[YCMI_CBDS_Summer_Course]_Summative_Activity_and_Applying_Methods_Learned_solutions.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of [YCMI_CBDS Summer Course] Summative Activity and Applying Methods Learned - solutions",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6k1iS6FNJq9",
        "colab_type": "text"
      },
      "source": [
        "For this exercise, we'll need several things from `sklearn`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEUNfyJoNKtq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import decomposition\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_nXgPU4rbK8",
        "colab_type": "text"
      },
      "source": [
        "We'll also need three other modules: `pandas`, `plotnine`, `sqlite3`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwtfi4AQr4jO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import plotnine as p9\n",
        "import sqlite3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFUYGCwrr6PA",
        "colab_type": "text"
      },
      "source": [
        "Run the following cell.\n",
        "\n",
        "It copies a database file called `friday.db` from the web into the file system hosting this notebook. This data is modified from that used in [Smith et al, 1988](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2245318/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pw3nPPSjPbG-",
        "colab_type": "code",
        "outputId": "657bc0b8-3fe9-452a-9a06-0a33dd73384d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "!wget https://github.com/YCMI/summer-course-2020/raw/master/friday.db"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-06-12 06:57:56--  https://github.com/YCMI/summer-course-2020/raw/master/friday.db\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/YCMI/summer-course-2020/master/friday.db [following]\n",
            "--2020-06-12 06:57:56--  https://raw.githubusercontent.com/YCMI/summer-course-2020/master/friday.db\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 172032 (168K) [application/octet-stream]\n",
            "Saving to: ‘friday.db.1’\n",
            "\n",
            "\rfriday.db.1           0%[                    ]       0  --.-KB/s               \rfriday.db.1         100%[===================>] 168.00K  --.-KB/s    in 0.03s   \n",
            "\n",
            "2020-06-12 06:57:56 (6.55 MB/s) - ‘friday.db.1’ saved [172032/172032]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pT5-G-3MP0xg",
        "colab_type": "text"
      },
      "source": [
        "The database contains three tables, with columns as below:\n",
        "\n",
        "**demographics**: patient_id, dob, gender, children, state\n",
        "\n",
        "**patienthistory**: patient_id, diabetespedigree, diagnoses\n",
        "\n",
        "**testresults**: patient_id, diastolic, systolic, bicepskinthickness, seruminsulin, glucose, bmi\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjsR2GJUVoPQ",
        "colab_type": "text"
      },
      "source": [
        "Connect to the `friday.db` database and look at a sample of each table to see how the data is structured."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxMKHFLTV46N",
        "colab_type": "code",
        "outputId": "bd20d7ff-aef1-4a29-82ad-6e495d0ba2a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "conn = sqlite3.connect('friday.db')\n",
        "demographics = pd.read_sql('SELECT * from demographics', conn)\n",
        "demographics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>dob</th>\n",
              "      <th>gender</th>\n",
              "      <th>children</th>\n",
              "      <th>state</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20935</td>\n",
              "      <td>Sep 01, 1973</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>Rhode Island</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26574</td>\n",
              "      <td>Nov 07, 1994</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "      <td>Massachussetts</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12718</td>\n",
              "      <td>Aug 01, 1998</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36959</td>\n",
              "      <td>Aug 20, 1996</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>Connecticut</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>92337</td>\n",
              "      <td>Jun 01, 1992</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "      <td>Rhode Island</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>1463</th>\n",
              "      <td>66247</td>\n",
              "      <td>Jan 30, 1979</td>\n",
              "      <td>F</td>\n",
              "      <td>10</td>\n",
              "      <td>Massachussetts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1464</th>\n",
              "      <td>27385</td>\n",
              "      <td>Jul 19, 1998</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>Massachussetts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>14989</td>\n",
              "      <td>Oct 07, 1968</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>Massachussetts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>86520</td>\n",
              "      <td>May 02, 1987</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>75807</td>\n",
              "      <td>Aug 05, 1970</td>\n",
              "      <td>F</td>\n",
              "      <td>9</td>\n",
              "      <td>Rhode Island</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1468 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_id           dob gender  children           state\n",
              "0          20935  Sep 01, 1973      F         1    Rhode Island\n",
              "1          26574  Nov 07, 1994      F         3  Massachussetts\n",
              "2          12718  Aug 01, 1998      F         1        New York\n",
              "3          36959  Aug 20, 1996      F         0     Connecticut\n",
              "4          92337  Jun 01, 1992      F         3    Rhode Island\n",
              "...          ...           ...    ...       ...             ...\n",
              "1463       66247  Jan 30, 1979      F        10  Massachussetts\n",
              "1464       27385  Jul 19, 1998      M         0  Massachussetts\n",
              "1465       14989  Oct 07, 1968      F         2  Massachussetts\n",
              "1466       86520  May 02, 1987      M         0        New York\n",
              "1467       75807  Aug 05, 1970      F         9    Rhode Island\n",
              "\n",
              "[1468 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "pd.read_sql('SELECT * from patienthistory', conn)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>diabetespedigree</th>\n",
              "      <th>diagnoses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20935</td>\n",
              "      <td>0.349</td>\n",
