Cancer_detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/akhilesh-kanmanthreddy/AI_Camp_2023/blob/main/Cancer_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Who is ready to learn? (Our Machines Are!)\n",
        "\n",
        "Our task at hand -- building an AI that can save lives.\n",
        "\n",
        "Today we are going to work to create an AI that can predict whether a breast cancer tumor is benign or malignant. This diagnosis has incredibly important medical implications for those who are diagnosed with breast cancer.\n",
        "\n",
        "Before we get started, you should make sure you know what the difference between benign and malignant tumors are and discuss them with your team.\n",
        "\n",
        "**Hint:** we recommend you heavily refer to your EDA mini project from last week for this activity!"
      ]
    },
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        "### Step 1: Imports and Preprocessing\n",
        "\n",
        "We need to `import pandas as pd` and read in our data. It is currently a .csv (comma-separated value) file, and we generally use the pd.read_csv() function from pandas to read our data into a dataframe."
      ]
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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        "id": "BvK5i4DcvcTg"
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      "source": [
        "breast_cancer_df = pd.read_csv(\"https://raw.githubusercontent.com/pkmklong/Breast-Cancer-Wisconsin-Diagnostic-DataSet/master/data.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rdwjqFR8xdE2"
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      "source": [
        "**TODO:** Do you remember how to print the first five rows of a pandas dataframe? How about all of the columns? How about the number of rows? Please do that below and google anything you are unsure about."
      ]
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              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0  ...          17.33           184.60      2019.0            0.1622   \n",
              "1  ...          23.41           158.80      1956.0            0.1238   \n",
              "2  ...          25.53           152.50      1709.0            0.1444   \n",
              "3  ...          26.50            98.87       567.7            0.2098   \n",
              "4  ...          16.67           152.20      1575.0            0.1374   \n",
              "\n",
              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0             0.6656           0.7119                0.2654          0.4601   \n",
              "1             0.1866           0.2416                0.1860          0.2750   \n",
              "2             0.4245           0.4504                0.2430          0.3613   \n",
              "3             0.8663           0.6869                0.2575          0.6638   \n",
              "4             0.2050           0.4000                0.1625          0.2364   \n",
              "\n",
              "   fractal_dimension_worst  Unnamed: 32  \n",
              "0                  0.11890          NaN  \n",
              "1                  0.08902          NaN  \n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "# print first five rows of breast_cancer\n",
        "breast_cancer_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qQ5KjWIMwGaP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ff14d5f-758b-425c-a4e5-79aa9fa678a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
              "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
              "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
              "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
              "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
              "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
              "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
              "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
              "       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "# print columns of breast_cancer\n",
        "breast_cancer_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LxELBWOdxsrl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97744c02-744b-4496-e3d4-e77768ee067b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "569"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "# print the number of rows in breast_cancer\n",
        "breast_cancer_df.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lFnrZickyynD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61432e6f-ae29-4f0b-c9ea-5105cd7abe0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.11"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "# explore anything else you are interested in here\n",
        "breast_cancer_df['radius_mean'].max()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_df['radius_mean'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wOPzoZNFriP",
        "outputId": "7a3757d5-a16e-4d0f-c8d1-6c6548b36857"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.981"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8MwFiBX0Zdu"
      },
      "source": [
        "**TODO:** Can you figure out how many rows are malignant (M) and how many are benign (B)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0tYK-EaL0g8y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f1a524e-fd38-4ece-f6cd-3756b9e39d20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "212\n",
            "357\n"
          ]
        }
      ],
      "source": [
        "# Figure out how many rows are malignant and how many are benign. Hint: use the .count() function!\n",
        "malignant = 0\n",
        "benign = 0\n",
        "for i in breast_cancer_df[\"diagnosis\"]:\n",
        "  if i == \"M\":\n",
        "    malignant = malignant + 1\n",
        "  else:\n",
        "    benign = benign + 1\n",
        "\n",
        "print(malignant)\n",
        "print(benign)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjS_4CFYxx52"
      },
      "source": [
        "### Step 2: Do Your Research\n",
        "That is a ton of columns. That is a ton of data! Where do we start?\n",
        "\n",
