example-data-science-notebook/Example Machine Learning Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example machine learning notebook\n",
    "\n",
    "### Notebook by [Randal S. Olson](http://www.randalolson.com/)\n",
    "#### Supported by [Jason H. Moore](http://www.epistasis.org/)\n",
    "#### [University of Pennsylvania Institute for Bioinformatics](http://upibi.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It is recommended to [view this notebook in nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) for the best viewing experience.**\n",
    "\n",
    "**You can also [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=example-data-science-notebook%2FExample%20Machine%20Learning%20Notebook.ipynb) - no local installation required.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "\n",
    "1. [Introduction](#Introduction)\n",
    "\n",
    "2. [License](#License)\n",
    "\n",
    "3. [Required libraries](#Required-libraries)\n",
    "\n",
    "4. [The problem domain](#The-problem-domain)\n",
    "\n",
    "5. [Step 1: Answering the question](#Step-1:-Answering-the-question)\n",
    "\n",
    "6. [Step 2: Checking the data](#Step-2:-Checking-the-data)\n",
    "\n",
    "7. [Step 3: Tidying the data](#Step-3:-Tidying-the-data)\n",
    "\n",
    "    - [Bonus: Testing our data](#Bonus:-Testing-our-data)\n",
    "\n",
    "8. [Step 4: Exploratory analysis](#Step-4:-Exploratory-analysis)\n",
    "\n",
    "9. [Step 5: Classification](#Step-5:-Classification)\n",
    "\n",
    "    - [Cross-validation](#Cross-validation)\n",
    "\n",
    "    - [Parameter tuning](#Parameter-tuning)\n",
    "\n",
    "10. [Step 6: Reproducibility](#Step-6:-Reproducibility)\n",
    "\n",
    "11. [Conclusions](#Conclusions)\n",
    "\n",
    "12. [Further reading](#Further-reading)\n",
    "\n",
    "13. [Acknowledgements](#Acknowledgements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "In the time it took you to read this sentence, terabytes of data have been collectively generated across the world — more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.\n",
    "\n",
    "In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields — statistics, physics, computer science, design, and many more — the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.\n",
    "\n",
    "In this notebook, I'm going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like.\n",
    "\n",
    "In addition to providing code examples, I also hope to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist.\n",
    "\n",
    "I will be following along with the data analysis checklist from [The Elements of Data Analytic Style](https://leanpub.com/datastyle), which I strongly recommend reading as a free and quick guidebook to performing outstanding data analysis.\n",
    "\n",
    "**This notebook is intended to be a public resource. As such, if you see any glaring inaccuracies or if a critical topic is missing, please feel free to point it out or (preferably) submit a pull request to improve the notebook.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required libraries\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\n",
    "\n",
    "This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\n",
    "\n",
    "* **NumPy**: Provides a fast numerical array structure and helper functions.\n",
    "* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\n",
    "* **scikit-learn**: The essential Machine Learning package in Python.\n",
    "* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\n",
    "* **Seaborn**: Advanced statistical plotting library.\n",
    "* **watermark**: A Jupyter Notebook extension for printing timestamps, version numbers, and hardware information.\n",
    "\n",
    "To make sure you have all of the packages you need, install them with `conda`:\n",
    "\n",
    "    conda install numpy pandas scikit-learn matplotlib seaborn\n",
    "    \n",
    "    conda install -c conda-forge watermark\n",
    "\n",
    "`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so.\n",
    "\n",
    "**Note:** I will not be providing support for people trying to run this notebook outside of the Anaconda Python distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The problem domain\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\n",
    "\n",
    "We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\n",
    "\n",
    "<img src=\"images/petal_sepal.jpg\" />\n",
    "\n",
    "We've been given a [data set](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\n",
    "\n",
    "### *Iris setosa*\n",
    "\n",
    "<img src=\"images/iris_setosa.jpg\" />\n",
    "\n",
    "### *Iris versicolor*\n",
    "<img src=\"images/iris_versicolor.jpg\" />\n",
    "\n",
    "### *Iris virginica*\n",
    "<img src=\"images/iris_virginica.jpg\" />\n",
    "\n",
    "The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\n",
    "\n",
    "**Note:** The data set we're working with is the famous [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Answering the question\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.\n",
    "\n",
    ">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\n",
    "\n",
    "We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.\n",
    "\n",
    ">Did you define the metric for success before beginning?\n",
    "\n",
    "Let's do that now. Since we're performing classification, we can use [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision) — the fraction of correctly classified flowers — to quantify how well our model is performing. Our company's Head of Data has told us that we should achieve at least 90% accuracy.\n",
    "\n",
    ">Did you understand the context for the question and the scientific or business application?\n",
    "\n",
    "We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.\n",
    "\n",
    ">Did you record the experimental design?\n",
    "\n",
    "Our company's Head of Data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company GitHub repository.\n",
    "\n",
    ">Did you consider whether the question could be answered with the available data?\n",
    "\n",
    "The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.\n",
    "\n",
    "**Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked.** Don't skip it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Checking the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "The next step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\n",
    "\n",
    "Generally, we're looking to answer the following questions:\n",
    "\n",
    "* Is there anything wrong with the data?\n",
    "* Are there any quirks with the data?\n",
    "* Do I need to fix or remove any of the data?\n",
    "\n",
    "Let's start by reading the data into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "0              5.1             3.5              1.4             0.2   \n",
       "1              4.9             3.0              1.4             0.2   \n",
       "2              4.7             3.2              1.3             0.2   \n",
       "3              4.6             3.1              1.5             0.2   \n",
       "4              5.0             3.6              1.4             0.2   \n",
       "\n",
       "         class  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_data = pd.read_csv('iris-data.csv')\n",
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're in luck! The data seems to be in a usable format.\n",
    "\n",
    "The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\n",
    "\n",
    "Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\n",
    "\n",
    "**One of the first things we should look for is missing data.** Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\n",
    "\n",
    "We can tell pandas to automatically identify missing values if it knows our missing value marker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data = pd.read_csv('iris-data.csv', na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voilà! Now pandas knows to treat rows with 'NA' as missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, it's always a good idea to look at the distribution of our data — especially the outliers.\n",
    "\n",
    "Let's start by printing out some summary statistics about the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.644627</td>\n",
       "      <td>3.054667</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.236552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312781</td>\n",
       "      <td>0.433123</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.755058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.055000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
       "count       150.000000      150.000000       150.000000      145.000000\n",
       "mean          5.644627        3.054667         3.758667        1.236552\n",
       "std           1.312781        0.433123         1.764420        0.755058\n",
       "min           0.055000        2.000000         1.000000        0.100000\n",
       "25%           5.100000        2.800000         1.600000        0.400000\n",
       "50%           5.700000        3.000000         4.350000        1.300000\n",
       "75%           6.400000        3.300000         5.100000        1.800000\n",
       "max           7.900000        4.400000         6.900000        2.500000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\n",
    "\n",
    "If you ask me, though, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers.\n",
    "\n",
    "Since we know we're going to be plotting in this section, let's set up the notebook so we can plot inside of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\n",
    "\n",
    "We can even have the plotting package color each entry by its class to look for trends within the classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We have to temporarily drop the rows with 'NA' values\n",
    "# because the Seaborn plotting function does not know\n",
    "# what to do with them\n",
    "sb.pairplot(iris_data.dropna(), hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the scatterplot matrix, we can already see some issues with the data set:\n",
    "\n",
    "1. There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "3. We had to drop those rows with missing values.\n",
    "\n",
    "In all of these cases, we need to figure out what to do with the erroneous data. Which takes us to the next step..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Tidying the data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis.\n",
    "\n",
    "Let's walk through the issues one-by-one.\n",
    "\n",
    ">There are five classes when there should only be three, meaning there were some coding errors.\n",
    "\n",
    "After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\n",
    "\n",
    "Let's use the DataFrame to fix these errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\n",
    "\n",
    "iris_data['class'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! Now we only have three class types. Imagine how embarrassing it would've been to create a model that used the wrong classes.\n",
    "\n",
    ">There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\n",
    "\n",
    "Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data. (i.e., \"This data didn't fit my hypothesis\" will not stand peer review.)\n",
    "\n",
    "In the case of the one anomalous entry for `Iris-setosa`, let's say our field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\n",
    "iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\n",
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent! Now all of our `Iris-setosa` rows have a sepal width greater than 2.5.\n",
    "\n",
    "The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.067</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.060</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.057</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.055</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "77            0.067             3.0              5.0             1.7   \n",
       "78            0.060             2.9              4.5             1.5   \n",
       "79            0.057             2.6              3.5             1.0   \n",
       "80            0.055             2.4              3.8             1.1   \n",
       "81            0.055             2.4              3.7             1.0   \n",
       "\n",
       "              class  \n",
       "77  Iris-versicolor  \n",
       "78  Iris-versicolor  \n",
       "79  Iris-versicolor  \n",
       "80  Iris-versicolor  \n",
       "81  Iris-versicolor  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
    "              (iris_data['sepal_length_cm'] < 1.0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\n",
    "\n",
    "After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADa5JREFUeJzt3W2MXOdZxvHrwtuqtrdxilymkVOxrRRZKlkB8SgkiohmsQLOiwgfUuEI0iYqWqAQCjJC5gP0EyIfCCIgVGSlbYoIWanuC1ETQqvCUpBIxKxj2CRu1BC5jR3XaYE4bLAUrbj54CmyhvW8nOfsHO89/59k7bw8Z557bj1z6ezxnD2OCAEAtr7va7oAAEA9CHQASIJAB4AkCHQASIJAB4AkCHQASIJAB4AkCHQASIJAB4AkZiY52e7du2Nubm6SU0qS3nzzTe3cuXPi824V9Gcw+jMcPRqstD8rKyvfjYh3Dxs30UCfm5tTt9ud5JSSpOXlZXU6nYnPu1XQn8Hoz3D0aLDS/tj+5ijjOOQCAEkQ6ACQBIEOAEkQ6ACQBIEOAEkMDXTbn7L9mu3nLnrs+21/xfY3ej/ftbllAgCGGWUP/RFJB/oeOyzpqxFxjaSv9u4DABo0NNAj4muS/qPv4TslfaZ3+zOSfqbmugAAY6p6DL0VEWckqffzB+orCQBQhUe5SLTtOUlfiohre/dfj4grL3r+PyNiw+PothclLUpSq9Xat7S0VEPZ41lbW9Ps7OzE590q+vuzevpcY7XM79nV2NyXwvoZjh4NVtqfhYWFlYhoDxtX9dT/s7aviogztq+S9NqlBkbEEUlHJKndbkcTpwdzWvJg/f259/ATjdVy8uc6Q8dMGutnOHo02KT6U/WQy+OSPty7/WFJf1VPOQCAqkb52uJjkv5J0l7bp2x/RNIDkm6x/Q1Jt/TuAwAaNPSQS0TcfYmn9tdcCwCgAGeKAkASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJFEU6LZ/w/bztp+z/Zjtd9RVGABgPJUD3fYeSb8mqR0R10raJulgXYUBAMZTeshlRtJ22zOSdkh6tbwkAEAVlQM9Ik5L+gNJ35J0RtK5iPhyXYUBAMbjiKi2of0uSZ+T9LOSXpf0WUlHI+Iv+sYtSlqUpFartW9paamo4CrW1tY0Ozs78Xm3iv7+rJ4+11gt83t2NTLvoPfc2i6dPb95czf1nuvEZ2yw0v4sLCysRER72LiSQP+gpAMR8ZHe/Q9JuiEiPnqpbdrtdnS73UrzlVheXlan05n4vFtFf3/mDj/RWC0nH7i9kXkHvedD8+t6cHVm0+Zu6j3Xic/YYKX9sT1SoJccQ/+WpBts77BtSfslnSh4PQBAgZJj6M9IOirpmKTV3msdqakuAMCYin6PjIiPS/p4TbUAAApwpigAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJEGgA0ASBDoAJFEU6LavtH3U9tdtn7B9Y12FAQDGM1O4/UOSnoqIu2y/XdKOGmoCAFRQOdBtXyHpZkn3SlJEvCXprXrKAgCMq+SQy/slfUfSp20/a/th2ztrqgsAMCZHRLUN7bakpyXdFBHP2H5I0hsR8Tt94xYlLUpSq9Xat7S0VFjy+NbW1jQ7OzvxeUutnj43kXla26Wz5ycy1VDze3Y1Mu+gXm92f5p6z3Xaqp+xSSntz8LCwkpEtIeNKwn090h6OiLmevd/XNLhiLj9Utu02+3odruV5iuxvLysTqcz8XlLzR1+YiLzHJpf14Orpf+dUo+TD1xy+WyqQb3e7P409Z7rtFU/Y5NS2h/bIwV65UMuEfFtSa/Y3tt7aL+kF6q+HgCgTOlux/2SHu19w+VlSfeVlwQAqKIo0CPiuKShvwYAADYfZ4oCQBIEOgAkQaADQBIEOgAkQaADQBIEOgAkQaADQBIEOgAkQaADQBIEOgAkQaADQBIEOgAkQaADQBIEOgAkQaADQBKXx3XHgJ5JXXbvctLUe27y0nfT+J4ngT10AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJAh0AEiCQAeAJIoD3fY228/a/lIdBQEAqqljD/1jkk7U8DoAgAJFgW77akm3S3q4nnIAAFWV7qH/kaTfkvQ/NdQCACjgiKi2oX2HpNsi4qO2O5J+MyLu2GDcoqRFSWq1WvuWlpYqzbd6+lyl7SSptV06e77atvN7dlWet1TJex5HSX+mQdb+1Lm219bWNDs7O/L4Sa3tfk19nsftT7+FhYWViGgPG1cS6L8v6R5J65LeIekKSZ+PiJ+/1Dbtdju63W6l+UquEn5ofl0Prs5U2nYaroxe0p9pkLU/da7t5eVldTqdkcdPam33a+rzPG5/+tkeKdArH3KJiN+OiKsjYk7SQUl/OyjMAQCbi++hA0AStfweGRHLkpbreC0AQDXsoQNAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACRBoANAEgQ6ACSR70KJAEZS53U9D82v696GrhM6jqauZfrIgZ0TmYc9dABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQqB7rt99r+O9snbD9v+2N1FgYAGE/JFYvWJR2KiGO23ylpxfZXIuKFmmoDAIyh8h56RJyJiGO92/8l6YSkPXUVBgAYjyOi/EXsOUlfk3RtRLzR99yipEVJarVa+5aWlirNsXr6XOX6Wtuls+erbTu/Z1fleUuVvOdxlPRnGtCf4ejRYO/btU2zs7OVt19YWFiJiPawccWBbntW0t9L+r2I+Pygse12O7rdbqV5Si7uemh+XQ+uVju6dPKB2yvPW2pSF7Qt6c80oD/D0aPBHjmwU51Op/L2tkcK9KJvudh+m6TPSXp0WJgDADZXybdcLOmTkk5ExB/WVxIAoIqSPfSbJN0j6SdsH+/9u62mugAAY6p80Csi/lGSa6wFAFCAM0UBIAkCHQCSINABIAkCHQCSINABIAkCHQCSINABIAkCHQCSINABIAkCHQCSINABIAkCHQCSINABIAkCHQCSINABIAkuAjjEpK7rCQCl2EMHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIgkAHgCQIdABIoijQbR+w/aLtl2wfrqsoAMD4Kge67W2S/lTSrZI+IOlu2x+oqzAAwHhK9tCvl/RSRLwcEW9JWpJ0Zz1lAQDGVRLoeyS9ctH9U73HAAANcERU29D+oKSfiohf6N2/R9L1EXF/37hFSYu9u3slvVi93Mp2S/puA/NuFfRnMPozHD0arLQ/PxgR7x42aKZgglOS3nvR/aslvdo/KCKOSDpSME8x292IaDdZw+WM/gxGf4ajR4NNqj8lh1z+WdI1tt9n++2SDkp6vJ6yAADjqryHHhHrtn9V0t9I2ibpUxHxfG2VAQDGUnLIRRHxpKQna6plMzV6yGcLoD+D0Z/h6NFgE+lP5f8UBQBcXjj1HwCSSBPotk/aXrV93HZ3g+dt+497f6bgX21f10SdTRqhRx3b53rPH7f9u03U2RTbV9o+avvrtk/YvrHvedbQ8B5N7Rqyvfei933c9hu2f71vzKauoaJj6JehhYi41Hc9b5V0Te/fj0n6RO/ntBnUI0n6h4i4Y2LVXF4ekvRURNzV++bWjr7nWUPDeyRN6RqKiBcl/Yj0f38a5bSkL/QN29Q1lGYPfQR3SvrzuOBpSVfavqrponB5sH2FpJslfVKSIuKtiHi9b9hUr6ERe4QL9kv6t4j4Zt/jm7qGMgV6SPqy7ZXe2an9+FMFw3skSTfa/hfbf237hyZZXMPeL+k7kj5t+1nbD9ve2Tdm2tfQKD2SpncNXeygpMc2eHxT11CmQL8pIq7ThV9pfsX2zX3Pe4Ntpu0rPsN6dEwXTjH+YUl/IumLky6wQTOSrpP0iYj4UUlvSur/k9DTvoZG6dE0ryFJUu9Q1E9L+uxGT2/wWG1rKE2gR8SrvZ+v6cJxq+v7hoz0pwoyG9ajiHgjItZ6t5+U9DbbuydeaDNOSToVEc/07h/VhfDqHzPNa2hoj6Z8DX3PrZKORcTZDZ7b1DWUItBt77T9zu/dlvSTkp7rG/a4pA/1/pf5BknnIuLMhEttzCg9sv0e2+7dvl4X1se/T7rWJkTEtyW9Yntv76H9kl7oGzbVa2iUHk3zGrrI3dr4cIu0yWsoy7dcWpK+0FtHM5L+MiKesv1LkhQRf6YLZ7TeJuklSf8t6b6Gam3KKD26S9Iv216XdF7SwZiuM8/ul/Ro71fmlyXdxxr6f4b1aKrXkO0dkm6R9IsXPTaxNcSZogCQRIpDLgAAAh0A0iDQASAJAh0AkiDQASAJAh0AkiDQASAJAh0AkvhfN5H/aaUr2UQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\n",
    "              (iris_data['sepal_length_cm'] < 1.0),\n",
