House_Sales_in_King_Count_USA-20231003-1696291200.jupyterlite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"300\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Final Project: House Sales in King County, USA </font></h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "    <li><a href=\"#Instructions\">Instructions</a></li>\n",
    "    <li><a href=\"#About-the-Dataset\">About the Dataset</a></li>\n",
    "    <li><a href=\"#Module-1:-Importing-Data-Sets\">Module 1: Importing Data </a></li>\n",
    "    <li><a href=\"#Module-2:-Data-Wrangling\">Module 2: Data Wrangling</a> </li>\n",
    "    <li><a href=\"#Module-3:-Exploratory-Data-Analysis\">Module 3: Exploratory Data Analysis</a></li>\n",
    "    <li><a href=\"#Module-4:-Model-Development\">Module 4: Model Development</a></li>\n",
    "    <li><a href=\"#Module-5:-Model-Evaluation-and-Refinement\">Module 5: Model Evaluation and Refinement</a></li>\n",
    "</a></li>\n",
    "</div>\n",
    "<p>Estimated Time Needed: <strong>75 min</strong></p>\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instructions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, you are a Data Analyst working at a Real Estate Investment Trust. The Trust would like to start investing in Residential real estate. You are tasked with determining the market price of a house given a set of features. You will analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on. This is a template notebook; your job is to complete the ten questions. Some hints to the questions are given.\n",
    "\n",
    "As you are completing this notebook, take and save the **screenshots** of the final outputs of your solutions (e.g., final charts, tables, calculation results etc.). They will need to be shared in the following Peer Review section of the Final Project module.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About the Dataset\n",
    "\n",
    "This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It was taken from [here](https://www.kaggle.com/harlfoxem/housesalesprediction?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-wwwcourseraorg-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01). It was also slightly modified for the purposes of this course. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Variable      | Description                                                                                                 |\n",
    "| ------------- | ----------------------------------------------------------------------------------------------------------- |\n",
    "| id            | A notation for a house                                                                                      |\n",
    "| date          | Date house was sold                                                                                         |\n",
    "| price         | Price is prediction target                                                                                  |\n",
    "| bedrooms      | Number of bedrooms                                                                                          |\n",
    "| bathrooms     | Number of bathrooms                                                                                         |\n",
    "| sqft_living   | Square footage of the home                                                                                  |\n",
    "| sqft_lot      | Square footage of the lot                                                                                   |\n",
    "| floors        | Total floors (levels) in house                                                                              |\n",
    "| waterfront    | House which has a view to a waterfront                                                                      |\n",
    "| view          | Has been viewed                                                                                             |\n",
    "| condition     | How good the condition is overall                                                                           |\n",
    "| grade         | overall grade given to the housing unit, based on King County grading system                                |\n",
    "| sqft_above    | Square footage of house apart from basement                                                                 |\n",
    "| sqft_basement | Square footage of the basement                                                                              |\n",
    "| yr_built      | Built Year                                                                                                  |\n",
    "| yr_renovated  | Year when house was renovated                                                                               |\n",
    "| zipcode       | Zip code                                                                                                    |\n",
    "| lat           | Latitude coordinate                                                                                         |\n",
    "| long          | Longitude coordinate                                                                                        |\n",
    "| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n",
    "| sqft_lot15    | LotSize area in 2015(implies-- some renovations)                                                            |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Import the required libraries**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented.\n",
    "# !mamba install -qy pandas==1.3.4 numpy==1.21.4 seaborn==0.9.0 matplotlib==3.5.0 scikit-learn==0.20.1\n",
    "# Note: If your environment doesn't support \"!mamba install\", use \"!pip install\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Surpress warnings:\n",