        "You should probably do some research on this dataset to find out what the columns mean. I got it on kaggle - you can read about it here: [Wisconsin Breast Cancer Dataset](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujuEOdIYyaV8"
      },
      "source": [
        "### Step 3: Cleaning our data up!\n",
        "\n",
        "Maybe there are some columns we don't need. The last column makes zero sense right? Do we need an id column for our purposes? You should drop **any** columns that you feel are not worth keeping track of.\n",
        "\n",
        "Pandas' `drop()` documentation: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html. Remember to use `inplace = True` if you are changing your dataframe and note creating a new one!\n",
        "\n",
        "**Note:** This data is pretty clean! We only need a few lines for this dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GsTMmM8EzBJ9"
      },
      "outputs": [],
      "source": [
        "breast_cancer_df.drop('Unnamed: 32', axis=1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYufgpaiMBfZ",
        "outputId": "1e36bf9c-b0e2-40c9-fcb1-d0afa08a7565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
              "       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
              "       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
              "       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
              "       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
              "       'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
              "       'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
              "       'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
              "       'symmetry_worst', 'fractal_dimension_worst'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_df.drop('id', axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "G-PBSgT3HI9l",
        "outputId": "38472f4e-8558-4271-cd31-27666d68b6af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0           M        17.99         10.38          122.80     1001.0   \n",
              "1           M        20.57         17.77          132.90     1326.0   \n",
              "2           M        19.69         21.25          130.00     1203.0   \n",
              "3           M        11.42         20.38           77.58      386.1   \n",
              "4           M        20.29         14.34          135.10     1297.0   \n",
              "..        ...          ...           ...             ...        ...   \n",
              "564         M        21.56         22.39          142.00     1479.0   \n",
              "565         M        20.13         28.25          131.20     1261.0   \n",
              "566         M        16.60         28.08          108.30      858.1   \n",
              "567         M        20.60         29.33          140.10     1265.0   \n",
              "568         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "0           0.2419  ...        25.380          17.33           184.60   \n",
              "1           0.1812  ...        24.990          23.41           158.80   \n",
              "2           0.2069  ...        23.570          25.53           152.50   \n",
              "3           0.2597  ...        14.910          26.50            98.87   \n",
              "4           0.1809  ...        22.540          16.67           152.20   \n",
              "..             ...  ...           ...            ...              ...   \n",
              "564         0.1726  ...        25.450          26.40           166.10   \n",
              "565         0.1752  ...        23.690          38.25           155.00   \n",
              "566         0.1590  ...        18.980          34.12           126.70   \n",
              "567         0.2397  ...        25.740          39.42           184.60   \n",
              "568         0.1587  ...         9.456          30.37            59.16   \n",
              "\n",
              "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0        2019.0           0.16220            0.66560           0.7119   \n",
              "1        1956.0           0.12380            0.18660           0.2416   \n",
              "2        1709.0           0.14440            0.42450           0.4504   \n",
              "3         567.7           0.20980            0.86630           0.6869   \n",
              "4        1575.0           0.13740            0.20500           0.4000   \n",
              "..          ...               ...                ...              ...   \n",
              "564      2027.0           0.14100            0.21130           0.4107   \n",
              "565      1731.0           0.11660            0.19220           0.3215   \n",
              "566      1124.0           0.11390            0.30940           0.3403   \n",
              "567      1821.0           0.16500            0.86810           0.9387   \n",
              "568       268.6           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                  0.2654          0.4601                  0.11890  \n",
              "1                  0.1860          0.2750                  0.08902  \n",
              "2                  0.2430          0.3613                  0.08758  \n",
              "3                  0.2575          0.6638                  0.17300  \n",
              "4                  0.1625          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "564                0.2216          0.2060                  0.07115  \n",
              "565                0.1628          0.2572                  0.06637  \n",
              "566                0.1418          0.2218                  0.07820  \n",
              "567                0.2650          0.4087                  0.12400  \n",
              "568                0.0000          0.2871                  0.07039  \n",
              "\n",
              "[569 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-71f1b923-96fe-42e2-acc1-de6871da39aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>...</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</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",
