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Design an End-to-End Housing Price Prediction Machine Learning System
Nischal Subedi
Nischal Subedi
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Design an End-to-End Housing Price Prediction Machine Learning System

Design an end-to-end housing price prediction machine learning system and deploy final model artifact using AWS Sagemaker.

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Apply now
Fridays
 at
3:00
P.M.
 ET /
12:00
P.M.
PT
8 weeks, 2-3 hours per week
Expert
No experience required
No experience required
Some experience required
Degree and experience required

Description

Predictive modeling is a critical skill for data scientists, widely used across various industries. In real estate, predictive analytics and valuation modeling are gaining momentum, with both startups and established companies increasingly adopting data-driven approaches to forecast housing prices.  

In this Build Project, you will retrieve, clean, and preprocess real-world housing data, transforming it into a format suitable for machine learning. You will explore and visualize the data to gain insights, then apply supervised learning algorithms to predict the sales price for each house. Throughout the project, you'll focus on optimizing model performance through feature selection and hyperparameter tuning. Additionally, you'll deploy your final model using AWS SageMaker, making it accessible via cloud infrastructure. This hands-on project will not only enhance your predictive modeling skills but also give you practical experience in cloud-based machine learning deployment.

Session timeline

  • Applications open
    December 1, 2024
  • Application deadline
    January 15, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    February 3, 2025
  • Project end date
    Week of

What you will learn

  • Preprocess and analyze various data types, including categorical and numerical, using techniques like Exploratory Data Analysis (EDA) and data visualization.
  • Understand and apply supervised learning algorithms, with a focus on feature selection and hyperparameter tuning to optimize model performance as well as perform model selection.
  • Utilize Git for version control to track project progress and maintain a clean, organized repository.
  • Deploy the final machine learning model to the cloud, ensuring scalable and reliable access through AWS infrastructure.
Build Projects are 8-week experiences that operate on a rolling basis. Selected participants engage in weekly live workshops with a Build Fellow and 2-15 other students.

Project workshops

1
Introduction to Project - Setup and Tools
2
Exploratory Data Analysis (EDA)
3
Data Preprocessing and Feature Engineering
4
Building Supervised Machine Learning Models
5
Hyperparameter Tuning and Model Optimization
6
Model Evaluation and Selection
7
Cloud Deployment with AWS SageMaker
8
Final Presentation and Documentation

Prerequisites

  • Python Programming Proficiency: Ability to write Python code with an understanding of functions, loops, conditionals, and object-oriented programming concepts like classes.
  • Experience with Data Manipulation Libraries: Familiarity with libraries such as Pandas and NumPy for data cleaning, transformation, and handling missing values.
  • Understanding of Cloud Computing Basics: Awareness of how cloud services work, including concepts like virtual machines, storage, and cloud-based machine learning environments (e.g., AWS).
  • Supervised Machine Learning Knowledge: Familiarity of how supervised learning models work, including how to train, validate, and evaluate models.
  • Git Version Control Skills: Experience using Git for managing project code, including committing changes, creating branches, and pushing updates to a remote repository like GitHub.

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About the expert

Nischal Subedi is a Data Science Fellow at Open Avenues, where he mentors students and leads projects in data science, machine learning, and AI. Additionally, as a Data Scientist at Home Partners of America, a company within the Blackstone Inc. portfolio, he develops pricing strategies to enhance leasing operations across the company.

He brings over five years of experience to the data science field and holds a master's degree in Statistics from the University of Delaware.

Fun Fact: Nepal, known for Mount Everest, is not just Nischal's birthplace but also the source of his affinity for hiking.

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