Comprehensive Literature Review

Comprehensive Literature Review: Require a more comprehensive survey of existing approaches.

· Comparative Study: Expect more detailed benchmarking of at least 8 to 10 machine learning models.

· Additional Experiments:

· Conduct feature selection or dimensionality reduction as an extra step.

· Explore ensemble methods or advanced techniques beyond the basics

2. Project Objectives

The main goal of this project is to provide hands-on research experience in machine learning. Students will:

· Identify and define a research-worthy problem.

· Explore existing machine learning approaches.

· Develop, implement, and test their own solution.

· Critically evaluate their work in comparison with related methods.

3. Project Topic Selection

· Topics should be relevant to machine learning in a domain that all group members agree on.

· Each group must submit a 1-page project proposal (12-point font, single spacing, 1-inch margins) that includes:

· Problem statement

· Dataset(s) selection

· Plan and methodology

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

Step 1: Break Down the Requirements

• Literature Review: Summarize existing research and identify gaps.

• Comparative Study: Benchmark 8–10 machine learning models.

• Additional Experiments: Apply feature selection/dimensionality reduction and test ensemble methods.

• Final Proposal (1 page): Include problem statement, dataset(s), plan, and methodology.

Step 2: Conduct the Literature Review

• Search for 15–20 scholarly sources (Google Scholar, IEEE Xplore, Springer).

• For each study, record:

  • The machine learning task

  • Datasets used

  • Models tested

  • Evaluation metrics and outcomes

• End with a gap analysis that justifies your project.
  📖 Resource: Writing Literature Reviews (University of Toronto)

Step 3: Select a Research Topic

• Choose a problem relevant to machine learning (healthcare prediction, NLP sentiment analysis, fraud detection, image classification, etc.).

• Ensure all team members agree and it is feasible within your timeframe.
  📖 Resource: Kaggle Datasets

Step 4: Define the Problem Statement

• Write 3–5 sentences explaining:

  • What the problem is

  • Why it matters

  • The expected outcome (e.g., “Developing a predictive model for credit card fraud detection using multiple ML approaches”).

Step 5: Select Dataset(s)

• Pick a relevant, high-quality dataset.

• Describe:

  • Data source (e.g., UCI ML Repository, Kaggle)

  • Size (# of records, # of features)

  • Target variable
    📖 Resource: UCI Machine Learning Repository

Step 6: Plan Your Methodology

• Preprocessing: Clean, normalize, and handle missing data.

• Models: Benchmark 8–10 models such as Logistic Regression, Decision Tree, Random Forest, XGBoost, LightGBM, SVM, k-NN, Naïve Bayes, Neural Networks, and Ensemble methods.

• Additional Experiments: Feature selection (PCA, RFE) and ensemble learning (bagging, boosting, stacking).

• Evaluation Metrics: Accuracy, Precision, Recall, F1, ROC-AUC (classification) or RMSE/MAE (regression).
  📖 Resource: Scikit-learn Documentation

Step 7: Draft the Proposal (1 Page)

• Problem Statement – 3–5 sentences.

• Dataset(s) Selection – Source, size, and relevance.

• Plan and Methodology – Summarize preprocessing, models, and experiments in 1–2 short paragraphs.
  📖 Resource: MIT Proposal Writing Guide

Step 8: Proofread & Finalize

• Ensure proposal is 1 page, 12-pt font, single spacing, 1-inch margins.

• Use academic tone and concise wording.

• Double-check clarity and grammar before submission.

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