              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>...</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>...</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>...</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-71f1b923-96fe-42e2-acc1-de6871da39aa button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-71f1b923-96fe-42e2-acc1-de6871da39aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H7H0OTAhzoKQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a06d8fa0-a958-4f2d-9271-50c645ec4795"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of            id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0      842302         M        17.99         10.38          122.80     1001.0   \n",
              "1      842517         M        20.57         17.77          132.90     1326.0   \n",
              "2    84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3    84348301         M        11.42         20.38           77.58      386.1   \n",
              "4    84358402         M        20.29         14.34          135.10     1297.0   \n",
              "..        ...       ...          ...           ...             ...        ...   \n",
              "564    926424         M        21.56         22.39          142.00     1479.0   \n",
              "565    926682         M        20.13         28.25          131.20     1261.0   \n",
              "566    926954         M        16.60         28.08          108.30      858.1   \n",
              "567    927241         M        20.60         29.33          140.10     1265.0   \n",
              "568     92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0            0.11840           0.27760         0.30010              0.14710   \n",
              "1            0.08474           0.07864         0.08690              0.07017   \n",
              "2            0.10960           0.15990         0.19740              0.12790   \n",
              "3            0.14250           0.28390         0.24140              0.10520   \n",
              "4            0.10030           0.13280         0.19800              0.10430   \n",
              "..               ...               ...             ...                  ...   \n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
              "566          0.08455           0.10230         0.09251              0.05302   \n",
              "567          0.11780           0.27700         0.35140              0.15200   \n",
              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0    ...        25.380          17.33           184.60      2019.0   \n",
              "1    ...        24.990          23.41           158.80      1956.0   \n",
              "2    ...        23.570          25.53           152.50      1709.0   \n",
              "3    ...        14.910          26.50            98.87       567.7   \n",
              "4    ...        22.540          16.67           152.20      1575.0   \n",
              "..   ...           ...            ...              ...         ...   \n",
              "564  ...        25.450          26.40           166.10      2027.0   \n",
              "565  ...        23.690          38.25           155.00      1731.0   \n",
              "566  ...        18.980          34.12           126.70      1124.0   \n",
              "567  ...        25.740          39.42           184.60      1821.0   \n",
              "568  ...         9.456          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "0                  0.2654          0.4601                  0.11890  \n",
              "1                  0.1860          0.2750                  0.08902  \n",
              "2                  0.2430          0.3613                  0.08758  \n",
              "3                  0.2575          0.6638                  0.17300  \n",
              "4                  0.1625          0.2364                  0.07678  \n",
              "..                    ...             ...                      ...  \n",
              "564                0.2216          0.2060                  0.07115  \n",
              "565                0.1628          0.2572                  0.06637  \n",
              "566                0.1418          0.2218                  0.07820  \n",
              "567                0.2650          0.4087                  0.12400  \n",
              "568                0.0000          0.2871                  0.07039  \n",
              "\n",
              "[569 rows x 32 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "# drop any n/a values remaining! Remember inplace!\n",
        "breast_cancer_df.info\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer_df.dropna(axis=1, how = 'any', inplace = True)"
      ],
      "metadata": {
        "id": "xkONQYuPKkzm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tL-d7BNRzz_V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "9a7b2345-2663-4968-9187-09697a405283"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "564  926424         M        21.56         22.39          142.00     1479.0   \n",
              "565  926682         M        20.13         28.25          131.20     1261.0   \n",
              "566  926954         M        16.60         28.08          108.30      858.1   \n",
              "567  927241         M        20.60         29.33          140.10     1265.0   \n",
              "568   92751         B         7.76         24.54           47.92      181.0   \n",
              "\n",
              "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "564          0.11100           0.11590         0.24390              0.13890   \n",
              "565          0.09780           0.10340         0.14400              0.09791   \n",
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              "568          0.05263           0.04362         0.00000              0.00000   \n",
              "\n",
              "     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "564  ...        25.450          26.40           166.10      2027.0   \n",
              "565  ...        23.690          38.25           155.00      1731.0   \n",
              "566  ...        18.980          34.12           126.70      1124.0   \n",
              "567  ...        25.740          39.42           184.60      1821.0   \n",
