Task: Dataset Selection and Neural Network Experimentation

https://www.kaggle.com/datasets/iamsouravbanerjee/…       

Task 6 Task: Dataset Selection and Neural Network Experimentation Deadline for Presentation: Thursday, April 25th Team Members: Maximum three allowed Objective: The objective of this task is to enable trainees to select a classification dataset from either Kaggle or Hugging Face, build and compare several neural network architectures, perform hyperparameter tuning, and report on overfitting. Trainees will present their findings in a concise presentation to their peers within a limited timeframe. 1. Dataset Selection (15 marks): • Choose a classification dataset from either Kaggle or Hugging Face. • Ensure the dataset is approved by the trainer and meets the following criteria: o Number of Rows: At least 10,000 rows of data are generally considered suitable o Number of Categorical Columns: A minimum of 2 categorical columns is desirable o Total Number of Columns: at least 7, including both numerical and categorical features. o Offers a clear classification task with labeled data. o Provides features that are relevant and appropriate for the classification task. 2. Neural Network Experimentation (40 marks): • Experiment with building and comparing several neural network architectures on the selected dataset. • Explore various aspects of neural network design, including different architectures, activation functions, regularization techniques, and optimization algorithms. • Perform hyperparameter tuning on the neural network models to optimize performance. • Report on the presence of overfitting and discuss strategies for mitigating it. 3. Presentation Preparation (15 marks): • Prepare a presentation consisting of a maximum of 10 slides. • Ensure the presentation covers the following aspects: o Introduction to the selected dataset, including its source, size, and classification task. o Overview of the neural network architectures experimented with, including their design and hyperparameters. o Comparison of the performance of different neural network architectures and their hyperparameter-tuned versions. o Discussion of overfitting in the neural network models and strategies employed to address it. o Summary of findings and conclusions drawn from the experimentation process. 4. Presentation Delivery (30 marks): • Present the findings to peers in the class within a time limit of 5 minutes. • Deliver the presentation clearly and effectively, addressing all key points outlined in the presentation preparation. • Engage with peers by encouraging questions and discussion regarding the presented findings. Total Marks: 100 Note: • Seek approval from the trainer before finalizing the dataset for experimentation. • Collaboration, knowledge sharing, and feedback from peers and the trainer are encouraged throughout the experimentation and presentation process. • The presentation aims to provide a concise summary of the dataset, experimentation process, and findings related to neural network experimentation.