
+       "      <th>T2</th>\n",
+       "      <th>RH_2</th>\n",
+       "      <th>T3</th>\n",
+       "      <th>RH_3</th>\n",
+       "      <th>T4</th>\n",
+       "      <th>...</th>\n",
+       "      <th>T9</th>\n",
+       "      <th>RH_9</th>\n",
+       "      <th>T_out</th>\n",
+       "      <th>Press_mm_hg</th>\n",
+       "      <th>RH_out</th>\n",
+       "      <th>Windspeed</th>\n",
+       "      <th>Visibility</th>\n",
+       "      <th>Tdewpoint</th>\n",
+       "      <th>rv1</th>\n",
+       "      <th>rv2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>19735</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "      <td>19735.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>19735</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>2016-03-03 11:20:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>97.694958</td>\n",
+       "      <td>3.801875</td>\n",
+       "      <td>21.686571</td>\n",
+       "      <td>40.259739</td>\n",
+       "      <td>20.341219</td>\n",
+       "      <td>40.420420</td>\n",
+       "      <td>22.267611</td>\n",
+       "      <td>39.242500</td>\n",
+       "      <td>20.855335</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.485828</td>\n",
+       "      <td>41.552401</td>\n",
+       "      <td>7.411665</td>\n",
+       "      <td>755.522602</td>\n",
+       "      <td>79.750418</td>\n",
+       "      <td>4.039752</td>\n",
+       "      <td>38.330834</td>\n",
+       "      <td>3.760707</td>\n",
+       "      <td>24.988033</td>\n",
+       "      <td>24.988033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>102.524891</td>\n",
+       "      <td>7.935988</td>\n",
+       "      <td>1.606066</td>\n",
+       "      <td>3.979299</td>\n",
+       "      <td>2.192974</td>\n",
+       "      <td>4.069813</td>\n",
+       "      <td>2.006111</td>\n",
+       "      <td>3.254576</td>\n",
+       "      <td>2.042884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.014712</td>\n",
+       "      <td>4.151497</td>\n",
+       "      <td>5.317409</td>\n",
+       "      <td>7.399441</td>\n",
+       "      <td>14.901088</td>\n",
+       "      <td>2.451221</td>\n",
+       "      <td>11.794719</td>\n",
+       "      <td>4.194648</td>\n",
+       "      <td>14.496634</td>\n",
+       "      <td>14.496634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.790000</td>\n",
+       "      <td>27.023333</td>\n",
+       "      <td>16.100000</td>\n",
+       "      <td>20.463333</td>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>28.766667</td>\n",
+       "      <td>15.100000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.890000</td>\n",
+       "      <td>29.166667</td>\n",
+       "      <td>-5.000000</td>\n",
+       "      <td>729.300000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-6.600000</td>\n",
+       "      <td>0.005322</td>\n",
+       "      <td>0.005322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>20.760000</td>\n",
+       "      <td>37.333333</td>\n",
+       "      <td>18.790000</td>\n",
+       "      <td>37.900000</td>\n",
+       "      <td>20.790000</td>\n",
+       "      <td>36.900000</td>\n",
+       "      <td>19.530000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>38.500000</td>\n",
+       "      <td>3.666667</td>\n",
+       "      <td>750.933333</td>\n",
+       "      <td>70.333333</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>12.497889</td>\n",
+       "      <td>12.497889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21.600000</td>\n",
+       "      <td>39.656667</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>40.500000</td>\n",
+       "      <td>22.100000</td>\n",
+       "      <td>38.530000</td>\n",
+       "      <td>20.666667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.390000</td>\n",
+       "      <td>40.900000</td>\n",
+       "      <td>6.916667</td>\n",
+       "      <td>756.100000</td>\n",
+       "      <td>83.666667</td>\n",
+       "      <td>3.666667</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>3.433333</td>\n",
+       "      <td>24.897653</td>\n",
+       "      <td>24.897653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>22.600000</td>\n",
+       "      <td>43.066667</td>\n",
+       "      <td>21.500000</td>\n",
+       "      <td>43.260000</td>\n",
+       "      <td>23.290000</td>\n",
+       "      <td>41.760000</td>\n",
+       "      <td>22.100000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.600000</td>\n",
+       "      <td>44.338095</td>\n",
+       "      <td>10.408333</td>\n",
+       "      <td>760.933333</td>\n",
+       "      <td>91.666667</td>\n",
+       "      <td>5.500000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>6.566667</td>\n",
+       "      <td>37.583769</td>\n",
+       "      <td>37.583769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1080.000000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>26.260000</td>\n",
+       "      <td>63.360000</td>\n",
+       "      <td>29.856667</td>\n",
+       "      <td>56.026667</td>\n",
+       "      <td>29.236000</td>\n",
+       "      <td>50.163333</td>\n",
+       "      <td>26.200000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.500000</td>\n",
+       "      <td>53.326667</td>\n",
+       "      <td>26.100000</td>\n",
+       "      <td>772.300000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>66.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>49.996530</td>\n",
+       "      <td>49.996530</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>11 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       date    Appliances        lights            T1  \\\n",
+       "count                 19735  19735.000000  19735.000000  19735.000000   \n",
+       "unique                19735           NaN           NaN           NaN   \n",
+       "top     2016-03-03 11:20:00           NaN           NaN           NaN   \n",
+       "freq                      1           NaN           NaN           NaN   \n",
+       "mean                    NaN     97.694958      3.801875     21.686571   \n",
+       "std                     NaN    102.524891      7.935988      1.606066   \n",
+       "min                     NaN     10.000000      0.000000     16.790000   \n",
+       "25%                     NaN     50.000000      0.000000     20.760000   \n",
+       "50%                     NaN     60.000000      0.000000     21.600000   \n",
+       "75%                     NaN    100.000000      0.000000     22.600000   \n",
+       "max                     NaN   1080.000000     70.000000     26.260000   \n",
+       "\n",
+       "                RH_1            T2          RH_2            T3          RH_3  \\\n",
+       "count   19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
+       "unique           NaN           NaN           NaN           NaN           NaN   \n",
+       "top              NaN           NaN           NaN           NaN           NaN   \n",
+       "freq             NaN           NaN           NaN           NaN           NaN   \n",
+       "mean       40.259739     20.341219     40.420420     22.267611     39.242500   \n",
+       "std         3.979299      2.192974      4.069813      2.006111      3.254576   \n",
+       "min        27.023333     16.100000     20.463333     17.200000     28.766667   \n",
+       "25%        37.333333     18.790000     37.900000     20.790000     36.900000   \n",
+       "50%        39.656667     20.000000     40.500000     22.100000     38.530000   \n",
+       "75%        43.066667     21.500000     43.260000     23.290000     41.760000   \n",
+       "max        63.360000     29.856667     56.026667     29.236000     50.163333   \n",
+       "\n",
+       "                  T4  ...            T9          RH_9         T_out  \\\n",
+       "count   19735.000000  ...  19735.000000  19735.000000  19735.000000   \n",
+       "unique           NaN  ...           NaN           NaN           NaN   \n",
+       "top              NaN  ...           NaN           NaN           NaN   \n",
+       "freq             NaN  ...           NaN           NaN           NaN   \n",
+       "mean       20.855335  ...     19.485828     41.552401      7.411665   \n",
+       "std         2.042884  ...      2.014712      4.151497      5.317409   \n",
+       "min        15.100000  ...     14.890000     29.166667     -5.000000   \n",
+       "25%        19.530000  ...     18.000000     38.500000      3.666667   \n",
+       "50%        20.666667  ...     19.390000     40.900000      6.916667   \n",
+       "75%        22.100000  ...     20.600000     44.338095     10.408333   \n",
+       "max        26.200000  ...     24.500000     53.326667     26.100000   \n",
+       "\n",
+       "         Press_mm_hg        RH_out     Windspeed    Visibility     Tdewpoint  \\\n",
+       "count   19735.000000  19735.000000  19735.000000  19735.000000  19735.000000   \n",
+       "unique           NaN           NaN           NaN           NaN           NaN   \n",
+       "top              NaN           NaN           NaN           NaN           NaN   \n",
+       "freq             NaN           NaN           NaN           NaN           NaN   \n",
+       "mean      755.522602     79.750418      4.039752     38.330834      3.760707   \n",
+       "std         7.399441     14.901088      2.451221     11.794719      4.194648   \n",
+       "min       729.300000     24.000000      0.000000      1.000000     -6.600000   \n",
+       "25%       750.933333     70.333333      2.000000     29.000000      0.900000   \n",
+       "50%       756.100000     83.666667      3.666667     40.000000      3.433333   \n",
+       "75%       760.933333     91.666667      5.500000     40.000000      6.566667   \n",
+       "max       772.300000    100.000000     14.000000     66.000000     15.500000   \n",
+       "\n",
+       "                 rv1           rv2  \n",
+       "count   19735.000000  19735.000000  \n",
+       "unique           NaN           NaN  \n",
+       "top              NaN           NaN  \n",
+       "freq             NaN           NaN  \n",
+       "mean       24.988033     24.988033  \n",
+       "std        14.496634     14.496634  \n",
+       "min         0.005322      0.005322  \n",
+       "25%        12.497889     12.497889  \n",
+       "50%        24.897653     24.897653  \n",
+       "75%        37.583769     37.583769  \n",
+       "max        49.996530     49.996530  \n",
+       "\n",
+       "[11 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include ='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b8b2a8e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date           0\n",
+       "Appliances     0\n",
+       "lights         0\n",
+       "T1             0\n",
+       "RH_1           0\n",
+       "T2             0\n",
+       "RH_2           0\n",
+       "T3             0\n",
+       "RH_3           0\n",
+       "T4             0\n",
+       "RH_4           0\n",
+       "T5             0\n",
+       "RH_5           0\n",
+       "T6             0\n",
+       "RH_6           0\n",
+       "T7             0\n",
+       "RH_7           0\n",
+       "T8             0\n",
+       "RH_8           0\n",
+       "T9             0\n",
+       "RH_9           0\n",
+       "T_out          0\n",
+       "Press_mm_hg    0\n",
+       "RH_out         0\n",
+       "Windspeed      0\n",
+       "Visibility     0\n",
+       "Tdewpoint      0\n",
+       "rv1            0\n",
+       "rv2            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "24f62118",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "column_names = {'date': 'Date', 'Appliances': 'Appliances', 'lights':'Lights', 'T1':'Temp_Kitchen',\n",
+    "                'RH_1':'Humidity_Kitchen', 'T2':'Temp_LivingRoom', 'RH_2':'Humidity_LivingRoom', \n",
+    "                'T3':'Temp_LaundryRoom', 'RH_3':'Humidity_LaundryRoom', 'T4':'Temp_Office', \n",
+    "                'RH_4':'Humidity_Office', 'T5':'Temp_Bathroom', 'RH_5':'Humidity_Bathroom', \n",
+    "                'T6': 'Temp_Outside_Building', 'RH_6': 'Humidity_Outside_Building', \n",
+    "                'T7': 'Temp_IroningRoom', 'RH_7': 'Humidity_IroningRoom',\n",
+    "                'T8': 'Temp_TeenagerRoom', 'RH_8': 'Humidity_TeenagerRoom', \n",
+    "                'T9': 'Temp_ParentsRoom', 'RH_9': 'Humidity_ParentsRoom', 'T_out': 'Temp_Outside', \n",
+    "                'Press_mm_hg': 'Press_mm_hg', 'RH_out': 'Humidity_Outside', 'Windspeed': 'Windspeed', \n",
+    "                'Visibility': 'Visibility', 'Tdewpoint': 'T_Dewpoint', 'rv1': 'Random_Var1', 'rv2': 'Random_Var2'}\n",
+    "\n",
+    "df = df.rename(columns = column_names)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c994f4e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Appliances</th>\n",
+       "      <th>Lights</th>\n",
+       "      <th>Temp_Kitchen</th>\n",
+       "      <th>Humidity_Kitchen</th>\n",
+       "      <th>Temp_LivingRoom</th>\n",
+       "      <th>Humidity_LivingRoom</th>\n",
+       "      <th>Temp_LaundryRoom</th>\n",
+       "      <th>Humidity_LaundryRoom</th>\n",
+       "      <th>Temp_Office</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Temp_ParentsRoom</th>\n",
+       "      <th>Humidity_ParentsRoom</th>\n",
+       "      <th>Temp_Outside</th>\n",
+       "      <th>Press_mm_hg</th>\n",
+       "      <th>Humidity_Outside</th>\n",
+       "      <th>Windspeed</th>\n",
+       "      <th>Visibility</th>\n",
+       "      <th>T_Dewpoint</th>\n",
+       "      <th>Random_Var1</th>\n",
+       "      <th>Random_Var2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-01-11 17:00:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>47.596667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.730000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.033333</td>\n",
+       "      <td>45.53</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>733.5</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>13.275433</td>\n",
+       "      <td>13.275433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-01-11 17:10:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.693333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.722500</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.066667</td>\n",
+       "      <td>45.56</td>\n",
+       "      <td>6.483333</td>\n",
+       "      <td>733.6</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.666667</td>\n",
+       "      <td>59.166667</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>18.606195</td>\n",
+       "      <td>18.606195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-01-11 17:20:00</td>\n",
+       "      <td>50</td>\n",
+       "      <td>30</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.300000</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.626667</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.933333</td>\n",
