gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abstract-destruction",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA) on Property Sales Dataset\n",
    "\n",
    "This Jupyter notebook presents an exploratory data analysis (EDA) of a property sales dataset. <br/>\n",
    "The purpose of this analysis is to: \n",
    "1. gain insights into the dataset\n",
    "2. identify any anomalies or suspicious data points\n",
    "3. explore the price difference between similar properties in New South Wales (NSW) and Queensland (QLD). \n",
    "\n",
    "By performing various statistical analyses and visualizations, I aim to uncover patterns, trends, and relationships within the data, and provide meaningful insights for further decision-making.<br/> \n",
    "\n",
    "## A data science project consists of the following steps:\n",
    "1. Data Analysis\n",
    "2. Feature Engineering\n",
    "3. Feature Selection\n",
    "4. Building Model\n",
    "5. Finetuning Model\n",
    "6. Model Deployment\n",
    "\n",
    "For the purpose of this expercise I will be working on the first step and answer a few questions along the way.\n",
    "\n",
    "Let's dive into the data and explore the fascinating world of property sales!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sudden-appraisal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import sys\n",
    "\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# For tree Graph\n",
    "import squarify\n",
    "\n",
    "import seaborn as sbn\n",
    "import plotly.express as px\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "applicable-attraction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from the dataset\n",
    "df = pd.read_csv(\"dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "martial-cooperation",
   "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>property_id</th>\n",
       "      <th>sale_price</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>property_type</th>\n",
       "      <th>suburb</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>land</th>\n",
       "      <th>floorplate</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>garages</th>\n",
       "      <th>slope</th>\n",
       "      <th>max_roof_height</th>\n",
       "      <th>year_built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1452430</td>\n",
       "      <td>1061100.0</td>\n",
       "      <td>2/06/2011</td>\n",
       "      <td>House</td>\n",
       "      <td>MOUNT ANNAN</td>\n",
       "      <td>2567</td>\n",
       "      <td>NSW</td>\n",
       "      <td>450.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.032485</td>\n",
       "      <td>7.177677</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6915958</td>\n",
       "      <td>543900.0</td>\n",
       "      <td>16/01/2013</td>\n",
       "      <td>Strata</td>\n",
       "      <td>HILLCREST</td>\n",
       "      <td>4118</td>\n",
       "      <td>QLD</td>\n",
       "      <td>279.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.042645</td>\n",
       "      <td>5.870000</td>\n",
       "      <td>1982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>841815</td>\n",
       "      <td>1102500.0</td>\n",
       "      <td>20/07/2014</td>\n",
       "      <td>House</td>\n",
       "      <td>EMERTON</td>\n",
       "      <td>2770</td>\n",
       "      <td>NSW</td>\n",
       "      <td>552.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-9.114863</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4393085</td>\n",
       "      <td>1183800.0</td>\n",
       "      <td>15/08/2019</td>\n",
       "      <td>Strata</td>\n",
       "      <td>PACIFIC PINES</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.786286</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2782313</td>\n",
       "      <td>805200.0</td>\n",
       "      <td>30/10/2013</td>\n",
       "      <td>Strata</td>\n",
       "      <td>MARSFIELD</td>\n",
       "      <td>2122</td>\n",
       "      <td>NSW</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.141627</td>\n",
       "      <td>12.207000</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   property_id  sale_price   sale_date property_type         suburb  postcode  \\\n",
       "0      1452430   1061100.0   2/06/2011         House    MOUNT ANNAN      2567   \n",
       "1      6915958    543900.0  16/01/2013        Strata      HILLCREST      4118   \n",
       "2       841815   1102500.0  20/07/2014         House        EMERTON      2770   \n",
       "3      4393085   1183800.0  15/08/2019        Strata  PACIFIC PINES      4211   \n",
       "4      2782313    805200.0  30/10/2013        Strata      MARSFIELD      2122   \n",
       "\n",
       "  state    land  floorplate  bedrooms  bathrooms  garages     slope  \\\n",
       "0   NSW   450.0       267.0       3.0        4.0      2.0 -5.032485   \n",
       "1   QLD   279.0       205.0       1.0        2.0      1.0 -1.042645   \n",
       "2   NSW   552.0       104.0       1.0        3.0      2.0 -9.114863   \n",
       "3   QLD   380.0         NaN       2.0        4.0      2.0 -2.786286   \n",
       "4   NSW  3627.0      1239.0       2.0        2.0      2.0 -1.141627   \n",
       "\n",
       "   max_roof_height  year_built  \n",
       "0         7.177677      2000.0  \n",
       "1         5.870000      1982.0  \n",
       "2         4.140000      1988.0  \n",
       "3         6.890000      1988.0  \n",
       "4        12.207000      2000.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "artificial-xerox",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52248, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "married-kinase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52248 entries, 0 to 52247\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   property_id      52248 non-null  int64  \n",
      " 1   sale_price       52248 non-null  float64\n",
      " 2   sale_date        52248 non-null  object \n",
      " 3   property_type    52248 non-null  object \n",
      " 4   suburb           52248 non-null  object \n",
      " 5   postcode         52248 non-null  int64  \n",
      " 6   state            52248 non-null  object \n",
      " 7   land             52207 non-null  float64\n",
      " 8   floorplate       52127 non-null  float64\n",
      " 9   bedrooms         51949 non-null  float64\n",
      " 10  bathrooms        52005 non-null  float64\n",
      " 11  garages          51754 non-null  float64\n",
      " 12  slope            51475 non-null  float64\n",
      " 13  max_roof_height  51451 non-null  float64\n",
      " 14  year_built       50903 non-null  float64\n",
      "dtypes: float64(9), int64(2), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Variables information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clinical-timber",
   "metadata": {},
   "source": [
    "### Now that the data is imported properly, I would like to explore it further to find some interesting points.\n",
    "So Far I know that:<br/>\n",
    "1. The number of rows in the data set are 52248.\n",
    "2. The number of columns in the data set are 15.\n",
    "3. Property Id can be set as a primary key\n",
    "4. Sales data can be converted into datetime\n",
    "5. **Note:** Sales price can be an dependant feature\n",
    "\n",
    "I do steps 3 and 4 below and then look at the summary of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cooperative-crest",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set property id as primary key\n",
    "df.set_index('property_id', inplace=True)\n",
    "\n",
    "# convert the 'Date' column to datetime format\n",
    "df['sale_date']= pd.to_datetime(df['sale_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "medical-murder",
   "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>sale_price</th>\n",
       "      <th>postcode</th>\n",
       "      <th>land</th>\n",
       "      <th>floorplate</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>garages</th>\n",
       "      <th>slope</th>\n",
       "      <th>max_roof_height</th>\n",
       "      <th>year_built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.224800e+04</td>\n",
       "      <td>52248.000000</td>\n",
       "      <td>52207.000000</td>\n",
       "      <td>52127.000000</td>\n",
       "      <td>51949.000000</td>\n",
       "      <td>52005.000000</td>\n",
       "      <td>51754.000000</td>\n",
       "      <td>51475.000000</td>\n",
       "      <td>51451.000000</td>\n",
       "      <td>50903.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.007536e+06</td>\n",
       "      <td>3358.348913</td>\n",
       "      <td>953.025686</td>\n",
       "      <td>476.039135</td>\n",
       "      <td>1.688021</td>\n",
       "      <td>3.187155</td>\n",
       "      <td>1.389091</td>\n",
       "      <td>0.057454</td>\n",
       "      <td>7.633694</td>\n",
       "      <td>1986.253698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.818537e+05</td>\n",
       "      <td>915.242623</td>\n",
       "      <td>2302.996295</td>\n",
       "      <td>902.449902</td>\n",
       "      <td>0.642744</td>\n",
       "      <td>0.840039</td>\n",
       "      <td>0.657981</td>\n",
       "      <td>10.070129</td>\n",
       "      <td>6.573843</td>\n",
       "      <td>14.188864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.781000e+05</td>\n",
       "      <td>2074.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-42.051853</td>\n",
       "      <td>2.140668</td>\n",
       "      <td>1860.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.068000e+05</td>\n",
       "      <td>2560.000000</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-6.776442</td>\n",
       "      <td>4.647691</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.775000e+05</td>\n",
       "      <td>3395.500000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067975</td>\n",
       "      <td>6.396876</td>\n",
       "      <td>1989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.171200e+06</td>\n",
       "      <td>4215.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.911060</td>\n",
       "      <td>7.697807</td>\n",
       "      <td>1996.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.883200e+06</td>\n",
       "      <td>4510.000000</td>\n",
       "      <td>230400.000000</td>\n",
       "      <td>5843.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>43.770452</td>\n",
       "      <td>51.598239</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sale_price      postcode           land    floorplate      bedrooms  \\\n",
       "count  5.224800e+04  52248.000000   52207.000000  52127.000000  51949.000000   \n",
       "mean   1.007536e+06   3358.348913     953.025686    476.039135      1.688021   \n",
       "std    2.818537e+05    915.242623    2302.996295    902.449902      0.642744   \n",
       "min    3.781000e+05   2074.000000      51.000000     38.000000      1.000000   \n",
       "25%    8.068000e+05   2560.000000     293.000000    153.000000      1.000000   \n",
       "50%    9.775000e+05   3395.500000     550.000000    212.000000      2.000000   \n",
       "75%    1.171200e+06   4215.000000     714.000000    302.000000      2.000000   \n",
       "max    2.883200e+06   4510.000000  230400.000000   5843.000000      7.000000   \n",
       "\n",
       "          bathrooms       garages         slope  max_roof_height    year_built  \n",
       "count  52005.000000  51754.000000  51475.000000     51451.000000  50903.000000  \n",
       "mean       3.187155      1.389091      0.057454         7.633694   1986.253698  \n",
       "std        0.840039      0.657981     10.070129         6.573843     14.188864  \n",
       "min        1.000000      0.000000    -42.051853         2.140668   1860.000000  \n",
       "25%        3.000000      1.000000     -6.776442         4.647691   1980.000000  \n",
       "50%        3.000000      1.000000      0.067975         6.396876   1989.000000  \n",
       "75%        4.000000      2.000000      6.911060         7.697807   1996.000000  \n",
       "max        7.000000      8.000000     43.770452        51.598239   2012.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe gives statistical information about numerical columns in the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "insured-default",
   "metadata": {},
   "source": [
    "# Data Analysis Phase\n",
    "\n",
    "**In this phase I will analyse to find out more about the following:**\n",
    "1. Missing Values\n",
    "2. Numerical Values\n",
    "3. Distribution of Numerical Values\n",
    "4. Categorical Variables if there are any\n",
    "5. Outliers\n",
    "6. Relation between independant and dependant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "reverse-ladder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sale_price            0\n",
       "sale_date             0\n",
       "property_type         0\n",
       "suburb                0\n",
       "postcode              0\n",
       "state                 0\n",
       "land                 41\n",
       "floorplate          121\n",
       "bedrooms            299\n",
       "bathrooms           243\n",
       "garages             494\n",
       "slope               773\n",
       "max_roof_height     797\n",
       "year_built         1345\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "geographic-distributor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "land 0.0008  % missing values\n",
      "floorplate 0.0023  % missing values\n",
      "bedrooms 0.0057  % missing values\n",
      "bathrooms 0.0047  % missing values\n",
      "garages 0.0095  % missing values\n",
      "slope 0.0148  % missing values\n",
      "max_roof_height 0.0153  % missing values\n",
      "year_built 0.0257  % missing values\n"
     ]
    }
   ],
   "source": [
    "# 1-step make the list of features which has missing values\n",
    "features_with_na=[features for features in df.columns if df[features].isnull().sum()>1]\n",
    "\n",
    "# 2-step print the feature name and the percentage of missing values\n",
    "for feature in features_with_na:\n",
    "    print(feature, np.round(df[feature].isnull().mean(), 4),  ' % missing values')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valuable-stuart",
   "metadata": {},
   "source": [
    "### There are a few missing values.\n",
    "Since I am considering sales price as a dependant feature, I would like to see the relationship between missing values and Sales Price .<br/>\n",
    "\n",
    "The purpose of the below code will be to visually examine whether the presence of missing values in each feature has any significant impact on the median sale price. The bar plots will allow me to quickly compare the median sale price between observations with missing values and those without missing values for each feature. \n",
    "\n",
    "This analysis can help identify if missing data in specific features might be related to variations in the sale_price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "front-first",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YmuQZwAVV9YX+/j9fyWGlLnywSGvVNcAMcGlVfTfJvcCP9o/9z8C6x4Gn0PsG4EMbWtU8Q9da9QzggX7MrwCeu9jiqnoE+FaSV/Z3XbPM80lL5hm61qq/AD6T5AhwG/DVDp9zLbA/yaP0fmi6tKr46L8kNcJLLpLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY0w6JLUCIMuSY34X92QSKLFZwPQAAAAAElFTkSuQmCC\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"
    },
    {
     "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"
    },
    {
     "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": [
    "# Plot diagram to see relationship\n",
    "for feature in features_with_na:\n",
    "    df_copy = df.copy()\n",
    "    \n",
    "    # let's make a variable that indicates 1 if the observation was missing or zero otherwise\n",
    "    df_copy[feature] = np.where(df_copy[feature].isnull(), 1, 0)\n",
    "    \n",
    "    # let's calculate the mean SalePrice where the information is missing or present\n",
    "    df_copy.groupby(feature)['sale_price'].median().plot.bar()\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-source",
   "metadata": {},
   "source": [
    "### The Bar graphs show that there is significant impact for each feature on the median sale price.\n",
    "\n",
    "Takeaways:\n",
    "1. The Object Type values do not have Null Values.\n",
    "2. Numerical Features have null values\n",
    "\n",
    "I will be replacing the missing values after finding out skewness and outliers Because: \n",
    "1. If data is normally distributed and outliers are not heavily skewing the distribution, I may use Mean Imputation\n",
    "2. If there are many outliers then I may use Median Imputation.\n",
    "3. Alternate option: If there is strong correlation between features is to use regression Imputation where I can predict missing values based on relavent features.\n",
    "\n",
    "#### To make this decision I will move further ahead with the data analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "secret-accuracy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of numerical variables:  11\n"
     ]
    },
    {
     "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>sale_price</th>\n",
       "      <th>sale_date</th>\n",
       "      <th>postcode</th>\n",
       "      <th>land</th>\n",
       "      <th>floorplate</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>garages</th>\n",
       "      <th>slope</th>\n",
       "      <th>max_roof_height</th>\n",
       "      <th>year_built</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1452430</th>\n",
       "      <td>1061100.0</td>\n",
       "      <td>2011-02-06</td>\n",
       "      <td>2567</td>\n",
       "      <td>450.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-5.032485</td>\n",
       "      <td>7.177677</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6915958</th>\n",
       "      <td>543900.0</td>\n",
       "      <td>2013-01-16</td>\n",
       "      <td>4118</td>\n",
       "      <td>279.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.042645</td>\n",
       "      <td>5.870000</td>\n",
       "      <td>1982.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841815</th>\n",
       "      <td>1102500.0</td>\n",
       "      <td>2014-07-20</td>\n",
       "      <td>2770</td>\n",
       "      <td>552.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-9.114863</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4393085</th>\n",
       "      <td>1183800.0</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>4211</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-2.786286</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>1988.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782313</th>\n",
       "      <td>805200.0</td>\n",
       "      <td>2013-10-30</td>\n",
       "      <td>2122</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.141627</td>\n",
       "      <td>12.207000</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sale_price  sale_date  postcode    land  floorplate  bedrooms  \\\n",
       "property_id                                                                  \n",
       "1452430       1061100.0 2011-02-06      2567   450.0       267.0       3.0   \n",
       "6915958        543900.0 2013-01-16      4118   279.0       205.0       1.0   \n",
       "841815        1102500.0 2014-07-20      2770   552.0       104.0       1.0   \n",
       "4393085       1183800.0 2019-08-15      4211   380.0         NaN       2.0   \n",
       "2782313        805200.0 2013-10-30      2122  3627.0      1239.0       2.0   \n",
       "\n",
       "             bathrooms  garages     slope  max_roof_height  year_built  \n",
       "property_id                                                             \n",
       "1452430            4.0      2.0 -5.032485         7.177677      2000.0  \n",
       "6915958            2.0      1.0 -1.042645         5.870000      1982.0  \n",
       "841815             3.0      2.0 -9.114863         4.140000      1988.0  \n",
       "4393085            4.0      2.0 -2.786286         6.890000      1988.0  \n",
       "2782313            2.0      2.0 -1.141627        12.207000      2000.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numeric Variables\n",
    "numerical_features = [feature for feature in df.columns if df[feature].dtypes != 'O']\n",
    "\n",
    "print('Number of numerical variables: ', len(numerical_features))\n",
    "\n",
    "# visualise the numerical variables\n",
    "df[numerical_features].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stable-pharmacy",
   "metadata": {},
   "source": [
    "### Analyse Temporal Variable (Sale Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "accompanied-support",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             sale_date\n",
      "property_id           \n",
      "4678863     2010-01-01\n",
      "1663525     2010-01-02\n",
      "6614865     2010-01-02\n",
      "986844      2010-01-02\n",
      "976901      2010-01-02\n",
      "...                ...\n",
      "1923189     2020-12-31\n",
      "1923189     2020-12-31\n",
      "4799145     2020-12-31\n",
      "1923189     2020-12-31\n",
      "4799145     2020-12-31\n",
      "\n",
      "[52248 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# let me see the unique values in sale_date\n",
    "print(df[['sale_date']].sort_values(by=['sale_date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "frozen-milwaukee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract year and month information\n",
    "df['year'] = df['sale_date'].dt.year\n",
    "df['month'] = df['sale_date'].dt.month\n",
    "\n",
    "# I will now see if any 1 month has more sales than the other months\n",
    "monthly_sales = df.groupby('month')['sale_price'].sum()\n",
    "\n",
    "sales_percent = monthly_sales / monthly_sales.sum() * 100\n",
    "\n",
    "month_labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(sales_percent, labels=month_labels, autopct='%1.1f%%')\n",
    "plt.title('Percentage of Total Sales by Month')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forward-department",
   "metadata": {},
   "source": [
    "### From the pie chart I can observe that the months with the least sales are December, January and February.\n",
    "\n",
    "This must be because at the end and the start of the year. Most people suspend their listings from around Thanksgiving to the New Year because they assume buyers are scarce.\n",
    "\n",
    "Also, there are many sales between March and October"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reflected-authority",
   "metadata": {},
   "source": [
    "### Now I will analyse Object Type variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "specified-validity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['property_type', 'suburb', 'state']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data type is \"Object\" \n",
    "object_type_features=[feature for feature in df.columns if df[feature].dtypes=='O']\n",
    "object_type_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "exotic-football",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each property type and plot the graph\n",
    "property_counts = df['property_type'].value_counts()\n",
    "\n",
    "plt.pie(property_counts, labels=property_counts.index, autopct='%1.1f%%', startangle=90)\n",
    "plt.title('Distribution of Property Types')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historical-treasure",
   "metadata": {},
   "source": [
    "##### I have the following observations from the graph:\n",
    "1. There are 2 property types in the dataset: Houses and Strata.\n",
    "2. Number of Strata and Houses are almost equally distributed. \n",
    "\n",
    "I will drill down further into each state to see the split of Strata and Houses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "exterior-blowing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I will now see the total number of sales by proeprty type in NSW and QLD\n",
    "# Group the data by property type and state, and calculate the count of property_id\n",
    "df_count = pd.read_csv('dataset.csv')\n",
    "grouped_data = df_count.groupby(['property_type', 'state'])['property_id'].count().reset_index()\n",
    "\n",
    "# Pivot the data to have property types as columns and states as rows\n",
    "pivot_data = grouped_data.pivot(index='state', columns='property_type', values='property_id')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "pivot_data.plot(kind='bar', ax=ax)\n",
    "\n",
    "ax.set_xlabel('State')\n",
    "ax.set_ylabel('Total Number of Sales')\n",
    "ax.set_title('Total Number of Sales by Property Type and State')\n",
    "plt.legend(title='Property Type')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "final-concert",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot to see the total value of sales for each property type in each state.\n",
    "subset_data = df[['property_type', 'state', 'sale_price']]\n",
    "\n",
    "# Group and calculate the sum of sale_price by property_type and state\n",
    "grouped_data = subset_data.groupby(['property_type', 'state'])['sale_price'].sum()\n",
    "\n",
    "# Pivot the data\n",
    "pivot_table = grouped_data.reset_index().pivot_table(index='state', columns='property_type', values='sale_price')\n",
    "\n",
    "ax = pivot_table.plot.bar(stacked=False, figsize=(10, 6))\n",
    "ax.set_xlabel('State')\n",
    "ax.set_ylabel('Total Sale Value')\n",
    "ax.set_title('Total Sale Value by Property Type and State')\n",
    "ax.legend(title='Property Type')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "accessible-spain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the CSV file into a DataFrame\n",
    "df = pd.read_csv('dataset.csv')\n",
    "\n",
    "# Convert 'sale_date' to datetime format\n",
    "df['sale_date'] = pd.to_datetime(df['sale_date'])\n",
    "\n",
    "# Extract year from 'sale_date' column\n",
    "df['year'] = df['sale_date'].dt.year\n",
    "\n",
    "# Group data by state, year, and property type\n",
    "grouped_data = df.groupby(['state', 'year', 'property_type']).size().reset_index(name='count')\n",
    "\n",
    "# Plot line graphs for each state and property type\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(data=grouped_data, x='year', y='count', hue='property_type', style='state')\n",
    "plt.title('Number of Sales Over the Years by State and Property Type')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Sales')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "peaceful-receptor",
   "metadata": {},
   "source": [
    "#### Froom these graphs I can make the following inferences:\n",
    "\n",
    "1. Over 10 years there are more number of strata sales in both NSW and QLD compared to the houses in respective states.\n",
    "\n",
    "**Digging further into the total prices for each proeprty type in each state:**\n",
    "1. There are more number of valued sales for Strata and Houses in NSW compared to QLD. \n",
    "2. Although the number of sales in both states are the same but, total sales value is more this means that, \"The value of houses and Strata in NSW is higher than those in QLD.\"\n",
    "3. Over the years the number of sales for both Strata and Houses have been decreasing in NSW, while QLD saw a steady increase between the period of 2012 and 2017. \n",
    "\n",
    "\n",
    "Now I will analyse the last Object variable \"Suburbs\" by drilling down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "prospective-shoulder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I will calculate total sales by suburb and state\n",
    "sales_by_suburb_state = df.groupby(['suburb', 'state'])['sale_price'].sum().reset_index()\n",
    "top_20_sales = sales_by_suburb_state.nlargest(20, 'sale_price')\n",
    "plt.rcParams['figure.figsize'] = (40, 20)\n",
    "plt.rcParams['font.size'] = 12\n",
    "\n",
    "# Tree map for top 20 sales by suburb and state\n",
    "plt.figure()\n",
    "squarify.plot(sizes=top_20_sales['sale_price'], label=top_20_sales.apply(lambda x: f\"{x['suburb']}, {x['state']}\", axis=1), alpha=0.8)\n",
    "plt.axis('off')\n",
    "plt.title('Tree Map: Top 20 Sales by Suburb and State')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "curious-orlando",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for the specified suburbs and states\n",
    "suburbs = ['HORNSBY', 'WAITARA', 'NERANG']\n",
    "states = ['NSW', 'NSW', 'QLD']\n",
    "filtered_data = df[df['suburb'].isin(suburbs) & df['state'].isin(states)]\n",
    "filtered_data\n",
    "\n",
    "# Group data by suburb and calculate the number of sales and mean sale price\n",
    "sales_counts = filtered_data.groupby('suburb').size()\n",
    "mean_sale_prices = filtered_data.groupby('suburb')['sale_price'].mean()\n",
    "\n",
    "# Plotting the number of sales\n",
    "plt.figure(figsize=(10, 6))\n",
    "sales_counts.plot(kind='bar', color='blue')\n",
    "plt.title('Number of Sales for Specified Suburbs')\n",
    "plt.xlabel('Suburb')\n",
    "plt.ylabel('Number of Sales')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# Plotting the mean sale prices\n",
    "plt.figure(figsize=(10, 6))\n",
    "mean_sale_prices.plot(kind='bar', color='green')\n",
    "plt.title('Mean Sale Prices for Specified Suburbs')\n",
    "plt.xlabel('Suburb')\n",
    "plt.ylabel('Mean Sale Price ($)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chronic-breakdown",
   "metadata": {},
   "source": [
    "### The takeaway from suburb analytics:\n",
    "1. The suburbs with most sale values in 10 years are Hornsby, Waitara in NSW and Nerang in QLD.\n",
    "2. Nerang showed more number of sales compared to both Waitara and Hornsby. \n",
    "3. The total value of sales is more for Waitara in NSW although it has less number of sales because we can see that the median sales price is high for Waitara.\n",
    "4. The total value of sales is also high for Hornsby although it has the least number of sales compared to Nerang in QLD and Waitara in NSW because of high median sales price.\n",
    "\n",
    "\n",
    "**Note: The total value of sales in a suburb should not be the only consdiering factor while buying a property in a suburb because it doesnot tell the compelete scenerio.\n",
    "If these 3 suburbs were picked to buy a property then I will suggest buying in Nerang QLD.This is because it has low median sales price coupled with high number of sales over last 10 years may mean that the suburb is about to boom. However, Remember that Housing market has it's ups and downs. So, if other information about supply and demand was given, we can make a much more accurate prediction on which suburbs is a better place to buy.**\n",
    "\n",
    "\n",
    "### Let me now Analyse the Discrete and Continuous Variables\n",
    "\n",
    "Discrete features are those that can only take on a limited number of distinct values and are typically represented by categorical variables. <br/>Continuous features, can take on any numeric value within a certain range and are typically represented by numerical variables.\n",
    "\n",
    "Discrete Variables:\n",
    "1. Bedrooms\n",
    "2. Bathrooms\n",
    "3. Garages\n",
    "\n",
    "Continuous Variables:\n",
    "1. sale_price\n",
    "2. land (land area in square meters)\n",
    "3. floorplate (area of the base of the building in square meters)\n",
    "4. slope (slope of land measured in degrees)\n",
    "5. max_roof_height (maximum height of the roof in meters)\n",
    "6. year_built\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "second-haiti",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_discrete = df[['suburb','bedrooms', 'bathrooms', 'garages', 'sale_price']]\n",
    "df_continuous = df[['property_type', 'suburb','land', 'floorplate', 'slope', 'max_roof_height', 'year_built' ,'sale_price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "numeric-fountain",
   "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>suburb</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>garages</th>\n",
       "      <th>sale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52243</th>\n",
       "      <td>TWEED HEADS</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1070500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52244</th>\n",
       "      <td>CRESTMEAD</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1117500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52245</th>\n",
       "      <td>BALD HILLS</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>908900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52246</th>\n",
       "      <td>RABY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1201800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52247</th>\n",
       "      <td>MOUNT DRUITT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>974400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             suburb  bedrooms  bathrooms  garages  sale_price\n",
       "52243   TWEED HEADS       1.0        3.0      1.0   1070500.0\n",
       "52244     CRESTMEAD       1.0        3.0      NaN   1117500.0\n",
       "52245    BALD HILLS       2.0        4.0      2.0    908900.0\n",
       "52246          RABY       1.0        3.0      1.0   1201800.0\n",
       "52247  MOUNT DRUITT       1.0        3.0      2.0    974400.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_discrete.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "frozen-bidding",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of zero values in 'bedrooms': 0\n",
      "Count of zero values in 'bathrooms': 0\n",
      "Count of zero values in 'garages': 3567\n"
     ]
    }
   ],
   "source": [
    "# Count the number of zero values in 'bedrooms', 'bathrooms', and 'garages' columns\n",
    "zero_bedrooms_count = (df_discrete['bedrooms'] == 0).sum()\n",
    "zero_bathrooms_count = (df_discrete['bathrooms'] == 0).sum()\n",
    "zero_garages_count = (df_discrete['garages'] == 0).sum()\n",
    "\n",
    "# Print the counts of zero values\n",
    "print(\"Count of zero values in 'bedrooms':\", zero_bedrooms_count)\n",
    "print(\"Count of zero values in 'bathrooms':\", zero_bathrooms_count)\n",
    "print(\"Count of zero values in 'garages':\", zero_garages_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opening-april",
   "metadata": {},
   "source": [
    "###### This tells that there are zero values only in Garages. If there were Zero in bedrooms then I was planning to remove such rows, because they will not mean anything. One other thing we can assume from zero bedrooms and having atleast 1 bathroom is that the proeprty may be a piece of land. it is neither a house nor a strata property type. However, for this scenerio, we do not have any zero's in bedrooms and bathrooms making our case easy to analyse.\n",
    "\n",
    "### I will do the following analysis on Discrete data:\n",
    "1. Grouping data by state to help identify trends\n",
    "2. Time Series Analysis: Examine trends and seasonality\n",
    "3. Correlation Analysis\n",
    "4. Distribution of features. This will give insights into outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "domestic-track",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1. Group data by state and calculate the mean values for bedrooms, bathrooms, and garages\n",
    "state_grouped = df.groupby('state').agg({'bedrooms': 'mean', 'bathrooms': 'mean', 'garages': 'mean'})\n",
    "\n",
    "state_grouped.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title(\"Average Bedrooms, Bathrooms, and Garages by State\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel(\"Average Count\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "distributed-expense",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2 Time Series Analysis\n",
    "df = pd.read_csv('dataset.csv')\n",
    "df['sale_date'] = pd.to_datetime(df['sale_date'])\n",
    "df.set_index('sale_date', inplace=True)\n",
    "\n",
    "monthly_data = df.groupby(['state', 'property_type']).resample('M').mean()\n",
    "monthly_data = monthly_data.reset_index()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.lineplot(data=monthly_data, x='sale_date', y='sale_price', hue='state', style='property_type')\n",
    "plt.title('Monthly Average Sale Price by State and Property Type')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Sale Price')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "devoted-optimization",
   "metadata": {},
   "source": [
    "#### From 1 and 2 graphs,  I can conclude that :\n",
    "1. The number of bathrooms is twice the number of bedrooms in both NSW and QLD.\n",
    "2. Houses in both queensland and NSW have a higher sales price compared to Strata.\n",
    "3. Over the last 10 years total sales prices of houses have growth 600 Thousand compared to  Strata that only grew by 400 thousand\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "operational-helena",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UZAElWRERSQPLUJIFJVkREUkDy9SQH9DAJxERkbRRS1ZERFJOx2QDJVkREUk5HZMNlGRFRCTl1JINdExWREQkTdSSFRGRlNPFKAIlWRERSTnLUEcpKMmKiEgaaOBToCQrIiIpp4FPgdrzIiIiaaKWrIiIpJy6iwMlWRERSTkNfAqUZEVEJOXUkg2UZEVEJOU08ClQe15ERCRN1JIVEZGUU3dxoCQrIiIpp4FPgZKsiIiknFqygX5qiIiIpIlasgkMvPAUtj3tODrtMowljz3Lp2df1dwhtWrrNpVw40vTeWfBcrrm5TLxoF04fKcBNepNmTmfG1+aTm5WZlXZnyccyF7bbdOU4bYa64o2c93jU3lnzkK6dczjkiMO4Ig9dqxR74UZc7j7pXdZtaGI7MxMDho+kCsnjKKgQy4A+11zd7X6xaVlfP+AXblqwuimeButyrqizVz31DTe+fIbunXswCXj9+WI3YfW+Zpz7p3CB/OW8OFN55KV2X7aNWrJBkqyCRQv+Za5t95Fr/EHk5GX29zhtHq/evVjsjIzmHr+0XyxYi0/mfwmw3p1YXDPLjXq7ta3B5N+OKYZomx9bp08jeysTF677hxmL1nJxElTGNa3J0P69KhWb49BfXngohPp1jGPouISbnryNe548V2unDAKgHdvuaCqblFJKWNvuJfxu9WdONqrW6e8SXZmJq9dfTqzl65k4gMvMKxvD4b07p6w/nMz5lBe4U0cZcugJBu0n59VSVj29Mssn/IKJavWNncord6m0jJe+fIbLjxwZ/Jzstijf08OGdyP5z5f2NyhtWpFJaVM/WwuFx26H/m5OYzcvh+jRmzPsx/NrlG3T9dOdOuYV/U8w4xFtezbUz+dS/eCPEZu3y9dobdaRSWlTJ05j4u+tzf5udmMHNSXUTsN5NmP5ySsv2FzMX955UMuPWy/Jo60ZbCMjEZNDVqHWXczm2xmG81sgZmdXEu9v5hZYcxUbGYbYuZPM7PNMfO/SNFmUEtW0mvBmg1kmjGwW6eqsmG9uvDhNysT1p/97VrG3jWFzh1yOHLEAM7cZzhZGqVYw4IVa8g0Y1CvblVlO/btxfR53ySs/9HXS5g4aQqFm0vokJ3FH08/MmG9KdM/5+g9h2OmVki8BSvXhW3es2tV2Y59ezD966UJ69/+n/f5/r4j6NEpL+H8tq6JLkZxJ1AC9AZ2B54zs0/cfWZsJXc/Hzi/8rmZ3Q9UxC3rYne/N9UBbnWSNbMxQLm7v5HCeKSNKSopoyA3u1pZQU42RSWlNeqO7N+TJ04fT9/O+Xy1cj1XPvcumRkZnLXP8KYKt9XYVFxadUy1UkFeDkXFNbcrwMjt+/HWTeezfF0hT733f/Tr3rlGnaVrNvDhvMVc//1xaYm5tQvbPKdaWUGHHIqKS2rUnfnNt8xYuIzLjzqQ5esLmyrEdsXMOgLHA7u4eyHwpplNAX4EXNmA1x3VFHE2uIlgZq+b2YHR4yuAR4FHzOzqel53rplNN7PpL1asbVSw0vrk52SxsaSsWtnGkjLyc7Jr1N22awH9u3Qkw4yhvbpw7n4jmDonccusvcvLzWZj3Jd74eYS8nNrbtdYvbsUcOCOA7niny/WmPfMh5+zx/Z92bZ7zWPlUrnNq/+IKdxcSn5u9cRbUeHcMuW/XH7kge1qoFM8y7DGTTG5I5rOjVvFMEJDL7a//hNg53pCOx5YAcQ3EG8zs5Vm9paZjW7Me4+VTEt2F+Dd6PGPgdFAIfAWcGttL3L3e4B7AJ7L3rF9jgBoxwZ260RZRQUL12xgQNRlPGfFWgb3qNmSqkE9lrUa2KsbZRUVLFixloG9ugIwZ8lKBscNekqkrML5ZtW6GuXPfDibs8bsmepQ24yBPbuEbb5yLQOjLuM5y1YxeJtu1eoVFpcwa/EKLn/0ZSAkXYDxv36Q3500npHb923SuJtLYy9GEZs7alEAxO/I64BOCerGOh34h7vH5qMrgFmErucfAs+Y2e7u/lVyUdeUzFbIANzMBgPm7p+7+yKgWz2va3UsM5OM3BwsMyPmcWb9L5Qa8rKzGDu0P3e/PYtNpWXMWLyS179awpEJTuF56+ulrNq4GYCvV6/n3nc/Z9RgDcBJJD8nm3G7DOaul96lqKSUj79ewrRZ8zhqZM2u9ec+ms3SNRtwd5asWc8dL77NPkO2q1ZnxvylfLuuUKOK65Cfk824Edtz19TpYZsvWMq0WfM5ao9h1ep16pDD1CtP4/GJJ/L4xBO544wjAHjkouPZtR2djtbYlmwDFALxv9Y7AxsS1A0xmW0HjAL+EVvu7u+5+wZ3L3b3BwiNxyOSesO1SKYl+yZwB9AXmBwFPBhIPIKlFRty9QUM++XEqufbnnIMc268nS9vuqMZo2q9rho7khtems64u5+ha14OV40byeCeXVi6vogTHvgP/zr9UPp2zuf9hd9y3X+mU1RSRo+OHThi+AAdj63DNceN4brHpzLm+r/RtWMHrjluDEP69GDpmg0c+7uHmHzZqfTt1ol5y1fzp+ffYn1RMZ3zczl4+CAuOfyAasuaMv1zxu06mI5xxxylumuOOZjrnpzGmFseoGt+B6455mCG9O7O0rUbOPZPjzH5pz+gb9dO9OyUX/Wa4rJwuKRHQX676j5uglN45gBZZjbU3b+Myr4DzKzjNacBb7v7vHqW7aSoL82qt5jrqGjWA/g5UAr81t0LzexIYKi7/6khy1B3cfqNvuOE5g6hzcvsq9Z1kyitOaBIUqvD8ZemLRMuOHdCo77vB97zdL2xmdmjhIR4DmF08fPAAfGji2PqfwH82t0nxZR1BfYFXgfKgB8QuqlHunujT+VpcEvW3VcBV8eVPdfYAEREpO1pohsEXAhMAr4FVgEXuPtMMxtAOMY6wt0XApjZ/sC2wBNxy8gGbgaGA+XAbGBCKhIsJJFkzSwLOAnYg3DAuYq7x4/6EhGRdqwprvjk7quBCQnKF1IzT70DdExQdwWwd5pCTOqY7EPArsALwPL0hCMiIm2BbnUXJJNkDwO2c/daR26JiIjIFskk2VlAd+oYHi0iIgKALs0JJJdkTwXuNbOXiOsudvd/JH6JiIi0R7oLT5BMkj0DOJhw8YlNMeVO3Im9IiLSvumYbJBMkv0JsIe7f56uYEREpG1QSzZI5qfGckA3ARUREWmgZFqyfwT+aWa/Ipz4W6UBl6gSEZF2RN3FQTJJ9s7o7//ElTugq+eLiEgVdRcHyVxWUT9LRESkQZRkg2RasgBE14TsD3wT3epORESkOnUXA0kMfDKzvmb2OjAXeAr4yszeMDPdkkRERCSBZH5q3A18AnRz976E82U/Bv6SjsBERKT1MrNGTW1FMt3FBwF93b0UwN03mtnlwOK0RCYiIq2WRhcHySTZNcAIQmu20o7A2lQGJCIirZ8GPgXJJNnfAFPN7D5gATAQOBO4Nh2BiYiItHbJnMLzNzP7CjgZ2A1YApzk7q+mKzgREWml1F0MJHkKT5RQlVRFRKRO6i4OkjmFJ9vMbjCzeWa2Ofp7g5nlpDNAERFpfcwyGjW1Fckek90HOJ8tx2SvBToDl6Y+NBERabXUkgWSS7InAt9x91XR8y/M7CPCaGMlWRERkTjJJNnafpbo54qIiFSj82SDZJLsE8AzZnYD4b6yA4FfAI+nIzAREWm9NPApSCbJXk5IqncC/QhXenoUuDkNcYmISGvWhgYvNUaDkqyZZQJ/A85191+mNyQREWnt1JINGvRTw93LgfFARXrDERERaTuSac//EdB5sSIiUr+MjMZNbUQyx2QnAn2An5nZCsArZ7j7gFQHJiIirVdbul1dYySTZE9NWxQiItK2tKHWaGMkc4OA19MZiIiISFtTZ5I1sxsbshCNOBYRkVgaXRzU157fLmYaClwJjAOGAGOj50PTGaCIiLRCltG4qSGrMOtuZpPNbKOZLTCzk2upd4aZlZtZYcw0OtnlbI06W7LufmZMEI8S7h/7ZEzZcYRrGouIiGzRNC3ZO4ESoDewO/CcmX3i7jMT1H3H3Q9KwXKSksyR6cOBp+PK/g0c0dggRESkbUn3re7MrCNwPHCtuxe6+5vAFOBHycWZmuXUJpnRxXOBi4D/jSm7EPiqoQsYfccJSaxOtsa0i//V3CG0eeMeOru5Q2gX1rw/o7lDaPP6Ht/cEdTOzM4Fzo0pusfd74l5Pgwod/c5MWWfAKNqWeQeZrYSWA08CNzm7mVbsZykJJNkzwEmm9nlhOsW9wfKgONSEYiIiLQhjewujhLqPXVUKQDWxZWtAzolqPsGsAvhXug7A48R8tdtSS4nacmcwvOxmQ0F9iPcIGApoY+7NBWBiIhI29EEt7orBDrHlXUGNsRXdPd5MU8/i86c+X+EJNvg5WyNrd4K7v4GkBP1Z4uIiGxh1ripfnOArKjxV+k7QEMGKzlb7oXemOXUq8FJ1sx2jYL5G3BfVDwKmJSKQERERBrK3TcCTwE3mllHMzsQOIZwvLUaMzvczHpHj4cD1xIG7ia1nK2RTEv2buCX7j4cqOwifh2obUi0iIi0V01zg4ALgTzgW+AR4AJ3n2lmA6JzYSuvqz8O+NTMNgLPE5LqrfUtp/EbIbmBTzsDD0WPHcIvADPLS0UgIiLShjTBDQLcfTUwIUH5QsKApsrnlwGXJbucVEgmyc4H9gSmVxaY2T6EU3tERESqNMHAp1YhmSR7LeEqGH8Bcs3sKuACwqk9IiIiWzTw0ohtXYO3grs/CxwG9AJeAwYAx7r7S2mKTUREpFVrcEvWzHKAY4HxhPNkFwMrzWymu29OU3wiItIa6S48QHLdxXcDOwITCVfNGABcTbjy01mpD01ERFqrhlx/uD1IJslOAAa7+9ro+Swze58w8ElJVkREtlBLFkguyS4D8oG1MWV5hMsrioiIbKGWLFBPkjWzsTFPHwReNLPbgW8IN3K/CPhH+sITERFpvepryd6XoOzquOfnAb9OTTgiItImNMHFKFqDOpOsu2/fVIGIiEgbootRAMkdkxUREWkYHZMFGnGrOxEREambWrIiIpJ6OoUHUJIVEZF0UHcxoCQrIiLpoNHFgJKsiIikg0YXAxr4JCIikjZqyYqISOqpuxhQkhURkXTQwCdASVZERNJBx2QBJVkREUkHdRcDGvgkIiKSNmrJiohI6umYLKAkKyIi6aDuYkBJVkRE0kEDnwAdkxUREUkbtWRFRCTlXN3FgJKsiIikgwY+AUqyIiKSDkqygJIs6zaVcONL03lnwXK65uUy8aBdOHynATXqTZk5nxtfmk5uVmZV2Z8nHMhe223TlOG2KQMvPIVtTzuOTrsMY8ljz/Lp2Vc1d0ityrqiYq779395Z+5iuuXncsn39uaI3QbX+Zpz/v48H3y9lA+vO5OszPAleNW/pvH+vCVsKi2jR0EeZx60G8ftuWNTvIUWz/I60vUH55IzbFcqNm5gw/OPsfnjtxPWzey+DZ2PPY2cHXaC8jKK3p/GhmcfAaD3rZOqLzc7h6K3X2b95AfS/h6ai7qLg3afZH/16sdkZWYw9fyj+WLFWn4y+U2G9erC4J5datTdrW8PJv1wTDNE2TYVL/mWubfeRa/xB5ORl9vc4bQ6tz73NtmZGbx2+cnMXraKiQ+9xLA+3RmyTbeE9Z/7ZC7lFRU1ys8+5DvcMOFgcrIy+XrFWs7++/MM79uDEf16pvsttHhdjj8TLy/j2+svIKv/ILqf/f8oW7KAsuWLq1fMzKT7eVdR9NbLrH3wdryigqxefapmL7/6rKrHlpPLNtffzeZP3muqtyHNqF235zeVlvHKl99w4YE7k5+TxR79e3LI4H489/nC5g6tXVj29Mssn/IKJavWNncorU5RSSlTZ83norF7kp+bzciBfRg1fADPzpibsP6GzSX8ZdrHXDp+nxrzhmzTjZyoh8YMDFi0en06w28VLCeXDrvuw4YXnsBLiin9+gs2z/yQvL0OrlE3b+9RVKxfw8Y3nsdLiqGslLKlixIut8Nu+1BRuJ6SebPT/Raal2U0bmrIKsy6m9lkM9toZgvM7ORa6p1uZh+a2Xoz+8bMfmNmWTHzp5nZZjMrjKYvUrQVtr4la2Y7AOXuviBVwTS1BWs2kGnGwG6dqsqG9erCh9+sTFh/9rdrGXvXFDp3yOHIEQM4c5/hZOlcMGkGC1atI9OMQTE9Ljv27sH0+UsT1r996nS+v/dO9CjISzj/lmfeYsqML9lcWs7wvj04eOh2aYm7Ncns1Qe8gvKVy6rKypYuDN3BcXIGDqFs9Qq6nXM5OQMGU7p0EesnP0DZspqJNm+vQ9g0/b9pjb1FaJru4juBEqA3sDvwnJl94u4z4+rlAz8F3gN6AVOAy4BfxdS52N3vTXWADc4QZvaImR0QPT4TmAnMMrOzUx1UUykqKaMgN7taWUFONkUlpTXqjuzfkydOH8/UC47mt0fvz4uzF/GP6XOaKlSRajYVl1HQIadaWUGHxPvuzMUrmLFwOSftO6LW5V1z9IG8fc1p/P3sIxm300CyY8YetFeW04GKTUXVyio2FWG5HWrUzezSnbw99qfozf+w/IYLKf58Bt3O+jlkVt+OGV17kDN4JzZNfyOtsbcIGRmNm+phZh2B44Fr3b3Q3d8kJM8fxdd197vd/b/uXuLui4F/Agem+B0nlEwzbBwwPXr8M+C7wD7AlXW9yMzONbPpZjZ90n8/3roo0yQ/J4uNJWXVyjaWlJGfk12j7rZdC+jfpSMZZgzt1YVz9xvB1DnfNFWoItXk5WaxsbikWllhcWmNfbeiwrnl2be5/PD9qgY61SYzI4ORA/uwfH0Rj3/wecpjbm28ZDMZHaq3/DM65OHFm2vWLS2h5OsvKJ79CZSXs3Has2TkF5C1Tf9q9fL3OpiSr7+gfPWKtMbeErhZo6bY3BFN58atYhihNzW2tfMJsHMDwjuE0FCMdZuZrTSzt8xs9Fa/8TjJdBfnuHuJmfUHurv7WwBm1ruuF7n7PcA9ABv/eo1vdaRpMLBbJ8oqKli4ZgMDoi7jOSvWMrhH5/pfrIFz0owG9uhCWYWzYNU6BvYIXcZzlq1mcNygp8LiEmYtWcnlT7wGhKQLMP73j/K7749l5KA+xCuvqOAbHZOlfMUyyMgks2efqi7jrH4DKVte88d16dJF5AwaVu8y8/Y6mMJXp6Q81rYoNnfUogBYF1e2DuiUoG6VqCd2L+CcmOIrgFmErucfAs+Y2e7u/lWyccdLpiU7w8yuAq4FnouC7Q+02v/GvOwsxg7tz91vz2JTaRkzFq/k9a+WcGSCU3je+nopqzaGX7Bfr17Pve9+zqjB/Zo65DbFMjPJyM3BMjNiHqubsiHyc7IZt9NA7nr1I4pKSvl4wXKmzV7AUbsPqVavU4ccpl52Eo9fMIHHL5jAHT8aD8Aj5x/Drtv2YlXhJl747CuKikspr6jgrS+/4YXP5rHP9tq3vaSYzZ99QKfDTsBycskeNIwOO++Z8Hjqpg/fJGfgEHKG7gJmdDzkcCo2bqDs2y2jkLMHDSWjc7f2M6o4/QOfCoH4FlFnYEOtIZlNIByHPdzdqwbfuPt77r7B3Yvd/QHgLeCIZN9yIsm0ZM8GbgJKgf8Xle1P6Ntuta4aO5IbXprOuLufoWteDleNG8ngnl1Yur6IEx74D/86/VD6ds7n/YXfct1/plNUUkaPjh04YvgAztpneHOH36oNufoChv1yYtXzbU85hjk33s6XN93RjFG1HtccdSDXPf0GY379MF3zc7nm6AMZsk03lq4t5Ng7nmTyxcfTt2sBPTvlV72muKwcgB4d88jKzMAMnnh/Nrc88zYV7vTtUsDlh+/LmJ0GNtfbalHWPTmJrj88j22uvxsvKmTdk5MoW76YjK496HX5b1nxm/9HxdpVlK9YytqH76LLCWeRUdCZ0m/ms2bS76C8vGpZeXsdwubPPkjY3dwWefovRjEHyDKzoe7+ZVT2HWp2AwNgZocBfwOOdPfP6lm2k6L+SnNvuh7cltZd3BZNu/hfzR1CmzfuoVY71q9VWfP+jOYOoc3r+/uH03bgq/C9Zxr1fV+w79H1xmZmjxIS4jmE0cXPAwfEjy42s7HAE8Cx7v5G3LyuwL7A60AZ8ANCN/VId2/0qTxJncJjZgcDexD6wqu4+62NDURERCRJFwKTgG+BVcAF7j7TzAYQjrGOcPeFhMOcXYDnbcupRf9198OBbOBmYDhQDswGJqQiwUISSdbMbge+D/wX2BQzS61TERGppgm6i3H31cCEBOULiWkMunutl+pz9xXA3umID5JryZ4C7OLuS9IVjIiItBG6djGQXJJdBBSnKxAREWlDdBceIPnRxX8zs0eA5bEz4g8ki4hI+6a78ATJJNk9gcMJV8qIPyZb88RSERGRdi6ZJHsrcLS7T01XMCIi0kaouxhILsluBNQtLCIi9XJdexZI7rKKvwT+ZGZ9zCwjdkpXcCIi0jq5ZTRqaiuSaclOiv6eF1NmhGOyuuCsiIhs0YYSZWMkk2S3T1sUIiIibVCDk6y7LwCIuod7A8vdvSJdgYmISOulU3iCBrfnzayzmf0D2AwsBjaZ2QNm1iVt0YmISKukY7JBMu/kf4GOwC5AHrArkB+Vi4iIbGHWuKmNSOaY7GHADu5eFD2fE91hvtF3jhcREWmLkkmym4FewIKYsp7oesYiIhKnLXX5NkYySfZe4GUz+wMh0Q4ELiXc3FZERKSKLkYRJJNkbwGWACcD/aLHv2HL+bMiIiKAWrKVkjmFxwkJVUlVRETq1oYGLzVGUj81zOxMM3vVzL6I/p6ZrsBERERauwa3ZM3sGuA04PdsOSZ7uZn1c/db0hSfiIi0Qp5cG67NSuaY7DnA6MorPwGY2X8Id+ZRkhURkSq64lOQTJLtCKyIK1tFuDCFiIhIFQ18CpLZCi8C/zSzHc0sz8yGAw8A/0lPaCIi0lo51qiprUgmyV4MbAA+AQqBGYQbuU9MfVgiIiKtX4O6i80sE7gMOBc4g3Clp5W6C4+IiCSi7uKgQVvB3cuBi4ASd69w92+VYEVEpDZu1qiprUjmp8YDwPnpCkRERNoOHZMNkhldvA8w0cwuBxYBXjnD3Q9JdWAiIiKtXTJJ9m/RJCIiUicdkw2SuXbxA+kMRERE2o621OXbGMm0ZDGz8cDuQEFsubv/MoUxiYhIK6eWbNDgrWBmdwAPAXsC28VNIiIiVZpi4JOZdTezyWa20cwWmNnJddS91MyWmdk6M5tkZrlbs5xkJdOSPQnY3d0XpWrlIiIijXAnUAL0JvSyPmdmn7j7zNhKZnYocCUwlnAv9MnADVFZg5ezNZJpz68C1jZ2hSIi0va5ZTRqqo+ZdQSOB65190J3fxOYAvwoQfXTgfvcfaa7rwFuIlxYKdnlJK3OlqyZ7RDz9PeEaxffBiyPrefu8xqyssy+/ZIOUJIz7qGzmzuENu+VU+9r7hDahbEPnNHcIUgjNMHAp2FAubvPiSn7BBiVoO7OwL/j6vU2sx7AgCSWk7T6uovnEs6Hjd1aR8XVcSAzFcGIiEjb0NirNpnZuYRL+Va6x93viXleAKyLe9k6oFOCxcXXrXzcKcnlJK3OJOvuGh4mIiJJc29cko0S6j11VCkEOseVdSbcyKa+upWPNyS5nKQlM7r4f2sp/1MqAhEREUnCHCDLzIbGlH0HSDRYaWY0L7becndfleRykpZMS/WMWspTcnBYRETaDiejUVO9y3ffCDwF3GhmHc3sQOAY4MEE1f8BnG1mI8ysG/AL4P6tWE7S6j2Fx8zOqqwb87jSDsDKVAQiIiJtRxNd8elCYBLwLeEMmAvcfaaZDQBmASPcfaG7v2hmvwFeA/KAJ4Hr6ltOKgJsyHmylS3VHKq3Wp0wyvj0VAQiIiJtR1MkWXdfDUxIUL6Qmlcm/APwh2SWkwr1Jll3HwNgZje7+y/SEYSIiEhblMwNAqoSrJkZMaf16AbuIiISSzcICJIZXdwvurbjKqAMKI2ZREREquim7UEyo4v/Sri24zjCeUUjCZeeOj8NcYmISCvmbo2a2opkbhBwADDA3Teambv7J2Z2NvA2upm7iIjEaEut0cZIpiVbTugmBlhrZr2AjUD/lEclIiLSBiTTkn0POIJwi6D/AI8Bm4DpaYhLRERaMbVkg2SS7I/YMqL4J8BlhPOQ/pzqoEREpHVTkg2S6S4uAi41sy8JV8Q4ndB9vCYdgYmISOulgU9BMkn2bsJd5S8B9o7+HgLclYa4REREWr1kuosnAIPdfW30fJaZvUe452z8NY1FRKQdq1B3MZBckl0G5ANrY8rygKWpDEhERFo/HZMN6kyyZjY25umDwItmdjvwDbAdcBHhFkIiIiJV2tJx1caoryV7X4Kyq+Oenwf8OjXhiIhIW6CWbFBnknX37ZsqEBERkbYmmWOyIiIiDaLu4kBJVkREUk7dxYGSrIiIpJxasoGSrIiIpFxFcwfQQiRzxScRERFJglqyIiKScuouDpRkRUQk5TTwKVCSFRGRlFNLNtAxWRERkTRRS1ZERFJO3cWBkqyIiKRchTd3BC2DkqyIiKScWrKBkqyIiKScBj4FGvgkIiKSJmrJiohIyrmOyQLtMMmuK9rMdY9P5Z05C+nWMY9LjjiAI/bYsUa9F2bM4e6X3mXVhiKyMzM5aPhArpwwioIOuQDsd83d1eoXl5bx/QN25aoJo5vibbR464qKue7f/+WduYvplp/LJd/bmyN2G1zna875+/N88PVSPrzuTLIyQyfLVf+axvvzlrCptIweBXmcedBuHLdnzc9LEht44Slse9pxdNplGEsee5ZPz76quUNqVdZtKub6KW/xzldL6Jafy8Rxe3LErjvU+ZofP/AiH8xfxvRrTyMro3pn4YJV6znx7qf57ohB3HrcIekMvdlV6Jgs0A6T7K2Tp5Gdlclr153D7CUrmThpCsP69mRInx7V6u0xqC8PXHQi3TrmUVRcwk1PvsYdL77LlRNGAfDuLRdU1S0qKWXsDfcyfrehTfpeWrJbn3ub7MwMXrv8ZGYvW8XEh15iWJ/uDNmmW8L6z30yl/KKmpcUP/uQ73DDhIPJycrk6xVrOfvvzzO8bw9G9OuZ7rfQJhQv+Za5t95Fr/EHk5GX29zhtDq3Pf8u2ZkZvHrZD/hi2WomPjyVYb271b4ff/oV5XUMq73t+XfZuX/72HdbwjFZM+sO3AeMB1YCV7n7w7XUPR24BBgKrAceBq5297Jo/jRgP6Aseslid6/3F/9WH5M1szFm1qp+ihWVlDL1s7lcdOh+5OfmMHL7fowasT3PfjS7Rt0+XTvRrWNe1fMMMxatWptwuVM/nUv3gjxGbt8vXaG3KkUlpUydNZ+Lxu5Jfm42Iwf2YdTwATw7Y27C+hs2l/CXaR9z6fh9aswbsk03crIyATADAxatXp/O8NuUZU+/zPIpr1BSy74rtdtUUsrUWQu4aMwe5Odks8eA3ozacTue+/SrhPU3bC7hr69/wk+/t2fC+S/+3zw6dchhn+37pjPsFsO9cVOK3AmUAL2BU4C7zWznWurmAz8FegL7AuOAy+LqXOzuBdHUoC61Brdkzex1QlZ/y8yuAH4GlJnZne5+a0OX05wWrFhDphmDem35Fbpj315Mn/dNwvoffb2EiZOmULi5hA7ZWfzx9CMT1psy/XOO3nM4Zs3/y60lWLBqXdjOPbtUle3YuwfT5y9NWP/2qdP5/t470aMgL+H8W555iykzvmRzaTnD+/bg4KHbpSVukVgLVq0nM8MY2GPLfjysd3c+XLAsYf3bX/mIE/faMeF+XFhcwl2vzeCe0w5l8sdz0hazbGFmHYHjgV3cvRB408ymAD8Croyv7+6xxwAXm9k/gTGNjSOZluwuwLvR4x8DowlN5/PrepGZnWtm081s+n3/eXOrgkyVTcWlVcdUKxXk5VBUXJqw/sjt+/HWTefz0i/O4ozRI+nXvXONOkvXbODDeYs5eq+d0hJza7SpuIyCDjnVygo6ZFNUUnM7z1y8ghkLl3PSviNqXd41Rx/I29ecxt/PPpJxOw0kO2rZiqRTUUkZBbnZ1coKcnPYmOD7YuaSlcxYtJyT9k38PXDnqx9z7B5D6dOlY1pibYkca9QUmzui6dwkQxgGlLt77K+aT4DaWrLxDgFmxpXdZmYrzewtMxvdkIUkk2QzADezwYC5++fuvghIfHAi4u73uPte7r7X2YcelMTqUi8vN5uNxSXVygo3l5Af948Ur3eXAg7ccSBX/PPFGvOe+fBz9ti+L9t275Lgle1TXm5Wze1cXEp+TvXtXFHh3PLs21x++H5VA51qk5mRwciBfVi+vojHP/g85TGLxMvPyaqRUDcWl9Ax7vuiwp1bn3uXyw/bt8ZAJ4DZy1bx3tdLOXX/2n9ItkUV3rgpNndE0z1JhlAArIsrWwd0qu+FZnYmsBfwu5jiK4AdgP7APcAzUT6sUzIDn94E7gD6ApOjQAYTDia3CgN7daOsooIFK9YysFdXAOYsWcnguEFPiZRVON+siv+84JkPZ3PWmMTHYNqrgT26UFbhLFi1rqqrbc6y1QyOGyxSWFzCrCUrufyJ14CQdAHG//5Rfvf9sYwc1KfGsssrKvhGx2SlCQzs0Tnaj9czsEfoxZqzfA2Do++OSpX78RX/mgaEpAtw6B8e57cnjmHW0pUsWVvIYX98Aggt5Ap3fvjXKTx63v802ftpauke+BQNRBpVy+y3gIlAfPdjZ2BDPcudAPwK+K67V+U3d38vptoDZnYScARwe13LSybJngH8HFgB/DYqGw78OYllNKv8nGzG7TKYu156l+tOHMcXi1cwbdY8HrjoxBp1n/toNiO370+frgUsXbuBO158m32GVD8WOGP+Ur5dV6hRxXHyc7IZt9NA7nr1I6475iC+WLqaabMX8MCPj65Wr1OHHKZedlLV82XrN3LKX6fwyPnH0D2/A6sKN/H+10sYNWwAudmZvPvVEl74bB6/OmF0E7+j1ssyM7GsTCwzA8vMJCM3By8rx8vLmzu0Fi8vJ5txOw3g7mkfc93RBzB72WqmfbGQ+886olq9Trk5vPzz71c9X7auiFPvfZaHzz2a7h07sFO/Hhy2y/ZV8//x9kyWrC3k6iP3b7L30ha5++i65kfHZLPMbKi7fxkVf4eaXcCxrzkM+BtwpLt/Vl8IUP95Sg1Osu6+Crg6ruy5hr6+pbjmuDFc9/hUxlz/N7p27MA1x41hSJ8eLF2zgWN/9xCTLzuVvt06MW/5av70/FusLyqmc34uBw8fxCWHH1BtWVOmf864XQfTMe74o8A1Rx3IdU+/wZhfP0zX/FyuOfpAhmzTjaVrCzn2jieZfPHx9O1aQM9O+VWvKS4LX/w9OuaRlZmBGTzx/mxueeZtKtzp26WAyw/flzE7DWyut9XqDLn6Aob9cmLV821POYY5N97Olzfd0YxRtR5XH7k/1/37Tcb87jG65uVy9ZH7h/14XSHH3fk0T100gb5dCuhZkGA/LsgjKyOD7EzIy97yVZuXk0VOVibdO3Zo8vfTlJr7YhTuvtHMngJuNLNzgN2BY4ADEtU3s7HAP4Fj3f39uHldCSOOXyecwvMDwjHbn9YXh3kDt4SZ5QK/BE4Cerh7FzMbDwxz9wb9x26ecqeuAZJumzY2dwRt3iun3tfcIbQLYx84o7lDaPPyTr4qbX26z35U1qjv+6NGZjU6tug82UnA94BVwJWV58ma2QBgFjDC3Rea2WvAwcDmmEX8190PN7NewPOE3ttyYDZwrbu/XF8MyXQX/5FwwPcU4IWobGZUrp/FIiJSpblbsiEGXw1MqGXeQsLgqMrntZ6u4+4rgL23JoZkkuyxwJCoCV4RrXixmfXfmhWLiEjb1RKu+NQSJHMKTwlxSTlqQq9KaUQiIiJtRDJJ9gnCsOXtAcysL6Gb+NF0BCYiIq1XY8+TbSuSSbJXA/OBz4CuwJfAEuCGlEclIiKtWgu5dnGzS+YUnhLCcOWfRt3EK72hQ5NFRKRdcd3qDkjuBgHxN1HsFF0QvxhY6u4171MmIiLtUlvq8m2MZEYXz6XmFS4qN2NFdHeDC919eaqCExERac2SOSb7Y8LVMIYCHQh3OHgIuBDYlZCw70x1gCIi0vromGyQTEv2BsJ5spVXw5hrZhcAc9z9r2Z2BmEwlIiItHNtKVE2RjJJNgMYRLicVKUBQOXNPQuTXJ6IiLRRFboYBZBcUvwT8KqZ/R1YBGwLnBmVAxwJvJPK4ERERFqzZE7h+Y2ZfQqcCIwElgJnu/uL0fyngafTEKOIiLQy6i4OkurejRLqi2mKRURE2ggl2SCpJGtmuxNuBdSTmFN53P2XqQ1LRERaM50nGyRzMYpzCbe1ewk4nHC7u/HAv9MTmoiItFa6C0+QzHmylwOHufuxwKbo7wlAaVoiExERaeWS6S7ext3/Gz2uMLMMd3/BzP6ZjsBERKT10jHZIJkk+42ZDXL3+cAc4BgzW0m4z6yIiEgVHZMNkkmyvwF2Itzu7kbgX0AOcEnqwxIRkdZMLdmgQUnWwu123gAWAkTdxN2AHHcvTGN8IiLSCinJBg0a+BTdN/YzoCKmrEQJVkREpHbJdBd/TLjzzuz6KoqISPumY7JBMkl2GvCimd1PuHZx1SZ090mpDUtERFozdRcHySTZA4GvgVFx5Q4oyYqISJWKivrrtAfJ3CBgTDoDERERaWu26v6v0Wjj2GsX6zeLiIhUUXdx0ODLKppZfzObbGargDLC5RQrJxERkSrujZvaimSuXfwXwtWdxgGFhHvKTgHOT0NcIiLSilV446a2Ipnu4gOAAe6+0czc3T8xs7OBt4G/pSc8ERFpjbzRzdG2cRefZFqy5YRuYoC1ZtYL2Aj0T3lUIiIibUAyLdn3gCOAycB/gMeATcD0NMQlIiKtWFs6rtoYybRkf0S4IAXAT4FXgf8DTkptSCIi0tpVVDRuSgUz6x4N2N1oZgvM7OQ66p5hZuVmVhgzjd6aZcVKpiX7s2hFsWXFwNlm9g3worsvT2J5IiLSRrWQluydhAG7vYHdgefM7BN3n1lL/Xfc/aAULQtIriU7DLgCGAMMif5eDuwBXADMM7PDklieiIi0Uc09utjMOgLHA9e6e6G7v0k4I+ZHTbmsZFqyGcAP3X1yzIqPAU529/3M7HTgV8CLtS6hVPd3T7c1789o7hDavLEPnNHcIbQLr55+f3OH0OYdefJVzR1CrczsXODcmKJ73P2eJBYxDCh39zkxZZ9Q89LAsfYws5XAauBB4DZ3L9vKZQHJJdlDqXn89dkoEICHgDuSWJ6IiLRRje0ujhJqMkk1XgGwLq5sHdCplvpvALsAC4CdCYN7y4DbtmJZVZLpLv6K0C0c6/yoHKAn4ZQeERFp57zCGzXVx8ymmZnXMr1JuGhS57iXdQY2JIzXfZ67f+3uFe7+GXAjcEI0O6llxUqmJXsO8JSZXQEsJpwfWw4cF83fEbg2ieWJiEgble6rNrn76LrmR8dRs8xsqLt/GRV/B6hzoFLsKthyRYw5W7usZO7C85GZDQX2A/oBSwkjsUqj+W8QmtsiIiLNKro64VPAjWZ2DmFE8DGEqxfWYGaHAx+5+3IzG05oND6xNcuKldRdeKKE+t9kXiMiIu1PCzmF50LC/c6/BVYBF1SecmNmA4BZwAh3X0i4Lv/9ZlYALCeMM7q1Icuqy1bd6k5ERKQuFS3gKv/uvhqYUMu8hYQBTZXPLwMu25pl1UVJVkREUq6FtGSbnZKsiIiknJJskMwpPCIiIpIEtWRFRCTlKtSUBZRkRUQkDTxFd9Jp7ZRkRUQk5VwtWUBJVkRE0iBV94Rt7TTwSUREJE3UkhURkZRTd3GgJCsiIinXAi741CIoyYqISMo15HZ17YGOyYqIiKSJWrIiIpJyOiQbKMmKiEjKtYS78LQESrIiIpJyGl0cKMmKiEjK6bKKgQY+iYiIpIlasiIiknK6C0+gJCsiIimnY7KBkqyIiKScRhcHOiYrIiKSJmrJiohIyqm3OFCSFRGRlNO1iwMlWRERSTmNLg6UZEVEJOXUkg008ElERCRN1JIVEZGUU0s2UJIVEZGUU44NlGRFRCTl1JIN2n2SXVe0meuemsY7X35Dt44duGT8vhyx+9A6X3POvVP4YN4SPrzpXLIydVg7EcvrSNcfnEvOsF2p2LiBDc8/xuaP305YN7P7NnQ+9jRydtgJyssoen8aG559BIDet06qvtzsHIrefpn1kx9I+3to6dZtKub6KW/xzldL6Jafy8Rxe3LErjvU+ZofP/AiH8xfxvRrTyMro/q+u2DVek68+2m+O2IQtx53SDpDb1MGXngK2552HJ12GcaSx57l07Ovau6QWgRdVjFo90n21ilvkp2ZyWtXn87spSuZ+MALDOvbgyG9uyes/9yMOZTrF1q9uhx/Jl5exrfXX0BW/0F0P/v/UbZkAWXLF1evmJlJ9/Ououitl1n74O14RQVZvfpUzV5+9VlVjy0nl22uv5vNn7zXVG+jRbvt+XfJzszg1ct+wBfLVjPx4akM692NIdt0S1j/uU+/qnPfve35d9m5f890hdtmFS/5lrm33kWv8QeTkZfb3OFIC9Oum2FFJaVMnTmPi763N/m52Ywc1JdROw3k2Y/nJKy/YXMxf3nlQy49bL8mjrR1sZxcOuy6DxteeAIvKab06y/YPPND8vY6uEbdvL1HUbF+DRvfeB4vKYayUsqWLkq43A677UNF4XpK5s1O91to8TaVlDJ11gIuGrMH+TnZ7DGgN6N23I7nPv0qYf0Nm0v46+uf8NPv7Zlw/ov/N49OHXLYZ/u+6Qy7TVr29Mssn/IKJavWNncoLUpFhTdqSgUz625mk81so5ktMLOT66j7FzMrjJmKzWxDzPxpZrY5Zv4XDYmhwUnWgh+b2atm9mlUdoiZfb+hy2hpFqxcR6YZg3p2rSrbsW8Pvvp2TcL6t//nfb6/7wh6dMproghbp8xefcArKF+5rKqsbOlCsnpvW6NuzsAhlK1eQbdzLqf3jX+l+wW/IKvPdgmXm7fXIWya/t+0xd2aLFi1nswMY2CPLlVlw3p356sVaxPWv/2Vjzhxrx3pUVBz3y0sLuGu12bw8/F7pytcaYfcvVFTitwJlAC9gVOAu81s51riPd/dCyon4BHgibhqF8fU2bEhASTTkr0ROBu4BxgQlX0DXJHEMlqUTcWlFHTIqVZW0CGHouKSGnVnfvMtMxYu46T9d22q8Foty+lAxaaiamUVm4qw3A416mZ26U7eHvtT9OZ/WH7DhRR/PoNuZ/0cMjOr1cvo2oOcwTuxafobaY29tSgqKaMgN7taWUFuDhuLS2vUnblkJTMWLeekfXdKuKw7X/2YY/cYSp8uHdMSq7RPXuGNmhrLzDoCxwPXunuhu78JTAF+lMRrGz34I5kkewZwlLs/ClRuga+BOkdamNm5ZjbdzKbf9/I7WxdlmuTlZtf4UircXEp+bvXEW1Hh3DLlv1x+5IEa6NQAXrKZjA7VW0wZHfLw4s0165aWUPL1FxTP/gTKy9k47Vky8gvI2qZ/tXr5ex1MyddfUL56RVpjby3yc7Jq7Lsbi0voGJd4K9y59bl3ufywfWsMdAKYvWwV7329lFP3H5HWeEWSFZs7ouncJBcxDCh399jjf58ACVuycY4HVgDxv+pvM7OVZvaWmY1uSBDJDHzKBAqjx5VJtiCmLCF3v4fQ+mXzk39sUSOGBvbsQllFBQtWrmVg1GU8Z9kqBscNHCksLmHW4hVc/ujLwJb7JI7/9YP87qTxjNRxrGrKVyyDjEwye/ap6jLO6jeQsuXf1KhbunQROYOG1bvMvL0OpvDVKSmPtbUa2KMzZRXOglXrGdijMwBzlq9hcK+u1eoVFpcwa8lKrvjXNGDL9WQP/cPj/PbEMcxaupIlaws57I+hV6yopIwKd3741yk8et7/NNn7kbansa3R2NyxlQqAdXFl64BODXjt6cA/vHq/9RXALEL38w+BZ8xsd3dPPBAikkySfR74g5ldCuEYLXAT8EwSy2hR8nOyGTdie+6aOp3rjhvFF0tXMm3WfB44f0K1ep065DD1ytOqni9bV8gpdz3FIxcdT/eOOj4bz0uK2fzZB3Q67ATWPf43svoNpMPOe7Lq9utq1N304ZsUjDqCnKG7UDJ3Jh0PPoyKjRso+3bLKOTsQUPJ6NxNo4pj5OVkM26nAdw97WOuO/oAZi9bzbQvFnL/WUdUq9cpN4eXf75l2MSydUWceu+zPHzu0XTv2IGd+vXgsF22r5r/j7dnsmRtIVcfuX+TvZfWzjIzsaxMLDMDy8wkIzcHLyvHy8ubO7Rmle4bBJjZNGBULbPfAiYCnePKOwMbalavttztouX+OLbc3WO/gB4ws5OAI4Db61peMn2fPwP6EX4JdCG0YAfSio/JAlxzzMEUl5Yx5pYHuPLRV7jmmIMZ0rs7S9duYL/r72Xp2g2YGT075VdN3TqGY4s9CvLJzsqsZw3t07onJ2HZOWxz/d10O/Vi1j05ibLli8no2oPet04io2sPAMpXLGXtw3fR5YSz6H3z38jdeU/WTPodxHxB5e11CJs/+yBhd3N7dvWR+7O5tIwxv3uMq558g6uP3J8h23Rj6bpC9r/1IZauKwz7bkF+1dStYzjFpEdBHtmZmeRlZ1Wbn5eTRU5WJt071jx+LokNufoCDi/8jCFXnMe2pxwTHl99QXOH1ezSfUzW3Ue7u9UyHQTMAbLMLPbCB98BZtaz6NOAt919Xn0hAFZfnJbsKC4z24aQXBe5+7L66sdqad3FbdGatz9o7hDavK57avBbU3j19PubO4Q278jSL+pNElvrtGuXNur7/h839W10bGZWOYboHGB3Qo/sAe5ea6KNTs35tbtPiinrCuwLvA6UAT8gdGWPdPc6T+VpcHexmY0H5kcHkb+NynYEBrj7yw1djoiISBO5EJhEyFmrgAsqE6yZDSAcYx3h7gujsv2Bbal56k42cDMwHCgHZgMT6kuwkNwx2TuB+GutbYjK6x+5IiIi7UaqLijRGO6+GphQy7yFhMFRsWXvADXOZXP3FcBWnUieTJLdxt2XxpUtBfokqiwiIu2XbhAQJDPwaZ6ZjY0rG004V1ZERKRKC7niU7NLpiV7PfCUmd0HfAUMBs6MJhERkSpeUdHcIbQIDW7Juvu/gfGE/uojo7+HRuUiIiISJ6lb3bn7+8D7aYpFRETaiJYw8KklqDPJmtk17n5L9PjG2uq5+y9THZiIiLRebem4amPU15KNvTdZ4vuPbbmOsYiICKDRxZXqTLLufgGAmWUADwJvuXtxUwQmIiLS2jXomKy7V5jZv929IXcvEBGRdk4t2SCZgU9vmNl+7v5u2qIREZE2ocJ1Cg8kl2QXAC+Y2b+BRcQci9XAJxERiaWWbJBMks0Dno4exw6I0pYUEZFqlGSDBidZd9eVnURERJKQ1MUoopvffp9w8/YlwOPu/mU6AhMRkdZL58kGDb6sopmdDHwM7AZsBHYFPorKRUREqlRUVDRqaiuSacneDBzh7m9UFpjZwYTzZx9OdWAiItJ66ZhskEyS7QS8E1f2LglucCsiIu2b6xQeILn7yf4BuNXMOgCYWR5wS1QuIiIicZJpyV4I9AF+YmZrgG6AAUvN7ILKSu4+ILUhiohIa6Pu4iCZJHtq2qIQEZE2RUk2SOY82dfrq2NmzwH11hMRkbZNl1UMkjkm2xAHp3h5IiIirVZSF6MQERFpCHUXB0qyIiKSct6GLijRGEqyIiKScmrJBqlOspbi5YmISCuki1EEqR74dGuKlyciItJqJXODgFwzu8XM5pnZuqhsvJldXFnH3W9LR5AiItK6VFR4o6a2IpmW7B+BXYBT2HKj9pnABbW+QkRE2iWvqGjU1FYkc0z2WGCIu280swoAd19sZv3TE5qIiLRWGvgUJNOSLSEuKZtZL2BVSiMSEZFWz72iUVMqmNnFZjbdzIrN7P4G1L/UzJaZ2Tozm2RmuTHzupvZZDPbaGYLGnov9WSS7BPAA2a2fbTCvsAdwKNJLENERKSpLCHcC31SfRXN7FDgSmAcMAjYAbghpsqdhMZmb8Jh07vNbOf6lptMkr0amA98BnQFviS8gRtqf4mIiLRHXuGNmlISg/tT7v40DetxPR24z91nuvsa4CbgDAAz6wgcD1zr7oXu/iYwBfhRfQtN5gYBJcBPgZ9G3cQr3V2d7iIiUkMrHLy0M/DvmOefAL3NrAcwACh39zlx80fVt9A6k6yZ7VDH7E5m4doT7j6vvhUBdDj+0lZ3sQozO9fd72nuOBqq7/HNHUHyWts2bo1a4zY+8uSrmjuEpLXG7Zwubz4zqlHf92Z2LnBuTNE9ad62BcC6mOeVjzslmFc5v1N9C62vu3guoVt4bh3Tl/WtpJU7t/4q0kjaxumnbdw0tJ1TxN3vcfe9YqZqCdbMppmZ1zK9uRWrLAQ6xzyvfLwhwbzK+RvqW2idLVl3T/UVoURERBrN3UeneJEzge8Aj0fPvwMsd/dVZrYZyDKzoe7+Zcz8mfUtVElURETaJDPLMrMOQCaQaWYdzKy2xuU/gLPNbISZdQN+AdwP4O4bgaeAG82so5kdCBwDPFhfDMlcVjHLzC4xsyfN7HUze6NyaugyWikdX0k/beP00zZuGtrOLcsvgE2EU3NOjR7/AsDMBphZoZkNAHD3F4HfAK8BC6LpuphlXQjkAd8CjwAXuHu9LVlr6ABhM7sdGEvYiW4BriFcUvFRd7++QQsRERFpR5JJsouB/d19oZmtdfeuZjYc+Ku71zuMWUREpL1J5phsPrAoerzJzPLdfTawR+rDSszM5pvZd1OwnPvN7OZUxNRWpGrbRstyMxuSimWJNKXm2HfN7C9mdm1TrlOaTjI3CPgc2Bt4H5gOXG9m64HF6QhMWgczmwY85O73NncsIq2Ru5/f3DFI+iTTkv0JUBo9/hkwEjiKNnZeWB0jzyQNtL1TzwKdOdAKmFlmc8cg6ZXMP2IBsDZ6XEi4bvEsmv5iFHub2SwzW2Nmf4+GZ2NmR5nZDDNba2Zvm9lulS8wsz3M7CMz22BmjwEdYuaNNrNvzOwKM1sG/D26Qf2fzGxJNP0p7m4MPzazuWa22symmFm/mHluZhea2ZfR+m4ys8Fm9o6ZrTezx80sJ6rb08yejWJebWb/beYvxxrb1sy6RTGuiMqfNbNto/hvAQ4G7ohG6d0Rs6zvRttgjZndadHlwczsDDN7y8z+aGarCT0iXczsH9E6FpjZLyq3g5llRM8XmNm3Ub0u0bxB0fY+08wWRes638z2NrNPo+1aFZOZDbEwMn6dma2M9oUWxcxGmtnH0b7zhJk9ZmY31/U5RK+bZma3mNlbQBGwQ7RdPo+WNc/Mzotb1+VmtjTax8+xmK7S6H/gd2a20MyWW+jSzIvmtbT9NqHof3px9P6/MLNxZrZP9L+4Nnrvd1T+PyZ4fa3boI51Vn6fXB3tY/PN7JSY+feb2d1m9ryZbQTGWNzhKzM7xsJ32Xoz+8rMDovKu5jZfVHci6P9Qkm6pXP3Bk2E7uIB0eOHo+k+YEpDl9HYiXCDgv8DtgO6A28R7rAwkjCsel/C+VCnR3VzgRzCUOxLgWzgBEKL/OZomaOBMuDXUf084EbgXWAboBfwNnBTVH8ssDJaZy5wO/BGTIxOuHB0Z8K1MIuBVwh3dOhC+GFyelT3NuAvUVzZhIRlTbU9G7htexAujJ1PuITYE8DTMa+bBpwTtywHniXcSGIAsAI4LJp3RrS9JxIOV+QRzk/7d7T8QcAc4Oyo/lmEK4vtQPih9xTwYDRvULSuvxB+OI0HNgNPR59d/2i/GBXVf4QwKj4jqn9Qc2zrOj6Dyn31J9H+cBzhrh8N/RwWRvtcVvT6I4HBgBGusVoEjIzqHwYsi+rnE873c8I9owH+FO3H3aP1PQPc1tL22zq25Y6EMST9YvaVwcCewH7RNhpE+F77ady+W+82qGO9o6P9+w+E74dRwEZgx2j+/YTL8R0Ysx/ez5bvo32i+d+L5vcHhkfzngb+CnSM9u/3gfOae1trqmdfTGKnXR/9zSLc0aAg+lJY2WTBhkRwfszzI4CvgLuJkmDMvC+iHfwQQqvbYua9TfUkWwJ0iJn/FXBEzPNDgfnR4/uA38TMKyAk7UHRcwcOjJn/IXBFzPPfA3+KHt9ISC5Dmn1HqGXbJqi3O7Am5vk0EifZg2KePw5cGT0+A1gYMy+T8ENkREzZecC06PErwIUx83aMtnfll6QD/WPmrwJ+EPP8SaIvUUIyvwfYtrm3dy2fwSGEMQ6x++qblftqAz6HG+tZ/tPAT6LHk4hJGMCQaFsOISTljcDgmPn7A1+3tP22jvc6hPAD67tAdh31fgpMjtt3690GdSxvNCHJdowpe5xw9xYICfUfca+5ny3fR38F/phgub2j/5O8mLKTgNeae1trqntKpotnvZn1JiSuWe5eGJVnJ7GMVFgU83gB0A8YCPw86gJaa2ZrCS2yftG02KO9MuZ1sVa4++aY5/3i6lSup8a8aDusIvzirLQ85vGmBM8Lose/JbTSXoq6865M+I6bTo1ta2b5ZvbXqLt2PfAG0LUB3VTLYh4XseU9x6+nJ1tacLHrrtyeiT6LLMKXTqWGbu/LCV+e75vZTDM7q5730NQS7auLABr4OcRuV8zscDN7N+rSXUv44dQzZl2LanltL0Lr9sOY/6cXo3JoefttDe4+l5BArwe+NbNHzayfmQ2LurqXRdvxVrZsk1j1bYO6rPFwhaBKsd8fEPc5xdmO8CM/3kDCd+3SmHj+SmjRSguWTJK9HfgA+Cfh5rUQujxmpzqoemwX83gAoZW6CLjF3bvGTPnu/giwFOhvZhb3uljxJwsvIezU8eupMc/CfQZ7sBWjrN19g7v/3N13AI4GfmZm45JdTgol2rY/J7Qe93X3zoTWFoRkBTW3XUPEvmYloWUav70rt2eiz6KM6om0YSt1X+buP3b3foTW8l3Wsk41SrSvVn4m9X0OELNdLYwheBL4HdDb3bsCz8fUXwpsG/Pa2M9+JeHHyc4x/09d3L0AWuR+m5C7P+zuBxH2HyccErqb8J01NNqOV1N9G1aqcxvUo1v0vVAp9vsD6v6fWUTo1k5UXgz0jImns7vXe9NwaV4NTrLu/mtC18uB7v5oVLwYOCcdgdXhIjPb1sy6E/5BHgP+BpxvZvta0NHMjjSzTsA7hC/lSyxcGvI4wnGPujwC/MLMeplZT+CXwEPRvIeBM81s9+iL7FbgPXefn+wbsTBYa0j0pboeKI+m5pJo23YifNmsjcqvi3vNcsLx0q3i7uWE7rRbzKyTmQ0kjF6v3N6PAJea2fZmVkDY3o+5e1my6zKzE23LYKE1hC+75tze8d4hxHNxtK8ew5Z9tb7PIV4O4ZjgCqDMzA4nHLOu9DhhP97JzPIJ+zgA7l5B+J/6o5ltA2Bm/c3s0OhxS9tvazCzHc1sbPQ/upmw7coJ23E9UGjhYjoXJHp9fdugAW4wsxwzO5hwFsYTDXzdfYTPZZyFQX/9zWy4uy8FXgJ+b2ado3mDzUwXAmrhkhoR6O5z3P2ruOefpT6sOj1M2NnmRdPN7j4d+DFwB+HLcy7RHe093Gz+uOj5GuAHhMEzdbmZcC7wp8BnwEdRGe7+CnAtoZWwlPCr84db+V6GAlMJo7XfAe5y92lbuaxUqLFtCYM/8gi/7N8ldJnF+jNwgoURr/+7leudSDj+NY9wDPJhwjFDor8PErpHvyZ8YU7cyvXsDbxnZoWEAS0/cfevt3JZKRezr55NGMl/KmEAWTH1fw7xy9oAXEJIpmuAkwnvuXL+C8D/Eq7TOpew/xGtC+CKqPzdqFt1KqElDS1vv00kF/gVYXstI3SrXg1cRtgWGwhJtK4R5nVtg7osI2zzJYSev/M9XLinXu7+PnAm8EfCAKjX2dKTcxrhx9OsaPn/Avo2ZLnSfBp8WUURaXpm9h7wF3f/e5rXsxNhdHnu1vQSSGBmowkXZ9m2nqrSTrS4c9tE2jMzG2VmfaLu4tOB3ain1dqIdR0bdWl2IxyvfEYJViS1lGRFWpYdgU8IXYU/B06Ijselw3mEY7ZfEY5XJjw+KdVZuNBEYYLpheaOTVoedReLiIikiVqyIiIiaaIkKyIikiZKsiIiImmiJCsiIpImSrIiIiJpoiQrIiKSJv8fFE8ME75xTZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Correlation analysis\n",
    "# Select the columns of interest\n",
    "columns_of_interest = ['bedrooms', 'bathrooms', 'garages', 'sale_price']\n",
    "subset_df = df[columns_of_interest]\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = subset_df.corr()\n",
    "\n",
    "# Plot the correlation matrix as a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Coefficients with Sales Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "intelligent-internship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4. Distribution Analysis\n",
    "columns_of_interest = ['bedrooms', 'bathrooms', 'garages', 'sale_price']\n",
    "subset_df = df[columns_of_interest]\n",
    "\n",
    "# Plot histograms\n",
    "subset_df.hist(bins=10, figsize=(10, 8), grid=False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Create box plots\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.boxplot(data=df, x='bedrooms')\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.boxplot(data=df, x='bathrooms')\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.boxplot(data=df, x='garages')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sound-groove",
   "metadata": {},
   "source": [
    "#### From these graphs I can understand that:\n",
    "1. Bathrooms have a relatively high correlation to sales price compared to other discerete variables\n",
    "2. Bedrooms, Garage data is Right Tailed. meaning most properties have less bedrooms and garages.\n",
    "3. The box plots show outlliers for bedrooms, bathrooms and garages\n",
    "    \n",
    "### I will analyse the outliers further.\n",
    "\n",
    "Steps to analyse outliers:\n",
    "1. Data quality: I am assuming the these are not data entry errors\n",
    "2. Stat techniques: Z score. Using this for Bathrooms because graph is following gausian distribution\n",
    "3. Inter quartile Ranges for bedrooms and garages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "variable-cholesterol",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Select the columns of interest\n",
    "columns_of_interest = ['bathrooms']\n",
    "subset_df = df[columns_of_interest]\n",
    "\n",
    "# Calculate z-scores for bathrooms\n",
    "z_scores = (subset_df - subset_df.mean()) / subset_df.std()\n",
    "\n",
    "# More than 3 standard devitations then they are outliers\n",
    "threshold = 3\n",
    "\n",
    "# Identify outliers based on the threshold\n",
    "outliers = (np.abs(z_scores) > threshold)\n",
    "\n",
    "# Print the rows where outliers are True\n",
    "outliers_bathrooms_df = df[outliers.any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "assigned-lying",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(393, 14)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the total count of outliers.\n",
    "outliers_bathrooms_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "constitutional-revolution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6670384310237281\n",
      "5.707271731460649\n",
      "3.1871550812421883\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assuming Bathrooms follows A Gaussian Distribution, I will calculate the boundaries which differentiates the outliers\n",
    "\n",
    "uppper_boundary=subset_df['bathrooms'].mean() + 3* subset_df['bathrooms'].std()\n",
    "lower_boundary=subset_df['bathrooms'].mean() - 3* subset_df['bathrooms'].std()\n",
    "print(lower_boundary), print(uppper_boundary),print(subset_df['bathrooms'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-header",
   "metadata": {},
   "source": [
    "#### we have the upper boundary as 5 bathrooms. and lower bowndary close to 0\n",
    "**Note: There will be nothing less than 0 bathrooms. Also more than 5 bathrooms are considered to be outliers**\n",
    "\n",
    "### Bedrooms and Garages are right skewed. Hence will do the following to find the outliers range using IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cultural-cassette",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Outliers with lower bridge =  -0.5  and upper bridge =  3.5\n",
      "Extreme Outliers with lower bridge =  -2.0  and upper bridge =  5.0\n"