    "              'sepal_length_cm'] *= 100.0\n",
    "\n",
    "iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew! Good thing we fixed those outliers. They could've really thrown our analysis off.\n",
    "\n",
    ">We had to drop those rows with missing values.\n",
    "\n",
    "Let's take a look at the rows with missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5             NaN   \n",
       "8               4.4             2.9              1.4             NaN   \n",
       "9               4.9             3.1              1.5             NaN   \n",
       "10              5.4             3.7              1.5             NaN   \n",
       "11              4.8             3.4              1.6             NaN   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same *Iris* type — this error could potentially bias our analysis.\n",
    "\n",
    "One way to deal with missing data is **mean imputation**: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement.\n",
    "\n",
    "Let's see if we can do that here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "7               5.0             3.4              1.5            0.25   \n",
       "8               4.4             2.9              1.4            0.25   \n",
       "9               4.9             3.1              1.5            0.25   \n",
       "10              5.4             3.7              1.5            0.25   \n",
       "11              4.8             3.4              1.6            0.25   \n",
       "\n",
       "          class  \n",
       "7   Iris-setosa  \n",
       "8   Iris-setosa  \n",
       "9   Iris-setosa  \n",
       "10  Iris-setosa  \n",
       "11  Iris-setosa  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_petal_width = iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].mean()\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'].isnull()),\n",
    "              'petal_width_cm'] = average_petal_width\n",
    "\n",
    "iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\n",
    "              (iris_data['petal_width_cm'] == average_petal_width)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\n",
    "              (iris_data['sepal_width_cm'].isnull()) |\n",
    "              (iris_data['petal_length_cm'].isnull()) |\n",
    "              (iris_data['petal_width_cm'].isnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! Now we've recovered those rows and no longer have missing data in our data set.\n",
    "\n",
    "**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\n",
    "\n",
    "    iris_data.dropna(inplace=True)\n",
    "\n",
    "After all this hard work, we don't want to repeat this process every time we work with the data set. Let's save the tidied data file *as a separate file* and work directly with that data file from now on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data.to_csv('iris-data-clean.csv', index=False)\n",
    "\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take a look at the scatterplot matrix now that we've tidied the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, I purposely inserted numerous errors into this data set to demonstrate some of the many possible scenarios you may face while tidying your data.\n",
    "\n",
    "The general takeaways here should be:\n",
    "\n",
    "* Make sure your data is encoded properly\n",
    "\n",
    "* Make sure your data falls within the expected range, and use domain knowledge whenever possible to define that expected range\n",
    "\n",
    "* Deal with missing data in one way or another: replace it if you can or drop it\n",
    "\n",
    "* Never tidy your data manually because that is not easily reproducible\n",
    "\n",
    "* Use code as a record of how you tidied your data\n",
    "\n",
    "* Plot everything you can about the data at this stage of the analysis so you can *visually* confirm everything looks correct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Testing our data\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "At SciPy 2015, I was exposed to a great idea: We should test our data. Just how we use unit tests to verify our expectations from code, we can similarly set up unit tests to verify our expectations about a data set.\n",
    "\n",
    "We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example,\n",
    "\n",
    "```Python\n",
    "assert 1 == 2\n",
    "```\n",
    "\n",
    "will raise an `AssertionError` and stop execution of the notebook because the assertion failed.\n",
    "\n",
    "Let's test a few things that we know about our data set now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And so on. If any of these expectations are violated, then our analysis immediately stops and we have to return to the tidying stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Exploratory analysis\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Now after spending entirely too much time tidying our data, we can start analyzing it!\n",
    "\n",
    "Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors. We'll be looking to answer questions such as:\n",
    "\n",
    "* How is my data distributed?\n",
    "\n",
    "* Are there any correlations in my data?\n",
    "\n",
    "* Are there any confounding factors that explain these correlations?\n",
    "\n",
    "This is the stage where we plot all the data in as many ways as possible. Create many charts, but don't bother making them pretty — these charts are for internal use.\n",
    "\n",
    "Let's return to that scatterplot matrix that we used earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is normally distributed for the most part, which is great news if we plan on using any modeling methods that assume the data is normally distributed.\n",
    "\n",
    "There's something strange going on with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 823.5x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.pairplot(iris_data_clean, hue='class')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sure enough, the strange distribution of the petal measurements exist because of the different species. This is actually great news for our classification task since it means that the petal measurements will make it easy to distinguish between `Iris-setosa` and the other `Iris` types.\n",
    "\n",
    "Distinguishing `Iris-versicolor` and `Iris-virginica` will prove more difficult given how much their measurements overlap.\n",
    "\n",
    "There are also correlations between petal length and petal width, as well as sepal length and sepal width. The field biologists assure us that this is to be expected: Longer flower petals also tend to be wider, and the same applies for sepals.\n",
    "\n",
    "We can also make **violin plots** of the data to compare the measurement distributions of the classes. Violin plots contain the same information as [box plots](https://en.wikipedia.org/wiki/Box_plot), but also scales the box according to the density of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "for column_index, column in enumerate(iris_data_clean.columns):\n",
    "    if column == 'class':\n",
    "        continue\n",
    "    plt.subplot(2, 2, column_index + 1)\n",
    "    sb.violinplot(x='class', y=column, data=iris_data_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enough flirting with the data. Let's get to modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Classification\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Wow, all this work and we *still* haven't modeled the data!\n",
    "\n",
    "As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\n",
    "\n",
    "Remember: **Bad data leads to bad models.** Always check your data first.\n",
    "\n",
    "<hr />\n",
    "\n",
    "Assured that our data is now as clean as we can make it — and armed with some cursory knowledge of the distributions and relationships in our data set — it's time to make the next big step in our analysis: Splitting the data into training and testing sets.\n",
    "\n",
    "A **training set** is a random subset of the data that we use to train our models.\n",
    "\n",
    "A **testing set** is a random subset of the data (mutually exclusive from the training set) that we use to validate our models on unforseen data.\n",
    "\n",
    "Especially in sparse data sets like ours, it's easy for models to **overfit** the data: The model will learn the training set so well that it won't be able to handle most of the cases it's never seen before. This is why it's important for us to build the model with the training set, but score it with the testing set.\n",
    "\n",
    "Note that once we split the data into a training and testing set, we should treat the testing set like it no longer exists: We cannot use any information from the testing set to build our model or else we're cheating.\n",
    "\n",
    "Let's set up our data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# We're using all four measurements as inputs\n",
    "# Note that scikit-learn expects each entry to be a list of values, e.g.,\n",
    "# [ [val1, val2, val3],\n",
    "#   [val1, val2, val3],\n",
    "#   ... ]\n",
    "# such that our input data set is represented as a list of lists\n",
    "\n",
    "# We can extract the data in this format from pandas like this:\n",
    "all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "# Similarly, we can extract the class labels\n",
    "all_labels = iris_data_clean['class'].values\n",
    "\n",
    "# Make sure that you don't mix up the order of the entries\n",
    "# all_inputs[5] inputs should correspond to the class in all_labels[5]\n",
    "\n",
    "# Here's what a subset of our inputs looks like:\n",
    "all_inputs[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our data is ready to be split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "(training_inputs,\n",
    " testing_inputs,\n",
    " training_classes,\n",
    " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our data split, we can start fitting models to our data. Our company's Head of Data is all about decision tree classifiers, so let's start with one of those.\n",
    "\n",
    "Decision tree classifiers are incredibly simple in theory. In their simplest form, decision tree classifiers ask a series of Yes/No questions about the data — each time getting closer to finding out the class of each entry — until they either classify the data set perfectly or simply can't differentiate a set of entries. Think of it like a game of [Twenty Questions](https://en.wikipedia.org/wiki/Twenty_Questions), except the computer is *much*, *much* better at it.\n",
    "\n",
    "Here's an example decision tree classifier:\n",
    "\n",
    "<img src=\"iris_dtc.png\" />\n",
    "\n",
    "Notice how the classifier asks Yes/No questions about the data — whether a certain feature is <= 1.75, for example — so it can differentiate the records. This is the essence of every decision tree.\n",
    "\n",
    "The nice part about decision tree classifiers is that they are **scale-invariant**, i.e., the scale of the features does not affect their performance, unlike many Machine Learning models. In other words, it doesn't matter if our features range from 0 to 1 or 0 to 1,000; decision tree classifiers will work with them just the same.\n",
    "\n",
    "There are several [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune for decision tree classifiers, but for now let's use a basic decision tree classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97368421052631582"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Create the classifier\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# Train the classifier on the training set\n",
    "decision_tree_classifier.fit(training_inputs, training_classes)\n",
    "\n",
    "# Validate the classifier on the testing set using classification accuracy\n",
    "decision_tree_classifier.score(testing_inputs, testing_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heck yeah! Our model achieves 97% classification accuracy without much effort.\n",
    "\n",
    "However, there's a catch: Depending on how our training and testing set was sampled, our model can achieve anywhere from 80% to 100% accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Hk+vzSEO91hM/rmc9QWLKV0EWcPoAsY5fO+Cz+sWGfdjwJO0zxO0vrXASa19OvA4R7iQe6zXyehiz6+39mvbL0yA1/H9F2Of4Ni9GLuaOucO1sXoAuBXgVNnXdMq6jwdeFlr/wnw/tY+Ffhy+/1d29rHXJ2rrPG4eW2O1TLP4S/GXs73X4z93LSey5k/EFN8gC8DvsjoCO69re/9wFvHxrwPuO6Q7X4CeKj9Aj4EbJ11Laupk9GFrX9u9dwPvHls2/e27R4DLp11LdOok9G53kda/xeAn591Laus820t4L4IfOhg8LV1v8Hoovpe4JpZ1zLpGo/D1+bHGJ06/B9GR+lbgXcA72jrw+iPNH2p1bNpWs+ln4yVpM71eo5ektQY9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kde7/AC40cbqbQG9XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_accuracies = []\n",
    "\n",
    "for repetition in range(1000):\n",
    "    (training_inputs,\n",
    "     testing_inputs,\n",
    "     training_classes,\n",
    "     testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
    "    \n",
    "    decision_tree_classifier = DecisionTreeClassifier()\n",
    "    decision_tree_classifier.fit(training_inputs, training_classes)\n",
    "    classifier_accuracy = decision_tree_classifier.score(testing_inputs, testing_classes)\n",
    "    model_accuracies.append(classifier_accuracy)\n",
    "    \n",
    "plt.hist(model_accuracies)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's obviously a problem that our model performs quite differently depending on the subset of the data it's trained on. This phenomenon is known as **overfitting**: The model is learning to classify the training set so well that it doesn't generalize and perform well on data it hasn't seen before.\n",
    "\n",
    "### Cross-validation\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This problem is the main reason that most data scientists perform ***k*-fold cross-validation** on their models: Split the original data set into *k* subsets, use one of the subsets as the testing set, and the rest of the subsets are used as the training set. This process is then repeated *k* times such that each subset is used as the testing set exactly once.\n",
    "\n",
    "10-fold cross-validation is the most common choice, so let's use that here. Performing 10-fold cross-validation on our data set looks something like this:\n",
    "\n",
    "(each square is an entry in our data set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "def plot_cv(cv, features, labels):\n",
    "    masks = []\n",
    "    for train, test in cv.split(features, labels):\n",
    "        mask = np.zeros(len(labels), dtype=bool)\n",
    "        mask[test] = 1\n",
    "        masks.append(mask)\n",
    "    \n",
    "    plt.figure(figsize=(15, 15))\n",
    "    plt.imshow(masks, interpolation='none', cmap='gray_r')\n",
    "    plt.ylabel('Fold')\n",
    "    plt.xlabel('Row #')\n",
    "\n",
    "plot_cv(StratifiedKFold(n_splits=10), all_inputs, all_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\n",
    "\n",
    "We can perform 10-fold cross-validation on our model with the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0kEWHSqrqriT/BjgHWAO8r6quXORmm1ci3JT1mBnMPU09ZgZzT9NUMqfqXsPVkqRVzG9OSlJnLG5J6sySinuxr74nWZ/k00m+kGRrkuePLHt8kouSXJnkH5PcbyXuwCRzJ9kzyZ+3vNuSvH4VZT4kyfkt75YkB40sOzHJV9t04rQyLyd3kiNGHh9bk7y0h9wjy/dN8s0k7+ohc3vMn9se11clme0k91vbY2Rbkncmme/jypPI/L4kNy/0/ZQM3tnu09YkTxpZtvLPx6oaa2J4Y/Jq4FBgL+ByYMOcdTYD/7rNbwCubfNrga3AE9rlhwJrxt33cqZl5j4B+GCbfwBwLTC7SjJ/CDixzR8DvL/NPwS4pv374Db/4FV0rBfKfRjw6Db/COAmYL/Vnntk+R8AZwLv6iEzsAV4Vpt/IPCA1Z4b+AXgs20ba4CLgKOnlPvpwJOAKxZY/nzgbIbvvTwF+Fy7fiLPx6WccY/z1fcC9m3zD+LHn/d+NrC1qi4HqKpvV9XdS9j3ciwndwH7JFkL3B/4AXDH5COPlXkDcH6b//TI8ucA51XVbVX1HeA84LlTyAzLyF1VX6mqr7b5G4GbgZmppF7e8SbJk4EDgHOnkHWHXc6c4XcNra2q8wCqantV/fN0Yi/rWBdwP4bC3xvYE/iniScGquoC4LadrPJC4C9qcDGwX5KHM6Hn41KKe76vvh84Z503AK9IcgPwCeC32/WHAZXknCSXJXndLubdFcvJ/WHgToazv28Ab6uqnf3nrZRxMl8OvLjNvwhYl+ShY952UpaT+/9LciTDk/PqCeWca5dzJ9kDeDvw2omnvKflHOvDgNuTfKQND/73DL9Mbhp2OXdVXcRQ5De16Zyq2jbhvONa6H5N5Pm4lOIe56vvLwNOr6qDGF46vL89sNcCTwNe3v59UZJf2oW8u2I5uY8E7mZ46f5I4HeTHDrJsM04mX8PeEaSLwDPAL4J3DXmbSdlObmHDQxnKe8HfqOqfjSpoHMsJ/dvAp+oquuZruVkXgsc1Zb/LMOwxUkTS3pPu5w7yaOAxzF8e/tA4JgkT59k2CVY6H5N5Pk4zu8q2WGcr76fTHsZUFUXZXgDcv92289U1a0AST7BMF50PpO3nNwnAJ+sqh8CNyf5LLCRYZxqt2ZuwwnHAyR5IPDiqvpue9Vw9Jzbbplk2BG7nLtd3hc4C/hP7eXmtCzneP88cFSS32QYK94ryfaqmvTvrV/uY+QLVXVNW/bXDOOyfzrhzMvNvQm4uKq2t2Vnt9wXTCH3Yha6X5N5Pi5hcH4tQ2E9kh+/qXD4nHXOBk5q849rwcMwKH8Zwxt8a4H/DRw7pTcVlpP73wN/1ub3Aa4CHr9KMu8P7NHm3wy8sX78ZsjX2zF/cJt/yCo61gvl3ovhB/lrppF1pXLPWeckpvfm5HKO9Zq2/ky7/GfAb3WQ+6WtO9YyjG+fD7xgio+TWRZ+c/JY7vnm5D+06yfyfFxq8OcDX2EYe/yP7bo3Ase1+Q0M7/peDnwRePbIbV8BXAlcAbx1Wgd7ObkZzqA+1HJfBbx2FWX+FeCrbZ33AnuP3PZVwNfa9Bur7FjPm7s9Pn7Yjv+O6YjVnnvONk5iSsW9Ao+RZzF80usfgdOBvVZ7boYfOO8BtrXn4zummPkDDOPqP2Q4iz4ZOAU4pS0Pwx+cubod040jt13x56NfeZekzvjNSUnqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOvP/ANCoyZRRxeHrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "# cross_val_score returns a list of the scores, which we can visualize\n",
    "# to get a reasonable estimate of our classifier's performance\n",
    "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "plt.hist(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a much more consistent rating of our classifier's general classification accuracy.\n",
    "\n",
    "### Parameter tuning\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Every Machine Learning model comes with a variety of parameters to tune, and these parameters can be vitally important to the performance of our classifier. For example, if we severely limit the depth of our decision tree classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier(max_depth=1)\n",
    "\n",
    "cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "plt.hist(cv_scores)\n",
    "plt.title('Average score: {}'.format(np.mean(cv_scores)))\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the classification accuracy falls tremendously.\n",
    "\n",
    "Therefore, we need to find a systematic method to discover the best parameters for our model and data set.\n",
    "\n",
    "The most common method for model parameter tuning is **Grid Search**. The idea behind Grid Search is simple: explore a range of parameters and find the best-performing parameter combination. Focus your search on the best range of parameters, then repeat this process several times until the best parameters are discovered.\n",
    "\n",
    "Let's tune our decision tree classifier. We'll stick to only two parameters for now, but it's possible to simultaneously explore dozens of parameters if we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.959731543624161\n",
      "Best parameters: {'max_depth': 3, 'max_features': 3}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the grid search to see how the parameters interact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dSUWsi0i+ARrMPvOtItI2f8FJ7L3xvXz9pCPtpKrjVLWDqnYIViIHWLt9H/UTKlO3WiViXEKXZjWZt3m3X5nt+/7gpPq+7oaUxCrEuiXqEjnA/NUeWqTWonG9GsTGuLiiZxumzV7jV6Z2YlxBC37ENV2Z+MViAK5/9BNa/e0FWvf/N/e+8jXvTV8SsYkcYP4qDy1Sk2hcvyaxMW6uOOdkps1a5VemdmLVghbciOu6M3HqAgBqVK9CpVh3QZnTT27Mqg1ZRKqKWBeR3GcezJb5dYBf5nPe8XudiLwWxPMGJE/hjZ8288C5LXCJ8N1vO0jf9TtXtqvPuu37mb95NxN/8XBzl0b0blMXVXjpx8LRGi9f3oa4Sm5iXEKnRjV4ZPpav5EwkcTrVYY//wVTRl+D2yVM/Hwxq9K2MfKGHixcvYVpc9bQrW0TRg3uiSrMWrKRYc9/Eeqwg8LrzWP4c5OZMuZ63G5h4tQFrNqQxcgbz2Hh6nSmzVpNt1ObMermc311sXgDw571fXOgdeO6vHh3P/LyFJdLGP3OTL+RH5GmItZFGObogIkW7wQLI5e/tTB8gytn0yZMDXUI4eNgZP7SNMGVO+fxv5yK2z/yfcA5Z8HIs8Iq9VfYcebGGFNcJLfMLZkbY4wjkm+AWjI3xhhHON7YDJQlc2OMcURwLrdkbowx+axlbowxUSCCc7klc2OMyWctc2OMiQI2msUYY6KAtcyNMSYKRHAut2RujDH5rGVujDFRIIJzuSVzY4zJZzdAjTEmCrgiuGkecDIXkVbACKBx0f1UtWcQ4jLGmHIXwbm8VC3zj4BXgdcBb3DCMcaY0KkoN0APqeorQYvEGGNCLIK7zI+dzEWkljM7RURuBT4BDuRvV9WgfaF19twNwTp05LGv6xTKzTl2GWP+hGhvmS8AFMi/yhFFtinQrKyDMsaYUIjqG6Cq2hRARKqoql/zUESqBCswY4wpb5HczeIqRdk5Aa4zxpiIJCIBT+EmkD7zekAKECci7SjsbkkAqgYxNmOMKVdhmKMDFkif+XnAQCAVeK7I+j3AfUGIyRhjQiKS+8yP2c2iqhNV9SxgoKqeVWTqo6ofl0OMxhhTLkQCn459LDlfRH4VkbUick8J2xuJyPciskhElorIhUW23evs96uInBdI7KUZZz5DRP4NdMU3imUWMEpVd5TiGMYYE7bK6t0sIuIGXgJ6AenAPBGZrKorixR7APhQVV8RkROAz4Emznx/oA3QAPhGRFqp6lEf1izNDdAPgG3AZcDlzvx/SrG/McaENZdIwNMxdALWqup6Vf0DX/7sW6yM4rv3CJAIbHHm+wIfqOoBVd0ArHWOd/TYA7xGgFqq+oiqbnCmR4EapdjfGGPCmpRmEhksIvOLTIOLHCoF2FxkOd1ZV9S/gAEiko6vVT60FPsepjTdLN+LSH/gQ2f5cmBaKfY3xpiwVpohh6o6Dhh3pEOVtEux5auACar6rIicDrwjIicGuO9hSpPMbwLuAN51DuwG9onIHYCqasLRdjbGmHBXhg8NpQMNiyynUtiNkm8QcD6Aqs51HsJMCnDfwwTczaKq1VXVpaoxqhrrzFd3JkvkxpiI53JJwNMxzANaikhTEamE74bm5GJlNgFnA4jI8UAVfPciJwP9RaSyiDQFWgK/HOuEpXmfuQDXAE1V9RERaQjUV9VjnsQYYyJBWT3ZqaqHRGQIMB1fL8Z4VV0hIqOA+ao6GbgTeF1EhuPr7RioqgqsEJEPgZXAIeC2Y41kgdJ1s7wM5AE9gUeAvfiG3nQsxTGMMSZsleW7WVT1c3w3Nouue7DI/EqgyxH2fQx4rDTnK00yP01VTxWRRc7Jsp0/H4wxJiqE4ztXAlWaZH7QGQivACJSB19L3RhjokLkpvLSJfN/4/swRV0ReQzf0MQHghJVOTmrTTKjrjwFt0t4b9YGxn65xm97Sq04Xri+AwlxlXC7hMc+Xs53y7cS6xaeHnAqpzSpSV6eMvI/S5i7ZnuIrqJs9DqtJaOH9cbtdjFhyjxGv/OD3/ZG9Wrw6n2XkVSjKtk5udzw8Id4tvk+ErH3x0dZvm4rAJszd3PF3e+Ue/xlqdcZxzN6xOW4XS4mfDqH0W997be9Uf2avPrQAJJqViM7Zz833D8RT9YuABrWq8nLD15NanJNFKXfkFfYlBG077cEXUWri0h+N0vAyVxVJ4nIAnx3XwXop6qrghZZkLkEHr+6LVeOmUVG9n6+uK8nXy3JYE3GnoIywy48nsnzPbw9cz2t6lfn3aFd6HTfl1xzZlMAej78DbWrV+a9/+vC+Y9/hx5zJGh4crmE5+/qw0W3j8eTlcOsN29l6o+rWZ2WVVDmiSEXMOmLhUz6YhHd2zdj1C3nMWjURwDkHjhI54FjQxV+mXK5hOfv+RsX3TIWT+YuZk0awdSZy1i9fmtBmSeGX8Kkab8wacrPdO/YilFD+zBo5NsAvPHIdTz1xnS++3k18XGVyIvUHwoqZl2U1eP8oXDMoYkiUit/ArKA94H3gMwin5SLOO2a1iItax+btu/joFf5bF46553SwK+MolSv4vt9Vz0ulq27fd/maFU/gVmrfYlux54D7N5/kFMa1yzfCyhDHU9IZV36DtK2ZHPwkJePvllK7zOP9yvTukldZsxfB8DMBesP2x4tOp7YhHWbt5Pm2eGri+kL6d3jZL8yrZvVZ8bPvwIwc94aevc4yVlfjxi3i+9+Xg3Avtw/yP39YPleQBmqiHVRli/aKm+BjDNfAMx3/n8bsAb4zZlfcLQdRaSTiHR05k8QkTuKvhkslOrViMOzc3/BcsauXOrVjPMrM3rKKi7r3IgFT13Au0O78MD7iwFYmb6b89o2wO0SGtauysmNa5BSy3/fSNKgTiLpmbsLlj3bdpNSx//RgWVrt9LvrBMB6Nu9DQnxVaiV4LvmKpVimPXmrcwcdzMXd4vsJN+gbiLpmdkFy57MbFLqJPqVWbbGQ7+z2wLQt+cpJFSLo1ZiPC0b1WXXnlw+GH0jc9+/m8eH9Yvoll5FrIsyfDdLuQvkFbhNVbUZvvGSF6tqkqrWBnoDR3wFrog8hK+f/RUReQIYC1QD7hGR+8sk+r+gpH8LLfZn4CUdG/KfORtpf/cXDHhxNi/e0BEReH92GhnZuXx5f09GXXkK89ft5JA3/P+EPJISnx0udjn3jv2cM9s2Ze6EIZzZrimerN0c8vruf7e69Gm6DnqZv//rPzxze2+apkTsH2xICbVR/F/23jGfcGb7Fsx9/27ObN8CT2Y2h7xeYmJcdGnXnHvGfELXAc/QNDWJa/t0Lp/Ag6Ai1kUkt8xLcwO0o6renL+gql+IyCNHKX850BaoDGwFUlU1R0SeAX7mCGMonZfVDAZI6HoTVY/vVYoQA5eRnUtKrcIPJdWvEUfmLr9PnHJV1yZc/cIsABas30nlWDe1qlVmx54DPPTh0oJyk+/uwYasvUGJszx4tu0mNbmwxZVSJ5Et23P8ymRs30P/+yYBEB9XiX492pCz70DBNoC0Ldn8sHA9bVs1YIMnvG90HYknaxepyYVdZinJNdmybbdfmYxtu+l/1xuAUxdntyVn7+94Mnex5Nd00jy+t0JP/n4JnU5qykTmlt8FlKGKWBeRPDSxNG9N3C4iD4hIExFp7LSuj/Yu80Oq6lXV/cA6Vc0BUNVcjjKkUVXHqWoHVe0QrEQOsDgtm6Z1q9GwdlVi3ULfjqlMX+L/+gPPzv10bV0XgJb1qlM51sWOPQeIq+QmrpIbgG7H18XrzfO7cRpp5q/y0CI1icb1axIb4+aKc05m2iz/e9u1E6sW/KCPuK47E6f6ethqVK9CpVh3QZnTT27Mqg1ZRKr5KzbSolEdGjeo7auL805l2oylfmVq14gvrIsbzmPiZz8V7FsjIY6kmtUA6NHxOL+bhZGmItaFqxRTuClNy/wq4CF8wxMV+MFZdyR/iEhVJ5m3z18pIomEwfh0b55y3/uLeX9YV9wu4YPZaazJ2MOIPiewZGM2Xy3J4OGPlvLMtacy+JwWKDBswnwAalevzPu3d0VVydj1O0PHzw/txfxFXm8ew5+bzJQx1+N2CxOnLmDVhixG3ngOC1enM23Warqd2oxRN5+LKsxavIFhz/peM9G6cV1evLsfeXmKyyWMfmem3yiYSOP15jH8qQ+Z8vJtuF3CxM9+YtX6rYy85SIWrtzEtJnL6NahJaOG9vHVxcK1DHvC9yLRvDzl3uc+5fNXhyIiLFq1ifEfzw7xFf15FbEu3BHQr38kUryf+E8fSORFVR1aZLmyqh4ooVwSvne6LDvWMesP/l/kdkSXsV3Lj3qvuWLJzTl2GVPh5C4a+5cz8R2TVwecc57r0zqsMn9pWubH4veOgZISubN+OxDZT9gYY6JSJPeZl2UyN8aYiBbBvSyWzI0xJl8EN8zLNJlHcDUYYwzERHA2D3iEjfNJo+LrkoosvlAmERljTIhE8kNDpRkuOU9ECh7hEpHLgDn5y6o6oQzjMsaYchfJj/OXppvlamC8iMwAGgC18X11yBhjokIY5uiAleYVuMuc95i/A+wBuqlqetAiM8aYclYhRrOIyJtAc+BkoBUwRUTGqupLwQrOGGPKUzh2nwSqNN0sy4Ebna9Hb3D6z58LTljGGFP+3OH40pUAlaabZUyx5d3AoDKPyBhjQqSk1/5GitJ0s7QEngBOAAqGKTrvOjfGmIgXyX3mpfmj4i3gFeAQcBbwNr6bocYYExVcEvgUbkqTzONU9Vt8b1rcqKr/woYmGmOiiIgEPIWb0twA/V1EXMBvIjIE8AB1gxOWMcaUv3BscQeqNMl8GFAV+D/gEXxdLdcFIyhjjAmFSP44RWmSueLrI28MxDrrXsc37twYYyJeBOfyUiXzScAIYBnl9Nm3ynGVy+M0kcG+rmNM0IVhV3jASpPMt6nq5KBFYowxIeaqCOPMgYdE5A3gW6Dgk3Cq+nGZR2WMMSFQUVrm1wOt8fWX53ezKGDJ3BgTFWIiuNO8NMn8FFU9KWiRGGNMiEVyy7w0Dw39JCInBC0SY4wJsYrycYquwN9FZAO+PnMBVFVtaKIxJiqEYY4OWGmS+flBi8IYY8JABL8Bt1SvwN0YzECMMSbUwvGdK4EqTcvcGGOimtuSuTHGRL7ITeWR3UVkjDFlSiTw6djHkvNF5FcRWSsi95SwfYyILHamNSKyy1nfVkTmisgKEVkqIlcGEru1zI0xxlFWfeYi4gZeAnoB6cA8EZmsqivzy6jq8CLlhwLtnMX9wHWq+puINAAWiMh0Vd11tHNay9wYYxyuUkzH0AlYq6rrVfUP4AOg71HKXwW8D6Cqa1T1N2d+C5AF1AkkdmOMMZTuoSERGSwi84tMg4scKgXYXGQ53Vl3GBFpDDQFvithWyegErDuWLFbN4sxxjhK082iquOAcUc6VEm7HKFsf+C/quotFkt9fN+Q+LuqHvO145bMjTHGUYZdFelAwyLLqcCWI5TtD9xWdIWIJADTgAdU9adATmjdLMYY4yjDDzrPA1qKSFMRqYQvYR/2PQgROQ6oCcwtsq4S8Anwtqp+FGjsFbpl3r11HR68tA1ul/Cfnzbxyjf+3VINalbh2WvakhAXi8slPDVlNTNWZtG3fQo39WxWUK51gwR6j/6RlZ7I/RpQrzOOZ/SIy3G7XEz4dA6j3/rab3uj+jV59aEBJNWsRnbOfm64fyKeLN/N9Yb1avLyg1eTmlwTRek35BU2ZewMxWWUCauLQhWtLspqnLmqHnI+fD8dcAPjVXWFiIwC5hf50M9VwAeqWrQL5m9AN6C2iAx01g1U1cVHjd3/GOGlye1TgxacS+D7B85iwMs/s3VXLpPvPJOhExeyNnNvQZnHrzyJlek5vDt7Iy2SqzHhpk50HeV/j+K4+tV5/cYOdHvk+2CFCkDmD18G7dgul7Ds0we56JaxeDJ3MWvSCP5+7wRWr99aUGbS0zfw+Y8rmDTlZ7p3bMV1fTozaOTbAEx//XaeemM63/28mvi4SuSpkvv7waDFG0xWF4UirS5yF439y7n4s2VbA845fU+qF1bPGJVrN4uIvF2e5zuato1rsHHbPjbv2M9BrzJloYdzT0r2L6RQrYrvj5eEuBh76BR2AAARV0lEQVQyc34/7Dh92jdg8sIjdYVFho4nNmHd5u2keXZw8JCXj6YvpHcP/5dhtm5Wnxk//wrAzHlr6N3jJGd9PWLcLr77eTUA+3L/iNjkBVYXRVXEunCLBDyFm6AlcxGZXGyaAlyavxys8wYqOTGOLbsKk3PGrt9JTozzKzPmyzX065DC3IfP5q2bOvHQf1ccdpze7SI/mTeom0h6ZnbBsiczm5Q6iX5llq3x0O/stgD07XkKCdXiqJUYT8tGddm1J5cPRt/I3Pfv5vFh/XBF8NdarC4KVcS6kFL8L9wEs2WeCuQAzwHPOtOeIvMhVdIvVi02cqjPqQ347y/pnP7Qt1z/2i+Mubat335tG9cg9w8vazL2BDna4CrpB7P435r3jvmEM9u3YO77d3Nm+xZ4MrM55PUSE+OiS7vm3DPmE7oOeIamqUlc26dz+QQeBFYXhSpiXZTl4/zlLZg3QDsAtwP3AyNUdbGI5KrqzKPt5Ay8HwxQq+dtVD8xOK9R37orlwY1qhQs169Rhazd/t0oV3ZuxN9f/RmAhWm7qBzjolZ8JXbs/QOAi0+N/FY5gCdrF6nJNQuWU5JrsmXbbr8yGdt20/+uNwCIj6tEv7PbkrP3dzyZu1jyazppnh0ATP5+CZ1OasrEwpvzEcXqolBFrAtXGLa4AxW0lrmq5qnqGHwfgr5fRMYSwC8PVR2nqh1UtUOwEjnAkk27aVInntRaccS6hYtPTeHr5Zl+ZbZk59KlVRIAzZOrUTnWXZDIReDCtvWZEgXJfP6KjbRoVIfGDWoTG+PmivNOZdqMpX5lateILxiONeKG85j42U8F+9ZIiCOpZjUAenQ8zu8GWaSxuihUEevCWuZHoarpwBUichG+bpew4M1THvzfCt6+5TTcLuHDnzbz29a9DL+gFcs27+ab5Zk8+ulKnux/MoN6NENVuWtS4cig05rXZuuu39m8Y38Ir6JseL15DH/qQ6a8fBtulzDxs59YtX4rI2+5iIUrNzFt5jK6