+       "      <td>18.926667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>6.366667</td>\n",
+       "      <td>733.7</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.333333</td>\n",
+       "      <td>55.333333</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>28.642668</td>\n",
+       "      <td>28.642668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-01-11 17:30:00</td>\n",
+       "      <td>50</td>\n",
+       "      <td>40</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.066667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.590000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.250000</td>\n",
+       "      <td>733.8</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>51.500000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>45.410389</td>\n",
+       "      <td>45.410389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-01-11 17:40:00</td>\n",
+       "      <td>60</td>\n",
+       "      <td>40</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.333333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.530000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.133333</td>\n",
+       "      <td>733.9</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>5.666667</td>\n",
+       "      <td>47.666667</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>10.084097</td>\n",
+       "      <td>10.084097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Date  Appliances  Lights  Temp_Kitchen  Humidity_Kitchen  \\\n",
+       "0  2016-01-11 17:00:00          60      30         19.89         47.596667   \n",
+       "1  2016-01-11 17:10:00          60      30         19.89         46.693333   \n",
+       "2  2016-01-11 17:20:00          50      30         19.89         46.300000   \n",
+       "3  2016-01-11 17:30:00          50      40         19.89         46.066667   \n",
+       "4  2016-01-11 17:40:00          60      40         19.89         46.333333   \n",
+       "\n",
+       "   Temp_LivingRoom  Humidity_LivingRoom  Temp_LaundryRoom  \\\n",
+       "0             19.2            44.790000             19.79   \n",
+       "1             19.2            44.722500             19.79   \n",
+       "2             19.2            44.626667             19.79   \n",
+       "3             19.2            44.590000             19.79   \n",
+       "4             19.2            44.530000             19.79   \n",
+       "\n",
+       "   Humidity_LaundryRoom  Temp_Office  ...  Temp_ParentsRoom  \\\n",
+       "0             44.730000    19.000000  ...         17.033333   \n",
+       "1             44.790000    19.000000  ...         17.066667   \n",
+       "2             44.933333    18.926667  ...         17.000000   \n",
+       "3             45.000000    18.890000  ...         17.000000   \n",
+       "4             45.000000    18.890000  ...         17.000000   \n",
+       "\n",
+       "   Humidity_ParentsRoom  Temp_Outside  Press_mm_hg  Humidity_Outside  \\\n",
+       "0                 45.53      6.600000        733.5              92.0   \n",
+       "1                 45.56      6.483333        733.6              92.0   \n",
+       "2                 45.50      6.366667        733.7              92.0   \n",
+       "3                 45.40      6.250000        733.8              92.0   \n",
+       "4                 45.40      6.133333        733.9              92.0   \n",
+       "\n",
+       "   Windspeed  Visibility  T_Dewpoint  Random_Var1  Random_Var2  \n",
+       "0   7.000000   63.000000         5.3    13.275433    13.275433  \n",
+       "1   6.666667   59.166667         5.2    18.606195    18.606195  \n",
+       "2   6.333333   55.333333         5.1    28.642668    28.642668  \n",
+       "3   6.000000   51.500000         5.0    45.410389    45.410389  \n",
+       "4   5.666667   47.666667         4.9    10.084097    10.084097  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bd0de804",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dropping Columns\n",
+    "df.drop(['Date', 'Lights'], inplace=True, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f34809bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Appliances</th>\n",
+       "      <th>Temp_Kitchen</th>\n",
+       "      <th>Humidity_Kitchen</th>\n",
+       "      <th>Temp_LivingRoom</th>\n",
+       "      <th>Humidity_LivingRoom</th>\n",
+       "      <th>Temp_LaundryRoom</th>\n",
+       "      <th>Humidity_LaundryRoom</th>\n",
+       "      <th>Temp_Office</th>\n",
+       "      <th>Humidity_Office</th>\n",
+       "      <th>Temp_Bathroom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Temp_ParentsRoom</th>\n",
+       "      <th>Humidity_ParentsRoom</th>\n",
+       "      <th>Temp_Outside</th>\n",
+       "      <th>Press_mm_hg</th>\n",
+       "      <th>Humidity_Outside</th>\n",
+       "      <th>Windspeed</th>\n",
+       "      <th>Visibility</th>\n",