    "def warn(*args, **kwargs):\n",
    "    pass\n",
    "\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.warn = warn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 1: Importing Data Sets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset by running the cell below.\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import piplite\n",
    "await piplite.install('seaborn')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from pyodide.http import pyfetch\n",
    "\n",
    "async def download(url, filename):\n",
    "    response = await pyfetch(url)\n",
    "    if response.status == 200:\n",
    "        with open(filename, \"wb\") as f:\n",
    "            f.write(await response.bytes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv\""
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "await download(filepath, \"housing.csv\")\n",
    "file_name=\"housing.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the csv:\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df = pd.read_csv(file_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This version of the lab is working on JupyterLite, which requires the dataset to be downloaded to the interface.While working on the downloaded version of this notebook on their local machines(Jupyter Anaconda), the learners can simply **skip the steps above,** and simply use the URL directly in the `pandas.read_csv()` function. You can uncomment and run the statements in the cell below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/kc_house_data_NaN.csv\"\n",
    "df = pd.read_csv(filepath)\n",
    "\n",
    "# saving raw data in local\n",
    "df.to_csv(\"housing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method <code>head</code> to display the first 5 columns of the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Display the data types of each column using the function dtypes. Take a screenshot of your code and output. You will need to submit the screenshot for the final project. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method describe to obtain a statistical summary of the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2: Data Wrangling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\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. Make sure the <code>inplace</code> parameter is set to <code>True</code>. Take a screenshot of your code and output. You will need to submit the screenshot for the final project. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>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>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",
       "      <th>count</th>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613</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",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20140623T000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>NaN</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",
       "      <th>std</th>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>NaN</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",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>NaN</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",
       "      <th>25%</th>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>NaN</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",
       "      <th>50%</th>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>NaN</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",
       "      <th>75%</th>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>NaN</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",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>NaN</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>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id             date         price      bedrooms  \\\n",
       "count   2.161300e+04            21613  2.161300e+04  21600.000000   \n",
       "unique           NaN              372           NaN           NaN   \n",
       "top              NaN  20140623T000000           NaN           NaN   \n",
       "freq             NaN              142           NaN           NaN   \n",
       "mean    4.580302e+09              NaN  5.400881e+05      3.372870   \n",
       "std     2.876566e+09              NaN  3.671272e+05      0.926657   \n",
       "min     1.000102e+06              NaN  7.500000e+04      1.000000   \n",
       "25%     2.123049e+09              NaN  3.219500e+05      3.000000   \n",
       "50%     3.904930e+09              NaN  4.500000e+05      3.000000   \n",
       "75%     7.308900e+09              NaN  6.450000e+05      4.000000   \n",
       "max     9.900000e+09              NaN  7.700000e+06     33.000000   \n",
       "\n",
       "           bathrooms   sqft_living      sqft_lot        floors    waterfront  \\\n",
       "count   21603.000000  21613.000000  2.161300e+04  21613.000000  21613.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean        2.115736   2079.899736  1.510697e+04      1.494309      0.007542   \n",
       "std         0.768996    918.440897  4.142051e+04      0.539989      0.086517   \n",
       "min         0.500000    290.000000  5.200000e+02      1.000000      0.000000   \n",
       "25%         1.750000   1427.000000  5.040000e+03      1.000000      0.000000   \n",
       "50%         2.250000   1910.000000  7.618000e+03      1.500000      0.000000   \n",
       "75%         2.500000   2550.000000  1.068800e+04      2.000000      0.000000   \n",
       "max         8.000000  13540.000000  1.651359e+06      3.500000      1.000000   \n",
       "\n",
