Energy Consumption-XGBoost,dtree.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Predicting Energy consumption - XGBoost and Decision tree<h1></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"Understanding the dataset\">\n",
    "    <h2>Understanding the dataset</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Energy consumption dataset Contains information from https://www.kaggle.com/claytonmiller/annual-energy-consumption-from-singapore-buildings accessed on 07/8/2021 from https://data.gov.sg/dataset/building-energy-performance-data which is made available under the terms of the Singapore Open Data Licence version 1.0 https://data.gov.sg/open-data-licence"
     ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine an Energy provider, recording their customer building's energy usage patterns, classifying the buildings annual energy usage as High or Low. In this project, we aim to learn energy patterns of buildings using a baseline decision tree classifier and compare its performance to performance of decision trees that uses boosting strategy namely XGBoost"
     ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('E:\\Datasets\\data\\Dataset2018.csv')\n",
    "df2 = pd.read_excel('E:\\Datasets\\data\\Dataset2019.xlsx')\n",
    "\n",
    "frames = [df,df2]\n",
    "my_data = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata = pd.DataFrame(my_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"data\">\n",
    "    <h2>Data Visualization </h2> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>energyuseintensity(year1)</td>\n",
       "      <td>2041.0</td>\n",
       "      <td>259.482423</td>\n",
       "      <td>205.466214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>321.936358</td>\n",
       "      <td>2.750351e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>energyuseintensity(year2)</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>256.999141</td>\n",
       "      <td>200.228148</td>\n",
       "      <td>1.0</td>\n",
       "      <td>143.043546</td>\n",
       "      <td>211.371424</td>\n",
       "      <td>319.155566</td>\n",
       "      <td>2.750000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>greenmarkyearaward</td>\n",
       "      <td>639.0</td>\n",
       "      <td>2015.302034</td>\n",
       "      <td>2.732226</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.019000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>grossfloorarea</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>31202.073530</td>\n",
       "      <td>88811.664136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5375.750000</td>\n",
       "      <td>13051.500000</td>\n",
       "      <td>30475.750000</td>\n",
       "      <td>2.865168e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count          mean           std     min  \\\n",
       "energyuseintensity(year1)  2041.0    259.482423    205.466214     0.0   \n",
       "energyuseintensity(year2)  2047.0    256.999141    200.228148     1.0   \n",
       "greenmarkyearaward          639.0   2015.302034      2.732226  2006.0   \n",
       "grossfloorarea             1708.0  31202.073530  88811.664136   115.0   \n",
       "\n",
       "                                   25%           50%           75%  \\\n",
       "energyuseintensity(year1)   143.000000    213.000000    321.936358   \n",
       "energyuseintensity(year2)   143.043546    211.371424    319.155566   \n",
       "greenmarkyearaward         2014.000000   2016.000000   2017.000000   \n",
       "grossfloorarea             5375.750000  13051.500000  30475.750000   \n",
       "\n",
       "                                    max  \n",
       "energyuseintensity(year1)  2.750351e+03  \n",
       "energyuseintensity(year2)  2.750000e+03  \n",
       "greenmarkyearaward         2.019000e+03  \n",
       "grossfloorarea             2.865168e+06  "
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "newdata.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the dataset, energyuseintensity,grossfloorarea appear to be right skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000015A9AFF95C8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdata.hist(column='grossfloorarea', bins=50)\n",
    "newdata.hist(column='energyuseintensity(year1)', bins=50)\n",
    "newdata.hist(column='energyuseintensity(year2)', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"data\">\n",
    "    <h2>Data Cleaning</h2> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyse the  dataset for null rows, remove the null rows of the columns we aim to use for bulding our classifcation model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buildingaddress               361\n",
       "buildingname                  689\n",
       "buildingsize                  280\n",
       "buildingtype                    0\n",
       "energyuseintensity(year1)      28\n",
       "energyuseintensity(year2)      22\n",
       "greenmarkrating              1430\n",
       "greenmarkstatus                 0\n",
       "greenmarkyearaward           1430\n",
       "grossfloorarea                361\n",
       "voluntarydisclosure             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = newdata.dropna(subset=['energyuseintensity(year1)','buildingsize','energyuseintensity(year2)','greenmarkstatus','grossfloorarea'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buildingaddress                0\n",
       "buildingname                 255\n",
       "buildingsize                   0\n",
       "buildingtype                   0\n",
       "energyuseintensity(year1)      0\n",
       "energyuseintensity(year2)      0\n",
       "greenmarkrating              931\n",
       "greenmarkstatus                0\n",
       "greenmarkyearaward           931\n",
       "grossfloorarea                 0\n",
