House_pricing_explore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# House Pricing Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "In this notebook I'm trying to do some Analysis on <a href='https://www.kaggle.com/c/house-prices-advanced-regression-techniques'> house prices</a> based on the features of each house and the relation between the price and the features \n",
    "\n",
    "by answering the following questions:-\n",
    "- <a href=\"#features\">What are the most features that affect the price ? </a>\n",
    "- <a href=\"#sale\">What is the salling price dustribution ?</a>\n",
    "- <a href=\"#areadist\">What is the area distribution in sq feet ? </a>\n",
    "- <a href = \"#cars\">How many cars can most of the houses hold ? </a>\n",
    "- <a href='#qual'>What is the most common over all qualtiy and what is its percentage ?</a>\n",
    "- <a href='#cond'>What is the most common over all condition and what is its percentage ? </a>\n",
    "- <a href=\"#top5ne\">What are the top 5 common neighborhoods ? </a>\n",
    "- <a href =\"#top5exp\">What are the top 5 expensive neighborhoods ? </a>\n",
    "- <a href =\"#saletype\">What is the most common selling type ? </a>\n",
    "- <a href =\"#salecond\">What is the most common sale condition ? </a>\n",
    "- <a href=\"#yearprice\"> What is the relation between the building year of a building and the selling price ? </a>\n",
    "- <a href =\"#areapricezone\">What is the destribution of the classification zones to the sale price and the area of the proerty ? </a>\n",
    "- <a href = \"#areaprice\"> What is the relation between the area and the price</a>\n",
    "-<a href ='#areashape'>What is the relation between the neighborhoods and the shape of the house?</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df = pd.read_csv('train.csv')\n",
    "house_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Util Functions\n",
    "some functions to help with the work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def normalize_y_axis(data_series):\n",
    "    n_points = house_df.shape[0]\n",
    "    max_count = data_series.value_counts().max()\n",
    "    max_prop = max_count / n_points\n",
    "\n",
    "    # generate tick mark locations and names\n",
    "    tick_props = np.arange(0, max_prop, 0.05)\n",
    "    tick_names = ['{:0.2f}'.format(v) for v in tick_props]\n",
    "    plt.yticks(tick_props * n_points, tick_names)\n",
    "    plt.ylabel('proportion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Data Cleaning\n",
    "First we will start by exploring the data and handling the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "house_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Get correlation between the numerical fields and the price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtFinSF2       0.011378\n",
       "BsmtHalfBath     0.016844\n",
       "MiscVal          0.021190\n",
       "Id               0.021917\n",
       "LowQualFinSF     0.025606\n",
       "YrSold           0.028923\n",
       "3SsnPorch        0.044584\n",
       "MoSold           0.046432\n",
       "OverallCond      0.077856\n",
       "MSSubClass       0.084284\n",
       "PoolArea         0.092404\n",
       "ScreenPorch      0.111447\n",
       "EnclosedPorch    0.128578\n",
       "KitchenAbvGr     0.135907\n",
       "BedroomAbvGr     0.168213\n",
       "BsmtUnfSF        0.214479\n",
       "BsmtFullBath     0.227122\n",
       "LotArea          0.263843\n",
       "HalfBath         0.284108\n",
       "OpenPorchSF      0.315856\n",
       "2ndFlrSF         0.319334\n",
       "WoodDeckSF       0.324413\n",
       "LotFrontage      0.351799\n",
       "BsmtFinSF1       0.386420\n",
       "Fireplaces       0.466929\n",
       "MasVnrArea       0.477493\n",
       "GarageYrBlt      0.486362\n",
       "YearRemodAdd     0.507101\n",
       "YearBuilt        0.522897\n",
       "TotRmsAbvGrd     0.533723\n",
       "FullBath         0.560664\n",
       "1stFlrSF         0.605852\n",
       "TotalBsmtSF      0.613581\n",
       "GarageArea       0.623431\n",
       "GarageCars       0.640409\n",
       "GrLivArea        0.708624\n",
       "OverallQual      0.790982\n",
       "SalePrice        1.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_corr_to_price = house_df.corr().abs()['SalePrice'].sort_values()\n",
