index-tina.ipynb

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    "# Module 1 Final Project\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this lesson, we'll review all of the guidelines and specifications for the final project for Module 1. \n",
    "\n",
    "## Objectives\n",
    "You will be able to:\n",
    "* Describe all required aspects of the final project for Module 1\n",
    "* Describe all required deliverables\n",
    "* Describe what constitutes a successful project\n",
    "* Describe what the experience of the project review should be like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Project Summary\n",
    "\n",
    "It's time to put all our newfound data science skills to use with a final project!\n",
    "\n",
    "You should expect this project to take **20-25 hours** of solid, focused effort.\n",
    "\n",
    "If you're done way quicker, go back and dig in deeper or try some of the optional \"level up\" suggestions. If you're worried that you're going to get to 30 hrs and still not even have the data imported, reach out to an instructor in Slack ASAP to get some help!\n",
    "\n",
    "## Your  Data\n",
    "\n",
    "* `\"kc_house_data.csv\"`\n",
    "    * King County House Sales dataset. We've modified the dataset to make it a bit more fun and challenging. \n",
    "\n",
    "* `\"column_names.md\"`\n",
    "    * The description of the column names. As with most real world data sets, the column names are not perfectly described, so you'll have to do some research or use your best judgment if you have questions relating to what the data means.\n",
    "\n",
    "You'll clean, explore, and model this dataset with a multivariate linear regression to predict the sale price of houses as accurately as possible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Deliverables\n",
    "\n",
    "There will be three deliverables for this project:\n",
    "\n",
    "1. A well documented **Jupyter Notebook** containing any code you've written for this project and comments explaining it. This work will need to be pushed to your GitHub repository in order to submit your project.\n",
    "\n",
    "2. A short **Keynote/PowerPoint/Google Slides presentation** (delivered as a PDF export) giving a high-level overview of your methodology and recommendations for **non-technical** stakeholders. Make sure to also add and commit this pdf of your non-technical presentation to your repository with a file name of presentation.pdf.\n",
    "\n",
    "3. A **Live Demo** of your non-technical presentation. You will have an opportunity to practice and get feedback the day before the final presentation so that you grow more confident on your speaking skills."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Process\n",
    "\n",
    "### 1. Getting Started\n",
    "\n",
    "Please start by reviewing this document. If you have any questions, please ask them in slack ASAP so (a) we can answer the questions and (b) so we can update this repository to make it clearer.\n",
    "\n",
    "### 2. The Project Review\n",
    "\n",
    "> **When you start on the project, please also reach out to an instructor immediately to schedule your project review** (if you're not sure who to schedule with, please ask in slack!)\n",
    "\n",
    "\n",
    "#### 1. Deliver your PDF presentation to a non-technical stakeholder. \n",
    "In this phase of the review (~10 mins) your instructor will play the part of a non-technical stakeholder that you are presenting your findings to. The presentation should not exceed 5 minutes, giving the \"stakeholder\" 5 minutes to ask questions.\n",
    "\n",
    "In the first half of the presentation (2-3 mins), you should summarize your methodology in a way that will be comprehensible to someone with no background in data science and that will increase their confidence in you and your findings. In the second half (the remaining 2-3 mins) you should summarize your findings and be ready to answer a couple of non-technical questions from the audience. The questions might relate to technical topics (sampling bias, confidence, etc) but will be asked in a non-technical way and need to be answered in a way that does not assume a background in statistics or machine learning. You can assume a smart, business stakeholder, with a non-quantitative college degree.\n",
    "\n",
    "#### 2. Go through the Jupyter Notebook, answering questions about how you made certain decisions. Be ready to explain things like:\n",
    "    * \"how did you pick the question(s) that you did?\"\n",
    "    * \"why are these questions important from a business perspective?\"\n",
    "    * \"how did you decide on the data cleaning options you performed?\"\n",
    "    * \"why did you choose a given method or library?\"\n",
    "    * \"why did you select those visualizations and what did you learn from each of them?\"\n",
    "    * \"why did you pick those features as predictors?\"\n",
    "    * \"how would you interpret the results?\"\n",
    "    * \"how confident are you in the predictive quality of the results?\"\n",
    "    * \"what are some of the things that could cause the results to be wrong?\"\n",
    "\n",
    "What won't happen:\n",
    "* You won't be yelled at, belittled, or scolded\n",
    "* You won't be put on the spot without support\n",
    "* There's nothing you can do to instantly fail or blow it\n",
    "\n",
    "**Please note: We need to receive the URL of your repository at least 24 hours before and please have the project finished at least 3 hours before your review so we can look at your materials in advance.** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements\n",
    "\n",
    "This section outlines the rubric we'll use to evaluate your project.\n",
    "\n",
    "### 1. Technical Report Must-Haves\n",
    "\n",
    "For this project, your Jupyter Notebook should meet the following specifications:\n",
    "\n",
    "#### Organization/Code Cleanliness\n",
    "\n",
    "* The notebook should be well organized, easy to follow,  and code should be commented where appropriate.\n",
    "    * Your notebook should contain 1 - 2 paragraphs briefly explaining your general approach to this project as an intro.\n",
    "    * **Level Up**: The notebook contains well-formatted, professional looking markdown cells explaining any substantial code.  All functions have docstrings that act as professional-quality documentation\n",
    "    \n",
    "* The notebook is written for technical audiences with a way to both understand your approach and reproduce your results. The target audience for this deliverable is other data scientists looking to validate your findings.\n",
    "\n",
    "#### Visualizations & EDA\n",
    " \n",
    "* You pose at least 3 meaningful questions and answer them through EDA.  These questions should be well labeled and easy to identify inside the notebook. \n",
    "    * **Level Up**: Each question is clearly answered with a visualization that makes the answer easy to understand. \n",
    "* Your project contains at least 4 meaningful data **visualizations**, with corresponding interpretations. All visualizations are well labeled with axes labels, a title, and a legend (when appropriate) \n",
    "    \n",
    "#### Model Quality/Approach\n",
    "\n",
    "* Your notebook shows an **iterative approach** to modeling, and details the parameters and results of the model at each iteration.  \n",
    "    * **Level Up**: Whenever necessary, you briefly explain the changes made from one iteration to the next, and why you made these choices.  \n",
    "\n",
    "\n",
    "* Your **final model**\n",
    "    * You pick at least 3 coefficients from your final model and explain their impact on the price of a house in this dataset.\n",
    "        * Your model should not include any predictors with p-values greater than .05.\n",
    "    * You explain your rationale for selecting your model (not just the learner, also the parameters and variables)\n",
    "        * Minimum 1 paragraph\n",
    "\n",
    "\n",
    "\n",
    "### 2. Non-Technical Presentation Must-Haves\n",
    "\n",
    "The second deliverable should be a Keynote, PowerPoint or Google Slides presentation delivered as a pdf file in your fork of this repository with the file name of `presentation.pdf` detailing the results of your project.  Your target audience is non-technical people interested in using your findings to maximize their profit when selling their home. \n",
    "\n",
    "Your presentation should:\n",
    "* Content:\n",
    "   * _Based on the results of your models, your presentation should discuss at least two concrete features that highly influence housing prices._\n",
    "* Format:\n",
    "    * Contain between 5 - 10 professional-quality slides.\n",
    "        * **Level Up**: The slides should use visualizations whenever possible, and avoid walls of text.\n",
    "        * Avoid technical jargon and explain the results in a clear, actionable way for non-technical audiences.   \n",
    "    * Take no more than 5 minutes to present.   \n",
    "\n",
    "\n",
    "## Submitting your Project\n",
    "\n",