+       "      <th>T_Dewpoint</th>\n",
+       "      <th>Random_Var1</th>\n",
+       "      <th>Random_Var2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>60</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>47.596667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.730000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>45.566667</td>\n",
+       "      <td>17.166667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.033333</td>\n",
+       "      <td>45.53</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>733.5</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>63.000000</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>13.275433</td>\n",
+       "      <td>13.275433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.693333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.722500</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.790000</td>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>45.992500</td>\n",
+       "      <td>17.166667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.066667</td>\n",
+       "      <td>45.56</td>\n",
+       "      <td>6.483333</td>\n",
+       "      <td>733.6</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.666667</td>\n",
+       "      <td>59.166667</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>18.606195</td>\n",
+       "      <td>18.606195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>50</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.300000</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.626667</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>44.933333</td>\n",
+       "      <td>18.926667</td>\n",
+       "      <td>45.890000</td>\n",
+       "      <td>17.166667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.50</td>\n",
+       "      <td>6.366667</td>\n",
+       "      <td>733.7</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.333333</td>\n",
+       "      <td>55.333333</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>28.642668</td>\n",
+       "      <td>28.642668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>50</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.066667</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.590000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>45.723333</td>\n",
+       "      <td>17.166667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.250000</td>\n",
+       "      <td>733.8</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>51.500000</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>45.410389</td>\n",
+       "      <td>45.410389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>60</td>\n",
+       "      <td>19.89</td>\n",
+       "      <td>46.333333</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>44.530000</td>\n",
+       "      <td>19.79</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>18.890000</td>\n",
+       "      <td>45.530000</td>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>45.40</td>\n",
+       "      <td>6.133333</td>\n",
+       "      <td>733.9</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>5.666667</td>\n",
+       "      <td>47.666667</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>10.084097</td>\n",
+       "      <td>10.084097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Appliances  Temp_Kitchen  Humidity_Kitchen  Temp_LivingRoom  \\\n",
+       "0          60         19.89         47.596667             19.2   \n",
+       "1          60         19.89         46.693333             19.2   \n",
+       "2          50         19.89         46.300000             19.2   \n",
+       "3          50         19.89         46.066667             19.2   \n",
+       "4          60         19.89         46.333333             19.2   \n",
+       "\n",
+       "   Humidity_LivingRoom  Temp_LaundryRoom  Humidity_LaundryRoom  Temp_Office  \\\n",
+       "0            44.790000             19.79             44.730000    19.000000   \n",
+       "1            44.722500             19.79             44.790000    19.000000   \n",
+       "2            44.626667             19.79             44.933333    18.926667   \n",
+       "3            44.590000             19.79             45.000000    18.890000   \n",
+       "4            44.530000             19.79             45.000000    18.890000   \n",
+       "\n",
+       "   Humidity_Office  Temp_Bathroom  ...  Temp_ParentsRoom  \\\n",
+       "0        45.566667      17.166667  ...         17.033333   \n",
+       "1        45.992500      17.166667  ...         17.066667   \n",
+       "2        45.890000      17.166667  ...         17.000000   \n",
+       "3        45.723333      17.166667  ...         17.000000   \n",
+       "4        45.530000      17.200000  ...         17.000000   \n",
+       "\n",
+       "   Humidity_ParentsRoom  Temp_Outside  Press_mm_hg  Humidity_Outside  \\\n",
+       "0                 45.53      6.600000        733.5              92.0   \n",