       "                view  ...         grade    sqft_above  sqft_basement  \\\n",
       "count   21613.000000  ...  21613.000000  21613.000000   21613.000000   \n",
       "unique           NaN  ...           NaN           NaN            NaN   \n",
       "top              NaN  ...           NaN           NaN            NaN   \n",
       "freq             NaN  ...           NaN           NaN            NaN   \n",
       "mean        0.234303  ...      7.656873   1788.390691     291.509045   \n",
       "std         0.766318  ...      1.175459    828.090978     442.575043   \n",
       "min         0.000000  ...      1.000000    290.000000       0.000000   \n",
       "25%         0.000000  ...      7.000000   1190.000000       0.000000   \n",
       "50%         0.000000  ...      7.000000   1560.000000       0.000000   \n",
       "75%         0.000000  ...      8.000000   2210.000000     560.000000   \n",
       "max         4.000000  ...     13.000000   9410.000000    4820.000000   \n",
       "\n",
       "            yr_built  yr_renovated       zipcode           lat          long  \\\n",
       "count   21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "unique           NaN           NaN           NaN           NaN           NaN   \n",
       "top              NaN           NaN           NaN           NaN           NaN   \n",
       "freq             NaN           NaN           NaN           NaN           NaN   \n",
       "mean     1971.005136     84.402258  98077.939805     47.560053   -122.213896   \n",
       "std        29.373411    401.679240     53.505026      0.138564      0.140828   \n",
       "min      1900.000000      0.000000  98001.000000     47.155900   -122.519000   \n",
       "25%      1951.000000      0.000000  98033.000000     47.471000   -122.328000   \n",
       "50%      1975.000000      0.000000  98065.000000     47.571800   -122.230000   \n",
       "75%      1997.000000      0.000000  98118.000000     47.678000   -122.125000   \n",
       "max      2015.000000   2015.000000  98199.000000     47.777600   -121.315000   \n",
       "\n",
       "        sqft_living15     sqft_lot15  \n",
       "count    21613.000000   21613.000000  \n",
       "unique            NaN            NaN  \n",
       "top               NaN            NaN  \n",
       "freq              NaN            NaN  \n",
       "mean      1986.552492   12768.455652  \n",
       "std        685.391304   27304.179631  \n",
       "min        399.000000     651.000000  \n",
       "25%       1490.000000    5100.000000  \n",
       "50%       1840.000000    7620.000000  \n",
       "75%       2360.000000   10083.000000  \n",
       "max       6210.000000  871200.000000  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "df.drop(columns=[\"Unnamed: 0\"], inplace=True)\n",
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we have missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df[\"bedrooms\"].mean()\n",
    "df[\"bedrooms\"].replace(np.nan, mean, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df[\"bathrooms\"].mean()\n",
    "df[\"bathrooms\"].replace(np.nan, mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\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 data frame. Take a screenshot of your code and output. You will need to submit the screenshot for the final project. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>10680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>8241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>1910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count\n",
       "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "df[\"floors\"].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\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. Take a screenshot of your code and boxplot. You will need to submit the screenshot for the final project. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='waterfront', ylabel='price'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"waterfront\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\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. Take a screenshot of your code and scatterplot. You will need to submit the screenshot for the final project. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 8081250.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "sns.regplot(data=df, x=\"sqft_above\", y=\"price\", line_kws={\"color\": \"red\"})\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to calculate 'corr()', we need to transform df.date into a numerical value type serie. \n",
    "df.date = pd.to_numeric(pd.to_datetime(df.date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            1.000000\n",
       "sqft_living      0.702035\n",
       "grade            0.667434\n",
       "sqft_above       0.605567\n",
       "sqft_living15    0.585379\n",
       "bathrooms        0.525738\n",
       "view             0.397293\n",
       "sqft_basement    0.323816\n",
       "bedrooms         0.308797\n",
       "lat              0.307003\n",
       "waterfront       0.266369\n",
       "floors           0.256794\n",
       "yr_renovated     0.126434\n",
       "sqft_lot         0.089661\n",
       "sqft_lot15       0.082447\n",
       "yr_built         0.054012\n",
       "condition        0.036362\n",
       "long             0.021626\n",
       "date            -0.004357\n",
       "id              -0.016762\n",
       "zipcode         -0.053203\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"price\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4: Model Development\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00046769430149007363\n"
     ]
    }
   ],
   "source": [
    "X = df[[\"long\"]]\n",