    "cols_corr_to_price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "drop all the numerical columns with correlation less than 25%, note that `OverallCond` and `MSSubClass` are categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "keys_to_drop = cols_corr_to_price[cols_corr_to_price < .25].keys().tolist()\n",
    "cat_list = ['OverallCond','MSSubClass']\n",
    "keys_to_drop = set(keys_to_drop)  - set(cat_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.drop(columns = list(keys_to_drop),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id=\"features\"></a>\n",
    "What are the most features that affect the price ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'correlation to Sale Price')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_corr_to_price = house_df.corr().abs()['SalePrice'].sort_values()\n",
    "corr = cols_corr_to_price[-6:-1]\n",
    "corr.plot(kind='bar')\n",
    "plt.xlabel('Top 5 numerical variables correlated to price')\n",
    "plt.ylabel('correlation to Sale Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass          0\n",
      "Foundation          0\n",
      "SaleCondition       0\n",
      "BsmtFinSF1          0\n",
      "TotalBsmtSF         0\n",
      "Heating             0\n",
      "HeatingQC           0\n",
      "CentralAir          0\n",
      "1stFlrSF            0\n",
      "2ndFlrSF            0\n",
      "GrLivArea           0\n",
      "ExterCond           0\n",
      "FullBath            0\n",
      "KitchenQual         0\n",
      "TotRmsAbvGrd        0\n",
      "Functional          0\n",
      "Fireplaces          0\n",
      "GarageCars          0\n",
      "GarageArea          0\n",
      "PavedDrive          0\n",
      "WoodDeckSF          0\n",
      "OpenPorchSF         0\n",
      "SaleType            0\n",
      "HalfBath            0\n",
      "ExterQual           0\n",
      "SalePrice           0\n",
      "LandSlope           0\n",
      "LandContour         0\n",
      "Neighborhood        0\n",
      "Condition1          0\n",
      "Condition2          0\n",
      "BldgType            0\n",
      "HouseStyle          0\n",
      "OverallQual         0\n",
      "OverallCond         0\n",
      "LotConfig           0\n",
      "YearBuilt           0\n",
      "LotShape            0\n",
      "RoofStyle           0\n",
      "RoofMatl            0\n",
      "Street              0\n",
      "LotArea             0\n",
      "MSZoning            0\n",
      "Exterior1st         0\n",
      "Exterior2nd         0\n",
      "YearRemodAdd        0\n",
      "Utilities           0\n",
      "Electrical          1\n",
      "MasVnrType          8\n",
      "MasVnrArea          8\n",
      "BsmtFinType1       37\n",
      "BsmtQual           37\n",
      "BsmtCond           37\n",
      "BsmtExposure       38\n",
      "BsmtFinType2       38\n",
      "GarageQual         81\n",
      "GarageCond         81\n",
      "GarageFinish       81\n",
      "GarageYrBlt        81\n",
      "GarageType         81\n",
      "LotFrontage       259\n",
      "FireplaceQu       690\n",
      "Fence            1179\n",
      "Alley            1369\n",
      "MiscFeature      1406\n",
      "PoolQC           1453\n"
     ]
    }
   ],
   "source": [
    "nulls_series = house_df.columns[house_df.isna().any()].tolist()\n",
    "print(house_df.isnull().sum().sort_values().to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "drop the most nullable columns `PoolQC`,`MiscFeature`,`Alley` and `Fence`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.drop(columns=['PoolQC','MiscFeature','Alley','Fence'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "drop the nullable row `Electrical`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.drop(house_df[house_df['Electrical'].isna()].index[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass         0\n",
      "SaleCondition      0\n",
      "BsmtFinSF1         0\n",
      "TotalBsmtSF        0\n",
      "Heating            0\n",
      "HeatingQC          0\n",
      "CentralAir         0\n",
      "Electrical         0\n",
      "1stFlrSF           0\n",
      "2ndFlrSF           0\n",
      "GrLivArea          0\n",
      "Foundation         0\n",
      "FullBath           0\n",
      "KitchenQual        0\n",
      "TotRmsAbvGrd       0\n",
      "Functional         0\n",
      "Fireplaces         0\n",
      "GarageCars         0\n",
      "GarageArea         0\n",
      "PavedDrive         0\n",
      "WoodDeckSF         0\n",
      "OpenPorchSF        0\n",
      "SaleType           0\n",
      "HalfBath           0\n",
      "ExterCond          0\n",
      "SalePrice          0\n",