       "voluntarydisclosure            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Categorical variable for target column:<h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, we create a conditional column which gives us a categorical variable based on the EUI values of Buildings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:376: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "E:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "row_indexes=data[data['energyuseintensity(year1)']>=200].index\n",
    "data.loc[row_indexes,'EUI(Year1)']=\"High\"\n",
    "row_indexes=data[data['energyuseintensity(year1)']<200].index\n",
    "data.loc[row_indexes,'EUI(Year1)']=\"Low\"\n",
    "\n",
    "row_indexes=data[data['energyuseintensity(year2)']>=200].index\n",
    "data.loc[row_indexes,'EUI(Year2)']=\"High\"\n",
    "row_indexes=data[data['energyuseintensity(year2)']<200].index\n",
    "data.loc[row_indexes,'EUI(Year2)']=\"Low\"\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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>buildingaddress</th>\n",
       "      <th>buildingname</th>\n",
       "      <th>buildingsize</th>\n",
       "      <th>buildingtype</th>\n",
       "      <th>energyuseintensity(year1)</th>\n",
       "      <th>energyuseintensity(year2)</th>\n",
       "      <th>greenmarkrating</th>\n",
       "      <th>greenmarkstatus</th>\n",
       "      <th>greenmarkyearaward</th>\n",
       "      <th>grossfloorarea</th>\n",
       "      <th>voluntarydisclosure</th>\n",
       "      <th>EUI(Year1)</th>\n",
       "      <th>EUI(Year2)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>160 CHANGI ROAD, SINGAPORE 419728</td>\n",
       "      <td>HEXACUBE</td>\n",
       "      <td>Small</td>\n",
       "      <td>Mixed Development</td>\n",
       "      <td>81.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5036.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>180 KITCHENER ROAD, SINGAPORE 208539</td>\n",
       "      <td>CITY SQUARE MALL</td>\n",
       "      <td>Large</td>\n",
       "      <td>Retail</td>\n",
       "      <td>382.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>65640.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9 RAFFLES PLACE, SINGAPORE 048619</td>\n",
       "      <td>REPUBLIC PLAZA</td>\n",
       "      <td>Large</td>\n",
       "      <td>Office</td>\n",
       "      <td>212.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>102356.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1 MAGAZINE ROAD, SINGAPORE 059567</td>\n",
       "      <td>CENTRAL MALL</td>\n",
       "      <td>Large</td>\n",
       "      <td>Office</td>\n",
       "      <td>203.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>15769.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>11 TANJONG KATONG ROAD, SINGAPORE 437157</td>\n",
       "      <td>KINEX</td>\n",
       "      <td>Large</td>\n",
       "      <td>Retail</td>\n",
       "      <td>431.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>28938.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            buildingaddress      buildingname buildingsize  \\\n",
       "0         160 CHANGI ROAD, SINGAPORE 419728          HEXACUBE        Small   \n",
       "2      180 KITCHENER ROAD, SINGAPORE 208539  CITY SQUARE MALL        Large   \n",
       "3         9 RAFFLES PLACE, SINGAPORE 048619    REPUBLIC PLAZA        Large   \n",
       "4         1 MAGAZINE ROAD, SINGAPORE 059567      CENTRAL MALL        Large   \n",
       "5  11 TANJONG KATONG ROAD, SINGAPORE 437157             KINEX        Large   \n",
       "\n",
       "        buildingtype  energyuseintensity(year1)  energyuseintensity(year2)  \\\n",
       "0  Mixed Development                       81.0                      105.0   \n",
       "2             Retail                      382.0                      365.0   \n",
       "3             Office                      212.0                      183.0   \n",
       "4             Office                      203.0                      181.0   \n",
       "5             Retail                      431.0                      396.0   \n",
       "\n",
       "  greenmarkrating greenmarkstatus  greenmarkyearaward  grossfloorarea  \\\n",
       "0             NaN              No                 NaN          5036.0   \n",
       "2        Platinum             Yes              2018.0         65640.0   \n",
       "3        Platinum             Yes              2018.0        102356.0   \n",
       "4        Platinum             Yes              2017.0         15769.0   \n",
       "5            Gold             Yes              2015.0         28938.0   \n",
       "\n",
       "  voluntarydisclosure EUI(Year1) EUI(Year2)  \n",
       "0                   Y        Low        Low  \n",
       "2                   Y       High       High  \n",
       "3                   Y       High        Low  \n",
       "4                   Y        Low        Low  \n",
       "5                   Y        Low        Low  "
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Pop and insert the columns that we are gonna use in the beginning for prediction purposes after fitting model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "sixth_column = dataframe.pop('EUI(Year2)')\n",
    "dataframe.insert(0, 'EUI(Year2)', sixth_column)\n",
    "\n",
    "\n",
    "fifth_column = dataframe.pop('grossfloorarea')\n",
    "dataframe.insert(0, 'grossfloorarea', fifth_column)\n",
    "\n",
    "\n",
    "fourth_column = dataframe.pop('greenmarkstatus')\n",
    "dataframe.insert(0, 'greenmarkstatus', fourth_column)\n",
    "\n",
    "third_column = dataframe.pop('energyuseintensity(year2)')\n",
    "dataframe.insert(0, 'energyuseintensity(year2)', third_column)\n",
    "\n",
    "second_column = dataframe.pop('energyuseintensity(year1)')\n",
    "dataframe.insert(0, 'energyuseintensity(year1)', second_column)\n",
    "\n",
    "first_column = dataframe.pop('buildingsize')\n",