              "568  ...         9.456          30.37            59.16       268.6   \n",
              "\n",
              "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
              "564                0.2216          0.2060                  0.07115  \n",
              "565                0.1628          0.2572                  0.06637  \n",
              "566                0.1418          0.2218                  0.07820  \n",
              "567                0.2650          0.4087                  0.12400  \n",
              "568                0.0000          0.2871                  0.07039  \n",
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              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
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              "  </tbody>\n",
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              "<p>5 rows × 32 columns</p>\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "# print the last five rows of your dataframe\n",
        "breast_cancer_df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1yX_YUAz8ti"
      },
      "source": [
        "**TODO:** at this point, all of our data should be numeric except for our diagnosis column. This is because supervised machine learning models work off of numerical data. Use breast_cancer.info() below to check that all our data besides 'diagnosis' are numeric."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gAPOpEtcz7U2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83600a93-5953-4551-d464-4dd1d041a405"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 34 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            " 33  outcome                  569 non-null    uint8  \n",
            "dtypes: float64(31), int64(1), object(1), uint8(1)\n",
            "memory usage: 147.4+ KB\n"
          ]
        }
      ],
      "source": [
        "breast_cancer.info()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4ocJucC3-p2"
      },
      "source": [
        "### Dummy Variables - Preprocessing\n",
        "Our target variable (the variable we are trying to predict) is `diagnosis`. Our only problem is that the diagnosis variable will either be an 'M' or a 'B', which is not something our model will understand. We need to change our M's to 1's and B's to 0's so that our model can think like computers do -- using numbers!  \n",
        "\n",
        "To do so, we use something called a dummy variable. I'll show you how to do so below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k2w_QD0e3-WY",
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          "height": 206
        },
        "outputId": "bf2e7c1c-0776-41a5-f22c-a23f376acad2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   B  M\n",
              "0  0  1\n",
              "1  0  1\n",
              "2  0  1\n",
              "3  0  1\n",
              "4  0  1"
            ],
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "dummies = pd.get_dummies(breast_cancer_df[\"diagnosis\"])\n",
        "dummies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXI5tmDd5CKh"
      },
      "source": [
        "**Ok, we've got our dummy variables. What now?** Now we have to pick one and add it to our dataframe -- we also have to delete the old diagnosis column!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QtT1BaBF5M3V",
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          "height": 317
        },
        "outputId": "954ba77c-b5aa-4eb0-f5d2-5067079ac3be"
      },
      "outputs": [
        {
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          "data": {
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              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0  ...          17.33           184.60      2019.0            0.1622   \n",
              "1  ...          23.41           158.80      1956.0            0.1238   \n",
              "2  ...          25.53           152.50      1709.0            0.1444   \n",
              "3  ...          26.50            98.87       567.7            0.2098   \n",
              "4  ...          16.67           152.20      1575.0            0.1374   \n",
              "\n",
              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0             0.6656           0.7119                0.2654          0.4601   \n",
              "1             0.1866           0.2416                0.1860          0.2750   \n",
              "2             0.4245           0.4504                0.2430          0.3613   \n",
              "3             0.8663           0.6869                0.2575          0.6638   \n",
              "4             0.2050           0.4000                0.1625          0.2364   \n",
              "\n",
              "   fractal_dimension_worst  outcome  \n",
              "0                  0.11890        1  \n",
              "1                  0.08902        1  \n",
              "2                  0.08758        1  \n",
              "3                  0.17300        1  \n",
              "4                  0.07678        1  \n",
              "\n",
              "[5 rows x 33 columns]"
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
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              "      <td>842302</td>\n",
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              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
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              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
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              "      <td>0.6656</td>\n",
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              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
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              "      <td>0.08902</td>\n",
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              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
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              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
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              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "breast_cancer_df[\"outcome\"] = dummies[\"M\"]\n",
