Category: Machine Learning

What will we cover?

In this tutorial we will cover the Data Science Workflow and…

• Why Data Science?
• Understand the Problem as a Data Scientist.
• The Data Science Workflow
• Explore it with a Student Grade Prediction problem.

We will use Python and pandas with our initial Data Science problem.

Part 1: Why Data Science?

Did you know you check your phone 58 times per day?

Let’s say you are awake 16 hours – that is, you check your phone every 17 minutes during all your waking hours.

Estimates approximate that 66% of all smartphone users are addicted to their phones.

Does that surprise you?

How do we know that?

Data.

We live in a world where you know that the above statements are possibly not wild guesses, there is data to confirm them.

This tutorial is not about helping your phone addiction – it is about Data Science.

With a world full of data you can learn just about anything, make your own analysis and understand the aspects better. You can help make data driven decisions, to avoid blind guesses.

This is one reason to love Data Science.

How did Data Science start?

• Data Analysis Helped Defeat Cholera
• First Modern Weahter Forecast

Part 2: Understanding the problem in Data Science

The key to success in Data Science is understanding the problem. Get the right question.

What is the problem we try to solve? This will form the Data Science Problem.

Examples

• Sales figure and call center logs: evaluate a new product
• Sensor data from multiple sensors: detect equipment failure
• Customer data + marketing data: better targeted marketing

Part of understanding the problem included to asses the situation – this will help you understand your context, your problem better.

In the end, it is all about defining the object of your Data Science research. What is the success criteria?

The key to a successful Data Science project is to understand the object and success criteria, this will guide you in your search to understand the research better.

Part 3: Data Science Workflow

Most get Data Science wrong!

At least, at first.

Deadly wrong!

The assume – not to blame them – that Data Science is about knowing the most tools to solve the problem.

This series of tutorials will teach you something different.

The key to a successful Data Scientist is to understand the Data Science Workflow.

Looking at the above flow – you will realize, that most beginners only focus on a narrow aspect of it.

That is a big mistake – the real value is in step 5, where you use the insight to make measurable goals from data driven insights.

Let’s take an example of how a simple Data Science Workflow could be.

• Step 1
  • Problem: Predict weather tomorrow
  • Data: Time series on Temperateture, Air pressure, Humidity, Rain, Wind speed, Wind direction, etc.
  • Import: Collect data from sources
• Step 2
  • Explore: Data quality
  • Visualize: A great way to understand data
  • Cleaning: Handle missing or faulty data
• Step 3
  • Features: Select features to use in model
  • Model: Example in predicting rain/no rain (Tutorial + Video + Code + Project)
  • Analyze: Apply the prediction model
• Step 4
  • Present: Weather forecast
  • Visualize: Charts, maps, etc.
  • Credibility: Inaccurate results, too high confidence, not presenting full findings
• Step 5
  • Insights: What to wear, impact on outside events, etc.
  • Impact: Sales and weather forecast (umbrella, ice cream, etc.)
  • Main goal: This is what makes Data Science valuable

Now, while this looks straight forward – the can be many iterations back into a previous step. Even at step 5, you can consult the client and realize you need more data and start another iteration from step 1, to enrich the process again.

Part 4: Student Grade Prediction

To get started with a simple project, we will explore the Portuguese high school student dataset from Kaggle.

It consists of features and targets.

The features are column data for each student. That is, each studen as a row in the dataset, and each row has data for each of the features.

The the target is what we want to predict from student data.

That is, given a row of features, can we predict the targets.

Here we will look at a smaller problem.

Problem: Propose activities to improve G3 grades.

Our Goal

• To guide the school how they helps students getting higher grades

Yes – we need to explore the data and get ideas on how to help the students to get higher grades.

Now, let’s explore our Data Science Workflow.

Step 1: Acquire

• Explore problem
• Identify data
• Import data

Get the right questions

• This forms the data science problem
• What is the problem

We need to understand a bit about the context.

Understand context

• Student age?
• What is possible?
• What is the budget?

We have an idea about these things, not exact figures, but we have an idea about the age (high school students). This tells us what kind of activities we should propose. If it were kids in age 8-10 years, we should propose something different.

What is possible – well, your imagination must guide you with your rational mind. Also, what is the budget – we cannot propose ideas which are too expensive for a normal high school budget.

Let’s get started with some code, to get acquainted with the data.

import pandas as pd
data = pd.read_csv('https://raw.githubusercontent.com/LearnPythonWithRune/DataScienceWithPython/main/files/student-mat.csv')
print(len(data))

We will see it has 395 students in the dataset.

print(data.head())
print(data.columns)

This will show the first 5 lines of the dataset as well as the columns. The columns contains the feature and targets.