     ]
    }
   ],
   "source": [
    "# Bedrooms - Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.bedrooms.quantile(0.75)-df.bedrooms.quantile(0.25)\n",
    "\n",
    "# Normal Outliers bedrooms\n",
    "lower_bridge=df['bedrooms'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['bedrooms'].quantile(0.75)+(IQR*1.5)\n",
    "print(\"Normal Outliers with lower bridge = \",lower_bridge,\" and upper bridge = \",upper_bridge)\n",
    "\n",
    "# Extreme outliers bedrooms\n",
    "lower_bridge_extreme=df['bedrooms'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge_extreme=df['bedrooms'].quantile(0.75)+(IQR*3)\n",
    "print(\"Extreme Outliers with lower bridge = \",lower_bridge_extreme,\" and upper bridge = \",upper_bridge_extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "premium-height",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Outliers with lower bridge =  -0.5  and upper bridge =  3.5\n",
      "Extreme Outliers with lower bridge =  -2.0  and upper bridge =  5.0\n"
     ]
    }
   ],
   "source": [
    "# Garages - Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.garages.quantile(0.75)-df.garages.quantile(0.25)\n",
    "\n",
    "# Normal Outliers bedrooms\n",
    "lower_bridge=df['garages'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['garages'].quantile(0.75)+(IQR*1.5)\n",
    "print(\"Normal Outliers with lower bridge = \",lower_bridge,\" and upper bridge = \",upper_bridge)\n",
    "\n",
    "# Extreme outliers bedrooms\n",
    "lower_bridge_extreme=df['garages'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge_extreme=df['garages'].quantile(0.75)+(IQR*3)\n",
    "print(\"Extreme Outliers with lower bridge = \",lower_bridge_extreme,\" and upper bridge = \",upper_bridge_extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fallen-bennett",
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers_df = df[(df['bedrooms'] > 5) | (df['bathrooms'] > 5) | (df['garages'] > 5)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "swedish-salon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(416, 14)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "third-gregory",
   "metadata": {},
   "source": [
    "#### Summary of outlier analysis for discrete data:\n",
    "1. Bedrooms, Bathrooms and garages all indicate that values greater than 5 are outliers.\n",
    "2. Number of properties having bathrooms greater than 5 are 393. While total properties with Bedrooms or garages greater than 5 are only 16. \n",
    "3. High correlation between bedrooms and sale price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "competitive-version",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the frequency of outliers for each state\n",
    "statewise_outliers = outliers_df['state'].value_counts()\n",
    "\n",
    "# Plot the distribution of outliers statewise\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(data=outliers_df, x='state', order=statewise_outliers.index)\n",
    "plt.title(\"Distribution of Outliers Statewise\")\n",
    "plt.xlabel(\"State\")\n",
    "plt.ylabel(\"Outlier Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "living-designation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in columns_of_interest:\n",
    "    df_outliers =df_discrete.copy()\n",
    "    df_outliers.groupby(feature)['sale_price'].median().plot.bar()\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('SalePrice')\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stuffed-homework",
   "metadata": {},
   "source": [
    "### There are more outliers in NSW compared to Queensland. \n",
    "\n",
    "we can observe from previous graphs that as bedrooms, bathrooms are increasing sales price is also increasing. Hence there is a possibility that NSW usually has properties with more bedrooms/bathrooms than Queensland.\n",
    "\n",
    "### I will now Analyse the continuous variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "statistical-artist",
   "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>property_type</th>\n",
       "      <th>suburb</th>\n",
       "      <th>land</th>\n",
       "      <th>floorplate</th>\n",
       "      <th>slope</th>\n",
       "      <th>max_roof_height</th>\n",
       "      <th>year_built</th>\n",
       "      <th>sale_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>House</td>\n",
       "      <td>MOUNT ANNAN</td>\n",
       "      <td>450.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>-5.032485</td>\n",
       "      <td>7.177677</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1061100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Strata</td>\n",
       "      <td>HILLCREST</td>\n",
       "      <td>279.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>-1.042645</td>\n",
       "      <td>5.870000</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>543900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>House</td>\n",
       "      <td>EMERTON</td>\n",
       "      <td>552.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>-9.114863</td>\n",
       "      <td>4.140000</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1102500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Strata</td>\n",
       "      <td>PACIFIC PINES</td>\n",
       "      <td>380.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.786286</td>\n",
       "      <td>6.890000</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1183800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Strata</td>\n",
       "      <td>MARSFIELD</td>\n",
       "      <td>3627.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>-1.141627</td>\n",
       "      <td>12.207000</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>805200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  property_type         suburb    land  floorplate     slope  max_roof_height  \\\n",
       "0         House    MOUNT ANNAN   450.0       267.0 -5.032485         7.177677   \n",
       "1        Strata      HILLCREST   279.0       205.0 -1.042645         5.870000   \n",
       "2         House        EMERTON   552.0       104.0 -9.114863         4.140000   \n",
       "3        Strata  PACIFIC PINES   380.0         NaN -2.786286         6.890000   \n",
       "4        Strata      MARSFIELD  3627.0      1239.0 -1.141627        12.207000   \n",
       "\n",
       "   year_built  sale_price  \n",
       "0      2000.0   1061100.0  \n",
       "1      1982.0    543900.0  \n",
       "2      1988.0   1102500.0  \n",
       "3      1988.0   1183800.0  \n",
       "4      2000.0    805200.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continuous.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "liable-shuttle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['property_type', 'suburb', 'land', 'floorplate', 'slope',\n",
       "       'max_roof_height', 'year_built', 'sale_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continuous.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "occasional-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the columns of interest\n",
    "columns_of_interest = ['land', 'floorplate', 'slope', 'max_roof_height', 'year_built', 'sale_price']\n",
    "subset_df_2 = df[columns_of_interest]\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix_2 = subset_df_2.corr()\n",
    "\n",
    "# Plot the correlation matrix as a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix_2, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Correlation Coefficients with Sales Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "chinese-synthesis",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akhil/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/Users/akhil/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akhil/anaconda3/lib/python3.7/site-packages/pandas/core/arraylike.py:358: RuntimeWarning: invalid value encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using a log function to calculate scatter plots for each property type\n",
    "continuous_features = ['land', 'floorplate', 'slope', 'max_roof_height']\n",
    "\n",
    "for feature in continuous_features:\n",
    "    if 0 in df_continuous[feature].unique():\n",
    "        pass\n",
    "    else:\n",
    "        fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "        for i, property_type in enumerate(['House', 'Strata']):\n",
    "            data = df_continuous[df_continuous['property_type'] == property_type]\n",
    "            data[feature] = np.log(data[feature])\n",
    "            data['sale_price'] = np.log(data['sale_price'])\n",
    "\n",
    "            axes[i].scatter(data[feature], data['sale_price'])\n",
    "            axes[i].set_xlabel(feature)\n",
    "            axes[i].set_ylabel('Sales Price')\n",
    "            axes[i].set_title(f'{feature} - {property_type}')\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "reliable-edition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5I0QJLsneSSJPck2Zrk5iSv6Kk/KsnGJDuS3JjkwJ66JDk/yYPt8p4k6akfadvsaLdxdN++j2/3uz3JtUmWzk6vJWnumAxL0mBZAHwbeCnw48BZwJ+2iex+wLq2bCmwHvh4T9s1wGuBlcDzgFcDp/bUXwXcDOwLvB34ZJJlAElWABcBJwH7AzuAC2ekh5I0QEyGJWmAVNX2qlpbVXdX1T9V1Z8D3wReABwDbKiqT1TVw8BaYGWSQ9rmJwMXVNW9VXUfcAFwCkCSg4HDgbOramdVXQ3cDqxu254AXFdVn6mqbTQJ9zFJFs9GvyVprpgMS9IAS7I/cDCwAVgB3DpaV1XbgTvbcvrr28e9dXdV1dZd1Pdu+07g0Xbf/TGtSbI+yfrNmzfveeckaQCYDEvSgEryVOAK4MNVtRFYBGzpW20LMHr2tr9+C7ConTc82bb99Y+rqouralVVrVq2bNnkOiVJA8ZkWJIGUJKnAB+lOTt7Wlu8DVjSt+oSYOs49UuAbVVVe9C2v16ShtJuk2GvbJak2dWOk5fQXMi2uqp+2FZtoLk4bnS9fYCD2vIn1bePe+uW980B7q/v3fZyYG9g0zR0SZIG1kTODHtlsyTNrg8APwO8pqp29pRfAxyaZHWShcA7gNvaKRQAHwHemuSAJM8ETgcuA6iqTcAtwNlJFiZ5Hc24fHXb9grgNUmOaJPsc4B1fXOMJWno7DYZ9spmSZo97bdrpwKHAd9Nsq1dTqiqzTRj5LuAh4AXAcf1NL8IuI5mLP0qcH1bNuo4YFXb9jzg2HabVNUG4E00SfH3aeYKv3mGuilJA2PBZBv0Xdn8W/Rd2Zxk9MrmjUz9yubP92z7ziSjVzZ/ZbJxS9J8UFX3ANlF/Q3AIePUFfC2dhmr/m7gyF1s+0rgyolHK0nz36QuoBvUK5u9zY8kSZL2xIST4UG+stnb/EiSJGlPTCgZ9spmSZIkDaOJnhn2ymZJkiQNnYncZ9grmyVJkjSUdns3Ca9sliRJ0rDy55glSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJA2QJKclWZ/kkSSX9dUdlWRjkh1JbkxyYE9dkpyf5MF2eU+S9NSPtG12tNs4um/bxye5J8n2JNcmWTrjnZWkAWAyLEmD5TvAucClvYVJ9gPWAWcBS4H1wMd7VlkDvBZYCTwPeDVwak/9VcDNwL7A24FPJlnWbnsFcBFwErA/sAO4cHq7JUmDyWRYkgZIVa2rqmuBB/uqjgE2VNUnquphYC2wMskhbf3JwAVVdW9V3QdcAJwCkORg4HDg7KraWVVXA7cDq9u2JwDXVdVnqmobTcJ9TJLFM9VPSRoUJsOSND+sAG4dfVJV24E72/In1bePe+vuqqqtu6jv3fadwKPAwdMYvyQNJJNhSZofFgFb+sq2AIvHqd8CLGrnDU+2bX/9EyRZ085rXr958+ZJdUKSBo3JsCTND9uAJX1lS4Ct49QvAbZVVe1B2/76J6iqi6tqVVWtWrZs2aQ6IUmDxmRYkuaHDTQXxwGQZB/goLb8SfXt49665X1zgPvre7e9HNgb2DSN8UvSQDIZlqQBkmRBkoXAXsBeSRYmWQBcAxyaZHVb/w7gtqra2Db9CPDWJAckeSZwOnAZQFVtAm4Bzm639zqaO05c3ba9AnhNkiPaJPscYF3fHGNJGkomw5I0WM4EdgJnACe2j8+sqs00d394F/AQ8CLguJ52FwHX0dwl4qvA9W3ZqOOAVW3b84Bj221SVRuAN9Ekxd+nmSv85pnpniQNlgVzHYAk6Ueqai3NbdPGqrsBOGScugLe1i5j1d8NHLmL/V4JXDmZWCVpGHhmWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6a8FcByBJ0kwYOeP6WdvX3ee9atb2JWl6mQxLkjRFs5V4m3RL02+gp0kkWZrkmiTbk9yT5Pi5jkmShpVjrqQuGvQzw+8HHgX2Bw4Drk9ya1VtmNOoJGk4OeZq1s3mdJbZMoxn8If524+BTYaT7AOsBg6tqm3AZ5N8CjgJOGNOg5OkIeOYq37DmKRKYxnYZBg4GHisqjb1lN0KvLR/xSRrgDXt021J7pjkvvYDHtijKCcp58/GXsY1a/2cQ/ZxeMz3fh441wFM0myMufP1mA5M3JP4P2RgYp6k+Rj3k2Ke4//rJ2ogX+vdvHbjxTyl8XaQk+FFwJa+si3A4v4Vq+pi4OI93VGS9VW1ak/bzxdd6Kd9HB5d6ecAmfExd74e0/kY93yMGeZn3PMxZpifcc9UzIN8Ad02YElf2RJg6xzEIknDzjFXUicNcjK8CViQ5Dk9ZSsBL+SQpOnnmCupkwY2Ga6q7cA64Jwk+yR5MfCrwEdnYHd7PMVinulCP+3j8OhKPwfCLI258/WYzse452PMMD/jno8xw/yMe0ZiTlXNxHanRZKlwKXAy4AHgTOq6sq5jUqShpNjrqQuGuhkWJIkSZpJAztNQpIkSZppJsOSJEnqrE4nw0mWJrkmyfYk9yQ5fq5jGk+Sm5I8nGRbu9zRU3dUko1JdiS5McmBPXVJcn6SB9vlPUnSUz/SttnRbuPovv0e374225Nc284pnK4+nZZkfZJHklzWVzcnfUqyd5JLk/xjku8meetM9LGNsXqO57YkZ83TPu6d5JJ2f1uT3JzkFT31Q3EstecyIGPtXI05U4x5zj5fU4z78iT3t5+/TUl+c9Bj7tnHc9L8f3v5fIg5c5QfTFPsxyX5epqx4c4kR8xJ3FXV2QW4Cvg4zc3mX0Jzg/kVcx3XOLHeBPzmGOX7tXH/W2Ah8F+BL/TUnwrcATwLOAD4GvCmnvq/A/4QeBrNT7H+AFjW1q2gucfoL7av0ZXAx6axT8cArwU+AFw2CH0C3g38H+AngJ8Bvgv88gz0cQQoYME47eZTH/cB1rZ9egrw6nb/I8N0LF32fGFAxtpdfB5n7H06DTHPyedrGuJeAezdPj6k/fy9YJBj7tnH/2rHjssH/f3Rbv8mZjk/mKa4XwbcA/xc+94+oF1mPe4ZGXDmw0IzwDwKHNxT9lHgvLmObZx4x3uzrwE+39evncAh7fPPA2t66t8w+qai+fnVR4DFPfX/Z/RNBfwBcGVP3UHta7Z4OvrUs91zeeJ/THPWJ+A+4Jd66t/JNPwBMEYfR9h1Mjzv+tgX/23tIDR0x9Jl0u+FgRtrZ3PMmaH4Z/zzNc3xPhe4H/h3gx4zcBzwpzR/gIwmw4Me803Mcn4wTXF/HnjDIMTd5WkSBwOPVdWmnrJbaf6aHVTvTvJAks8lObItW0ETN/D4vULv5Ef9eEI9T+zjCuCuqtq6i/rebd9J+5/adHRmF+akT0l+AnjmLrY9E+5Jcm+SP0myX0/5vO1jkv1p3iMbxohlmI+lxjYfxtqZfJ9Oq1n8fE1HrBcm2QFspEmGPz3IMSdZApwDnN5XNbAx95jt/GBKkuwFrAKWJflG+//g+5I8bS7i7nIyvIjmNHyvLcDiOYhlIn4PWE7zlcDFwHVJDmL3/eiv3wIsaufXTLZtf/1Mmas+Lep5Plbb6fQA8ELgQJqvDhcDV/TUz8s+JnkqTT8+XFUb9yDWedFPTcp8GGtn8n06bWb58zVlVfXmdntH0PygyyMDHvM7gUuq6tt95YMcM8xNfjBV+wNPBY6leX8cBjwfOHMu4u5yMrwNWNJXtoRmLtbAqaovVtXWqnqkqj4MfA54JbvvR3/9EmBbNd8dTLZtf/1Mmas+bet5PlbbaVNV26pqfVX9v6r6HnAa8EvtmYmx4hz4PiZ5Cs3X34/S9Gd3sYxVP/D91KTNh7F2Jt+n02IOPl/Toqoeq6rP0szv/K1BjTnJYcDRwH8bo3ogYx41R/nBVO1s//0fVXV/VT1AM893TuLucjK8CViQ5Dk9ZStpvnqaDwoITbwrRwuT7EMzd3K0H0+o54l93AAsT7J4F/W9214O7E3z2s2kOelTVT1E81XeeNueSTUa0lhxMuB9bP8iv4Tmr/3VVfXDcWLpwrHUE82HsXYm36dTNkefr+m2oCe2QYz5SJprOb6V5LvA7wKrk/z9AMc8ntnID6YWYDNG38uP/u/rNftxT9dE6Pm4AB+jucp5H+DFDOjdJICnAy+nuapyAXACsJ3mooRlbdyr2/rzeeJVl28Cvk7z9ckz2zdE71WXXwDe27Z9HU++Wv8fab7C2Ae4nOm9m8SCdr/vpjnjMdq/OesTcB7wtzR3IDiEJqGayp0Wxuvji9rj9xRgX5or7W+cj31st/nBNqZFfeVDcyxdpvT+GIixdhefxxl7n05T3LP++ZpivM+guRBtEbAXzf9f24FfHeCY/xnwkz3Le4FPtvEOZMzttp/OHOQH0xT7OcCX2/fLT9Bc6PbOuYh7VgeiQVuApcC17RvnW8Dxcx3TOHEua98wW9uD+gXgZT31R9NcoLCT5qrSkZ66AO8B/qFd3kP7M9xt/UjbZifNrUqO7tv38e1rsx34M2DpNPZrLc1fhb3L2rnsE82ZxUtpkqzvAW+diT4Crwe+2cZwP/AR4CfnaR8PbPv1MM1XVKPLCcN0LF2m9B4ZiLF2vM9jWzdj79Mpxjxnn68pxLyM5g/RH7Sfv9uBN/bUD1zM47xXLh/0mJnD/GAaYn8qcGEb93eBPwYWzkXcaRtKkiRJndPlOcOSJEnqOJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yG1RlJKsmJcx2HJOnJkhyZ5KtJfpjkpgmsPy1jepK7k5w5yTY3JfnQVPetwbBgrgOQJEkCPgB8GXgFsH0W9/tCYMd0b7RNlp9dVUdO97Y1vTwzLEmSJi2Np07jJp8D/O+q+nZV/cM0bneXqmpzVc1m8q0BYzKsoZLkJUk+l2Rru9ya5OXjrPtTST6W5AdJdrZfe63qqT+y/RruNUm+lOThJBuSvKxvO89OcnW7nYeS/K8k/3Km+yppeLXj0SVJzk3y/XZ8eVeSpyR5R5LvJdmc5F09bY5P8sUkW5I8kOT6JAf31P+7JI8m+Vc9Zb/Wjm3Pn0BMpyT5f0n+dZKbgUeAlydZnOSiNp6Hk6xP8kt9bZ/bxrOtXa5L8uy27sgkBewFfKQdd0+Z4Eu1JMlH2/H+20ne1rffBUnWJvlmzxh+at86T5gmkWTfJJ9Isr19nd+Z5MNJbhjjNTkryXeT/EOSy5Ls05avBd4AvLTtz2T6pFlmMqyhkWQv4FPAF4HD22UtY3z9lSTAtcAhwKuBfwV8D/jfSfbrW/0PgXOA5wNfAD6V5IB2O/sDnwW+DxwB/BxwB3BTkmXT2kFJXXMs8FTgJcBbgf8C/DmwiGa8+V3gvyR5Rbv+3sA7aca+lwGPAdcn+TGAqvpT4MPAVUmWtIny+4H/VFU3TzCmpwDvAU6nGT+/CFwKvBw4kWac/Bzw50kOAUjyNOB/AQuBl7bLIuAv29g+D/xUu/3T2scfn2A8ZwOfAQ4D/itwfpJ/3VP/IeAY4FTgZ2jG8vOTvGEX2/wTYCXN/w3/BngW8Nox1jsWWAocCRzfrjOajL8XuBL4u7Y/k+mTZltVubgMxQL8BFDAkePUF3Bi+/io9vnP9tTvDdwPvKN9fmS7zht61lkA3AOc2z5fC3yhbz8B7gTeMteviYuLy/xcgJuAW/rKNgC395XdCrx3nG0sbcewF/eUPa3dzp8CNwPXTiKmU9rtHdFT9uy27JV96/49cGn7+A00JyX266nfH9gJ/FpP2eNj9ATjKeCP+8o2Au9uH/8L4J+AQ/rWeUfvawvcDZzZPn5Ou92jeuqfCnwbuKHv+NzWt90PAn/X8/xDwE1z/V5y2f3iBXQaGlX1UHvBwl8l+Rvgb4FrquqOMVZfATxYVV/raf9Iki+2db3+rmed/5fkS8DPtkUvBF6QZFtfm6fRDKqStKdu7Xv+3XbpL3sGQJLDaM6UHgbsR/OHOcCBNGdrqaqdSf49cAvNt2FH7UFcX+55PDoWfqZvnc8AP98+XgF8raoeGK2squ8luYMnj7eTdUvf8/toEm2AVTSvwfrmy8DHLaA5az6W0f58oSfWHyZZDyyewL5/Cc07JsMaKlX1xiR/RDMgvQx4Z5LTquqisVYfoyzjlPevM+opwF/TfLXXb8sEQpak8fyw73mNU/aUJP+MZirCZ4Hf4EdJ8wbgx/ravKT99+k0ifRkLlZ7rKoensB6/WPpno63u/No3/PiR1NAR//9BZ48XW53+51IXLvat+YRD5qGTlV9tar+sKpeAVwCrBljtQ3AfklGzwKQZG+aucMb+tb9uZ51FtCcDf56W7Se5szGfVX1jb5l8/T1SpJ26WeAZcDbq+rGqvo6zdSxJ5wSTbKC5jqIU4G/AD7Wjn17anS8/MW+8iN66jYAK3qvx2ivtziYJ4+30+kr7b8/Pcb4fOc4bUa/LRw9qz067r9gD/b/KM1FgRpwJsMaGu1dHc5Pc0eJA5P8PM2A/LUxVv8b4EvAlUlenORQ4CM0F3h8oG/dM5K8MsnPtHX796zzPprB7tokRyQZaff/riS/MAPdlKSx3ENzd4ffTnJQkqOAP6LnDGeShcDHgE9V1SXAG2kS5vfu6U7bpPITwIVJXp7kkPbbuUNpLmiD5kKyzcDHkxye5AVtHPcxgxeVVdU3aC7u+59JTmr/j1iZ5DeS/N44bf4vcB3w/iQvbU+YXAQsYfJnsb8JHJJkRZL9pvhHh2aQybCGyXaaebofAzYBV9NcpfykKQxVVTRX/m4ErqeZA/eTwMt657W1fpfmCu1bgBcDv1pV97bb+R7NGYQHgHU0d5K4gmaO3v3T2TlJGk87bp1IMz1sA02C+7s0F5CN+m/APjRnhamqh4ATgDcl+ZUp7P43gb8CLqeZ5/xi4NVVtbHdz06aqWuP0Mwl/lua8fqXq6p/qsF0W0PT77fTnBj5a+Bk4K5dtPl14Ks0Z85vokna/zcwkekhvS6h+b/l8zR/DLx+ku01S9LkBJL6JTkSuBH456PJrySpW9rbdm6kOaN++lzHo+nnBXSSJEmtJL9Ic2HhzTR3kPiPwAhw2dxFpZnkNAlJkkTPr8ONtfyXWY7lg7uIZSYvuoPmOpAzaaZ83AgsB/51Vd0+w/vVHHGahCRJYvTnkcfxD1U1mVuwTTWWZ9BctDaWH1bVPbMVi4afybAkSZI6a+jmDO+33341MjIy12FI6rivfOUrD1TVsrmOY6Y55kqaa1Mdb4cuGR4ZGWH9+vVzHYakjkvSia9xHXMlzbWpjrdeQCdJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZQ3cBnYbXyBnXz8p+7j7vVbOyH2ksSS4HjgL2Ab4LvKeqPtTWHQW8H/hp4IvAKaP3W00S4DzgN9tNXQL8XrX3z0wyAvwJ8CLgW8BpVXVDz36PB94N7Af8b+A3Zuq+sn6WJQ0SzwxL0mB5NzBSVUuAXwHOTfKCJPsB64CzgKXAeuDjPe3WAK8FVgLPA14NnNpTfxXNz8vuC7wd+GSSZQBJVgAXAScB+wM7gAtnqH+SNFBMhiVpgFTVhqp6ZPRpuxwEHANsqKpPVNXDwFpgZZJD2nVPBi6oqnur6j7gAuAUgCQHA4cDZ1fVzqq6GrgdWN22PQG4rqo+U1XbaBLuY5IsnuHuStKcMxmWpAGT5MIkO4CNwP3Ap4EVwK2j61TVduDOtpz++vZxb91dVbV1F/W9274TeBQ4eJq6JEkDa0LJcJLTkqxP8kiSy3rKR5JUkm09y1k99UlyfpIH2+U97by23vY3JtmRZGOSo/v2e3ySe5JsT3JtkqXT0GdJGmhV9WZgMXAEzdSIR4BFwJa+Vbe06zFG/RZgUTvmTrZtf/0TJFnT/p+wfvPmzRPtliQNpImeGf4OcC5w6Tj1T6+qRe3yzp5y57BJ0h6oqseq6rPAs4DfArYBS/pWWwKMnu3tr18CbGsvoJts2/76/tgurqpVVbVq2bKh/8VpSUNuQneTqKp1AElW0QzME/X4HLa2/QXAG4EP9sxh+6Wq2glcneQtNHPYPkjPHLa27VnA15Ms7vuqT5pWs3WlO3i1uyZkAc2c4Q00YyoASfbpKaf9dyXwpfb5yr665X3j50rgyr62o9teDuwNbJruzkjSoJmuOcP3JLk3yZ+0VzyPmpU5bH5lJ2kYJHlGkuOSLEqyV5KXA68H/ga4Bjg0yeokC4F3ALdV1ca2+UeAtyY5IMkzgdOBywCqahNwC3B2koVJXkfzbd3VbdsrgNckOaJNss8B1nniQVIXTDUZfgB4IXAg8AKa+WVX9NTPyhw2v7KTNCSKZkrEvcBDwHuBt1TVn1XVZppvzt7V1r0IOK6n7UXAdTR3ifgqcH1bNuo4YFXb9jzg2HabVNUG4E004/f3acbZN89MFyVpsEzpRzfaW/Csb59+L8lpwP1JllTVP7KLOWxJpnUOmyTNd21y+tJd1N8AHDJOXQFva5ex6u8GjtzFtq/kR9MmJKkzpvvWatX+O3rHiCfMQ2OcOWy7qHcOmyRJkmbMRG+ttqCdo7YXsFc752xBkhcleW6SpyTZF/hj4KaqGp3e4Bw2SZIkDayJnhk+E9gJnAGc2D4+E1gO/CXN1IWv0twL8/U97ZzDJkmSpIE10VurraX56c+xXLWLds5hkyRJ0sDy55glSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkDZAkeye5JMk9SbYmuTnJK3rqj0qyMcmOJDcmObCnLknOT/Jgu7wnSXrqR9o2O9ptHN237+Pb/W5Pcm2SpbPTa0maOybDkjRYFgDfBl4K/DhwFvCnbSK7H7CuLVsKrAc+3tN2DfBaYCXwPODVwKk99VcBNwP7Am8HPplkGUCSFcBFwEnA/sAO4MIZ6aEkDRCTYUkaIFW1varWVtXdVfVPVfXnwDeBFwDHABuq6hNV9TCwFliZ5JC2+cnABVV1b1XdB1wAnAKQ5GDgcODsqtpZVVcDtwOr27YnANdV1WeqahtNwn1MksWz0W9JmisTSoaTnJZkfZJHklzWV+dXdpI0Q5LsDxwMbABWALeO1lXVduDOtpz++vZxb91dVbV1F/W9274TeLTdtyQNrYmeGf4OcC5waW+hX9lJ0sxJ8lTgCuDDVbURWARs6VttCzB69ra/fguwqD0JMdm2/fW9ca1pT5Cs37x58+Q6JUkDZkLJcFWtq6prgQf7qvzKTpJmQJKnAB+lOTt7Wlu8DVjSt+oSYOs49UuAbVVVe9C2v/5xVXVxVa2qqlXLli2bcJ8kaRBNdc7wQHxl51kKScOkPZN7Cc23Yqur6odt1Qaab9pG19sHOKgtf1J9+7i3bnnfCYX++t5tLwf2BjZNQ5ckaWBNNRkeiK/sPEshach8APgZ4DVVtbOn/Brg0CSrkywE3gHc1k6hAPgI8NYkByR5JnA6cBlAVW0CbgHOTrIwyetopq9d3ba9AnhNkiPaJPscYF3fCQtJGjpTTYYH4is7SRoW7UXIpwKHAd9Nsq1dTqiqzTRTyd4FPAS8CDiup/lFwHU0U86+Clzflo06DljVtj0POLbdJlW1AXgTTVL8fZoTD2+eoW5K0sBYMMX2G2jmBQO7/MruS+3zMb+y6znzsBK4sq/t6Lb9yk7S0Kuqe4Dsov4G4JBx6gp4W7uMVX83cOQutn0lPxqDJakTJnprtQXtV3J7AXu1X7EtwK/sJEmSNI9NdJrEmcBO4AzgxPbxmX5lJ0mSpPlsQtMkqmotzW3TxqrzKztJkiTNS/4csyRJkjrLZFiSJEmdZTIsSZKkzprqrdUkRs64fq5DkKQnmc2x6e7zXjVr+5I0vTwzLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZC+Y6AKnLRs64flb2c/d5r5qV/UiSNN94ZliSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxL0gBJclqS9UkeSXJZX91RSTYm2ZHkxiQH9tQlyflJHmyX9yRJT/1I22ZHu42j+7Z9fJJ7kmxPcm2SpTPeWUkaANOSDCe5KcnDSba1yx09dTM2eEvSEPoOcC5waW9hkv2AdcBZwFJgPfDxnlXWAK8FVgLPA14NnNpTfxVwM7Av8Hbgk0mWtdteAVwEnATsD+wALpzebknSYJrOM8OnVdWidnkuzOzgLUnDqKrWVdW1wIN9VccAG6rqE1X1MLAWWJnkkLb+ZOCCqrq3qu4DLgBOAUhyMHA4cHZV7ayqq4HbgdVt2xOA66rqM1W1jWbMPibJ4pnqpyQNipmeJjGTg7ckdckK4NbRJ1W1HbizLX9Sffu4t+6uqtq6i/rebd8JPAocPI3xS9JAms5k+N1JHkjyuSRHtmUzOXhLUpcsArb0lW0BFo9TvwVY1E49m2zb/vonSLKmnde8fvPmzZPqhCQNmulKhn8PWA4cAFwMXJfkIGZ28H6cA7OkDtgGLOkrWwJsHad+CbCtqmoP2vbXP0FVXVxVq6pq1bJlzlyTNL9NSzJcVV+sqq1V9UhVfRj4HPBKZnbw7t2/A7OkYbeB5voKAJLsAxzUlj+pvn3cW7e8bw5wf33vtpcDewObpjF+SRpIMzVnuIAws4O3JA2dJAuSLAT2AvZKsjDJAuAa4NAkq9v6dwC3VdXGtulHgLcmOSDJM4HTgcsAqmoTcAtwdru919FctHx12/YK4DVJjmjH6XOAdX3T1CRpKE05GU7y9CQvHx2wk5wA/CLwV8zs4C1Jw+hMYCdwBnBi+/jMqtpMcwHxu4CHgBcBx/W0uwi4juZC468C17dlo44DVrVtzwOObbdJVW0A3kSTFH+fZjram2eme5I0WBZMwzaeSnNPzEOAx4CNwGur6g6AJKuB9wGXA1/kyYP3cprBG+BDPHnwvoxm8P4WPYO3JA2jqlpLc+edsepuoBlrx6or4G3tMlb93cCRu9jvlcCVk4lVkobBlJPhNjl94S7qZ2zwliRJkqbCn2OWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnbVgrgPQzBg54/q5DkGSJGngeWZYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps7yATuqA2byg8u7zXjVr+5Ikaao8MyxJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrL+wxLkqQnma37k3tvcs01zwxLkiSpswb6zHCSpcAlwC8BDwD/uaqunNuopmY2fwlMkiZjGMfc2eJZVGn+GuhkGHg/8CiwP3AYcH2SW6tqw5xGJUnDyTFX0piG+Q++gU2Gk+wDrAYOraptwGeTfAo4CThjToOTNK5hHjCHmWOu5opjhubawCbDwMHAY1W1qafsVuCl/SsmWQOsaZ9uS3LHNMaxH83XhV3SxT5DN/s9b/uc86fUfDb6feAMb3+6TXXMnbfvpV0YuD5N8X0PA9inabLbfk3DazfbhvFYzdRxmtJ4O8jJ8CJgS1/ZFmBx/4pVdTFw8UwEkWR9Va2aiW0Pqi72GbrZ7y72Gbrb792Y0pg7jK+pfZo/hrFf9mn2DPLdJLYBS/rKlgBb5yAWSRp2jrmSOmmQk+FNwIIkz+kpWwl4IYckTT/HXEmdNLDJcFVtB9YB5yTZJ8mLgV8FPjrLoczI9IsB18U+Qzf73cU+Q3f7Pa5pGHOH8TW1T/PHMPbLPs2SVNVcxzCu9p6XlwIvAx4EzvCel5I0MxxzJXXRQCfDkiRJ0kwa2GkSkiRJ0kwzGZYkSVJnmQzvQpLnJHk4yeV95Ucl2ZhkR5Ibk8y3m+s/SZK9k1yS5J4kW5PcnOQVfesMXb+hmSeZ5Jok29v+Hz/XMU233R3fYT22o8b6LA97n2fDsHx2kpyWZH2SR5Jc1lc3L98nw/qZT3J5kvuT/GOSTUl+s6duXvZp1LCNU0luavuzrV3u6KkbqH6ZDO/a+4Ev9xYk2Y/miuuzgKXAeuDjsx/atFsAfJvm16Z+nKZ/f5pkBIa639Ac50eB/YETgA8kWTG3IU27cY/vkB/bUU/4LHekz7NhWD473wHOpbl48HHz/H0yrJ/5dwMjVbUE+BXg3CQvmOd9GjWM49RpVbWoXZ4Lg9kvL6AbR5LjgGOArwHPrqoT2/I1wClV9Qvt831oflrw+VW1ca7inQlJbgN+v6quHtZ+t/14CDh09Gdok3wUuK+qzpjT4GbY6PEF9mUIj+2osT7Lw/p+nk3D+NlJci7wrKo6pX0+VO+TYfvMJ3kucBPwO8DTmcd9GsZxKslNwOVV9aG+8oHrl2eGx5BkCXAOcPoY1SuAW0eftPfmvLMtHxpJ9gcO5kc33B/Wfh8MPDb6n3nrVuZ/v3ap7/gO67Hd1Wd5aPs8i7rw2Rma98kwfeaTXJhkB7ARuB/4NPO4T0M+Tr07yQNJPpfkyLZs4PplMjy2dwKXVNW3x6hbBGzpK9sCLJ7xqGZJkqcCVwAf7vkrbVj7Paz9GtcYx3eYX4PxPsvD3OfZ0oXXcCj6OGyf+ap6M02sR9B83f4I87tPwzpO/R6wHDiA5sc2rktyEAPYr84lw+2E7hpn+WySw4Cjgf82zia2AUv6ypYAW2cw7CnbXb971nsKzS9OPQqc1rOJednvCRjWfo1pnOM7lK/Bbj7LQ9nnWdaF13De93FYP/NV9VhVfRZ4FvBbzNM+DfM4VVVfrKqtVfVIVX0Y+BzwSgawXwvmasdzpaqO3FV9krcAI8C3kkDzF8xeSX62qg6n+Yrp5J719wEO4kfTCQbS7voNkKbDl9BcDPPKqvphT/W87PcEbAIWJHlOVf3ftmwl879fT7KL4zusx/ZIxvksAx9kOPs8m7rw2ZnXn42OfOYX8KPY52OfjqQ741QBYRCPVVW59CzAPwN+smd5L/BJYFlbv4zmdP5qYCFwPvCFuY57mvr+QeALwKIx6oa53x8DrgL2AV7c9nPFXMc1W8d3WI/trj7Lw9rnOXiNh+KzQ5NQLaS5U8FH28cL5vv7ZNg+88AzgONoE0bg5cB24FfncZ+GcpyiuaDx5T2fpRPaY/XcQezXnL9gg74Aa2muhuwtO5pm4v5OmitZR+Y6zmno54E0f7U9TPMVxuhywjD3u+3XUuDa9oP6LeD4uY5pto/vsB7bvtfgCZ/lLvR5Fl7TofjstO+N6lvWzuf3yTB+5tsk6m+BHwD/CNwOvLGnft71aYw+DsU41R6rL9NMffgBzR9lLxvUfnlrNUmSJHVW5y6gkyRJkkaZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMqyhkOTIJJXkWQMQy0gby0vmOhZJkrRrC+Y6AGkIfRv4KeDBuQ5EkiTtmsmwNI2S/FhVPQp8d65jkSRJu+c0CQ2MJC9J8rkkW9vl1iQvb+veleTrSXYk+XaSDyb58d1s79lJrk7ygyQPJflfSf7lBGMZnepwUpK/TrIzyTeTnDDGOick+XSS7cAfjDVNIskzkvxJku8leTjJHUl+YzpilSRJe85kWAMhyV7Ap4AvAoe3y1pgR7vKTmAN8LPAKcCRwB/vYnv7A58Fvg8cAfwccAdwU5JlkwjtfOBS4DDgCuCjSVaNsc6VwL8E3j9GLE8D/hZYCZzQ9uG3R/s2jbFKkqRJSlXNdQwSSX4C+AfgX1fVTRNY/3XAx4CnVdU/JTkSuBH451V1b5K1wC9X1c/1tAnwDeB/VNV/3832R4BvAudW1Vk95Z8H7qqqE3vWeUdVvXOMtkdU1WeTvIEmSX52Vd07xr6mFKskSdpzzhnWQKiqh5J8CPirJH9Dcyb1mqq6AyDJMcBbgGcDS2i+1fgx4CeB74yxyRcCL0iyra/8acBzJhHa3/U9/xxwVF/Zl3azjRcAXxsrEW5NV6ySJGmSTIY1MKrqjUn+CPgl4GXAO5OcBtwCfAJ4N/CfgIdophJ8mCYhHstTgL8GThujbssUwswYZdsn0G5XX8HMVKySJGk3TIY1UKrqq8BXgT9M8kGaecJXAg9U1Zmj6yU5djebWk8zt/i+qto5hZB+Dvh0z/OfB74+yW18BfiNJM8a5+zwdMUqSZImyQvoNBDauymc395R4sAkP09zMdnXaC4mW5bkDUmWJ/k14M272eT7gL2Aa5Mc0d7h4SXtXSl+YRKhvSHJ8UkOTnIOTTL83yfZvauAe4BPJTk6yb9IclSSfz/NsUqSpEkyGdag2E4zP/ZjwCbgauDzwGlV9efAu4A/AG4HjqOZLjGuqvoeTeL6ALCOJqG+AjgQuH8ScZ1Bc3b6NuDXgJOr6suTaE9V7QBeSnPG+2M0Z5bfTzMneDpjlSRJk+TdJKQx9N8RYo7DkSRJM8Qzw5IkSeosk2F1UpK/SLJtnOUv5jo+SZI0O5wmoU5KcgDtnN0x7Kyq+2YzHkmSNDdMhiVJktRZQ3ef4f32269GRkbmOgxJHfeVr3zlgapaNtdxSJJ2beiS4ZGREdavXz/XYUjquCT3zHUMkqTd8wI6SZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmcN3d0k1Bg54/pZ29fd571q1vYlSZI0nTwzLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWRNKhpOclmR9kkeSXNZTPpKkkmzrWc7qqU+S85M82C7vSZK+9jcm2ZFkY5Kj+/Z7fJJ7kmxPcm2SpdPQZ0mSJAmY+Jnh7wDnApeOU//0qlrULu/sKV8DvBZYCTwPeDVwak/9VcDNwL7A24FPJlkGkGQFcBFwErA/sAO4cILxSpIkSbs1oWS4qtZV1bXAg5Pc/snABVV1b1XdB1wAnAKQ5GDgcODsqtpZVVcDtwOr27YnANdV1WeqahtwFnBMksWTjEGSJEka03TNGb4nyb1J/iTJfj3lK4Bbe57f2paN1t1VVVt3Uf9426q6E3gUOHiaYpYkSVLHTTUZfgB4IXAg8AJgMXBFT/0iYEvP8y3AonbecH/daP3icdr21z8uyZp2TvP6zZs372FXJEmS1DVTSoaraltVra+q/1dV3wNOA34pyZJ2lW3Akp4mS4BtVVVj1I3Wbx2nbX99bxwXV9Wqqlq1bNmyqXRJkiRJHbJgmrdX7b+jd4zYQHPx3Jfa5yvbstG65UkW90yVWAlc2de22WCyHNgb2DTNMc+qkTOun+sQJEmS1JrordUWJFkI7AXslWRhW/aiJM9N8pQk+wJ/DNxUVaPTGz4CvDXJAUmeCZwOXAZQVZuAW4Cz2+29juaOE1e3ba8AXpPkiCT7AOcA6/rmGEuSJEl7bKLTJM4EdgJnACe2j88ElgN/STN14avAI8Dre9pdBFxHc5eIrwLXt2WjjgNWAQ8B5wHHVtVmgKraALyJJin+Ps1c4TdPtoOSJEnSeCY0TaKq1gJrx6m+ahftCnhbu4xVfzdw5C7aX8mPpk1IkiRJ08qfY5YkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6y2RYkiRJnWUyLEmSpM6aUDKc5LQk65M8kuSyvrqjkmxMsiPJjUkO7KlLkvOTPNgu70mSnvqRts2OdhtH9237+CT3JNme5NokS6fYX0mSJOlxEz0z/B3gXODS3sIk+wHrgLOApcB64OM9q6wBXgusBJ4HvBo4taf+KuBmYF/g7cAnkyxrt70CuAg4Cdgf2AFcOOGeSZIkSbsxoWS4qtZV1bXAg31VxwAbquoTVfUwsBZYmeSQtv5k4IKqureq7gMuAE4BSHIwcDhwdlXtrKqrgduB1W3bE4DrquozVbWNJuE+JsniPeuqJEmS9EQLpth+BXDr6JOq2p7kzrZ8Y399+3hFT9u7qmrrLuo/37PtO5M8ChwMfGWKcWsajZxx/azs5+7zXjUr+5EkSd0x1QvoFgFb+sq2AIvHqd8CLGrnDU+2bX/945Ksaec0r9+8efOkOyFJkqRummoyvA1Y0le2BNg6Tv0SYFtV1R607a9/XFVdXFWrqmrVsmXLJt0JSZIkddNUk+ENNBfHAZBkH+CgtvxJ9e3j3rrlfXOA++t7t70c2BvYNMWYJUmSJGDit1ZbkGQhsBewV5KFSRYA1wCHJlnd1r8DuK2qNrZNPwK8NckBSZ4JnA5cBlBVm4BbgLPb7b2O5o4TV7dtrwBek+SINsk+B1jXN8dYkiRJ2mMTPTN8JrATOAM4sX18ZlVtprn7w7uAh4AXAcf1tLsIuI7mLhFfBa5vy0YdB6xq254HHNtuk6raALyJJin+Ps1c4TdPuoeSJEnSONJM3x0eq1atqvXr1891GOOarTsvDCPvJqH5JMlXqmrVXMchSdo1f45ZkiRJnWUyLEmSpM4yGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzjIZliRJUmeZDEuSJKmzTIYlSZLUWSbDkiRJ6iyTYUmSJHWWybAkSZI6a1qS4SQ3JXk4ybZ2uaOn7qgkG5PsSHJjkgN76pLk/CQPtst7kqSnfqRts6PdxtHTEa8kSZIE03tm+LSqWtQuzwVIsh+wDjgLWAqsBz7e02YN8FpgJfA84NXAqT31VwE3A/sCbwc+mWTZNMYsSZKkDpvpaRLHABuq6hNV9TCwFliZ5JC2/mTggqq6t6ruAy4ATgFIcjBwOHB2Ve2sqquB24HVMxyzJEmSOmI6k+F3J3kgyeeSHNmWrQBuHV2hqrYDd7blT6pvH/fW3VVVW8epf1ySNUnWJ1m/efPm6eiLJEmSOmC6kuHfA5YDBwAXA9clOQhYBGzpW3cLsLh93F+/BVjUzhveXdvHVdXFVbWqqlYtW+YsCkmSJE3MtCTDVfXFqtpaVY9U1YeBzwGvBLYBS/pWXwKMnu3tr18CbKuqmkBbSZIkaUpmas5wAQE20FwcB0CSfYCD2nL669vHvXXLkywep16SJEmakiknw0menuTlSRYmWZDkBOAXgb8CrgEOTbI6yULgHcBtVbWxbf4R4K1JDkjyTOB04DKAqtoE3AKc3W77dTR3nLh6qjFLkiRJAAumYRtPBc4FDgEeAzYCr62qOwCSrAbeB1wOfBE4rqftRTRzjW9vn3+oLRt1HE1y/BDwLeDYqvIKOUmSJE2LKSfDbXL6wl3U30CTKI9VV8Db2mWs+ruBI6caoyRJkjQWf45ZkiRJnTUd0ySkWTFyxvWzsp+7z3vVrOxHkiTNPc8MS5IkqbM8M9yarbOOkiRJGhyeGZYkSVJnmQxLkiSps0yGJUmS1Fkmw5IkSeosk2FJkiR1lsmwJEmSOstkWJIkSZ1lMixJkqTOMhmWJElSZ5kMS5IkqbNMhiVJktRZJsOSJEnqLJNhSZIkdZbJsCRJkjrLZFiSJEmdZTIsSZKkzlow1wFIg2bkjOtnbV93n/eqWduXJEl6Ms8MS5IkqbNMhiVJktRZA50MJ1ma5Jok25Pck+T4uY5JkiRJw2PQ5wy/H3gU2B84DLg+ya1VtWFOo5IkSdJQGNhkOMk+wGrg0KraBnw2yaeAk4Az5jQ4aZrM1sV6XqgnSdLYBjYZBg4GHquqTT1ltwIvnaN4pHnLO2RIkjS2QU6GFwFb+sq2AIv7V0yyBljTPt2W5I4Zjg1gP+CBWdjPbLJPg2/g+5PzJ91k4Pu0B/YDDpzrICRJuzfIyfA2YElf2RJga/+KVXUxcPFsBDUqyfqqWjWb+5xp9mnwDVt/YKj7NDLXcUiSdm+Q7yaxCViQ5Dk9ZSsBL56TJEnStBjYZLiqtgPrgHOS7JPkxcCvAh+d28gkSZI0LAY2GW69GXga8H3gKuC3Bui2arM6LWOW2KfBN2z9AfskSZpDqaq5jkGSJEmaE4N+ZliSJEmaMSbDkiRJ6iyT4V1IsjTJNUm2J7knyfHjrHdKkseSbOtZjpzdaHcvyWlJ1id5JMllu1n3Pyb5bpItSS5NsvcshTkpE+3TPDpGeye5pH2/bU1yc5JX7GL9gT5Ok+nPfDlGAEkuT3J/kn9MsinJb+5i3YE+RpLUdSbDu/Z+4FFgf+AE4ANJVoyz7t9V1aKe5abZCnISvgOcC1y6q5WSvJzmJ6+PAkaA5cDvz3Rwe2hCfWrNh2O0APg2zS8t/jhwFvCnSUb6V5wnx2nC/WnNh2ME8G5gpKqWAL8CnJvkBf0rzZNjJEmdZjI8jiT7AKuBs6pqW1V9FvgUcNLcRrbnqmpdVV0LPLibVU8GLqmqDVX1EPBO4JQZDm+PTKJP80JVba+qtVV1d1X9U1X9OfBN4EmJFvPgOE2yP/NG+5o/Mvq0XQ4aY9WBP0aS1HUmw+M7GHisqjb1lN0KjHdm+PlJHmi/Mj0rySD/ut/urKDp66hbgf2T7DtH8UyXeXeMkuxP814c65aC8+447aY/MI+OUZILk+wANgL3A58eY7V5d4wkqWtMhse3CNjSV7YFWDzGup8BDgWeQXM2+fXAf5rR6GZWf99HH4/V9/li3h2jJE8FrgA+XFUbx1hlXh2nCfRnXh2jqnozzWt9BM0PBD0yxmrz6hhJUheZDI9vG7Ckr2wJsLV/xaq6q6q+2X4NfDtwDnDsLMQ4U/r7Pvr4SX2fL+bbMUryFJpfW3wUOG2c1ebNcZpIf+bbMQKoqsfaKVTPAn5rjFXmzTGSpK4yGR7fJmBBkuf0lK1k/K93exWQGYlqdmyg6euolcD3qmoo5uW2BvYYJQlwCc2Fm6ur6ofjrDovjtMk+tNvYI/RGBYw9pzheXGMJKnLTIbHUVXbab76PCfJPkleDPwqzdmtJ0j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      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution Analysis\n",
