heart.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOR8tURF6ytW9mmKfQknYNl"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Predicting heart disease using machine learning\n","\n","The dataset is from www.archive.ics.uci.edu/ml/datasets/heart+Disease\n","Also from www.kaggle.com/ronitf/geart-disease-uci\n","https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data"],"metadata":{"id":"_w5DMLbjtApl"}},{"cell_type":"code","source":["# Import all the tools we need\n","\n","# Regular EDA (exploratory data analysis) and plotting libraries\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# We want our plots to appear inside the notebook\n","%matplotlib inline\n","\n","#Models from SciKit-Learn\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.ensemble import RandomForestClassifier\n","\n","# Model Evaluations\n","from sklearn.model_selection import train_test_split, cross_val_score\n","from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n","from sklearn.metrics import confusion_matrix, classification_report\n","from sklearn.metrics import precision_score, recall_score, f1_score\n","from sklearn.metrics import RocCurveDisplay\n","\n","from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Dywvb0OxuAY8","executionInfo":{"status":"ok","timestamp":1701837668710,"user_tz":-330,"elapsed":31671,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"8f5a1011-3c6c-4761-f1d0-e92444d52761"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"Ko4roN-Ms5MJ","executionInfo":{"status":"ok","timestamp":1701837670065,"user_tz":-330,"elapsed":1372,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"bc07d24d-9374-4722-a046-166c068dca91"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","0     63    1   3       145   233    1        0      150      0      2.3   \n","1     37    1   2       130   250    0        1      187      0      3.5   \n","2     41    0   1       130   204    0        0      172      0      1.4   \n","3     56    1   1       120   236    0        1      178      0      0.8   \n","4     57    0   0       120   354    0        1      163      1      0.6   \n","..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  target  \n","0        0   0     1       1  \n","1        0   0     2       1  \n","2        2   0     2       1  \n","3        2   0     2       1  \n","4        2   0     2       1  \n","..     ...  ..   ...     ...  \n","298      1   0     3       0  \n","299      1   0     3       0  \n","300      1   2     3       0  \n","301      1   1     3       0  \n","302      1   1     2       0  \n","\n","[303 rows x 14 columns]"],"text/html":["\n","  <div id=\"df-8223f3e7-89fd-49c6-b54d-69f1e097077b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>298</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>140</td>\n","      <td>241</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>123</td>\n","      <td>1</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>299</th>\n","      <td>45</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>110</td>\n","      <td>264</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>132</td>\n","      <td>0</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>300</th>\n","      <td>68</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>144</td>\n","      <td>193</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>141</td>\n","      <td>0</td>\n","      <td>3.4</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>301</th>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>130</td>\n","      <td>131</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>115</td>\n","      <td>1</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>302</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>174</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>303 rows × 14 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8223f3e7-89fd-49c6-b54d-69f1e097077b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-8223f3e7-89fd-49c6-b54d-69f1e097077b 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-8223f3e7-89fd-49c6-b54d-69f1e097077b');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-b243eae8-2987-4cc1-a509-56a67d03023a\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b243eae8-2987-4cc1-a509-56a67d03023a')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-b243eae8-2987-4cc1-a509-56a67d03023a button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":2}],"source":["df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/ZTM Course/End-to-End Heart Disease Classification/heart-disease.csv\")\n","df"]},{"cell_type":"code","source":["df[\"target\"].value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NFHeT8Fm2YaW","executionInfo":{"status":"ok","timestamp":1701837670068,"user_tz":-330,"elapsed":181,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"ae7db729-8b7c-4b19-d09b-9c88a234ea6b"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    165\n","0    138\n","Name: target, dtype: int64"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["df[\"target\"].value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":443},"id":"zAVNOOF82eAL","executionInfo":{"status":"ok","timestamp":1701837670070,"user_tz":-330,"elapsed":172,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"5badbacf-d513-4346-b106-eb2a8fecf234"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2BPbF9x725FT","executionInfo":{"status":"ok","timestamp":1701837670073,"user_tz":-330,"elapsed":158,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"b67bc38c-6a0f-49d5-867d-ab697ce54873"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 303 entries, 0 to 302\n","Data columns (total 14 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   age       303 non-null    int64  \n"," 1   sex       303 non-null    int64  \n"," 2   cp        303 non-null    int64  \n"," 3   trestbps  303 non-null    int64  \n"," 4   chol      303 non-null    int64  \n"," 5   fbs       303 non-null    int64  \n"," 6   restecg   303 non-null    int64  \n"," 7   thalach   303 non-null    int64  \n"," 8   exang     303 non-null    int64  \n"," 9   oldpeak   303 non-null    float64\n"," 10  slope     303 non-null    int64  \n"," 11  ca        303 non-null    int64  \n"," 12  thal      303 non-null    int64  \n"," 13  target    303 non-null    int64  \n","dtypes: float64(1), int64(13)\n","memory usage: 33.3 KB\n"]}]},{"cell_type":"code","source":["df.isna().sum()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"roACrsLk3fzj","executionInfo":{"status":"ok","timestamp":1701837670075,"user_tz":-330,"elapsed":152,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"0409fa4f-bb42-4aee-ef24-88a854390864"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age         0\n","sex         0\n","cp          0\n","trestbps    0\n","chol        0\n","fbs         0\n","restecg     0\n","thalach     0\n","exang       0\n","oldpeak     0\n","slope       0\n","ca          0\n","thal        0\n","target      0\n","dtype: int64"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["df.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"A7MBwnND3nSi","executionInfo":{"status":"ok","timestamp":1701837670078,"user_tz":-330,"elapsed":146,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"e63eebc0-dc6f-4b88-abf9-e9ca0828af95"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              age         sex          cp    trestbps        chol         fbs  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n","std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n","min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n","25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n","50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n","75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n","max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n","\n","          restecg     thalach       exang     oldpeak       slope          ca  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n","std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n","min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n","25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n","50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n","75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n","max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n","\n","             thal      target  \n","count  303.000000  303.000000  \n","mean     2.313531    0.544554  \n","std      0.612277    0.498835  \n","min      0.000000    0.000000  \n","25%      2.000000    0.000000  \n","50%      2.000000    1.000000  \n","75%      3.000000    1.000000  \n","max      3.000000    1.000000  "],"text/html":["\n","  <div id=\"df-540ffebd-4838-49c9-9dc7-4ec71197961c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>54.366337</td>\n","      <td>0.683168</td>\n","      <td>0.966997</td>\n","      <td>131.623762</td>\n","      <td>246.264026</td>\n","      <td>0.148515</td>\n","      <td>0.528053</td>\n","      <td>149.646865</td>\n","      <td>0.326733</td>\n","      <td>1.039604</td>\n","      <td>1.399340</td>\n","      <td>0.729373</td>\n","      <td>2.313531</td>\n","      <td>0.544554</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.082101</td>\n","      <td>0.466011</td>\n","      <td>1.032052</td>\n","      <td>17.538143</td>\n","      <td>51.830751</td>\n","      <td>0.356198</td>\n","      <td>0.525860</td>\n","      <td>22.905161</td>\n","      <td>0.469794</td>\n","      <td>1.161075</td>\n","      <td>0.616226</td>\n","      <td>1.022606</td>\n","      <td>0.612277</td>\n","      <td>0.498835</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>71.