visualizing_machine_learning_model_accuracy.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing Machine Learning Model Accuracy"
      ],
      "metadata": {
        "id": "V4vVRfyVKLSn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When working with machine learning models, the ability to interpret which hyper-parameters create the most accurate model is important. For a simple, quick illustration on how to visualize a model's complexity I will use [seaborn](https://seaborn.pydata.org/), a graphing library, to show the accuracy of differing hyperparameters for a [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier).\n",
        "\n",
        "Ultimately, something like sklearn.model_selection's [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#), which brute-force tests all hyperparameters, can provide optimum results. However, for simplicity's sake, we will simply iterate over increasing numbers of nearest neighbors. A prepossessed dataset of telecom churn will be used with the 'churn' column as the target designating turnover.\n",
        "\n",
        "First quietly install the required Python third party modules and import them."
      ],
      "metadata": {
        "id": "wyVKvZa7KQfE"
      }
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    {
      "cell_type": "code",
      "source": [
        "# install third-party modules quietly\n",
        "!pip install numpy -q\n",
        "!pip install pandas -q\n",
        "!pip install seaborn -q\n",
        "!pip install matplotlib -q\n",
        "!pip install scikit-learn -q"
      ],
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        "id": "g7Vk9ZH7tPWe"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# import modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "Iy1ET_08tZmQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, set the seaborn theme and read the [CSV data from a Github Gist](https://gist.githubusercontent.com/johnziebro/1fbf5aa62eda5a7d6a299d65764d93e6/raw/941d13e7e169095976a0875250cc1f7e8b83ee29/churn.csv) into a Pandas dataframe:"
      ],
      "metadata": {
        "id": "TiqqnvFhLU6W"
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    {
      "cell_type": "code",
      "source": [
        "# set seaborn style\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "\n",
        "# load data from Github gist\n",
        "data_url = \"https://gist.githubusercontent.com/johnziebro/1fbf5aa62eda5a7d6a299d65764d93e6/raw/941d13e7e169095976a0875250cc1f7e8b83ee29/churn.csv\"\n",
        "churn_df = pd.read_csv(data_url, index_col=0)\n",
        "churn_df.head()"
      ],
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   account_length  area_code  international_plan  voice_mail_plan  \\\n",
              "0             128        415                   0                1   \n",
              "1             107        415                   0                1   \n",
              "2             137        415                   0                0   \n",
              "3              84        408                   1                0   \n",
              "4              75        415                   1                0   \n",
              "\n",
              "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
              "0                     25              265.1              110   \n",
              "1                     26              161.6              123   \n",
              "2                      0              243.4              114   \n",
              "3                      0              299.4               71   \n",
              "4                      0              166.7              113   \n",
              "\n",
              "   total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
              "0             45.07              197.4               99             16.78   \n",
              "1             27.47              195.5              103             16.62   \n",
              "2             41.38              121.2              110             10.30   \n",
              "3             50.90               61.9               88              5.26   \n",
              "4             28.34              148.3              122             12.61   \n",
              "\n",
              "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
              "0                244.7                 91               11.01   \n",
              "1                254.4                103               11.45   \n",
              "2                162.6                104                7.32   \n",
              "3                196.9                 89                8.86   \n",
              "4                186.9                121                8.41   \n",
              "\n",
              "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
              "0                10.0                 3               2.70   \n",
              "1                13.7                 3               3.70   \n",
              "2                12.2                 5               3.29   \n",
              "3                 6.6                 7               1.78   \n",
              "4                10.1                 3               2.73   \n",
              "\n",
              "   customer_service_calls  churn  \n",
              "0                       1      0  \n",
              "1                       1      0  \n",
              "2                       0      0  \n",
              "3                       2      0  \n",
              "4                       3      0  "
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "To build the model and determine accuracy, the data will be split between the features which led to the churn as X, and the target churn result represented by y.\n",
        "\n",
        "The data is then again randomly split into two sets of observations holding back a small percentage in order to test the accuracy of the fitted model.  We use sklearn's ubiquitous [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split) to accomplish the split."
      ],
      "metadata": {
        "id": "w7j0jQFALvb_"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0DTVHU4rngF",
        "outputId": "65c1c815-c7cf-4c04-b047-7699ba3f3863"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8740629685157422\n"
          ]
        }
      ],
      "source": [
        "# Import the module\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = churn_df.drop(\"churn\", axis=1).values\n",
        "y = churn_df[\"churn\"].values\n",
        "\n",
        "# Split into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# Fit the classifier to the training data\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# Print the accuracy\n",
        "print(knn.score(X_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the model will be fitted to the data using a K Nearest Neighbors classifier ranging from 1 to 12 neighbors. K Nearest Neighbors uses a number of similar observations to determine a prediction for previously unseen data. In simplified terms if a customer has similar usage features as other customers, it is likely their resulting churn will also be similar."