    " You’re almost done! In order to submit your project for review, include the following links to your work in the corresponding fields on the right-hand side of Learn.\n",
    "\n",
    " 1. **GitHub Repo:**\n",
    "    - Start a git repo for your project at the very beginning and added it to this \n",
    "         [sheet](https://docs.google.com/spreadsheets/d/1PMKPCBfwh1v7HyeWC6AtIwAJsk1BsC7r5iyKy9C2Glo/edit#gid=0)\n",
    "    - Keep committing and pushing your work so that you don't lose it!\n",
    "\n",
    "2. **Tell us about your progress!**\n",
    "    - Check the boxes in the [sheet](https://docs.google.com/spreadsheets/d/1PMKPCBfwh1v7HyeWC6AtIwAJsk1BsC7r5iyKy9C2Glo/edit#gid=0) when each of the following is ready:\n",
    "    \n",
    "        - Your **jupyter notebook** is finished and pushed to the repo\n",
    "        - Your **slides** are PDFed and pushed to your repo\n",
    "        - You are ready for your **dry run**"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "The end of module projects and project reviews are a critical part of the programme. They give you a chance to both bring together all the skills you've learned into realistic projects and to practice key \"business judgement\" and communication skills that you otherwise might not get as much practice with.\n",
    "\n",
    "The projects are serious and important. They are not graded, but they can be passed and they can be failed. Take the project seriously, put the time in, ask for help from your peers or instructors early and often if you need it, and treat the review as a job interview and you'll do great. We're rooting for you to succeed and we're only going to ask you to take a review again if we believe that you need to. We'll also provide open and honest feedback so you can improve as quickly and efficiently as possible.\n",
    "\n",
    "Finally, this is your first project. We don't expect you to remember all of the terms or to get all of the answers right. If in doubt, be honest. If you don't know something, say so. If you can't remember it, just say so. It's very unusual for someone to complete a project review without being asked a question they're unsure of, we know you might be nervous which may affect your performance. Just be as honest, precise and focused as you can be, and you'll do great!\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: General approach to project  ->\n",
    "\n",
    "The approach we are taking in an ultimate aim to explain house sale prices using the provided data can be divded into the following steps\n",
    "\n",
    "Step 1: To understand the data provided by looking at summary statistics alongside given data definitions, to make sense of what each data series (column) is representing, whether it's continous, binary or categorical, what values it takes, whether its affected by duplicates and nulls.\n",
    "\n",
    "Step 2: Based on the finding from previous step, we will clean the data, replacing, converting or deleting Nulls, duplicates as appropriate, \n",
    "\n",
    "Step 3: Based on the given data, and with the ultimate aim of predicting house price in mind. We ask a few questions that may review interesting trends or useful insights.\n",
    "\n",
    "Step 4: We use visulisation to help answer the questions we have raised above, as well as studying the distribution of potential key independent variables as indicated by correlation matrix. We will also check for a linear relationship between independent variables and the dependent variable via scatter plots. We will also be looking for any obvious data outliers in the graphs.\n",
    "\n",
    "Step 5: modelling fitting..... \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries needed\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.precision', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the data as dataframe\n",
    "house_df = pd.read_csv('kc_house_data.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Understanding data provided"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
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       "      <th>date</th>\n",
       "      <td>10/13/2014</td>\n",
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       "      <td>2/25/2015</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>2/18/2015</td>\n",
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       "      <th>price</th>\n",
       "      <td>2.22e+05</td>\n",
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       "      <td>6.04e+05</td>\n",
       "      <td>5.1e+05</td>\n",
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       "      <th>bedrooms</th>\n",
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       "      <th>bathrooms</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1180</td>\n",
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       "      <td>1680</td>\n",
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       "      <th>sqft_lot</th>\n",
       "      <td>5650</td>\n",
       "      <td>7242</td>\n",
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       "      <td>5000</td>\n",
       "      <td>8080</td>\n",
       "    </tr>\n",
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       "      <th>floors</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <th>grade</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
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       "      <th>sqft_above</th>\n",
       "      <td>1180</td>\n",
       "      <td>2170</td>\n",
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       "      <td>1680</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
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       "      <td>910.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>yr_built</th>\n",
       "      <td>1955</td>\n",
       "      <td>1951</td>\n",
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       "      <td>1987</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>zipcode</th>\n",
       "      <td>98178</td>\n",
       "      <td>98125</td>\n",
       "      <td>98028</td>\n",
       "      <td>98136</td>\n",
       "      <td>98074</td>\n",
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       "      <td>47.5</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.5</td>\n",
       "      <td>47.6</td>\n",
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       "      <th>long</th>\n",
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       "      <td>-122</td>\n",
       "      <td>-122</td>\n",
       "      <td>-122</td>\n",
       "      <td>-122</td>\n",
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       "      <th>sqft_living15</th>\n",
       "      <td>1340</td>\n",
       "      <td>1690</td>\n",
       "      <td>2720</td>\n",
       "      <td>1360</td>\n",
       "      <td>1800</td>\n",
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       "      <th>sqft_lot15</th>\n",
       "      <td>5650</td>\n",
       "      <td>7639</td>\n",
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       "      <td>5000</td>\n",
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       "                        0           1           2           3           4\n",
       "id             7129300520  6414100192  5631500400  2487200875  1954400510\n",
       "date           10/13/2014   12/9/2014   2/25/2015   12/9/2014   2/18/2015\n",
       "price            2.22e+05    5.38e+05     1.8e+05    6.04e+05     5.1e+05\n",
       "bedrooms                3           3           2           4           3\n",
       "bathrooms               1        2.25           1           3           2\n",
       "sqft_living          1180        2570         770        1960        1680\n",
       "sqft_lot             5650        7242       10000        5000        8080\n",
       "floors                  1           2           1           1           1\n",
       "waterfront            NaN           0           0           0           0\n",
       "view                    0           0           0           0           0\n",
       "condition               3           3           3           5           3\n",
       "grade                   7           7           6           7           8\n",
       "sqft_above           1180        2170         770        1050        1680\n",
       "sqft_basement         0.0       400.0         0.0       910.0         0.0\n",
       "yr_built             1955        1951        1933        1965        1987\n",
       "yr_renovated            0    1.99e+03         NaN           0           0\n",
       "zipcode             98178       98125       98028       98136       98074\n",
       "lat                  47.5        47.7        47.7        47.5        47.6\n",
       "long                 -122        -122        -122        -122        -122\n",
       "sqft_living15        1340        1690        2720        1360        1800\n",
       "sqft_lot15           5650        7639        8062        5000        7503"
      ]
     },
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     "output_type": "display_data"
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      "id               21597 non-null int64\n",