              "      <td>Diabetes, Cancer, Influenza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26574</td>\n",
              "      <td>0.279</td>\n",
              "      <td>Influenza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12718</td>\n",
              "      <td>1.394</td>\n",
              "      <td>Diabetes, Influenza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36959</td>\n",
              "      <td>0.431</td>\n",
              "      <td>Diabetes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>92337</td>\n",
              "      <td>0.268</td>\n",
              "      <td>Diabetes, Influenza, Depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1463</th>\n",
              "      <td>66247</td>\n",
              "      <td>0.205</td>\n",
              "      <td>Diabetes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1464</th>\n",
              "      <td>27385</td>\n",
              "      <td>0.355</td>\n",
              "      <td>Diabetes, Influenza</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>14989</td>\n",
              "      <td>0.259</td>\n",
              "      <td>Diabetes, Arthritis, Influenza, Depression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>86520</td>\n",
              "      <td>1.162</td>\n",
              "      <td>Cancer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>75807</td>\n",
              "      <td>1.282</td>\n",
              "      <td>Diabetes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1468 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_id  diabetespedigree                                   diagnoses\n",
              "0          20935             0.349                 Diabetes, Cancer, Influenza\n",
              "1          26574             0.279                                   Influenza\n",
              "2          12718             1.394                         Diabetes, Influenza\n",
              "3          36959             0.431                                    Diabetes\n",
              "4          92337             0.268             Diabetes, Influenza, Depression\n",
              "...          ...               ...                                         ...\n",
              "1463       66247             0.205                                    Diabetes\n",
              "1464       27385             0.355                         Diabetes, Influenza\n",
              "1465       14989             0.259  Diabetes, Arthritis, Influenza, Depression\n",
              "1466       86520             1.162                                      Cancer\n",
              "1467       75807             1.282                                    Diabetes\n",
              "\n",
              "[1468 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPG9Yf2nV5JB",
        "colab_type": "code",
        "outputId": "a062fc3f-74ed-46bb-ff1b-210ac209d80b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "pd.read_sql('SELECT * from testresults', conn)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>diastolic</th>\n",
              "      <th>systolic</th>\n",
              "      <th>bicepskinthickness</th>\n",
              "      <th>seruminsulin</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bmi</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>20935</td>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.3 cm</td>\n",
              "      <td>156</td>\n",
              "      <td>126</td>\n",
              "      <td>30.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26574</td>\n",
              "      <td>62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>74</td>\n",
              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12718</td>\n",
              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3 cm</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36959</td>\n",
              "      <td>65</td>\n",
              "      <td>91.214016</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>92337</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18 mm</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1463</th>\n",
              "      <td>66247</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1464</th>\n",
              "      <td>27385</td>\n",
              "      <td>72</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32 mm</td>\n",
              "      <td>159</td>\n",
              "      <td>147</td>\n",
              "      <td>31.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1465</th>\n",
              "      <td>14989</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25 mm</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>86520</td>\n",
              "      <td>84</td>\n",
              "      <td>112.768350</td>\n",
              "      <td>22 mm</td>\n",
              "      <td>200</td>\n",
              "      <td>167</td>\n",
              "      <td>29.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1467</th>\n",
              "      <td>75807</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24 mm</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1468 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_id  diastolic    systolic  ... seruminsulin  glucose   bmi\n",
              "0          20935         60         NaN  ...          156      126  30.1\n",
              "1          26574         62         NaN  ...           74       99  21.8\n",
              "2          12718         76         NaN  ...          210      199  42.9\n",
              "3          36959         65   91.214016  ...          130      140  42.6\n",
              "4          92337         70         NaN  ...          105      163  31.6\n",
              "...          ...        ...         ...  ...          ...      ...   ...\n",
              "1463       66247         70         NaN  ...          115      125  31.1\n",
              "1464       27385         72         NaN  ...          159      147  31.2\n",
              "1465       14989         80         NaN  ...          210      108  27.0\n",
              "1466       86520         84  112.768350  ...          200      167  29.3\n",
              "1467       75807         82         NaN  ...          150      112  28.2\n",
              "\n",
              "[1468 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "st-kyRTOctp8",
        "colab_type": "text"
      },
      "source": [
        "Make a bar chart comparing the number of male and female patients per state in our dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL8gJlQJc_1z",
        "colab_type": "code",
        "outputId": "eaed6cab-a9d5-4626-e265-7dddbebdebba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "p9.ggplot(demographics, p9.aes(x='state', fill='gender')) + p9.geom_bar(position='dodge')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8788884189844)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGBU8leSV5k7",
        "colab_type": "text"