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>47.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>120.000000</td>\n","      <td>211.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>133.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>55.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>130.000000</td>\n","      <td>240.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>153.000000</td>\n","      <td>0.000000</td>\n","      <td>0.800000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>140.000000</td>\n","      <td>274.500000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>166.000000</td>\n","      <td>1.000000</td>\n","      <td>1.600000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>77.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>200.000000</td>\n","      <td>564.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>202.000000</td>\n","      <td>1.000000</td>\n","      <td>6.200000</td>\n","      <td>2.000000</td>\n","      <td>4.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-540ffebd-4838-49c9-9dc7-4ec71197961c')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-540ffebd-4838-49c9-9dc7-4ec71197961c 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-540ffebd-4838-49c9-9dc7-4ec71197961c');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-07fa7e4b-7856-41a7-973f-c20e31b868ba\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-07fa7e4b-7856-41a7-973f-c20e31b868ba')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-07fa7e4b-7856-41a7-973f-c20e31b868ba button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["   #Heart disease according to sex\n","   df.sex.value_counts()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T_-c5RQx3rJK","executionInfo":{"status":"ok","timestamp":1701837670083,"user_tz":-330,"elapsed":144,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"ba100ba2-28de-476a-9a2f-3ab0bcf1ef34"},"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    207\n","0     96\n","Name: sex, dtype: int64"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["#Comparing target columns with the sex column\n","pd.crosstab(df.target, df.sex)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"qNHCGSwFcw1n","executionInfo":{"status":"ok","timestamp":1701837670085,"user_tz":-330,"elapsed":137,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"3713b997-74a6-491e-82ad-7accf4de5abd"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["sex      0    1\n","target         \n","0       24  114\n","1       72   93"],"text/html":["\n","  <div id=\"df-b79e4a3b-d176-49a2-a3b9-537116938255\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>sex</th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","    <tr>\n","      <th>target</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>24</td>\n","      <td>114</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>72</td>\n","      <td>93</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b79e4a3b-d176-49a2-a3b9-537116938255')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-b79e4a3b-d176-49a2-a3b9-537116938255 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-b79e4a3b-d176-49a2-a3b9-537116938255');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-8410da43-1334-4569-8f71-a97e68709d3c\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8410da43-1334-4569-8f71-a97e68709d3c')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-8410da43-1334-4569-8f71-a97e68709d3c button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["#Create a plot of crosstab\n","pd.crosstab(df.target, df.sex).plot(kind=\"bar\", figsize=(10, 6), color=[\"salmon\", \"lightblue\"])\n","\n","plt.title(\"Heart Disease frequency for Sex\")\n","plt.xlabel(\"0 = No Disease, 1 = Disease\")\n","plt.ylabel(\"No of people\")\n","plt.legend({\"Female\", \"Male\"})\n","plt.xticks(rotation=0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":582},"id":"CA8UvQNAdRVf","executionInfo":{"status":"ok","timestamp":1701837671129,"user_tz":-330,"elapsed":1173,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"f1f8ff6c-4516-43c8-9d97-66207c366538"},"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1]), [Text(0, 0, '0'), Text(1, 0, '1')])"]},"metadata":{},"execution_count":10},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Create another figure\n","plt.figure(figsize=(10, 6))\n","\n","#Scatter with positive examples\n","plt.scatter(df.age[df.target==1],\n","            df.thalach[df.target==1],\n","            c=\"salmon\")\n","\n","#Scatter with negative examples\n","plt.scatter(df.age[df.target==0],\n","            df.thalach[df.target==0],\n","            c=\"lightblue\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":540},"id":"P8PN92p5rJX5","executionInfo":{"status":"ok","timestamp":1701837671132,"user_tz":-330,"elapsed":97,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"384ed34d-2f33-4f49-fa83-675a03177e7d"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f15eb272860>"]},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df.age.plot.hist()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"AWn-NNvne3l-","executionInfo":{"status":"ok","timestamp":1701837671133,"user_tz":-330,"elapsed":76,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"fc3cf2d9-013b-428e-ae2e-a35e708905c5"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: ylabel='Frequency'>"]},"metadata":{},"execution_count":12},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Heart Disease Frequency per Chest Pain Type\n","pd.crosstab(df.cp, df.target)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"B6REnddnqGqF","executionInfo":{"status":"ok","timestamp":1701837671135,"user_tz":-330,"elapsed":67,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"1c5d6c7b-3852-4cf9-9b4c-8a84e7894c88"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["target    0   1\n","cp             \n","0       104  39\n","1         9  41\n","2        18  69\n","3         7  16"],"text/html":["\n","  <div id=\"df-71ea1736-6e7e-4de7-a314-1016265758dc\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>target</th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","    <tr>\n","      <th>cp</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>104</td>\n","      <td>39</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9</td>\n","      <td>41</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>18</td>\n","      <td>69</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>7</td>\n","      <td>16</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71ea1736-6e7e-4de7-a314-1016265758dc')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-71ea1736-6e7e-4de7-a314-1016265758dc 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-71ea1736-6e7e-4de7-a314-1016265758dc');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-75d2de4c-4a6a-4647-8852-43cb084a4f56\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75d2de4c-4a6a-4647-8852-43cb084a4f56')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-75d2de4c-4a6a-4647-8852-43cb084a4f56 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["#Make the crosstab more visual\n","pd.crosstab(df.cp, df.target).plot(kind=\"bar\", figsize=(10, 6), color=['salmon', \"lightblue\"])\n","\n","#Add some communication\n","plt.title(\"Heart disease Frequency Per Chest Pain Type\")\n","plt.xlabel(\"Chest Pain Type\")\n","plt.ylabel(\"No of persons\")\n","plt.legend([\"No Disease\", \"Disease\"])\n","plt.xticks(rotation=0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":601},"id":"1QLgz2qZq0_L","executionInfo":{"status":"ok","timestamp":1701837672038,"user_tz":-330,"elapsed":964,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"5a1c7e04-4e11-420d-832e-fe7ed466c1c0"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([0, 1, 2, 3]),\n"," [Text(0, 0, '0'), Text(1, 0, '1'), Text(2, 0, '2'), Text(3, 0, '3')])"]},"metadata":{},"execution_count":14},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Make a correlation matrix\n","df.corr()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"sECzig2Orvgd","executionInfo":{"status":"ok","timestamp":1701837672039,"user_tz":-330,"elapsed":17,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"323ae803-bb86-4ec7-b7b3-8eceee1bc399"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["               age       sex        cp  trestbps      chol       fbs  \\\n","age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n","sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n","cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n","trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n","chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n","fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n","restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n","thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n","exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n","oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n","slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n","ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n","thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n","target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n","\n","           restecg   thalach     exang   oldpeak     slope        ca  \\\n","age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n","sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n","cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n","trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n","chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n","fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n","restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n","thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n","exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n","oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n","slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n","ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n","thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n","target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n","\n","              thal    target  \n","age       0.068001 -0.225439  \n","sex       0.210041 -0.280937  \n","cp       -0.161736  0.433798  \n","trestbps  0.062210 -0.144931  \n","chol      0.098803 -0.085239  \n","fbs      -0.032019 -0.028046  \n","restecg  -0.011981  0.137230  \n","thalach  -0.096439  0.421741  \n","exang     0.206754 -0.436757  \n","oldpeak   0.210244 -0.430696  \n","slope    -0.104764  0.345877  \n","ca        0.151832 -0.391724  \n","thal      1.000000 -0.344029  \n","target   -0.344029  1.000000  "],"text/html":["\n","  <div id=\"df-576dd985-30ce-4a9d-8c9b-bbe9e6be31f8\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>1.000000</td>\n","      <td>-0.098447</td>\n","      <td>-0.068653</td>\n","      <td>0.279351</td>\n","      <td>0.213678</td>\n","      <td>0.121308</td>\n","      <td>-0.116211</td>\n","      <td>-0.398522</td>\n","      <td>0.096801</td>\n","      <td>0.210013</td>\n","      <td>-0.168814</td>\n","      <td>0.276326</td>\n","      <td>0.068001</td>\n","      <td>-0.225439</td>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <td>-0.098447</td>\n","      <td>1.000000</td>\n","      <td>-0.049353</td>\n","      <td>-0.056769</td>\n","      <td>-0.197912</td>\n","      <td>0.045032</td>\n","      <td>-0.058196</td>\n","      <td>-0.044020</td>\n","      <td>0.141664</td>\n","      <td>0.096093</td>\n","      <td>-0.030711</td>\n","      <td>0.118261</td>\n","      <td>0.210041</td>\n","      <td>-0.280937</td>\n","    </tr>\n","    <tr>\n","      <th>cp</th>\n","      <td>-0.068653</td>\n","      <td>-0.049353</td>\n","      <td>1.000000</td>\n","      <td>0.047608</td>\n","      <td>-0.076904</td>\n","      <td>0.094444</td>\n","      <td>0.044421</td>\n","      <td>0.295762</td>\n","      <td>-0.394280</td>\n","      <td>-0.149230</td>\n","      <td>0.119717</td>\n","      <td>-0.181053</td>\n","      <td>-0.161736</td>\n","      <td>0.433798</td>\n","    </tr>\n","    <tr>\n","      <th>trestbps</th>\n","      <td>0.279351</td>\n","      <td>-0.056769</td>\n","      <td>0.047608</td>\n","      <td>1.000000</td>\n","      <td>0.123174</td>\n","      <td>0.177531</td>\n","      <td>-0.114103</td>\n","      <td>-0.046698</td>\n","      <td>0.067616</td>\n","      <td>0.193216</td>\n","      <td>-0.121475</td>\n","      <td>0.101389</td>\n","      <td>0.062210</td>\n","      <td>-0.144931</td>\n","    </tr>\n","    <tr>\n","      <th>chol</th>\n","      <td>0.213678</td>\n","      <td>-0.197912</td>\n","      <td>-0.076904</td>\n","      <td>0.123174</td>\n","      <td>1.000000</td>\n","      <td>0.013294</td>\n","      <td>-0.151040</td>\n","      <td>-0.009940</td>\n","      <td>0.067023</td>\n","      <td>0.053952</td>\n","      <td>-0.004038</td>\n","      <td>0.070511</td>\n","      <td>0.098803</td>\n","      <td>-0.085239</td>\n","    </tr>\n","    <tr>\n","      <th>fbs</th>\n","      <td>0.121308</td>\n","      <td>0.045032</td>\n","      <td>0.094444</td>\n","      <td>0.177531</td>\n","      <td>0.013294</td>\n","      <td>1.000000</td>\n","      <td>-0.084189</td>\n","      <td>-0.008567</td>\n","      <td>0.025665</td>\n","      <td>0.005747</td>\n","      <td>-0.059894</td>\n","      <td>0.137979</td>\n","      <td>-0.032019</td>\n","      <td>-0.028046</td>\n","    </tr>\n","    <tr>\n","      <th>restecg</th>\n","      <td>-0.116211</td>\n","      <td>-0.058196</td>\n","      <td>0.044421</td>\n","      <td>-0.114103</td>\n","      <td>-0.151040</td>\n","      <td>-0.084189</td>\n","      <td>1.000000</td>\n","      <td>0.044123</td>\n","      <td>-0.070733</td>\n","      <td>-0.058770</td>\n","      <td>0.093045</td>\n","      <td>-0.072042</td>\n","      <td>-0.011981</td>\n","      <td>0.137230</td>\n","    </tr>\n","    <tr>\n","      <th>thalach</th>\n","      <td>-0.398522</td>\n","      <td>-0.044020</td>\n","      <td>0.295762</td>\n","      <td>-0.046698</td>\n","      <td>-0.009940</td>\n","      <td>-0.008567</td>\n","      <td>0.044123</td>\n","      <td>1.000000</td>\n","      <td>-0.378812</td>\n","      <td>-0.344187</td>\n","      <td>0.386784</td>\n","      <td>-0.213177</td>\n","      <td>-0.096439</td>\n","      <td>0.421741</td>\n","    </tr>\n","    <tr>\n","      <th>exang</th>\n","      <td>0.096801</td>\n","      <td>0.141664</td>\n","      <td>-0.394280</td>\n","      <td>0.067616</td>\n","      <td>0.067023</td>\n","      <td>0.025665</td>\n","      <td>-0.070733</td>\n","      <td>-0.378812</td>\n","      <td>1.000000</td>\n","      <td>0.288223</td>\n","      <td>-0.257748</td>\n","      <td>0.115739</td>\n","      <td>0.206754</td>\n","      <td>-0.436757</td>\n","    </tr>\n","    <tr>\n","      <th>oldpeak</th>\n","      <td>0.210013</td>\n","      <td>0.096093</td>\n","      <td>-0.149230</td>\n","      <td>0.193216</td>\n","      <td>0.053952</td>\n","      <td>0.005747</td>\n","      <td>-0.058770</td>\n","      <td>-0.344187</td>\n","      <td>0.288223</td>\n","      <td>1.000000</td>\n","      <td>-0.577537</td>\n","      <td>0.222682</td>\n","      <td>0.210244</td>\n","      <td>-0.430696</td>\n","    </tr>\n","    <tr>\n","      <th>slope</th>\n","      <td>-0.168814</td>\n","      <td>-0.030711</td>\n","      <td>0.119717</td>\n","      <td>-0.121475</td>\n","      <td>-0.004038</td>\n","      <td>-0.059894</td>\n","      <td>0.093045</td>\n","      <td>0.386784</td>\n","      <td>-0.257748</td>\n","      <td>-0.577537</td>\n","      <td>1.000000</td>\n","      <td>-0.080155</td>\n","      <td>-0.104764</td>\n","      <td>0.345877</td>\n","    </tr>\n","    <tr>\n","      <th>ca</th>\n","      <td>0.276326</td>\n","      <td>0.118261</td>\n","      <td>-0.181053</td>\n","      <td>0.101389</td>\n","      <td>0.070511</td>\n","      <td>0.137979</td>\n","      <td>-0.072042</td>\n","      <td>-0.213177</td>\n","      <td>0.115739</td>\n","      <td>0.222682</td>\n","      <td>-0.080155</td>\n","      <td>1.000000</td>\n","      <td>0.151832</td>\n","      <td>-0.391724</td>\n","    </tr>\n","    <tr>\n","      <th>thal</th>\n","      <td>0.068001</td>\n","      <td>0.210041</td>\n","      <td>-0.161736</td>\n","      <td>0.062210</td>\n","      <td>0.098803</td>\n","      <td>-0.032019</td>\n","      <td>-0.011981</td>\n","      <td>-0.096439</td>\n","      <td>0.206754</td>\n","      <td>0.210244</td>\n","      <td>-0.104764</td>\n","      <td>0.151832</td>\n","      <td>1.000000</td>\n","      <td>-0.344029</td>\n","    </tr>\n","    <tr>\n","      <th>target</th>\n","      <td>-0.225439</td>\n","      <td>-0.280937</td>\n","      <td>0.433798</td>\n","      <td>-0.144931</td>\n","      <td>-0.085239</td>\n","      <td>-0.028046</td>\n","      <td>0.137230</td>\n","      <td>0.421741</td>\n","      <td>-0.436757</td>\n","      <td>-0.430696</td>\n","      <td>0.345877</td>\n","      <td>-0.391724</td>\n","      <td>-0.344029</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-576dd985-30ce-4a9d-8c9b-bbe9e6be31f8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-576dd985-30ce-4a9d-8c9b-bbe9e6be31f8 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-576dd985-30ce-4a9d-8c9b-bbe9e6be31f8');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-fe53113a-4772-4766-b55a-7bf170ef2361\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fe53113a-4772-4766-b55a-7bf170ef2361')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-fe53113a-4772-4766-b55a-7bf170ef2361 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["#Let's make the correlation matrix a little more pretty\n","corr_matrix = df.corr()\n","fig, ax = plt.subplots(figsize=(15, 10))\n","ax = sns.heatmap(corr_matrix,\n","                 annot=True,\n","                 linewidths=0.5,\n","                 fmt=\".2f\",\n","                 cmap=\"YlGnBu\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":836},"id":"hHl0Ow-Q1Ylz","executionInfo":{"status":"ok","timestamp":1701837673596,"user_tz":-330,"elapsed":1570,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"900c7127-8200-4062-cd19-a21a11335951"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#MODELLING\n","#Splitting data\n","x = df.drop(\"target\", axis=1)\n","\n","y = df[\"target\"]"],"metadata":{"id":"9eYaste92B_V","executionInfo":{"status":"ok","timestamp":1701837673598,"user_tz":-330,"elapsed":158,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["x"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"hKjHWhGs465a","executionInfo":{"status":"ok","timestamp":1701837673600,"user_tz":-330,"elapsed":156,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"a7cc09ef-494b-4b0d-9942-5ca58333d897"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","0     63    1   3       145   233    1        0      150      0      2.3   \n","1     37    1   2       130   250    0        1      187      0      3.5   \n","2     41    0   1       130   204    0        0      172      0      1.4   \n","3     56    1   1       120   236    0        1      178      0      0.8   \n","4     57    0   0       120   354    0        1      163      1      0.6   \n","..