      "date             21597 non-null object\n",
      "price            21597 non-null float64\n",
      "bedrooms         21597 non-null int64\n",
      "bathrooms        21597 non-null float64\n",
      "sqft_living      21597 non-null int64\n",
      "sqft_lot         21597 non-null int64\n",
      "floors           21597 non-null float64\n",
      "waterfront       19221 non-null float64\n",
      "view             21534 non-null float64\n",
      "condition        21597 non-null int64\n",
      "grade            21597 non-null int64\n",
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      "sqft_basement    21597 non-null object\n",
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      "lat              21597 non-null float64\n",
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      "dtypes: float64(8), int64(11), object(2)\n",
      "memory usage: 3.5+ MB\n"
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     "data": {
      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "id                  0\n",
       "date                0\n",
       "price               0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "sqft_living         0\n",
       "sqft_lot            0\n",
       "floors              0\n",
       "waterfront       2376\n",
       "view               63\n",
       "condition           0\n",
       "grade               0\n",
       "sqft_above          0\n",
       "sqft_basement       0\n",
       "yr_built            0\n",
       "yr_renovated     3842\n",
       "zipcode             0\n",
       "lat                 0\n",
       "long                0\n",
       "sqft_living15       0\n",
       "sqft_lot15          0\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get overview of the dataset\n",
    "\n",
    "display(house_df.head().T)\n",
    "display(house_df.info())\n",
    "house_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings\n",
    "From initial looking at the datatype, two data columns that will need converting is date to datetime,\n",
    "and sqft_basement to int64 to be same as all the other sqrt data columns\n",
    "only three data columns have Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>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>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</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",
       "      <th>is_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.160e+04</td>\n",
       "      <td>2.160e+04</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>2.160e+04</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>19221.000</td>\n",
       "      <td>21534.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>17755.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.00</td>\n",
       "      <td>21597.000</td>\n",
       "      <td>21597.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580e+09</td>\n",
       "      <td>5.403e+05</td>\n",
       "      <td>3.373</td>\n",
       "      <td>2.116</td>\n",
       "      <td>2080.322</td>\n",
       "      <td>1.510e+04</td>\n",
       "      <td>1.494</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.234</td>\n",
       "      <td>3.410</td>\n",
       "      <td>7.658</td>\n",
       "      <td>1788.597</td>\n",
       "      <td>1971.000</td>\n",
       "      <td>83.637</td>\n",
       "      <td>98077.952</td>\n",
       "      <td>47.560</td>\n",
       "      <td>-122.214</td>\n",
       "      <td>1986.62</td>\n",
       "      <td>12758.284</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.877e+09</td>\n",
       "      <td>3.674e+05</td>\n",
       "      <td>0.926</td>\n",
       "      <td>0.769</td>\n",
       "      <td>918.106</td>\n",
       "      <td>4.141e+04</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.651</td>\n",
       "      <td>1.173</td>\n",
       "      <td>827.760</td>\n",
       "      <td>29.375</td>\n",
       "      <td>399.946</td>\n",
       "      <td>53.513</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.141</td>\n",
       "      <td>685.23</td>\n",
       "      <td>27274.442</td>\n",
       "      <td>0.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000e+06</td>\n",
       "      <td>7.800e+04</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>370.000</td>\n",
       "      <td>5.200e+02</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>370.000</td>\n",
       "      <td>1900.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98001.000</td>\n",
       "      <td>47.156</td>\n",
       "      <td>-122.519</td>\n",
       "      <td>399.00</td>\n",
       "      <td>651.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.123e+09</td>\n",
       "      <td>3.220e+05</td>\n",
       "      <td>3.000</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1430.000</td>\n",
       "      <td>5.040e+03</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1190.000</td>\n",
       "      <td>1951.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98033.000</td>\n",
       "      <td>47.471</td>\n",
       "      <td>-122.328</td>\n",
       "      <td>1490.00</td>\n",
       "      <td>5100.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.905e+09</td>\n",
       "      <td>4.500e+05</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2.250</td>\n",
       "      <td>1910.000</td>\n",
       "      <td>7.618e+03</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1560.000</td>\n",
       "      <td>1975.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98065.000</td>\n",
       "      <td>47.572</td>\n",
       "      <td>-122.231</td>\n",
       "      <td>1840.00</td>\n",
       "      <td>7620.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.309e+09</td>\n",
       "      <td>6.450e+05</td>\n",
       "      <td>4.000</td>\n",
       "      <td>2.500</td>\n",
       "      <td>2550.000</td>\n",
       "      <td>1.068e+04</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>2210.000</td>\n",
       "      <td>1997.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>98118.000</td>\n",
       "      <td>47.678</td>\n",
       "      <td>-122.125</td>\n",
       "      <td>2360.00</td>\n",
       "      <td>10083.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900e+09</td>\n",
       "      <td>7.700e+06</td>\n",
       "      <td>33.000</td>\n",
       "      <td>8.000</td>\n",
       "      <td>13540.000</td>\n",
       "      <td>1.651e+06</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>13.000</td>\n",
       "      <td>9410.000</td>\n",
       "      <td>2015.000</td>\n",
       "      <td>2015.000</td>\n",
       "      <td>98199.000</td>\n",
       "      <td>47.778</td>\n",
       "      <td>-121.315</td>\n",
       "      <td>6210.00</td>\n",
       "      <td>871200.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id      price   bedrooms  bathrooms  sqft_living   sqft_lot  \\\n",
       "count  2.160e+04  2.160e+04  21597.000  21597.000    21597.000  2.160e+04   \n",
       "mean   4.580e+09  5.403e+05      3.373      2.116     2080.322  1.510e+04   \n",
       "std    2.877e+09  3.674e+05      0.926      0.769      918.106  4.141e+04   \n",
       "min    1.000e+06  7.800e+04      1.000      0.500      370.000  5.200e+02   \n",
       "25%    2.123e+09  3.220e+05      3.000      1.750     1430.000  5.040e+03   \n",
       "50%    3.905e+09  4.500e+05      3.000      2.250     1910.000  7.618e+03   \n",
       "75%    7.309e+09  6.450e+05      4.000      2.500     2550.000  1.068e+04   \n",
       "max    9.900e+09  7.700e+06     33.000      8.000    13540.000  1.651e+06   \n",
       "\n",
       "          floors  waterfront       view  condition      grade  sqft_above  \\\n",
       "count  21597.000   19221.000  21534.000  21597.000  21597.000   21597.000   \n",
       "mean       1.494       0.008      0.234      3.410      7.658    1788.597   \n",
       "std        0.540       0.087      0.766      0.651      1.173     827.760   \n",
       "min        1.000       0.000      0.000      1.000      3.000     370.000   \n",
       "25%        1.000       0.000      0.000      3.000      7.000    1190.000   \n",
       "50%        1.500       0.000      0.000      3.000      7.000    1560.000   \n",
       "75%        2.000       0.000      0.000      4.000      8.000    2210.000   \n",
       "max        3.500       1.000      4.000      5.000     13.000    9410.000   \n",
       "\n",
       "        yr_built  yr_renovated    zipcode        lat       long  \\\n",
       "count  21597.000     17755.000  21597.000  21597.000  21597.000   \n",
       "mean    1971.000        83.637  98077.952     47.560   -122.214   \n",
       "std       29.375       399.946     53.513      0.139      0.141   \n",
       "min     1900.000         0.000  98001.000     47.156   -122.519   \n",
       "25%     1951.000         0.000  98033.000     47.471   -122.328   \n",
       "50%     1975.000         0.000  98065.000     47.572   -122.231   \n",
       "75%     1997.000         0.000  98118.000     47.678   -122.125   \n",
       "max     2015.000      2015.000  98199.000     47.778   -121.315   \n",
       "\n",
       "       sqft_living15  sqft_lot15  is_renovated  \n",