Step 2: Prepare

• Explore data
• Visualize ideas
• Cleaning data

This step is also about understand if data quality is as expected. We will learn a lot more about this later.

For now explore the data types of the columns.

print(data.dtypes)

This will print out the data types. We see some are integers (int64) others are objects (that is strings/text in this case).

school        object
sex           object
age            int64
address       object
famsize       object
Pstatus       object
Medu           int64
Fedu           int64
Mjob          object
Fjob          object
reason        object
guardian      object
traveltime     int64
studytime      int64
failures       int64
schoolsup     object
famsup        object
paid          object
activities    object
nursery       object
higher        object
internet      object
romantic      object
famrel         int64
freetime       int64
goout          int64
Dalc           int64
Walc           int64
health         int64
absences       int64
G1             int64
G2             int64
G3             int64
dtype: object

And if there are any missing values.

print(data.isnull().any())

The output below tells us (all the False values) that there is no missing data.

school        False
sex           False
age           False
address       False
famsize       False
Pstatus       False
Medu          False
Fedu          False
Mjob          False
Fjob          False
reason        False
guardian      False
traveltime    False
studytime     False
failures      False
schoolsup     False
famsup        False
paid          False
activities    False
nursery       False
higher        False
internet      False
romantic      False
famrel        False
freetime      False
goout         False
Dalc          False
Walc          False
health        False
absences      False
G1            False
G2            False
G3            False
dtype: bool

Step 3: Analyze

• Feature selection
• Model selection
• Analyze data

We are interested to see what has impact on end grades (G3). We can use correlation for that.

For now, correlation is just a number saying if something is correlated or not.

A correlation number is between (including both) -1 and 1. If close to -1 or 1 (that is not close to 0), then it is correlated.

print(data.corr())

age          -0.161579
Medu          0.217147
Fedu          0.152457
traveltime   -0.117142
studytime     0.097820
failures     -0.360415
famrel        0.051363
freetime      0.011307
goout        -0.132791
Dalc         -0.054660
Walc         -0.051939
health       -0.061335
absences      0.034247
G1            0.801468
G2            0.904868
G3            1.000000
Name: G3, dtype: float64

This shows us to learnings.

First of all, the grades G1, G2, and G3 are highly correlated, while almost non of the others are.

Second, it only considers the numeric features.

But how can we use non-numeric features you might ask.

Let’s consider the feature higher (wants to take higher education (binary: yes or no)).

print(data.groupby('higher')['G3'].mean())

This gives.

higher
no      6.800
yes    10.608
Name: G3, dtype: float64

This shows that this is a good indicator of whether a student gets good or bad grades. That is, if we assume the questions were asked in the beginning at high school, you can say that students answering no will get 6.8, while students answering yes till get 10.6 on average (grades are in range 0 – 20).

That is a big indicator.

But how many are in each group?

You can get that by.

print(data.groupby('higher')['G3'].count())

Resulting in.

higher
no      20
yes    375
Name: G3, dtype: int64

Now, that is not many. But maybe this is good enough. Finding 20 students which we really can help improve grades.

Later we will learn more about standard deviation, but for now we leave our analysis at this.

Step 4: Report

• Present findings
• Visualize results
• Credibility counts

This is about how to present our results. We have learned nothing visual yet, so we will keep it simple.

We cannot do much more than present the findings.

higher mean grades
no 6.800
yes 10.608

higher count
no 20
yes 375

I am sure you can make a nicer power point presentation than this.

Step 5: Actions

• Use insights
• Measure impact
• Main goal

Now this is where we need to find ideas. We have identified 20 students, now we need to find activities that the high school can use to improve.

This is where I will let it be your ideas.

How can you measure?

Well, one way is to collect the same data each year and see if the activities have impact.

Now, you can probably do better than I did. Hence, I encourage you to play around with the dataset and find better indicators to get ideas to awesome activities.

Want to learn more?

Want to learn more about Data Science to become a successful Data Scientist?

This is one lesson of a 15 part Expert Data Science Blueprint course with the following resources.

• 15 video lessons – covers the Data Science Workflow and concepts, demonstrates everything on real data, introduce projects and shows a solution (YouTube video).
• 30 JuPyter Notebooks – with the full code and explanation from the lectures and projects (GitHub).
• 15 projects – structured with the Data Science Workflow and a solution explained in the end of video lessons (GitHub).