Naive_Bayes_Case_Study.ipynb

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        "# Predicting Health Outcomes of Horses with Machine Learning\n",
        "\n",
        "### Context\n",
        "\n",
        "Welcome to the **Scaler Health Analytics** team! Our objective is to leverage advanced analytics to predict health outcomes in horses, enabling veterinarians to make more informed decisions that can improve treatment strategies and overall horse health management. Utilizing machine learning techniques, particularly classification algorithms, you will analyze clinical data from horses to predict various health outcomes.\n",
        "\n",
        "### Dataset Description\n",
        "\n",
        "You have been provided with a comprehensive dataset containing clinical records of horses treated in a veterinary hospital. The data encapsulates a variety of clinical attributes that offer insights into the general health and medical condition of each horse. Each record in the dataset is described by the following features:\n",
        "\n",
        "### Clinical Attributes:\n",
        "- **id:** Unique identifier for each horse.\n",
        "- **surgery:** Whether the horse had surgery (Yes/No).\n",
        "- **age:** Age of the horse.\n",
        "- **hospital_number:** Unique number assigned to the horse at the hospital.\n",
        "- **rectal_temp:** Rectal temperature of the horse.\n",
        "- **pulse:** Pulse rate in beats per minute.\n",
        "- **respiratory_rate:** Respiratory rate in breaths per minute.\n",
        "- **temp_of_extremities:** Temperature of extremities (a possible indicator of shock).\n",
        "- **peripheral_pulse:** Quality of the peripheral pulse.\n",
        "- **mucous_membrane:** Color of the mucous membranes, which can indicate blood circulation quality.\n",
        "- **capillary_refill_time:** Time taken for color to return to mucous membrane after pressure is applied.\n",
        "- **pain:** Horse's pain level (graded).\n",
        "- **peristalsis:** Intestinal activity observed.\n",
        "- **abdominal_distention:** Any distention of the abdomen.\n",
        "- **nasogastric_tube:** Whether a nasogastric tube has been placed.\n",
        "- **nasogastric_reflux:** Any nasogastric reflux noted.\n",
        "- **nasogastric_reflux_ph:** pH of the nasogastric reflux.\n",
        "- **rectal_exam_feces:** Findings of the rectal examination of feces.\n",
        "- **abdomen:** Detailed examination findings of the abdomen.\n",
        "- **packed_cell_volume:** Packed cell volume, indicating hydration status and blood loss.\n",
        "- **total_protein:** Total protein levels in blood.\n",
        "- **abdomo_appearance:** Appearance of abdominal fluid.\n",
        "- **abdomo_protein:** Protein level in abdominal fluid.\n",
        "- **surgical_lesion:** Presence of surgical lesions.\n",
        "- **lesion_1, lesion_2, lesion_3:** Type and location of lesions identified.\n",
        "- **cp_data:** Clinical pathology data.\n",
        "- **outcome:** Health outcome of the horse (e.g., recovered, euthanized, died).\n",
        "\n",
        "Your task is to use machine learning models to predict the 'outcome' for each horse and identify key predictors of health outcomes. This project will contribute significantly to improving the predictive models used in veterinary practices."
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            "--2024-08-20 17:41:29--  https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/072/101/original/health_outcome_dataset.csv\n",
            "Resolving d2beiqkhq929f0.cloudfront.net (d2beiqkhq929f0.cloudfront.net)... 18.164.173.18, 18.164.173.58, 18.164.173.110, ...\n",
            "Connecting to d2beiqkhq929f0.cloudfront.net (d2beiqkhq929f0.cloudfront.net)|18.164.173.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 229201 (224K) [text/plain]\n",
            "Saving to: ‘health_outcome_dataset.csv’\n",
            "\n",
            "\r          health_ou   0%[                    ]       0  --.-KB/s               \rhealth_outcome_data 100%[===================>] 223.83K  --.-KB/s    in 0.04s   \n",
            "\n",
            "2024-08-20 17:41:29 (5.91 MB/s) - ‘health_outcome_dataset.csv’ saved [229201/229201]\n",
            "\n"
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      "source": [
        "!wget https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/072/101/original/health_outcome_dataset.csv"
      ]
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        "import warnings\n",
        "\n",
        "# Ignore all warnings\n",
        "warnings.filterwarnings(\"ignore\")"
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        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"health_outcome_dataset.csv\")\n",
        "df.head()"
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            "text/plain": [
              "   id surgery    age  hospital_number  rectal_temp  pulse  respiratory_rate  \\\n",
              "0   0     yes  adult           530001         38.1  132.0              24.0   \n",
              "1   1     yes  adult           533836         37.5   88.0              12.0   \n",
              "2   2     yes  adult           529812         38.3  120.0              28.0   \n",
              "3   3     yes  adult          5262541         37.1   72.0              30.0   \n",
              "4   4      no  adult          5299629         38.0   52.0              48.0   \n",
              "\n",
              "  temp_of_extremities peripheral_pulse mucous_membrane  ...  \\\n",
              "0                cool          reduced   dark_cyanotic  ...   \n",
              "1                cool           normal   pale_cyanotic  ...   \n",
              "2                cool          reduced       pale_pink  ...   \n",
              "3                cold          reduced       pale_pink  ...   \n",
              "4              normal           normal     normal_pink  ...   \n",
              "\n",
              "  packed_cell_volume total_protein abdomo_appearance abdomo_protein  \\\n",
              "0               57.0           8.5     serosanguious            3.4   \n",
              "1               33.0          64.0     serosanguious            2.0   \n",
              "2               37.0           6.4     serosanguious            3.4   \n",
              "3               53.0           7.0            cloudy            3.9   \n",
              "4               47.0           7.3            cloudy            2.6   \n",
              "\n",
              "  surgical_lesion lesion_1  lesion_2 lesion_3 cp_data     outcome  \n",
              "0             yes     2209         0        0      no        died  \n",
              "1             yes     2208         0        0      no  euthanized  \n",
              "2             yes     5124         0        0      no       lived  \n",
              "3             yes     2208         0        0     yes       lived  \n",
              "4              no        0         0        0     yes       lived  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <th>id</th>\n",
              "      <th>surgery</th>\n",
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              "      <th>lesion_1</th>\n",
              "      <th>lesion_2</th>\n",
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              "      <th>0</th>\n",
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              "      <td>57.0</td>\n",
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              "      <td>37.5</td>\n",
              "      <td>88.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>cool</td>\n",
              "      <td>normal</td>\n",
              "      <td>pale_cyanotic</td>\n",
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              "      <td>33.0</td>\n",
              "      <td>64.0</td>\n",
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              "      <td>38.3</td>\n",
              "      <td>120.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>cool</td>\n",
              "      <td>reduced</td>\n",
              "      <td>pale_pink</td>\n",