      "Condition1         0\n",
      "LandSlope          0\n",
      "Neighborhood       0\n",
      "ExterQual          0\n",
      "Condition2         0\n",
      "BldgType           0\n",
      "HouseStyle         0\n",
      "OverallQual        0\n",
      "OverallCond        0\n",
      "YearBuilt          0\n",
      "YearRemodAdd       0\n",
      "RoofStyle          0\n",
      "RoofMatl           0\n",
      "LandContour        0\n",
      "Exterior1st        0\n",
      "LotShape           0\n",
      "Exterior2nd        0\n",
      "Street             0\n",
      "LotArea            0\n",
      "MSZoning           0\n",
      "LotConfig          0\n",
      "Utilities          0\n",
      "MasVnrArea         8\n",
      "MasVnrType         8\n",
      "BsmtFinType1      37\n",
      "BsmtCond          37\n",
      "BsmtQual          37\n",
      "BsmtFinType2      38\n",
      "BsmtExposure      38\n",
      "GarageType        81\n",
      "GarageYrBlt       81\n",
      "GarageFinish      81\n",
      "GarageQual        81\n",
      "GarageCond        81\n",
      "LotFrontage      259\n",
      "FireplaceQu      689\n"
     ]
    }
   ],
   "source": [
    "print(house_df.isnull().sum().sort_values().to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "now let's do some investigation on Fireplaces we know that we have a column `Fireplaces` that represents the number of fireplaces in the house, we will try to find the number of houses with zero fireplace and compare that number with `FireplaceQu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "689\n"
     ]
    }
   ],
   "source": [
    "print(len(house_df.groupby('Fireplaces').get_group(0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "we find that the number of `0 Fireplaces` is the same as the number of `Nan` values in `FirePlaceQu` which means there are no null values in `FireplaceQu` now we will replace the `Nan` value to `NA` in `FireplaceQu`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df.FireplaceQu.fillna('NA',inplace=True)\n",
    "house_df.FireplaceQu.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Now if we check the `Garage` attributes we will find that some of them have the same number of nulls `81` so we will asume that those units have no Garage and we can sure make sure by checking the number of `GarageArea == 0`, so lets replace the `nan` value with `NA` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(house_df.query('GarageArea == 0'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass         0\n",
      "TotalBsmtSF        0\n",
      "Heating            0\n",
      "HeatingQC          0\n",
      "CentralAir         0\n",
      "Electrical         0\n",
      "1stFlrSF           0\n",
      "2ndFlrSF           0\n",
      "GrLivArea          0\n",
      "FullBath           0\n",
      "HalfBath           0\n",
      "KitchenQual        0\n",
      "TotRmsAbvGrd       0\n",
      "BsmtFinSF1         0\n",
      "Functional         0\n",
      "FireplaceQu        0\n",
      "GarageType         0\n",
      "GarageYrBlt        0\n",
      "GarageFinish       0\n",
      "GarageCars         0\n",
      "GarageArea         0\n",
      "GarageQual         0\n",
      "GarageCond         0\n",
      "PavedDrive         0\n",
      "WoodDeckSF         0\n",
      "OpenPorchSF        0\n",
      "SaleType           0\n",
      "Fireplaces         0\n",
      "SaleCondition      0\n",
      "SalePrice          0\n",
      "OverallQual        0\n",
      "Utilities          0\n",
      "LotConfig          0\n",
      "LandSlope          0\n",
      "Neighborhood       0\n",
      "Condition1         0\n",
      "Condition2         0\n",
      "BldgType           0\n",
      "HouseStyle         0\n",
      "Street             0\n",
      "OverallCond        0\n",
      "YearBuilt          0\n",
      "YearRemodAdd       0\n",
      "RoofStyle          0\n",
      "RoofMatl           0\n",
      "Exterior1st        0\n",
      "Exterior2nd        0\n",
      "ExterQual          0\n",
      "ExterCond          0\n",
      "LotArea            0\n",
      "Foundation         0\n",
      "MSZoning           0\n",
      "LandContour        0\n",
      "LotShape           0\n",
      "MasVnrType         8\n",
      "MasVnrArea         8\n",
      "BsmtQual          37\n",
      "BsmtFinType1      37\n",
      "BsmtCond          37\n",
      "BsmtFinType2      38\n",
      "BsmtExposure      38\n",
      "LotFrontage      259\n"
     ]
    }
   ],
   "source": [
