Assignment II

Programming with Python

Introduction

This is the second of the two assignments for this course. It will be graded as pass/fail and you and your group of up to 3 students will need to submit it before the start of the last lecture. Please hand in one assignment per group via the e-mail address vlcek@beyondsimulations.com.

In the assignment, you will practice the concepts you learned in the second part of the course. You will find a lot of information online and you are welcome to use generative AI to help you with the assignment. However, you are not allowed to copy the code from other groups and you have to indicate where and how you used AI to help you. Try to use comments to structure and to explain your code. Furthermore, use descriptive variable names and format your code nicely in order to make it more readable.

Redact secret information

In this excercise, you will create a program that is able to redact secret information in a text. The program should be able to redact the following: zip codes, names, email addresses and phone numbers. The program should ask the user for a filename and then read the file and redact the secret information. The program should then print the redacted text to the console and write it to a new file called redacted.txt. You can find a file with secret information in the git repository under assignments/secret-text.txt.

# Secret information redactor
# TODO: Create a program that is able to redact secret information in a text.
# YOUR CODE HERE

Dice roll simulator

In this excercise, you will create a program that is able to simulate dice rolls and visualizes their distribution. The program should ask the user for the number of sides on the dice and the number of dices to roll. Then, the program should simulate the dice rolls 10000 times and visualize the distribution of the dice rolls using a histogram.

# Dice roll simulator
# TODO: Create a program that is able to simulate a dice roll.
# YOUR CODE HERE

Regression analysis

Note

You will either have to to this excercise or the next one about dashboards.

In this excercise, you will create a program that performs linear regression on a dataset with housing prices. The dataset can be found in the git repository under assignments/hamburg_housing_prices.csv. The dataset contains the following columns:

  • Price: The price of the house in Euros.
  • Area: The area of the house in square meters.
  • Rooms: The number of rooms in the house.
  • Year_Built: The year the house was built.
  • Distance_to_Center: The distance to the center of Hamburg in kilometers.

Your task is to perform a linear regression analysis on the dataset and to predict the price of a house based on its area, number of rooms, year it was built and its distance to the center of Hamburg. You can use the pandas library to read the dataset and the numpy library to perform the linear regression analysis. After performing the linear regression analysis, use the matplotlib library to visualize the relationship between the price and the other features as well as the predicted prices for a given set of features.

# Regression analysis
# TODO: Create a program that performs linear regression analysis on the dataset.
# YOUR CODE HERE

Dashboards

Note

You will either have to to this excercise or the previous one about regression analysis.

In this exercise, you will create a program that visualizes a data set of your choice in a dashboard. For this excercise, you can choose any data set of your interest. The data should be visualized in a dashboard with at least two plots. You can use one of the dashboard libraries we discussed in the lecture.

Note

The dashboard should be programmed in a separate file I can call for evaluation.

# Dashboards
# TODO: Create a program that visualizes a data set of your choice in a dashboard.
# YOUR CODE HERE