dWjJqKF9UIVZC9cy7IkPAcjLU+597lM+f3UoIsKiVZsY//HsEF/Rn2d1Uagi1kU4JulAVdihiZEmmEMTjYkGZTE08dvV2wPOOWe3Tgqr1F+hHxoyxpiiwnGUSqAsmRtjjCOSu1ksmRtjjMNa5sYYEwUi4LmmI7JkbowxDlcE97NYMjfGGEfkpnJL5sYYU8Ba5sYYEwUiN5VbMjfGmEIRnM0tmRtjjMOGJhpjTBSwoYnGGBMNLJkbY0zks24WY4yJAhE8MtGSuTHG5IvgXG7J3BhjCkRwNrdkbowxDnsCNEiy0rNCHUL4iEsIdQTh4+Dvxy5jzJ8Quak8zJO5McaUqwjO5pbMjTHGYUMTjTEmCkRwl7klc2OMyWfJ3BhjooB1sxhjTBSwlrkxxkSBCM7llsyNMaZABGdzS+bGGOOwPnNjjIkC9nEKY4yJBpbMjTEm8lk3izHGRAEbmmiMMVEggnM5rlAHYIwxYUNKMR3rUCLni8ivIrJWRO45Qpm/ichKEVkhIu8V25YgIh4RGRtI6NYyN8YYR1l9nEJE3MBLQC8gHZgnIpNVdWWRMi2Be4EuqpotInWLHeYRYGag56zQybxX2xSeuaEzbpcw4ds1PPvJUr/tDevE8+qtZ5KUWIXsPQcY9MJMPDv3A/DZA+fSsVUd5q7K5LInvglF+GWq12ktGT2sN263iwlT5jH6nR/8tjeqV4NX77uMpBpVyc7J5YaHP8SzLQeAvT8+yvJ1WwHYnLmbK+5+p9zjL0u9Tm/N6Dv74Xa5mPDZT4ye+J3f9kb1avLqg1eSVKMa2Tn7ueHBSXiydgOw96fRLF+XAcDmrdlccef4co+/LFW0uijDbpZOwFpVXQ8gIh8AfYGVRcr8A3hJVbMBVLXgazwi0h5IBr4EOgRywgqbzF0uYcw/Tqf3qOl4duzjx6f6MG3eJlan7yoo88R1nXhv5lomzVhL9xPr8/CADtz4b1+SG/PZMqpWjmFQr+NCdQllxuUSnr+rDxfdPh5PVg6z3ryVqT+uZnVa4ZeenhhyAZO+WMikLxbRvX0zRt1yHoNGfQRA7oGDdB4Y0F+CYc/lEp7/56VcNORVPJm7mTVxOFN/WMHqDZkFZZ64/WImTZvPpGnz6d6hBaNuu4hBD/n+Qs49cJDO1zwbqvDLVIWsi1JkcxEZDAwusmqcqo5z5lOAzUW2pQOnFTtEK+c4swE38C9V/VJEXMCzwLXA2YHGU2H7zDu0SGLd1hzSMvdw8FAe/521nt4dG/mVad2wBjOWbgFg5vIMv+0zlmWwJ/dgucYcLB1PSGVd+g7StmRz8JCXj75ZSu8zj/cr07pJXWbMXwfAzAXrD9seLTq2acS6zdtJ8+z01cXXi+jd/US/Mq2b1WPGvN8AmDl/Lb27nVjSoSJeRawLKcX/VHWcqnYoMo3zO9ThtNhyDNAS6AFcBbwhIjWAW4HPVXUzpVBuyVxEuorIHSJybnmd82ga1IrHs31fwbJn5z4a1K7qV2ZZ2k76nt4EgL6nNSahaiVqVatcnmGWiwZ1EknP3F2w7Nm2m5Q6/t8cXbZ2K/3O8v2H2rd7GxLiq1ArIQ6AKpVimPXmrcwcdzMXd4vsJO+ri8K/zjyZu0ipk+hXZtmaLfTreTIAfc86iYRqVaiV6PvZqVIphlkThzNz/O1c3D2yE1tFrAuRwKdjSAcaFllOBbaUUOYzVT2oqhuAX/El99OBISKSBowGrhORJ491wqB1s4jIL6rayZn/B3Ab8AnwkIicqqrHDC6YSvrH0GK/N++b+AvP3Xg61/ZoyaxVW/Hs2MehvLzyCbAcldiEKFYX9479nDF39GHAhacye3EanqzdHPL66qLVpU+TsX0PTRrU5MsXb2T5ukw2eHYGP/AgkBJ+MLRYZdz7wmTG/PNSBvTuyOxF6/Fk7uLQIacuLn6EjO05NEmpxZcv38rytRls8Owol9jLWkWsizJ8nH8e0FJEmgIeoD9wdbEyn+JrkU8QkSR83S7rVfWa/AIiMhDooKoljoYpKph95rFF5gcDvVR1m4iMBn4CSkzmRfuhYttdR0zT7kEJzrNjHylJ8QXLKbXiyXBububLyM7lqmd8N3ziq8TQr3MTcvZHR9dKUZ5tu0lNLmxxpdRJZMv2HL8yGdv30P++SQDEx1WiX4825Ow7ULANIG1LNj8sXE/bVg0iNpl7snaRmlyjYDkluUYJdZFD/39OAJy6OOtkcvb9XrANIM2zkx8WrqXtcSlhn8COpGLWRdlkc1U9JCJDgOn4+sPHq+oKERkFzFfVyc62c0VkJeAFRqjqn66gYHazuESkpojUBkRVtwGo6j7g0JF2KtoPFaxEDrBg7XZa1E+kcd1qxMa4uLxrM6bN3+RXpnb1ygUt+BGXnsLb360JWjyhNH+VhxapSTSuX5PYGDdXnHMy02at8itTO7FqQUttxHXdmTh1AQA1qlehUqy7oMzpJzdm1YYsItX8lZtp0agOjRvU8tVFr3ZM+2G5X5naifGFdTHwbCZO+QWAGtXjitRFPKef3JRVRW4WRpqKWBdl2M2Cqn6uqq1UtbmqPuase9BJ5KjPHap6gqqepKoflHCMCao6JJDYg9kyTwQW4PtVpyJST1W3ikg1wuBBK2+ecscbc5k88jzcLuHt735j1eZdjOzfjoVrtzNt/mbObFOfUQPaowqzV25l2OtzC/b/+pELaZWSSLUqsfw27kpueXkW3yz2hPCK/jyvN4/hz01mypjrcbuFiVMXsGpDFiNvPIeFq9OZNms13U5txqibz0UVZi3ewLBnJwPQunFdXry7H3l5issljH5npt8omEjj9eYx/OmPmfLvwbjdLiZO/oVV6zMZedP5LFy1mWk/rKBb++aMuu0iVJVZi9Yz7On/AdC6aTIv3ntFYV1M/M5v5EekqYh1EfLE9BdI8T6woJ9QpCqQ7HT4H1XVy8aXb3BhTDPWhjqE8HHw91BHYMJQ7rzn/nIuztj9R8A5p35ipbDK/eU+zlxV9wPHTOTGGFPe7K2JxhgTBeyticYYEwUsmRtjTBSwbhZjjIkGkZvLLZkbY0y+CM7llsyNMSaf9ZkbY0wUKKuPU4RChX0FrjHGRBNrmRtjjCOCG+aWzI0xJp8NTTTGmChgLXNjjIkClsyNMSYKWDeLMcZEAWuZG2NMFIjgXG7J3BhjCkRwNrdkbowxjkjuMy/3z8ZFIhEZrKrjQh1HOLC6KGR14WP1EB7scf7ADA51AGHE6qKQ1YWP1UMYsGRujDFRwJK5McZEAUvmgbH+wEJWF4WsLnysHsKA3QA1xpgoYC1zY4yJApbMj0JExotIlogsD3UsoSQiDUXkexFZJSIrROT2UMcUKiJSRUR+EZElTl08HOqYQk1E3CKySESmhjqWisyS+dFNAM4PdRBh4BBwp6oeD3QGbhORE0IcU6gcAHqq6ilAW+B8Eekc4phC7XZgVaiDqOgsmR+Fqv4A7Ax1HKGmqhmqutCZ34PvP9yU0EYVGuqz11mMdaYKe+NJRFKBi4A3Qh1LRWfJ3JSKiDQB2gE/hzaS0HG6FRYDWcDXqlph6wJ4HvgnkBfqQCo6S+YmYCJSDfgfMExVc0IdT6ioqldV2wKpQCcROTHUMYWCiPQGslR1QahjMZbMTYBEJBZfIp+kqh+HOp5woKq7gBlU3PsqXYA+IpIGfAD0FJF3QxtSxWXJ3ByTiAjwJrBKVZ8LdTyhJCJ1RKSGMx8HnAOsDm1UoaGq96pqqqo2AfoD36nqgBCHVWFZMj8KEXkfmAscJyLpIjIo1DGFSBfgWnwtr8XOdGGogwqR+sD3IrIUmIevz9yG5JmQsydAjTEmCljL3BhjooAlc2OMiQKWzI0xJgpYMjfGmChgydwYY6KAJXNjjIkClsxNWBKRyiLyjTOm/co/sX+/CvxmR1MBxYQ6AGOOoB0Q67wD5c/oB0wFVga6g4jEqOqhP3k+Y0LKWuamVESkiYisFpE3RGS5iEwSkXNEZLaI/CYinZxpjvPBgjkicpyz7x0iMt6ZP8nZv2oJ56gLvAu0dVrmzUWkvYjMFJEFIjJdROo7Zf8hIvOcj0X8T0SqisgZQB/gmSL7zxCRDs4+Sc77RBCRgSLykYhMAb5y1o1wjrk0/+MTIhIvItOc8yz/M38tGBNUqmqTTQFPQBN8H6s4CV9jYAEwHhCgL/ApkADEOOXPAf7nzLuAH4BLgPlAl6Ocpwcw1ZmPBeYAdZzlK4HxznztIvs8Cgx15icAlxfZNgPo4MwnAWnO/EAgHajlLJ+L7wPF4sQ7FegGXAa8XuR4iaH+t7DJpqKTdbOYP2ODqi4DEJEVwLeqqiKyDF+yTwQmikhLfB9uiAVQ1TwRGQgsBV5T1dkBnu844ETga987v3ADGc62E0XkUaAGUA2Y/ieu52tVzf8IybnOtMhZrga0BH4ERovIU/h+yfz4J85jTNBYMjd/xoEi83lFlvPw/Uw9Anyvqpc4H7OYUaR8S2Av0KAU5xNghaqeXsK2CUA/VV3i/KLocYRjHKKwW7FKsW37ip3rCVV97bAgRNoDFwJPiMhXqjoq4CswJsisz9wEQyLgceYH5q8UkUTgBXzdFrVF5PIAj/crUEdETneOEysibZxt1YEM533r1xTZZ4+zLV8a0N6ZP9p5pwM3OB/iQERSRKSuiDQA9qvqu8Bo4NQAYzemXFgyN8HwNL7W62x8XSL5xgAvq+oaYBDwpHOz86hU9Q98CfgpEVkCLAbOcDaPxPcJu6/xf6/4B8AI5yZsc3wJ+BYRmYOvz/xI5/oKeA+Y63Qb/RffL4WTgF+cz8Xdj69/3piwYa/ANcaYKGAtc2OMiQJ2A9SElIhcD9xebPVsVb0tFPEYE6msm8UYY6KAdbMYY0wUsGRujDFRwJK5McZEAUvmxhgTBSyZG2NMFPh/e8Spzu8N4NwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_visualization = grid_search.cv_results_['mean_test_score']\n",
    "grid_visualization.shape = (5, 4)\n",
    "sb.heatmap(grid_visualization, cmap='Blues', annot=True)\n",
    "plt.xticks(np.arange(4) + 0.5, grid_search.param_grid['max_features'])\n",
    "plt.yticks(np.arange(5) + 0.5, grid_search.param_grid['max_depth'])\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('max_depth')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a better sense of the parameter space: We know that we need a `max_depth` of at least 2 to allow the decision tree to make more than a one-off decision.\n",
    "\n",
    "`max_features` doesn't really seem to make a big difference here as long as we have 2 of them, which makes sense since our data set has only 4 features and is relatively easy to classify. (Remember, one of our data set's classes was easily separable from the rest based on a single feature.)\n",
    "\n",
    "Let's go ahead and use a broad grid search to find the best settings for a handful of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9664429530201343\n",
      "Best parameters: {'criterion': 'entropy', 'max_depth': 3, 'max_features': 3, 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "decision_tree_classifier = DecisionTreeClassifier()\n",
    "\n",
    "parameter_grid = {'criterion': ['gini', 'entropy'],\n",
    "                  'splitter': ['best', 'random'],\n",
    "                  'max_depth': [1, 2, 3, 4, 5],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(decision_tree_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take the best classifier from the Grid Search and use that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,\n",
       "            max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_classifier = grid_search.best_estimator_\n",
    "decision_tree_classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even visualize the decision tree with [GraphViz](http://www.graphviz.org/) to see how it's making the classifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.tree as tree\n",
    "from sklearn.externals.six import StringIO\n",
    "\n",
    "with open('iris_dtc.dot', 'w') as out_file:\n",
    "    out_file = tree.export_graphviz(decision_tree_classifier, out_file=out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"iris_dtc.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(This classifier may look familiar from earlier in the notebook.)\n",
    "\n",
    "Alright! We finally have our demo classifier. Let's create some visuals of its performance so we have something to show our company's Head of Data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P/aKkVyW9IunnMb7Yo8qs9FnS+ijza5K+4yjzrZJ2R/s8IumyIY/9kqSW6LbMe+bofdEXvfZnb/O85r3gOe5STCWdg/fFjUp/wmq7pLWSxnrOrPRfLL+RtCM6/n4Z4+v8B6XnwfuUPjteLumrkr4abTdJD0c/03ZJ84c8Nm/HH984BADH+MYhADhGSQOAY5Q0ADhGSQOAY5Q0ADhGSQOAY5Q0ADhGSQOAY/8F1NyQCK+nzg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\n",
    "\n",
    "sb.boxplot(dt_scores)\n",
    "sb.stripplot(dt_scores, jitter=True, color='black')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... that's a little boring by itself though. How about we compare another classifier to see how they perform?\n",
    "\n",
    "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\n",
    "\n",
    "**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\n",
    "\n",
    "Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\n",
    "\n",
    "Let's see if a Random Forest classifier works better here.\n",
    "\n",
    "The great part about scikit-learn is that the training, testing, parameter tuning, etc. process is the same for all models, so we only need to plug in the new classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9664429530201343\n",