    "#so from above as we can see that the units with 0 of GarageArea = 81 so let's replace the null values with NA\n",
    "house_df.fillna({'GarageCond':'NA','GarageQual':'NA','GarageFinish':'NA','GarageType':0,'GarageYrBlt':0},inplace=True)\n",
    "print(house_df.isnull().sum().sort_values().to_string())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "replace the nan values in `MasVnrType` with `NA` and `MasVnrArea` with `0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.fillna({'MasVnrType':'NA','MasVnrArea':0},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Now let's replace the `Null` basements with `NA` values also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.fillna({'BsmtExposure':'NA','BsmtFinType2':'NA','BsmtFinType1':'NA','BsmtQual':'NA','BsmtCond':'NA'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Finally we will drop the rows of the `null` `LotFrontage`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.drop(house_df[house_df['LotFrontage'].isna()].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# Data Exploration Phase\n",
    "now we will start be exploring different variables and explore them using visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='sale'></a>\n",
    "### What is the salling price distrebution ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Property Sale Price')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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At1UZfxG4aoRt1gPrx53Oqppfx+mg25Yca8ppo4Hbr2v4bZpZ4/kVqmZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGXK5m5llqGa5S5on6WFJT0vaLenWND5L0lZJe9PlzNI26yT1S9oj6epm3gEzMztVPUfux4DbIuIfAu8E1khaBKwFtkXEQmBbuk5atwJYDCwD7pQ0pRnhzcysuprlHhEHI+L7afkV4GlgDrAc2JCmbQBuSMvLgY0R8VpE7AP6gcsbHdzMzEZ2WufcJc0H3gY8CnRExEEofgEAb0rT5gDPlzbbn8bMzGyCKCLqmyhVgP8FrI+Ir0s6GhEzSuuPRMRMSZ8DvhsRd6fxu4C/ioh7h93eamA1QEdHx9Le3l4qlUpj7lUTDQ4OHs+564WXJjnNyDrOhkM/b/ztLpkzveG3Wf6etjLnbKx2yQmtm7W7u3tHRHRWWze1nhuQdCZwL3BPRHw9DR+SNDsiDkqaDRxO4/uBeaXN5wIHht9mRPQAPQCdnZ1RqVTo6uqqJ86k6uvrO57z5rUPTG6YUdy25Bif2VXXP+9pGbipq+G3Wf6etjLnbKx2yQntlXVIPc+WEXAX8HRE/OfSqi3AyrS8Eri/NL5C0jRJC4CFwPbGRTYzs1rqObS7AvggsEvSzjT2CeB2YJOkVcBzwI0AEbFb0ibgKYpn2qyJiNcbntzMzEZUs9wj4juARlh91QjbrAfWjyOXmZmNg1+hamaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWocZ/gvIkmD+BH1R925JjLf3B2GZm4CN3M7MsudzNzDLkcjczy5DL3cwsQy53M7MMudzNzDLkcjczy5DL3cwsQ1m8iMkmTjNeMFbPC8MGbr+u4fs1y1nNI3dJX5B0WNKTpbFZkrZK2psuZ5bWrZPUL2mPpKubFdzMzEZWz2mZLwHLho2tBbZFxEJgW7qOpEXACmBx2uZOSVMaltbMzOpSs9wj4lvAz4YNLwc2pOUNwA2l8Y0R8VpE7AP6gcsblNXMzOqkiKg9SZoPfDMiLknXj0bEjNL6IxExU9IdwCMRcXcavwt4MCI2V7nN1cBqgI6OjqW9vb1UKpUx3YldL7w0pu3GouNsOPTzCdvdmLVLTqgv65I50ycmzCgGBwfH/DM6kZyz8Vo1a3d3946I6Ky2rtEPqKrKWNXfHhHRA/QAdHZ2RqVSoaura0w7nch3abxtyTE+s6v1H4dul5xQX9aBm7omJswo+vr6xvwzOpGcs/HaKeuQsT4V8pCk2QDp8nAa3w/MK82bCxwYezwzMxuLsZb7FmBlWl4J3F8aXyFpmqQFwEJg+/gimpnZ6ar5d7ukrwBdwPmS9gN/BNwObJK0CngOuBEgInZL2gQ8BRwD1kTE603KbmZmI6hZ7hHx/hFWXTXC/PXA+vGEMjOz8fHbD5iZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlyuZuZZcjlbmaWIZe7mVmGXO5mZhlqj09zsF968yfwA1mGG7j9uknbt9lY+cjdzCxDLnczswy53M3MMuRyNzPLkMvdzCxDLnczswy53M3MMuRyNzPLkMvdzCxDLnczswz57QfMahh664Pblhzj5gl8GwS/7YGNh4/czcwy5HI3M8tQ08pd0jJJeyT1S1rbrP2YmdmpmnLOXdIU4HPAPwH2A9+TtCUinmrG/sxyNNa3OW7EYwM+39/+mvWA6uVAf0Q8CyBpI7AccLmbtYGJeP/8iX6AejyambVZv0gVEY2/UelfAMsi4pZ0/YPAOyLid0pzVgOr09W3AC8CP214mMY7H+dstHbJ6pyN1S45oXWzXhQRF1Rb0awjd1UZO+m3SET0AD3HN5Aei4jOJuVpGOdsvHbJ6pyN1S45ob2yDmnWA6r7gXml63OBA03al5mZDdOscv8esFDSAklnASuALU3al5mZDdOU0zIRcUzS7wAPAVOAL0TE7hqb9dRY3yqcs/HaJatzNla75IT2ygo06QFVMzObXH6FqplZhlzuZmY5iohJ/QKWAXuAfmBtE/fzBeAw8GRpbBawFdibLmeW1q1LmfYAV5fGlwK70rr/yolTW9OAr6bxR4H5pW1Wpn3sBVbWyDkPeBh4GtgN3NqKWYE3ANuBJ1LOP27FnKX5U4DHgW+2eM6BtI+dwGOtmhWYAWwGnqH4WX1Xq+WkeP3MztLXy8DHWy1ns74mdGcj/If7IXAxcBZFUSxq0r7eDVzGyeX+adIvFGAt8KdpeVHKMg1YkDJOSeu2px9kAQ8C16Tx3wb+Ii2vAL5a+o/5bLqcmZZnjpJzNnBZWj4X+D8pT0tlTbdZSctnph/sd7ZazlLefwt8mRPl3qo5B4Dzh421XFZgA3BLWj6LouxbLuewrvkxcFEr52xo503kzqp8w98FPFS6vg5Y18T9zefkct8DzE7Ls4E91XJQPOvnXWnOM6Xx9wOfL89Jy1MpXs2m8py07vPA+08j8/0U79HTslmBNwLfB97RijkpXmexDbiSE+XecjnTnAFOLfeWygqcB+wjHb22as5h2d4D/G2r52zk12Sfc58DPF+6vj+NTZSOiDgIkC7fVCPXnLQ8fPykbSLiGPAS8Cuj3FZNkuYDb6M4Km65rJKmSNpJcbpra0S0ZE7gz4HfB/5faawVc0LxSu7/IWlHeouOVsx6MfAT4IuSHpfUK+mcFsxZtgL4Slpu5ZwNM9nlXvNtCibJSLlGyzuWbUYOIFWAe4GPR8TLo00dw34bkjUiXo+ISymOjC+XdEmr5ZR0PXA4InaMku2kTcawz0b+218REZcB1wBrJL17lLmTlXUqxSnO/xYRbwNepTi90Wo5ixsqXkj5PuBro80b4z4b+v++kSa73Cf7bQoOSZoNkC4P18i1Py0PHz9pG0lTgenAz0a5rRFJOpOi2O+JiK+3claAiDgK9FE8ON5qOa8A3idpANgIXCnp7hbMCUBEHEiXh4H7KN5htdWy7gf2p7/UoHhg9bIWzDnkGuD7EXEoXW/VnI01keeAqpwHm0rxQMMCTjyguriJ+5vPyefc/4yTH1j5dFpezMkPrDzLiQdWvkfxwOHQAyvXpvE1nPzAyqa0PIvi/OTM9LUPmDVKRgF/Cfz5sPGWygpcAMxIy2cD3waub7WcwzJ3ceKce8vlBM4Bzi0t/2+KX5itmPXbwFvS8qdSxpbLmbbZCHyoVf8vNa3vJnJnI3zjr6V4RsgPgU82cT9fAQ4Cv6D4rbqK4tzYNoqnKm0rf/OBT6ZMe0iPjKfxTuDJtO4OTjwl6g0Uf/b1UzyyfnFpmw+n8f7yD9kIOf8xxZ9vP+DEU7iubbWswD+ieGrhD9I+/jCNt1TOYZm7OFHuLZeT4lz2E5x4euknWzjrpcBj6d//GxQF1oo530jxduLTS2Mtl7MZX377ATOzDE32OXczM2sCl7uZWYZc7mZmGXK5m5llyOVuZpYhl7uZWYZc7mZmGfr/0nMt0DYwdb0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "house_df['SalePrice'].hist();\n",
