Phase 3 Project

Congratulations! You've made it through another intense module, and now you're ready to show off your newfound Machine Learning skills!

All that remains in Phase 3 is to put your new skills to use with another large project! This project should take 20 to 30 hours to complete.

Project Overview

For this project, you will engage in the full data science process from start to finish, solving a classification problem using a dataset of your choice.

The Data

You have the option to either choose a dataset from a curated list or choose your own dataset not on the list. The goal is to choose a dataset appropriate to the type of business problem and/or classification methods that most interests you. It is up to you to define a stakeholder and business problem appropriate to the dataset you choose. If you are feeling overwhelmed or behind, we recommend you choose dataset #2 or #3 from the curated list.

If you choose a dataset from the curated list, inform your instructor which dataset you chose and jump right into the project. If you choose your own dataset, run the dataset and business problem by your instructor for approval before starting your project.

Curated List of Datasets

You may select any of the four datasets below - we provide brief descriptions of each. Follow the links to learn more about the dataset and business problems before making a final decision.

1) Chicago Car Crashes

Note this links also to Vehicle Data and to Driver/Passenger Data.

Build a classifier to predict the primary contributory cause of a car accident, given information about the car, the people in the car, the road conditions etc. You might imagine your audience as a Vehicle Safety Board who's interested in reducing traffic accidents, or as the City of Chicago who's interested in becoming aware of any interesting patterns. Note that there is a multi-class classification problem. You will almost certainly want to bin or trim or otherwise limit the number of target categories on which you ultimately predict. Note e.g. that some primary contributory causes have very few samples.

2) Terry Traffic Stops

In Terry v. Ohio, a landmark Supreme Court case in 1967-8, the court found that a police officer was not in violation of the "unreasonable search and seizure" clause of the Fourth Amendment, even though he stopped and frisked a couple of suspects only because their behavior was suspicious. Thus was born the notion of "reasonable suspicion", according to which an agent of the police may e.g. temporarily detain a person, even in the absence of clearer evidence that would be required for full-blown arrests etc. Terry Stops are stops made of suspicious drivers.

Build a classifier to predict whether an arrest was made after a Terry Stop, given information about the presence of weapons, the time of day of the call, etc. Note that this is a binary classification problem.

Note that this dataset also includes information about gender and race. You may use this data as well. You may, e.g. pitch your project as an inquiry into whether race (of officer or of subject) plays a role in whether or not an arrest is made.

If you do elect to make use of race or gender data, be aware that this can make your project a highly sensitive one; your discretion will be important, as well as your transparency about how you use the data and the ethical issues surrounding it.

3) SyriaTel Customer Churn

Build a classifier to predict whether a customer will ("soon") stop doing business with SyriaTel, a telecommunications company. Note that this is a binary classification problem.

Most naturally, your audience here would be the telecom business itself, interested in losing money on customers who don't stick around very long. Are there any predictable patterns here?

4) Tanzanian Water Well Data

This dataset is part of an active competition until August 1st, 2022!

Tanzania, as a developing country, struggles with providing clean water to its population of over 57,000,000. There are many waterpoints already established in the country, but some are in need of repair while others have failed altogether.

Build a classifier to predict the condition of a water well, using information about the sort of pump, when it was installed, etc. Note that this is a ternary classification problem.

Sourcing Your Own Data

Sourcing new data is a valuable skill for data scientists, but it requires a great deal of care. An inappropriate dataset or an unclear business problem can lead you spend a lot of time on a project that delivers underwhelming results. The guidelines below will help you complete a project that demonstrates your ability to engage in the full data science process.

Your dataset must be...

1. Appropriate for classification. It should have a categorical outcome or the data needed to engineer one.

2. Usable to solve a specific business problem. This solution must rely on your classification model.

3. Somewhat complex. It should contain a minimum of 1000 rows and 10 features.

4. Unfamiliar. It can't be one we've already worked with during the course or that is commonly used for demonstration purposes (e.g. MNIST).

5. Manageable. Stick to datasets that you can model using the techniques introduced in Phase 3.

Once you've sourced your own dataset and identified the business problem you want to solve with it, you must to run them by your instructor for approval.

Problem First, or Data First?

There are two ways that you can source your own dataset: Problem First or Data First. The less time you have to complete the project, the more strongly we recommend a Data First approach to this project.

Problem First: Start with a problem that you are interested in that you could potentially solve with a classification model. Then look for data that you could use to solve that problem. This approach is high-risk, high-reward: Very rewarding if you are able to solve a problem you are invested in, but frustrating if you end up sinking lots of time in without finding appropriate data. To mitigate the risk, set a firm limit for the amount of time you will allow yourself to look for data before moving on to the Data First approach.

Data First: Take a look at some of the most popular internet repositories of cool data sets we've listed below. If you find a data set that's particularly interesting for you, then it's totally okay to build your problem around that data set.

There are plenty of amazing places that you can get your data from. We recommend you start looking at data sets in some of these resources first:

• UCI Machine Learning Datasets Repository
• Kaggle Datasets
• Awesome Datasets Repo on Github
• New York City Open Data Portal
• Inside AirBNB

The Deliverables

There are three deliverables for this project:

• A GitHub repository
• A Jupyter Notebook
• A non-technical presentation

Review the "Project Submission & Review" page in the "Milestones Instructions" topic for instructions on creating and submitting your deliverables. Refer to the rubric associated with this assignment for specifications describing high-quality deliverables.

Key Points

• Your deliverables should explicitly address each step of the data science process. Refer to the Data Science Process lesson from Topic 19 for more information about process models you can use.

• Your Jupyter Notebook should demonstrate an iterative approach to modeling. This means that you begin with a basic model, evaluate it, and then provide justification for and proceed to a new model. We encourage you to try a bunch of different models: logistic regression, decision trees, or anything else you think would be appropriate.

• You must choose appropriate classification metrics and use them to evaluate your models. Choosing the right classification metrics is a key data science skill, and should be informed by data exploration and the business problem itself. You must then use this metric to evaluate your model performance using both training and testing data.

Getting Started

Create a new repository for your project to get started. We recommend structuring your project repository similar to the structure in the Phase 1 Project Template. You can do this either by creating a new fork of that repository to work in or by building a new repository from scratch that mimics that structure.

Project Submission and Review

Review the "Project Submission & Review" page in the "Milestones Instructions" topic to learn how to submit your project and how it will be reviewed. Your project must pass review for you to progress to the next Phase.

Summary

This project is an opportunity to expand your data science toolkit by evaluating, choosing, and working with new datasets. Spending time up front making sure you have a good dataset for a solvable problem will help avoid the major problems that can sometimes derail data science projects. You've got this!