House Sales_in_King_Count_USA (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n",
    "\n",
    "<h1 align=center><font size = 5>Data Analysis with Python</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# House Sales in King County, USA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>id</b> : A notation for a house\n",
    "\n",
    "<b> date</b>: Date house was sold\n",
    "\n",
    "\n",
    "<b>price</b>: Price is prediction target\n",
    "\n",
    "\n",
    "<b>bedrooms</b>: Number of bedrooms\n",
    "\n",
    "\n",
    "<b>bathrooms</b>: Number of bathrooms\n",
    "\n",
    "<b>sqft_living</b>: Square footage of the home\n",
    "\n",
    "<b>sqft_lot</b>: Square footage of the lot\n",
    "\n",
    "\n",
    "<b>floors</b> :Total floors (levels) in house\n",
    "\n",
    "\n",
    "<b>waterfront</b> :House which has a view to a waterfront\n",
    "\n",
    "\n",
    "<b>view</b>: Has been viewed\n",
    "\n",
    "\n",
    "<b>condition</b> :How good the condition is overall\n",
    "\n",
    "<b>grade</b>: overall grade given to the housing unit, based on King County grading system\n",
    "\n",
    "\n",
    "<b>sqft_above</b> : Square footage of house apart from basement\n",
    "\n",
    "\n",
    "<b>sqft_basement</b>: Square footage of the basement\n",
    "\n",
    "<b>yr_built</b> : Built Year\n",
    "\n",
    "\n",
    "<b>yr_renovated</b> : Year when house was renovated\n",
    "\n",
    "<b>zipcode</b>: Zip code\n",
    "\n",
    "\n",
    "<b>lat</b>: Latitude coordinate\n",
    "\n",
    "<b>long</b>: Longitude coordinate\n",
    "\n",
    "<b>sqft_living15</b> : Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area\n",
    "\n",
    "\n",
    "<b>sqft_lot15</b> : LotSize area in 2015(implies-- some renovations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will require the following libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1: Importing Data Sets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Load the csv:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'\n",
    "df=pd.read_csv(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We use the method <code>head</code> to display the first 5 columns of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n",
       "0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n",
       "1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n",
       "2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n",
       "3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n",
       "4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n",
       "\n",
       "   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n",
       "0         1180      5650     1.0           0  ...      7        1180   \n",
       "1         2570      7242     2.0           0  ...      7        2170   \n",
       "2          770     10000     1.0           0  ...      6         770   \n",
       "3         1960      5000     1.0           0  ...      7        1050   \n",
       "4         1680      8080     1.0           0  ...      8        1680   \n",
       "\n",
       "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
       "0              0      1955             0    98178  47.5112 -122.257   \n",
       "1            400      1951          1991    98125  47.7210 -122.319   \n",
       "2              0      1933             0    98028  47.7379 -122.233   \n",
       "3            910      1965             0    98136  47.5208 -122.393   \n",
       "4              0      1987             0    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1 \n",
    "Display the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         int64\n",
       "id                 int64\n",
       "date              object\n",
       "price            float64\n",
       "bedrooms         float64\n",
       "bathrooms        float64\n",
       "sqft_living        int64\n",
       "sqft_lot           int64\n",
       "floors           float64\n",
       "waterfront         int64\n",
       "view               int64\n",
       "condition          int64\n",
       "grade              int64\n",
       "sqft_above         int64\n",
       "sqft_basement      int64\n",
       "yr_built           int64\n",
       "yr_renovated       int64\n",
       "zipcode            int64\n",
       "lat              float64\n",
       "long             float64\n",
       "sqft_living15      int64\n",
       "sqft_lot15         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method describe to obtain a statistical summary of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>21613.00000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21600.000000</td>\n",
       "      <td>21603.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>10806.00000</td>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.372870</td>\n",
       "      <td>2.115736</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>...</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>6239.28002</td>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926657</td>\n",
       "      <td>0.768996</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>...</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5403.00000</td>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>10806.00000</td>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>16209.00000</td>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>21612.00000</td>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0            id         price      bedrooms     bathrooms  \\\n",
