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Homework answers / question archive / Important points: 1

Important points: 1

Statistics

Important points:
1. please strictly follow the review parameters & scoring criteria- very critical.
2. please work and showcase minimum 3 models eg. linear regression, white box / black box ensembled model etc and then compare and elaborate the same and finally show which model works best in comparison to all.
3. kindly change the recommendations given earlier in notes or in PPT, feedback received that the recommendation are not at all correct, just mentioning top 5 features is not a recommendation given to management of company.
4. objective of the project is to derive agent bonus for top performers & upskill low performers..recommendations have to be made separately for both basis the models built what can be suggested to be done to meet the objective.
5. ** 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.
PLS MAKE SURE THE ABOVE POINTERS ARE FOLLOWED**
Review Parameters Review Points

1. Introduction

 3

- Brief introduction about the problem statement and the need of solving it.

  
   

2. EDA and Business Implication

 5

- Uni-variate / Bi-variate / Multi-variate analysis to understand relationship b/w variables. How your analysis is impacting the business?

  

- Both visual and non-visual understanding of the data.

  
   

3. Data Cleaning and Pre-processing

 8

- Approach used for identifying and treating missing values and outlier treatment (and why)

  

- Need for variable transformation (if any)

  

- Variables removed or added and why (if any)

  
   

4. Model building

 8

- Clear on why was a particular model(s) chosen.

  

- Effort to improve model performance.

  
   

5. Model validation

 8

- How was the model validated? Just accuracy, or anything else too?

  
   

6. Final interpretation / recommendation

 8

- Detailed recommendations for the management/client based on the analysis done.

  

 

Please note the following:

  • You have to submit 2 files :
    1. Business Report: In this, you should cover all the topics given above 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.

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.

 

Scoring guide (Rubric) - Some Rubric (1) (1)
Criteria Points
Introduction - What did you wish to achieve while doing the project ?
 3
EDA - Uni-variate / Bi-variate / Multi-variate analysis to understand relationship b/w variables. - Both visual and non-visual understanding of the data.
 5
Data Cleaning and Pre-processing - Approach used for identifying and treating missing values and outlier treatment (and why) - Need for variable transformation (if any) - Variables removed or added and why (if any)
 8
Model building - Clear on why was a particular model(s) chosen. - Effort to improve model performance.
 8
Model validation - How was the model validated ? Just accuracy, or anything else too ?
 8
Final interpretation / recommendation - Very clear and crisp on what recommendations do you want to give to the management / client.
 8
Points 40
 
 

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