       "count       21597.00   21597.000     21597.000  \n",
       "mean         1986.62   12758.284         0.034  \n",
       "std           685.23   27274.442         0.182  \n",
       "min           399.00     651.000         0.000  \n",
       "25%          1490.00    5100.000         0.000  \n",
       "50%          1840.00    7620.000         0.000  \n",
       "75%          2360.00   10083.000         0.000  \n",
       "max          6210.00  871200.000         1.000  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df.describe().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "condition\n",
       "1       29\n",
       "2      170\n",
       "3    14020\n",
       "4     5677\n",
       "5     1701\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "grade\n",
       "3        1\n",
       "4       27\n",
       "5      242\n",
       "6     2038\n",
       "7     8974\n",
       "8     6065\n",
       "9     2615\n",
       "10    1134\n",
       "11     399\n",
       "12      89\n",
       "13      13\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "floors\n",
       "1.0    10673\n",
       "1.5     1910\n",
       "2.0     8235\n",
       "2.5      161\n",
       "3.0      611\n",
       "3.5        7\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "view\n",
       "0.0    19422\n",
       "1.0      330\n",
       "2.0      957\n",
       "3.0      508\n",
       "4.0      317\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "waterfront\n",
       "0.0    19075\n",
       "1.0      146\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0       17011\n",
       "2014.0       73\n",
       "2003.0       31\n",
       "2013.0       31\n",
       "2007.0       30\n",
       "Name: yr_renovated, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.0      12826\n",
       "?          454\n",
       "600.0      217\n",
       "500.0      209\n",
       "700.0      208\n",
       "Name: sqft_basement, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking at the statistical summary of the numerical data columns, it seems waterfront, view and yr_renovation \n",
    "# contains a lot of zeros (with at least 75% of the data being zero) \n",
    "\n",
    "for col in (['condition', 'grade', 'floors', 'view', 'waterfront']):\n",
    "    display(house_df.groupby(col).size())\n",
    "    \n",
    "display(house_df['yr_renovated'].value_counts().head())\n",
    "\n",
    "house_df['sqft_basement'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings\n",
    "The range of condition and grade confirms with the definition of building condition and building grade which we found on the King County gov website, it is also a sign of the quality / reliability of the underlying data\n",
    "\n",
    "Its clear now that zero values account for majority of the data for waterfront, view and yr_renovated.\n",
    "\n",
    "While this makes sense for waterfront, as most houses are likely NOT by water, and it may also make sense for yr_renovated, however there is a chance that zero may also mean Null, as we only have 744 entries out of 21,597 houses, it seems quite low unless the houses were mostly built recently.\n",
    "\n",
    "However for view, this many zeros does not make sense as the data shows sold house prices, and there  shouldn't be this many houses sold without ever being viewed.\n",
    "\n",
    "Last observation from above, sqft_basement has been identified as object (i.e. string) as it contains some value of '?'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <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",
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       "  </tbody>\n",
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       "<p>0 rows × 21 columns</p>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df[house_df.duplicated(keep = False)]\n",
    "\n",
    "# no duplicated rows present in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(353, 21)\n"
     ]
    },
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     "data": {
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       "      <th>date</th>\n",
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       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>7/25/2014</td>\n",
       "      <td>4.300e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1580</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1290</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98117</td>\n",
       "      <td>47.687</td>\n",
       "      <td>-122.386</td>\n",
       "      <td>1570</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6021501535</td>\n",
       "      <td>12/23/2014</td>\n",
       "      <td>7.000e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1580</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1290</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98117</td>\n",
       "      <td>47.687</td>\n",
       "      <td>-122.386</td>\n",
       "      <td>1570</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>4139480200</td>\n",
       "      <td>6/18/2014</td>\n",
       "      <td>1.380e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4290</td>\n",
       "      <td>12103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2690</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.550</td>\n",
       "      <td>-122.102</td>\n",
       "      <td>3860</td>\n",
       "      <td>11244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>4139480200</td>\n",
       "      <td>12/9/2014</td>\n",
       "      <td>1.400e+06</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4290</td>\n",
       "      <td>12103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>2690</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.550</td>\n",
       "      <td>-122.102</td>\n",
       "      <td>3860</td>\n",
       "      <td>11244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>7520000520</td>\n",
       "      <td>9/5/2014</td>\n",
       "      <td>2.320e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1240</td>\n",
       "      <td>12092</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>960</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1922</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.496</td>\n",
       "      <td>-122.352</td>\n",
       "      <td>1820</td>\n",
       "      <td>7460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>7520000520</td>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>2.405e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1240</td>\n",
       "      <td>12092</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>960</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1922</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.496</td>\n",
       "      <td>-122.352</td>\n",
       "      <td>1820</td>\n",
       "      <td>7460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>3969300030</td>\n",
       "      <td>7/23/2014</td>\n",
       "      <td>1.650e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>7134</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.490</td>\n",
       "      <td>-122.240</td>\n",
       "      <td>1020</td>\n",
       "      <td>7138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>3969300030</td>\n",
       "      <td>12/29/2014</td>\n",
       "      <td>2.399e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>7134</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.490</td>\n",
       "      <td>-122.240</td>\n",
       "      <td>1020</td>\n",
       "      <td>7138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>2231500030</td>\n",
       "      <td>10/1/2014</td>\n",
       "      <td>3.150e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2180</td>\n",
       "      <td>10754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1100</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.771</td>\n",
       "      <td>-122.341</td>\n",
       "      <td>1810</td>\n",
       "      <td>6929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>2231500030</td>\n",
       "      <td>3/24/2015</td>\n",
       "      <td>5.300e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2180</td>\n",
       "      <td>10754</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1100</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.771</td>\n",
       "      <td>-122.341</td>\n",
       "      <td>1810</td>\n",
       "      <td>6929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>8820903380</td>\n",
       "      <td>7/28/2014</td>\n",
       "      <td>4.520e+05</td>\n",
       "      <td>6</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2660</td>\n",
       "      <td>13579</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1937</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.714</td>\n",
       "      <td>-122.286</td>\n",
       "      <td>1120</td>\n",
       "      <td>8242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>8820903380</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>7.300e+05</td>\n",
       "      <td>6</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2660</td>\n",