    "plt.title('Property Sale Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='areadist'></a>\n",
    "### What is the area distribution in sq feet ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEzCAYAAADKCUOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAcp0lEQVR4nO3de7RcZZ3m8e9joCMDKpccMCQhiRJm5NIGjVGHntUIKAhqYEY0aEu0aWFGtLGXq+3gzLTYvaJot62umYZpFDS60JBWkYzgBTNeFopAoCOXcIsmknBJooCArawmPPPH3oHiUOecOqdO3d7zfNaqdarevd/9/urk5Klde7+1S7aJiIiyPKfXBURExORLuEdEFCjhHhFRoIR7RESBEu4REQVKuEdEFCjhHhFRoIR79JSkzZJ+J+kxSdskfV7SXr2uC0CSJR3c5jbmS3pS0gWTVVdEKxLu0Q/eaHsv4GXAK4D/MXwFSbt1q5hJHut04CFgqaTpXRozIuEe/cP2vcC3gMPhqT3nsyXdDdxdt71b0kZJD0paI+nAXf3r9f9c0i8k/UrS30l6TsPyP5V0u6SHJH1H0txhfZ8aS9KP6kU/q99VvFXSrZLe2NBn93qchaM8rdOpXqz+DXhj44IRnt8bJK2X9LCkn0j6w4b1l0v6uaRHJW2QdMr4fsMxpdjOLbee3YDNwHH1/TnAbcDf1o8NXA3sC+wBHAP8imoPfzrwv4AfNWzLwPfr9Q8C7gL+rF52MrAReAmwG1Xg/mRY36fGamg7uGGdDwKXNTxeAtwyynP7T8DjwD51rWuGLR/+/F4GbAdeCUwDltW/n+n1+qcCB1LtlL0V+C0ws9f/hrn1563nBeQ2tW91eD0GPAz8ErhgWLge07DuxcAnGh7vRbVHPK9h/RMalr8HWFvf/xZwRsOy5wD/CsxtNlZDW2O4Hwg8Cjy/fvxV4IOjPLfPAd+o77+6rnX/YdtvfH4X7npha2i7E/jjEba/HljS63/D3PrzlsMy0Q9Otr237bm232P7dw3LtjTcP5DqBQAA248BvwZmjbD+L+s+AHOBz9SHOx4GHgQ0St9nsX0f8GPgv0jaG3g9cGmzdSXtQbWnfWnd91rgHuBtw1ZtHHMu8IFdNdZ1ztn1HCSd3nDI5mGqw1czRqs5pq6Ee/S7xsuW3kcVgABI2hPYD7i3YZ05DfcPqvtAFaJn1S8iu2572P7JCGONZCXwJ1TBfa2r8wTNnAI8H7hA0gOSHqB6ITl9lOe3BVgxrMZ/Z/sr9fmBzwLvBfazvTdwK9ULVMSzJNxjkHwZeJekhfXMk48C19ne3LDOX0raR9Ic4Bzgsrr9/wDnSjoMQNILJJ06xnjbgBcNa/sG1bHxc4AvjtJ3GXAJcASwsL4dBSyUdMQIfT4L/FdJr1RlT0knSXoesCfVC8GOuv53UZ94jmgm4R4Dw/Za4H8CXwPuB14MLB222hXAjVTHo6+kOk6P7cuBjwOrJD1Ctdf7+jGGPA9YWR8GeUu9nd/V488Hvt6sk6RZwLHAp20/0HC7Efg2VfA3e37rgHcD/5tq+uRG4J31sg3AJ4FrqV50jqA6RBTRlOx8WUeUQZKBBbY3dnicvwYOsf0nnRwnoh354ETEOEjaFzgDeEeva4kYTQ7LRLRI0rupTnp+y/aPxlo/opdyWCYiokDZc4+IKFDCPSKiQH1xQnXGjBmeN29er8uIiBgoN954469sDzVb1hfhPm/ePNatW9frMiIiBoqkX460LIdlIiIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAvXFh5iifPOWXzmhfpvPP2mSK4mYGrLnHhFRoIR7RESBEu4REQVKuEdEFCjhHhFRoIR7RESBEu4REQVKuEdEFCjhHhFRoIR7RESBEu4REQUaM9wlPVfS9ZJ+Juk2SR+p28+TdK+k9fXtxIY+50raKOlOScd38glERMSztXLhsMeBY2w/Jml34BpJ36qXfcr23zeuLOlQYClwGHAg8D1Jh9jeOZmFR0TEyMbcc3flsfrh7vXNo3RZAqyy/bjtTcBGYHHblUZERMtaOuYuaZqk9cB24Grb19WL3ivpZkmXSNqnbpsFbGnovrVui4iILmkp3G3vtL0QmA0slnQ4cCHwYmAhcD/wyXp1NdvE8AZJZ0paJ2ndjh07JlR8REQ0N67ZMrYfBn4AnGB7Wx36TwKf5elDL1uBOQ3dZgP3NdnWRbYX2V40NDQ0oeIjIqK5VmbLDEnau76/B3AccIekmQ2rnQLcWt9fAyyVNF3SfGABcP3klh0REaNpZbbMTGClpGlULwarbX9T0pckLaQ65LIZOAvA9m2SVgMbgCeAszNTJiKiu8YMd9s3A0c2aX/HKH1WACvaKy0iIiYqn1CNiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokCtfEH2cyVdL+lnkm6T9JG6fV9JV0u6u/65T0OfcyVtlHSnpOM7+QQiIuLZWtlzfxw4xvZLgYXACZJeBSwH1tpeAKytHyPpUGApcBhwAnBB/eXaERHRJWOGuyuP1Q93r28GlgAr6/aVwMn1/SXAKtuP294EbAQWT2rVERExqpaOuUuaJmk9sB242vZ1wAG27weof+5frz4L2NLQfWvdFhERXdJSuNveaXshMBtYLOnwUVZXs008ayXpTEnrJK3bsWNHa9VGRERLxjVbxvbDwA+ojqVvkzQToP65vV5tKzCnodts4L4m27rI9iLbi4aGhiZQekREjKSV2TJDkvau7+8BHAfcAawBltWrLQOuqO+vAZZKmi5pPrAAuH6yC4+IiJHt1sI6M4GV9YyX5wCrbX9T0rXAaklnAPcApwLYvk3SamAD8ARwtu2dnSk/IiKaGTPcbd8MHNmk/dfAsSP0WQGsaLu6iIiYkHxCNSKiQAn3iIgCJdwjIgqUcI+IKFDCPSKiQAn3iIgCJdwjIgqUcI+IKFDCPSKiQAn3iIgCJdwjIgqUcI+IKFDCPSKiQAn3iIgCJdwjIgqUcI+IKFDCPSKiQAn3iIgCtfIdqhE9M2/5lePus/n8kzpQScRgGXPPXdIcSd+XdLuk2ySdU7efJ+leSevr24kNfc6VtFHSnZKO7+QTiIiIZ2tlz/0J4AO2b5L0POBGSVfXyz5l++8bV5Z0KLAUOAw4EPiepENs75zMwiMiYmRj7rnbvt/2TfX9R4HbgVmjdFkCrLL9uO1NwEZg8WQUGxERrRnXCVVJ84AjgevqpvdKulnSJZL2qdtmAVsaum1l9BeDiIiYZC2Hu6S9gK8B77f9CHAh8GJgIXA/8Mldqzbp7ibbO1PSOknrduzYMe7CIyJiZC2Fu6TdqYL9UttfB7C9zfZO208Cn+XpQy9bgTkN3WcD9w3fpu2LbC+yvWhoaKid5xAREcO0MltGwMXA7bb/oaF9ZsNqpwC31vfXAEslTZc0H1gAXD95JUdExFhamS1zFPAO4BZJ6+u2DwGnSVpIdchlM3AWgO3bJK0GNlDNtDk7M2UiIrprzHC3fQ3Nj6NfNUqfFcCKNuqKiIg25PIDEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBSolS/IniPp+5Jul3SbpHPq9n0lXS3p7vrnPg19zpW0UdKdko7v5BOIiIhna2XP/QngA7ZfArwKOFvSocByYK3tBcDa+jH1sqXAYcAJwAWSpnWi+IiIaG7McLd9v+2b6vuPArcDs4AlwMp6tZXAyfX9JcAq24/b3gRsBBZPduERETGycR1zlzQPOBK4DjjA9v1QvQAA+9erzQK2NHTbWrdFRESXtBzukvYCvga83/Yjo63apM1NtnempHWS1