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              "      <td>37.0</td>\n",
              "      <td>6.4</td>\n",
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              "      <td>yes</td>\n",
              "      <td>5124</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>72.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>47.0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>cloudy</td>\n",
              "      <td>2.6</td>\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c67d0bc1-3bc8-4b97-9c9b-732f04f8da3a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "OnIhQ0UA9g1C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "outputId": "bc166038-9bef-49fd-f2b7-acb25efdced3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                         0\n",
              "surgery                    0\n",
              "age                        0\n",
              "hospital_number            0\n",
              "rectal_temp                0\n",
              "pulse                      0\n",
              "respiratory_rate           0\n",
              "temp_of_extremities       39\n",
              "peripheral_pulse          60\n",
              "mucous_membrane           21\n",
              "capillary_refill_time      6\n",
              "pain                      44\n",
              "peristalsis               20\n",
              "abdominal_distention      23\n",
              "nasogastric_tube          80\n",
              "nasogastric_reflux        21\n",
              "nasogastric_reflux_ph      0\n",
              "rectal_exam_feces        190\n",
              "abdomen                  213\n",
              "packed_cell_volume         0\n",
              "total_protein              0\n",
              "abdomo_appearance         48\n",
              "abdomo_protein             0\n",
              "surgical_lesion            0\n",
              "lesion_1                   0\n",
              "lesion_2                   0\n",
              "lesion_3                   0\n",
              "cp_data                    0\n",
              "outcome                    0\n",
              "dtype: int64"
            ],
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              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>hospital_number</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rectal_temp</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>pulse</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>respiratory_rate</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>temp_of_extremities</th>\n",
              "      <td>39</td>\n",
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              "      <th>peripheral_pulse</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
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              "      <th>mucous_membrane</th>\n",
              "      <td>21</td>\n",
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              "      <td>6</td>\n",
              "    </tr>\n",
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              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>peristalsis</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>abdominal_distention</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nasogastric_tube</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nasogastric_reflux</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nasogastric_reflux_ph</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rectal_exam_feces</th>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>abdomen</th>\n",
              "      <td>213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>packed_cell_volume</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_protein</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>abdomo_appearance</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>abdomo_protein</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surgical_lesion</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lesion_1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lesion_2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lesion_3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp_data</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>outcome</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "df.drop('lesion_3', inplace = True, axis = 1)\n",
        "\n",
        "# Create imputer objects\n",
        "num_imputer = SimpleImputer(strategy='median')  # Imputer for numerical data\n",
        "cat_imputer = SimpleImputer(strategy='most_frequent')  # Imputer for categorical data\n",
        "\n",
        "# Define columns by type\n",
        "num_cols = ['rectal_temp', 'pulse', 'respiratory_rate']  # Numerical columns\n",
        "cat_cols = ['temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time',\n",
        "            'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube',\n",
        "            'nasogastric_reflux', 'abdomen', 'abdomo_appearance', 'rectal_exam_feces']  # Categorical columns\n",
        "\n",
        "# Apply imputation\n",
        "df[num_cols] = num_imputer.fit_transform(df[num_cols])\n",
        "df[cat_cols] = cat_imputer.fit_transform(df[cat_cols])"
      ],
      "metadata": {
        "id": "xxmaSkFe-9fP"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['outcome'].value_counts()"
      ],
      "metadata": {
        "id": "kIChJZ7TUypt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "048a3548-b71d-4825-8ec7-0b83fcce9d46"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "outcome\n",
              "lived         574\n",
              "died          410\n",
              "euthanized    251\n",
              "Name: count, dtype: int64"
            ],
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              "      <th>outcome</th>\n",
              "      <th></th>\n",
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              "      <td>251</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "# Assesments for Naive Bayes-I\n"
      ],
      "metadata": {
        "id": "mWqrTpO91_LW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "### Pulse Rate Distribution\n",
        "\n",
        "#### Context:\n",
        "Analyzing the pulse rate distribution among horses with different health outcomes can provide critical insights into physiological states associated with various health conditions. This analysis can help veterinarians understand how pulse rate might correlate with the severity or type of condition a horse is facing.\n",
        "\n",
        "#### Task:\n",
        "Create a visual representation to analyze the pulse rate distribution across different health outcomes using the 'pulse' and 'outcome' columns in the dataset.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Group and Count Data:** Use the dataset to group entries by 'outcome' and calculate the average 'pulse' for each outcome category.\n",
        "2. **Visualize Data:** Generate a box plot to display the distribution of pulse rates for each health outcome category. This will help in visually comparing the pulse rate variability associated with different outcomes.\n",
        "3. **Analyze Trends:** Examine the box plot to determine which health outcome is associated with the highest average pulse rate and which shows the most variability in pulse rates.\n",
        "\n",
        "#### Question:\n",
        "After analyzing the box plot representing the pulse rate distribution for different health outcomes, identify the correct statements regarding pulse rates.\n",
        "\n",
        "#### Options:\n",
        "A) The average pulse rate is highest among horses that lived, indicating recovery from potentially distressing conditions.\n",
        "\n",
        "B) The average pulse rate is lowest for horses that were euthanized, suggesting less physiological distress before euthanasia.\n",
        "\n",
        "C) The average pulse rate is highest among horses that died, suggesting a correlation between high pulse rates and critical health conditions leading to death.\n",
        "\n",
        "D) The box plot shows no significant difference in the average pulse rates across different health outcomes."