+       "1                 45.56      6.483333        733.6              92.0   \n",
+       "2                 45.50      6.366667        733.7              92.0   \n",
+       "3                 45.40      6.250000        733.8              92.0   \n",
+       "4                 45.40      6.133333        733.9              92.0   \n",
+       "\n",
+       "   Windspeed  Visibility  T_Dewpoint  Random_Var1  Random_Var2  \n",
+       "0   7.000000   63.000000         5.3    13.275433    13.275433  \n",
+       "1   6.666667   59.166667         5.2    18.606195    18.606195  \n",
+       "2   6.333333   55.333333         5.1    28.642668    28.642668  \n",
+       "3   6.000000   51.500000         5.0    45.410389    45.410389  \n",
+       "4   5.666667   47.666667         4.9    10.084097    10.084097  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5249c0c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data Normalization\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "scaler = MinMaxScaler()\n",
+    "normalised_df = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)\n",
+    "features_df = normalised_df.drop(columns = ['Appliances'])\n",
+    "appliances_target = normalised_df['Appliances']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4bb31164",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Temp_Kitchen</th>\n",
+       "      <th>Humidity_Kitchen</th>\n",
+       "      <th>Temp_LivingRoom</th>\n",
+       "      <th>Humidity_LivingRoom</th>\n",
+       "      <th>Temp_LaundryRoom</th>\n",
+       "      <th>Humidity_LaundryRoom</th>\n",
+       "      <th>Temp_Office</th>\n",
+       "      <th>Humidity_Office</th>\n",
+       "      <th>Temp_Bathroom</th>\n",
+       "      <th>Humidity_Bathroom</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Temp_ParentsRoom</th>\n",
+       "      <th>Humidity_ParentsRoom</th>\n",
+       "      <th>Temp_Outside</th>\n",
+       "      <th>Press_mm_hg</th>\n",
+       "      <th>Humidity_Outside</th>\n",
+       "      <th>Windspeed</th>\n",
+       "      <th>Visibility</th>\n",
+       "      <th>T_Dewpoint</th>\n",
+       "      <th>Random_Var1</th>\n",
+       "      <th>Random_Var2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.32735</td>\n",
+       "      <td>0.566187</td>\n",
+       "      <td>0.225345</td>\n",
+       "      <td>0.684038</td>\n",
+       "      <td>0.215188</td>\n",
+       "      <td>0.746066</td>\n",
+       "      <td>0.351351</td>\n",
+       "      <td>0.764262</td>\n",
+       "      <td>0.175506</td>\n",
+       "      <td>0.381691</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.223032</td>\n",
+       "      <td>0.677290</td>\n",
+       "      <td>0.372990</td>\n",
+       "      <td>0.097674</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.953846</td>\n",
+       "      <td>0.538462</td>\n",
+       "      <td>0.265449</td>\n",
+       "      <td>0.265449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.32735</td>\n",
+       "      <td>0.541326</td>\n",
+       "      <td>0.225345</td>\n",
+       "      <td>0.682140</td>\n",
+       "      <td>0.215188</td>\n",
+       "      <td>0.748871</td>\n",
+       "      <td>0.351351</td>\n",
+       "      <td>0.782437</td>\n",
+       "      <td>0.175506</td>\n",
+       "      <td>0.381691</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.226500</td>\n",
+       "      <td>0.678532</td>\n",
+       "      <td>0.369239</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>0.476190</td>\n",
+       "      <td>0.894872</td>\n",
+       "      <td>0.533937</td>\n",
+       "      <td>0.372083</td>\n",
+       "      <td>0.372083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.32735</td>\n",
+       "      <td>0.530502</td>\n",
+       "      <td>0.225345</td>\n",
+       "      <td>0.679445</td>\n",
+       "      <td>0.215188</td>\n",
+       "      <td>0.755569</td>\n",
+       "      <td>0.344745</td>\n",
+       "      <td>0.778062</td>\n",
+       "      <td>0.175506</td>\n",
+       "      <td>0.380037</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.219563</td>\n",
+       "      <td>0.676049</td>\n",
+       "      <td>0.365488</td>\n",
+       "      <td>0.102326</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>0.452381</td>\n",
+       "      <td>0.835897</td>\n",
+       "      <td>0.529412</td>\n",
+       "      <td>0.572848</td>\n",
+       "      <td>0.572848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.32735</td>\n",
+       "      <td>0.524080</td>\n",
+       "      <td>0.225345</td>\n",
+       "      <td>0.678414</td>\n",
+       "      <td>0.215188</td>\n",
+       "      <td>0.758685</td>\n",
+       "      <td>0.341441</td>\n",
+       "      <td>0.770949</td>\n",
+       "      <td>0.175506</td>\n",
+       "      <td>0.380037</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.219563</td>\n",
+       "      <td>0.671909</td>\n",
+       "      <td>0.361736</td>\n",