    "Y = df[\"price\"]\n",
    "lm = LinearRegression()\n",
    "lm.fit(X, Y)\n",
    "print(lm.score(X, Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question  6\n",
    "\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. You will need to submit it for the final project.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4928532179037931\n"
     ]
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "X1 = df[[\"sqft_living\"]]\n",
    "Y = df[\"price\"]\n",
    "lm = LinearRegression()\n",
    "lm.fit(X1, Y)\n",
    "print(lm.score(X1, Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "Fit a linear regression model to predict the <code>'price'</code> using the list of features:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 and the value of the R^2. You will need to submit it for the final project.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6576912429594147\n"
     ]
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "Z = df[features]\n",
    "Y = df[[\"price\"]]\n",
    "lm = LinearRegression()\n",
    "lm.fit(Z, Y)\n",
    "print(lm.score(Z, Y))"
   ]
  },
  {
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Input = [\n",
    "    (\"scale\", StandardScaler()),\n",
    "    (\"polynomial\", PolynomialFeatures(include_bias=False)),\n",
    "    (\"model\", LinearRegression()),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 8\n",
    "\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. Take a screenshot of your code and the value of the R^2. You will need to submit it for the final project.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7506104506478922\n"
     ]
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "Z, Y = df[features], df[\"price\"]\n",
    "pipe = Pipeline(Input)\n",
    "Z = Z.astype(\"float\")\n",
    "pipe.fit(Z, Y)\n",
    "y_pipe = pipe.predict(Z)\n",
    "print(r2_score(Y, y_pipe))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 5: Model Evaluation and Refinement\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the necessary modules:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will split the data into training and testing sets:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of test samples: 3242\n",
      "number of training samples: 18371\n"
     ]
    }
   ],
   "source": [
    "features = [\n",
    "    \"floors\",\n",
    "    \"waterfront\",\n",
    "    \"lat\",\n",
    "    \"bedrooms\",\n",
    "    \"sqft_basement\",\n",
    "    \"view\",\n",
    "    \"bathrooms\",\n",
    "    \"sqft_living15\",\n",
    "    \"sqft_above\",\n",
    "    \"grade\",\n",
    "    \"sqft_living\",\n",
    "]\n",
    "X = df[features]\n",
    "Y = df[\"price\"]\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.15, random_state=1\n",
    ")\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",
    "\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. Take a screenshot of your code and the value of the R^2. You will need to submit it for the final project.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6478759163939114\n"
     ]
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "Ridge_Model = Ridge(alpha=0.1)\n",
    "Ridge_Model.fit(x_train, y_train)\n",
    "y_hat_Ridge = Ridge_Model.predict(x_test)\n",
    "print(r2_score(y_test, y_hat_Ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 10\n",
    "\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. You will need to submit it for the final project.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7002744282531637\n"
     ]
    }
   ],
   "source": [
    "# Enter Your Code, Execute and take the Screenshot\n",
    "polly_reg = PolynomialFeatures(degree=2)\n",
    "x_train_pr = polly_reg.fit_transform(x_train)\n",
    "x_test_pr = polly_reg.fit_transform(x_test)\n",
    "\n",
    "Ridge_Model = Ridge(alpha=0.1)\n",
    "Ridge_Model.fit(x_train_pr, y_train)\n",
    "yhat_Ridge = Ridge_Model.predict(x_test_pr)\n",
    "\n",
    "print(r2_score(y_test,yhat_Ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Once you complete your notebook you will have to share it. You can download the notebook by navigating to \"File\" and clicking on \"Download\" button.\n",
    "        <p><img width=\"600\" src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%206/images/DA0101EN_FA_Image21.png\" alt=\"share notebook\" style=\"display: block; margin-left: auto; margin-right: auto;\"></p>\n",
    "        <p></p>\n",
    "<p>This will save the (.ipynb) file on your computer. Once saved, you can upload this file in the \"My Submission\" tab, of the \"Peer-graded Assignment\" section.  \n",
    "          \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Michelle Carey</a>, <a href=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDA0101ENSkillsNetwork20235326-2022-01-01\">Mavis Zhou</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "<!--## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description                           |\n",
    "| ----------------- | ------- | --------------- | -------------------------------------------- |\n",
    "| 2020-12-01        | 2.2     | Aije Egwaikhide | Coverted Data describtion from text to table |\n",
    "| 2020-10-06        | 2.1     | Lakshmi Holla   | Changed markdown instruction of Question1    |\n",
    "| 2020-08-27        | 2.0     | Malika Singla   | Added lab to GitLab                          |\n",
    "| 2022-06-13        | 2.3     | Svitlana Kramar | Updated Notebook sharing instructions        |\n",
    "| <hr>              |         |                 |                                              |\n",
    "\n",
    "\n",
    "--!>\n",
    "<p>\n"
   ]
  }
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