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John Deere - Parts Demand Forecasting
Open for Registration- Lecture time: Friday: 11:30:00 - 12:20:00 ET
- Lab time: Monday: 11:30:00 - 13:20:00 ET
- Domain: Digital Agriculture
- Keywords: Forecasting, Machine Learning, Probabilistic Demand Forecasting, Supply Chain, Time Series Analysis
- Tools: Data Analytics, Optimization, Probabilistic Forecasting, Python, Time Series Data Analysis and Forecasting
- Citizenship: Open to all students
Summary
Develop a probabilistic demand forecasting method to predict the next 12 months of demand for the given part-location combinations. The accuracy of the methods will be evaluated using a variety of metrics associated with different demand time series patterns and weighted
Inari - Simulation and optimization for crop improvement
Open for Registration- Lecture time: Thursday: 13:30:00 - 14:20:00 ET
- Lab time: Tuesday: 13:30:00 - 15:20:00 ET
- Domain: Digital Agriculture
- Keywords: Agriculture, Genetics, Optimization, Predictive Modeling, Simulation modeling
- Tools: Optimization, Python, R, Stimulation
- Citizenship: Open to all students
Summary
The project will explore, develop, and evaluate multi-generational simulation and optimization frameworks to breed improved genetics.
BASF - Market Indicators for Trend Identification
Open for Registration- Lecture time: Thursday: 13:30:00 - 14:20:00 ET
- Lab time: Tuesday: 13:30:00 - 15:20:00 ET
- Domain: Agriculture
- Keywords: Agriculture, Market Development
- Tools: Data Exploration, Feature Engineering, Machine Learning, Python, Time Series Data Analysis
- Citizenship: Open to all students
Summary
In the ever-evolving world of agri-business, understanding market trends and predicting future prices is crucial for strategic decision-making. The project aims to combine various market indicators to identify trends and forecast prices in key market areas.
AgReliant Genetics - Customer Segmentation & Prediction
Open for Registration- Lecture time: Tuesday: 13:30:00 - 14:20:00 ET
- Lab time: Thursday: 13:30:00 - 15:20:00 ET
- Domain: Agriculture
- Keywords: Agriculture, Classification, Customer Behavior, Data Exploration, Data Exploration, Machine Learning, Prediction
- Tools: Classification, Excel, Machine Learning, marketing analytics, Python, R, Snowflake, Tableau
- Citizenship: Open to all students
Summary
AgReliant wants to greatly increase the efficiency with which AgReliant sells to customers. We want to use Data Science and Machine Learning techniques to gain a better understanding of our customer base through segmentation and predictive behavior modeling.
- Lecture time: Tuesday: 15:30:00 - 16:20:00 ET
- Lab time: Thursday: 15:30:00 - 17:20:00 ET
- Domain: Real Estate
- Keywords: Data Scraping, Machine Learning, Real Estate Property
- Tools: Databases, Data Modeling, GitHub, Machine Learning, MongoDB, Optimization, Predictive Models, Python
- Citizenship: Open to all students
Summary
Create a data model within the parameters of our current website/tech stack that identifies employment, life development, and housing opportunities based on end-user statistics.
NIIMBL - Simulated Manufacturing
Open for Registration- Lecture time: Monday: 09:30:00 - 10:20:00 ET
- Lab time: Friday: 09:30:00 - 11:20:00 ET
- Domain: Pharmaceutical
- Keywords: Data Analytics, Experimental Data Modeling, Manufacturing
- Tools: Data Modeling, Ontology Development, Python, R
- Citizenship: Open to all students
Summary
Create rich manufacturing datasets by combining models based on data from small, designed experiments with simulated inputs which have random variance built in.
Viasat - Art of Space Warfare: Orbital Modeling of Cascading Effects from Debris based on Space Consumption
Open for Registration- Lecture time: Tuesday: 13:30:00 - 14:20:00 ET
- Lab time: Thursday: 13:30:00 - 15:20:00 ET
- Domain: Research and Development
- Keywords: Orbital Modeling, Satellite, Space
- Tools: Data Modeling, GitHub, Machine Learning, Python, R
- Citizenship: Open to all students
Summary
This project seeks to understand (via modeling) the thresholds of over population in space to assess a sustainable carrying capacity (considerate of risk/impact) that is driven by multiple threat scenarios.
Inogen - Field-Repairable Oxygen Concentrator
Open for Registration- Lecture time: Monday: 15:30:00 - 16:20:00 ET
- Lab time: Wednesday: 15:30:00 - 17:20:00 ET
- Domain: Biomedical Engineering
- Keywords: Biomedical Engineering, Documentation, Multi-Disciplinary, Oxygen Concentrator, Prototyping
- Tools: Electrical Engineering, Mechanical Engineering, Python, Software Engineering
- Citizenship: Open to all students
Summary
This Project will be in collaboration with mentors from Inogen and Purdue Biomedical Engineering students in EPICS. Mission: Build an oxygen concentrator (hardware & software) out of generic (non-proprietary) parts and prepare instructions how to repair the build if it breaks
- Lecture time: Friday: 15:30:00 - 16:20:00 ET
- Lab time: Monday: 15:30:00 - 17:20:00 ET
- Domain: Aerospace
- Keywords: Aerospace, Machine Learning, Prediction, Predictive Modeling, Trajectory Analytics
- Tools: Git, Jupyter Notebooks, Machine Learning, Python, Tracktable (Sandia Python library)
- Citizenship: U.S. Citizens Required
Summary
This project will focus on trajectory prediction and analysis. Observing trajectories that have crossed an area or recovering lost segments. Predicting where the trajectory will be after a certain amount of time and where the vehicle will be.
- Lecture time: Thursday: 12:30:00 - 13:20:00 ET
- Lab time: Tuesday: 11:30:00 - 13:20:00 ET
- Domain: Manufacturing
- Keywords: Aerospace, Analytics, Business Intelligence, Data Cleaning, data mining, Data Visualization, Machine Learning, Predictive Analysis, Sales Forecasting
- Tools: Data Analytics, Machine Learning, Power BI, Python, R, Tableau
- Citizenship: Open to all students
Summary
Develop a predictive model by leveraging advanced analytics and machine learning techniques, the project aims to provide an accurate sales forecast that in turn enhances internal inventory management, optimizes supply chain operations, and improves sales strategies.