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Machine Learning (2026-2027) - Final Project Submission

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Machine Learning, Week 11

Final Project Submission

Put ten weeks of milestones together, hand in the project, and compete for the best one.

Objectives

  • List the five required deliverables and the one additional deliverable
  • Say which weekly milestone produced each part of your submission
  • Check your team against the submission checklist and plan what is left
  • Plan a project video that fits in exactly 5 minutes
  • Name the topics to explore next: SVM, GBMs, multiple linear regression

Week 11 of the plan

Where This Sits in the Course

  • No notebook and no dataset this week: it is the project final submission
  • Code on GitHub, a 5-minute video, the proposal, the presentation, a logo, and additionally a user interface
  • The best projects win the course competitions

Plan for the Session

PartWhat we doTime
1The six deliverables15 min
2From weekly milestones to the submission10 min
3The competition and the topics for the future5 min
4Practice: your team's final check30 min

Part 1

The Final Submission

Five required deliverables

#DeliverableWhat you hand in
1Project codeA GitHub repo with your notebook and data
2Project videoA video of 5 minutes
3Project proposalThe proposal of week 2, checked
4Project presentationThe slides of weeks 8 to 10, updated
5Project logoA logo for the project

Deliverable 1: Code on a GitHub Repo

  • The notebook runs top to bottom: load (week 1), clean (week 3), features and target (week 4), models (weeks 5 to 7)
  • The dataset, or a link to it when it is too large
  • A short README: the problem, the data, how to run it, each model's score on the same test split

Deliverable 2: A 5-Minute Video

5 minutes are 300 seconds, so time every part

PartSecondsFrom, to
The problem and the data450:00, 0:45
Cleaning, features and target600:45, 1:45
Models and their scores901:45, 3:15
A demo: notebook or interface603:15, 4:15
What you learned, what comes next454:15, 5:00
45 + 60 + 90 + 60 + 45 = 300 s

Deliverables 3, 4 and 5

DeliverableComes fromFinish in week 11
ProposalWeek 2Check it still describes the project you built
PresentationWeeks 8 to 10Add the discussion feedback and final scores
LogoNo earlier milestoneDesign it, then use it on the slides, video and README

Part 2

Ten Weeks of Milestones

Timeline diagram: Weeks 1-2 then Week 3 then Week 4 then Weeks 5-7 then Weeks 8-10 then Week 11.

  1. Weeks 1-2

    Team, idea, proposal

  2. Week 3

    Explore and clean

  3. Week 4

    Features and target

  4. Weeks 5-7

    Models: KNN, trees, Naive Bayes

  5. Weeks 8-10

    Presentations and discussions

  6. Week 11

    Final submission

Try It: The Submission Checklist

Predict: How Much Is Done by Week 10?

A team met every milestone from week 1 to week 10

2 + 1 + 1 + 1 + 1 + 2 = 8, 813 = 0.6154 ≈ 62%

Part 3

The Course Competition

The best projects win the course competitions

  • All five required deliverables are in; the interface is a bonus
  • Every member can explain every step, not only their own part
  • The numbers in the slides, README and video are the ones the notebook prints
  • Name one thing that did not work and what you learned from it

Explore in the Future

The plan's additional points

TopicBuilds onscikit-learn
Multiple linear regressionWeek 3: the notebook already used five featuresLinearRegression
SVM, support vector machinesWeeks 4 to 7: another classifier, same fit, predict, scoreSVC
GBMs, gradient boosting machinesWeek 6: many decision trees combined into one modelGradientBoostingClassifier

About 5 minutes

Practice 1: Name the Milestone Week

  1. Naming the target variable of your project
  2. Choosing K for KNN with the elbow loop
  3. Comparing the training score and the test score of a decision tree
  4. Choosing between MultinomialNB and GaussianNB
  5. Stating your idea in two or three sentences and naming the dataset

Answers

Practice 1: Answer

Piece of workWeek
1. The target variable4: features and target
2. K for KNN with the elbow loop5: KNN
3. Training against test score of a tree6: trees and forest
4. MultinomialNB or GaussianNB7: Naive Bayes
5. The idea and the dataset2: the proposal

About 5 minutes

Practice 2: Time a Video

Four parts are planned: 30, 75, 105 and 50 seconds

30 + 75 + 105 + 50 = 260 s
300 - 260 = 40 s, 260 s = 4:20

About 20 minutes

Practice 3: Your Team's Final Check

  1. Open the checklist widget and tick what your team has really finished
  2. Write down every missing item
  3. Give each missing item one owner and a date before the deadline
  4. Write your 5-part video plan and check it adds up to 300 seconds

Key Takeaways

  1. Five required deliverables: code on GitHub, a 5-minute video, the proposal, the presentation, the logo
  2. A user interface is additional
  3. The milestones of weeks 1 to 10 built 8 of 13 checklist items
  4. Week 11 finishes: push, record, check, update, design
  5. Next: explore SVM, GBMs and multiple linear regression on your own data

Open this lesson

Mahmoud AbasFinal Project Submission