        "breast_cancer_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kXUk3fFs5VxV"
      },
      "source": [
        "**TODO:** Now that our outcome column is there, we need to remove the diagnosis column that had the bad data, right? Please do so below.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_e-rGtPu5VIL"
      },
      "outputs": [],
      "source": [
        "#Drop the 'diagnosis' column from your dataframe. Remember to use inplace if you are changing the dataframe itself!\n",
        "breast_cancer_df.drop('diagnosis', axis=1, inplace = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhMq9yAj3x78"
      },
      "source": [
        "### Visualize our data\n",
        "Now that we've cleaned our data and it is all ready for our model, we should understand some of the relationships. Use your learnings from last week to create two plots -- you can always start with simple [scatter plots](https://plotly.com/python/line-and-scatter/) or maybe a [scatterplot matrix](https://plotly.com/python/splom/). Get creative!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QtEadcGS3sP3"
      },
      "outputs": [],
      "source": [
        "import plotly.express as px\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zAXnnF9f1Mp-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "403c269f-a6c2-4f13-c9e9-bce55ccd9d44"
      },
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "array([[<Axes: title={'center': 'id'}>,\n",
              "        <Axes: title={'center': 'radius_mean'}>,\n",
              "        <Axes: title={'center': 'texture_mean'}>,\n",
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              "        <Axes: title={'center': 'concave points_mean'}>,\n",
              "        <Axes: title={'center': 'symmetry_mean'}>,\n",
              "        <Axes: title={'center': 'fractal_dimension_mean'}>,\n",
              "        <Axes: title={'center': 'radius_se'}>],\n",
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              "        <Axes: title={'center': 'compactness_se'}>,\n",
              "        <Axes: title={'center': 'concavity_se'}>],\n",
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              "        <Axes: title={'center': 'symmetry_se'}>,\n",
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              "        <Axes: title={'center': 'outcome'}>, <Axes: >, <Axes: >,\n",
              "        <Axes: >, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "breast_cancer_df.hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oQXBTNye681E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "1a2b5523-f043-4199-ecac-79cf3b9aa621"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='radius_mean', ylabel='area_mean'>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "breast_cancer_df.plot.scatter(x = \"radius_mean\", y = \"area_mean\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvyKXnr27SaE"
      },
      "source": [
        "### Are you ready to perform machine learning?\n",
        "Do you think deciding whether a tumor is benign or malignant is classification or regression? Talk with your team for a minute.\n",
        "\n",
        "Either way, we need to tell our code what our target variable is, and what our input is going to be. <br> **TODO:** Can you do so below?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tZOfTkVx8w2K"
      },
      "outputs": [],
      "source": [
        "# Create a variable named target and set it equal to the [\"outcome\"] column in our breast_cancer dataframe.\n",
        "target = breast_cancer_df[\"outcome\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X6wHkQax9chF"
      },
      "outputs": [],
      "source": [
        "# Create a variable named input_columns and set it equal to breast_cancer.loc[:, breast_cancer.columns != \"outcome\"]. This means our inputs are every single column except for column!\n",
        "input_columns = breast_cancer_df.loc[:, breast_cancer_df.columns != \"outcome\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsxggVkp81js"
      },
      "source": [
        "### Splitting the Data\n",
        "\n",
        "For every single supervised machine learning problem, we will follow the same flow with `sklearn`:\n",
        "\n",
        "> *Split, Fit, Predict, and Evaluate.*\n",
        "\n",
        "What is first? Splitting our data into training and testing sets. You can watch a video on why we do that [in the first 36 seconds of this video](https://www.youtube.com/watch?v=_vdMKioCXqQ).\n",
        "\n",
        "We will split our data using the [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function on `sklearn` using the same flow as this tutorial: https://realpython.com/train-test-split-python-data/#application-of-train_test_split. The code to do so is below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9I03IMDT8tdn"
      },
      "outputs": [],
      "source": [
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(input_columns, target, test_size=0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcQ-PJQF9XSA"
      },
      "source": [
        "**TODO:** can you look at the shape of the x_train, x_test, y_train, and y_test columns? Why does the shape of these columns make sense?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ok87vW5k-GTS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f9a89d8-fe65-4b9f-8a22-4ba94dbbb36c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(455, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "#hint: start with x_train.shape\n",
        "x_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tY5UwAsS9sCV"
      },
      "source": [
        "**TODO:** Can you print the outputs with print(y_train) and print(y_test)? What do these 1's and 0's represent?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4wMcDBPW9n3C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e3b2b93-9e80-4139-e8ac-7cc671b98a1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "565    1\n",
            "261    1\n",
            "323    1\n",
            "499    1\n",
            "302    1\n",
            "      ..\n",
            "292    0\n",
            "316    0\n",
            "305    0\n",
            "528    0\n",
