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

Statistics Jan 27, 2022

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 neighborhood 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.

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

Description

Dear Participants,

Please submit your project notes -1 here.

Review Parameters	Review points
1) Introduction of the business problem	4
a) Defining problem statement
b) Need of the study/project
c) Understanding business/social opportunity

2)Data Report	2
a) Understanding how data was collected in terms of time, frequency and methodology
b) Visual inspection of data (rows, columns, descriptive details)
c) Understanding of attributes (variable info, renaming if required)

3) Exploratory data analysis	10
a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones)
b) Bivariate analysis (relationship between different variables , correlations)
a) Removal of unwanted variables (if applicable)
b) Missing Value treatment (if applicable)
d) Outlier treatment (if required)
e) Variable transformation (if applicable)
f) Addition of new variables (if required)

4) Business insights from EDA	4
a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business
b) Any business insights using clustering (if applicable)
c) Any other business insights

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 the evaluation will be based on the Business Report. The Python-code/.ipynb file is only for reference. If you fail to submit the Business report ZERO Marks will be awarded.
Any notes found copied/plagiarized from other sources 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) - Project Note 1 (1)

Criteria	Points
1. Problem Understanding a) Defining problem statement b) Need of the study/project c) Understanding business/social opportunity	4
2. Data Report a) Understanding how data was collected in terms of time, frequency and methodology b) Visual inspection of data (rows, columns, descriptive details) c) Understanding of attributes (variable info, renaming if required)	2
3. Exploratory Data Analysis a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones) b) Bivariate analysis (relationship between different variables , correlations) a) Removal of unwanted variables (if applicable) b) Missing Value treatment (if applicable) d) Outlier treatment (if required) e) Variable transformation (if applicable) f) Addition of new variables (if required)	10
4. Business insights from EDA a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business b) Any business insights using clustering (if applicable) c) Any other business insights	4

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