Year
2023
Category
Project
Product Duration
3 Days
Automated feature selection: The framework has techniques for working with datasets that have both category and numerical characteristics. To choose the most pertinent characteristics for analysis, it employs the proper feature selection algorithms. For both classification and regression tasks, the code uses a number of machine learning methods, including decision trees, support vector machines, logistic regression, random forests, and multi-layer perceptrons.
Optimisation of hyperparameters: The framework uses grid search to automatically improve the hyperparameters of the chosen machine learning models, enhancing their performance on the provided dataset. Evaluation of cross-validation: To assess the efficacy of each model, the code carries out cross-validation. When doing classification or regression tasks, it employs accuracy ratings and mean squared error (MSE).
Modify the code to load your dataset and specify the relevant columns for features and the target variable. Run the code using a Python interpreter (main.py). The code will automatically perform feature selection, train multiple machine learning models, optimize their hyperparameters, and evaluate their performance. The best classification model and the best regression model will be displayed, along with their respective accuracy scores or MSE scores.
The code requires the following dependencies to be installed: pandas, numpy and scikit-learn