       "      <td>13579</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1937</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.714</td>\n",
       "      <td>-122.286</td>\n",
       "      <td>1120</td>\n",
       "      <td>8242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>726049190</td>\n",
       "      <td>10/2/2014</td>\n",
       "      <td>2.875e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1810</td>\n",
       "      <td>7200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1130</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.749</td>\n",
       "      <td>-122.351</td>\n",
       "      <td>1810</td>\n",
       "      <td>8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>726049190</td>\n",
       "      <td>2/18/2015</td>\n",
       "      <td>4.310e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1810</td>\n",
       "      <td>7200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1130</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1954</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98133</td>\n",
       "      <td>47.749</td>\n",
       "      <td>-122.351</td>\n",
       "      <td>1810</td>\n",
       "      <td>8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>8682262400</td>\n",
       "      <td>7/18/2014</td>\n",
       "      <td>4.300e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1350</td>\n",
       "      <td>4003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.718</td>\n",
       "      <td>-122.033</td>\n",
       "      <td>1350</td>\n",
       "      <td>4479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>8682262400</td>\n",
       "      <td>5/13/2015</td>\n",
       "      <td>4.200e+05</td>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1350</td>\n",
       "      <td>4003</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.718</td>\n",
       "      <td>-122.033</td>\n",
       "      <td>1350</td>\n",
       "      <td>4479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>9834200885</td>\n",
       "      <td>7/17/2014</td>\n",
       "      <td>3.600e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2080</td>\n",
       "      <td>4080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.572</td>\n",
       "      <td>-122.290</td>\n",
       "      <td>1340</td>\n",
       "      <td>4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>9834200885</td>\n",
       "      <td>4/20/2015</td>\n",
       "      <td>5.500e+05</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2080</td>\n",
       "      <td>4080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1040</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>1962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98144</td>\n",
       "      <td>47.572</td>\n",
       "      <td>-122.290</td>\n",
       "      <td>1340</td>\n",
       "      <td>4080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>8062900070</td>\n",
       "      <td>9/9/2014</td>\n",
       "      <td>2.720e+05</td>\n",
       "      <td>5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>6300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1560</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.501</td>\n",
       "      <td>-122.172</td>\n",
       "      <td>1380</td>\n",
       "      <td>6300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>8062900070</td>\n",
       "      <td>2/13/2015</td>\n",
       "      <td>3.690e+05</td>\n",
       "      <td>5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>6300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1560</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.501</td>\n",
       "      <td>-122.172</td>\n",
       "      <td>1380</td>\n",
       "      <td>6300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id        date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "93    6021501535   7/25/2014  4.300e+05         3       1.50         1580   \n",
       "94    6021501535  12/23/2014  7.000e+05         3       1.50         1580   \n",
       "313   4139480200   6/18/2014  1.380e+06         4       3.25         4290   \n",
       "314   4139480200   12/9/2014  1.400e+06         4       3.25         4290   \n",
       "324   7520000520    9/5/2014  2.320e+05         2       1.00         1240   \n",
       "325   7520000520   3/11/2015  2.405e+05         2       1.00         1240   \n",
       "345   3969300030   7/23/2014  1.650e+05         4       1.00         1000   \n",
       "346   3969300030  12/29/2014  2.399e+05         4       1.00         1000   \n",
       "371   2231500030   10/1/2014  3.150e+05         4       2.25         2180   \n",
       "372   2231500030   3/24/2015  5.300e+05         4       2.25         2180   \n",
       "717   8820903380   7/28/2014  4.520e+05         6       2.25         2660   \n",
       "718   8820903380    1/2/2015  7.300e+05         6       2.25         2660   \n",
       "823    726049190   10/2/2014  2.875e+05         3       1.00         1810   \n",
       "824    726049190   2/18/2015  4.310e+05         3       1.00         1810   \n",
       "836   8682262400   7/18/2014  4.300e+05         2       1.75         1350   \n",
       "837   8682262400   5/13/2015  4.200e+05         2       1.75         1350   \n",
       "1084  9834200885   7/17/2014  3.600e+05         4       2.50         2080   \n",
       "1085  9834200885   4/20/2015  5.500e+05         4       2.50         2080   \n",
       "1127  8062900070    9/9/2014  2.720e+05         5       1.50         2550   \n",
       "1128  8062900070   2/13/2015  3.690e+05         5       1.50         2550   \n",
       "\n",
       "      sqft_lot  floors  waterfront  view     ...      grade  sqft_above  \\\n",
       "93        5000     1.0         0.0   0.0     ...          8        1290   \n",
       "94        5000     1.0         0.0   0.0     ...          8        1290   \n",
       "313      12103     1.0         0.0   3.0     ...         11        2690   \n",
       "314      12103     1.0         0.0   3.0     ...         11        2690   \n",
       "324      12092     1.0         NaN   0.0     ...          6         960   \n",
       "325      12092     1.0         0.0   0.0     ...          6         960   \n",
       "345       7134     1.0         0.0   0.0     ...          6        1000   \n",
       "346       7134     1.0         0.0   0.0     ...          6        1000   \n",
       "371      10754     1.0         NaN   0.0     ...          7        1100   \n",
       "372      10754     1.0         0.0   0.0     ...          7        1100   \n",
       "717      13579     2.0         0.0   0.0     ...          7        2660   \n",
       "718      13579     2.0         0.0   0.0     ...          7        2660   \n",
       "823       7200     1.0         0.0   0.0     ...          7        1130   \n",
       "824       7200     1.0         NaN   0.0     ...          7        1130   \n",
       "836       4003     1.0         0.0   0.0     ...          8        1350   \n",
       "837       4003     1.0         0.0   0.0     ...          8        1350   \n",
       "1084      4080     1.0         0.0   0.0     ...          7        1040   \n",
       "1085      4080     1.0         0.0   0.0     ...          7        1040   \n",
       "1127      6300     1.0         0.0   0.0     ...          7        1560   \n",
       "1128      6300     1.0         0.0   0.0     ...          7        1560   \n",
       "\n",
       "      sqft_basement yr_built  yr_renovated  zipcode     lat     long  \\\n",
       "93            290.0     1939           0.0    98117  47.687 -122.386   \n",
       "94            290.0     1939           0.0    98117  47.687 -122.386   \n",
       "313          1600.0     1997           0.0    98006  47.550 -122.102   \n",
       "314          1600.0     1997           0.0    98006  47.550 -122.102   \n",
       "324           280.0     1922        1984.0    98146  47.496 -122.352   \n",
       "325           280.0     1922        1984.0    98146  47.496 -122.352   \n",
       "345             0.0     1943           0.0    98178  47.490 -122.240   \n",
       "346             0.0     1943           NaN    98178  47.490 -122.240   \n",
       "371          1080.0     1954           0.0    98133  47.771 -122.341   \n",
       "372          1080.0     1954           0.0    98133  47.771 -122.341   \n",
       "717             0.0     1937        1990.0    98125  47.714 -122.286   \n",
       "718             0.0     1937        1990.0    98125  47.714 -122.286   \n",
       "823           680.0     1954           0.0    98133  47.749 -122.351   \n",
       "824           680.0     1954           0.0    98133  47.749 -122.351   \n",
       "836             0.0     2004           0.0    98053  47.718 -122.033   \n",
       "837             0.0     2004           0.0    98053  47.718 -122.033   \n",
       "1084         1040.0     1962           0.0    98144  47.572 -122.290   \n",
       "1085         1040.0     1962           0.0    98144  47.572 -122.290   \n",
       "1127          990.0     1959           0.0    98056  47.501 -122.172   \n",
       "1128          990.0     1959           NaN    98056  47.501 -122.172   \n",