      "Best parameters: {'criterion': 'gini', 'max_features': 1, 'n_estimators': 25}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=1, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest_classifier = RandomForestClassifier()\n",
    "\n",
    "parameter_grid = {'n_estimators': [10, 25, 50, 100],\n",
    "                  'criterion': ['gini', 'entropy'],\n",
    "                  'max_features': [1, 2, 3, 4]}\n",
    "\n",
    "cross_validation = StratifiedKFold(n_splits=10)\n",
    "\n",
    "grid_search = GridSearchCV(random_forest_classifier,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(all_inputs, all_labels)\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n",
    "\n",
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KOjDk9O/QbdBhRXXupfqFx5qtY1Zo9bpavnD7H1i+djMAh+/XiYcvOIhOHUr7sr7n+VVc+V/JYNcSXPeF/pw1vFezbaY8u4pJ//1mk8sP7lPOry88mO6dykp8F3u3Us9ZzAN+I+khYF19YUQ8nEtUraRs/Wo6L5qRXbGNi9jCW7/7dWEBbz91LwO0ptl2mzZ+SPW8Jz5qVreZ6ien8LHPHLu1bPPGD6me93iDOu8U1dmTlK1fTXLtRetasWIFq9e256Kne7Z2KDvk9TeWb00UAAvf/JAzf1XHfv1K65U++T+Lt05HwFUz32V2zcFN1o8InvyfRc2u8w/vbuLLv9zEoIGl97rbmo11olf7FbtkW6Umi17AKhpeARXAHpMshgwZ0tohtJi6ujqeiy0NysoUDD+o+S+9Dz74gDlFncfOHdo1aFdTU8PzRXW6FNXZs/RrE/tGjx492LBhQ2uHscO2tNv2q2ZLu3LadepWUvu6LQ3357q6Lc22jQi2lHAgZEu7DiXH0BZ1Jtk3domI2CNew4cPD/vIpZdeGiQJPYC4/vrrS2p3yimnNGh33333bVPni1/8YoM69957b0uHb3uYpUuXRvfu3bfuM/379481a9aU3H7ixIkN9rkRI0Zktrn88ssbtCl+9e7dO1asWLEzb2uPAFRGCd+xihKyr6QpbDtUBxHxtZ1LVS2noqIiKiv90L56W7Zs4f7776eyspLjjjuOsWPHZjcCNm7cyMknn8yaNWu4+eabGTlyZKN17rrrLhYtWsTYsWM59thjWzh62xMtXbqUKVOm0LFjRy644AL222+/kttGBFOnTuXZZ5/ltddeY8iQIfzoRz/KbPPggw/y3HPP0bVrV2pqahg0aBBr165FEl/72tcYNGjQzr6t3Z6kuVHCQ+xKTRanF8x2Ihn0b2VEXLrjIbYsJ4uW881vfhMg88No1hq8f7asUpNFqQMJ/rJo5fcDTzRR3czM9jA7epHxUMD9NzOzvUSpo85+QMNzFm+RPOPCzMz2AqUehtp9ry0zM7OdVtJhKEmnStq3YL6HpC/mF5aZmbUlpZ6zuDoi3qufiYi1JM+3MDOzvUCpyaKxeqXe/W1mZru5UpNFpaSbJB0s6SBJNwNz8wzMzMzajlKTxSXAJuBBYCqwAbg4r6DMzKxtKfVqqHWAn4FtZraXKvVqqMcl9SiY7ylpZn5hmZlZW1LqYag+6RVQAETEGkp4BreZme0ZSk0WWyRtHd5D0mAaGYW2mKQxkhZLqpK0zWEsSQdImiXpRUlPSRqYlh8p6RlJC9NlXy4xTjMzy0Gpl79+H/hfSU+n88cA45trkD67+1bgBGA5MEfS9Ih4paDajcDdEXGXpFHAtcBXgfXA30bEEkn9gbmSZhb2bszMbNcpqWcREY8CFcBikiuivkNyRVRzRgBVEbE0IjYBDwCnFNUZBsxKp2fXL4+I1yJiSTq9EngH6FtKrGZm1vJKPcH9dyRf6t9JX/cAkzKaDQCWFcwvT8sKLQDqn5VxKtBNUoOH8koaAZQDf2gkrvGSKiVVVldXl/JWzMxsB5R6zuKbwKeANyLiOOAoIOvbWY2UFZ/nuBwYKWkeMBJYAdRuXYG0H0liOj+i6KHSQETcHhEVEVHRt687HmZmeSn1nMWHEfGhJCR1jIhFkj6e0WY5sH/B/EBgZWGF9BDTaQCSugKn149BJak78F/AlRHxbIlxmplZDkpNFsvT+yx+DTwuaQ1FX/yNmAMMlXQgSY/hLOCcwgqS+gCr017DFcAdaXk58CuSk98PlfpmzMwsH6XewX1qOjlJ0mxgX+DRjDa1kiYAM4Ey4I6IWChpMlAZEdOBY4FrJQXwWz4aQuRMkiuueksal5aNi4j5Jb8zMzNrMds9cmxEPJ1da2vdGcCMorKrCqanAdMaafcL4BfbG5uZmeVjR5/BbWZmexEnCzMzy+RkYWZmmZwszMwsk5OFmZllcrIwM7NMThZmZpbJycLMzDI5WZiZWSYnCzMzy+RkYWZmmZwszMwsk5OFmZllcrIwM7NMThZmZpbJycLMzDI5WZiZWSYnCzMzy+RkYWZmmZwszMwsU67JQtIYSYslVUma2MjyAyTNkvSipKckDSxYdp6kJenrvDzjNDOz5uWWLCSVAbcCJwHDgLMlDSuqdiNwd0QcAUwGrk3b9gKuBj4NjACultQzr1jNzKx5efYsRgBVEbE0IjYBDwCnFNUZBsxKp2cXLP8b4PGIWB0Ra4DHgTE5xmpmZs3IM1kMAJYVzC9PywotAE5Pp08FuknqXWJbJI2XVCmpsrq6usUCNzOzhvJMFmqkLIrmLwdGSpoHjARWALUltiUibo+Iioio6Nu3787Ga2ZmTWif47qXA/sXzA8EVhZWiIiVwGkAkroCp0fEe5KWA8cWtX0qx1jNzKwZefYs5gBDJR0oqRw4C5heWEFSH0n1MVwB3JFOzwROlNQzPbF9YlpmZmatILdkERG1wASSL/lXgakRsVDSZElj02rHAoslvQb0A65J264G/okk4cwBJqdlZmbWCvI8DEVEzABmFJVdVTA9DZjWRNs7+KinYWZmrch3cJuZWSYnCzMzy+RkYWZmmZwszMwsk5OFmZllcrIwM7NMThZmZpbJycLMzDI5WZiZWSYnCzMzy+RkYWZmmZwszMwsk5OFmZllcrIwM7NMThZmZpbJycLMzDI5WZiZWSYnCzMzy+RkYWZmmXJNFpLGSFosqUrSxEaWD5I0W9I8SS9KOjkt7yDpLkkvSXpV0hV5xmlmZs3LLVlIKgNuBU4ChgFnSxpWVO1KYGpEHAWcBfxbWv4loGNEfBIYDlwkaXBesZqZWfPy7FmMAKoiYmlEbAIeAE4pqhNA93R6X2BlQfk+ktoDnYFNwPs5xmpmZs3IM1kMAJYVzC9PywpNAs6VtByYAVySlk8D1gFvAn8CboyI1TnGamZmzcgzWaiRsiiaPxu4MyIGAicD90hqR9IrqQP6AwcC35F00DYbkMZLqpRUWV1d3bLRm5nZVnkmi+XA/gXzA/noMFO9C4CpABHxDNAJ6AOcAzwaEZsj4h3gd0BF8QYi4vaIqIiIir59++bwFszMDPJNFnOAoZIOlFROcgJ7elGdPwGjASQdRpIsqtPyUUrsA3wGWJRjrGZm1ozckkVE1AITgJnAqyRXPS2UNFnS2LTad4ALJS0A7gfGRUSQXEXVFXiZJOlMiYgX84rVzMya1z7PlUfEDJIT14VlVxVMvwJ8tpF2NSSXz5qZWRvgO7jNzCyTk4WZmWVysjAzs0xOFmZmlsnJwszMMjlZmJlZJicLMzPL5GRhZmaZnCzMzCyTk4WZmWVysjAzs0xOFnuZtWvXkozVmNi4cSPr1q1rxYjMWlZNTQ2bN29u7TD2OE4We4klS5Zw1FFH0bNnTw455BCef/55rrvuOnr37k2PHj248MILqa2tbe0wzXbY+vXrOeOMM+jevTv9+vXj5z//eWuHtEdR4a/M3VlFRUVUVla2dhg77ZZbbqGqqqrF1/urX/2KZcs+esptt27d+OCDDxrUOf744xk2bNjW7Q8ZMqTF49heQ4YM4ZJLLsmuaLnLa9/cXk3tn88//zzPPvvs1nlJjBs3jm7duuUSx56yb0qaGxHbPFyuWK5DlFvbUfzY2eJEUVinc+fOuyQmsx3R1P5ZvI9HBKtWrcotWext3LPYS5x11lk8+OCDW+ePPvpoFixYQF1d3day3/zmN4wdO7ax5mZt3m233cY3vvGNrfNdu3Zl2bJl9OjRoxWjavvcs7AGbr31VsrKypg9ezYVFRXccsstvPDCC0yaNIl169Zx8cUXO1HYbu2iiy7izTff5O6776Zfv35cd911ThQtyD0LM7O9WKk9C18NZWZmmZwszMwsU67JQtIYSYslVUma2MjyQZJmS5on6UVJJxcsO0LSM5IWSnpJUqc8YzUzs6bldoJbUhlwK3ACsByYI2l6RLxSUO1KYGpE3CZpGDADGCypPfAL4KsRsUBSb8C3ZJqZtZI8exYjgKqIWBoRm4AHgFOK6gTQPZ3eF1iZTp8IvBgRCwAiYlVE1GFmZq0iz2QxAFhWML88LSs0CThX0nKSXkX97ZCHACFppqQXJH03xzjNzCxDnslCjZQVX6d7NnBnRAwETgbukdSO5PDYXwFfSf89VdLobTYgjZdUKamy+O5NMzNrOXkmi+XA/gXzA/noMFO9C4CpABHxDNAJ6JO2fToi3o2I9SS9jqOLNxARt0dERURU9O3bN4e3YGZmkG+ymAMMlXSgpHLgLGB6UZ0/AaMBJB1GkiyqgZnAEZK6pCe7RwKvYGZmrSLXO7jTS2F/CJQBd0TENZImA5URMT29Auo/gK4kh6i+GxGPpW3PBa5Iy2dERLPnLSRVA2/k9mb2Pn2Ad1s7CLMmeP9sOQdEROahmT1muA9rWZIqSxkCwKw1eP/c9XwHt5mZZXKyMDOzTE4W1pTbWzsAs2Z4/9zFfM7CzMwyuWdhZmaZnCzaAEl1kuZLelnSI5Ja5PFekgZLerkl1lW03kmSVqQxz5d0XUtvo2BbRxaORmytq2BfXShpgaRvp6Mu7Mi6Jks6vpnlX5f0tzseLUj6ZMF+ulrS6+n0Ezuz3r2RD0O1AZJqIqJrOn0X8FpEXNMC6x0M/GdEfGJn11W03klATUTcuANty7ZnUEhJ44CKiJiwvduylle0r34MuA/4XURc3bqRZZN0J8nnYVojy9pHRO2uj2r34Z5F2/MM6YCLkrpKmpUOpviSpFPS8sGSXpX0H+kvvMckdU6XDU9/8T0DXFy/UkmdJE1J1zNP0nFp+ThJv057NK9LmpD+Wpwn6VlJvUoNXNLotN1Lku6Q1DEt/6OkqyT9L/AlSQdLelTSXEn/I+nQtN6X0t7VAkm/Te/8nwx8Of01+OUW+R+2FhER7wDjgQlKlEm6QdIcJc+nuai+rqTvpvvFgvqeqKQ7JZ2RTl8n6ZW03Y1p2SRJl6fTR6b744uSfiWpZ1r+lKTrJT0v6TVJf11q/JKOl/SEpAeAeWnZeem65kv6t/pek6STlDxf5wVJD0rap0X+E3cnEeFXK79IfqVDcqf7Q8CYdL490D2d7gNUkQzQOBioBY5Ml00Fzk2nXwRGptM3AC+n098BpqTTh5IMtdIJGJeutxvQF3gP+Hpa72bgW43EOwlYAcxPX3+TrmsZcEha5+76tsAfSe7Or28/CxiaTn8aeDKdfgkYkE73SP8dB/yktf9GfjXcV4vK1gD9SBLHlWlZR6ASOBA4Cfg90CVd1iv9907gDKAXsJiPjnTU/+0nAZc3sl9PBn6YTj8F/Gs6fTLwRDOx3wmcUTB/PFADDErnPwH8Gmifzt8OnAN8DHi6IP7vA99r7b/Frn7l9vAj2y6dJc0nSQJzgcfTcgH/LOkYYAtJj6Nfuuz1iJifTs8leWjUviQftKfT8ntIPqiQjN57C0BELJL0BslQ8ACzI+ID4ANJ7wGPpOUvAUc0EfPNUXAYStKfpzG9lhbdRdKz+WE6/2Baryvwl8BD0taBiTum//4OuFPSVODhJrZrbU/9H/JEkjHdzkjn9wWGknwpT4lkUFAiYnVR+/eBD4GfSfov4D8brHzb/foukh9V9er3lbkkn6Ht8UxE/CmdPh74FFCZ7pudSX4ArQeGAb9Py8uB/93O7ez2nCzahg0RcWT6ofhPki/ZH5MM0d4XGB4RmyX9keQXPMDGgvZ1JDu22HYY+HqNDRlfr3BdWwrmt1D6PtLc+gHWpf+2A9ZGxJHFFSLi65I+DXwOmC9pmzrWtkg6iGT/e4dkH7gkImYW1RlD0/slEVEraQTJoKJnAROAUdsRRv3+Wsf2f6etK5gWyRh2PyisIOlU4NGI+Op2rnuP4nMWbUhEvAdcClwuqQPJL7N30kRxHHBARvu1wHuS/iot+krB4t/Wz0s6BBhE0vVvKYtIejdD0vmvknTdi2N8H3hd0pfSWJT2SpB0cEQ8FxFXkQwStz/wAckhMmtjJPUFfkpymDBIRov++3TfRdIh6bH9x4CvSeqSlvcqWk9XYN+ImAF8C2jwIyH9XKwpOB/R6L7VAp4AzpTUJ42rt6RBJIfQRqaJEUn7SBqaw/bbNPcs2piImCcGrr+dAAAC4klEQVRpAckvrHuBRyRVkpwbWFTCKs4H7pC0nuTDW+/fgJ9KeonkfMe4iNhYcChoZ+P+UNL5JIeX2pMMUf/TJqp/BbhN0pVAB5JH7i4Abkg/hCI5r7GA5NzKxPQw3bUR8WCLBGw7qv6QaQeS/ege4KZ02c9IDgO9oGTHqga+GBGPpr3ESkmbSJ5P872CdXYDfiOpE8nf/rJGtnseyf7bBVhKsp+3qIh4SdI/Ak+kJ7Y3k5y/myPpAuDB9KIL0viXtHQMbZkvnTUzs0w+DGVmZpmcLMzMLJOThZmZZXKyMDOzTE4WZmaWyZfOmmXQTgyc2MT6fh8Rf5lO30AyTMUM4A/A+oi4uyW2Y9aSnCzMdrH6RJG6COgbERubqt8UeaRU24V8GMqsiKS/TUc3XSDpnqJlF6ajqi6Q9MuCu5IbjJiblh1eMILpi/V3/UqqSf+dDuwDPCfpy0WjrDY1Mu+dkm6SNBu4fpf9p9hezzflmRWQdDjJwHSfjYh306EpLiU9DCWpd0SsSuv+f+DtiLglvTN+TESskNQjItZKugV4NiLuTe/8LYuIDWr4TIjC6UkF25lFcvfwknS8rGsjYpSSZzL0AU6J7XguiNnO8mEos4ZGAdMi4l1IRkgtGhLlE2mS6AF05aMhVRobMfcZ4PuSBgIPR0RJw0NkjMwL8JAThe1qPgxl1lBzI/dC8kyECRHxSeAfSUcBjoivA1eSDH44P+2B3AeMBTYAMyWVOpLq1pF5C16HFSxf11RDs7w4WZg1NItk5NHesO0IqSSD3r2Zjqy6dVTfxkbMTUcpXRoRPwam0/SzQRpobmRes9biZGFWICIWAtcAT6ej/95UVOUHwHMkD6gqHAX4BiWPDX2ZZDj4BcCXgZfTUVoPJXl6YKm+AlyQxrAQOGVH3o9ZS/EJbjMzy+SehZmZZXKyMDOzTE4WZmaWycnCzMwyOVmYmVkmJwszM8vkZGFmZpmcLMzMLNP/AZiKyjAR1SPoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_forest_classifier = grid_search.best_estimator_\n",
    "\n",
    "rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10),\n",
    "                       'classifier': ['Random Forest'] * 10})\n",
    "dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10),\n",
    "                      'classifier': ['Decision Tree'] * 10})\n",
    "both_df = rf_df.append(dt_df)\n",
    "\n",
    "sb.boxplot(x='classifier', y='accuracy', data=both_df)\n",