      },
      "source": [
        "We wish to test the hypotheses that for women, age, number of children, diabetespedigree (family history), blood pressure, serum insulin, glucose levels, and BMI are predictive of whether or not they have diabetes.\n",
        "\n",
        "Use SQL to get a DataFrame of just the females with information on their: dob, number of children, diabetespedigree, diagnoses, diastolic, systolic, bicepskinthickness, seruminsulin, glucose, and bmi."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiMt499fXiFS",
        "colab_type": "code",
        "outputId": "ec7534da-5a7f-4ea0-d643-2aa724ef1240",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "data = pd.read_sql('''\n",
        "  SELECT dob, children, diabetespedigree, diagnoses, diastolic, systolic, bicepskinthickness, seruminsulin, glucose, bmi\n",
        "  FROM demographics\n",
        "  INNER JOIN patienthistory ON demographics.patient_id = patienthistory.patient_id\n",
        "  INNER JOIN testresults on testresults.patient_id = demographics.patient_id\n",
        "  WHERE gender='F'\n",
        "''', conn)\n",
        "\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>dob</th>\n",
              "      <th>children</th>\n",
              "      <th>diabetespedigree</th>\n",
              "      <th>diagnoses</th>\n",
              "      <th>diastolic</th>\n",
              "      <th>systolic</th>\n",
              "      <th>bicepskinthickness</th>\n",
              "      <th>seruminsulin</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bmi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sep 01, 1973</td>\n",
              "      <td>1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>Diabetes, Cancer, Influenza</td>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.3 cm</td>\n",
              "      <td>156</td>\n",
              "      <td>126</td>\n",
              "      <td>30.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nov 07, 1994</td>\n",
              "      <td>3</td>\n",
              "      <td>0.279</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>74</td>\n",
              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aug 01, 1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1.394</td>\n",
              "      <td>Diabetes, Influenza</td>\n",
              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3 cm</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aug 20, 1996</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>65</td>\n",
              "      <td>91.214016</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jun 01, 1992</td>\n",
              "      <td>3</td>\n",
              "      <td>0.268</td>\n",
              "      <td>Diabetes, Influenza, Depression</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18 mm</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Jan 11, 1998</td>\n",
              "      <td>2</td>\n",
              "      <td>0.525</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>117.558697</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>27.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>Jul 14, 1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0.559</td>\n",
              "      <td></td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9 cm</td>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>27.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>Jan 30, 1979</td>\n",
              "      <td>10</td>\n",
              "      <td>0.205</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>Oct 07, 1968</td>\n",
              "      <td>2</td>\n",
              "      <td>0.259</td>\n",
              "      <td>Diabetes, Arthritis, Influenza, Depression</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25 mm</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>Aug 05, 1970</td>\n",
              "      <td>9</td>\n",
              "      <td>1.282</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24 mm</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              dob  children  diabetespedigree  ... seruminsulin  glucose   bmi\n",
              "0    Sep 01, 1973         1             0.349  ...          156      126  30.1\n",
              "1    Nov 07, 1994         3             0.279  ...           74       99  21.8\n",
              "2    Aug 01, 1998         1             1.394  ...          210      199  42.9\n",
              "3    Aug 20, 1996         0             0.431  ...          130      140  42.6\n",
              "4    Jun 01, 1992         3             0.268  ...          105      163  31.6\n",
              "..            ...       ...               ...  ...          ...      ...   ...\n",
              "763  Jan 11, 1998         2             0.525  ...          100       91  27.3\n",
              "764  Jul 14, 1999         1             0.559  ...           25       89  27.8\n",
              "765  Jan 30, 1979        10             0.205  ...          115      125  31.1\n",
              "766  Oct 07, 1968         2             0.259  ...          210      108  27.0\n",
              "767  Aug 05, 1970         9             1.282  ...          150      112  28.2\n",
              "\n",
              "[768 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMPYDdgsZIh5",
        "colab_type": "text"
      },
      "source": [
        "Let's clean up this data a bit."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlGH3tcLXidq",
        "colab_type": "text"
      },
      "source": [
        "Create a new column `age` and estimate it from the date of birth by assuming that every year has exactly 365.25 days.\n",
        "\n",
        "Hint: You'll want to subtract dates and divide by `pd.to_timedelta('365.25 days')`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuKR35noRQyk",
        "colab_type": "code",
        "outputId": "f65bd45a-5ac8-405a-82bf-1c939af38caf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "data['age'] = (pd.to_datetime('June 12, 2020') - pd.to_datetime(data['dob'])) / pd.to_timedelta('365.25 days')\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dob</th>\n",
              "      <th>children</th>\n",
              "      <th>diabetespedigree</th>\n",
              "      <th>diagnoses</th>\n",
              "      <th>diastolic</th>\n",
              "      <th>systolic</th>\n",
              "      <th>bicepskinthickness</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Sep 01, 1973</td>\n",
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              "      <td>60</td>\n",
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              "      <td>156</td>\n",
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              "      <td>Nov 07, 1994</td>\n",
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              "      <td>62</td>\n",