u3YsaPVMiIiogUthbuk3amC/VLbX6+bt0maWS+fCWyv27cCcxq6zwbuG75N2xfZXmR70dDQ0ETrj4iIJlqZLSPgYuB22//QsGgNsKy+vwy4oqF9qaTpkuYDC4DrJ6/kiIgYy24trHMU8A7gFknr67YPAecDqyWdAdwDnApg+zZJq4ENVDNtzra9c9Irj4iIEY0Z7ravoflxdIBjR+izAljRRl0REdGGfEI1IqJACfeIiAIl3CMiCpRwj4goUCuzZSKeYd7yK3tdQkSMIXvuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFSrhHRBQo4R4RUaCEe0REgRLuEREFauULsi+RtF3SrQ1t50m6V9L6+nZiw7JzJW2UdKek4ztVeEREjKyVPfcvACc0af+U7YX17SoASYcCS4HD6j4XSJo2WcVGRERrxgx32z8CHmxxe0uAVbYft70J2AgsbqO+iIiYgHaOub9X0s31YZt96rZZwJaGdbbWbRER0UUTDfcLgRcDC4H7gU/W7WqyrpttQNKZktZJWrdjx44JlhEREc1MKNxtb7O90/aTwGd5+tDLVmBOw6qzgftG2MZFthfZXjQ0NDSRMiIiYgQTCndJMxsengLsmkmzBlgqabqk+cAC4Pr2SoyIiPEa8wuyJX0FOBqYIWkr8GHgaEkLqQ65bAbOArB9m6TVwAbgCeBs2zs7U3pERIxkzHC3fVqT5otHWX8FsKKdoiIioj35hGpERIES7hERBUq4R0QUKOEeEVGghHtERIES7hERBUq4R0QUKOEeEVGghHtERIES7hERBUq4R0QUKOEeEVGghHtERIES7hERBUq4R0QUKOEeEVGghHtERIES7hERBRrza/YiBs285VeOu8/m80/qQCURvTPmnrukSyRtl3RrQ9u+kq6WdHf9c5+GZedK2ijpTknHd6rwiIgYWSuHZb4AnDCsbTmw1vYCYG39GEmHAkuBw+o+F0iaNmnVRkRES8YMd9s/Ah4c1rwEWFnfXwmc3NC+yvbjtjcBG4HFk1RrRES0aKInVA+wfT9A/XP/un0WsKVhva11W0REdNFkz5ZRkzY3XVE6U9I6Set27NgxyWVERExtEw33bZJmAtQ/t9ftW4E5DevNBu5rtgHbF9leZHvR0NDQBMuIiIhmJhrua4Bl9f1lwBUN7UslTZc0H1gAXN9eiRERMV5jznOX9BXgaGCGpK3Ah4HzgdWSzgDuAU4FsH2bpNXABuAJ4GzbOztUe0REjGDMcLd92giLjh1h/RXAinaKioiI9uTyAxERBcrlB6a4iXxUPyL6X/bcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIAiXcIyIKlHCPiChQwj0iokAJ94iIArX1ZR2SNgOPAjuBJ2wvkrQvcBkwD9gMvMX2Q+2VGRER4zEZe+6vsb3Q9qL68XJgre0FwNr6cUREdFEnDsssAVbW91cCJ3dgjIiIGEW74W7gu5JulHRm3XaA7fsB6p/7tzlGRESMU7tfkH2U7fsk7Q9cLemOVjvWLwZnAhx00EFtlhEREY3a2nO3fV/9cztwObAY2CZpJkD9c/sIfS+yvcj2oqGhoXbKiIiIYSYc7pL2lPS8XfeB1wG3AmuAZfVqy4Ar2i0yIiLGp53DMgcAl0vatZ0v2/62pBuA1ZLOAO4BTm2/zIiIGI8Jh7vtXwAvbdL+a+DYdoqKiIj25BOqEREFSrhHRBSo3amQ0UfmLb+y1yVERJ/InntERIES7hERBUq4R0QUKOEeEVGghHtERIEyW6YPZdZL903kd775/JM6UEnE5Miee0REgRLuEREFSrhHRBRoyh5zzzHWiCjZlA33iciJzogYFAn3iAma6It93gFGN+SYe0REgRLuEREFSrhHRBQo4R4RUaCOnVCVdALwGWAa8Dnb53dqrMxiiYh4po6Eu6RpwD8CrwW2AjdIWmN7QyfGiyhdPpcR49WpPffFwEbbvwCQtApYAiTcY8rr1jvNbr6jzQtJ/+lUuM8CtjQ83gq8skNjRUSP5Z1FpZ9+D50KdzVp8zNWkM4EzqwfPibpzjbGmwH8qo3+Y3kB8Js+3v5Enn+rY7ay3ljjj7aNkZa182/a6b+HTo/R7t/DRPqPp08r6475b66Pt11HPxled8t/HyP8HkbbdqO5I/ayPek34NXAdxoenwuc24mx6u2v69S26+1f1M/bn8jzb3XMVtYba/zRtjHSsnb+TTv999DpMSbh72Hc/cfTp8W/iXH/m0/Gc+/VbXjdk/n3MdHfSaemQt4ALJA0X9IfAEuBNR0aqxv+74Bvv50xJ6O20bbRi+fe79r9nUyk/3j6tLLuRP/NB/XvoZN1T2jbql8ZJp2kE4FPU02FvMT2io4MVI21zvaiTm2/3/X6+Xdi/Ha22Y3fR69/59Hf+uHvo2Pz3G1fBVzVqe0Pc1GXxulXvX7+nRi/nW124/fR69959Lee/310bM89IiJ6J5cfiIgoUMI9IqJACfeIiALlm5gGkKTjgZOpPgls4D7gCtvfngrj94qkfQHbfqjXtUT/kjQfOBLYYPuOntUxiCdUp2q4AEj6NHAI8EWqyzoAzAZOB+62fc4gj99OgHYifCUdBHwCOBZ4mOrT188H/h+w3PbmyRorBpOkb9g+ub6/hGoK+A+A/wh8zPYXelLXoIV7r8Ot1yTdZfuQJu0C7rK9YNDGbydAOx2+kq6l+s/6Vds767ZpwKnA+22/qp3tx+CT9C+2j6zv/wR4u+1NkmYAa22/tBd1DeIx9xNtn2h7le1r6tsq4CTgxF4X1wW/l7S4SfsrgN8P6PiXAZcDL7S9wPbBwEzgG8CqDvZtxQzbl+0KdgDbO+u/uf0mYfsx+Br3kHezvQnA9q+AJ3tT0mDuud8M/Jnt64e1LwYutn1EbyrrDkkvAy4EnsfT71zmAI8A77F946CNL+nukfb4R1vWbt8Wa1sFPAis5Okrnc4BllEF/1va2X4MPkk7gd9SvWucDhxk+4H60ivrbP9hT+oawHDvabj1C0kvpDrnIGCr7QcGdfx2ArTT4Vv/Bz2D6vsInnq+VNdKutj24+1sP8olaW/gJbav7cn4gxbuu/Q63HqpPr69mGeeUL7eXfrHnOzx2wnQhG/0I0kvs31TT2sYxHDvdbj1kqTXARcAdwP31s2zgYOp3rl8t+Txe2Eqz86KsdVHE57RBFwBvJEqY3sS8gMX7lMxXBpJuh14/fBZIPXc2qtsv2QQx28nQDsZvlN9dlaMTdKTwE+BxneJr6rbbPuYntQ1gOHe03DrNUl3Ux3He2JY+x9QfWji4EEbv50A7cK8+55OPY3+J+nNwPuAj9dXw0XSJtvze1nXIH5CdTee/k/c6F5g9y7X0guXADfUJxIbTyAuBS4e0PFPHCFALwPuAkYL6Hb6tuL3khYPn51F96aeRp+z/VVJ3wb+VtK7gA8w7GtFe2EQw73X4dZTtj8m6QrgTVRfZ7jrBOLbbW8Y0PHbCdBOh+87gQslNZud9c5J2H4UwPZjwF9IWkg1c2uvHpc0eIdlACQdShUuz5gd0Y1wi8nXzvTWbk2Nncqzs2J86kN2z7P9SE/rGMRwn8okvYDqC8dPBobq5u1UZ+fPt/3woI7fToB2Mnyn8uysaE19Un821eUGNje0/6ntS3pR08BdfkDSCyVdKOkfJe0n6TxJN0taLWlmr+vrgtXAQ8DRtvezvR/wGqrrqvzzoI5fB+jc+jYHmFu3dbRvC9t+HdXMrPOoLm9xEvAR4O56WUxxkj4K/HfgCGCtpPc1LH5vb6oawD33+sTFlcCewNuAS4GvUH2I5TjbS3pYXsdJutP2vx/vsn4ev53prZ2eGjvVZ2fF2CTdAhxp+4n6U6lfBu60/ReNFxXrel0DGO6NV2C7x/ZBDcvW217Yu+o6T9J3ge8BK21vq9sOoDq591rbxw3a+O0EaKfDt9dTT6P/Sbq98e9M1VVDL6K6Oumhtg/rRV0Dd1iGZ9b8xWHLpnWzkB55K9XVCH8o6SFJD1FdO3o/oBsXsRo+/oP1+Pu2MX4701s7PTV21+ysv5L0tvr2V8B1TIHZWdGSn0v6410P6quGngHcCfTsnd0g7rn/DfCJeupRY/vBVCf03tybynpH0pdsv6NLY/05cLntLWOu3Po2z6V6YWg2vXW17Y91ou846svsrBiRpD0AbP+uybJZtu99dq/OG7hwH42kd9n+fK/r6CRJa5o0H0P15RTYflOHx/8N1eVNf051bPGfXV23ut3tTjhAE77RTyTtRfWp6V90evbaqHUUFu7POAZfIkk3ARuAz1FNyxPVCeWlALZ/2OHx/wV4OXAc1SGaNwE31jV83fajnRy/23o99TT6n6QLbL+nvv9HVDs9P6c6qX/WrksSdL2uQQt3VV/W0XQRcIjt6d2sp9skPYfqI/UnAn9pe72kX9h+UZfGv8n2yxoe7w68HjiNarbS0IidR97mhAO0nt/+YapvvPlrqmt8/GfgDuAc2/ePt55h2/8O