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  \n","0        0   0     1  \n","1        0   0     2  \n","2        2   0     2  \n","3        2   0     2  \n","4        2   0     2  \n","..     ...  ..   ...  \n","298      1   0     3  \n","299      1   0     3  \n","300      1   2     3  \n","301      1   1     3  \n","302      1   1     2  \n","\n","[303 rows x 13 columns]"],"text/html":["\n","  <div id=\"df-6392e232-6471-44d5-9b48-fcbf20fd4304\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>298</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>140</td>\n","      <td>241</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>123</td>\n","      <td>1</td>\n","      <td>0.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>299</th>\n","      <td>45</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>110</td>\n","      <td>264</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>132</td>\n","      <td>0</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>300</th>\n","      <td>68</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>144</td>\n","      <td>193</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>141</td>\n","      <td>0</td>\n","      <td>3.4</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>301</th>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>130</td>\n","      <td>131</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>115</td>\n","      <td>1</td>\n","      <td>1.2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>302</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>174</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>303 rows × 13 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6392e232-6471-44d5-9b48-fcbf20fd4304')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-6392e232-6471-44d5-9b48-fcbf20fd4304 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-6392e232-6471-44d5-9b48-fcbf20fd4304');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-380f3148-dd49-4f3a-9423-d0c8f7cce6cc\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-380f3148-dd49-4f3a-9423-d0c8f7cce6cc')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-380f3148-dd49-4f3a-9423-d0c8f7cce6cc button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["y"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PHkS1O_D48uh","executionInfo":{"status":"ok","timestamp":1701837673601,"user_tz":-330,"elapsed":148,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"15ba0fd2-936d-4eff-cd22-db67b779c6f6"},"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0      1\n","1      1\n","2      1\n","3      1\n","4      1\n","      ..\n","298    0\n","299    0\n","300    0\n","301    0\n","302    0\n","Name: target, Length: 303, dtype: int64"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["#Splitting into train and test data sets\n","np.random.seed(42)\n","\n","#Split\n","x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"],"metadata":{"id":"u2g2RIFi5Fja","executionInfo":{"status":"ok","timestamp":1701837673606,"user_tz":-330,"elapsed":143,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["y_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1mnrtSWY5h2K","executionInfo":{"status":"ok","timestamp":1701837673608,"user_tz":-330,"elapsed":143,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"4781995f-38ea-4307-e369-772fabff3cef"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["132    1\n","202    0\n","196    0\n","75     1\n","176    0\n","      ..\n","188    0\n","71     1\n","106    1\n","270    0\n","102    1\n","Name: target, Length: 242, dtype: int64"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["#|Put models in a dictionary\n","models = {\"Logistic Regression\": LogisticRegression(),\n","          \"KNN\": KNeighborsClassifier(),\n","          \"Random Forest\": RandomForestClassifier()}\n","\n","#Create a function to fit and score models\n","def fit_and_score(models, x_train, x_test, y_train, y_test):\n","  #Set random seed\n","  np.random.seed(42)\n","  #Make dictionary to keep model scores\n","  model_scores = {}\n","  #Loop through models\n","  for name, model in models.items():\n","    #Fit model to the data\n","    model.fit(x_train, y_train)\n","    #Evaluate and append it's score to model_scores\n","    model_scores[name] = model.score(x_test, y_test)\n","    return model_scores"],"metadata":{"id":"9fBRZ1ej5nai","executionInfo":{"status":"ok","timestamp":1701837673610,"user_tz":-330,"elapsed":137,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["model_scores = fit_and_score(models=models,\n","                             x_train=x_train,\n","                             x_test=x_test,\n","                             y_train=y_train,\n","                             y_test=y_test)\n","\n","model_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kIekCmn3Fw1q","executionInfo":{"status":"ok","timestamp":1701837673613,"user_tz":-330,"elapsed":138,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"c289c045-bba1-4123-f544-3c5d952af356"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"output_type":"execute_result","data":{"text/plain":["{'Logistic Regression': 0.8852459016393442}"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["modelLR = LogisticRegression()\n","modelLR.fit(x_train, y_train)\n","modelLR.score(x_test, y_test)"],"metadata":{"id":"Mdl09SKuG3Xg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701837909357,"user_tz":-330,"elapsed":27,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"bf5f5130-047e-4af7-9286-ef774da6609b"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  n_iter_i = _check_optimize_result(\n"]},{"output_type":"execute_result","data":{"text/plain":["0.8852459016393442"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["modelKNN = KNeighborsClassifier()\n","modelKNN.fit(x_train, y_train)\n","modelKNN.score(x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X-AtI9X2caKv","executionInfo":{"status":"ok","timestamp":1701838053574,"user_tz":-330,"elapsed":557,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"f22172fb-ed15-46ac-9eb2-7626ddf3f1d8"},"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.6885245901639344"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["modelRF = RandomForestClassifier(x_train, y_train)\n","modelRF.fit(x_train, y_train)\n","modelRF.score(x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"id":"ErVg9pFdc9cc","executionInfo":{"status":"error","timestamp":1701838239443,"user_tz":-330,"elapsed":34,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"a7e46159-2a68-4a64-937c-97266b9f4a80"},"execution_count":27,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-27-c6fc88cb7865>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodelRF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmodelRF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodelRF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mTypeError\u001b[0m: RandomForestClassifier.