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              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "      <td>25.596167</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aug 01, 1998</td>\n",
              "      <td>1</td>\n",
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              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3 cm</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "      <td>21.864476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aug 20, 1996</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>65</td>\n",
              "      <td>91.214016</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "      <td>23.811088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jun 01, 1992</td>\n",
              "      <td>3</td>\n",
              "      <td>0.268</td>\n",
              "      <td>Diabetes, Influenza, Depression</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18 mm</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "      <td>28.030116</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Jan 11, 1998</td>\n",
              "      <td>2</td>\n",
              "      <td>0.525</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>117.558697</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>27.3</td>\n",
              "      <td>22.417522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>Jul 14, 1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0.559</td>\n",
              "      <td></td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9 cm</td>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>27.8</td>\n",
              "      <td>20.914442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>Jan 30, 1979</td>\n",
              "      <td>10</td>\n",
              "      <td>0.205</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "      <td>41.366188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>Oct 07, 1968</td>\n",
              "      <td>2</td>\n",
              "      <td>0.259</td>\n",
              "      <td>Diabetes, Arthritis, Influenza, Depression</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25 mm</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "      <td>51.679671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>Aug 05, 1970</td>\n",
              "      <td>9</td>\n",
              "      <td>1.282</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24 mm</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "      <td>49.853525</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              dob  children  diabetespedigree  ... glucose   bmi        age\n",
              "0    Sep 01, 1973         1             0.349  ...     126  30.1  46.778919\n",
              "1    Nov 07, 1994         3             0.279  ...      99  21.8  25.596167\n",
              "2    Aug 01, 1998         1             1.394  ...     199  42.9  21.864476\n",
              "3    Aug 20, 1996         0             0.431  ...     140  42.6  23.811088\n",
              "4    Jun 01, 1992         3             0.268  ...     163  31.6  28.030116\n",
              "..            ...       ...               ...  ...     ...   ...        ...\n",
              "763  Jan 11, 1998         2             0.525  ...      91  27.3  22.417522\n",
              "764  Jul 14, 1999         1             0.559  ...      89  27.8  20.914442\n",
              "765  Jan 30, 1979        10             0.205  ...     125  31.1  41.366188\n",
              "766  Oct 07, 1968         2             0.259  ...     108  27.0  51.679671\n",
              "767  Aug 05, 1970         9             1.282  ...     112  28.2  49.853525\n",
              "\n",
              "[768 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NV7IbYd2Yyg8",
        "colab_type": "text"
      },
      "source": [
        "Derive a new column `has_diabetes` (with `True` or `False` values) based on the `diagnoses` column.\n",
        "\n",
        "Hint: you can do this in one line."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4ivEraxZBbz",
        "colab_type": "code",
        "outputId": "e3fede15-a4f6-42cd-9394-3bbd3c28beda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        }
      },
      "source": [
        "data['has_diabetes'] = data['diagnoses'].str.contains('Diabetes')\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <td>Sep 01, 1973</td>\n",
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              "      <td>62</td>\n",
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              "      <td>19 mm</td>\n",
              "      <td>74</td>\n",
              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "      <td>25.596167</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aug 01, 1998</td>\n",
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              "      <td>Diabetes, Influenza</td>\n",
              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3 cm</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "      <td>21.864476</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aug 20, 1996</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>65</td>\n",
              "      <td>91.214016</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "      <td>23.811088</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jun 01, 1992</td>\n",
              "      <td>3</td>\n",
              "      <td>0.268</td>\n",
              "      <td>Diabetes, Influenza, Depression</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18 mm</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "      <td>28.030116</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Jan 11, 1998</td>\n",
              "      <td>2</td>\n",
              "      <td>0.525</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>117.558697</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>27.3</td>\n",
              "      <td>22.417522</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>Jul 14, 1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0.559</td>\n",
              "      <td></td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9 cm</td>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>27.8</td>\n",
              "      <td>20.914442</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>Jan 30, 1979</td>\n",
              "      <td>10</td>\n",
              "      <td>0.205</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "      <td>41.366188</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>Oct 07, 1968</td>\n",
              "      <td>2</td>\n",
              "      <td>0.259</td>\n",
              "      <td>Diabetes, Arthritis, Influenza, Depression</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25 mm</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "      <td>51.679671</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>Aug 05, 1970</td>\n",
              "      <td>9</td>\n",