       "\n",
       "      sqft_living15  sqft_lot15  \n",
       "93             1570        4500  \n",
       "94             1570        4500  \n",
       "313            3860       11244  \n",
       "314            3860       11244  \n",
       "324            1820        7460  \n",
       "325            1820        7460  \n",
       "345            1020        7138  \n",
       "346            1020        7138  \n",
       "371            1810        6929  \n",
       "372            1810        6929  \n",
       "717            1120        8242  \n",
       "718            1120        8242  \n",
       "823            1810        8100  \n",
       "824            1810        8100  \n",
       "836            1350        4479  \n",
       "837            1350        4479  \n",
       "1084           1340        4080  \n",
       "1085           1340        4080  \n",
       "1127           1380        6300  \n",
       "1128           1380        6300  \n",
       "\n",
       "[20 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(house_df[house_df.duplicated('id', keep = False)].shape)\n",
    "house_df[house_df.duplicated('id', keep = False)].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Findings\n",
    "\n",
    "there are duplicate id present in the dataset, meaning that the same house appears more than once. This could indeed be valid data as some houses do change hands more than once, looking at the data, most houses appear twice, so 353 duplicate rows is around 176 houses, out of the whole dataset, this is < 1% which seems reasonable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Cleaning the data set\n",
    "\n",
    "based on the above findings, we intend to do the following data cleaning\n",
    "\n",
    "a) drop column view: as it contains mostly zeros which does not make intuitive sense\n",
    "\n",
    "b) drop column sqrt_basement: as it contains invalid data '?'. Alternative choice here is that we turn the ? into Null and convert the data series into int. However given that this only contains part of the data about the size of the house and we have the more complete data seires sqrt_living available, we would not need to use sqrt_basement. For example, even if we cleaned sqrt_basement and it turns out to be highly correlated with house prices, it would be correlated with sqrt_living too, and we'd prefer to use sqrt_living in the model as it makes more intuitive sense as a better predictor.\n",
    "\n",
    "c) convert date from string object to datetime\n",
    "\n",
    "d) create a new binary data series based on yr_renovated, similar to waterfront. Intuitively it would make sense that recently renovated houses sell for more.\n",
    "\n",
    "later on, before building the model, we will delete the rows that contain Nulls in waterfront."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_df['is_renovated'] = house_df['yr_renovated'].map(lambda x: 0 if not x else (0 if math.isnan(x) else 1))\n",
    "\n",
    "clean_df = house_df.drop(columns = ['view','sqft_basement'])\n",
    "\n",
    "clean_df.date = clean_df.date.map(lambda x: pd.to_datetime(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-05-02 00:00:00\n",
      "2015-05-27 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(clean_df.date.min())\n",
    "print(clean_df.date.max())\n",
    "\n",
    "# looks like the house sales data is from 1 year timespan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>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>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</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",
       "      <th>is_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>1955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.511</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.721</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>2015-02-25</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>1933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.738</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>2014-12-09</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.521</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>1987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.617</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520 2014-10-13  221900.0         3       1.00         1180   \n",
       "1  6414100192 2014-12-09  538000.0         3       2.25         2570   \n",
       "2  5631500400 2015-02-25  180000.0         2       1.00          770   \n",
       "3  2487200875 2014-12-09  604000.0         4       3.00         1960   \n",
       "4  1954400510 2015-02-18  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  condition  grade  sqft_above  yr_built  \\\n",
       "0      5650     1.0         NaN          3      7        1180      1955   \n",
       "1      7242     2.0         0.0          3      7        2170      1951   \n",
       "2     10000     1.0         0.0          3      6         770      1933   \n",
       "3      5000     1.0         0.0          5      7        1050      1965   \n",
       "4      8080     1.0         0.0          3      8        1680      1987   \n",
       "\n",
       "   yr_renovated  zipcode     lat     long  sqft_living15  sqft_lot15  \\\n",
       "0           0.0    98178  47.511 -122.257           1340        5650   \n",
       "1        1991.0    98125  47.721 -122.319           1690        7639   \n",
       "2           NaN    98028  47.738 -122.233           2720        8062   \n",
       "3           0.0    98136  47.521 -122.393           1360        5000   \n",
       "4           0.0    98074  47.617 -122.045           1800        7503   \n",
       "\n",
       "   is_renovated  \n",
       "0             0  \n",
       "1             1  \n",
       "2             0  \n",
       "3             0  \n",
       "4             0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>bathrooms</th>\n",
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       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
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       "      <th>yr_renovated</th>\n",
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       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.017</td>\n",
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       "      <td>-0.004</td>\n",
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       "      <td>0.008</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-1.201e-02</td>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-2.701e-03</td>\n",
       "      <td>-0.139</td>\n",
       "      <td>-1.062e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.017</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.054</td>\n",
       "      <td>1.296e-01</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>0.307</td>\n",
       "      <td>0.022</td>\n",
       "      <td>5.852e-01</td>\n",
       "      <td>0.083</td>\n",
       "      <td>1.175e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.309</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.515</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.178</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.156</td>\n",
       "      <td>1.849e-02</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.132</td>\n",
       "      <td>3.934e-01</td>\n",
       "      <td>0.031</td>\n",
       "      <td>1.763e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.005</td>\n",
       "      <td>0.526</td>\n",
       "      <td>0.515</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.507</td>\n",
       "      <td>5.105e-02</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.225</td>\n",
       "      <td>5.699e-01</td>\n",
       "      <td>0.088</td>\n",
       "      <td>4.674e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.756</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.318</td>\n",
       "      <td>5.566e-02</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.241</td>\n",
       "      <td>7.564e-01</td>\n",
       "      <td>0.184</td>\n",
       "      <td>5.083e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot</th>\n",
       "      <td>-0.132</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.053</td>\n",
       "      <td>4.513e-03</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1.448e-01</td>\n",
       "      <td>0.718</td>\n",
       "      <td>5.091e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floors</th>\n",
       "      <td>0.019</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.503</td>\n",
       "      <td>0.354</td>\n",
       "      <td>-0.005</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.489</td>\n",
       "      <td>3.535e-03</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.126</td>\n",
       "      <td>2.801e-01</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>3.713e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.276</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.022</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>8.724e-02</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>8.886e-02</td>\n",
       "      <td>0.032</td>\n",