      ],
      "metadata": {
        "id": "L7g1lN5b1iv2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# TODO: Group the dataframe by 'outcome' and calculate the average 'pulse' for each outcome category.\n",
        "pulse_outcome_data = df.groupby('outcome')['pulse'].mean()  # Consider what function might summarize the data effectively.\n",
        "print(pulse_outcome_data)\n",
        "\n",
        "# Set a custom color palette (optional)\n",
        "custom_palette = sns.color_palette(\"pastel\")\n",
        "\n",
        "# TODO: Utilize seaborn to create a box plot that shows variation within grouped data.\n",
        "sns.boxplot(x='outcome', y='pulse', data=df, palette=custom_palette)\n",
        "plt.title('Pulse Rate Distribution by Health Outcome')\n",
        "plt.xlabel('Health Outcome')\n",
        "plt.ylabel('Pulse Rate')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()  # Adjust layout to not cut off labels\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "OwG1FsdlzvPC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "38d5a79f-0034-4e52-c058-ac51f0843df8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "outcome\n",
            "died          90.207317\n",
            "euthanized    86.573705\n",
            "lived         68.918118\n",
            "Name: pulse, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Rectal Temperature Correlation\n",
        "\n",
        "#### Context:\n",
        "Understanding how rectal temperature correlates with health outcomes in horses can provide valuable insights for veterinary treatment strategies. Rectal temperature is a critical clinical parameter and its deviations from the norm can indicate various health conditions.\n",
        "\n",
        "#### Task:\n",
        "Analyze the impact of different rectal temperature categories on health outcomes. The rectal temperatures will be categorized into hypothermia, normal, and fever to observe how these conditions correlate with health outcomes.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Create Temperature Categories:** Add a new column to the dataset, 'temp_category', which categorizes rectal temperature into hypothermia (<37.5°C), normal (37.5-38.5°C), and fever (>38.5°C).\n",
        "2. **Group Data by Temperature Category:** Group the dataset by 'temp_category' and 'outcome' to analyze the distribution of health outcomes within each temperature category.\n",
        "3. **Visualize and Analyze Data:** Create a bar chart to visualize the count of each health outcome within the different temperature categories. This will help identify any significant correlations between rectal temperature and health outcomes.\n",
        "\n",
        "#### Question:\n",
        "After analyzing the bar chart which depicts the count of health outcomes within each rectal temperature category, identify the correct statement regarding the correlation between rectal temperature categories and health outcomes.\n",
        "\n",
        "#### Options:\n",
        "A) Horses with fever have the highest survival rate.\n",
        "\n",
        "B) Normal rectal temperature is associated with the best health outcomes.\n",
        "\n",
        "C) Horses with hypothermia are more likely to be euthanized.\n",
        "\n",
        "D) There is no significant relationship between rectal temperature categories and health outcomes.\n"
      ],
      "metadata": {
        "id": "0YJZ51QR3Dxk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define function to categorize rectal temperature\n",
        "def categorize_temp(temp):\n",
        "    # TODO: Return the category based on temperature values (use if-elif-else structure).\n",
        "    if temp < 37.5:\n",
        "        return 'Hypothermia'\n",
        "    elif 37.5 <= temp <= 38.5:\n",
        "        return 'Normal'\n",
        "    else:\n",
        "        return 'Fever'\n",
        "\n",
        "# Apply function to create a new column\n",
        "df['temp_category'] = df['rectal_temp'].apply(categorize_temp)  # TODO: Choose the appropriate DataFrame method to apply a function.\n",
        "\n",
        "# Grouping data by temperature category and outcome for plotting\n",
        "grouped_data = df.groupby(['temp_category', 'outcome']).count()['id'].reset_index()  # TODO: Group by two columns and count a unique identifier.\n",
        "grouped_data.columns = ['Temperature Category', 'Outcome', 'Count']\n",
        "\n",
        "# Create a pivot table for visualization\n",
        "pivot_table = pd.pivot_table(grouped_data, values='Count', index='Temperature Category', columns='Outcome', fill_value=0)  # TODO: Fill the blanks to correctly set up the pivot table parameters.\n",
        "\n",
        "# Plotting the data\n",
        "pivot_table.plot(kind='bar', stacked=True)\n",
        "plt.title('Health Outcomes by Rectal Temperature Category')\n",
        "plt.xlabel('Temperature Category')\n",
        "plt.ylabel('Count of Outcomes')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend(title='Health Outcome')\n",
        "plt.tight_layout()  # Adjust layout to not cut off labels\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SNEr0RjO1k5Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "a8f8f1ca-c167-49f3-ddbf-b1471d2b0305"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Naive Bayes Assumption and Its Impact\n",
        "\n",
        "#### Context:\n",
        "The Naive Bayes classifier, while simple and effective, assumes feature independence which may affect its application in complex datasets like \"Predict Horse Health Outcomes.\"\n",
        "\n",
        "#### Task:\n",
        "Evaluate how the Naive Bayes assumption of feature independence affects its performance on a dataset with interdependent features such as surgery, age, pulse, and mucous membrane status.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Review the Assumption:** Consider the independence assumption of Naive Bayes.\n",
        "2. **Assess Potential Impacts:** Reflect on how this assumption might influence the model's effectiveness, especially where features might be interrelated.\n",
        "3. **Select the Most Accurate Statement:** Choose the statement that best describes Naive Bayes' assumption and its impact on the dataset.\n",
        "\n",
        "#### Statements:\n",
        "1. **Naive Bayes treats all features as equally interdependent.** This is suited for datasets with strong feature correlations, potentially making it less effective for independent feature scenarios.\n",
        "   \n",
        "2. **Naive Bayes assumes independence among features.** This could pose challenges if features like pulse and mucous membrane status, which may be related, are not truly independent.\n",
        "   \n",
        "3. **Naive Bayes expects numerical features to behave like categorical variables.** This might complicate handling datasets with a mix of feature types.\n",
        "   \n",
        "4. **Naive Bayes requires features to be independent and may need preprocessing to handle correlations like those between pulse and mucous membrane status.**\n",
        "\n",
        "#### Question:\n",
        "Considering the assumption and potential impacts of Naive Bayes on the horse health outcomes dataset, which statement number is most accurate?\n",
        "\n",
        "#### Options:\n",
        "A) Statement 1\n",
        "\n",
        "B) Statement 2\n",
        "\n",
        "C) Statement 3\n",
        "\n",
        "D) Statement 4"
      ],
      "metadata": {
        "id": "ge2Ock_O6YkO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Analyzing Pain and Euthanasia Decisions\n",
        "\n",
        "#### Context:\n",
        "Understanding the decision-making process in veterinary care, particularly euthanasia, can be crucial in managing horse health outcomes. Analyzing how pain levels influence these decisions is particularly significant.\n",
        "\n",
        "#### Task:\n",
        "Using Bayes' Theorem, calculate the probability that a horse is euthanized given it experiences severe pain and assess what this indicates about the role of pain in euthanasia decisions.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Review Given Probabilities:**\n",
