[SOLVED] ISOM3400 Assignment 2-Automobile-mileage-price

Learning Outcomes

Assignment 2

1. Identify business objective
2. Perform data cleansing
3. Perform data visualization and exploration
4. Coding with Python and its libraries
5. Identify and fix logical and run-time errors in programs

Learning goals at programming level

• Function, pandas, control statement, data structure, e.g., list, dictionary Dataset

Automobile dataset:

• car: manufacturer brand
• price: seller’s price in advertisement (in USD)
• body: car body type
• mileage: as mentioned in advertisement (‘000 Km)
• engV: rounded engine volume (‘000 cubic cm)
• engType: type of fuel (“Other” in this case should be treated as NA)
• registration: whether car registered in Ukraine or not
• year: year of production
• model: specific model name
• drive: drive type

  Background

  This assignment focuses on data visualization and exploration. Students are expected to derive a business objective from a given dataset, and perform data exploration and visualization on the given dataset in an attempt to respond to the stated business objective.

  Tasks

1. Identify a meaningful business objective from the given dataset (20 words max). The stated objective needs to be solvable by a predictive model that we can implement in the future.
2. Data cleansing: clean up unknown data in the given dataset.
3. Analyze the data statistically (not more than 10 lines of code) and present the data and results

   graphically (not more than 5 graphs). This task is highly dependent on task 1.

4. Write up a report. The conclusion part cannot be more than 50 words.

Report

1. Write up a report in the Jupyter Notebook format (.ipynb) and store the program in the same file.
2. Write down your name, student ID, and task numbers (i.e., Task (1), Task (2), and Task (3)) in the Text cells of the notebook page.
3. Students need to write comments for their codes. For example, write down what they try to do before running the code and comment on the findings or results after running the codes.

ISOM3400

Python Programming for Business Analytics

4. The last part of this report is conclusion. Students need to mention the business objective being addressed, and some concluding remarks based on the analysis in Task 3.
5. Students may include their assumptions in the last part of the report when needed.

Grading criteria

1. Meaningful and solvable business objective
2. Use of Python programming
3. Data Cleansing
4. Data Visualization and Exploration (with explanation, justification, and conclusion)
5. Data analysis based on a business objective

General guidelines and requirements

• The proposed business objective must be meaningful (not trivial), and can be solved by predictive model.
• Each programming statement or a block of codes needs to have explanations. Explain what the programming statement does.
• Conclusions must be supported by statistical analysis
• The given dataset (in CSV format) cannot be modified/changed/updated. And the

  Python program should not access other Excel files for any processing or analysis.

• On grading a student submission, we will first go to “Runtime” and click “Run all” to

  check possible bugs. Be prepared for that.

• On grading a student submission, we will simply unzip all files (including sub-folders),

  and then upload them to our Google Drive folder for grading. If we cannot run the program due to missing files and other reasons, we deduct marks for these problems.

  Submission Guidelines