       "      <td>7.960e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.026</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.059</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.264</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>-0.159</td>\n",
       "      <td>-0.362</td>\n",
       "      <td>-6.186e-02</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>-9.307e-02</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-5.538e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.763</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.459</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.448</td>\n",
       "      <td>1.667e-02</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.200</td>\n",
       "      <td>7.139e-01</td>\n",
       "      <td>0.121</td>\n",
       "      <td>1.526e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.075</td>\n",
       "      <td>-0.159</td>\n",
       "      <td>0.756</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.424</td>\n",
       "      <td>2.214e-02</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.345</td>\n",
       "      <td>7.318e-01</td>\n",
       "      <td>0.195</td>\n",
       "      <td>2.057e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <td>0.022</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.489</td>\n",
       "      <td>-0.026</td>\n",
       "      <td>-0.362</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.424</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-2.252e-01</td>\n",
       "      <td>-0.347</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.410</td>\n",
       "      <td>3.264e-01</td>\n",
       "      <td>0.071</td>\n",
       "      <td>-2.028e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_renovated</th>\n",
       "      <td>-0.012</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.022</td>\n",
       "      <td>-0.225</td>\n",
       "      <td>1.000e+00</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-0.072</td>\n",
       "      <td>-2.472e-04</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <td>-0.008</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.205</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.262</td>\n",
       "      <td>-0.347</td>\n",
       "      <td>6.985e-02</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.267</td>\n",
       "      <td>-0.564</td>\n",
       "      <td>-2.793e-01</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>6.238e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.307</td>\n",
       "      <td>-0.010</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.015</td>\n",
       "      <td>0.114</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>3.215e-02</td>\n",
       "      <td>0.267</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>4.868e-02</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>2.791e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long</th>\n",
       "      <td>0.021</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.126</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.410</td>\n",
       "      <td>-7.216e-02</td>\n",
       "      <td>-0.564</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.356e-01</td>\n",
       "      <td>0.256</td>\n",
       "      <td>-6.451e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_living15</th>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.570</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.326</td>\n",
       "      <td>-2.472e-04</td>\n",
       "      <td>-0.279</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.336</td>\n",
       "      <td>1.000e+00</td>\n",
       "      <td>0.184</td>\n",
       "      <td>6.223e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_lot15</th>\n",
       "      <td>-0.139</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.718</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.032</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.071</td>\n",
       "      <td>3.852e-03</td>\n",
       "      <td>-0.147</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>0.256</td>\n",
       "      <td>1.835e-01</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.380e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_renovated</th>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.080</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>1.000e+00</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.065</td>\n",
       "      <td>6.223e-04</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "id             1.000 -0.017     0.001      0.005       -0.012    -0.132   \n",
       "price         -0.017  1.000     0.309      0.526        0.702     0.090   \n",
       "bedrooms       0.001  0.309     1.000      0.515        0.578     0.032   \n",
       "bathrooms      0.005  0.526     0.515      1.000        0.756     0.088   \n",
       "sqft_living   -0.012  0.702     0.578      0.756        1.000     0.173   \n",
       "sqft_lot      -0.132  0.090     0.032      0.088        0.173     1.000   \n",
       "floors         0.019  0.257     0.178      0.503        0.354    -0.005   \n",
       "waterfront    -0.004  0.276    -0.002      0.067        0.110     0.023   \n",
       "condition     -0.024  0.036     0.026     -0.126       -0.059    -0.009   \n",
       "grade          0.008  0.668     0.357      0.666        0.763     0.115   \n",
       "sqft_above    -0.011  0.605     0.479      0.687        0.876     0.184   \n",
       "yr_built       0.022  0.054     0.156      0.507        0.318     0.053   \n",
       "yr_renovated  -0.012  0.130     0.018      0.051        0.056     0.005   \n",
       "zipcode       -0.008 -0.053    -0.154     -0.205       -0.200    -0.130   \n",
       "lat           -0.002  0.307    -0.010      0.024        0.052    -0.086   \n",
       "long           0.021  0.022     0.132      0.225        0.241     0.230   \n",
       "sqft_living15 -0.003  0.585     0.393      0.570        0.756     0.145   \n",
       "sqft_lot15    -0.139  0.083     0.031      0.088        0.184     0.718   \n",
       "is_renovated  -0.011  0.118     0.018      0.047        0.051     0.005   \n",
       "\n",
       "               floors  waterfront  condition  grade  sqft_above  yr_built  \\\n",
       "id              0.019      -0.004     -0.024  0.008      -0.011     0.022   \n",
       "price           0.257       0.276      0.036  0.668       0.605     0.054   \n",
       "bedrooms        0.178      -0.002      0.026  0.357       0.479     0.156   \n",
       "bathrooms       0.503       0.067     -0.126  0.666       0.687     0.507   \n",
       "sqft_living     0.354       0.110     -0.059  0.763       0.876     0.318   \n",
       "sqft_lot       -0.005       0.023     -0.009  0.115       0.184     0.053   \n",
       "floors          1.000       0.022     -0.264  0.459       0.524     0.489   \n",
       "waterfront      0.022       1.000      0.018  0.087       0.075    -0.026   \n",
       "condition      -0.264       0.018      1.000 -0.147      -0.159    -0.362   \n",
       "grade           0.459       0.087     -0.147  1.000       0.756     0.448   \n",
       "sqft_above      0.524       0.075     -0.159  0.756       1.000     0.424   \n",
       "yr_built        0.489      -0.026     -0.362  0.448       0.424     1.000   \n",
       "yr_renovated    0.004       0.087     -0.062  0.017       0.022    -0.225   \n",
       "zipcode        -0.060       0.031      0.003 -0.186      -0.262    -0.347   \n",
       "lat             0.049      -0.013     -0.015  0.114      -0.001    -0.148   \n",
       "long            0.126      -0.040     -0.106  0.200       0.345     0.410   \n",
       "sqft_living15   0.280       0.089     -0.093  0.714       0.732     0.326   \n",
       "sqft_lot15     -0.011       0.032     -0.003  0.121       0.195     0.071   \n",
       "is_renovated    0.004       0.080     -0.055  0.015       0.021    -0.203   \n",
       "\n",
       "               yr_renovated  zipcode    lat   long  sqft_living15  sqft_lot15  \\\n",
       "id               -1.201e-02   -0.008 -0.002  0.021     -2.701e-03      -0.139   \n",
       "price             1.296e-01   -0.053  0.307  0.022      5.852e-01       0.083   \n",
       "bedrooms          1.849e-02   -0.154 -0.010  0.132      3.934e-01       0.031   \n",
       "bathrooms         5.105e-02   -0.205  0.024  0.225      5.699e-01       0.088   \n",
       "sqft_living       5.566e-02   -0.200  0.052  0.241      7.564e-01       0.184   \n",
       "sqft_lot          4.513e-03   -0.130 -0.086  0.230      1.448e-01       0.718   \n",
       "floors            3.535e-03   -0.060  0.049  0.126      2.801e-01      -0.011   \n",
       "waterfront        8.724e-02    0.031 -0.013 -0.040      8.886e-02       0.032   \n",
       "condition        -6.186e-02    0.003 -0.015 -0.106     -9.307e-02      -0.003   \n",
       "grade             1.667e-02   -0.186  0.114  0.200      7.139e-01       0.121   \n",
       "sqft_above        2.214e-02   -0.262 -0.001  0.345      7.318e-01       0.195   \n",
       "yr_built         -2.252e-01   -0.347 -0.148  0.410      3.264e-01       0.071   \n",