      ],
      "metadata": {
        "id": "6DdtDG7jL_Ce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create neighbors\n",
        "neighbors = np.arange(1, 13)\n",
        "train_accuracies = {}\n",
        "test_accuracies = {}\n",
        "\n",
        "for neighbor in neighbors:\n",
        "  \n",
        "\t# Set up a KNN Classifier\n",
        "\tknn = KNeighborsClassifier(n_neighbors=neighbor)\n",
        "  \n",
        "\t# Fit the model\n",
        "\tknn.fit(X_train, y_train)\n",
        "  \n",
        "\t# Compute accuracy\n",
        "\ttrain_accuracies[neighbor] = knn.score(X_train, y_train)\n",
        "\ttest_accuracies[neighbor] = knn.score(X_test, y_test)\n",
        " \n",
        "print(\"Train Accuracies:\", train_accuracies, '\\n', \"Test Accuracies:\", test_accuracies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11s8-IVoruDs",
        "outputId": "edfa3731-9c58-4566-9312-3df27ed04691"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracies: {1: 1.0, 2: 0.9036009002250562, 3: 0.9114778694673669, 4: 0.8945986496624156, 5: 0.8953488372093024, 6: 0.8893473368342085, 7: 0.8885971492873218, 8: 0.8863465866466617, 9: 0.8870967741935484, 10: 0.8840960240060015, 11: 0.8874718679669917, 12: 0.8837209302325582} \n",
            " Test Accuracies: {1: 0.7946026986506747, 2: 0.8605697151424287, 3: 0.8500749625187406, 4: 0.8695652173913043, 5: 0.8740629685157422, 6: 0.8650674662668666, 7: 0.8710644677661169, 8: 0.863568215892054, 9: 0.8725637181409296, 10: 0.8665667166416792, 11: 0.8710644677661169, 12: 0.8710644677661169}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now create a seaborn friendly dataframe and determine the number of k nearest neighbors which produced the most accurate tests. Once we have the dataframe, we can us [panda's .idxmax() method](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html) to return the first column index of the maximum value in the 'test_accuracies' row."
      ],
      "metadata": {
        "id": "584A4QDXMLFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create DataFrame from accuracies\n",
        "accuracies = pd.DataFrame(\n",
        "    [train_accuracies, test_accuracies], \n",
        "    columns=neighbors, \n",
        "    index=['train accuracies', 'test accuracies']\n",
        ")\n",
        "accuracies"
      ],
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          "height": 112
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        "id": "Wiryu8oFrw7d",
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      "execution_count": null,
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        {
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              "                        1         2         3         4         5         6   \\\n",
              "train accuracies  1.000000  0.903601  0.911478  0.894599  0.895349  0.889347   \n",
              "test accuracies   0.794603  0.860570  0.850075  0.869565  0.874063  0.865067   \n",
              "\n",
              "                        7         8         9         10        11        12  \n",
              "train accuracies  0.888597  0.886347  0.887097  0.884096  0.887472  0.883721  \n",
              "test accuracies   0.871064  0.863568  0.872564  0.866567  0.871064  0.871064  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train accuracies</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.903601</td>\n",
              "      <td>0.911478</td>\n",
              "      <td>0.894599</td>\n",
              "      <td>0.895349</td>\n",
              "      <td>0.889347</td>\n",
              "      <td>0.888597</td>\n",
              "      <td>0.886347</td>\n",
              "      <td>0.887097</td>\n",
              "      <td>0.884096</td>\n",
              "      <td>0.887472</td>\n",
              "      <td>0.883721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test accuracies</th>\n",
              "      <td>0.794603</td>\n",
              "      <td>0.860570</td>\n",
              "      <td>0.850075</td>\n",
              "      <td>0.869565</td>\n",
              "      <td>0.874063</td>\n",
              "      <td>0.865067</td>\n",
              "      <td>0.871064</td>\n",
              "      <td>0.863568</td>\n",
              "      <td>0.872564</td>\n",
              "      <td>0.866567</td>\n",
              "      <td>0.871064</td>\n",
              "      <td>0.871064</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b05d8cb8-d77c-4d6a-b2a8-a2874093f596')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b05d8cb8-d77c-4d6a-b2a8-a2874093f596 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b05d8cb8-d77c-4d6a-b2a8-a2874093f596');\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",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# determine the most accurate number of neighbors\n",
        "most_accurate_neighbors_count = accuracies.loc[\"test accuracies\"].idxmax(0)\n",
        "most_accurate_neighbors_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPbzrQs_ICwF",
        "outputId": "341b2e33-9fb6-42cc-b8fa-950ebbe95f91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, since we've trained and tested the KNN classifier with 1 to 12 neighbors and determined that 5 neighbors at 87.4% produce the most accurate results, we can graph the results of each of the iterations."
      ],
      "metadata": {
        "id": "q1tjc99lMwuA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot training accuracies\n",
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches(16, 8)\n",
        "sns.lineplot(data=accuracies.T, ax=ax)\n",
        "ax.set(title='KNN: Varying Accuracy by Number of Neighbors', xlabel=\"Accuracy\", ylabel=\"Number of Neighbors\")\n",
        "\n",
        "# add annotation\n",
        "ax.axvline(most_accurate_neighbors_count, ls='--', linewidth=1, color='grey');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "MTmtAyBdVM7H",
        "outputId": "7d528ea4-8a7c-4dd9-a959-6f4aef927d2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}