        "   - P(Severe Pain) = 0.20\n",
        "   - P(Euthanized) = 0.25\n",
        "   - P(Severe Pain | Euthanized) = 0.50\n",
        "\n",
        "2. **Calculate the Probability:** Use Bayes' Theorem to find P(Euthanized | Severe Pain).\n",
        "\n",
        "3. **Interpret the Result:** Consider what the calculated probability implies about the impact of severe pain on euthanasia decisions.\n",
        "\n",
        "\n",
        "#### Question:\n",
        "Based on the calculation using Bayes' Theorem, what is the probability that a horse is euthanized given that it is experiencing severe pain? What does this probability suggest about the impact of severe pain on the decision to euthanize?\n",
        "\n",
        "#### Options:\n",
        "A) 0.40\n",
        "\n",
        "B) 0.50\n",
        "\n",
        "C) 0.75\n",
        "\n",
        "D) 0.625\n"
      ],
      "metadata": {
        "id": "A7pJUe_b73uB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Given probabilities\n",
        "P_SeverePain = 0.20\n",
        "P_Euthanized = 0.25\n",
        "P_SeverePain_Euthanized = 0.50\n",
        "\n",
        "# Bayes' Theorem calculation\n",
        "P_Euthanized_SeverePain = (P_SeverePain_Euthanized * P_Euthanized)/P_SeverePain  # TODO: Calculate the probability using Bayes' Theorem components given above.\n",
        "print(\"Probability of being euthanized given severe pain: {:.3f}\".format(P_Euthanized_SeverePain))"
      ],
      "metadata": {
        "id": "qapca5kA6GF0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb9b2d61-55b0-4bea-e7a9-bf2d41e7949d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of being euthanized given severe pain: 0.625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Evaluating Feature Significance with Chi-square Test\n",
        "\n",
        "#### Context:\n",
        "In statistical analysis for data, the Chi-square test of independence is essential for determining the association between features. This test helps identify which features are statistically significant in relation to the outcome variable in a dataset.\n",
        "\n",
        "#### Task:\n",
        "Perform a Chi-square test of independence on each Categorical feature against the 'outcome' variable to determine their significance in predicting health outcomes of horses. Given the significance level of 0.05, identify which Categoricalfeature is not significantly associated with the outcome.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Perform Chi-square Tests:** Apply the Chi-square test to each Categorical feature against the 'outcome' variable.\n",
        "2. **Interpret Results:** Evaluate the p-values to determine which Categorical feature(s) fail to show a significant association with the outcome, suggesting they may not be useful for predictive modeling in this context.\n",
        "\n",
        "\n",
        "#### Question:\n",
        "Based on a significance level of 0.05, which Categorical feature was found to be not significantly associated with the 'outcome' variable, therefore failing the Chi-square test?\n",
        "\n",
        "#### Options:\n",
        "A) temp_of_extremities\n",
        "\n",
        "B) peripheral_pulse\n",
        "\n",
        "C) pain\n",
        "\n",
        "D) All columns passed"
      ],
      "metadata": {
        "id": "wiPhYuckKxAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chi2_contingency  # TODO: Import the necessary function for performing the Chi-square test.\n",
        "\n",
        "threshold = .05\n",
        "\n",
        "print(f'{\"Column\":<25} | Test result')\n",
        "print('----------------------------------------')\n",
        "\n",
        "for column in cat_cols:\n",
        "    # Create a contingency table\n",
        "    contingency_table = pd.crosstab(df[column], df['outcome'])  # TODO: Create a contingency table from two categorical columns.\n",
        "\n",
        "    # Perform the Chi-Square test\n",
        "    chi2, p, dof, expected_freq = chi2_contingency(contingency_table)  # TODO: Apply the imported function to compute the Chi-square statistic and p-value.\n",
        "\n",
        "    print(f'{column:<25} |   ', '\\033[32mPassed' if p < threshold else '\\033[31mFailed', '\\033[0m')"
      ],
      "metadata": {
        "id": "y9PP-lsPX56r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62bc84ed-04e9-4ef8-dd67-a5b8270885fd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column                    | Test result\n",
            "----------------------------------------\n",
            "temp_of_extremities       |    \u001b[32mPassed \u001b[0m\n",
            "peripheral_pulse          |    \u001b[32mPassed \u001b[0m\n",
            "mucous_membrane           |    \u001b[32mPassed \u001b[0m\n",
            "capillary_refill_time     |    \u001b[32mPassed \u001b[0m\n",
            "pain                      |    \u001b[32mPassed \u001b[0m\n",
            "peristalsis               |    \u001b[32mPassed \u001b[0m\n",
            "abdominal_distention      |    \u001b[32mPassed \u001b[0m\n",
            "nasogastric_tube          |    \u001b[32mPassed \u001b[0m\n",
            "nasogastric_reflux        |    \u001b[32mPassed \u001b[0m\n",
            "abdomen                   |    \u001b[32mPassed \u001b[0m\n",
            "abdomo_appearance         |    \u001b[32mPassed \u001b[0m\n",
            "rectal_exam_feces         |    \u001b[32mPassed \u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "### Data Preprocessing for Modelling\n",
        "\n",
        "#### Context:\n",
        "In preparation for building a Gaussian Naive Bayes model to predict horse health outcomes, the initial step involves preprocessing the dataset. This includes dropping less relevant columns and applying appropriate transformations to the remaining data.\n",
        "\n",
        "#### Task:\n",
        "Prepare the data by dropping specified columns and applying transformations suitable for a Gaussian Naive Bayes model.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Drop Columns:**\n",
        "   - Remove the 'id' columns from the dataset.\n",
        "2. **Apply Transformations:**\n",
        "   - Use `StandardScaler` to normalize numerical data.\n",
        "   - Apply `OrdinalEncoder` to encode categorical data that handles unknown values.\n",
        "3. **Split the Data:**\n",
        "   - Use `train_test_split` with a `random_state=0` to divide the data into training and test sets, targeting 'outcome' as the label. `test_size = 0.2`\n",
        "\n",
        "#### Question:\n",
        "After preprocessing, which categorical feature ends up with the highest number of unique ordinal values?\n",
        "\n",
        "#### Options:\n",
        "A) temp_of_extremities\n",
        "\n",
        "B) pain and mucous_membrane\n",
        "\n",
        "C) cp_data and abdomen\n",
        "\n",
        "D) nasogastric_reflux\n"
      ],
      "metadata": {
        "id": "7Xn4wefJRqYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, OrdinalEncoder  # TODO: Import necessary preprocessing classes.\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "# Drop unnecessary columns\n",
        "df_temp = df.drop(['id'], axis=1)\n",
        "\n",
        "# Identify numerical and categorical columns\n",
        "numerical_cols = df_temp.select_dtypes(include=['int', 'float']).columns.tolist()  # TODO: Specify data types to select numerical (int and float both) columns.\n",
        "categorical_cols = df_temp.select_dtypes(include=['object']).columns.tolist()  # TODO: Specify the data type for categorical(object).\n",
        "\n",
        "# Remove the target column from the feature lists\n",
        "categorical_cols.remove('outcome')\n",
        "\n",
        "# Prepare target and features\n",
        "X = df_temp.drop('outcome', axis=1)\n",
        "y = df_temp['outcome']\n",
        "\n",
        "# Split the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)  # TODO: Use the appropriate function to split the data.\n",
        "\n",
        "# Create transformers for numerical and categorical data\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', StandardScaler(), numerical_cols),  # TODO: Choose the correct transformer for numerical data.\n",
