heart-attack-prediction-logistic-regression.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/milicaradisavljevic/heart-attack-prediction-logistic-regression?scriptVersionId=183353690\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"f5f28cb8","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-06-13T17:52:44.370131Z","iopub.status.busy":"2024-06-13T17:52:44.369756Z","iopub.status.idle":"2024-06-13T17:52:45.28477Z","shell.execute_reply":"2024-06-13T17:52:45.283329Z"},"papermill":{"duration":0.928791,"end_time":"2024-06-13T17:52:45.288192","exception":false,"start_time":"2024-06-13T17:52:44.359401","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/heart-attack-analysis-prediction-dataset/o2Saturation.csv\n","/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","        \n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"markdown","id":"986db74b","metadata":{"papermill":{"duration":0.0087,"end_time":"2024-06-13T17:52:45.308326","exception":false,"start_time":"2024-06-13T17:52:45.299626","status":"completed"},"tags":[]},"source":["# Import the libraries"]},{"cell_type":"code","execution_count":2,"id":"1929489c","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:45.327607Z","iopub.status.busy":"2024-06-13T17:52:45.327107Z","iopub.status.idle":"2024-06-13T17:52:47.102541Z","shell.execute_reply":"2024-06-13T17:52:47.101358Z"},"papermill":{"duration":1.788125,"end_time":"2024-06-13T17:52:47.105192","exception":false,"start_time":"2024-06-13T17:52:45.317067","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd #managing the data\n","import numpy as np #mathematical operations\n","import matplotlib.pyplot as plt #vizualization\n","import seaborn as sns #visualization\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import OneHotEncoder, LabelEncoder, MinMaxScaler\n","from sklearn.model_selection import train_test_split, learning_curve\n","from sklearn.linear_model import LogisticRegression\n","from sklearn import metrics\n","from sklearn.metrics import classification_report, accuracy_score, confusion_matrix,ConfusionMatrixDisplay, precision_recall_fscore_support, precision_score, recall_score\n","import matplotlib.pyplot as plt\n","import seaborn as sns"]},{"cell_type":"markdown","id":"9e33f8a5","metadata":{"papermill":{"duration":0.008317,"end_time":"2024-06-13T17:52:47.122226","exception":false,"start_time":"2024-06-13T17:52:47.113909","status":"completed"},"tags":[]},"source":["# Load the data¶"]},{"cell_type":"code","execution_count":3,"id":"2725ad14","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.141711Z","iopub.status.busy":"2024-06-13T17:52:47.140587Z","iopub.status.idle":"2024-06-13T17:52:47.180917Z","shell.execute_reply":"2024-06-13T17:52:47.179791Z"},"papermill":{"duration":0.052475,"end_time":"2024-06-13T17:52:47.183243","exception":false,"start_time":"2024-06-13T17:52:47.130768","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trtbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalachh</th>\n","      <th>exng</th>\n","      <th>oldpeak</th>\n","      <th>slp</th>\n","      <th>caa</th>\n","      <th>thall</th>\n","      <th>output</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>"],"text/plain":["   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\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","   caa  thall  output  \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  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["data = pd.read_csv(\"/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\")\n","data.head()"]},{"cell_type":"markdown","id":"be3a6775","metadata":{"papermill":{"duration":0.008594,"end_time":"2024-06-13T17:52:47.200683","exception":false,"start_time":"2024-06-13T17:52:47.192089","status":"completed"},"tags":[]},"source":["# About the data"]},{"cell_type":"markdown","id":"d4cb1585","metadata":{"papermill":{"duration":0.008373,"end_time":"2024-06-13T17:52:47.218905","exception":false,"start_time":"2024-06-13T17:52:47.210532","status":"completed"},"tags":[]},"source":["Heart Attack Analysis & Prediction Dataset dataset from Kaggle will be used. The data set provides information about disease characteristics and a person's chances of having a heart attack\n","\n","Key Attributes:\n","- age: Age of the patient;\n","- sex: Sex of the patient (sex = 0 female, sex = 1 male);\n","- cp: Chest pain type ~ 0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = Asymptomatic;\n","- trtbps: Resting blood pressure (in mm Hg);\n","- chol: Cholestoral in mg/dl fetched via BMI sensor;\n","- fbs: (fasting blood sugar > 120 mg/dl) ~ 1 = True, 0 = False;\n","- restecg: Resting electrocardiographic results ~ 0 = Normal, 1 = ST-T wave normality, 2 = Left ventricular hypertrophy;\n","- thalachh: Maximum heart rate achieved;\n","- oldpeak: Previous peak;\n","- slp: Slope;\n","- caa: Number of major vessels;\n","- thall: Thalium Stress Test result ~ (0,3);\n","- exng: Exercise induced angina ~ 1 = Yes, 0 = No;\n","- output: Target variable.\n"]},{"cell_type":"markdown","id":"46454d08","metadata":{"papermill":{"duration":0.008517,"end_time":"2024-06-13T17:52:47.23619","exception":false,"start_time":"2024-06-13T17:52:47.227673","status":"completed"},"tags":[]},"source":["# Objectives"]},{"cell_type":"markdown","id":"7790d032","metadata":{"papermill":{"duration":0.0084,"end_time":"2024-06-13T17:52:47.253373","exception":false,"start_time":"2024-06-13T17:52:47.244973","status":"completed"},"tags":[]},"source":["Our goal is to train a model that will be able to predict whether a person can get a heart attack or not. The focus will be on both prediction accuracy and interpretability. The detailed analysis will be done with the exploratory data analysis (EDA)."]},{"cell_type":"markdown","id":"42ac6d95","metadata":{"papermill":{"duration":0.008711,"end_time":"2024-06-13T17:52:47.270855","exception":false,"start_time":"2024-06-13T17:52:47.262144","status":"completed"},"tags":[]},"source":["# EDA and data cleaning"]},{"cell_type":"code","execution_count":4,"id":"a69a1c59","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.290408Z","iopub.status.busy":"2024-06-13T17:52:47.289988Z","iopub.status.idle":"2024-06-13T17:52:47.31618Z","shell.execute_reply":"2024-06-13T17:52:47.314885Z"},"papermill":{"duration":0.038627,"end_time":"2024-06-13T17:52:47.318682","exception":false,"start_time":"2024-06-13T17:52:47.280055","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","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   trtbps    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   thalachh  303 non-null    int64  \n"," 8   exng      303 non-null    int64  \n"," 9   oldpeak   303 non-null    float64\n"," 10  slp       303 non-null    int64  \n"," 11  caa       303 non-null    int64  \n"," 12  thall     303 non-null    int64  \n"," 13  output    303 non-null    int64  \n","dtypes: float64(1), int64(13)\n","memory usage: 33.3 KB\n"]}],"source":["data.info()"]},{"cell_type":"markdown","id":"c21f4130","metadata":{"papermill":{"duration":0.008667,"end_time":"2024-06-13T17:52:47.336458","exception":false,"start_time":"2024-06-13T17:52:47.327791","status":"completed"},"tags":[]},"source":["As we can see, we dont have object dtype and null values. "]},{"cell_type":"code","execution_count":5,"id":"8c868a1f","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.355917Z","iopub.status.busy":"2024-06-13T17:52:47.355513Z","iopub.status.idle":"2024-06-13T17:52:47.364624Z","shell.execute_reply":"2024-06-13T17:52:47.363521Z"},"papermill":{"duration":0.021493,"end_time":"2024-06-13T17:52:47.366996","exception":false,"start_time":"2024-06-13T17:52:47.345503","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["1"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["#checking for duplicates \n","data.duplicated().sum()"]},{"cell_type":"code","execution_count":6,"id":"8f2ed08e","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.387241Z","iopub.status.busy":"2024-06-13T17:52:47.386395Z","iopub.status.idle":"2024-06-13T17:52:47.398561Z","shell.execute_reply":"2024-06-13T17:52:47.397487Z"},"papermill":{"duration":0.025135,"end_time":"2024-06-13T17:52:47.401248","exception":false,"start_time":"2024-06-13T17:52:47.376113","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(302, 14)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["#we have 1 duplicate, so we will remove it\n","data.drop_duplicates(inplace=True)\n","data.shape"]},{"cell_type":"code","execution_count":7,"id":"3371b820","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.42118Z","iopub.status.busy":"2024-06-13T17:52:47.42078Z","iopub.status.idle":"2024-06-13T17:52:47.671647Z","shell.execute_reply":"2024-06-13T17:52:47.670501Z"},"papermill":{"duration":0.263706,"end_time":"2024-06-13T17:52:47.674172","exception":false,"start_time":"2024-06-13T17:52:47.410466","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Axes: xlabel='sex', ylabel='count'>"]},"execution_count":7,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ax=plt.axis()\n","sns.countplot(x='sex', data=data, palette='pastel')"]},{"cell_type":"markdown","id":"e4d3623a","metadata":{"papermill":{"duration":0.008884,"end_time":"2024-06-13T17:52:47.692567","exception":false,"start_time":"2024-06-13T17:52:47.683683","status":"completed"},"tags":[]},"source":["From the diagram (bar plot), we can see that the male population is more prone to heart attacks\n"]},{"cell_type":"code","execution_count":8,"id":"befe3a44","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:47.713226Z","iopub.status.busy":"2024-06-13T17:52:47.712819Z","iopub.status.idle":"2024-06-13T17:52:48.237387Z","shell.execute_reply":"2024-06-13T17:52:48.236265Z"},"papermill":{"duration":0.5383,"end_time":"2024-06-13T17:52:48.240392","exception":false,"start_time":"2024-06-13T17:52:47.702092","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10, 5))\n","sns.countplot(x=data[\"age\"])\n","plt.title(\"PATIENTS AGE\", fontsize=10)\n","plt.xlabel(\"AGE\", fontsize=10)\n","plt.ylabel(\"COUNT\", fontsize=10)\n","plt.show()"]},{"cell_type":"markdown","id":"74a16c22","metadata":{"papermill":{"duration":0.009633,"end_time":"2024-06-13T17:52:48.260429","exception":false,"start_time":"2024-06-13T17:52:48.250796","status":"completed"},"tags":[]},"source":["Based on the data, we conclude that people in 58, 57 and 54 have the highest chances of getting a heart attack.\n"]},{"cell_type":"markdown","id":"7b9c4fa8","metadata":{"papermill":{"duration":0.009672,"end_time":"2024-06-13T17:52:48.280357","exception":false,"start_time":"2024-06-13T17:52:48.270685","status":"completed"},"tags":[]},"source":["# Feature Engineering"]},{"cell_type":"code","execution_count":9,"id":"7e8aa4f6","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.301995Z","iopub.status.busy":"2024-06-13T17:52:48.301611Z","iopub.status.idle":"2024-06-13T17:52:48.307871Z","shell.execute_reply":"2024-06-13T17:52:48.306755Z"},"papermill":{"duration":0.020329,"end_time":"2024-06-13T17:52:48.310605","exception":false,"start_time":"2024-06-13T17:52:48.290276","status":"completed"},"tags":[]},"outputs":[],"source":["#defining raw input and output\n","X_raw = data.iloc[:, :-1]\n","y_raw = data.iloc[:, -1:]"]},{"cell_type":"markdown","id":"ba194461","metadata":{"papermill":{"duration":0.012377,"end_time":"2024-06-13T17:52:48.338139","exception":false,"start_time":"2024-06-13T17:52:48.325762","status":"completed"},"tags":[]},"source":["### Scaling features"]},{"cell_type":"code","execution_count":10,"id":"4c7ea277","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.368121Z","iopub.status.busy":"2024-06-13T17:52:48.367631Z","iopub.status.idle":"2024-06-13T17:52:48.38078Z","shell.execute_reply":"2024-06-13T17:52:48.379627Z"},"papermill":{"duration":0.029542,"end_time":"2024-06-13T17:52:48.383322","exception":false,"start_time":"2024-06-13T17:52:48.35378","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The range of feature inputs are within 0.0 to 1.0\n"]}],"source":["#MinMaxScaler object\n","scaler = MinMaxScaler()\n","\n","# Scaling the raw input features\n","X = scaler.fit_transform(X_raw)\n","\n","#schecking what we done\n","print(f\"The range of feature inputs are within {X.min()} to {X.max()}\")"]},{"cell_type":"markdown","id":"9617310b","metadata":{"papermill":{"duration":0.011832,"end_time":"2024-06-13T17:52:48.405551","exception":false,"start_time":"2024-06-13T17:52:48.393719","status":"completed"},"tags":[]},"source":["### Label Encoder "]},{"cell_type":"code","execution_count":11,"id":"60abd777","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.429227Z","iopub.status.busy":"2024-06-13T17:52:48.428097Z","iopub.status.idle":"2024-06-13T17:52:48.438246Z","shell.execute_reply":"2024-06-13T17:52:48.436983Z"},"papermill":{"duration":0.024787,"end_time":"2024-06-13T17:52:48.44075","exception":false,"start_time":"2024-06-13T17:52:48.415963","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(array([0, 1]), array([138, 164]))"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# LabelEncoder object\n","label_encoder = LabelEncoder()\n","\n","# Encoding the y\n","y = label_encoder.fit_transform(y_raw.values.ravel())\n","# ravel() function flattens the vector.\n","\n","#schecking what we done\n","np.unique(y, return_counts=True)"]},{"cell_type":"markdown","id":"9a64e40e","metadata":{"papermill":{"duration":0.010415,"end_time":"2024-06-13T17:52:48.46145","exception":false,"start_time":"2024-06-13T17:52:48.451035","status":"completed"},"tags":[]},"source":["# Logistic Regression"]},{"cell_type":"markdown","id":"86b6b7ad","metadata":{"papermill":{"duration":0.009777,"end_time":"2024-06-13T17:52:48.481412","exception":false,"start_time":"2024-06-13T17:52:48.471635","status":"completed"},"tags":[]},"source":["### Split the data"]},{"cell_type":"code","execution_count":12,"id":"649a1111","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.505976Z","iopub.status.busy":"2024-06-13T17:52:48.505517Z","iopub.status.idle":"2024-06-13T17:52:48.516183Z","shell.execute_reply":"2024-06-13T17:52:48.515029Z"},"papermill":{"duration":0.027118,"end_time":"2024-06-13T17:52:48.518782","exception":false,"start_time":"2024-06-13T17:52:48.491664","status":"completed"},"tags":[]},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state = 123)"]},{"cell_type":"markdown","id":"ec0d75f5","metadata":{"papermill":{"duration":0.009738,"end_time":"2024-06-13T17:52:48.538803","exception":false,"start_time":"2024-06-13T17:52:48.529065","status":"completed"},"tags":[]},"source":["## Logistic Regression with L2 penalty"]},{"cell_type":"markdown","id":"7197a4f3","metadata":{"papermill":{"duration":0.009719,"end_time":"2024-06-13T17:52:48.558707","exception":false,"start_time":"2024-06-13T17:52:48.548988","status":"completed"},"tags":[]},"source":["### Defining params"]},{"cell_type":"code","execution_count":13,"id":"f7f5b5dd","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.581085Z","iopub.status.busy":"2024-06-13T17:52:48.580617Z","iopub.status.idle":"2024-06-13T17:52:48.586717Z","shell.execute_reply":"2024-06-13T17:52:48.585523Z"},"papermill":{"duration":0.020073,"end_time":"2024-06-13T17:52:48.589062","exception":false,"start_time":"2024-06-13T17:52:48.568989","status":"completed"},"tags":[]},"outputs":[],"source":["# L2 penalty to shrink weights without removing features from the model\n","penalty= 'l2'\n","# for L2 penalty and binomial classes we will use 'liblinear'\n","#in case of L2 and multinomial) - 'lbfgs'\n","solver = 'liblinear'\n","# Max iteration = 1000\n","max_iter = 1000"]},{"cell_type":"markdown","id":"3c461b66","metadata":{"papermill":{"duration":0.010175,"end_time":"2024-06-13T17:52:48.60957","exception":false,"start_time":"2024-06-13T17:52:48.599395","status":"completed"},"tags":[]},"source":["### Model"]},{"cell_type":"code","execution_count":14,"id":"a402cf45","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.632226Z","iopub.status.busy":"2024-06-13T17:52:48.631758Z","iopub.status.idle":"2024-06-13T17:52:48.642828Z","shell.execute_reply":"2024-06-13T17:52:48.641365Z"},"papermill":{"duration":0.026211,"end_time":"2024-06-13T17:52:48.646082","exception":false,"start_time":"2024-06-13T17:52:48.619871","status":"completed"},"tags":[]},"outputs":[],"source":["# logistic regression model with above arguments\n","l2_model = LogisticRegression(random_state=123, penalty=penalty, solver=solver, max_iter=max_iter)\n","l2_model.fit(X_train, y_train)\n","l2_preds = l2_model.predict(X_test)"]},{"cell_type":"code","execution_count":15,"id":"606e512b","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.672086Z","iopub.status.busy":"2024-06-13T17:52:48.6716Z","iopub.status.idle":"2024-06-13T17:52:48.686362Z","shell.execute_reply":"2024-06-13T17:52:48.685236Z"},"papermill":{"duration":0.030817,"end_time":"2024-06-13T17:52:48.688654","exception":false,"start_time":"2024-06-13T17:52:48.657837","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["{'accuracy': 0.8360655737704918,\n"," 'recall': array([0.71428571, 0.93939394]),\n"," 'precision': array([0.90909091, 0.79487179]),\n"," 'f1score': array([0.8       , 0.86111111])}"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["def evaluate_metrics(yt, yp):\n","    results_pos = {}\n","    results_pos['accuracy'] = accuracy_score(yt, yp)\n","    precision, recall, f_beta, _ = precision_recall_fscore_support(yt, yp)\n","    results_pos['recall'] = recall\n","    results_pos['precision'] = precision\n","    results_pos['f1score'] = f_beta\n","    return results_pos\n","\n","evaluate_metrics(y_test, l2_preds)"]},{"cell_type":"markdown","id":"ca0c718c","metadata":{"papermill":{"duration":0.009981,"end_time":"2024-06-13T17:52:48.709098","exception":false,"start_time":"2024-06-13T17:52:48.699117","status":"completed"},"tags":[]},"source":["## Logistic Regression with L1 penalty"]},{"cell_type":"markdown","id":"96fd5d84","metadata":{"papermill":{"duration":0.010047,"end_time":"2024-06-13T17:52:48.729727","exception":false,"start_time":"2024-06-13T17:52:48.71968","status":"completed"},"tags":[]},"source":["### Defining params"]},{"cell_type":"code","execution_count":16,"id":"1fb063b6","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.753596Z","iopub.status.busy":"2024-06-13T17:52:48.752573Z","iopub.status.idle":"2024-06-13T17:52:48.758522Z","shell.execute_reply":"2024-06-13T17:52:48.757357Z"},"papermill":{"duration":0.020224,"end_time":"2024-06-13T17:52:48.760975","exception":false,"start_time":"2024-06-13T17:52:48.740751","status":"completed"},"tags":[]},"outputs":[],"source":["# L1 penalty to shrink coefficients without removing any features from the model\n","penalty= 'l1'\n","# \"saga\" solver for L1 penalty and multinomial classes\n","# 'liblinear' for L1 and binomial\n","solver = 'liblinear'\n","# Max iteration = 1000\n","max_iter = 1000"]},{"cell_type":"markdown","id":"de1d96e1","metadata":{"papermill":{"duration":0.00999,"end_time":"2024-06-13T17:52:48.781527","exception":false,"start_time":"2024-06-13T17:52:48.771537","status":"completed"},"tags":[]},"source":["### Model"]},{"cell_type":"code","execution_count":17,"id":"0961ea29","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.80466Z","iopub.status.busy":"2024-06-13T17:52:48.804172Z","iopub.status.idle":"2024-06-13T17:52:48.815478Z","shell.execute_reply":"2024-06-13T17:52:48.814292Z"},"papermill":{"duration":0.02566,"end_time":"2024-06-13T17:52:48.818154","exception":false,"start_time":"2024-06-13T17:52:48.792494","status":"completed"},"tags":[]},"outputs":[],"source":["l1_model = LogisticRegression(random_state=123, penalty=penalty, solver=solver, max_iter = 1000)\n","l1_model.fit(X_train, y_train)\n","l1_preds = l1_model.predict(X_test)"]},{"cell_type":"markdown","id":"8e410a7c","metadata":{"papermill":{"duration":0.010535,"end_time":"2024-06-13T17:52:48.839449","exception":false,"start_time":"2024-06-13T17:52:48.828914","status":"completed"},"tags":[]},"source":[]},{"cell_type":"code","execution_count":18,"id":"d293b5f1","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.862511Z","iopub.status.busy":"2024-06-13T17:52:48.86199Z","iopub.status.idle":"2024-06-13T17:52:48.876154Z","shell.execute_reply":"2024-06-13T17:52:48.875053Z"},"papermill":{"duration":0.028587,"end_time":"2024-06-13T17:52:48.878558","exception":false,"start_time":"2024-06-13T17:52:48.849971","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["{'accuracy': 0.8360655737704918,\n"," 'recall': array([0.71428571, 0.93939394]),\n"," 'precision': array([0.90909091, 0.79487179]),\n"," 'f1score': array([0.8       , 0.86111111])}"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["evaluate_metrics(y_test, l1_preds)"]},{"cell_type":"markdown","id":"85591814","metadata":{"papermill":{"duration":0.010079,"end_time":"2024-06-13T17:52:48.899259","exception":false,"start_time":"2024-06-13T17:52:48.88918","status":"completed"},"tags":[]},"source":["### Confusion Matrix"]},{"cell_type":"code","execution_count":19,"id":"6156083e","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.9226Z","iopub.status.busy":"2024-06-13T17:52:48.921591Z","iopub.status.idle":"2024-06-13T17:52:48.928258Z","shell.execute_reply":"2024-06-13T17:52:48.927075Z"},"papermill":{"duration":0.02069,"end_time":"2024-06-13T17:52:48.930546","exception":false,"start_time":"2024-06-13T17:52:48.909856","status":"completed"},"tags":[]},"outputs":[],"source":["cf = confusion_matrix(y_test, l1_preds, normalize='true')"]},{"cell_type":"code","execution_count":20,"id":"125c3252","metadata":{"execution":{"iopub.execute_input":"2024-06-13T17:52:48.955267Z","iopub.status.busy":"2024-06-13T17:52:48.95424Z","iopub.status.idle":"2024-06-13T17:52:49.207208Z","shell.execute_reply":"2024-06-13T17:52:49.206069Z"},"papermill":{"duration":0.268474,"end_time":"2024-06-13T17:52:49.209659","exception":false,"start_time":"2024-06-13T17:52:48.941185","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.set_context('talk')\n","disp = ConfusionMatrixDisplay(confusion_matrix=cf,display_labels=l1_model.classes_)\n","disp.plot()\n","plt.show()"]},{"cell_type":"markdown","id":"8d75df4b","metadata":{"papermill":{"duration":0.010638,"end_time":"2024-06-13T17:52:49.29248","exception":false,"start_time":"2024-06-13T17:52:49.281842","status":"completed"},"tags":[]},"source":["- Accuracy measures the proportion of correctly predicted instances (both true positives and true negatives) out of the total instances.\n","- Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive instances that are correctly identified by the model.\n","- Precision measures the proportion of predicted positive instances that are actually positive. It indicates how precise the model is when it predicts a positive instance.\n","- F1-score is the harmonic mean of precision and recall. It provides a single metric that balances both precision and recall.\n","\n","Accuracy of 83.61% along with reasonably high recall and precision values indicates that model is performing well in both cases."]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":1226038,"sourceId":2047221,"sourceType":"datasetVersion"}],"dockerImageVersionId":30732,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"},"papermill":{"default_parameters":{},"duration":8.378434,"end_time":"2024-06-13T17:52:49.925075","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-06-13T17:52:41.546641","version":"2.5.0"}},"nbformat":4,"nbformat_minor":5}