              "      <td>1.282</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24 mm</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "      <td>49.853525</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              dob  children  diabetespedigree  ...   bmi        age  has_diabetes\n",
              "0    Sep 01, 1973         1             0.349  ...  30.1  46.778919          True\n",
              "1    Nov 07, 1994         3             0.279  ...  21.8  25.596167         False\n",
              "2    Aug 01, 1998         1             1.394  ...  42.9  21.864476          True\n",
              "3    Aug 20, 1996         0             0.431  ...  42.6  23.811088          True\n",
              "4    Jun 01, 1992         3             0.268  ...  31.6  28.030116          True\n",
              "..            ...       ...               ...  ...   ...        ...           ...\n",
              "763  Jan 11, 1998         2             0.525  ...  27.3  22.417522         False\n",
              "764  Jul 14, 1999         1             0.559  ...  27.8  20.914442         False\n",
              "765  Jan 30, 1979        10             0.205  ...  31.1  41.366188          True\n",
              "766  Oct 07, 1968         2             0.259  ...  27.0  51.679671          True\n",
              "767  Aug 05, 1970         9             1.282  ...  28.2  49.853525          True\n",
              "\n",
              "[768 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nHLwKngZZFD",
        "colab_type": "text"
      },
      "source": [
        "The `bicepskinthickness` column has values with two different units -- cm and mm. Standardize this into a new column `bicepskinthickness_cm` measured in cm without units.\n",
        "\n",
        "Hints: you could do this with the `re` module, but you don't need it. You'll also want a for loop, and an if statement to build the new column one entry at a time, although you can. You can convert a string `my_numstr` to a number using e.g. `my_num = float(my_numstr)`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_tQJ3RQvaVz5",
        "colab_type": "code",
        "outputId": "63771645-0fad-4ca8-8b0f-c113c819b66e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        }
      },
      "source": [
        "bicepskinthickness_cm = []\n",
        "for thickness in data['bicepskinthickness']:\n",
        "  num, units = thickness.split()\n",
        "  num = float(num)\n",
        "  if units == 'mm':\n",
        "    num = num / 10\n",
        "  else:\n",
        "    if units != 'cm':\n",
        "      print('unsupported unit')\n",
        "  bicepskinthickness_cm.append(num)\n",
        "\n",
        "data['bicepskinthickness_cm'] = bicepskinthickness_cm\n",
        "data\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "        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>dob</th>\n",
              "      <th>children</th>\n",
              "      <th>diabetespedigree</th>\n",
              "      <th>diagnoses</th>\n",
              "      <th>diastolic</th>\n",
              "      <th>systolic</th>\n",
              "      <th>bicepskinthickness</th>\n",
              "      <th>seruminsulin</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age</th>\n",
              "      <th>has_diabetes</th>\n",
              "      <th>bicepskinthickness_cm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sep 01, 1973</td>\n",
              "      <td>1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>Diabetes, Cancer, Influenza</td>\n",
              "      <td>60</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.3 cm</td>\n",
              "      <td>156</td>\n",
              "      <td>126</td>\n",
              "      <td>30.1</td>\n",
              "      <td>46.778919</td>\n",
              "      <td>True</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nov 07, 1994</td>\n",
              "      <td>3</td>\n",
              "      <td>0.279</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>74</td>\n",
              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "      <td>25.596167</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aug 01, 1998</td>\n",
              "      <td>1</td>\n",
              "      <td>1.394</td>\n",
              "      <td>Diabetes, Influenza</td>\n",
              "      <td>76</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.3 cm</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "      <td>21.864476</td>\n",
              "      <td>True</td>\n",
              "      <td>4.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aug 20, 1996</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>65</td>\n",
              "      <td>91.214016</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "      <td>23.811088</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Jun 01, 1992</td>\n",
              "      <td>3</td>\n",
              "      <td>0.268</td>\n",
              "      <td>Diabetes, Influenza, Depression</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18 mm</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "      <td>28.030116</td>\n",
              "      <td>True</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>Jan 11, 1998</td>\n",
              "      <td>2</td>\n",
              "      <td>0.525</td>\n",
              "      <td>Influenza</td>\n",
              "      <td>62</td>\n",
              "      <td>117.558697</td>\n",
              "      <td>19 mm</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>27.3</td>\n",
              "      <td>22.417522</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>Jul 14, 1999</td>\n",
              "      <td>1</td>\n",
              "      <td>0.559</td>\n",
              "      <td></td>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.9 cm</td>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>27.8</td>\n",
              "      <td>20.914442</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>Jan 30, 1979</td>\n",
              "      <td>10</td>\n",
              "      <td>0.205</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26 mm</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "      <td>41.366188</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>Oct 07, 1968</td>\n",
              "      <td>2</td>\n",
              "      <td>0.259</td>\n",
              "      <td>Diabetes, Arthritis, Influenza, Depression</td>\n",
              "      <td>80</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25 mm</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "      <td>51.679671</td>\n",
              "      <td>True</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>Aug 05, 1970</td>\n",
              "      <td>9</td>\n",
              "      <td>1.282</td>\n",
              "      <td>Diabetes</td>\n",
              "      <td>82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24 mm</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "      <td>49.853525</td>\n",