__init__() takes from 1 to 2 positional arguments but 3 were given"]}]},{"cell_type":"code","source":["#Let's tune KNN (by hand)\n","\n","train_scores = []\n","test_scores = []\n","\n","#Create a list of different values for n_neighbors\n","neighbors = range(1,21)\n","\n","#Set up KNN instance\n","knn = KNeighborsClassifier()\n","\n","#Loop through different n_neighbors\n","for i in neighbors:\n","  knn.set_params(n_neighbors=i)\n","\n","  #Fit algorithm\n","  knn.fit(x_train, y_train)\n","\n","  #Update the training scores list\n","  train_scores.append(knn.score(x_train, y_train))\n","\n","  #Update the test scores list\n","  test_scores.append(knn.score(x_test, y_test))"],"metadata":{"id":"giy5qIeUdj5I","executionInfo":{"status":"ok","timestamp":1701839189277,"user_tz":-330,"elapsed":2035,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["train_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q24qc3yVhSfn","executionInfo":{"status":"ok","timestamp":1701839207772,"user_tz":-330,"elapsed":857,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"40230a23-b95f-4433-c217-6de47d8dffe3"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[1.0,\n"," 0.8099173553719008,\n"," 0.7727272727272727,\n"," 0.743801652892562,\n"," 0.7603305785123967,\n"," 0.7520661157024794,\n"," 0.743801652892562,\n"," 0.7231404958677686,\n"," 0.71900826446281,\n"," 0.6942148760330579,\n"," 0.7272727272727273,\n"," 0.6983471074380165,\n"," 0.6900826446280992,\n"," 0.6942148760330579,\n"," 0.6859504132231405,\n"," 0.6735537190082644,\n"," 0.6859504132231405,\n"," 0.6652892561983471,\n"," 0.6818181818181818,\n"," 0.6694214876033058]"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["test_scores"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"am1xMhfZhXEz","executionInfo":{"status":"ok","timestamp":1701839212129,"user_tz":-330,"elapsed":626,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"79a11da1-0266-435f-ef8d-b1a530cbaff3"},"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.6229508196721312,\n"," 0.639344262295082,\n"," 0.6557377049180327,\n"," 0.6721311475409836,\n"," 0.6885245901639344,\n"," 0.7213114754098361,\n"," 0.7049180327868853,\n"," 0.6885245901639344,\n"," 0.6885245901639344,\n"," 0.7049180327868853,\n"," 0.7540983606557377,\n"," 0.7377049180327869,\n"," 0.7377049180327869,\n"," 0.7377049180327869,\n"," 0.6885245901639344,\n"," 0.7213114754098361,\n"," 0.6885245901639344,\n"," 0.6885245901639344,\n"," 0.7049180327868853,\n"," 0.6557377049180327]"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["plt.plot(neighbors, train_scores, label=\"Train score\")\n","plt.plot(neighbors, test_scores, label=\"Test score\")\n","plt.xlabel(\"Number of neighbors\")\n","plt.ylabel(\"Model Score\")\n","plt.legend()\n","\n","print(f\"Maximum KNN score on the test data: {max(test_scores)*100:.2f}%\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"pvumSJ2uhYNb","executionInfo":{"status":"ok","timestamp":1701841655909,"user_tz":-330,"elapsed":1355,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"458ce9ab-3eeb-475f-a1b2-01725bf68920"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["Maximum KNN score on the test data: 75.41%\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":["HYPERPARAMETER TUNING USING RANDOMIZEDCV"],"metadata":{"id":"24OJyMnkyxG2"}},{"cell_type":"code","source":["#Create a hyperparameter grid for LogisticRegression\n","log_reg_grid = {\"C\": np.logspace(-4, 4, 20),\n","                'solver': [\"liblinear\"]}\n","\n","#Create yperparameter grid for RandomForestClassifier\n","rf_grid = {\"n_estimators\": np.arange(10, 1000, 50),\n","           \"max_depth\": [None, 3, 5, 10],\n","           \"min_samples_split\": np.arange(2, 20, 2),\n","           \"min_samples_lead\": np.arange(1, 20, 2)}"],"metadata":{"id":"QWAo6qfUiVNV","executionInfo":{"status":"ok","timestamp":1701844989102,"user_tz":-330,"elapsed":549,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["# Tune Logistic Regression\n","\n","np.random.seed(42)\n","\n","#Setup random hyperparameter search for LogisticRegression\n","rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n","                                param_distributions=log_reg_grid,\n","                                cv=5,\n","                                n_iter=20,\n","                                verbose=True)\n","\n","#Fit the random hyperparameter search model for LogisticRegression\n","rs_log_reg.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":137},"id":"5I-kWIPM3amG","executionInfo":{"status":"ok","timestamp":1701846300559,"user_tz":-330,"elapsed":996,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"1d460b74-0069-42ae-b10e-e028fd2e4456"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n","                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n","       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n","       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n","       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n","       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n","                                        'solver': ['liblinear']},\n","                   verbose=True)"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n","                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n","       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n","       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n","       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n","       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n","                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n","                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n","                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n","       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n","       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n","       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n","       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n","                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n","                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["rs_log_reg.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ChsYQFi-8asi","executionInfo":{"status":"ok","timestamp":1701846378556,"user_tz":-330,"elapsed":36,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"c62c5f76-a022-40b4-86d1-49cefc5a5739"},"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'solver': 'liblinear', 'C': 0.23357214690901212}"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["rs_log_reg.score(x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2aBEqx-O8t_v","executionInfo":{"status":"ok","timestamp":1701846417467,"user_tz":-330,"elapsed":18,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"cc906bea-410a-4140-c5c0-2ab7b589d7e5"},"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8852459016393442"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["#Setup random seed\n","np.random.seed(42)\n","\n","#Setup random hyperparameter search for RandomForestClassifier\n","rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n","                           param_distributions=rf_grid,\n","                           cv=5,\n","                           n_iter=20,\n","                           verbose=True)\n","\n","#Fit random hyperparameter search model for RandomForestClassifier\n","rs_rf.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":427},"id":"4AXkTw2y81sY","executionInfo":{"status":"error","timestamp":1701848423491,"user_tz":-330,"elapsed":79,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"ba88ad6b-6255-4f59-8ebc-04ee272622d2"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"]},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-39-2a2de8a54064>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m#Fit random hyperparameter search model for RandomForestClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mrs_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m         \u001b[0;34m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m         evaluate_candidates(\n\u001b[0m\u001b[1;32m   1769\u001b[0m             ParameterSampler(\n\u001b[1;32m   1770\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    819\u001b[0m                     )\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    822\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    823\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m         \u001b[0;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0mcloned_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msafe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m         \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcloned_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mset_params\u001b[0;34m(self, **params)\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m                 \u001b[0mlocal_valid_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_param_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    206\u001b[0m                     \u001b[0;34mf\"Invalid parameter {key!r} for estimator {self}. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m                     \u001b[0;34mf\"Valid parameters are: {local_valid_params!r}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Invalid parameter 'min_samples_lead' for estimator RandomForestClassifier(min_samples_split=16, n_estimators=10). Valid parameters are: ['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start']."]}]},{"cell_type":"code","source":["# Setup random seed\n","np.random.seed(42)\n","\n","# Setup random hyperparameter search for RandomForestClassifier\n","rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n","                           param_distributions=rf_grid,\n","                           cv=5,\n","                           n_iter=20,\n","                           verbose=True)\n","\n","# Fit random hyperparameter search model for RandomForestClassifier()\n","rs_rf.