       "count  21613.00000  2.161300e+04  2.161300e+04  21600.000000  21603.000000   \n",
       "mean   10806.00000  4.580302e+09  5.400881e+05      3.372870      2.115736   \n",
       "std     6239.28002  2.876566e+09  3.671272e+05      0.926657      0.768996   \n",
       "min        0.00000  1.000102e+06  7.500000e+04      1.000000      0.500000   \n",
       "25%     5403.00000  2.123049e+09  3.219500e+05      3.000000      1.750000   \n",
       "50%    10806.00000  3.904930e+09  4.500000e+05      3.000000      2.250000   \n",
       "75%    16209.00000  7.308900e+09  6.450000e+05      4.000000      2.500000   \n",
       "max    21612.00000  9.900000e+09  7.700000e+06     33.000000      8.000000   \n",
       "\n",
       "        sqft_living      sqft_lot        floors    waterfront          view  \\\n",
       "count  21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean    2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n",
       "std      918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n",
       "min      290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n",
       "25%     1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n",
       "50%     1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n",
       "75%     2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n",
       "max    13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n",
       "\n",
       "       ...         grade    sqft_above  sqft_basement      yr_built  \\\n",
       "count  ...  21613.000000  21613.000000   21613.000000  21613.000000   \n",
       "mean   ...      7.656873   1788.390691     291.509045   1971.005136   \n",
       "std    ...      1.175459    828.090978     442.575043     29.373411   \n",
       "min    ...      1.000000    290.000000       0.000000   1900.000000   \n",
       "25%    ...      7.000000   1190.000000       0.000000   1951.000000   \n",
       "50%    ...      7.000000   1560.000000       0.000000   1975.000000   \n",
       "75%    ...      8.000000   2210.000000     560.000000   1997.000000   \n",
       "max    ...     13.000000   9410.000000    4820.000000   2015.000000   \n",
       "\n",
       "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n",
       "mean      84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n",
       "std      401.679240     53.505026      0.138564      0.140828     685.391304   \n",
       "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
       "25%        0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n",
       "50%        0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n",
       "75%        0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n",
       "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
       "\n",
       "          sqft_lot15  \n",
       "count   21613.000000  \n",
       "mean    12768.455652  \n",
       "std     27304.179631  \n",
       "min       651.000000  \n",
       "25%      5100.000000  \n",
       "50%      7620.000000  \n",
       "75%     10083.000000  \n",
       "max    871200.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2: Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2 \n",
    "Drop the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code> from axis 1 using the method <code>drop()</code>, then use the method <code>describe()</code> to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the <code>inplace</code> parameter is set to <code>True</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['id' 'Unnamed: 0'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-4fbed06705d1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Unnamed: 0'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4100\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4101\u001b[0m             \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4102\u001b[1;33m             \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4103\u001b[0m         )\n\u001b[0;32m   4104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   3912\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3913\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3914\u001b[1;33m                 \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3916\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m   3944\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3945\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3946\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3947\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   5338\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5339\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5340\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{} not found in axis\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5341\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5342\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['id' 'Unnamed: 0'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df.drop(['id','Unnamed: 0'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21600.000000</td>\n",