    "dataframe.insert(0, 'buildingsize', first_column)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the data for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "buildingsize                   0\n",
       "energyuseintensity(year1)      0\n",
       "energyuseintensity(year2)      0\n",
       "greenmarkstatus                0\n",
       "grossfloorarea                 0\n",
       "EUI(Year2)                     0\n",
       "buildingaddress                0\n",
       "buildingname                 255\n",
       "buildingtype                   0\n",
       "greenmarkrating              931\n",
       "greenmarkyearaward           931\n",
       "voluntarydisclosure            0\n",
       "EUI(Year1)                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there are type float64 objects, Decision tree classifier takes float32 as input thus we need to convert it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildingsize                  object\n",
      "energyuseintensity(year1)    float64\n",
      "energyuseintensity(year2)    float64\n",
      "greenmarkstatus               object\n",
      "grossfloorarea               float64\n",
      "EUI(Year2)                    object\n",
      "buildingaddress               object\n",
      "buildingname                  object\n",
      "buildingtype                  object\n",
      "greenmarkrating               object\n",
      "greenmarkyearaward           float64\n",
      "voluntarydisclosure           object\n",
      "EUI(Year1)                    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (dataframe.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dataframe = my_dataframe.astype({c: np.float32 for c in dataframe.select_dtypes(include='float64').columns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildingsize                  object\n",
      "energyuseintensity(year1)    float32\n",
      "energyuseintensity(year2)    float32\n",
      "greenmarkstatus               object\n",
      "grossfloorarea               float32\n",
      "EUI(Year2)                    object\n",
      "buildingtype                  object\n",
      "greenmarkrating               object\n",
      "greenmarkyearaward           float32\n",
      "voluntarydisclosure           object\n",
      "EUI(Year1)                    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (my_dataframe.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "scrolled": false
   },
   "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>buildingsize</th>\n",
       "      <th>energyuseintensity(year1)</th>\n",
       "      <th>energyuseintensity(year2)</th>\n",
       "      <th>greenmarkstatus</th>\n",
       "      <th>grossfloorarea</th>\n",
       "      <th>EUI(Year2)</th>\n",
       "      <th>buildingtype</th>\n",
       "      <th>greenmarkrating</th>\n",
       "      <th>greenmarkyearaward</th>\n",
       "      <th>voluntarydisclosure</th>\n",
       "      <th>EUI(Year1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Small</td>\n",
       "      <td>81.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>No</td>\n",
       "      <td>5036.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>Mixed Development</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Large</td>\n",
       "      <td>382.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>65640.0</td>\n",
       "      <td>High</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Large</td>\n",
       "      <td>212.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>102356.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>Office</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Large</td>\n",
       "      <td>203.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>15769.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>Office</td>\n",
       "      <td>Platinum</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Large</td>\n",
       "      <td>431.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28938.0</td>\n",
       "      <td>Low</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Gold</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buildingsize  energyuseintensity(year1)  energyuseintensity(year2)  \\\n",
       "0        Small                       81.0                      105.0   \n",
       "2        Large                      382.0                      365.0   \n",
       "3        Large                      212.0                      183.0   \n",
       "4        Large                      203.0                      181.0   \n",
       "5        Large                      431.0                      396.0   \n",
       "\n",
       "  greenmarkstatus  grossfloorarea EUI(Year2)       buildingtype  \\\n",
       "0              No          5036.0        Low  Mixed Development   \n",
       "2             Yes         65640.0       High             Retail   \n",
       "3             Yes        102356.0        Low             Office   \n",
       "4             Yes         15769.0        Low             Office   \n",
       "5             Yes         28938.0        Low             Retail   \n",
       "\n",
       "  greenmarkrating  greenmarkyearaward voluntarydisclosure EUI(Year1)  \n",
       "0             NaN                 NaN                   Y        Low  \n",
       "2        Platinum              2018.0                   Y       High  \n",
       "3        Platinum              2018.0                   Y       High  \n",
       "4        Platinum              2017.0                   Y        Low  \n",
       "5            Gold              2015.0                   Y        Low  "
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Pre-Processing<h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X is the Data present and\n",
    "Y is the Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Small', 81.0, 105.0, 'No', 5036.0],\n",
       "       ['Large', 382.0, 365.0, 'Yes', 65640.0],\n",
       "       ['Large', 212.0, 183.0, 'Yes', 102356.0],\n",