    "sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='black')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about that? They both seem to perform about the same on this data set. This is probably because of the limitations of our data set: We have only 4 features to make the classification, and Random Forest classifiers excel when there's hundreds of possible features to look at. In other words, there wasn't much room for improvement with this data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Reproducibility\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Ensuring that our work is reproducible is the last and — arguably — most important step in any analysis. **As a rule, we shouldn't place much weight on a discovery that can't be reproduced**. As such, if our analysis isn't reproducible, we might as well not have done it.\n",
    "\n",
    "Notebooks like this one go a long way toward making our work reproducible. Since we documented every step as we moved along, we have a written record of what we did and why we did it — both in text and code.\n",
    "\n",
    "Beyond recording what we did, we should also document what software and hardware we used to perform our analysis. This typically goes at the top of our notebooks so our readers know what tools to use.\n",
    "\n",
    "[Sebastian Raschka](http://sebastianraschka.com/) created a handy [notebook tool](https://github.com/rasbt/watermark) for this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randal S. Olson Thu Jul 12 2018 \n",
      "\n",
      "CPython 3.6.6\n",
      "IPython 6.4.0\n",
      "\n",
      "numpy 1.12.1\n",
      "pandas 0.23.1\n",
      "sklearn 0.19.1\n",
      "matplotlib 2.2.2\n",
      "seaborn 0.8.1\n",
      "\n",
      "compiler   : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 17.6.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%watermark -a 'Randal S. Olson' -nmv --packages numpy,pandas,sklearn,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's extract the core of our work from Steps 1-5 and turn it into a single pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.2  3.6  6.1  2.5]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 7.4  2.8  6.1  1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 5.8  2.7  5.1  1.9]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 6.1  2.6  5.6  1.4]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 6.9  3.1  5.4  2.1]\t-->\tIris-virginica\t(Actual: Iris-virginica)\n",
      "[ 4.9  3.   1.4  0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.1  3.7  1.5  0.4]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.   3.2  1.2  0.2]\t-->\tIris-setosa\t(Actual: Iris-setosa)\n",
      "[ 5.8  2.6  4.   1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n",
      "[ 5.5  2.6  4.4  1.2]\t-->\tIris-versicolor\t(Actual: Iris-versicolor)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GeQuV73kdknTLiPWbSjZqAZPp5cpMhDfq4w8BPj9inzpJZdH9ZyR9O7o/T1J1dH+apH2SvhDDBwCjzjxin4cV3weHxRznafr4A9o6ZU7qZueZU9H+M6Plf5X0La95s7ZvkbQ6jnOiBMf4AUlvR49Rocwc8B84z5ySNCO6v0RSmzKfXcR1vBuU/4PDu5T7wWFLtH66pN9EP4fTovvTS5apwOB3KlOw+yX9bbTu25L+MLp/X1QM+yT9c1ZhfFXSjuh/0g5Jj8Z4sEeVecRjPKyYSrrI43yTpJ3Rcd4p6RveM484P3ZK+pGkSud5G5T5a3lZXMe3yPMiJen7kvZI2i1p/TWQuSrKuluZF8QbYsz8E2Xm7i8o8+74G5Iek/RYtN0k/VP0nHZKWpb1Z/9cUkd0+7NS5uIbhwDgGN84BADHKGkAcIySBgDHKGkAcIySBgDHKGkAcIySBgDHKGkAcOz/AaiRFRRi6qcdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "# We can jump directly to working with the clean data because we saved our cleaned data set\n",
    "iris_data_clean = pd.read_csv('iris-data-clean.csv')\n",
    "\n",
    "# Testing our data: Our analysis will stop here if any of these assertions are wrong\n",
    "\n",
    "# We know that we should only have three classes\n",
    "assert len(iris_data_clean['class'].unique()) == 3\n",
    "\n",
    "# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\n",
    "assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5\n",
    "\n",
    "# We know that our data set should have no missing measurements\n",
    "assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['sepal_width_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_length_cm'].isnull()) |\n",
    "                               (iris_data_clean['petal_width_cm'].isnull())]) == 0\n",
    "\n",
    "all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\n",
    "                             'petal_length_cm', 'petal_width_cm']].values\n",
    "\n",
    "all_labels = iris_data_clean['class'].values\n",
    "\n",
    "# This is the classifier that came out of Grid Search\n",
    "random_forest_classifier = RandomForestClassifier(criterion='gini', max_features=3, n_estimators=50)\n",
    "\n",
    "# All that's left to do now is plot the cross-validation scores\n",
    "rf_classifier_scores = cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10)\n",
    "sb.boxplot(rf_classifier_scores)\n",
    "sb.stripplot(rf_classifier_scores, jitter=True, color='black')\n",
    "\n",
    "# ...and show some of the predictions from the classifier\n",
    "(training_inputs,\n",
    " testing_inputs,\n",
    " training_classes,\n",
    " testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\n",
    "\n",
    "random_forest_classifier.fit(training_inputs, training_classes)\n",
    "\n",
    "for input_features, prediction, actual in zip(testing_inputs[:10],\n",
    "                                              random_forest_classifier.predict(testing_inputs[:10]),\n",
    "                                              testing_classes[:10]):\n",
    "    print('{}\\t-->\\t{}\\t(Actual: {})'.format(input_features, prediction, actual))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There we have it: We have a complete and reproducible Machine Learning pipeline to demo to our company's Head of Data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "I hope you found this example notebook useful for your own work and learned at least one new trick by reading through it.\n",
    "\n",
    "If you've spotted any errors or would like to contribute to this notebook, please don't hestitate to get in touch. I can be reached in the following ways:\n",
    "\n",
    "* [Email](http://www.randalolson.com/contact/) me\n",
    "\n",
    "* [Tweet](https://twitter.com/randal_olson/) at me\n",
    "\n",
    "* [Submit an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues) on GitHub\n",
    "\n",
    "* Fork the [notebook repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/), make the fix/addition yourself, then send over a pull request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "This notebook covers a broad variety of topics but skips over many of the specifics. If you're looking to dive deeper into a particular topic, here's some recommended reading.\n",
    "\n",
    "**Data Science**: William Chen compiled a [list of free books](http://www.wzchen.com/data-science-books/) for newcomers to Data Science, ranging from the basics of R & Python to Machine Learning to interviews and advice from prominent data scientists.\n",
    "\n",
    "**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning. There's also a [curated list](https://github.com/josephmisiti/awesome-machine-learning) of Machine Learning frameworks, libraries, and software sorted by language.\n",
    "\n",
    "**Unit testing**: Dive Into Python 3 has a [great walkthrough](https://www.diveinto.org/python3/unit-testing.html) of unit testing in Python, how it works, and how it should be used\n",
    "\n",
    "**pandas** has [several tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) covering its myriad features.\n",
    "\n",
    "**scikit-learn** has a [bunch of tutorials](http://scikit-learn.org/stable/tutorial/index.html) for those looking to learn Machine Learning in Python. Andreas Mueller's [scikit-learn workshop materials](https://github.com/amueller/scipy_2015_sklearn_tutorial) are top-notch and freely available.\n",
    "\n",
    "**matplotlib** has many [books, videos, and tutorials](http://matplotlib.org/resources/index.html) to teach plotting in Python.\n",
    "\n",
    "**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgements\n",
    "\n",
    "[[ go back to the top ]](#Table-of-contents)\n",
    "\n",
    "Many thanks to [Andreas Mueller](http://amueller.github.io/) for some of his [examples](https://github.com/amueller/scipy_2015_sklearn_tutorial) in the Machine Learning section. I drew inspiration from several of his excellent examples.\n",
    "\n",
    "The photo of a flower with annotations of the petal and sepal was taken by [Eric Guinther](https://commons.wikimedia.org/wiki/File:Petal-sepal.jpg).\n",
    "\n",
    "The photos of the various *Iris* flower types were taken by [Ken Walker](http://www.signa.org/index.pl?Display+Iris-setosa+2) and [Barry Glick](http://www.signa.org/index.pl?Display+Iris-virginica+3)."
   ]
  }
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