              "      <td>True</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              dob  children  ...  has_diabetes bicepskinthickness_cm\n",
              "0    Sep 01, 1973         1  ...          True                   3.3\n",
              "1    Nov 07, 1994         3  ...         False                   1.9\n",
              "2    Aug 01, 1998         1  ...          True                   4.3\n",
              "3    Aug 20, 1996         0  ...          True                   2.6\n",
              "4    Jun 01, 1992         3  ...          True                   1.8\n",
              "..            ...       ...  ...           ...                   ...\n",
              "763  Jan 11, 1998         2  ...         False                   1.9\n",
              "764  Jul 14, 1999         1  ...         False                   1.9\n",
              "765  Jan 30, 1979        10  ...          True                   2.6\n",
              "766  Oct 07, 1968         2  ...          True                   2.5\n",
              "767  Aug 05, 1970         9  ...          True                   2.4\n",
              "\n",
              "[768 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03rM-F6ga4jG",
        "colab_type": "text"
      },
      "source": [
        "Are any of the columns missing data? Are they missing a little data or a lot of data? Should we keep the column(s) with missing data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0L_72ocbo9u",
        "colab_type": "code",
        "outputId": "51ccfe83-f50d-441d-f1f8-cc31eb35709c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "data.isnull().describe()\n",
        "# we see everything has no null entries EXCEPT systolic blood pressure, where\n",
        "# the majority are missing. Safe to drop the column so let's do it"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dob</th>\n",
              "      <th>children</th>\n",
              "      <th>diabetespedigree</th>\n",
              "      <th>diagnoses</th>\n",
              "      <th>diastolic</th>\n",
              "      <th>systolic</th>\n",
              "      <th>bicepskinthickness</th>\n",
              "      <th>seruminsulin</th>\n",
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              "      <th>has_diabetes</th>\n",
              "      <th>bicepskinthickness_cm</th>\n",
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
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              "      <td>768</td>\n",
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              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>538</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "      <td>768</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          dob children  ... has_diabetes bicepskinthickness_cm\n",
              "count     768      768  ...          768                   768\n",
              "unique      1        1  ...            1                     1\n",
              "top     False    False  ...        False                 False\n",
              "freq      768      768  ...          768                   768\n",
              "\n",
              "[4 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn1r8YysGU0u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop(['systolic'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukbzmzhweCqp",
        "colab_type": "text"
      },
      "source": [
        "While we're at it, drop the redundant columns: `dob`, `diagnoses`, and `bicepskinthickness`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7X9_ohHPGUJG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.drop(['dob', 'diagnoses', 'bicepskinthickness'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gAOPLdseZs1",
        "colab_type": "code",
        "outputId": "06681d6d-2435-4bb8-f07b-d033ea0b9dc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <td>0.349</td>\n",
              "      <td>60</td>\n",
              "      <td>156</td>\n",
              "      <td>126</td>\n",
              "      <td>30.1</td>\n",
              "      <td>46.778919</td>\n",
              "      <td>True</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0.279</td>\n",
              "      <td>62</td>\n",
              "      <td>74</td>\n",
              "      <td>99</td>\n",
              "      <td>21.8</td>\n",
              "      <td>25.596167</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1.394</td>\n",
              "      <td>76</td>\n",
              "      <td>210</td>\n",
              "      <td>199</td>\n",
              "      <td>42.9</td>\n",
              "      <td>21.864476</td>\n",
              "      <td>True</td>\n",
              "      <td>4.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>65</td>\n",
              "      <td>130</td>\n",
              "      <td>140</td>\n",
              "      <td>42.6</td>\n",
              "      <td>23.811088</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0.268</td>\n",
              "      <td>70</td>\n",
              "      <td>105</td>\n",
              "      <td>163</td>\n",
              "      <td>31.6</td>\n",
              "      <td>28.030116</td>\n",
              "      <td>True</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>763</th>\n",
              "      <td>2</td>\n",
              "      <td>0.525</td>\n",
              "      <td>62</td>\n",
              "      <td>100</td>\n",
              "      <td>91</td>\n",
              "      <td>27.3</td>\n",
              "      <td>22.417522</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>1</td>\n",
              "      <td>0.559</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>89</td>\n",
              "      <td>27.8</td>\n",
              "      <td>20.914442</td>\n",
              "      <td>False</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>10</td>\n",
              "      <td>0.205</td>\n",
              "      <td>70</td>\n",
              "      <td>115</td>\n",
              "      <td>125</td>\n",
              "      <td>31.1</td>\n",
              "      <td>41.366188</td>\n",
              "      <td>True</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>2</td>\n",
              "      <td>0.259</td>\n",
              "      <td>80</td>\n",
              "      <td>210</td>\n",
              "      <td>108</td>\n",
              "      <td>27.0</td>\n",
              "      <td>51.679671</td>\n",
              "      <td>True</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>9</td>\n",
              "      <td>1.282</td>\n",
              "      <td>82</td>\n",
              "      <td>150</td>\n",
              "      <td>112</td>\n",
              "      <td>28.2</td>\n",
              "      <td>49.853525</td>\n",
              "      <td>True</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     children  diabetespedigree  ...  has_diabetes  bicepskinthickness_cm\n",