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":427},"id":"274GwkdsOF_L","executionInfo":{"status":"error","timestamp":1701850950409,"user_tz":-330,"elapsed":497,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"9b67c666-1cd8-4fdb-8b7c-6e6f26e446a1"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"]},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-54-330304fdd077>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Fit random hyperparameter search model for RandomForestClassifier()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mrs_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    872\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1767\u001b[0m         \u001b[0;34m\"\"\"Search n_iter candidates from param_distributions\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1768\u001b[0;31m         evaluate_candidates(\n\u001b[0m\u001b[1;32m   1769\u001b[0m             ParameterSampler(\n\u001b[1;32m   1770\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    819\u001b[0m                     )\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 821\u001b[0;31m                 out = parallel(\n\u001b[0m\u001b[1;32m    822\u001b[0m                     delayed(_fit_and_score)(\n\u001b[1;32m    823\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         )\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1865\u001b[0m         \u001b[0;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1791\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1792\u001b[0;31m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1793\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1794\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0mcloned_parameters\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msafe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m         \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mcloned_parameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mset_params\u001b[0;34m(self, **params)\u001b[0m\n\u001b[1;32m    203\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_params\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m                 \u001b[0mlocal_valid_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_param_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    206\u001b[0m                     \u001b[0;34mf\"Invalid parameter {key!r} for estimator {self}. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m                     \u001b[0;34mf\"Valid parameters are: {local_valid_params!r}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Invalid parameter 'min_samples_lead' for estimator RandomForestClassifier(min_samples_split=16, n_estimators=10). Valid parameters are: ['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start']."]}]},{"cell_type":"code","source":["#look into 166"],"metadata":{"id":"qIOaW7u3EgkI","executionInfo":{"status":"ok","timestamp":1701848596885,"user_tz":-330,"elapsed":4,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["#Find the best hyperparameters\n","rs_rf.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":200},"id":"74Z3AL-JFKCe","executionInfo":{"status":"error","timestamp":1701848654842,"user_tz":-330,"elapsed":532,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"540bab3e-25c4-4075-9b66-a57ffe6ea489"},"execution_count":42,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-42-985a6589ec42>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Find the best hyperparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrs_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'RandomizedSearchCV' object has no attribute 'best_params_'"]}]},{"cell_type":"code","source":["#Find the best hyperparameters\n","rs_rf.cv_results_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":200},"id":"73e5uy-MGYad","executionInfo":{"status":"error","timestamp":1701848934802,"user_tz":-330,"elapsed":774,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"f8533b8b-6784-4f94-d966-9c06cc11fd4a"},"execution_count":43,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-43-fcffaffdc59e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Find the best hyperparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrs_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'RandomizedSearchCV' object has no attribute 'cv_results_'"]}]},{"cell_type":"code","source":["rs_rf.score(x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"id":"_xiR67TCFVif","executionInfo":{"status":"error","timestamp":1701849162064,"user_tz":-330,"elapsed":1044,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"15d7b68b-bbed-4cfa-e31a-dada0b1779b2"},"execution_count":44,"outputs":[{"output_type":"error","ename":"NotFittedError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-44-b87f1973f328>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrs_rf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    434\u001b[0m         \"\"\"\n\u001b[1;32m    435\u001b[0m         \u001b[0m_check_refit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    437\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscorer_\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             raise ValueError(\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m   1388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1389\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfitted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1390\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNotFittedError\u001b[0m: This RandomizedSearchCV instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."]}]},{"cell_type":"markdown","source":["GridSearchCV"],"metadata":{"id":"4TAPCdNjHjM1"}},{"cell_type":"code","source":["#Different hyperparameters for our LogisticRegression model\n","log_reg_grid = {\"C\": np.logspace(-1, 4, 30),\n","                \"solver\": [\"liblinear\"]}\n","\n","#Setup grid hyperparameter search for LogisticRegression\n","gs_log_reg = GridSearchCV(LogisticRegression(),\n","                          param_grid = log_reg_grid,\n","                          cv=5,\n","                          verbose=True)\n","\n","#Fit hyperparameter grid model\n","gs_log_reg.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":137},"id":"IakAOuXsHVln","executionInfo":{"status":"ok","timestamp":1701850606090,"user_tz":-330,"elapsed":1732,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"9fe0d36a-8fe7-4b00-cf56-a06fade45574"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"]},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={'C': array([1.00000000e-01, 1.48735211e-01, 2.21221629e-01, 3.29034456e-01,\n","       4.89390092e-01, 7.27895384e-01, 1.08263673e+00, 1.61026203e+00,\n","       2.39502662e+00, 3.56224789e+00, 5.29831691e+00, 7.88046282e+00,\n","       1.17210230e+01, 1.74332882e+01, 2.59294380e+01, 3.85662042e+01,\n","       5.73615251e+01, 8.53167852e+01, 1.26896100e+02, 1.88739182e+02,\n","       2.80721620e+02, 4.17531894e+02, 6.21016942e+02, 9.23670857e+02,\n","       1.37382380e+03, 2.04335972e+03, 3.03919538e+03, 4.52035366e+03,\n","       6.72335754e+03, 1.00000000e+04]),\n","                         'solver': ['liblinear']},\n","             verbose=True)"],"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={&#x27;C&#x27;: array([1.00000000e-01, 1.48735211e-01, 2.21221629e-01, 3.29034456e-01,\n","       4.89390092e-01, 7.27895384e-01, 1.08263673e+00, 1.61026203e+00,\n","       2.39502662e+00, 3.56224789e+00, 5.29831691e+00, 7.88046282e+00,\n","       1.17210230e+01, 1.74332882e+01, 2.59294380e+01, 3.85662042e+01,\n","       5.73615251e+01, 8.53167852e+01, 1.26896100e+02, 1.88739182e+02,\n","       2.80721620e+02, 4.17531894e+02, 6.21016942e+02, 9.23670857e+02,\n","       1.37382380e+03, 2.04335972e+03, 3.03919538e+03, 4.52035366e+03,\n","       6.72335754e+03, 1.00000000e+04]),\n","                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n","             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n","             param_grid={&#x27;C&#x27;: array([1.00000000e-01, 1.48735211e-01, 2.21221629e-01, 3.29034456e-01,\n","       4.89390092e-01, 7.27895384e-01, 1.08263673e+00, 1.61026203e+00,\n","       2.39502662e+00, 3.56224789e+00, 5.29831691e+00, 7.88046282e+00,\n","       1.17210230e+01, 1.74332882e+01, 2.59294380e+01, 3.85662042e+01,\n","       5.73615251e+01, 8.53167852e+01, 1.26896100e+02, 1.88739182e+02,\n","       2.80721620e+02, 4.17531894e+02, 6.21016942e+02, 9.23670857e+02,\n","       1.37382380e+03, 2.04335972e+03, 3.03919538e+03, 4.52035366e+03,\n","       6.72335754e+03, 1.00000000e+04]),\n","                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n","             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":48}]},{"cell_type":"code","source":["#Check the best hyperparameters\n","gs_log_reg.