+       "      <td>0.104651</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.776923</td>\n",
+       "      <td>0.524887</td>\n",
+       "      <td>0.908261</td>\n",
+       "      <td>0.908261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.32735</td>\n",
+       "      <td>0.531419</td>\n",
+       "      <td>0.225345</td>\n",
+       "      <td>0.676727</td>\n",
+       "      <td>0.215188</td>\n",
+       "      <td>0.758685</td>\n",
+       "      <td>0.341441</td>\n",
+       "      <td>0.762697</td>\n",
+       "      <td>0.178691</td>\n",
+       "      <td>0.380037</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.219563</td>\n",
+       "      <td>0.671909</td>\n",
+       "      <td>0.357985</td>\n",
+       "      <td>0.106977</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>0.404762</td>\n",
+       "      <td>0.717949</td>\n",
+       "      <td>0.520362</td>\n",
+       "      <td>0.201611</td>\n",
+       "      <td>0.201611</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Temp_Kitchen  Humidity_Kitchen  Temp_LivingRoom  Humidity_LivingRoom  \\\n",
+       "0       0.32735          0.566187         0.225345             0.684038   \n",
+       "1       0.32735          0.541326         0.225345             0.682140   \n",
+       "2       0.32735          0.530502         0.225345             0.679445   \n",
+       "3       0.32735          0.524080         0.225345             0.678414   \n",
+       "4       0.32735          0.531419         0.225345             0.676727   \n",
+       "\n",
+       "   Temp_LaundryRoom  Humidity_LaundryRoom  Temp_Office  Humidity_Office  \\\n",
+       "0          0.215188              0.746066     0.351351         0.764262   \n",
+       "1          0.215188              0.748871     0.351351         0.782437   \n",
+       "2          0.215188              0.755569     0.344745         0.778062   \n",
+       "3          0.215188              0.758685     0.341441         0.770949   \n",
+       "4          0.215188              0.758685     0.341441         0.762697   \n",
+       "\n",
+       "   Temp_Bathroom  Humidity_Bathroom  ...  Temp_ParentsRoom  \\\n",
+       "0       0.175506           0.381691  ...          0.223032   \n",
+       "1       0.175506           0.381691  ...          0.226500   \n",
+       "2       0.175506           0.380037  ...          0.219563   \n",
+       "3       0.175506           0.380037  ...          0.219563   \n",
+       "4       0.178691           0.380037  ...          0.219563   \n",
+       "\n",
+       "   Humidity_ParentsRoom  Temp_Outside  Press_mm_hg  Humidity_Outside  \\\n",
+       "0              0.677290      0.372990     0.097674          0.894737   \n",
+       "1              0.678532      0.369239     0.100000          0.894737   \n",
+       "2              0.676049      0.365488     0.102326          0.894737   \n",
+       "3              0.671909      0.361736     0.104651          0.894737   \n",
+       "4              0.671909      0.357985     0.106977          0.894737   \n",
+       "\n",
+       "   Windspeed  Visibility  T_Dewpoint  Random_Var1  Random_Var2  \n",
+       "0   0.500000    0.953846    0.538462     0.265449     0.265449  \n",
+       "1   0.476190    0.894872    0.533937     0.372083     0.372083  \n",
+       "2   0.452381    0.835897    0.529412     0.572848     0.572848  \n",
+       "3   0.428571    0.776923    0.524887     0.908261     0.908261  \n",
+       "4   0.404762    0.717949    0.520362     0.201611     0.201611  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d4ac60a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# spliting data into training and test set i 70-30 with 42 random_state.\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train, x_test, y_train, y_test = train_test_split(features_df, \n",
+    "                                                    appliances_target, test_size=0.3, \n",
+    "                                                    random_state=42)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9c38ba2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "linear_model = LinearRegression()\n",
+    "linear_model.fit(x_train, y_train)\n",
+    "predicted_values = linear_model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e83416a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "45.35"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Q.14\n",
+    "import numpy as np\n",
+    "rss = np.sum(np.square(y_test - predicted_values))\n",
+    "round(rss, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2ab99def",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.088"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Q.15\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "rms = np.sqrt(mean_squared_error(y_test, predicted_values))\n",