              "0           1             0.349  ...          True                    3.3\n",
              "1           3             0.279  ...         False                    1.9\n",
              "2           1             1.394  ...          True                    4.3\n",
              "3           0             0.431  ...          True                    2.6\n",
              "4           3             0.268  ...          True                    1.8\n",
              "..        ...               ...  ...           ...                    ...\n",
              "763         2             0.525  ...         False                    1.9\n",
              "764         1             0.559  ...         False                    1.9\n",
              "765        10             0.205  ...          True                    2.6\n",
              "766         2             0.259  ...          True                    2.5\n",
              "767         9             1.282  ...          True                    2.4\n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ADk-BZhcIPI",
        "colab_type": "text"
      },
      "source": [
        "Standardize your numeric data (should be everything except the `has_diabetes` column) to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "Hint: You can do this with simple arithmetic operations as we did on Wednesday or use a `StandardScaler` as you learned yesterday. The arithmetic solution can be done in two lines."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AXxXgno-dY5P",
        "colab_type": "code",
        "outputId": "01feda7c-9d52-48ee-b6a0-cd60eebf0b62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "for column in ['children', 'diabetespedigree', 'diastolic', 'seruminsulin', 'glucose', 'bmi', 'age', 'bicepskinthickness_cm']:\n",
        "  data[column] = (data[column] - data[column].mean()) / data[column].std()\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th>diastolic</th>\n",
              "      <th>seruminsulin</th>\n",
              "      <th>glucose</th>\n",
              "      <th>bmi</th>\n",
              "      <th>age</th>\n",
              "      <th>has_diabetes</th>\n",
              "      <th>bicepskinthickness_cm</th>\n",
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              "      <td>-0.499109</td>\n",
              "      <td>-1.007995</td>\n",
              "      <td>-0.747140</td>\n",
              "      <td>-0.922011</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>-0.844335</td>\n",
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              "      <td>-1.123985</td>\n",
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              "      <th>765</th>\n",
              "      <td>1.826623</td>\n",
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              "      <td>-0.191902</td>\n",
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              "      <th>766</th>\n",
              "      <td>-0.547562</td>\n",
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              "      <td>0.417376</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     children  diabetespedigree  ...  has_diabetes  bicepskinthickness_cm\n",
              "0   -0.844335         -0.370859  ...          True               0.324495\n",
              "1   -0.250789         -0.582130  ...         False              -0.990608\n",
              "2   -0.844335          2.783109  ...          True               1.263854\n",
              "3   -1.141108         -0.123371  ...          True              -0.333057\n",
              "4   -0.250789         -0.615330  ...          True              -1.084544\n",
              "..        ...               ...  ...           ...                    ...\n",
              "763 -0.547562          0.160335  ...         False              -0.990608\n",
              "764 -0.844335          0.262953  ...         False              -0.990608\n",
              "765  1.826623         -0.805473  ...          True              -0.333057\n",
              "766 -0.547562         -0.642493  ...          True              -0.426993\n",
              "767  1.529850          2.445076  ...          True              -0.520929\n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aDBaU-E_fUZO"
      },
      "source": [
        "### PCA\n",
        "\n",
        "A good starting place when looking at a new dataset is running PCA and visualizing at a couple of the first principle components.\n",
        "\n",
        "Remember, PCA is simply a rotation of our data that allows us to visualize the axis with the greatest variation. This can elucidate some separation in our data if it exists on those axis, but it may not. \n",
        "\n",
        "**Adapt yesterday's PCA code to work with today's data and visualize the data in PCA space. Color-code by `has_diabetes`.** (For concreteness, plot all possible pairs from the first three principal components... that is, show three graphs PC1-PC2, PC1-PC3, and PC2-PC3.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVilLlOKfuzy",
        "colab_type": "code",
        "outputId": "a571f1f8-7e1b-4fcf-a9ca-ea278bc4a7cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "y = data['has_diabetes']\n",
        "X = data.drop(['has_diabetes'], axis=1)\n",
        "\n",
        "pca = decomposition.PCA(n_components=3)\n",
        "X_reduced = pd.DataFrame(\n",
        "    pca.fit_transform(X),\n",
        "    columns=['PC1', 'PC2', 'PC3']\n",
        ")\n",
        "X_reduced['has_diabetes'] = y\n",
        "\n",
        "p9.ggplot(X_reduced, p9.aes(x='PC1', y='PC2', color='has_diabetes')) + p9.geom_point(alpha=0.7)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8788880516209)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5y9qxagJMQ-",
        "colab_type": "code",
        "outputId": "c85da387-741d-43bf-e828-5e1194b297fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "p9.ggplot(X_reduced, p9.aes(x='PC1', y='PC3', color='has_diabetes')) + p9.geom_point(alpha=0.7)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363247974272501)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9f36c0db-cd7e-46b1-c2fb-2f317ea6d374",
        "id": "lCMakrs2JM4E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        }
      },
      "source": [
        "p9.ggplot(X_reduced, p9.aes(x='PC2', y='PC3', color='has_diabetes')) + p9.geom_point(alpha=0.7)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8788880449800)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNGlSBl8iPe0",
        "colab_type": "text"
      },
      "source": [
        "What do you make of the graphs?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ac04tEm2gC2j",
        "colab_type": "text"
      },
      "source": [
        "# Training and testing sets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0K16fV3OfUBf",
        "colab_type": "text"
      },