       "       ['Large', 203.0, 181.0, 'Yes', 15769.0]], dtype=object)"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = my_dataframe[['buildingsize','energyuseintensity(year1)','energyuseintensity(year2)','greenmarkstatus','grossfloorarea']].values\n",
    "X[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Encode categorical values as decision tree only takes values as inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 81.0, 105.0, 0, 5036.0],\n",
       "       [0, 382.0, 365.0, 1, 65640.0],\n",
       "       [0, 212.0, 183.0, 1, 102356.0],\n",
       "       [0, 203.0, 181.0, 1, 15769.0],\n",
       "       [0, 431.0, 396.0, 1, 28938.0],\n",
       "       [0, 281.0, 277.0, 0, 65933.0]], dtype=object)"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(['Small','Large']) \n",
    "X[:,0] = le.transform(X[:,0])\n",
    "\n",
    "\n",
    "le_ = LabelEncoder()\n",
    "le_.fit([ 'No','Yes'])\n",
    "X[:,3] = le_.transform(X[:,3])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X[0:6]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Low\n",
       "2     High\n",
       "3      Low\n",
       "4      Low\n",
       "5      Low\n",
       "6      Low\n",
       "7      Low\n",
       "8      Low\n",
       "9     High\n",
       "10     Low\n",
       "Name: EUI(Year2), dtype: object"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = my_dataframe['EUI(Year2)']\n",
    "\n",
    "y[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifying shape of the data to be same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1499, 5) (1499,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Splitting data and modelling<h2>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use train_test_split to split the data for training and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "DecisionTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DecisionTree.fit(X_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Prediciton and accuracy<h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "predTree = DecisionTree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that our model predicts most of the Predicitions right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low' 'High' 'High' 'Low' 'High' 'Low' 'Low' 'Low' 'High' 'High']\n",
      "1075    High\n",
      "1011    High\n",
      "1148    High\n",
      "73       Low\n",
      "690     High\n",
      "1083     Low\n",
      "541      Low\n",
      "268      Low\n",
      "820     High\n",
      "552     High\n",
      "Name: EUI(Year2), dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (predTree [0:10])\n",
    "print (y_testset [0:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the model is found to be 82% and we aim to improve it by using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.8266666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \",metrics.accuracy_score(y_testset, predTree))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Visualizing the Decision tree<h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installing required libraries to visualize decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge pydotplus -y\n",
    "!conda install -c conda-forge python-graphviz -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  io import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15aa0a33e88>"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "filename = \"Decisiontree.png\"\n",
    "featureNames = dataframe.columns[0:5]\n",
    "targetNames = dataframe[\"EUI(Year2)\"].unique().tolist()\n",
    "out=tree.export_graphviz(DecisionTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png(filename)\n",
    "img = mpimg.imread(filename)\n",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above decision tree shows the classification of EUI for newer buildings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>New model:XGBoost Classifier<h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label encoding of the target vector is done as XGBoost requires the target to be values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "le_y = LabelEncoder()\n",
    "le_y.fit(['High','Low'])\n",
    "y= le_y.transform(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "my_model = XGBClassifier()\n",
    "\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a model with random hyperparameters that are later Tuned to achieve maximum accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[02:04:55] WARNING: ..\\src\\learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=0.8, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
       "              min_child_weight=2, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=1000, n_jobs=4, nthread=4, num_parallel_tree=1,\n",
       "              random_state=100, reg_alpha=0, reg_lambda=1, scale_pos_weight=0.1,\n",
       "              seed=100, subsample=0.8, tree_method='exact',\n",
       "              validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model = XGBClassifier(n_estimators=1000, learning_rate=0.01,max_depth=3,min_child_weight=2,                      \n",
    " subsample=0.8,\n",
    " nthread=4,\n",
    " colsample_bytree = 0.8,                       \n",
    " scale_pos_weight=0.1,\n",
    " seed=100\n",
    " )\n",
    "my_model.fit(X_trainset, y_trainset, early_stopping_rounds=5, \n",
    "             eval_set=[( X_testset, y_testset)], verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy of the model is found to be 84% using XGBoost classifier!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 84.00%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_prediction = my_model.predict(X_testset)\n",
    "predictions = [round(value) for value in y_prediction]\n",
    "# evaluate predictions\n",
    "accuracy = accuracy_score(y_testset, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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