       "      <td>21603.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.372870</td>\n",
       "      <td>2.115736</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.926657</td>\n",
       "      <td>0.768996</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
       "count  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \n",
       "mean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \n",
       "std    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \n",
       "min    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n",
       "25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n",
       "50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n",
       "75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \n",
       "max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n",
       "\n",
       "             floors    waterfront          view     condition         grade  \\\n",
       "count  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean       1.494309      0.007542      0.234303      3.409430      7.656873   \n",
       "std        0.539989      0.086517      0.766318      0.650743      1.175459   \n",
       "min        1.000000      0.000000      0.000000      1.000000      1.000000   \n",
       "25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n",
       "50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n",
       "75%        2.000000      0.000000      0.000000      4.000000      8.000000   \n",
       "max        3.500000      1.000000      4.000000      5.000000     13.000000   \n",
       "\n",
       "         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
       "count  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \n",
       "std      828.090978     442.575043     29.373411    401.679240     53.505026   \n",
       "min      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n",
       "25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n",
       "50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n",
       "75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \n",
       "max     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n",
       "\n",
       "                lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean      47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std        0.138564      0.140828     685.391304   27304.179631  \n",
       "min       47.155900   -122.519000     399.000000     651.000000  \n",
       "25%       47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%       47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%       47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max       47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 13\n",
      "number of NaN values for the column bathrooms : 10\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can replace the missing values of the column <code>'bedrooms'</code> with the mean of the column  <code>'bedrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code>inplace</code> parameter to <code>True</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df['bedrooms'].mean()\n",
    "df['bedrooms'].replace(np.nan,mean, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We also replace the missing values of the column <code>'bathrooms'</code> with the mean of the column  <code>'bathrooms' </code> using the method <code>replace()</code>. Don't forget to set the <code> inplace </code>  parameter top <code> True </code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=df['bathrooms'].mean()\n",
    "df['bathrooms'].replace(np.nan,mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column bedrooms : 0\n",
      "number of NaN values for the column bathrooms : 0\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n",
    "print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Use the method <code>value_counts</code> to count the number of houses with unique floor values, use the method <code>.to_frame()</code> to convert it to a dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>floors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>10680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.0</td>\n",
       "      <td>8241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.5</td>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.0</td>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.5</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     floors\n",
       "1.0   10680\n",
       "2.0    8241\n",
       "1.5    1910\n",
       "3.0     613\n",
       "2.5     161\n",
