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Project Overview
In an era where data-driven decisions are crucial, this project addresses the pressing need for effective classification models. You will dive into the MNIST dataset, implementing k-NN and SVM algorithms, while learning industry-standard practices that prepare you for professional challenges in data science and machine learning.
Project Sections
Understanding the MNIST Dataset
This section introduces the MNIST dataset, its structure, and significance in machine learning. You will learn about the data preprocessing steps necessary for effective model training, including normalization and reshaping of images.
Tasks:
- ▸Explore the MNIST dataset and understand its features and labels.
- ▸Implement data preprocessing techniques such as normalization and reshaping.
- ▸Visualize a few samples from the dataset to understand the data better.
- ▸Split the dataset into training and testing sets for model evaluation.
- ▸Document the preprocessing steps and their importance in model performance.
Resources:
- 📚MNIST Dataset Documentation
- 📚Data Preprocessing Techniques Article
- 📚Python Libraries for Data Manipulation
Reflection
Reflect on how data preprocessing impacts model performance and the challenges faced during this phase.
Checkpoint
Complete a data preprocessing report with visualizations.
Implementing k-Nearest Neighbors (k-NN)
In this section, you will implement the k-NN algorithm to classify handwritten digits. You will learn about hyperparameter tuning and the importance of distance metrics in classification tasks.
Tasks:
- ▸Implement the k-NN algorithm using Python.
- ▸Experiment with different values of k and evaluate their effects on model accuracy.
- ▸Use cross-validation to optimize model performance.
- ▸Document the choice of distance metrics and their impact on classification.
- ▸Visualize the performance of the k-NN model using confusion matrices.
Resources:
- 📚k-NN Algorithm Overview
- 📚Hyperparameter Tuning Techniques
- 📚Confusion Matrix Guide
Reflection
Consider the challenges of hyperparameter tuning and how it affects model accuracy.
Checkpoint
Achieve a minimum accuracy of 85% on the validation set.
Exploring Support Vector Machines (SVM)
This section focuses on implementing the SVM algorithm. You will learn about kernel functions and their role in transforming data for better classification.
Tasks:
- ▸Implement the SVM algorithm using Python.
- ▸Experiment with different kernel functions and their parameters.
- ▸Evaluate the SVM model's performance using ROC curves.
- ▸Compare the performance of k-NN and SVM on the same dataset.
- ▸Document the advantages and limitations of SVM in classification tasks.
Resources:
- 📚SVM Algorithm Basics
- 📚Understanding Kernel Functions
- 📚ROC Curve Analysis
Reflection
Reflect on the differences between k-NN and SVM and their respective use cases in industry.
Checkpoint
Submit an SVM implementation report with performance comparisons.
Feature Engineering Techniques
In this section, you will delve into feature engineering, understanding its significance in improving model performance. You will explore techniques to enhance the dataset for better classification results.
Tasks:
- ▸Identify potential features that could enhance model performance.
- ▸Implement feature scaling and transformation techniques.
- ▸Evaluate the impact of feature engineering on model accuracy.
- ▸Document the feature engineering process and its relevance to classification.
- ▸Create visualizations to illustrate the importance of features in model predictions.
Resources:
- 📚Feature Engineering Guide
- 📚Techniques for Feature Selection
- 📚Importance of Feature Scaling
Reflection
Think about how feature engineering can change the landscape of model performance and the challenges encountered.
Checkpoint
Create a feature engineering report summarizing your findings.
Model Evaluation and Performance Metrics
This section emphasizes the importance of model evaluation. You will learn to use various metrics to assess the performance of your classification models.
Tasks:
- ▸Define key performance metrics such as accuracy, precision, recall, and F1 score.
- ▸Implement performance evaluation methods on both k-NN and SVM models.
- ▸Create visualizations to compare model performances using ROC curves and confusion matrices.
- ▸Document the evaluation process and its significance in model selection.
- ▸Reflect on the trade-offs between different evaluation metrics.
Resources:
- 📚Performance Metrics Overview
- 📚Evaluating Classification Models
- 📚ROC Curve and AUC Explained
Reflection
Reflect on the importance of model evaluation and how it influences decision-making in real-world applications.
Checkpoint
Complete a comprehensive evaluation report comparing all models.
Final Project Presentation and Documentation
In this final section, you will compile all your work into a cohesive project presentation. You will create documentation that showcases your learning journey and the skills acquired throughout the course.
Tasks:
- ▸Compile all reports, visualizations, and findings into a single presentation.
- ▸Create a video or written summary of your project and its outcomes.
- ▸Prepare to present your project to peers for feedback.
- ▸Document lessons learned and areas for future improvement.
- ▸Submit the final project documentation and presentation.
Resources:
- 📚Presentation Best Practices
- 📚Creating Effective Documentation
- 📚Feedback Techniques
Reflection
Consider how this project has prepared you for future challenges in machine learning and data science.
Checkpoint
Deliver a presentation that effectively communicates your project outcomes.
Timeline
This project will span over 8 weeks, with each section taking approximately one week to complete, allowing for iterative review and adjustments.
Final Deliverable
Your final deliverable will be a comprehensive project report and presentation that includes your classification models, evaluation metrics, and insights gained throughout the course. This portfolio piece will demonstrate your readiness for real-world challenges in data science.
Evaluation Criteria
- ✓Depth of understanding of algorithms and their implementation.
- ✓Quality of feature engineering and its impact on model performance.
- ✓Clarity and effectiveness of documentation and presentations.
- ✓Ability to critically evaluate model performance using appropriate metrics.
- ✓Creativity in problem-solving and approach to challenges.
Community Engagement
Engage with peers through online forums or study groups to share insights, seek feedback, and collaborate on project presentations.