        "        ('cat', OrdinalEncoder(), categorical_cols)  # TODO: Choose the correct transformer for categorical data that handles unknown categories.\n",
        "    ])\n",
        "\n",
        "# Apply preprocessing\n",
        "X_train = preprocessor.fit_transform(X_train)  # TODO: Fit and Apply the preprocessor to training data.\n",
        "X_test = preprocessor.transform(X_test)  # TODO: Apply the preprocessor to test data.\n",
        "\n",
        "\n",
        "print(f'{\"Column\":<25} | Number of unique values')\n",
        "print('------'*10)\n",
        "for i in categorical_cols:\n",
        "    print(f'{i:<25} |   ', f'\\033[32m{df[i].unique()}' , '\\033[0m')\n"
      ],
      "metadata": {
        "id": "rLICqN4eRrpk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed0300d9-74c3-40d2-b340-f750d1df1e8e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column                    | Number of unique values\n",
            "------------------------------------------------------------\n",
            "surgery                   |    \u001b[32m['yes' 'no'] \u001b[0m\n",
            "age                       |    \u001b[32m['adult' 'young'] \u001b[0m\n",
            "temp_of_extremities       |    \u001b[32m['cool' 'cold' 'normal' 'warm'] \u001b[0m\n",
            "peripheral_pulse          |    \u001b[32m['reduced' 'normal' 'absent' 'increased'] \u001b[0m\n",
            "mucous_membrane           |    \u001b[32m['dark_cyanotic' 'pale_cyanotic' 'pale_pink' 'normal_pink' 'bright_pink'\n",
            " 'bright_red'] \u001b[0m\n",
            "capillary_refill_time     |    \u001b[32m['more_3_sec' 'less_3_sec' '3'] \u001b[0m\n",
            "pain                      |    \u001b[32m['depressed' 'mild_pain' 'extreme_pain' 'alert' 'severe_pain' 'slight'] \u001b[0m\n",
            "peristalsis               |    \u001b[32m['absent' 'hypomotile' 'normal' 'hypermotile' 'distend_small'] \u001b[0m\n",
            "abdominal_distention      |    \u001b[32m['slight' 'moderate' 'none' 'severe'] \u001b[0m\n",
            "nasogastric_tube          |    \u001b[32m['slight' 'none' 'significant'] \u001b[0m\n",
            "nasogastric_reflux        |    \u001b[32m['less_1_liter' 'more_1_liter' 'none' 'slight'] \u001b[0m\n",
            "rectal_exam_feces         |    \u001b[32m['decreased' 'absent' 'normal' 'increased' 'serosanguious'] \u001b[0m\n",
            "abdomen                   |    \u001b[32m['distend_small' 'distend_large' 'normal' 'firm' 'other'] \u001b[0m\n",
            "abdomo_appearance         |    \u001b[32m['serosanguious' 'cloudy' 'clear'] \u001b[0m\n",
            "surgical_lesion           |    \u001b[32m['yes' 'no'] \u001b[0m\n",
            "cp_data                   |    \u001b[32m['no' 'yes'] \u001b[0m\n",
            "temp_category             |    \u001b[32m['Normal' 'Hypothermia' 'Fever'] \u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Bernoulli Naive Bayes Classifier\n",
        "\n",
        "#### Context:\n",
        "Evaluating the performance of the Bernoulli Naive Bayes classifier not only through traditional classification metrics but also by analyzing the types of errors made can offer deeper insights into the model's behavior. A confusion matrix is particularly useful for this purpose as it visualizes the performance of an algorithm by showing the actual versus predicted classifications.\n",
        "\n",
        "#### Task:\n",
        "Generate a confusion matrix for the Bernoulli Naive Bayes model and analyze which outcome categories are most frequently misclassified.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Prepare and Train the Model:**\n",
        "   - Implement the Bernoulli Naive Bayes classifier and train it on the dataset.\n",
        "   - Predict the outcomes using the test set.\n",
        "\n",
        "2. **Generate a Confusion Matrix:**\n",
        "   - Create a confusion matrix to visualize the model’s predictions against the actual values.\n",
        "\n",
        "3. **Analyze Misclassifications:**\n",
        "   - Examine the confusion matrix to determine which category is most frequently misclassified as \"Euthanized.\"\n",
        "\n",
        "#### Question:\n",
        "According to the confusion matrix generated from the Bernoulli Naive Bayes model, which category is most frequently incorrectly predicted as \"Euthanized\"?\n",
        "\n",
        "#### Options:\n",
        "A) Died\n",
        "\n",
        "B) Lived\n",
        "\n",
        "C) Both 'Died' and 'Lived' are equally misclassified as 'Euthanized'\n",
        "\n",
        "D) Neither 'Died' nor 'Lived' is misclassified as 'Euthanized'"
      ],
      "metadata": {
        "id": "y6xy-nOxecZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB  # TODO: Correctly import the Bernoulli Naive Bayes classifier.\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Encoding the 'outcome' variable\n",
        "outcome_map = {'died': 0, 'euthanized': 1, 'lived': 2}\n",
        "y_train_encoded = y_train.map(outcome_map)  # TODO: Apply the mapping to the 'y_train' series.\n",
        "y_test_encoded = y_test.map(outcome_map)  # TODO: Apply the mapping to the 'y_test' series.\n",
        "\n",
        "# Train Bernoulli Naive Bayes classifier\n",
        "bernoulli_nb = BernoulliNB()\n",
        "bernoulli_nb.fit(X_train, y_train_encoded)  # TODO: Fit the Bernoulli Naive Bayes model using training data.\n",
        "y_pred_ber = bernoulli_nb.predict(X_test)  # TODO: Predict outcomes using the test set.\n",
        "\n",
        "print(\"Bernoulli Naive Bayes Classification Report:\")\n",
        "print(classification_report(y_test_encoded, y_pred_ber, target_names=['Died', 'Euthanized', 'Lived']))  # TODO: Fill in the classification report with the appropriate arguments.\n",
        "\n",
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(y_test_encoded, y_pred_ber)  # TODO: Provide the correct variables to generate the confusion matrix.\n",
        "categories = ['Died', 'Euthanized', 'Lived']\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=categories, yticklabels=categories)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix for Bernoulli Naive Bayes Classifier')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ezpJ4zCMedXa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "outputId": "0c212088-43f4-49a3-ae7a-82f56adbb748"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bernoulli Naive Bayes Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Died       0.66      0.78      0.71        89\n",
            "  Euthanized       0.55      0.67      0.60        42\n",
            "       Lived       0.74      0.58      0.65       116\n",
            "\n",
            "    accuracy                           0.66       247\n",
            "   macro avg       0.65      0.67      0.65       247\n",
            "weighted avg       0.68      0.66      0.66       247\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "# Assesments for Naive Bayes-II\n"
      ],
      "metadata": {
        "id": "mNqoWbeoXCjz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Mucous Membrane Condition\n",
        "\n",
        "#### Context:\n",
        "The condition of a horse's mucous membrane can provide valuable insights into its circulatory and respiratory health, which are critical factors in determining the overall health status. Analyzing how different conditions of mucous membranes correlate with health outcomes can help in early diagnosis and management of health issues.\n",
        "\n",
        "#### Task:\n",
        "Investigate the relationship between the condition of the mucous membrane and health outcomes in horses.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Group Data by Mucous Membrane Condition:** Use the dataset to group entries by 'mucous_membrane' and 'outcome' to analyze the distribution of health outcomes within each mucous membrane condition category.\n",
        "2. **Visualize and Analyze Data:** Create a bar chart to visualize the count of each health outcome within the different mucous membrane condition categories. This will help identify any significant correlations between mucous membrane condition and health outcomes.\n",
