# Final Project

## 🌟 Final Project Phase

The Final Project is the highlight of the Machine Learning & AI course and a powerful opportunity for learners to apply what they’ve learned to a real-world dataset or problem. This phase challenges students to build, train, and evaluate machine learning models—while telling a compelling story about their findings.

{% hint style="info" %}
A Final Project Kick-Off slide deck can be found in your class drive and linked in your course sheet. It can be used in the Kick-Off session to inform your students on project requirements, recommendations, and includes examples of previous projects.
{% endhint %}

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### 🚀 About the Project Phase

After completing the main curriculum, students transition into the final project phase, where they work in small teams to explore a machine learning problem from end to end.

Learners will:

* Work individually or in teams (up to 3 people)
* Select a dataset or receive one from the teaching team
* Define a clear machine learning goal
* Clean and preprocess the data
* Train, evaluate, and interpret ML models
* Present findings in a clear and insightful way

💡 **Goal:** Build a small, focused machine learning project that shows off practical understanding and supports portfolio or career goals.

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### 🧑‍💻 Project Requirements

Each project must include:

* A defined machine learning objective (e.g. classification, regression, clustering)
* A well-documented **Jupyter Notebook or py files**
* Use of libraries such as **scikit-learn**, **pandas**, and **matplotlib/seaborn**
* A final presentation with a summary of methods, results, and learnings

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### 💡 Project Ideas

Students are encouraged to explore topics they’re passionate about. Possible ideas include:

* Predicting loan approval or credit risk
* Classifying text reviews or news articles
* Predicting house prices or stock trends
* Clustering customer behavior patterns
* Any dataset that is realistic in scope and allows for meaningful ML application

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### 📂 Prior Projects

Need inspiration? Take a look at these projects from previous semesters:

* [Winter 2024 – ML & AI Final Projects](https://docs.google.com/presentation/d/1hQeBynGr7Cw8UMuN717UxK_wvWuLk9ermI2bH8mzpqM/edit?usp=sharing)

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### 📅 Timeline (Fall 2025)

| **Milestone**         | **Date**                 |
| --------------------- | ------------------------ |
| Project Kick-Off      | Wednesday, November 19th |
| **Class Demo Day**    | Monday, December 8th     |
| **Official Demo Day** | *Date to be confirmed*   |

📌 **Attendance and presentation in the Class Demo Day are required for students to receive a certificate.**

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### 🎤 Class Demo Day

The **Class Demo Day** is a supportive, internal event where students present their projects to classmates and the teaching team. The goal is to:

* Practice presenting technical results
* Receive peer and instructor feedback
* Reflect on the ML process and learnings
* Celebrate collaboration and growth

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### 🎉 Official Demo Day

The **Official Demo Day** is an on-site event where selected student teams present their projects to the wider ReDI community, including **partners and alumni.**

* It highlights projects across all tracks
* Teams selected to present will receive mentoring to prepare

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### 🗣️ Presentation Guidelines

To help learners prepare their pitch, here’s a suggested format:

* **Length:** Max. 5 minutes
* **Mode:** Slides and or Jupyter Notebook walkthrough

**Proposed Structure:**

1. **Intro** – What’s the project, and what problem does it solve?
2. **Demo** – Show the live demo (notebook, code, visuals) 
3. **Challenges & Learning** – What went well? What was hard? What did you learn?

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### 🧾 Certification Requirements

To receive a ReDI certificate, learners must:

* Attend at least 80% of the course
* Participate in 2 Career Workshops
* Complete and present a Final Project
* Attend the Internal Class Demo Day

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### 🤝 Class Culture & Support

💬 *“We grow by doing, and by helping each other.”*

* Encourage an open, collaborative environment
* Use class time for mentoring, troubleshooting, and peer feedback
* Support teams with check-ins and technical coaching

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Let’s celebrate the end of the semester together :tada: