The original dataset: https://www.kaggle.com/datasets/ulrikthygepedersen/airlines-delay
Step 01. Data Collection and Understanding: In this first step, the data is collected and studied. Missing values are treated or removed, and initial data exploration is performed. Descriptive statistics such as kurtosis, skewness, mean, mode, median, and standard deviation are calculated.
Step 02. Data Filtering: Data filtering removes irrelevant columns or rows that are not relevant to the analysis. For example, columns with customer ID or hash code, or rows with invalid or inconsistent data.
Step 03. Exploratory Data Analysis (EDA): The EDA phase involves univariate, bivariate, and multivariate analysis to gain insights into the dataset. Hypotheses generated in the previous step are tested through statistical analysis and data visualization.
Step 04. Data Preprocessing: In this step, the data is prepared for machine learning modeling. This involves techniques such as data encoding, feature scaling, handling missing values, and handling outliers. The data may also be transformed or normalized to improve the performance of machine learning models.
Step 05. Feature Selection: Feature selection techniques are applied to identify the most relevant features that contribute to the predictive task. This helps reduce dimensionality and minimize the risk of overfitting. Techniques such as Boruta or feature importance from machine learning models can be used.
Step 06. Machine Learning Modeling: In this step, various machine learning algorithms are trained and evaluated on the dataset. The selected algorithms are applied to the data, and their performance is assessed using appropriate evaluation metrics. Cross-validation techniques may be used to estimate model performance.
Step 07. Hyperparameter Tuning: Once the best model(s) have been selected, the hyperparameters of the model are fine-tuned to optimize its performance. Techniques such as grid search or randomized search are used to explore different combinations of hyperparameters and identify the optimal settings.
Step 08. Model Evaluation and Conclusion: The final model is evaluated using unseen data or a holdout dataset to assess its generalization performance. The model's predictions are compared with the actual outcomes, and various performance metrics are calculated. The results are interpreted in the context of the business problem, and conclusions are drawn regarding the applicability and usefulness of the model.
DecisionTreeClassifier optimized
| Metric | Score |
|---|---|
| F1-Score | 0.6342 |
| Precision | 0.6436 |
| Recall | 0.6343 |
| Accuracy | 0.6480 |