            "160    0\n",
            "Name: outcome, Length: 455, dtype: uint8\n",
            "22     1\n",
            "502    0\n",
            "235    0\n",
            "496    0\n",
            "336    0\n",
            "      ..\n",
            "329    1\n",
            "379    1\n",
            "62     1\n",
            "225    0\n",
            "69     0\n",
            "Name: outcome, Length: 114, dtype: uint8\n"
          ]
        }
      ],
      "source": [
        "#print outputs\n",
        "print(y_train)\n",
        "print(y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lWxo646-Fka"
      },
      "source": [
        "### We have split, now we gotta fit!\n",
        "Now that are data is preprocessed (all numerical), and split (80/20 train/test) for machine learning, we can create a model to **classify** whether a patient has malignant (1) or benign (0) breast cancer!\n",
        "\n",
        "The first model is a classification model called K Nearest Neighbors. You can watch a video on how K Nearest Neighbors works here: https://www.youtube.com/watch?v=0p0o5cmgLdE.\n",
        "\n",
        "First things first, we need to import our model using SKLearn. You can follow the [guide]( https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py) here to see how this tutorial works.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RDDyWqva92IH"
      },
      "outputs": [],
      "source": [
        "import sklearn #our favorite machine learning library\n",
        "from sklearn.neighbors import KNeighborsClassifier as KNN #this will import our model as KNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tv1P3go2_U5O"
      },
      "source": [
        "**Creating the model object:**  we need to create an instance of the model. This is like having a model and giving it a nickname so we can remember it, change it, and save it to work custom on our dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GzCrGDYI-4FR"
      },
      "outputs": [],
      "source": [
        "# Creating the KNN model object -- we need to tell it the number of neighbors to look at.\n",
        "k = 5 #number of neighbors to look at (look at 7 nearest neighbors)\n",
        "my_KNN_model = KNN(n_neighbors=k)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzQ8yZj5Q0Eg"
      },
      "source": [
        "And there you have it. We have a K Nearest Neighbors model that will look at the 7 nearest neighbors, and gave it a nice nickname, my_KNN_model. <br> <br>\n",
        "\n",
        "**Fitting (Training) the model object:** **Supervised machine learning** training involves learning from past results (`y_train`) and past inputs (`x_train`). We pass these to our model for learning to teach our model to answer the question \"*based on these inputs, what outputs can we expect?*\"\n",
        "<br>\n",
        "\n",
        "To do this, we want to let this model learn from our data. We do so by running our model object's `.fit()` function on our training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VseIx97TSHQP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "9b344516-d2b0-40f0-c84a-7aaecb97b862"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "my_KNN_model.fit(x_train, y_train) #train the model on our past results x_train (inputs) and y_train (outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRJKWrA6hhdf"
      },
      "source": [
        "**Prediction and Evaluation:** How do we know if our model is actually good? We need to **test** our model by giving it a two-step quiz.\n",
        "\n",
        "Step 1: We let our model use our testing inputs (`x_test`) to make predictions that guess the whether the patient has benign or malignant tumors (we call these predictions `y_hat`). In this case our `y_hat`'s are a bunch of 1's and 0's that represent whether our model thinks the patient's tumor is benign or malignant.\n",
        "\n",
        "We do this by running our model object's `.predict()` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CVBaqGOIiEfP"
      },
      "outputs": [],
      "source": [
        "#make some predictions based on your testing inputs (x_test) using the predict() function\n",
        "y_hat = my_KNN_model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-ifi-bGj7fx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45606e95-daa7-4ff0-f246-7526f739779f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "y_hat #these are our predictions for our x_test inputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TjLu8V3bj8_F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3648fe6b-cdbd-4825-93f1-fd9658ccf8ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "np.array(y_test) #these are what actually happened for our x_test inputs. Do you see some differences?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbywAxezkRN8"
      },
      "source": [
        "Step 2 - Evaluation: Here we want to compare `y_hat` to `y_test`. Discuss with your team about why we should do this.\n",
        "\n",
        "One we we can compare them is visual inspection. With that said, would you recommend going through by hand and making a tally for every time the one's and zero's don't match up?\n",
        "\n",
        "Please don't do this. Please save yourself this nasty headache when we can just do math. Below is a common way to do this, called mean squared error.\n",
        "\n",
        "To get mean squared error, we compute the difference between each element in `y_hat` and `y_test` and square it and then get the average of all of that to see how far off we are."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eLFffHy1kFBh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "906f4320-38a0-4149-f2d1-ddd153d5c1dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.21052631578947367\n"
          ]
        }
      ],
      "source": [
        "total_squared_error = (np.sum((y_test - y_hat)**2)) #get the sum of all the errors (error = what we want (y_test) - what we predicted (y_hat))\n",
        "mean_squared_error = total_squared_error/len(y_test) #divide this by how many rows/observations we have\n",
        "print(mean_squared_error)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYj4HCmOmH42"
      },
      "source": [
