This is a group assessment worth 40% of the total mark for FIT5196. It consists of three tasks related to data analysis and manipulation.

• Input files:Group_dirty_data.csv,Group_outlier_data.csv,Group_missing_data.csv,warehouse.csv
• Output files:Group_dirty_data_solution.csv,Group_outlier_data_solution.csv,Group_missing_data_solution.csv,Group_ass2_task1.ipynb,Group_ass2_task1.py

The dataset contains transactional retail data from an online electronics store (DigiCO) in Melbourne, Australia. Each row represents a single order with columns such asorder_id,customer_id,date, etc.

1. Detect and fix errors in_dirty_data.csv
2. Impute the missing values in_missing_data.csv
3. Detect and remove outlier rows in_outlier_data.csv(w.r.t. thedelivery_chargesattribute only)

Thegroup_id_ass2_task1.ipynbshould demonstrate the methodology to achieve correct results. This includes using appropriate Python functions for input, process, and output, and presenting the solution in an efficient and proper way.

• Input file:suburb_info.xlsx
• Output file:Group_ass2_task2.ipynb

Study the effect of different normalisation/transformation methods on columnsnumber_of_houses,number_of_units,population,aus_born_perc,median_income,median_house_priceto prepare data for a linear regression model to predictmedian_house_price.

• Input file: None
• Output file:Group_report.pdf

1. Feedback Session During Week 10 Applied Session: Present progress, future planning, record TA’s suggestions, and continue work based on suggestions.
2. Group Reflection Presentation (Hurdle): Present methodology and answer questions during Week 12 applied sessions. Mandatory attendance.
3. Reflective Report: Provide a report based on feedback, tailored solutions, and any related findings.

• Submit 6 files:Group_dirty_data_solution.csv,Group_missing_data_solution.csv,Group_outlier_data_solution.csv,Group_ass2_task1.ipynb,Group_ass2_task1.py,Group_ass2_task2.ipynb,Group_report.pdf
• Zip all files intoGroup_ass2.zip
• Follow file naming standards and ensure files are parsable and readable.

• Instructions for generating.pyfiles from notebooks.
• Submission checklist details.