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NS6A1_dsMr97","executionInfo":{"status":"ok","timestamp":1701850713268,"user_tz":-330,"elapsed":675,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"3d41dfc9-4faa-4ef9-a3e4-12c1ac28b93a"},"execution_count":51,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'C': 0.2212216291070449, 'solver': 'liblinear'}"]},"metadata":{},"execution_count":51}]},{"cell_type":"code","source":["##evaluate the grid search LogisticRegression model\n","gs_log_reg.score(x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sQ7W9w42NC7t","executionInfo":{"status":"ok","timestamp":1701850767158,"user_tz":-330,"elapsed":501,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"1b3851c6-7a55-40fb-99cc-c33f9c05a955"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8852459016393442"]},"metadata":{},"execution_count":52}]},{"cell_type":"markdown","source":["Evaluting our tuned machine learning classifier, beyond accuracy\n","ROC curve and AUC score\n","Confusion matrix\n","Classification report\n","Precision\n","Recall\n","F1-score\n","... and it would be great if cross-validation was used where possible.\n","\n","To make comparisons and evaluate our trained model, first we need to make predictions."],"metadata":{"id":"wiB55w7sR0jn"}},{"cell_type":"code","source":["#Make predictions for tuned model\n","y_preds = gs_log_reg.predict(x_test)"],"metadata":{"id":"Um5ps-t9NdRs","executionInfo":{"status":"ok","timestamp":1701852071593,"user_tz":-330,"elapsed":529,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":55,"outputs":[]},{"cell_type":"code","source":["y_preds"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HMyDxFqfScGv","executionInfo":{"status":"ok","timestamp":1701852082940,"user_tz":-330,"elapsed":29,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"67b98935-7d8e-4a0e-cd24-d09facdc64d5"},"execution_count":56,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n","       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"]},"metadata":{},"execution_count":56}]},{"cell_type":"code","source":["y_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GhIyt2x8Serc","executionInfo":{"status":"ok","timestamp":1701852094406,"user_tz":-330,"elapsed":526,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"aa5af267-291a-47a6-c374-bb6f192fba58"},"execution_count":57,"outputs":[{"output_type":"execute_result","data":{"text/plain":["179    0\n","228    0\n","111    1\n","246    0\n","60     1\n","      ..\n","249    0\n","104    1\n","300    0\n","193    0\n","184    0\n","Name: target, Length: 61, dtype: int64"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","source":["#Plot ROC and calculate AUC metric\n","RocCurveDisplay.from_estimator(gs_log_reg, x_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"EShhNV7pShcE","executionInfo":{"status":"ok","timestamp":1701852474149,"user_tz":-330,"elapsed":1470,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"3034e280-a35f-4f59-c07b-cb66ffe7ecff"},"execution_count":62,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7f15ed415960>"]},"metadata":{},"execution_count":62},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["#Confusion matrix\n","print(confusion_matrix(y_test, y_preds))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QaPXbP__UEBB","executionInfo":{"status":"ok","timestamp":1701852547410,"user_tz":-330,"elapsed":495,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"c1bf57ba-76da-4448-9a95-e01255b3d52f"},"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["[[25  4]\n"," [ 3 29]]\n"]}]},{"cell_type":"code","source":["sns.set(font_scale=1.5)\n","\n","def plot_conf_matrix(y_test, y_preds):\n","  \"\"\"\n","  Plots a nice looking confusion matrix using seaborn heatmap\n","  \"\"\"\n","\n","  fig, ax = plt.subplots(figsize=(3, 3))\n","  ac = sns.heatmap(confusion_matrix(y_test, y_preds),\n","                   annot=True,\n","                   cbar=False)\n","\n","  plt.xlabel(\"True label\")\n","  plt.ylabel(\"Predicted label\")\n","\n","plot_conf_matrix(y_test, y_preds)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"djWjaOpUUQR8","executionInfo":{"status":"ok","timestamp":1701852912963,"user_tz":-330,"elapsed":752,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"7b8cac2c-e914-4b3e-8594-45ba8516693b"},"execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 300x300 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Now we've got a ROC curve, an AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1-score."],"metadata":{"id":"x7vJeeludPCa"}},{"cell_type":"code","source":["print(classification_report(y_test, y_preds))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hHbVNxVQVjK9","executionInfo":{"status":"ok","timestamp":1701854963389,"user_tz":-330,"elapsed":92,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"b4065250-4966-46a8-eeb7-7a4f1964ca28"},"execution_count":66,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.89      0.86      0.88        29\n","           1       0.88      0.91      0.89        32\n","\n","    accuracy                           0.89        61\n","   macro avg       0.89      0.88      0.88        61\n","weighted avg       0.89      0.89      0.89        61\n","\n"]}]},{"cell_type":"markdown","source":["Calculate evaluation metrics using cross-validation\n","We're going to calculate accuracy, precision, recall and f1-score of our model using cross-validation and to do so we'll be using cross_val_score()."],"metadata":{"id":"GtMkhjJzdxpi"}},{"cell_type":"code","source":["#Check the best hyperparameters\n","gs_log_reg.best_params_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xqT7YBvJddpL","executionInfo":{"status":"ok","timestamp":1701855612499,"user_tz":-330,"elapsed":755,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"8f8245f9-da00-4792-cba3-3f9961098684"},"execution_count":67,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'C': 0.2212216291070449, 'solver': 'liblinear'}"]},"metadata":{},"execution_count":67}]},{"cell_type":"code","source":["#Create a new classifier with new parameters\n","clf = LogisticRegression(C=0.2212216291070449,\n","                         solver=\"liblinear\")"],"metadata":{"id":"K2MnOeVhf753","executionInfo":{"status":"ok","timestamp":1701855810685,"user_tz":-330,"elapsed":2,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}}},"execution_count":69,"outputs":[]},{"cell_type":"code","source":["#Cross_validated accuracy\n","cv_acc = cross_val_score(clf,\n","                         x,\n","                         y,\n","                         cv=5,\n","                         scoring=\"accuracy\")\n","\n","cv_acc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u1yBEzYzgfsf","executionInfo":{"status":"ok","timestamp":1701855935926,"user_tz":-330,"elapsed":522,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"61d93167-377d-43f9-a855-672cd97f2a80"},"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["cv_acc = np.mean(cv_acc)\n","cv_acc"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XMEPEyb4hLRy","executionInfo":{"status":"ok","timestamp":1701855984708,"user_tz":-330,"elapsed":4,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"36f6bb62-f387-4940-8ff4-99febfc7c8e3"},"execution_count":71,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8479781420765027"]},"metadata":{},"execution_count":71}]},{"cell_type":"code","source":["#Cross_validated precision\n","cv_precision = cross_val_score(clf,\n","                               x,\n","                               y,\n","                               cv=5,\n","                               scoring=\"precision\")\n","\n","cv_precision = np.mean(cv_precision)\n","cv_precision"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vU_X7ntMhXVD","executionInfo":{"status":"ok","timestamp":1701856196702,"user_tz":-330,"elapsed":4974,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"f720b5b7-e904-4273-fab1-078a31b695ba"},"execution_count":74,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8215873015873015"]},"metadata":{},"execution_count":74}]},{"cell_type":"code","source":["#Cross-validated recall\n","cv_recall = cross_val_score(clf,\n","                            x,\n","                            y,\n","                            cv=5,\n","                            scoring=\"recall\")\n","\n","cv_recall = np.mean(cv_recall)\n","cv_recall"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mxfxsYcmhhY9","executionInfo":{"status":"ok","timestamp":1701856276470,"user_tz":-330,"elapsed":665,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"70960d12-d5ea-460f-96f8-8c462caa8369"},"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9272727272727274"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["#Cross-validated f1-score\n","cv_f1 = cross_val_score(clf,\n","                        x,\n","                        y,\n","                        cv=5,\n","                        scoring=\"f1\")\n","\n","cv_f1 = np.mean(cv_f1)\n","cv_f1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2VzEm3NfhmFS","executionInfo":{"status":"ok","timestamp":1701856411638,"user_tz":-330,"elapsed":555,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"2ce723df-1124-4147-ebe8-2205b5e40450"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8705403543192143"]},"metadata":{},"execution_count":77}]},{"cell_type":"code","source":["#Visualize cross_validated metrics\n","cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n","                           \"Precision\": cv_precision,\n","                           \"Recall\": cv_recall,\n","                           \"F1\": cv_f1},\n","                          index=[0])\n","\n","cv_metrics.T.plot.bar(title=\"Cross-validated classification metrics\",\n","                      legend=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":569},"id":"YSvYkwCEi8zR","executionInfo":{"status":"ok","timestamp":1701856733970,"user_tz":-330,"elapsed":1507,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"0aa801cf-1e69-4330-8665-34ff95d53295"},"execution_count":81,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: title={'center': 'Cross-validated classification metrics'}>"]},"metadata":{},"execution_count":81},{"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":["Feature Importance\n","Feature importance is another as asking, \"which features contributed most to the outcomes of the model and how did they contribute?