The project is based on the analysis of the «2013 American Community Survey» dataset published on Kaggle and released under the public domain license (CC0).
The task is to implement from scratch a learning algorithm for regression with square loss (e.g., ridge regression). The label to be predicted must be selected among the following 5 attributes, removing the remaining 4 from the dataset:
PERNP (Person's earnings)
PINCP (Person's income)
WAGP (Wages or salary income past 12 months)
HINCP (Household income)
FINCP (Family income)
Important: the techniques used in order to infer the predictor should be time and space efficient, and scale up to larger datasets.
A short report is required.
8 freelances font une offre moyenne de 39 € pour ce travail
Hello, I am a Machine Learning expert and I can do this task perfectly. Please feel free to contact me to discuss more details
grettings i'm data scientist i can help you with you project i'm expert in ML and algorithms of regression. write me for talk about the project
Hello, I am a data science engineer andI have 4+ years of experience. Looking forward to work with [login to view URL],
Hello dear, this is mohamed , working as daata analyst with solid experience with python and its approaches in machine learning , I have made a regression projects before you can check from my GITHUB account [login to view URL] Plus
Hi, I am intrested with your project. I would like to discuss it with details. Hope to hear you soon
I have built a many linear regression, logistic,and polynomial regression models using python, and sklearn and also using numpy along with that,.......if you feel I can solve your problem, please do hire me....let's fi Plus
Dear, I am Electronics Engineer with expertise in Matlab. I am sure I can help you with this project. Looking forward to hear more from you. Thanks Abu Tabraiz