    "continuous_features = ['land', 'floorplate', 'slope', 'max_roof_height', 'sale_price']\n",
    "subset_df = df[continuous_features]\n",
    "\n",
    "# Plot histograms\n",
    "subset_df.hist(bins=10, figsize=(10, 8), grid=False)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Create box plots\n",
    "plt.figure(figsize=(30, 6))\n",
    "plt.subplot(1, 5, 1)\n",
    "sns.boxplot(data=df, x='land')\n",
    "plt.subplot(1, 5, 2)\n",
    "sns.boxplot(data=df, x='floorplate')\n",
    "plt.subplot(1, 5, 3)\n",
    "sns.boxplot(data=df, x='slope')\n",
    "plt.subplot(1, 5, 4)\n",
    "sns.boxplot(data=df, x='max_roof_height')\n",
    "plt.subplot(1, 5, 5)\n",
    "sns.boxplot(data=df, x='sale_price')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "convenient-bennett",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Select the columns of interest\n",
    "columns_of_interest = ['slope']\n",
    "subset_df_2 = df[columns_of_interest]\n",
    "\n",
    "# Calculate z-scores for slope\n",
    "z_scores_2 = (subset_df_2 - subset_df_2.mean()) / subset_df_2.std()\n",
    "\n",
    "# More than 3 standard devitations then they are outliers\n",
    "threshold = 3\n",
    "\n",
    "# Identify outliers based on the threshold\n",
    "outliers_2 = (np.abs(z_scores_2) > threshold)\n",
    "\n",
    "# Print the rows where outliers are True\n",
    "outliers_slope_df = df[outliers_2.any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "legal-worker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 14)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the total count of outliers.\n",
    "outliers_slope_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bored-killing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-30.152934184004135\n",
      "30.267842506303854\n",
      "0.05745416114985893\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None, None)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slope follows A Gaussian Distribution, I will calculate the boundaries which differentiates the outliers\n",
    "\n",
    "uppper_boundary=subset_df_2['slope'].mean() + 3* subset_df_2['slope'].std()\n",
    "lower_boundary=subset_df_2['slope'].mean() - 3* subset_df_2['slope'].std()\n",
    "print(lower_boundary), print(uppper_boundary),print(subset_df_2['slope'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recreational-standing",
   "metadata": {},
   "source": [
    "#### From the graph we can see that: \n",
    "1. slope follows a gaussian distribution\n",
    "2. Since it is gaussian distribution I use Z score to find values more than 3 S.T.D away.\n",
    "3. Outliers lie after 30 and below -30.\n",
    "\n",
    "Land, Floorplate, max_roof_height, sale_price are all right skewed.\n",
    "Hence I will use IQR to calculate their extreme outlier values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "experimental-pottery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Outliers with lower bridge =  -338.5  and upper bridge =  1345.5\n",
      "Extreme Outliers with lower bridge =  -970.0  and upper bridge =  1977.0\n"
     ]
    }
   ],
   "source": [
    "# Land - Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.land.quantile(0.75)-df.land.quantile(0.25)\n",
    "\n",
    "# Normal Outliers Land\n",
    "lower_bridge=df['land'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['land'].quantile(0.75)+(IQR*1.5)\n",
    "print(\"Normal Outliers with lower bridge = \",lower_bridge,\" and upper bridge = \",upper_bridge)\n",
    "\n",
    "# Extreme outliers Land\n",
    "lower_bridge_extreme=df['land'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge_extreme=df['land'].quantile(0.75)+(IQR*3)\n",
    "print(\"Extreme Outliers with lower bridge = \",lower_bridge_extreme,\" and upper bridge = \",upper_bridge_extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "initial-mediterranean",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Outliers with lower bridge =  -70.5  and upper bridge =  525.5\n",
      "Extreme Outliers with lower bridge =  -294.0  and upper bridge =  749.0\n"
     ]
    }
   ],
   "source": [
    "# floor plate - Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.floorplate.quantile(0.75)-df.floorplate.quantile(0.25)\n",
    "\n",
    "# Normal Outliers floor plate\n",
    "lower_bridge=df['floorplate'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['floorplate'].quantile(0.75)+(IQR*1.5)\n",
    "print(\"Normal Outliers with lower bridge = \",lower_bridge,\" and upper bridge = \",upper_bridge)\n",
    "\n",
    "# Extreme outliers floor plate\n",
    "lower_bridge_extreme=df['floorplate'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge_extreme=df['floorplate'].quantile(0.75)+(IQR*3)\n",
    "print(\"Extreme Outliers with lower bridge = \",lower_bridge_extreme,\" and upper bridge = \",upper_bridge_extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "tested-hearts",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Outliers with lower bridge =  0.07251831274999976  and upper bridge =  12.27297947875\n",
      "Extreme Outliers with lower bridge =  -4.502654624500001  and upper bridge =  16.848152416\n"
     ]
    }
   ],
   "source": [
    "# Max Roof Height - Lets compute the Interquantile range to calculate the boundaries\n",
    "IQR=df.max_roof_height.quantile(0.75)-df.max_roof_height.quantile(0.25)\n",
    "\n",
    "# Normal Outliers Max Roof Height\n",
    "lower_bridge=df['max_roof_height'].quantile(0.25)-(IQR*1.5)\n",
    "upper_bridge=df['max_roof_height'].quantile(0.75)+(IQR*1.5)\n",
    "print(\"Normal Outliers with lower bridge = \",lower_bridge,\" and upper bridge = \",upper_bridge)\n",
    "\n",
    "# Extreme outliers Max Roof Height\n",
    "lower_bridge_extreme=df['max_roof_height'].quantile(0.25)-(IQR*3)\n",
    "upper_bridge_extreme=df['max_roof_height'].quantile(0.75)+(IQR*3)\n",
    "print(\"Extreme Outliers with lower bridge = \",lower_bridge_extreme,\" and upper bridge = \",upper_bridge_extreme)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "manufactured-oklahoma",
   "metadata": {},
   "source": [
    "#### Takeaway:\n",
    "1. All continuous features show no Correlation with each other.\n",
    "2. Floor plate and max roof height show negative correlation to sale price however on digging further into each type\n",
    "    <ul>\n",
    "    2.1 Land and Floorplate of Houses have positive correlation with Sale price. This means as the ssquare rate increases, the sale price increases.</ul>\n",
    "3. No correlation for strata and Sale Price\n",
    "4. Slope follows gaussian distribution and hence used Z score to calculate outlier values which are 30 and -30\n",
    "5. Land, Floorplate, max_roof_height, sale_price are all right skewed and hence required IQR ranges to calculate outlier values. Because mean, median and mode are not the same as gaussian distributed data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-marsh",
   "metadata": {},
   "source": [
    "# Summary of Data Analysis And Outliers Analysis\n",
    "\n",
    "I have started the EDA by analysing missing values and I noticed the following: \n",
    "1. The Object Type values do not have Null Values.\n",
    "2. Numerical Features have null values\n",
    "3. from outlier analysis I found out that most of the data is skewed except Slope and Bathrooms. So for these I can use mean imputation to fill the missing values and other values I can use median imputation to fill missing values.\n",
    "\n",
    "Upon Digging further into each variable I conclude the following:<br/>\n",
    "<u>**Temporal analysis:**</u>\n",
    "1. The months with the least sales are December, January and February. This must be because most people suspend their listings from around Thanksgiving to the New Year because they assume buyers are scarce.\n",
    "\n",
    "<u>**Object Type Variables:**</u>\n",
    "1. There are 2 property types in the dataset: Houses and Strata (A group of buildings divided into lots such as apartments, townhouse or villa).\n",
    "2. Number of Strata and Houses are almost equally distributed.\n",
    "3. Over 10 years there are more number of strata sales in both NSW and QLD compared to the houses in respective states.\n",
    "4. Over 10 years, there are more number of total valued sales for Strata and Houses in NSW compared to QLD.\n",
    "5. Although the number of sales in both states are the same but, total sales value is more this means that, \"The value of houses and Strata in NSW is higher than those in QLD.\"\n",
    "6. Over the years the number of sales for both Strata and Houses have been decreasing in NSW, while QLD saw a steady increase between the period of 2012 and 2017.\n",
    "7. The suburbs with most sale values in 10 years are Hornsby, Waitara in NSW and Nerang in QLD.\n",
    "8. Nerang showed more number of sales compared to both Waitara and Hornsby. \n",
    "\n",
    "<u>**Discrete And Continuous Variables:**</u>\n",
    "1. The number of bathrooms is twice the number of bedrooms in both NSW and QLD.\n",
    "2. Houses in both queensland and NSW have a higher sales price compared to Strata.\n",
    "3. Over the last 10 years total sales prices of houses have growth 600 Thousand compared to Strata that only grew by 400 thousand\n",
    "4. Over the last 10 years NSW and QLD Houses prices have grown at the same rate.\n",
    "5. Over the last 10 years NSW and QLD Strata prices have at the same rate\n",
    "\n",
    "## Outlier Analysis Summary\n",
    "In the outlier Analysis I have taken a simple approach where I have plotted the distribution of data. This will help identify the methodology for finding the outliers. \n",
    "1. If data is assumed to be a Gaussian Distribution I will use Z score to find outliers\n",
    "2. If data is Skewed then I will use IQR to find outliers. In skewed distributions, the mean , median and mode are not equal and there is a possibility of long tail on one side. In these scenerios Extreme valies in tail can affect the mean and standard deviation this affecting the Z score and making it less reliable. \n",
    "\n",
    "<u>**Summary for discrete data:**</u>\n",
    "1. Bedrooms, Bathrooms and garages all indicate that values greater than 5 are outliers.\n",
    "2. Number of properties having bathrooms greater than 5 are 393. While total properties with Bedrooms or garages greater than 5 are only 16.\n",
    "3. High correlation between bedrooms and sale price means. To keep the outliers and build models, then one way is to use ensemble techniques like RandomForest or GrandientBoosting.\n",
    "\n",
    "<u>**Summary for Continuous data:**</u>\n",
    "\n",
    "1. All continuous features show no Correlation with each other.\n",
    "2. Floor plate and max roof height show negative correlation to sale price however on digging further into each type\n",
    "<ul>\n",
    "    2.1 Land and Floorplate of Houses have positive correlation with Sale price. This means as the ssquare rate increases, the sale price increases.</ul>\n",
    "3. No correlation for strata and Sale Price\n",
    "4. Slope follows gaussian distribution and hence used Z score to calculate outlier values which are 30 and -30\n",
    "5. Land, Floorplate, max_roof_height, sale_price are all right skewed and hence required IQR ranges to calculate outlier values. Because mean, median and mode are not the same as gaussian distributed data.\n