        "\n",
        "#### Question:\n",
        "After analyzing the bar chart which depicts the count of health outcomes within each mucous membrane condition category, identify the correct statement regarding the correlation between mucous membrane condition and health outcomes.\n",
        "\n",
        "#### Options:\n",
        "A) Horses with normal pink mucous membranes tend to have better health outcomes and higher survival rates.\n",
        "\n",
        "B) A pale or cyanotic mucous membrane is strongly associated with a higher survival rate.\n",
        "\n",
        "C) Horses with bright pink mucous membranes are more likely to be euthanized due to severe health issues.\n",
        "\n",
        "D) The color of the mucous membrane has no correlation with the health outcomes of horses.\n",
        "\n"
      ],
      "metadata": {
        "id": "NOXVtFR252TH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Grouping data by mucous membrane condition and outcome for plotting\n",
        "grouped_data = df.groupby(['mucous_membrane', 'outcome']).count()['id'].reset_index()  # TODO: Choose appropriate columns for grouping and a method to summarize the data.\n",
        "grouped_data.columns = ['Mucous Membrane Condition', 'Outcome', 'Count']\n",
        "\n",
        "# Create a pivot table for visualization\n",
        "pivot_table = pd.pivot_table(grouped_data, values='Count', index='Mucous Membrane Condition', columns='Outcome', fill_value=0)  # TODO: Determine which values will be summarized and how they should be arranged in the pivot table.\n",
        "\n",
        "# Plotting the data\n",
        "pivot_table.plot(kind='bar', stacked=True)\n",
        "plt.title('Health Outcomes by Mucous Membrane Condition')\n",
        "plt.xlabel('Mucous Membrane Condition')\n",
        "plt.ylabel('Count of Outcomes')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title='Health Outcome')\n",
        "plt.tight_layout()  # Adjust layout to not cut off labels\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "PHf8DxM73XGe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "2e4c8a35-e1fb-44d6-c339-5d29880a0375"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "### Training and Evaluating the Gaussian Model\n",
        "\n",
        "#### Context:\n",
        "After preprocessing the dataset for the \"Predict Health Outcomes of Horses\" and training a Gaussian Naive Bayes model, the next step is to evaluate the classifier’s performance. This involves encoding the target variable and analyzing the model's ability to predict each category.\n",
        "\n",
        "#### Task:\n",
        "Train and evaluate the Gaussian Naive Bayes classifier using the preprocessed features and encoded target. Use the classification report to identify which category the model has difficulty predicting.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Encode the Target Variable:**\n",
        "   - Map the 'outcome' categories to numeric values using the provided dictionary.\n",
        "2. **Model Training:**\n",
        "   - Train the Gaussian Naive Bayes classifier using the encoded training data.\n",
        "3. **Model Evaluation:**\n",
        "   - Evaluate the classifier's performance using the classification report to identify which category is most problematic.\n",
        "\n",
        "#### Question:\n",
        "Which category does the Gaussian Naive Bayes model struggle with the most, according to the classification report?\n",
        "\n",
        "#### Options:\n",
        "A) Died\n",
        "\n",
        "B) Euthanized\n",
        "\n",
        "C) Lived\n",
        "\n",
        "D) All categories have nearly identical recall"
      ],
      "metadata": {
        "id": "rFZ61ZosR7QA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB  # TODO: Import the Gaussian Naive Bayes classifier.\n",
        "from sklearn.metrics import classification_report, confusion_matrix  # TODO: Import necessary functions for evaluation.\n",
        "\n",
        "# Encoding the 'outcome' variable\n",
        "outcome_map = {'died': 0, 'euthanized': 1, 'lived': 2}\n",
        "y_train_encoded = y_train.map(outcome_map)  # TODO: Apply the mapping to the 'y_train' series.\n",
        "y_test_encoded = y_test.map(outcome_map)  # TODO: Apply the mapping to the 'y_test' series.\n",
        "\n",
        "# Initialize and train Gaussian Naive Bayes classifier\n",
        "model = GaussianNB()\n",
        "model.fit(X_train, y_train_encoded)  # TODO: Fit the model using the training data.\n",
        "\n",
        "# Predict on the test set\n",
        "y_pred = model.predict(X_test)  # TODO: Make predictions using the test data.\n",
        "\n",
        "# Generate the classification report\n",
        "report = classification_report(y_test_encoded, y_pred, target_names=['Died', 'Euthanized', 'Lived'])  # TODO: Provide the correct variables to generate the classification report.\n",
        "print(report)"
      ],
      "metadata": {
        "id": "ejLaPK40R3rM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3cda8d4-3ca2-4f8d-8b9d-3bf4d735216a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Died       0.49      0.98      0.65        89\n",
            "  Euthanized       0.56      0.57      0.56        42\n",
            "       Lived       0.88      0.20      0.32       116\n",
            "\n",
            "    accuracy                           0.54       247\n",
            "   macro avg       0.64      0.58      0.51       247\n",
            "weighted avg       0.69      0.54      0.48       247\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### Cross-Validation with Gaussian\n",
        "\n",
        "#### Context:\n",
        "Cross-validation is a robust technique for assessing the generalizability of a predictive model beyond the specific data used during training. Using 5-fold cross-validation with the Gaussian Naive Bayes classifier provides insight into the model's performance across different subsets of the dataset.\n",
        "\n",
        "#### Task:\n",
        "Apply 5-fold cross-validation using the Gaussian Naive Bayes Classifier on the \"Predict Health Outcomes of Horses\" dataset, utilizing the 'f1_weighted' scoring metric to evaluate the model's performance.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Set Up Cross-Validation:**\n",
        "   - Use the Gaussian Naive Bayes classifier.\n",
        "   - Implement 5-fold cross-validation.\n",
        "   - Apply the 'f1_weighted' metric to measure model performance across folds.\n",
        "\n",
        "2. **Calculate the Average Score:**\n",
        "   - Determine the average 'f1_weighted' score across all cross-validation folds to assess the overall effectiveness of the model.\n",
        "\n",
        "#### Question:\n",
        "1. What is the average 'f1_weighted' score value using 5-fold cross-validation with the Gaussian Naive Bayes Classifier?\n",
        "2. Is this 'f1_weighted' score higher or lower than the F1 score from the previously generated classification report for the category the model struggled with the most?\n",
        "\n",
        "#### Options:\n",
        "A) Mean F1 score: 0.5, Higher\n",
        "\n",
        "B) Mean F1 score: 0.6, Higher\n",
        "\n",
        "C) Mean F1 score: 0.7, Lower\n",
        "\n",
        "D) Mean F1 score: 0.8, Lower"
      ],
      "metadata": {
        "id": "VpitgxmbDZf_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score  # TODO: Import the function to perform cross-validation.\n",
        "\n",
        "# Perform 5-fold cross-validation using 'f1_weighted' as the scoring metric\n",
        "# Review the scoring parameters available in the documentation to ensure correct usage. https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter\n",
        "cv_scores = cross_val_score(model, X_train, y_train_encoded, cv=5, scoring='f1_weighted')  # TODO: Initialize the classifier, set the number of folds, and specify the scoring metric.\n",
        "\n",
        "# Calculate the average of the cross-validation scores\n",
        "average_cv_score = cv_scores.mean()  # TODO: Determine the appropriate method to calculate the mean of the scores.\n",