+    "round(rms, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "599d3b2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.05"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Q.13\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "mean = mean_absolute_error(y_test, predicted_values)\n",
+    "round(mean,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "fa27a198",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features               Humidity_Kitchen\n",
+      "Linear_Model_Weight            0.553547\n",
+      "Name: 25, dtype: object\n",
+      "Features               Humidity_LivingRoom\n",
+      "Linear_Model_Weight              -0.456698\n",
+      "Name: 0, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Q.17\n",
+    "def get_weights_df(model, feat, column_name):\n",
+    "    weights = pd.Series(model.coef_, feat.columns).sort_values()\n",
+    "    weights_df = pd.DataFrame(weights).reset_index()\n",
+    "    weights_df.columns = ['Features', column_name]\n",
+    "    weights_df[column_name].round(3)\n",
+    "    return weights_df\n",
+    "\n",
+    "linear_model_weights = get_weights_df(linear_model, x_train, 'Linear_Model_Weight')\n",
+    "\n",
+    "print(linear_model_weights.iloc[linear_model_weights['Linear_Model_Weight'].idxmax()])\n",
+    "\n",
+    "print(linear_model_weights.iloc[linear_model_weights['Linear_Model_Weight'].idxmin()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "4ba26e82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lasso Regression\n",
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "lasso_R = Lasso(alpha=0.001)\n",
+    "lasso_R.fit(x_train, y_train)\n",
+    "lasso_pred = lasso_R.predict(x_test)\n",
+    "\n",
+    "lasso_weight = get_weights_df(lasso_R, x_train, 'Lasso_weight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "68417d8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.025\n",
+      "                     Features  Lasso_weight\n",
+      "0            Humidity_Outside     -0.049557\n",
+      "1       Humidity_TeenagerRoom     -0.000110\n",
+      "2                Temp_Kitchen      0.000000\n",
+      "3                  T_Dewpoint      0.000000\n",
+      "4                  Visibility      0.000000\n",
+      "5                 Press_mm_hg     -0.000000\n",
+      "6                Temp_Outside      0.000000\n",
+      "7        Humidity_ParentsRoom     -0.000000\n",
+      "8            Temp_ParentsRoom     -0.000000\n",
+      "9           Temp_TeenagerRoom      0.000000\n",
+      "10       Humidity_IroningRoom     -0.000000\n",
+      "11                Random_Var1     -0.000000\n",
+      "12           Temp_IroningRoom     -0.000000\n",
+      "13      Temp_Outside_Building      0.000000\n",
+      "14          Humidity_Bathroom      0.000000\n",
+      "15              Temp_Bathroom     -0.000000\n",
+      "16            Humidity_Office      0.000000\n",
+      "17                Temp_Office     -0.000000\n",
+      "18       Humidity_LaundryRoom      0.000000\n",
+      "19           Temp_LaundryRoom      0.000000\n",
+      "20        Humidity_LivingRoom     -0.000000\n",
+      "21            Temp_LivingRoom      0.000000\n",
+      "22  Humidity_Outside_Building     -0.000000\n",
+      "23                Random_Var2     -0.000000\n",
+      "24                  Windspeed      0.002912\n",
+      "25           Humidity_Kitchen      0.017880\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lasso_R.score(x_train, y_train).round(3))\n",
+    "print(lasso_weight)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "bcfad006",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Q.19\n",
+    "print((lasso_weights_df['Lasso_weight'] != 0).sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "884998b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.094"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Q20\n",
+    "lasso_rmse = np.sqrt(mean_squared_error(y_test, lasso_pred))\n",
+    "round(lasso_rmse, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be32cb67",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Predicting Energy Efficiency of Buildings/README.md b/Predicting Energy Efficiency of Buildings/README.md
new file mode 100644
index 0000000..60c4eb4
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+++ b/Predicting Energy Efficiency of Buildings/README.md	
@@ -0,0 +1,5 @@
+we will develop a multivariate multiple regression model to study the effect of eight input variables on two output variables, which are the heating load and the cooling load, of residential buildings.
+you will learn about simple linear regression and the different assumptions made by simple linear regression models.
+you will learn about multiple linear regression and assumptions made by multiple linear regression models.
+you will learn about different evaluation metrics for measuring regression performance.
+ou will learn about regularization as a method to make complex models simpler by penalising coefficients to reduce their magnitude, variance in the training set and in turn, reduce overfitting in the model.