        "**Not too bad!** MSE represents  the average error (difference) between our model and the correct result. A low mean squared error means that our model makes the correct guess most of the time.\n",
        "\n",
        "But we have to think about the nature of the problem. As Data Scientists, do we want to tell someone that we have this average error when we are predicting something as serious as breast cancer?\n",
        "\n",
        "And what does an error mean? Are we telling them they don't have a malignant tumor (y_hat=0) when they actually do (y_test = 1)? Are we telling them they have a tumor (y_hat=1) when they actually don't (y_test = 0)?\n",
        "\n",
        "We can get insights on this through a confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WCZLL1wZnfTn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "545af857-ce92-4515-e6e2-c0ce1ecdeed9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "sns.heatmap(confusion_matrix(y_test, y_hat), annot=True, fmt='g')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCR5MW5gp0ni"
      },
      "source": [
        "**What is this???** I encourage you and your team to read about it here: https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62. You should define what true positives, true negatives, false positives, and false negatives represent for this scenario.\n",
        "\n",
        "Discuss what other  findings you can take away, and talk with your instructor about metrics like accuracy, precision and recall!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbo3cueVq7EV"
      },
      "source": [
        "**The last step!!** We need to change parameters and data to improve our model as we go. If we can make even 1 more correct cancer prediction, it is worth it, right?\n",
        "\n",
        "**TODO:** Go back and change the value of `k` in the **creating your model object** section and re-run all the code below it. See what changes with your MSE and confusion matrix!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObvQ_3pPmPBf"
      },
      "source": [
        "### Code Your Own Machine Learning Model\n",
        "\n",
        "It's time for us to create our own classifier. Let's go with an [SVM](https://www.youtube.com/watch?v=_YPScrckx28). We will do the first step for you.\n",
        "\n",
        "I know it was a lot of information in the last section, but the code is *basically* the same. Since our data is already split, you will follow the same steps to fit your model, make some predictions, evaluate the performance, and visualize your confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LW9bLcghmT5d"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC\n",
        "#Create your model object -- you can go back and change the kernel argument after evaluating your data.\n",
        "svm_model = SVC(kernel='linear', C=1.0, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bk4rua0er_QF"
      },
      "source": [
        "Do you remember how to fit a model? What parts of our data do we fit/train on? You can go look at the section above for inspiration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cu2cA4YBqrNL"
      },
      "outputs": [],
      "source": [
        "#fit your model\n",
        "svm_model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oAExynzsveF"
      },
      "source": [
        "Now create some predictions and set them equal to `y_hat`. You can look above or google how to make predictions on sklearn if you get confused!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hkrx7foxsuNZ"
      },
      "outputs": [],
      "source": [
        "#create some predictions on your testing inputs and set them equal to y_hat.\n",
        "y_pred = svm_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-ANsXzitBdu"
      },
      "source": [
        "Compute your MSE below. Is this higher or lower than KNN? Which one would you recommend so far?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zz8OFQxFs8N0"
      },
      "outputs": [],
      "source": [
        "#Compute your MSE.\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ge0IHJ7AtQI2"
      },
      "source": [
        "Now, create a confusion matrix! How are the results? Do we end up with more false negatives or false positives?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xlm2k_-otFgI"
      },
      "outputs": [],
      "source": [
        "#Create a confusion matrix\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_mat)\n",
        "print(\"Classification Report:\")\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6AyqrZd9v9BR"
      },
      "source": [
        "Amazing, now try changing your kernel and seeing how your results change. You can research what kernels are available here: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html. If your interested in more ML with SKLearn you should get used to pages like these!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hd9Jqq18wFkd"
      },
      "source": [
        "### Want more?\n",
        "\n",
        "There are dozens of models available on SKLearn: https://scikit-learn.org/stable/supervised_learning.html.\n",
        "\n",
        "You can try an implement your own below, I recommend starting with logistic regression or random forests :)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CC0NmaY7v7_K"
      },
      "outputs": [],
      "source": [
        "#have fun!\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)"
      ],
      "metadata": {
        "id": "HathOfPMNu_B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on the training data\n",
        "rf_model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "k7VYH6J0N4O0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = rf_model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "uc1MF7bfOfAA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "confusion_mat = confusion_matrix(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n"
      ],
      "metadata": {
        "id": "bMpssevTOf8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_mat)\n",
        "print(\"Classification Report:\")\n",
        "print(report)"
      ],
      "metadata": {
        "id": "kuPNGnZPOgqJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}