1builbvmztdjvhM41vZr29l+DL7G/xOSvg98wPZNkl5EdWhwUU/qGsBw3wYcTzXX+hmLgJ/YPrD7VXWfpNnAp4BtwJu69Y5ltKldkvZodtyxhW1OOEA7PTW211NPo/8NC/cbbb+8YVmmQrZK0sXA521f02TZl22/rQdl9Yykk4CjbH+oS+MdYvuuSd7mhAO001Njez31NPqfpH8FNlLtYM6j+pq9h+p32TfbPrwXdQ3chcPqKUYjLZtSwQ5g+0qqPddujTepwV77paQP0jxAx5qV0+mpsW8FllNN/TyA6jzHNqpvesr3pwY8e7rjrpl8+1IdKuyJgdtzj/JI2ocqQJcA+9fNuwL0fNvDD8E19u341FhJ/4HqU68/bRxH0gnOtzFFn0q4R19rZ3rrZEyNref1nw3cDiykOkl7Rb3sGSeXI4aT9C3br+/J2An36GftTG+djKmx9XVDXm37MUnzgK8CX7L9mV6eLIv+oWd/h+pTi4Bv2u7JBQ0H7ph7lGeM6a0HdKpvi6btOhRje7Oko4GvSppbjxFxA/BDmv897N3lWp6ScI9+cACjTG/tYN9WPCBpoe31UH3jjqQ3UF1z5ohJ2H4MvtupPqx09/AFkibtMh3jlXCPfvBNYK9dAdpI0g862LcVpwPPuCJkfYXI0yX90yRsPwbfeYx8Ecb3jdDecTnmHhHRpnpG1Szgun6ZUTWIl/yNiOgb9YyqK6j20m+V1Pip6I/2pqoclomIaNe7gZc3zqiSNM/2Z+jhSfeEe0REe/pyRlUOy0REtOcBSU9dw6gO+jcAM+jhjKqcUI2IaEN9hdYndl3RdNiyo2z/uAdlJdwjIkqUwzIREQVKuEdEFCjhHlOWpFMkuf4ASkRREu4xlZ0GXEP95eKNJE3GF31E9EzCPaYkSXsBRwFnUIe7pKMlfV/Sl4FbJE2T9HeSbpB0s6SzdvWVtFbSTZJuGfaJxIi+kA8xxVR1MvBt23dJerDhmtyLgcNtb5J0JvAb26+QNB34cf2dqluAU2w/ImkG8FNJa5ypZ9FHsuceU9VpwKr6/qr6McD1tjfV919HdfXH9cB1wH7AAqpPHX60vpb896guGDUZ146PmDTZc48pR9J+wDHA4ZJM9UXaBq4Cftu4KvA+298Z1v+dwBDV9UT+TdJm4LldKD2iZdlzj6nozcAXbc+1Pc/2HGAT8EfD1vsO8N8k7Q4g6RBJewIvALbXwf4aYG43i49oRcI9pqLTgMuHtX0NeNuwts8BG4CbJN0K/BPVu91LgUWS1gFvB+7obLkR45fLD0REFCh77hERBUq4R0QUKOEeEVGghHtERIES7hERBUq4R0QUKOEeEVGghHtERIH+P61PTK4NnD9vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_bins =10 ** np.arange(np.log10(house_df.LotArea.min()),np.log10(house_df.LotArea.max())+.1,.1)\n",
    "plt.hist(data = house_df,x='LotArea',bins=x_bins);\n",
    "plt.xscale('log');\n",
    "plt.title('Property Area')\n",
    "plt.xlabel('Area')\n",
    "tick_locs = [1300, 4000,7000, 10000, 13000,20000, 215245]\n",
    "plt.xticks(tick_locs, tick_locs,rotation=90);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='cars'></a>\n",
    "### How many cars can most of the houses hold ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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": [
    "house_df['GarageCars'].hist();\n",
    "plt.title('Size of Garage in Car Capacity');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='qual'></a>\n",
    "### What is the most common over all qualtiy and what is its percentage ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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": [
    "color_p = sns.color_palette()[0]\n",
    "sns.countplot(data = house_df, x = 'OverallQual',color = color_p,order = house_df['OverallQual'].value_counts().index)\n",
    "plt.title('Over All Quality')\n",
    "normalize_y_axis(house_df['OverallQual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id ='cond'></a>\n",
    "### What is the most common overall condition and what is its percentage ? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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": [
    "color_p = sns.color_palette()[0]\n",
    "sns.countplot(data = house_df, x = 'OverallCond',color = color_p,order = house_df['OverallCond'].value_counts().index)\n",
    "plt.title('Over All Condition')\n",
    "normalize_y_axis(house_df['OverallCond'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='saletype'></a>\n",
    "### What is the most common selling type ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "color_p = sns.color_palette()[0]\n",
    "sns.countplot(data = house_df, x = 'SaleType',color = color_p,order = house_df['SaleType'].value_counts().index)\n",
    "plt.title('Sale Type')\n",
    "\n",
    "# get proportion taken by most common group for derivation\n",
    "# of tick marks\n",
    "normalize_y_axis(house_df['SaleType'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='salecond'></a>\n",
    "### What is the most common sale condition ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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": [
    "color_p = sns.color_palette()[0]\n",
    "sns.countplot(data = house_df, x = 'SaleCondition',color = color_p,order = house_df['SaleCondition'].value_counts().index)\n",
    "plt.title('Sale Condition')\n",
    "normalize_y_axis(house_df['SaleCondition'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='top5ne'></a>\n",
    "### What are the top 5 common neighborhoods ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "#draw_bar_plot('Neighborhood','Neighborhood')\n",
    "color_p = sns.color_palette()[0]\n",
    "sns.countplot(data = house_df, x = 'Neighborhood',color = color_p, order = house_df['Neighborhood'].value_counts().index[:5])\n",
    "plt.title('Neighborhood');\n",
    "plt.xticks(rotation=90);\n",
    "normalize_y_axis(house_df['SaleCondition'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='top5exp'></a>\n",
    "### What are the top 5 expensive neighborhoods ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Neighborhood')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_p = sns.color_palette()[0]\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "sns.boxplot(data=house_df,x = 'Neighborhood',y = 'SalePrice',color = color_p,order = house_df.groupby('Neighborhood')['SalePrice'].median().sort_values(ascending=False).index[:5]);\n",
    "plt.xticks(rotation=90);\n",
    "plt.title('Top 5 Neighborhoods with relevance to price')\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Neighborhood')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='areaprice'></a>\n",
    "### What is the relation between the area and the price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "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": [
    "plt.scatter(data=house_df,x = 'LotArea', y ='SalePrice');\n",
    "plt.xlabel('Area');\n",
    "plt.ylabel('Price');\n",
    "plt.title('Area vs Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "we can see that we have a very large area, we will check it out to see if it's an outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313    2\n",
      "Name: FullBath, dtype: int64\n",
      "313    2\n",
      "Name: GarageCars, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "largest_house = house_df[house_df['LotArea']==house_df['LotArea'].max()]\n",
    "print(largest_house.FullBath)\n",
    "print(largest_house.GarageCars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "As we can see that the house with the largest area is probably an outlier, as house of this size will have more than 2 bathrooms and Garage of 2 cars capecity, so we will remove this house from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "house_df.drop(largest_house.index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "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": [
    "sns.regplot(data=house_df,x = 'LotArea', y ='SalePrice');\n",
    "plt.xlabel('Area');\n",
    "plt.ylabel('Price');\n",
    "plt.title('Area vs Price');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id ='areapricezone'></a>\n",
    "### What is the destribution of the classification zones to the sale price and the area of the proerty ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\a3ade\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:243: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x255db599fd0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data = house_df, hue = 'MSZoning', size = 5)\n",
    "g.map(plt.scatter, 'LotArea', 'SalePrice',alpha=.5)\n",
    "plt.xlabel('Area')\n",
    "plt.title('The Area, price with zones classification distribution');\n",
    "plt.ylabel('Price')\n",
    "plt.legend(labels=['Residential Low Density','Residential Medium Density','Commercial','Floating Village Residential','Residential High Density'],loc='center left',bbox_to_anchor=(1, 0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='yearprice'></a>\n",
    "### What is the relation between the building year of a building and the selling price ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 6.79999999999999, 'Year Built')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "g = sns.relplot(x=\"YearBuilt\", y=\"SalePrice\", ci=None,kind=\"line\", data=house_df);\n",
    "x_ticks = np.arange(1870,2030,10).tolist();\n",
    "g.fig.autofmt_xdate();\n",
    "plt.xticks(x_ticks,x_ticks);\n",
    "plt.title('Time line of the price accourding to the years');\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Year Built')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id='areashape'></a>\n",
    "### What is the relation between the neighborhoods and the shape of the house?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The relationship between the shape of the proerty and the neighborhoods')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "sns.countplot(data= house_df,x = 'Neighborhood', hue = 'LotShape')\n",
    "plt.xticks(rotation=90);\n",
    "plt.legend(labels = ['Regular','Slightly irregular','Moderately Irregular', 'Irregular']);\n",
    "plt.title('The relationship between the shape of the proerty and the neighborhoods')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1199, 62)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}