      "source": [
        "Split your data into training and test sets with 20% of the data being used for the test set. To ensure we all work with the same random split, let's specify a `random_state`, namely `random_state=42`.\n",
        "\n",
        "If your predictor variables are in `X` and your outcome vector is in `y`, this can be done using:\n",
        "\n",
        "```\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "```\n",
        "\n",
        "Hint: how can you split your DataFrame into a predictor matrix `X` and outcome vector `y`?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0TCdY1rhG51",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-bzGUNxlZHw",
        "colab_type": "text"
      },
      "source": [
        "# Cross-validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6SSk_i6lbP2",
        "colab_type": "text"
      },
      "source": [
        "Using 5-fold cross-validation (`cross_val_score`) on your *training* data only, plot the relationship between the number of estimators (between 10 and 100 counting by 10s) with a max depth of 4 and the average regression score for a corresponding `RandomForest`.\n",
        "\n",
        "Hint: you did cross-validation yesterday in the disease classification exercises with logistic regression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb9OntH7mX6P",
        "colab_type": "code",
        "outputId": "f56e5a39-3d74-4555-9adf-68998b6db5af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "# performance of random forest, iterating number of estimators\n",
        "scores = []\n",
        "# number of estimators\n",
        "N = 110\n",
        "# maximum depth\n",
        "D = 4\n",
        "estimator_choices = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "for n_estimators in estimator_choices:\n",
        "    clf = RandomForestClassifier(n_estimators=n_estimators, max_depth=D, random_state=0)\n",
        "    # 5-fold cross validation\n",
        "    sc = cross_val_score(clf, X_train, y_train, cv=5)\n",
        "    scores.append(sc.mean())\n",
        "\n",
        "(\n",
        "    p9.ggplot(\n",
        "      pd.DataFrame({'n_estimators': estimator_choices, 'score': scores}),\n",
        "      p9.aes(x='n_estimators', y='score')\n",
        "    ) + p9.geom_line()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363247975960106)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MFjeFPZmYMB",
        "colab_type": "text"
      },
      "source": [
        "# Testing\n",
        "\n",
        "Using the number of estimators that gave the best score in the validation phase, train a `RandomForest` on your entire training set, and `.score` its performance with the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBa7Ec4vmtOk",
        "colab_type": "code",
        "outputId": "966d3bac-d604-4270-ba64-29e4e0fc93f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_n_estimators = 30  # by inspection of the graph above\n",
        "\n",
        "clf = RandomForestClassifier(n_estimators=best_n_estimators, max_depth=D, random_state=0)\n",
        "clf.fit(X_train, y_train)\n",
        "print(f\"Optimal Non-Categorical Accuracy: {clf.score(X_test, y_test)}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal Non-Categorical Accuracy: 0.7987012987012987\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3kpmt6-mtZ4",
        "colab_type": "text"
      },
      "source": [
        "Print the confusion matrix. What does it mean?\n",
        "\n",
        "Hint: looking at `y_test.value_counts()` might help with the interpretation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4InslPDzmvRN",
        "colab_type": "code",
        "outputId": "9ea8e1d2-0a61-4ed8-8d11-4e1a306e7a89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "y_predicted = clf.predict(X_test)\n",
        "cm = confusion_matrix(y_predicted, y_test)\n",
        "print(cm)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[83 22]\n",
            " [ 9 40]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CKtEYXjNmvqo",
        "colab_type": "text"
      },
      "source": [
        "Finally, make a horizontal bar graph showing the fitted `RandomForest`'s `feature_importances_`, sorted in order of increasing importance.\n",
        "\n",
        "Hints: you used `feature_importances_` in the Warfarin exercises. You'll need to specify a `stat` to `geom_bar` and you'll need to modify the coordinate system to make the graphs horizontal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQS5qd8tTC31",
        "colab_type": "code",
        "outputId": "4ff0d8ab-2810-4ddf-aa24-58d50ff99a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "importances = pd.DataFrame({'Variable': X_train.keys(), 'Feature_Importance': clf.feature_importances_})\n",
        "importances"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Variable</th>\n",
              "      <th>Feature_Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>children</td>\n",
              "      <td>0.045159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>diabetespedigree</td>\n",
              "      <td>0.049930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>diastolic</td>\n",
              "      <td>0.039643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>seruminsulin</td>\n",
              "      <td>0.249238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>glucose</td>\n",
              "      <td>0.247303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>bmi</td>\n",
              "      <td>0.132287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>age</td>\n",
              "      <td>0.144485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>bicepskinthickness_cm</td>\n",
              "      <td>0.091956</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Variable  Feature_Importance\n",
              "0               children            0.045159\n",
              "1       diabetespedigree            0.049930\n",
              "2              diastolic            0.039643\n",
              "3           seruminsulin            0.249238\n",
              "4                glucose            0.247303\n",
              "5                    bmi            0.132287\n",
              "6                    age            0.144485\n",
              "7  bicepskinthickness_cm            0.091956"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6HG-tRfnuGo",
        "colab_type": "code",
        "outputId": "b9a1cb79-6a94-41ca-ab57-011af3e15b1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "source": [
        "(\n",
        "    p9.ggplot(\n",
        "        importances,\n",
        "        p9.aes(x='Variable', y='Feature_Importance')\n",
        "    )\n",
        "    + p9.geom_bar(stat='identity')\n",
        "    + p9.coord_flip()\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363247974325998)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    }
  ]
}