       "3.5       8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['floors'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "Use the function <code>boxplot</code> in the seaborn library  to  determine whether houses with a waterfront view or without a waterfront view have more price outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2439e6e7f48>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.boxplot(x='waterfront',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "Use the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2439f062c48>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TwASAqh4DzCFrGOuMaG9NNSZtNpaauRqegqoWoxciEgPmVLpURFwReUFE/nv4+goR+Z6IHBORPxWRRNieDF8fD9/fU3WNz4btr4vIR6rabwrbjovI3VXt8+7DMIzm+eAOHx3mtoNHeP99T3LbwSMW+zEWxFwNz1+LyL8E2kTkp4E/A/7bHM/9NeC1qtf3AV9S1X3AGPCpsP1TwJiqXgl8KTwOEbkK+ARwNXAT8B9DY+YCXwVuBq4CbguPnXcfhmEEzJYPzoQHxlIhqs0XLiLiEEzaHwYEeAz4mjY5WUQGgQeBfw38c+BngRFgm6qWReR9wO+o6kdE5LHw+dPhiuo80A/cDaCq/3d4zceA3wm7+B1V/UjY/tmw7d759jHb5zhw4IA+++yzTb8jw1jv3HbwyLSyDNlimYGuFN+444YVHJmxGhGR51T1QKP35rqPpw34A1X9z+EF3bCtWcTx94D/C4gUcJuBy6paDl8PATvD5zuB0wChwRgPj98JHKm6ZvU5p+va37vAPkarBy0idwB3AOzevbvJRzSMjUGU1LMaEx4YC2GurrYnCAxNRBvwl7OdICI/Awyr6nPVzQ0O1SbvLVV7s/6nGlQPquoBVT3Q39/f4BTD2Hjs6mvn4mSBEyMZjp6f4MRIhouTBRMeGPNmriuelKpmoheqmhGRZn9tPwHcIiIfBVJAN8EKqFdEYuGKZBA4Gx4/BOwChkI3WA9wqao9ovqcRu2jC+jDMJaFpcwKsNwZBt63dxPfP3UJR8ARKHo+w+kit/3dTS3r01ifzHXFMyki745eiMj1QG62E1T1s6o6qKp7CMQBT6rq/wb8FfDx8LDbgW+Hzw+FrwnffzKMvRwCPhEq0q4A9gHfB54B9oUKtkTYx6HwnPn2YRgtZymD8ysR6H/6xCX6OxMkXAc/LF/Q35ng6RN272bMj7mueH4d+DMRiVYO24FfWGCfdwEPi8jvAi8AXw/bvw78FxE5TrAK+QSAqr4iIt8EXgXKwKdV1QMQkV8lEDq4BDGoVxbSh2EsB9VZAQDaEzGyxfKCCp4t5bXmyumxLFs6k/R3pSptqmoxHmPezMnwqOozIrIfeAdBnOSoqpbm2omqHgYOh89PAO9pcEwe+LkZzv/XBMq4+vZHgUcbtM+7D2P9sxLJL6tZyuD8SgT6d/W1T1O12eZSYyHManhE5IOq+qSI/MO6t/aJCKr65y0cm2EsGZFrKu5KjWvqHliQ8YmM2BsXJih5SiLmsG+ga1ZjtlQT9+Gjw0zkSpwbz5GKufR3JelKxVtuBO78wF4+f+gVssUybXGXXMmzujnGgmgW4/nJ8PFnG/z8TAvHZRhLylImv4yM2MnRDBP5MrmSx3i2xKmLmVnjLM2yAsyn7/aEiyNC0fM5M5ZjNJNftBFolpVgts2lhjEfmm4gDTePflxVv7k8Q1pd2AbS9cH773uS3rY4IoGiPp0vMTyRp+Ap79mzifft3cTTJy7NyQ0XbaQ8P56n7ClOWLo55gjbelKzbqisLng2uAB3X/UmzolcidFMgULZpz3h8pVPvGtRCrloRVi9mjHDYiyURW0gVVU/DOJvSMNjrA+q3VzpfImzl/MoSirmcHI0w/dPXWKgK8HmjmRTN1wUXyl6Pm5oyCSUFzeLsyy24Fl1bKe7LU53WxxVZTxXWtR1V0KsYGxc5iqnflxEflNEdonIpuinpSMzjCWk2s01PBEYHYAtnUnS+TKOwESuPCc3XJTBOeE6RA4DDeXFrY6ztCp7tJVDMJaTuRqe/wP4FeCvgWerfgxjTVAdnyh4SsJ12NHTRne4cok2REbMNulGRqwrFcNHKfs+vq90t8VaHmxfijhRI6wcgrGczHUfz1UEhuf9BClm/gb4T60alGG0gsjNVZ/sMuE6FD2fhDt1HzbbpHvj/gHuIXBPlb0JiqGqbc/mzpZLtKv7XmicqJ7DR4cZmyxw6uIkccdha3eSmOuYYs1oGXPNTv1NgiJwfxw23Qb0qurPt3BsqwITF6xO5rMnp/7Y9+3dxCPPn6kE0kczBUYyxUqMpzqwDqzo3p9WUy0qKHs+F9IFSp7y9oFO7rpp/7r6rMbyMpu4YK6G50VVva5Z23rEDM/qo16BNZopMJYt0ZWKTdtLM5Na6+Pv3snTJy5VVg2Rqq16FQGse6WXlTowWsVSlEV4QURuUNUj4QXfC/ztUg3Q2HgsJotAtQJrIlfi4mRQHDdbKE9TpM2k1nr6xKVpE+tn6vq57eCRliu91lM2hXpW+rOtNBv988/GXMUF7wX+p4icEpFTwNPAT4rID0XkpZaNzliXLDbBZbUCazRTwEFwHaHk6zRF2mLUWq1SekUbNa//4l9w5x89x6mLmRWr6NkqUcFGr1a60T9/M+ZqeG4CriDIZPCT4fOPEmQv+NnWDM1Yryw2i0D1ZFn0fESm5MxQaxwWM7G2YlKunpDyJR9flYuZEplCeVHZFBZKq1RyS5kpYi2y0T9/M+aaJPRHrR6IsXGYj3unkbuiOmdYpEgThP6uJFBrHJrlF5vNHdKK3GTVE1K0AVWBkXSBrlS86fcwn9xwzb7HSOW31Co5sGqlG/3zN2OuMR7DmJH5+rLnmixzxsSet1zNPbdczQNPnWA8W6TsK5s64nQmY9Pu2GebWJslDm3FpFw9ISVch7KniDO1h2i276FY9pjIBxXdc0WvkhuuWaLTuXzOpY49bPRM1hv98zfDDI+xKBaS9XmuK4nZ0rh8444bapRrsxmHmSbWuaSJmenchQaOqyekLZ1Jzo7n8MuKr/DquXFijsOt1+1oOM6LmTIOUskNN5Ers60nNk3sUD+2sclCzecse8pwOs+df/Qc797d15Kg90bPZL3RP38zzPAYi2Iuk3ejSTpasVTLmR946gSf+/bLlWPm6q5Y6B17dP10vsRIukDR84k7wnhu9lJTiymxUD0hdaVidORcLufKuAKpmEtXKsYjz5/h2sHeyrXmkxuu0dhOXZxksLcNgIlcibPjOQTwVRddHmImWuXCWyts9M/fjJYZHhFJAU8BybCfR1T1C2H56oeBTcDzwD9W1aKIJIGHgOuBi8AvqOqp8FqfBT4FeMBnVPWxsP0m4MsEFUi/pqr3hu3z7sNYGM2Mw2zuskjOPNMxXckYuZLXMnfFrr52Tl3McDFTQoSKMi6dL3P46PCMk8RiEmrWT0i+wvaeJFs6p6p61l8rWiVVXHMyc264RmOLOw4X0gW62xIVFSACSddpaTLQVrjw1hIb/fPPxlxVbQuhAHww3GT6TuAmEbkBuA/4kqruA8YIDArh45iqXgl8KTwOEbmKoET11QTquv8oIq6IuMBXgZsJUvrcFh7LfPswFk4z5ddc1D0zHaOqLVFcQWDshsaynBsPVjqFsk/ZCzZT97XHZ1QffeUv3+DIyYu8OTLJK2fHuTCeAxqvxGaqb3Pj/gG+cccN/M1dH6S7Lc7mjmTNefXXapYb7n17N1X6ef6tMcpVOecAtnYnK99j0fNRFFUqYoyVCHo3q/1jrG9atuLRICVCJnwZD38U+CDwj8L2B4HfAe4Hbg2fAzwC/AcJiqfcCjysqgXgpIgcZ6qs9fGwzDUi8jBwq4i8Nt8+dC7pG4yGNPNlz8VdNtMx47kSX7z1mqbuipniLbO1/9YjLzKWrXWplX1loDPBls5kw4n4K3/5Bl9+8nglI7WvMJwJNq92tcVrVh4zreI+PnS5pu5PZ8Kdtqq7OFlgsuDx/vuenOaajHLDAUwWPHwt8dXDb7KpIzBgo+kCZy7nAaE7/E5jrsPbBzrpbU8wNBa42bb1pOhKBe8vd9B7qavBGmuPlsZ4wlXJc8CVBKuTN4HLqloODxkCdobPdwKnAVS1LCLjwOaw/UjVZavPOV3X/t7wnPn2MbroD7tBaebLnou6Z7Zjmrkrokms5AVVQM+N53j+rTE+es1WnntrvOHk9sBTJ0jny7hhkF41uCMSgcmiN218kQE7cvIiqhBzhLI/da8ynCniA7/996+qtDVyeY1m8nz18JsM9rVVxjSRKxFdqS3ucnGywHC6SH9notZgvTv4E04lYmxJuFycLNLdFuf8eB5ffS5mSiRjLtt6UgyN5biQztOVClyV47kS/Z1JTo9l2bulg5FMAdcJVpQrEfS22j9GSw2PqnrAO0WkF/gW8GONDgsfZYb3Zmpv5Cac7fjZ+qhBRO4A7gDYvXt3g1OMamYzDnNR9yxGAfTAUycoeV4lThN3HTxf+a8vnmNbd5KetiB2Uj25nR7LUvZ9Yq5DzHEoha4pVciXp+/zie7Oo5VOtdGJqP7DOnx0mOffGsPzfZIxl/6uJF2pOOPZEl6YXSESNOTLHknXpb8zyXiuxGTBo78zQX/X1LjrDdbxkQxlT+lI1u4FOj+erxjTQkk5P5FnS0cCIRAi9LbFyZU8BCoiilYHvRutOm2Pi7EsqjZVvSwih4EbgF4RiYUrkkHgbHjYELALGBKRGNADXKpqj6g+p1H76AL6qB/vQeAgBElCF/HRNzxzUffMVQE00yQ2ng2MjhMqvlyBkirj2VJN0D6a3Hb1tTOaLqAaCApgyvh0JGI1SUCr784dCdxrESKABo/dbVNxoc8feqUynrKvnL2cZ0cvFDyfVMytVEAVCVZPRc9nsujxxVuv4XPffnnapFxtsAA8X3Ek2HQaCQ4UpeApyZhDzAkk11HMrLstXrO6AOjrSPLd32htEtCZXGqN3Iu2x2Vj0UpVWz9QCo1OG/AhgmD+XwEfJ1Cd3Q58OzzlUPj66fD9J1VVReQQ8Cci8u+BHcA+4PsEN5n7QgXbGQIBwj8Kz5lXH636DoyAuah75upSa6R8OzeeI15VS0cVHAkm+mqiye3OD+ytxHhUgl+/6wjtcYcdPalA0v3U9LvzLR2JSkwn6ChYLg90JitGLTJUW7tSgWxZgwPPj+eJOQ5dqRgj6ULFMPk+JGNSEVw0cjtGBisi4QaGsuj57Ohp4+x4jpKnNWPa2pUi5gonRifZN9BZ8z0s1+piJpeaiFDyfNvjsoFppaptO/BXYRLRZ4DHVfW/A3cB/zwUCWwGvh4e/3Vgc9j+z4G7AVT1FeCbwKvAd4FPq6oXrmZ+FXgMeA34Zngs8+3DWP088NQJimWP8+N5Xr+Q5vx4nmLZQ1WJOYF7TVXxfcVH6U7FiDlOQ0XcjfsH+Lcfv459A52ICCLCtu4kibhLydeGd+cAW3vaGOhMVMYkAlu7kgx0pypGLUos2t0WZ0dPGzE3cIMp8Okb30Yi5pIveRTLPrmSR8HzyRaDz3XswkTD3GmRwYrY0pnEV3BF6ErF2NwRjMl1hJgrlcqqUYLTlaosOlOS1UyhXKkGO54rMdCVWlelJozmzKkez0bG6vGsDq7/4l8wkQ927kf7WHyUnlSMT75vD189/CaeH7iaulIxEjF3Ws2d2WIZM9WlSbgOk0WvpibPeK6EQGVyr64HVCz7tCfcSowmuk5U3+Yrf/kGX3riGPX/dq4DMcfhgf/9eqDW7VhfuK5aMJAplBnsa+dytkjR86eNP+4I2ZK/oJpCi03rb7V+NjZLUY/HMFaUyJXkOFM7931fKXrKZz70dq4d7K1M1h0JFxHhoSM/olgOJt1mzCbp/rnrB/na/zhJphC4idrjDjt72xARzo/nSBe8ipw5UqVBsDKpdiMdPjrM1/7HScLQUPA5wkffh01d8WnpgCKqP99gXzu//fevmpYmp5FAI1LazXcH/VJInjdS2piNXntHVSmUfQoln0LZayjAqcYMj7EmSMQcckUPX6d27qNBO0zFiOrl1QjkSnBytHFCzWjCGEkXGE0Xpu1v6Ui4PPL8GTqSLrmiBwL5ss/lXImyr2RLPmXfZyJXJhlzK2KGyYJXoxqDQHQwWSwTd4VCOfjHVIJ4lCPC5o7ktPQ3c53Mmgk05jsJLoXkeaOkjdmI+5KK5cDA5ENDU/KUy9kib1xIc/R8mmMXMrOeb4bHaAlLfQe4b6CLUxczTOSC3fcJ16G7I86ezbWB85qEmo6EAfwgDU6UUDM67thwmnS+TF97nG3dSc5czjM0lmNnrxJznaD8gOs0vN5YqDQr+0oyVqte29yRJOaU+Ju7PlgZV1TNNBVzKfuKI0FiUCeUgHLruXoAACAASURBVMccqYm9LGQyW8oULUsled4IaWPW+74kz9caI1Mo+WQKJd64kOH18+ng50Kac+P5OV/TDI+x5CzmDnAmgxW5bbrbAnlxvuxRnlRu+7ubas6vSahZ5ZaLEmoeuzBRGVu2UA4KsU0W2dHTxs7eNi6k85yfKHDF5nYSrsMbwxmSrlDwtOKyE4FC6MITAA0zRqOMpIPNmfXB+2PDabKFMkVPK3JoCOTZvq90d8Rr0t88/9ZYJcNAlEZoOSczS+s/d9bTvqR6l1mh7DNZKPPmSIajkZE5n+atS9npGyAJbqSu2NLBj23v5quz9GOGx1hyFnoH2Mhg/dYjL7K5I0Gm6IHvM5opohpkco67wlcPv8lDR35UKYpWk1DTn55Qs+gpPa7g+Uqu5Ff+eX50KYgNbe1KMlkoVwLysTA4D4GBiLtOzWbRhBsYHHxAlHzZnxbHOHx0mHS+TNn3K1kSIvWz6wg97cHKrVpE4KsiUFlFzVYkrv47XMhKs/68aCwbIT6zWNaykY5cZoVykK8wVyxzcnSyYmCOnk9zcnRyxpjNYF8b+7d18Y5tXbxjaxdXDnTSkYzRFnfN8BiLZz4T2kLLDdQbLC90aaULZa7s7+T4SAbV4I9dFc6GCTqzhTInRzN86sFnKhM6BIH7WLhK6UrFmciVggJqo5P4Oj1lRaHsc+Zynrgr9LQnKHta2VgKwfHBSgpiTrBaicYymilQKGtlAyoE7rXTY1kmciXiDhTKTOPXPngln/nQ2yvHR58/2hiKTFUnbTaZLXSl2ei8R54/My9V4GzXXu9B97UioohcZoWST77skSt6vHUpW3GVvX4+zfHhDIWy3/D8ga7klJHZ1sXbt3bR0xYnEXNIxtzw0anZUzcTZniMpsx3QltouYF6l8VIuoAjwT9MsOkwcFP96GK2sgEz5gr5sk8+XaD+pizcu4kSuNNUtbJpsxGer7hhHra2uMvJ8UnijoOviqdTMRkB3tbfycXJIq4TyJRjrlRkykDN93V+PD/jHeP9f/0m33xuiF197RwbTtORcDkxkiFX8iqfpwAcu5CmKxWryQdXT6OV5kg6z2cefoHutviME/9MK9SnT1xalOx5owTdV6OIot5lli95nLmcq6xiXr+Q5o0LaSYLXsPze9viFQMTGZstnckaA5OMOcTmYGQaYYbHaMp93z3K8EQeT4Nge39XsrLTvtE/150f2Mudf/QciuIgaDjRR+UGZvqHrHdZFD0/2BSq8Oq5CbyqydvXoJCZwjSDU01Zlb1bOhi6lKXgQ4PUfDVsao8zkily9Hy6Yiwi+bMTGlFHhLtvDtIONppsqlcuMD2paDW5ks+FiTyjmQLFss/YZJBN2q1Lz4M0G/l0wz2RK3Fxsoivyu5N7TNO/K2KUaz3oHs11SmWTocZLKrbW029y+zCeJ6j5ycqcZk3LqSnZWOP6Ei6vH1r4CqLjMyOnjaScZdkzKkYmoUamUaY4TFm5fDRYY6eTwPBxFf2PU5fyjLY1zbjxHTj/gE6ky75kl9RoG3pTNGVis06mdW7LAQo+RALVz0RkRHwtba9Eapw6mK26XFxV3BFuJQtEQ9jQNX9QdCfo8Hqq76YXTX1E3mzTdquBMY56jKm1LgMUzGHfQNdTSu71udAG80UwvPdWQUKrYpRrKegezOWc3VX7TIrlH0uThZ47exEZSXz+vk0w+lCw3OTMYcrBzqnVjJbu/g7mztIhUYmGXdIuEtrZBphhseYlXu/81rNnbZqUAb27OUcB/ZsnvG8t2/tbrhrfbbJrN5lEXMdyp6H6zqUy35l5SF1+0Hrk3fW4zcxOhBsUBU3ONaNOXgNjIUAng+DfSlcZ2rF18wAzODZq1DvUy/5Pn4Q3gmMYDiWZpVd60ssFMLvLCr4Vn+NiFbFKNZy0H2+tGp1V3GZhSua8WyJ185NVAzM0fNphsZyDc91HeGKLR0VA7N/WxdvC4P/CXf5jEwjzPAYs3LyYpaYE0y4EUqwEmk0MUWT8Ctnx0kXyohCW8KtpLFpNplVbwS984+eQxyZCvALxCUIsuzf1s0rZ8YbF7poQHW2gEYExksRh2kVPKtxHaErFUdVOXZhgpu+9NccG8kQdxy6UzGe+9GlSqG2hCts70lVKpvORP27ccepyMFFhHiovZ6psitMZZ1OuE5Y8C1Le8KlI+lWNsTWXyOiVTGKtRJ0XwqWanVXCqvhFkoe6UI5MC7nJoLHC+mKMKYeAXZvbmd/GPSPHrvb4lXuMreyxaAZrRaFmOExmuKI4LhSkQNDUM+lUdmCKGtAruhVViLZYpBC49M37p6ztPfzh15BCEociAQrEFWlFP7X/fDMOAAdcYe9/V28cX6CQoMJfo52iR09Kc5NFJq65JJhpoTRTIF0wSNTzOJKIJ6oyVwNFD3lR5ca341Wj296QajAEEZxoW3dyWllv2dL8fOdX/8AMHManUYTfys2es7HoK119dtCVne+r5WVzGTB4/hwmlfPTcVl3hzJTGUdr2N7T4p3bJ0K/l+1vZvejsSCjEw9y+E2NMNjzMreLR0cG87gOkF6GtVgRRCPOZXSzO/bu4mnT1yqbHqEIKdaTBx8UWKusK0nxaM/PFdT9jmaAOsnnOhufltPirOX80Fg3Vfq/wcFmCz6vHJmfNbVTKQ4m42hy3ma/Z8qsKUzQbZY5uJkEYFK6puFUn+2ENz1RvZve08KX2GgKzXvyq6rQW01F4O2HtRvzVZ3qkrR88mXfPKlMqdGs7x8ZrziMnvjQmZaFvGIzR2JGoXZ1Tt6GOhK1siYF2pkGrEcohAzPMas3HXTfn7rkReDDZCejxNWu+xOxehti3PqYobvn7pEf2eisumx6CmJYKlSyRpQ9nxOXcyxp6rswG8+8iLFkkfRD2TSo5kCv/nIi6gq23uCJJx97R4XZgiUVoL+s4xfCVZn0bhmw3UEf5Zjkq7ga3A9z9eg4FqT+FIjapKERnnnqsabcJ1A+h262iKJ9gNPnQhqBVVt8BxJ50nnyxTKgWvu1ut21PS1FlLWrAf1W72R39Hbxi/+vT38+GAPLw1d5qWhcY6en6hszJzIN9jUBXSlYpWVzDu2dnHNzh4G+9pqZMzOEhqZRiyHKMTKIjTByiJMuUGGxrKM50o1af9PjGQqyjWAshfc2SGBmsr3gxWPF27Z37e1q3Ldo+fGKfmQdIO9MqWqGTzuQF97gsu5MqUqF99CcR1BfW0a6J+N/s4E//bj1/HAUyd44fRYqESrHfeCxia1Krak6+CjlXo+jUozlDzl+t09PPryBcq+T9J16GkP0u70dyYZCeXZcVd4+9buVe3iev99T9LbFkeqVCOqyniuNt/daqXaZTacLvDS6cu8WhWXuVjngo1IxQO1YiRhvmZHD1dsaScVjy2bkWnEUpWzsLIIxqKovmuOJomIoufjhKuaqBqm60DZh3K4U7MrFeyNGexN1Vw3EnMFYoXaybvkw3CmiFu3Ilgonq9zjvc0whFI58vc992jpAtltnYlOTueD8ttL26AngbqtcgdGHOFLZ0puttCEcNwhsG+tmkrgieOjtS0T+RKjKRzgbpNmVNm7tXg4lpr6rdov8zYZJGXhsZ55ex4RWE2U6LMuCvs7e9kf7iauWZnD/u2dtIej1XUZSthZBqxHKIQMzzGvKifJBKuM5Utui1OrlhmdDK4w/MV2uMOV2zppK89KFRWTTRdl2dKJRBeo5k7K/p3nemQeJg5YaHmQQjiW76vvDGc4e0DnRVDO5opUFaZVZRQv6JpRMJ1cCXQUO/tn8q4Hfn9G1XynCx67K5qH80EmR5KnpKIOQ0zc1cblNXi4lrN6rdyqDKbyJV45ewEL58Z57XQZfbWpWzDv0tHYM/mjkpamWt2dHPVzm46k6HCbBUZmUYsR2ywZYZHRHYBDwHbCNzwB1X1yyKyCfhTYA9wCvh5VR2TYJ39ZeCjQBb4RVV9PrzW7cDnwkv/rqo+GLZfD/wh0AY8CvyaqupC+jDmRv0k0d0WYzhdpCsVYyJX5FK2hCPCrr5UpbRAdT2a6skl7gplT2c1Ko7AYF87Zy7nZtz9H97cNyThBoFXT705GbGI6nVMkBg0SNwZrEDSuI7Dpo44V2zpIFfyGM0UmCyUAalUQS2Hk36h7E+L5dSTLXps6UygMG0CvmJze82+IJiqFVTdXvT8yvcQea2qM3PX++hXywbP1SCCgCmX2WSxxNHzaX44NMGr54LVzImR2RNlvmNrF/u3d3H1jm6u3tFDb3uiklZG6jeerQFaHRts5YqnDPwLVX1eRLqA50TkceAXgSdU9V4RuRu4G7gLuBnYF/68F7gfeG9oRL4AHCCYC54TkUOqOhYecwdwhMDw3AR8J7zmnPto4Xewalmob79+ktizuZPb/u6Uqi3mClu7AjcRwEg6z6/8yfN4vuL5PgnXpT3psm+gi4HOBE+fHJu1P5/A9bSzN8W58TxFT4k7wtv6O7j75h/jV/7kebLFYFUQFVSLJghHoqwBYR0dVbTOrVVNtbGJHt1Q2lzyfMq+EhUzbU84nBsvcG48ED4kHOFnr93O+YlizeR573de4/jIJBCk75mJQDGXZP+2Tp44OsJkMTAs/+T9V3DtYG/DFcFP7e/n0ZcvVEp+o4ofGj5VpmXmrnddrSYX10qIIKIcZseHJ3lx6DKvnBmvJMrMz5Aos78zGajLQiNz7c5etnQl17SRWQlaZnhU9RxwLnyeFpHXgJ3ArcCN4WEPAocJjMKtwEMazBRHRKRXRLaHxz6uqpcAQuN1k4gcBrpV9emw/SHgYwSGZ159hGPdMFT79l2BF94a41MPPcO+/k7uvvnHmmYybmSwPkMQ/3ElcPmcHc/hiFAs+6FSK5j8i55POy7v27uJ3/+r403HmnQDKfGxCxPEXAfP9yircnwkw33fPYojQZ0cJ1yRVMeKpvK5eahGhgi8GfxejVo9BS+chByCPGoCjGdrVUlFX/nWD86xq6+t5jqZokd7XJgoNJc1XJjI88Zwhv7OBLs3BaucR54/w7WDvdxzy9U1K4JI1bapI16pT+SIkAjzwkUKPteBvlSyoetquV1cKylkKHs++ZLHW5dyvDg0xstnJoKNmRcyZBqlDQc6Ei5X7+wJJczdXDvYy86+tmDXvxmZRbEsMR4R2QO8C/gesDWa6FX1nIhEf3k7gdNVpw2FbbO1DzVoZwF9bCjDE/n2PV85N14IMkiLcOpSdtbgcmSwimWPdL7M+fE8z781xkev2crR8xnOjOWm3FNOYGQqKwcnUL2Jr2QKZe7/6zeb7q0BcCTIdvArf/xcpS4OQFnh9fNpXFfoSLiUPGWy2Hhyr7553dqVJFMoU/aUwiwZChrhQ0VGPdOZp8dyJGMOF8ZzfO/kxXlJrceypYqIob8rVRNz+cYdN9T8TqJEpD1tqUq57dFMntFMscbt6PlwaTJwhdYnrlxOF9dihQzzMVq+Hygrz13O88LpMX44NBWXmSlRZrRKTMYcOpMxHAdQ+Kfvv4IPXbXVjMwS03LDIyKdwP8L/LqqTszyC2z0xkzu+9naZx3OXM4RkTsIXHjs3r27ySXXHpFv/+ToZKW8gBJMqo2yTkf/9M+/NYbv+/gIMQmKpJU8n2/9YLrdrl55RF+65wf1bdSbam/2C8uXPf7ZN56vMToRPqBeYMj+zuYO3gxdWrMR7QlKxZw59V9POSx3PRsz1TNphhKssLJFj4lcie62mYu/NYrPjIfluF1HaiTqRS+oePrC6TE+9dCzvH2gk7tu2l9xby3HqmMxQoZmRqtY9hnNFHjx9GVeOnOZV84GRubCROP9X4mYw5X9HbxjWzfX7OzmusFe7v3Oa1yaLNKeiFWMTLZY5g/+9hQ/ffW2Jf0ujBYbHhGJExidP1bVPw+bL0TurdCVNhy2DwG7qk4fBM6G7TfWtR8O2wcbHL+QPmpQ1YPAQQj28cz5A68RIt9+dXnoKBZQP9FV/9N7vo/nB/VtXDeKmzTvLzJqpaoVUNQ+F9Iz1AyJrhFt6pwPM/nwVwMKDF3OMUgQ32oUc+lKxjg+nKkpVRGt4ByCUtylqsSqo5lSqJyDk6OTyy6bnk3I0Gw1U12WI+4ImzqDIn2f+/bLXPX9bo6eT3N6llLMe7d08o4oJjPYw1XbuyuJMiMjc36iMG0v0XrNpL0aaKWqTYCvA6+p6r+veusQcDtwb/j47ar2XxWRhwkC/uOh4XgM+Dci0hce92Hgs6p6SUTSInIDgQvvk8DvL6SPpf7sq53It++GclsBfJT2RKxS5fO2g0dq0te0J2IkY4GEFwIJtOu4czYe9VLquTIXwxZzhL6OJJBZUB+rEc9XzlzOsaO3bVrM5fDRYUYyhUpqnZLnMXkxixu6h6K5sz4jguPMvrJtJTMJGdT3ufOPnqsIJMqeXzGKP/mOfr7zw/McPZ8O4nMEUvHs2NRemerMzALs2tReE5O5Zmd3mCizVo4+1/Gt1r1Ea51Wrnh+AvjHwA9F5Adh278kMAbfFJFPAW8BPxe+9yiBzPk4gdT5lwBCA/NF4JnwuHsioQHwy0zJqb8T/jDfPjYakW//vu8e5Y3hDHEXepOxiv97Z2+q4s7IFsts6w5iCP1dSbIXgztLDQP3q4Gipxw5cXGlh7HklH3l4+/e2bBiaCw0IhECqA9umCXClaAx+hU5VcaofmW7HEH/RkKG8VypUg495gjFksdIyaM94fJbj7xIf1eK185NVFyQ9cRd4e+9bUtlJXPdYC+bO4McZksxvtWyl2g9YilzmrDeU+ZUx28E2NaTqqTRzxbLjKQLtCdc0vkyRc/H82ffd9MKYhKICdYT88nx1pl0+PGdfRWD8P77nmRsskjZ08pGRCVwZW7vSjIyWaLs+8RCabmngSw85gYB9B29QT2hKPFo5EqtnnDvueXqJTc+1amXdvSkGEkXOHkxG3wXNM9QIUwZUF/hP//j6/nQEsZfqse3GspXr3VmS5ljhqcJ693wRMyUL+v48AQz5DM0FsBCE+z0tsWIuQ79nUlOjE5SLPvEHIi5gQup5AUbZEWEff0diAiZQpnOZIx0rsi5dIG447C1O1nZ2BtJtBvl5Yrq+sxnFTTTyklVGRrL8fyPxnhp6DIvh9Uyo9VOIzqTMf7hu3fyt8dHKYbZGy5lS5W45J5N7Xz3N35yAd+ksVxYrjajKZ0Jl+MjmUAVJRKmcV94mpmlRAjiFsu90moFC/0Il3NlBLiYKU7FO3wALyxJHuzZSbpBeqCS5/PFW6+ZViZ7aCxbU2Lhc99+eVrQP8gknmXP5vY5S5+rRSgdCZeTIxk+8/AL7N7UzoV0gZEZMozD1O832mflCPyH295VKQgYXfeKLYnKiuzum39sgd+ksRoww7PBOHx0mPu+e5QTo4H0+IrN7Xz0x7dzMXTdgJKfw/6a5SSKKS0+HefapjqrQvRdlPxgonYdcMVhoHtq/8993z3aNHbTKKh+YaLQVPocGbJTFzP0tiUYyRTIFj1Knl+zP+vlsxOV564IV27t5Krt3fz4YA++p/zh/zyJp8p4tkTB84m5Dp++8W1AIHA5PZalKxmrZKs2F9j6wAzPBuLw0WF+65EXKxsVAd64kOH1C8dwJMhJ1qxmzUqyeke2vEQrv1DEhoiQdIWB7qn4XP2q5eRohjv/6Dm6UjH2DXRVguZjkwVOXZysdcP5PoO9bTV9tsVd3rqY4ZlTl3jk2dP8t5fOVYxMlDaoGb1tLp8N9w9FXDnQGazEnKm4ClCzbyda5VSv4Iy1jcV4mrDWYzzVfveJXIl8yUMk2INT9vxF15IxVo4oa/a7d/cxnM5T9oJiepEIxBXYv72HiVyJs+OB7DjuCNt72xjPlRCCfUIX0wWipA+7+tpQ32ckXAG7jhB3nYqRme2vxZGgQODlXOOgoOvAe/ZsblrTZanqwRgri8V4Nij1O77Pj+cp+0rCBSQszmasWZSgON3lbJGTo5PTYmCqQY2e0UwBB0GcIKNEeyLGmcs5fD902omQcJWyH+yLcarKOHieUvRqN/A6Au0Jl7a4S3siRnvCJVssc3osR2aWzb6ez5w2ZK6WrNlG6zDDs46pT1OSjDmUi4Hboux70yaqjR5DWYtM5Eok4y4O00uAK3B+PIcPoWAkrJ9U9iiWg82nwvRVTL23VYC+9jj3fOwa3r2rl3/xZy9NW5HEwsSZgdBh5r+iuWzItM2c65/577Qy1gynx7I1BcS2dCYriqhGc4MZnbXHRMHj/Hi+YiyivS5RKfKCpzgEmSOKZZ9cyeP1C5nK77/R71yAwd423j7QyTU7url6R5Bi5meu3cGOMA5T8pRssYxq8FjylI5kjCv7O5kpe5HAnDZkznR928y5fjDDs47Z1ddeqWAJ0N0Wx5VQvrpywzKWmHJVddXopqK6qmshLLY30w1HNdGq90I6z5nLOTKF8rTVxo37B7jnlqsZ6Eoxnisx0JXinluuZt9AF7mSR39nsuG1P/bO7XOv+dTg+iYsWD+Yq20d0ygNiA/s3tRGd1uCEyMZskXPVjprnFKDPHjVBsaRKYOiOrXKaeRajV77vlLCZ2gsR197nN/++1fVHDdTVuvPH3qFzlQgfx7JFCvS765UjPMTRQ4fHZ6z8TFDs36xFc86ptGd4/auJBfSBV45O86kGZ11wUyrGEfgf7lyEx0JFyQI0FcqZYbH1K98BSp54JRA9ba5IzFjfabbDh7h/fc9yW0HjwBU/t6ScZf927rY0plgb38Huze1VzaiHj46PO1axsbCVjzrkJlSl0T7eAol3wzOKiXmgCiUFvkLSrpCRzLG3755qWKYsiUvFBhIzaoneozaYo7gKezf1l3ZuFnPjDVybrm6Inm+7eCRoOLsAmrwGOsbMzxrlNmMS6MqoZ++8W08feISMVfM6KxilqpMkKeQLpRrV0Ph85Kn01Y6NYfplDhhJjXZXAq7mSzamAkzPGuQ6rvNQqnMkRMXefrExdBNAp2pGJmCh4OEd6/KVw+/SWfSJT1LYkZj/dBI0qx1j40I5NA+23raZlWTzcWomCzamAkzPGuQ6G5zIldiJDNlSKLcXWPZMq4E6SM1LAzmo+SKUtmhbqxe5lMyYa7Xc0VqslQkXaFQtWHHkSD1jivgOkFl2epkovXMxaishRo3y1GLyJiOGZ41SHS3eTJM9NmI6k2AWuXjN1Y/S57FSMGNCWUNbkRSMYd9W7s4en4CIcjRt7e/Mzg0jOn8zV0fnPWSczEqUcHB1VrjZsY4FctXEnyjYoZnDRLdbVqaNWM2ojiODxRDQYkr0NMW58RIhnKYe83TqWXwXF1hczUqq1kWPZc4ldEaWmZ4ROQPgJ8BhlX1mrBtE/CnwB7gFPDzqjomQfWxLxOUpc4Cv6iqz4fn3A58Lrzs76rqg2H79UyVvX4U+DVV1YX0sVaI3ALHhtOkrTqb0YSEK/S0xZnIl1GClEkxRxjLlhAJSkcXPcX3lYlcsVIgbq6usNVsVOaCiR9Wjlbu4/lD4Ka6truBJ1R1H/BE+BrgZmBf+HMHcD9UDNUXgPcC7wG+ICJ94Tn3h8dG5920kD7WCpFbYDidZ1t3ik0d8eYnGRuG+n/k4LUwliuxtTvJQFeSr3ziXeHeLQ2zVwiuI8QcOD9R2HAZAuoze4CJH5aLlq14VPUpEdlT13wrcGP4/EHgMHBX2P6QBjUajohIr4hsD499XFUvAYjI48BNInIY6FbVp8P2h4CPAd+Zbx+qem4pP3eriNwCZU85dim9quvmGMtPKu5S8nw81bC2Upijz4ezl3O0JWL85iMvUgj12mWURMxhsKeNrlSM8VxpxpIDh48Oc+93XuPkxWAlsHdLB3fV1dVZi6wF8cN6ZbljPFujiV5Vz4lI9Je7EzhdddxQ2DZb+1CD9oX0Mc3wiMgdBKsidu/ePc+P2BpOj2VxBU6P5Sy2Y1SIOcKWzgRXbOms/I2cGy/gqyISSKM9hS5HGM9OKSBdCSTX58dznLkcuN1u/r2nSBfK0/aF/eYjL3K5qnjgseEMv/XIi/zbj1+3po3Pahc/rGdWi7igUc5KXUD7QvqY3qh6EDgIQSG4JtddchpJPLuSMV6/kDajY1QQYFN7nIlcie+fukRHwqUj6bKjN8VIOigIpwSxnkLZx3GEhEhQKE4DBZsfXqeoyrHhDDt7UzXqrgeeOkGmUMYVwQktj6iSzq+PIPxaj1OtVZbb8FyI3FuhKy1K2jQE7Ko6bhA4G7bfWNd+OGwfbHD8QvpYNRw+Osx93z3KG8MZHBTHcTg/nuf7Jy9Oq5FibDyitDYCYWxGuJwroyipmEN7wmU4XaQnTNLp+UFW6pKnlDyPuCu4jkNca/f0JGJOILkXGM0U2dvfWVF3nR7LhtVMp+7bRILS2haENxbKchueQ8DtwL3h47er2n9VRB4mEBKMh4bjMeDfVAkKPgx8VlUviUhaRG4Avgd8Evj9hfTRws/alOqVTVcyxkimQCZfBtUgV5dvuz2NAAG2didJxhzOXM4DwYolMh9bOpN0t8XJlzzGwrLW0f6tyFgVPSWB4oggKHE3KGtd9PzAsEhQtwem1F27+toZzRRQPzA4hNeNOY4F4Y0F0zJVm4h8A3gaeIeIDInIpwiMwU+LyDHgp8PXEMihTwDHgf8M/ApAKCr4IvBM+HNPJDQAfhn4WnjOmwTCAubbx0pRrVKLNoNezpYq7hHDiGiPO+zf1sUVWzrxFa7s72DfQCflMKfajp42ukNZcMkLxAVtcZeE6xAP3WPR31TR8/FRYk6wUXVLZ5KEG6x4GuVou/MDe+lMxvBU8Xw//FG6UjELwhsLRlRtmpuNAwcO6LPPPrvk173t4JGalCPRLvJy6B4xDICk6+Cj9KRiPPvbH655r/5vCOC1cxMk3MCV5oogIpQ9j7IPjiP4qmxuj9PflWIkU6CnLU7Z8yurqJ29qcp+nkhavV5VbUZrEZHnWSvgJgAADURJREFUVPVAo/dWi7hgw1G/eS3hOpQ8HxFBqlwoxsbGcQTf14by+UZyYNcRetrjTOTKlL1A2eY4Du0xYVtPioGuVEU2Hbl6h8ayXNnfgYiQKZSn5WizALyx1FghuBWifvNaf1cSXwOZqxmd9ctMBdhmwg9LhiZi0/9VGxX6+/SNbyPuunSlYvgoZd/H95XutljDXGrfuOMGvnjrNfR1JEkXyiYpNpYFW/GsEPV3q64jJGIO2aIl8lzPxBxApBLsb0ax7BN3hf7OZKWtWUblawd7eeCpE5S9iUBQEHPYs7mzoUGxRJnGSmCGZ4Wo37wGmNHZADhOlMwG4m4gBpiJ6hIFI5lCpWR0M0MxH9eYJco0VgIzPMtM/d3qtu4EPzg9Rq5k0umNwBWb27n75h/j84de4czlLHEnqKFUTzxcGcVdhy2dSYqex2cefoFC2UeAbT0pRGTRhsISZRorgRmeZaTerfHDoTGetspsGwoRqax2P/PwC2SLHh0Jp7IPJ1ssMzSWY99AJxJunEnnS4ymi8F+nGC7DWcv59nRC12p+KIMhVUJNVYCExcsI9VujeGJPBkzOhuOkUwBCNxhX/nEu9jR28a2nhRdqVil1PTeLR01wpORdAEkKGuQcB0EQSRsZ3GG4s4P7KXkKdliGVWdtdy1YSwVZniWkdNjWdriLsMTeYYzxZUejrECFMtTNxuNVGn33HI1d920v8YY5MuBEdrSmWRLZxIfRVUplL1FG4qZxmDxHaOVmKttGdnV186pixkuhHeqxvokyqlWj+sECTurmUkIUC086UjEaE+4lewEABfSeURl2p6bhWD7dIzlxgzPMhAl/zw2nKZs3rV1S197jMvZckOjk4w59LXHuWJL55yuVW0MothgJL2PuWIrE2NNY4anxfzGw8/zX39wzjaFrgPirjSUPwsw2NeGiJCMuaTzZfIlD0+hty3GYLhZeKEuMasbY6w3zPC0kK/85Rt86wdrosCpMQNxB3xgZ28799xyNS8NXeb3/+p4xQAlXKGnLc4Xb70GqDUO79u7iadPXFoSY2HuMGM9YYanhfynp06s9BCMRZCMOXi+0pFwK26tG/cPVDIDNDIo9cbhMysxcMNY5ZjhaQHRJlHLRLB6cYVZi+v1JF22dKdqsjRH2OrDMBaHGZ4lpnqTqLE6cQTiMYd2JyjnnCmU8apEH1s64rQlYkuiGDMMYzpmeJaQfMnj3/3F62SLQUp6Y3mJOVD2g2C/CKTiLm0xqWzUHehK0pFwmSxOFTkDC9obxnKz4QyPiNwEfBlwga+p6r1NTmlIyfN540Kal4bGw5/LvH4+TdmquLWU9rjLR64e4PxEkaGxLJ3JGOl8iZFwQ+6V/UEutPkYDzM0hrG8bCjDIyIu8FWCkthDwDMickhVX53tPN9XToxO8sMzl3nx9DgvDl3m1bMTFGbYlBNzhPaES3sihqJcmLANo9V0p2L8k/dfMWuQ3jCM9cuGMjzAe4DjqnoCQEQeBm4FZjQ8J0YmufZf/QWZQrnh+1s6E1w72Mu1gz1cN9hLOlfi3z3+BnFXKlUhR50Cm9rjjGRKrfhMqwoB2hIu7QmXfQNdTY2JGRrD2HhsNMOzEzhd9XoIeO9sJ0wWyxWj05WK8eM7e7huVy/XDfZw7WAv28P09NV0t8Vr7uTjjlDylULJZ6Kw9pRu/+Cd2/nSJ94N1JZLtlWKYRgLYaMZnkZSs2lBGRG5A7gDoG/HXn7vF97JtYM97NncgeM0V6vVy20jpVt/dwomckwUljdvTtIVknEXCDY87tvavWCDYVJiwzAWy0YzPEPArqrXg8DZ+oNU9SBwEODAgQP6sXftXFSn1SlPimWfHb0umUKZs5fzRCYoGXPoTDiM5wNpbzOJQnvC5brBXltxGIax5thohueZ/7+9uw22qqrjOP79JQ8KqUCUqZiCMTVkyFMOkCY+jKJp9MKCYopsfGNTaQ02OPTGV47VNOlUmKmVYUqSUww+ECEvtAcMBETi6QYmKAWOgpYzqfXvxfpf7ul6uXIP5+7D5fw+M2fO2muvu+9af9a9/7v32ewFjJY0EngemAV8topv7DMFM7OipRJPRLwp6cvAMsrt1HdHxMYmd8vMrKW0VOIBiIiHgYeb3Q8zs1blFUjNzKxSTjxmZlYpJx4zM6uUE4+ZmVXKicfMzCqlCD9NuTuS9gJ/62LXcODFirtzJHIcOjgWHRyLDq0ai9Mj4t1d7XDiqZOk1RExqdn9aDbHoYNj0cGx6OBYvJUvtZmZWaWceMzMrFJOPPW7o9kdOEI4Dh0ciw6ORQfHohN/xmNmZpXyGY+ZmVXKiaeHJE2XtEVSm6R5ze5Pb5B0mqSVkjZJ2ijpuqwfJmm5pG35PjTrJem2jMnTkibUHGtOtt8maU6zxnQ4JB0jaa2kpbk9UtKqHNMiSQOyfmBut+X+M2qOcWPWb5F0aXNGcngkDZG0WNLmnBtTWnhOfC1/Np6RdJ+kY1t1XtQlIvw6xBdlKYW/AqOAAcB6YEyz+9UL4zwZmJDl44GtwBjgW8C8rJ8H3JLly4FHKCu8TgZWZf0wYHu+D83y0GaPr454fB34BbA0t38JzMry7cC1Wf4ScHuWZwGLsjwm58pAYGTOoWOaPa464vAz4JosDwCGtOKcAE4FdgDH1cyHL7TqvKjn5TOenjkHaIuI7RHxOnA/MKPJfWq4iNgdEU9l+VVgE+WHbQbllw/5/skszwDuieJPwBBJJwOXAssj4qWIeBlYDkyvcCiHTdII4OPAnbkt4EJgcTbpHIf2+CwGLsr2M4D7I+LfEbEDaKPMpT5D0gnAx4C7ACLi9YjYRwvOidQPOE5SP2AQsJsWnBf1cuLpmVOBnTXbu7LuqJWXBcYDq4CTImI3lOQEtC+perC4HA3x+h7wDTiwSvm7gH0R8WZu147pwHhz//5sfzTEYRSwF/hJXna8U9JgWnBORMTzwHeA5ygJZz+whtacF3Vx4ukZdVF31N4WKOmdwK+A6yPile6adlEX3dT3CZKuAPZExJra6i6axtvs69NxSP2ACcCCiBgP/Ityae1gjtpY5OdYMyiXx04BBgOXddG0FeZFXZx4emYXcFrN9gjghSb1pVdJ6k9JOvdGxINZ/Y+8XEK+78n6g8Wlr8fro8AnJD1Luax6IeUMaEheYoH/H9OB8eb+E4GX6PtxgDKGXRGxKrcXUxJRq80JgIuBHRGxNyLeAB4EptKa86IuTjw982dgdN69MoDyQeGSJvep4fL6813Apoj4bs2uJUD7XUhzgN/U1H8+72SaDOzPyy7LgEskDc2/Ei/Juj4hIm6MiBERcQbl3/qxiJgNrASuymad49Aen6uyfWT9rLy7aSQwGniyomE0RET8Hdgp6QNZdRHwF1psTqTngMmSBuXPSnssWm5e1K3Zdzf0tRflbp2tlDtQ5je7P700xnMpp/xPA+vydTnluvQKYFu+D8v2An6QMdkATKo51hcpH5q2AVc3e2yHEZNpdNzVNoryC6INeAAYmPXH5nZb7h9V8/XzMz5bgMuaPZ46YzAOWJ3z4teUu9Jack4ANwGbgWeAn1PuTGvJeVHPy08uMDOzSvlSm5mZVcqJx8zMKuXEY2ZmlXLiMTOzSjnxmJlZpZx4zMysUk48Zk2U/3nwd5LWSZop6XpJg+o81rOShje6j2aN1u/tm5hZLxoP9I+IcVCSB7AQeK2ZnTLrTT7jMWswSYMlPSRpfS4UNlNlAcHNkp7IBdKWSnoPJcmMyzOe6ygPnVwpaWU3x18gaXUuRHZTp903SHoyX+/P9qdLWpELsq2Q9D5JJ+YZ0juyzSBJOyX1l3SmpEclrZH0uKQP9lKorEU58Zg13nTghYg4OyLOAh4FfgxcCZwHvBcgIvYA1wCPR8S4iLiV8pDICyLigm6OPz8iJgFjgfMlja3Z90pEnAN8n/JAU7J8T0SMBe4FbouI/ZRFyM7PNlcCy6I89PIO4CsRMRGYC/zwcIJh1pkTj1njbQAulnSLpPMoj8/fERHbojyjauFhHv/Tkp4C1gIfoqxk2e6+mvcpWZ5CWUEVynPFzs3yImBmlmcBi3IpjKnAA5LWAT+irEhr1jD+jMeswSJiq6SJlAer3gz8lgats5JPMZ4LfCQiXpb0U8pDKA98+4OU6aJ+CXCzpGHAROAxytoy+9o/czLrDT7jMWswSacAr0XEQspKlVOBkZLOzCaf6ebLXwWO72b/CZRF2PZLOom3LkA2s+b9j1n+A+WMBmA28ARARPyT8rTkWylP3v5PlAX/dkj6VI5Fks7ubrxmPeUzHrPG+zDwbUn/Bd4ArgWGAw9JepHyi/+sg3ztHcAjknZ39TlPRKyXtBbYCGwHft+pyUBJqyh/VLYnuK8Cd0u6gbJ89dU17RdRHtk/raZuNrBA0jeB/pRF8NYfysDNDoWXRTCrmKRpwNyIuKLZfTFrBl9qMzOzSvmMx+wIlZfMBnaq/lxEbGhGf8waxYnHzMwq5UttZmZWKSceMzOrlBOPmZlVyonHzMwq5cRjZmaV+h/xdWxdk1lzNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='sqft_above',y='price',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zipcode         -0.053203\n",