        "print(f\"Average F1 weighted score across 5 folds: {average_cv_score:.2f}\")"
      ],
      "metadata": {
        "id": "rqF9aGaSYkKl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b06a75b6-25f6-4667-f657-41b6427c5d04"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average F1 weighted score across 5 folds: 0.50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Multinomial Naive Bayes Evaluation\n",
        "\n",
        "#### Context:\n",
        "After training a Gaussian Naive Bayes model on the \"Predict Health Outcomes of Horses\" dataset and obtaining its performance metrics, the next step involves evaluating a Multinomial Naive Bayes model. This evaluation will use the same dataset, but the continuous features will be scaled to `non-negative` values suitable for Multinomial Naive Bayes.\n",
        "\n",
        "#### Task:\n",
        "Evaluate the performance of the Multinomial Naive Bayes classifier after scaling the features to ensure all are non-negative, as required by the model.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Data Preparation:**\n",
        "   - Scale the continuous features using `MinMaxScaler` to ensure all feature values are non-negative.\n",
        "   \n",
        "2. **Model Training and Evaluation:**\n",
        "   - Train a Multinomial Naive Bayes classifier on the scaled data.\n",
        "   - Generate a classification report to assess the model's performance across different outcome categories.\n",
        "\n",
        "#### Question:\n",
        "Based on the classification report for the Multinomial Naive Bayes model, which category has the highest recall, indicating the best performance in correctly identifying true positive cases?\n",
        "\n",
        "#### Options:\n",
        "A) Died\n",
        "\n",
        "B) Euthanized\n",
        "\n",
        "C) Lived\n",
        "\n",
        "D) All categories have nearly identical recall\n"
      ],
      "metadata": {
        "id": "UvfcsGFobjRZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB  # TODO: Correctly import the Multinomial Naive Bayes classifier.\n",
        "from sklearn.preprocessing import MinMaxScaler  # TODO: Correctly import the MinMaxScaler for data normalization.\n",
        "\n",
        "# Initialize MinMaxScaler\n",
        "scaler = MinMaxScaler()  # TODO: Instantiate the scaler.\n",
        "\n",
        "# Scale X_train and X_test\n",
        "X_train_scaled = scaler.fit_transform(X_train)  # TODO: Fir and Apply the scaler to train data.\n",
        "X_test_scaled = scaler.transform(X_test)  # TODO: Apply the scaler to test data.\n",
        "\n",
        "# Train Multinomial Naive Bayes classifier\n",
        "multinomial_nb = MultinomialNB()\n",
        "multinomial_nb.fit(X_train_scaled, y_train_encoded)  # TODO: Fit the Multinomial Naive Bayes model to the scaled training data.\n",
        "y_pred_multinomial = multinomial_nb.predict(X_test_scaled)  # TODO: Predict the test set results with the trained model.\n",
        "\n",
        "print(\"Multinomial Naive Bayes Classification Report:\")\n",
        "print(classification_report(y_test_encoded, y_pred_multinomial, target_names=['Died', 'Euthanized', 'Lived']))  # TODO: Provide the correct arguments to generate the classification report."
      ],
      "metadata": {
        "id": "cdenScZNbkF1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ce7a3e6-259f-4748-a641-ae0d366e0fa7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Multinomial Naive Bayes Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Died       0.67      0.56      0.61        89\n",
            "  Euthanized       0.62      0.57      0.59        42\n",
            "       Lived       0.62      0.72      0.67       116\n",
            "\n",
            "    accuracy                           0.64       247\n",
            "   macro avg       0.64      0.62      0.62       247\n",
            "weighted avg       0.64      0.64      0.63       247\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Optimizing Laplace Smoothing for Gaussian Naive Bayes Classifier\n",
        "\n",
        "#### Context:\n",
        "Laplace smoothing is a technique used to handle the issue of zero probability in Naive Bayes classifiers. While typically more relevant for discrete data in Multinomial and Bernoulli Naive Bayes, experimenting with this parameter in Gaussian Naive Bayes can provide insights into its effect on model robustness, particularly when handling datasets with sparse features or small sample sizes.\n",
        "\n",
        "#### Task:\n",
        "Determine the best Laplace smoothing parameter (alpha) for a Gaussian Naive Bayes classifier applied to the \"Predict Health Outcomes of Horses\" dataset. This involves tuning the alpha parameter to improve model performance, especially in handling data with potential zero-frequency issues.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Setup Parameter Tuning:**\n",
        "   - Use a range of alpha values to determine which provides the best balance between bias and variance.\n",
        "   - Apply cross-validation to evaluate each model configuration under different alpha settings.\n",
        "\n",
        "2. **Evaluate Model Performance:**\n",
        "   - Measure the performance 'f1_weighted' of the Gaussian Naive Bayes classifier for each alpha setting.\n",
        "   - Identify the alpha value that results in the highest average performance across cross-validation folds.\n",
        "\n",
        "3. **Implement and Discuss Results:**\n",
        "   - Implement the model using the optimal alpha.\n",
        "   - Discuss how Laplace smoothing impacts the performance of a Gaussian Naive Bayes classifier.\n",
        "\n",
        "#### Question:\n",
        "After tuning the Laplace smoothing parameter for the Gaussian Naive Bayes model on the \"Predict Health Outcomes of Horses\" dataset, which alpha value resulted in the highest f1_weighted score?\n",
        "\n",
        "#### Options:\n",
        "A) Alpha = 0.0, Accuracy = 65%\n",
        "\n",
        "B) Alpha = 0.8, Accuracy = 64%\n",
        "\n",
        "C) Alpha = 0.5, Accuracy = 75%\n",
        "\n",
        "D) Alpha = 0.3, Accuracy = 53%\n"
      ],
      "metadata": {
        "id": "oQUs2htdVHFa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV  # TODO: Import the necessary class for optimizing hyperparameters.\n",
        "import numpy as np\n",
        "\n",
        "# Define a range of alpha values to test\n",
        "alpha_range = np.linspace(0.0, 1.0, 11)\n",
        "\n",
        "# Setup GridSearchCV to find the best alpha\n",
        "param_grid = {'var_smoothing': alpha_range}  # TODO: Specify the parameter name for Laplace smoothing in GaussianNB.\n",
        "gnb = GaussianNB()\n",
        "clf = GridSearchCV(gnb, param_grid, cv=5, scoring='f1_weighted')  # TODO: Fill in the GridSearchCV constructor with the correct model and parameters.\n",
        "clf.fit(X_train, y_train)  # TODO: Fit the GridSearchCV to the training data.\n",
        "\n",
        "# Best alpha and its score\n",
        "best_alpha = clf.best_params_['var_smoothing']  # TODO: Extract the best alpha value from clf.\n",
        "best_score = clf.best_score_  # TODO: Extract the best score from clf.\n",
        "print(f\"Best alpha: {best_alpha}\")\n",
        "print(f\"Best scoring f1_weighted: {best_score:.2f}\")"
      ],
      "metadata": {
        "id": "qIn5hPQWeWTh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6acf8d18-03d0-41ab-f221-d8b69b8cad6e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best alpha: 0.8\n",
            "Best scoring f1_weighted: 0.64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Match the Following attributes\n",
        "\n",
        "#### Context:\n",
        "Understanding how different Naive Bayes classifiers calculate feature importance is crucial for interpreting model outcomes and for feature selection. Each classifier type has specific methods or attributes that help in identifying the contribution of each feature towards making a prediction.\n",
        "\n",
        "#### Task:\n",
        "Match the following Naive Bayes models to the correct method or attribute used for determining feature importance, which provides insights such as $( P(x_i \\mid y) $) or the mean of each feature per class. Use the [scikit-learn documentation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes) to find the parameters that return these values.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Research**: Look into the scikit-learn documentation for Naive Bayes classifiers to identify the specific attributes associated with each model.\n",
        "2. **Match**: Connect each model listed on the left with its corresponding method or attribute on the right that is used to determine feature importance.\n",
        "\n",
        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
        "<head>\n",
        "<meta charset=\"UTF-8\">\n",
        "<title>Match the Following Characteristics</title>\n",
        "<style>\n",
        "  .matching-table {\n",
        "    width: 100%;\n",
        "    border-collapse: collapse;\n",
        "  }\n",
        "  \n",
        "  .matching-table th,\n",
        "  .matching-table td {\n",
        "    border: 1px solid black;\n",
        "    padding: 5px;\n",
        "    text-align: left;\n",
        "  }\n",
        "  \n",
        "  .matching-table th {\n",
        "    background-color: #f2f2f2;\n",
        "  }\n",
        "</style>\n",
        "</head>\n",
        "<body>\n",
        "\n",
        "<table class=\"matching-table\">\n",
        "  <tr>\n",
        "    <th>Models</th>\n",
        "    <th>Characteristics</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>A. Gaussian Naive Bayes\n",
        "</td>\n",
        "    <td>I. feature_log_prob_</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>B. Multinomial Naive Bayes</td>\n",
        "    <td>II. feature_importances_</td>\n",
        "  </tr>  \n",
        "\t<tr>\n",
        "    <td>C. Bernoulli Naive Bayes</td>\n",
        "    <td>III. epsilon_</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td></td>\n",
        "    <td>IV:  theta_</td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "</body>\n",
        "</html>\n",
        "\n",
        "#### Options:\n",
        "- A) A-I, B-III, C-II\n",
        "\n",
        "- B) A-IV, B-I, C-I\n",
        "\n",
        "- C) A-IV, B-I, C-II\n",
        "\n",
        "- D) A-I, B-IV, C-I\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hdq6J8w3lGGd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "### Evaluating Feature Importance in Gaussian Naive Bayes Model\n",
        "\n",
        "#### Context:\n",
        "Understanding which features most influence the predictions of a Gaussian Naive Bayes classifier can enhance interpretability and guide further model refinement. This model evaluates feature importance based on the mean values of features conditioned on each class, reflecting the significance of each feature in class discrimination.\n",
        "\n",
        "#### Task:\n",
        "Analyze feature importance in a Gaussian Naive Bayes model trained on a dataset, using the mean of each feature across classes to determine their impact on the model's predictions.\n",
        "\n",
        "#### Instructions:\n",
        "1. **Model Setup and Training:**\n",
        "   - Initialize the Gaussian Naive Bayes model with `var_smoothing` set to 0.3 to stabilize the calculation by adjusting the variance of each feature.\n",
        "   - Train the model using the training sets `X_train` and `y_train`.\n",
        "\n",
        "2. **Feature Importance Calculation:**\n",
        "   - Extract the mean of each feature for each class from the model. These means are critical as Gaussian Naive Bayes assumes features are normally distributed, and the mean of these distributions plays a significant role in class separation.\n",
        "   - Calculate the `absolute` values of these means to focus on the magnitude of feature values irrespective of their direction.\n",
        "\n",
        "3. **Summarize Feature Importance:**\n",
        "   - Aggregate these mean values across all classes to get a single importance score per feature.\n",
        "   - Rank these features based on their importance scores to identify the most influential features.\n",
        "\n",
        "4. **Visualization:**\n",
        "   - Plot these importance scores using a horizontal bar chart to visually compare the significance of each feature.\n",
        "\n",
        "\n",
        "#### Question:\n",
        "After implementing the above steps, which features were identified as the most important based on the mean absolute values of the class conditional distributions?\n",
        "\n",
        "#### Options:\n",
        "A) rectal_temp, age, peristalsis\n",
        "\n",
        "B) lesion_2, pain, cp_data\n",
        "\n",
        "C) nasogastric_reflux, nasogastric_tube, rectal_exam_feces\n",
        "\n",
        "D) total_protein, lesion_1, temp_category\n"
      ],
      "metadata": {
        "id": "0XdrIektahHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Gaussian Naive Bayes model\n",
        "model = GaussianNB(var_smoothing = 0.3) # TODO: Set the smoothing parameter to 0.3.\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# TODO: Use the appropriate numpy function to calculate the absolute values of the means\n",
        "feature_importances = np.abs(model.theta_)   # TODO: Access the attribute that stores the class conditional means.\n",
        "\n",
        "# Create a DataFrame to view these importances\n",
        "importance_df = pd.DataFrame(feature_importances, columns=X.columns)\n",
        "# Summing the importances across classes to get an overall importance score for each feature\n",
        "importance_df.loc['Mean Importance'] = importance_df.mean(axis=0)  # TODO: Aggregate the means to get a single importance score per feature\n",
        "sorted_importance = importance_df.loc['Mean Importance'].sort_values(ascending=False)   # TODO: Sort the features based on importance\n",
        "\n",
        "print(\"Feature Importance based on absolute means of class conditional distributions:\")\n",
        "print(sorted_importance)\n",
        "\n",
        "# Optionally, visualize these importances\n",
        "import matplotlib.pyplot as plt\n",
        "sorted_importance.plot(kind='barh')\n",
        "plt.title('Feature Importance in Gaussian Naive Bayes')\n",
        "plt.ylabel('Features')\n",
        "plt.xlabel('Importance (mean absolute values across classes)')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "C60BjqugCnIX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        },
        "outputId": "a380c8dd-2a33-4d23-fa3b-68d26f80771b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Importance based on absolute means of class conditional distributions:\n",
            "nasogastric_reflux       3.093584\n",
            "nasogastric_tube         2.627840\n",
            "rectal_exam_feces        2.094395\n",
            "abdomen                  1.809921\n",
            "total_protein            1.424576\n",
            "lesion_1                 1.359208\n",
            "nasogastric_reflux_ph    1.353643\n",
            "temp_category            1.339915\n",
            "abdominal_distention     1.192330\n",
            "packed_cell_volume       1.148624\n",
            "abdomo_appearance        1.042088\n",
            "surgical_lesion          0.939177\n",
            "abdomo_protein           0.811958\n",
            "lesion_2                 0.756599\n",
            "pain                     0.716326\n",
            "cp_data                  0.496992\n",
            "temp_of_extremities      0.440712\n",
            "hospital_number          0.333712\n",
            "pulse                    0.332115\n",
            "respiratory_rate         0.291780\n",
            "peripheral_pulse         0.215117\n",
            "surgery                  0.153676\n",
            "rectal_temp              0.151303\n",
            "peristalsis              0.055472\n",
            "capillary_refill_time    0.047601\n",
            "mucous_membrane          0.030234\n",
            "age                      0.028012\n",
            "Name: Mean Importance, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}