       "yr_renovated      1.000e+00    0.070  0.032 -0.072     -2.472e-04       0.004   \n",
       "zipcode           6.985e-02    1.000  0.267 -0.564     -2.793e-01      -0.147   \n",
       "lat               3.215e-02    0.267  1.000 -0.135      4.868e-02      -0.086   \n",
       "long             -7.216e-02   -0.564 -0.135  1.000      3.356e-01       0.256   \n",
       "sqft_living15    -2.472e-04   -0.279  0.049  0.336      1.000e+00       0.184   \n",
       "sqft_lot15        3.852e-03   -0.147 -0.086  0.256      1.835e-01       1.000   \n",
       "is_renovated      1.000e+00    0.062  0.028 -0.065      6.223e-04       0.004   \n",
       "\n",
       "               is_renovated  \n",
       "id               -1.062e-02  \n",
       "price             1.175e-01  \n",
       "bedrooms          1.763e-02  \n",
       "bathrooms         4.674e-02  \n",
       "sqft_living       5.083e-02  \n",
       "sqft_lot          5.091e-03  \n",
       "floors            3.713e-03  \n",
       "waterfront        7.960e-02  \n",
       "condition        -5.538e-02  \n",
       "grade             1.526e-02  \n",
       "sqft_above        2.057e-02  \n",
       "yr_built         -2.028e-01  \n",
       "yr_renovated      1.000e+00  \n",
       "zipcode           6.238e-02  \n",
       "lat               2.791e-02  \n",
       "long             -6.451e-02  \n",
       "sqft_living15     6.223e-04  \n",
       "sqft_lot15        4.380e-03  \n",
       "is_renovated      1.000e+00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.corr()\n",
    "\n",
    "# initial view at the correlation matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Interesting questions, insights on the dataset\n",
    "\n",
    "Question 1: Has houses got bigger over the years in King Country?\n",
    "\n",
    "Question 2: Is building condition / grade lower for older houses?\n",
    "\n",
    "Question 3: Where are the expensive house located? Where are the relatively cheaper houses located?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2: Is building quality in general lower for older houses?\n",
    "\n",
    "In order to answer the question, we will look at the condition versus the year_built, and grade versus the year_built. In order to get a sumary statistics that makes sense, and a better visual. We group the data by the year_built, and plot only the average. Plotting median, min, max, quantiles would not make sense as grade and condition are categorical data series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>87.0</td>\n",
       "      <td>3.529</td>\n",
       "      <td>0.860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>29.0</td>\n",
       "      <td>3.655</td>\n",
       "      <td>0.936</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>27.0</td>\n",
       "      <td>3.630</td>\n",
       "      <td>0.792</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>46.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.782</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>45.0</td>\n",
       "      <td>3.800</td>\n",
       "      <td>0.842</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count   mean    std  min  25%  50%  75%  max\n",
       "yr_built                                              \n",
       "1900       87.0  3.529  0.860  1.0  3.0  3.0  4.0  5.0\n",
       "1901       29.0  3.655  0.936  1.0  3.0  4.0  4.0  5.0\n",
       "1902       27.0  3.630  0.792  3.0  3.0  3.0  4.0  5.0\n",
       "1903       46.0  3.500  0.782  2.0  3.0  3.0  4.0  5.0\n",
       "1904       45.0  3.800  0.842  3.0  3.0  4.0  5.0  5.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>130.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>170.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>201.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>559.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count  mean  std  min  25%  50%  75%  max\n",
       "yr_built                                           \n",
       "2011      130.0   3.0  0.0  3.0  3.0  3.0  3.0  3.0\n",
       "2012      170.0   3.0  0.0  3.0  3.0  3.0  3.0  3.0\n",
       "2013      201.0   3.0  0.0  3.0  3.0  3.0  3.0  3.0\n",
       "2014      559.0   3.0  0.0  3.0  3.0  3.0  3.0  3.0\n",
       "2015       38.0   3.0  0.0  3.0  3.0  3.0  3.0  3.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition_yr = clean_df.groupby('yr_built')['condition'].describe()\n",
    "display(condition_yr.head())\n",
    "condition_yr.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>87.0</td>\n",
       "      <td>6.874</td>\n",
       "      <td>1.098</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>29.0</td>\n",
       "      <td>7.034</td>\n",
       "      <td>0.865</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>27.0</td>\n",
       "      <td>7.444</td>\n",
       "      <td>1.155</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>46.0</td>\n",
       "      <td>6.870</td>\n",
       "      <td>0.885</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>45.0</td>\n",
       "      <td>6.911</td>\n",
       "      <td>1.041</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count   mean    std  min  25%  50%  75%   max\n",
       "yr_built                                               \n",
       "1900       87.0  6.874  1.098  4.0  6.0  7.0  7.0  10.0\n",
       "1901       29.0  7.034  0.865  5.0  7.0  7.0  8.0   9.0\n",
       "1902       27.0  7.444  1.155  6.0  7.0  7.0  8.0  12.0\n",
       "1903       46.0  6.870  0.885  5.0  6.0  7.0  7.0   9.0\n",
       "1904       45.0  6.911  1.041  6.0  6.0  7.0  7.0  12.0"
      ]
     },
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     "output_type": "display_data"
    },
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr_built</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>130.0</td>\n",
       "      <td>8.185</td>\n",
       "      <td>0.938</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>170.0</td>\n",
       "      <td>8.182</td>\n",
       "      <td>0.902</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>201.0</td>\n",
       "      <td>8.468</td>\n",
       "      <td>0.938</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>559.0</td>\n",
       "      <td>8.560</td>\n",
       "      <td>0.827</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>38.0</td>\n",
       "      <td>8.921</td>\n",
       "      <td>0.941</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count   mean    std  min  25%  50%  75%   max\n",
       "yr_built                                               \n",
       "2011      130.0  8.185  0.938  7.0  8.0  8.0  9.0  11.0\n",
       "2012      170.0  8.182  0.902  7.0  8.0  8.0  9.0  10.0\n",
       "2013      201.0  8.468  0.938  7.0  8.0  8.0  9.0  11.0\n",
       "2014      559.0  8.560  0.827  6.0  8.0  8.0  9.0  11.0\n",
       "2015       38.0  8.921  0.941  6.0  8.0  9.0  9.0  11.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grade_yr = clean_df.groupby('yr_built')['grade'].describe()\n",
    "display(grade_yr.head())\n",
    "grade_yr.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2: Findings.\n",
    "\n",
    "Looking at the plots below, it seems that building condition has actually been declining over time. Checking the definition of building condition on King Country website(https://info.kingcounty.gov/assessor/esales/Glossary.aspx?type=r#b), building condition is the condition of the inside of the property, like wall surfaces, roofing, plumbing, heating, RELATIVE to age and grade. The work \"relative\" is key here as it means houses built in 1904 with mean of 3.8 is not directly comparable to the score of 3 given to houses build in 2014. \n",
    "\n",
    "Also, note by looking at the summary statistics above, we can see that older houses had a large standard deviation (around 1), meaning that while on average the mean is around 3.5, the range is from score of 1 or 2 to 5. Whereas for properties built more recently, all were given a score of 3.\n",
    "\n",
    "Building grade records the quality of the construction, and as expected this does improve with time. The stardard deviation remains around the 1.0 level, suggesting the scoring system is at least somewhat consistent throught time.\n",
    "\n",
    "We think the building grade data column is a better representation of the building quality, and this does indeed improve with time as we had assumed in the question/hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a2453f198>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.title('Building Condition Throughtout Time')\n",
    "plt.scatter(condition_yr.index, condition_yr[['mean']])\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('Building Grade Throughtout Time')\n",
    "plt.scatter(grade_yr.index, grade_yr[['mean']])\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.title('Standard Dev. of Building Condition Score Throughtout Time')\n",
    "plt.scatter(condition_yr.index, condition_yr[['std']])\n",
    "\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.title('Standard Dev. of Building Grade Score Throughtout Time')\n",
    "plt.scatter(grade_yr.index, grade_yr[['std']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}