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Problem statement: A house value is simply more than location and square footage

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Problem statement:

A house value is simply more than location and square footage. Like the features that make up a person, an educated party would want to know all aspects that give a house its value. For example, you want to sell a house and you don’t know the price which you may expect?—?it can’t be too low or too high. To find house price you usually try to find similar properties in your neighbourhood and based on gathered data you will try to assess your house price.

Objective:

Take advantage of all of the feature variables available below, use it to analyse and predict house prices.

1. cid: a notation for a house
2. dayhours: Date house was sold
3. price: Price is prediction target
4. room_bed: Number of Bedrooms/House
5. room_bath: Number of bathrooms/bedrooms
6. living_measure: square footage of the home
7. lot_measure: quare footage of the lot
8. ceil: Total floors (levels) in house
9. coast: House which has a view to a waterfront
10. sight: Has been viewed
11. condition: How good the condition is (Overall)
12. quality: grade given to the housing unit, based on grading system
13. ceil_measure: square footage of house apart from basement
14. basement_measure: square footage of the basement
15. yr_built: Built Year
16. yr_renovated: Year when house was renovated
17. zipcode: zip
18. lat: Latitude coordinate
19. long: Longitude coordinate
20. living_measure15: Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area
21. lot_measure15: lotSize area in 2015(implies-- some renovations)
22. furnished: Based on the quality of room 
23. total_area: Measure of both living and lot

Review Parameters	Review Points
1). Model building and interpretation.	10
a. Build various models (You can choose to build models for either or all of descriptive, predictive or prescriptive purposes)
b. Test your predictive model against the test set using various appropriate performance metrics
c.Interpretation of the model(s)
2). Model Tuning and business implication	10
a.Ensemble modelling, wherever applicable
b. Any other model tuning measures(if applicable)
c. Interpretation of the most optimum model and its implication on the business
Total	20

 

Please note the following:

• You have to submit 2 files : 
   
  1. Business Report: In this, you should cover all the topics given in the rubric in a sequential manner. It should include a detailed explanation of the approach used, insights, inferences, all outputs of codes like graphs, tables, etc. and their business implications. Your report should not be filled with codes. You will be evaluated based on the business report.
  2. Python Notebook file: This is a must and will be used for reference while evaluating.Failing to do so shall lead to ZERO marks in all the sections where code file is necessary.
• Please note that evaluation will happen on Business Report. The Python-code is only for reference. If you fail to submit the Business report ZERO Marks will be awarded.
• Any notes found copied/ plagiarized with other(s) will not be graded and marked as zero.
• Please ensure timely submission as the post-deadline assignment will not be accepted.

Standard Instructions for Business Report:

• All pages must be numbered.
• Tables/figures/charts/graphics (if any) must have number and title.
• Groups must make sure visualizations are clearly read at usual magnification and add value to the Report
  • All visualizations must be clearly labelled.
  • All axis labels and legends must be legible.
  • Tableau graphics default mode is not always conducive to normal copy-paste. A proper adjustment may be required. 

All raw codes and raw outputs must be in the Appendix. Illegible graphs and raw codes and raw outputs in the body of the report will mandate a heavy penalty.

 

Regards,

Program office.

Scoring guide (Rubric) - Note 2 Rubric (1)

Criteria	Points
1. Model building and interpretation. a. Build various models (You can choose to build models for either or all of descriptive, predictive or prescriptive purposes) b. Test your predictive model against the test set using various appropriate performance metrics c.Interpretation of the model(s)	10
2. Model Tuning a.Ensemble modelling, wherever applicable b. Any other model tuning measures(if applicable) c. Interpretation of the most optimum model and its implication on the business	10
Points	20