\"\n","\n","Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n","\n","Let's find the feature importance for our LogisticRegression model..."],"metadata":{"id":"2iSgWbmskY77"}},{"cell_type":"code","source":["#Fit an instance of Logistic Regression\n","clf = LogisticRegression(C=0.2212216291070449,\n","                         solver=\"liblinear\")\n","\n","clf.fit(x_train, y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"id":"EMtlyqC8kF5K","executionInfo":{"status":"ok","timestamp":1701857667379,"user_tz":-330,"elapsed":4431,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"dfba7458-c5f6-45d3-c3cd-da9071682f43"},"execution_count":82,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LogisticRegression(C=0.2212216291070449, solver='liblinear')"],"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.2212216291070449, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.2212216291070449, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":82}]},{"cell_type":"code","source":["#Check coefficient\n","clf.coef_"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CGjCPOZTnw31","executionInfo":{"status":"ok","timestamp":1701857729402,"user_tz":-330,"elapsed":518,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"83234488-0f87-4eb6-fb39-a67009466235"},"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.00348354, -0.88559313,  0.6692227 , -0.01160079, -0.001685  ,\n","         0.04648658,  0.32645411,  0.02466732, -0.61967584, -0.57313636,\n","         0.46276587, -0.64561816, -0.69121355]])"]},"metadata":{},"execution_count":83}]},{"cell_type":"code","source":["df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"bWXjjU2XoBSq","executionInfo":{"status":"ok","timestamp":1701858541779,"user_tz":-330,"elapsed":622,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"eded573d-681d-4971-d769-469a17b0767a"},"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n","0   63    1   3       145   233    1        0      150      0      2.3      0   \n","1   37    1   2       130   250    0        1      187      0      3.5      0   \n","2   41    0   1       130   204    0        0      172      0      1.4      2   \n","3   56    1   1       120   236    0        1      178      0      0.8      2   \n","4   57    0   0       120   354    0        1      163      1      0.6      2   \n","\n","   ca  thal  target  \n","0   0     1       1  \n","1   0     2       1  \n","2   0     2       1  \n","3   0     2       1  \n","4   0     2       1  "],"text/html":["\n","  <div id=\"df-405ba155-1506-46c8-bb4d-0708fca6ecaf\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-405ba155-1506-46c8-bb4d-0708fca6ecaf')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-405ba155-1506-46c8-bb4d-0708fca6ecaf 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-405ba155-1506-46c8-bb4d-0708fca6ecaf');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-4be7ee63-5531-4c58-ab38-ef6be864b9a2\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4be7ee63-5531-4c58-ab38-ef6be864b9a2')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-4be7ee63-5531-4c58-ab38-ef6be864b9a2 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":84}]},{"cell_type":"code","source":["#Match coef's of features to columns\n","feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n","feature_dict"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pkuotlBkrHYB","executionInfo":{"status":"ok","timestamp":1701858711180,"user_tz":-330,"elapsed":1189,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"5cac48b8-556a-4382-f553-497da4bb7fc5"},"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'age': 0.0034835426131471763,\n"," 'sex': -0.8855931337581155,\n"," 'cp': 0.6692226961496021,\n"," 'trestbps': -0.011600787499313711,\n"," 'chol': -0.0016850027266994956,\n"," 'fbs': 0.046486580947070796,\n"," 'restecg': 0.32645410877370834,\n"," 'thalach': 0.024667315723039693,\n"," 'exang': -0.6196758409270574,\n"," 'oldpeak': -0.5731363645217727,\n"," 'slope': 0.4627658691436633,\n"," 'ca': -0.6456181575503567,\n"," 'thal': -0.6912135456343571}"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","source":["#Visualize feature importance\n","feature_df = pd.DataFrame(feature_dict, index=[0])\n","feature_df.T.plot.bar(title=\"Feature Importance\", legend=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":559},"id":"bZEUiF2trr3y","executionInfo":{"status":"ok","timestamp":1701858838599,"user_tz":-330,"elapsed":1321,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"3d8e0c28-8e67-43ef-de77-096cc4e377f9"},"execution_count":87,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: title={'center': 'Feature Importance'}>"]},"metadata":{},"execution_count":87},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["pd.crosstab(df[\"sex\"], df[\"target\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"rDuWDr6tsPyo","executionInfo":{"status":"ok","timestamp":1701858937388,"user_tz":-330,"elapsed":556,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"cfa4c808-167a-4877-b5cc-a4c3fa9f9a09"},"execution_count":88,"outputs":[{"output_type":"execute_result","data":{"text/plain":["target    0   1\n","sex            \n","0        24  72\n","1       114  93"],"text/html":["\n","  <div id=\"df-e6f531a8-9ddd-4e38-87c9-b031cf2643f5\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>target</th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>24</td>\n","      <td>72</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>114</td>\n","      <td>93</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6f531a8-9ddd-4e38-87c9-b031cf2643f5')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e6f531a8-9ddd-4e38-87c9-b031cf2643f5 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-e6f531a8-9ddd-4e38-87c9-b031cf2643f5');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-cb55a1c8-9671-4696-8d66-aa5b36eca531\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb55a1c8-9671-4696-8d66-aa5b36eca531')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-cb55a1c8-9671-4696-8d66-aa5b36eca531 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":88}]},{"cell_type":"code","source":["pd.crosstab(df[\"slope\"], df[\"target\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"-vfte3G-soTI","executionInfo":{"status":"ok","timestamp":1701858968399,"user_tz":-330,"elapsed":10,"user":{"displayName":"Shigraf Salik","userId":"14202062023566691520"}},"outputId":"d95cb2e9-a7a7-4028-eac9-1e7d77a4b2e5"},"execution_count":89,"outputs":[{"output_type":"execute_result","data":{"text/plain":["target   0    1\n","slope          \n","0       12    9\n","1       91   49\n","2       35  107"],"text/html":["\n","  <div id=\"df-8a66232f-6ce4-40ef-b0a2-a85008f07562\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>target</th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","    <tr>\n","      <th>slope</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>12</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>91</td>\n","      <td>49</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>35</td>\n","      <td>107</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a66232f-6ce4-40ef-b0a2-a85008f07562')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .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","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\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","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-8a66232f-6ce4-40ef-b0a2-a85008f07562 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-8a66232f-6ce4-40ef-b0a2-a85008f07562');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-c2942848-dbf7-471c-9953-16e4521f921c\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2942848-dbf7-471c-9953-16e4521f921c')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\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-c2942848-dbf7-471c-9953-16e4521f921c button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":89}]},{"cell_type":"markdown","source":["slope - the slope of the peak exercise ST segment\n","\n","0: Upsloping: better heart rate with excercise (uncommon)\n","1: Flatsloping: minimal change (typical healthy heart)\n","2: Downslopins: signs of unhealthy heart"],"metadata":{"id":"AV8_GHkBvJsA"}}]}