Reference no: EM132416814

Over the past five decades, housing costs have risen faster than incomes, low-cost housing has been disappearing from the market, and racial disparities in homeownership rates have deepened.

A recent study by the economists at Apartment Lab explains:
Housing markets are exacerbating the gap between the rich and poor...As incomes are growing fastest at the top of the income distribution, housing costs are doing the opposite. Americans in the bottom ten percent of the income distribution have experienced the most rapid growth in housing costs over the past ten years. Moving up the income ladder, the richer a household gets the less it has seen rents and mortgage payments spike. At the top, we find that the top quartile of income earners has actually seen their housing costs fall in the last decade.

Housing markets place disproportionate financial stress on low-income households, who tend to face similar housing costs as middle-income Americans, despite lower paychecks.

Those in the lowest quartile make only 27% as much as the median household, but they still need to pay 79% of what the median household does each month to housing. America's poor earn just a fraction of the median family's income, but their housing costs are not too different.
Today, most poor renting families spend at least half of their income on housing costs, according to the Eviction Lab.

Housing issues are intertwined with many other social problems, including poverty, educational disparities, and health care. Digging into underlying patterns in housing costs can be an important first step toward greater visibility and better policies.

In previous capstone challenges, we looked at how poverty, evictions, and heart health are distributed across US counties. Now, we are challenging you to consider how poverty, income, health, ethnicity, and other sociodemographic factors are related to rent costs, and to use your skills to build a model for predicting median gross rent values in counties across the United States.

Problem Description
• About the Data
• Target Variable
o Submission Format
o Performance Metric
• Features
o Example Row
• References

About the Data
Your goal is to predict the median gross rent at the county level from other socioeconomic and demographic indicators.

Target Variable

We're trying to predict the variable gross_rent (a positive integer) for each row of the test data set.

Your job is to:

1. Train a model using the inputs in train_values.csv and the labels train_labels.csv
2. Predict floats for each row in test_values.csv for which you don't know the true number of evictions.
3. Output your predictions in a format that matches submission_format.csv exactly.
4. Upload your predictions to this competition in order to get a score.
5. Export your grading token (click the "Export Score for EdX" tab) and paste it into the assignment grader on edX to get your course grade.

Challenge Instructions

An important skill for a successful data scientist is the ability to communicate the insights gained from analysis in the form of a report. A good report can distill the analysis into an easy to digest explanation of what analysis was performed and what conclusions were reached, supported by evidence in the form of statistics and visualizations.

Report Requirements for this Challenge

In this challenge, you will create and submit a report that documents the analysis you have performed on the competition data and presents your findings and conclusions, with supporting statistics and data visualizations.

Specifically, your report must:

• Be written in English, and submitted as a PDF document with a maximum file size of 5 MB and a guideline length of between 1500 and 3000 words (a few less or more is permissible provided all other requirements are met). You can save Microsoft Word 2016 documents in PDF format, and there are numerous online tools to convert documents to PDF.
• Start with an executive summary or overview section that concisely summarizes the analysis you performed during this project, and the conclusions you reached.
• Continue by describing the data, the process used to explore and analyze it, and the key findings, conclusions, and recommendations you reached.
• Support your conclusions by presenting statistics and visualizations.

Attachment:- Problem Description.rar