       "long             0.021626\n",
       "condition        0.036362\n",
       "yr_built         0.054012\n",
       "sqft_lot15       0.082447\n",
       "sqft_lot         0.089661\n",
       "yr_renovated     0.126434\n",
       "floors           0.256794\n",
       "waterfront       0.266369\n",
       "lat              0.307003\n",
       "bedrooms         0.308797\n",
       "sqft_basement    0.323816\n",
       "view             0.397293\n",
       "bathrooms        0.525738\n",
       "sqft_living15    0.585379\n",
       "sqft_above       0.605567\n",
       "grade            0.667434\n",
       "sqft_living      0.702035\n",
       "price            1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['price'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4: Model Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00046769430149007363"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['long']]\n",
    "Y = df['price']\n",
    "lm = LinearRegression()\n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question  6\n",
    "Fit a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculate the R^2. Take a screenshot of your code and the value of the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49285321790379316"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = df[['sqft_living']]\n",
    "Y1 = df[['price']]\n",
    "lm1 = LinearRegression()\n",
    "lm1.fit(X1,Y1)\n",
    "lm1.score(X1,Y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "Fit a linear regression model to predict the <code>'price'</code> using the list of features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then calculate the R^2. Take a screenshot of your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6576951666037494"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm2 = LinearRegression()\n",
    "lm2.fit(df[features],df[['price']])\n",
    "lm2.score(df[features],df[['price']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This will help with Question 8\n",
    "\n",
    "Create a list of tuples, the first element in the tuple contains the name of the estimator:\n",
    "\n",
    "<code>'scale'</code>\n",
    "\n",
    "<code>'polynomial'</code>\n",
    "\n",
    "<code>'model'</code>\n",
    "\n",
    "The second element in the tuple  contains the model constructor \n",
    "\n",
    "<code>StandardScaler()</code>\n",
    "\n",
    "<code>PolynomialFeatures(include_bias=False)</code>\n",
    "\n",
    "<code>LinearRegression()</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list <code>features</code>, and calculate the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7513404614351351"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline(Input)\n",
    "pipe.fit(df[features],df[['price']])\n",
    "pipe.score(df[features],df[['price']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 5: Model Evaluation and Refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will split the data into training and testing sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples: 3242\n",
      "number of training samples: 18371\n"
     ]
    }
   ],
   "source": [
    "features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \n",
    "X = df[features]\n",
    "Y = df['price']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
    "\n",
    "\n",
    "print(\"number of test samples:\", x_test.shape[0])\n",
    "print(\"number of training samples:\",x_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6478759163939115"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RigeModel = Ridge(alpha = 0.1)\n",
    "RigeModel.fit(x_train,y_train)\n",
    "RigeModel.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 10\n",
    "Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7002744265869922"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = PolynomialFeatures(degree = 2)\n",
    "x_train_pr = pr.fit_transform(x_train)\n",
    "x_test_pr = pr.fit_transform(x_test)\n",
    "RigeModel1 = Ridge(alpha = 0.1)\n",
    "RigeModel1.fit(x_train_pr,y_train)\n",
    "RigeModel1.score(x_test_pr,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Once you complete your notebook you will have to share it. Select the icon on the top right a marked in red in the image below, a dialogue box should open, and select the option all&nbsp;content excluding sensitive code cells.</p>\n",
    "        <p><img width=\"600\" src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/save_notebook.png\" alt=\"share notebook\"  style=\"display: block; margin-left: auto; margin-right: auto;\"/></p>\n",
    "        <p></p>\n",
    "        <p>You can then share the notebook&nbsp; via a&nbsp; URL by scrolling down as shown in the following image:</p>\n",
    "        <p style=\"text-align: center;\"><img width=\"600\"  src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/url_notebook.png\" alt=\"HTML\" style=\"display: block; margin-left: auto; margin-right: auto;\" /></p>\n",
    "        <p>&nbsp;</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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