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Log Regression Confusion Matrix
Inspiration
All of the team members were following the Learners track at the TAMU Datathon. At each of the learning lessons, we had the opportunity to receive exposure to different machine learning models and regression methods. Consequently, we were inspired to apply what we had learned during the day to a real life, industrial problem faced by the oil and gas industry to hopefully minimize operating costs and reduce any chances of safety hazards.
What it does
Our project implements machine learning to learn from sensor data to predict potential equipment failure.
How we built it
We built our project in Python utilizing the knowledge we gained during our training throughout the day from the TAMU Datathon Learners track.
Challenges we ran into
We ran into challenges with processing and manipulating data, identifying appropriate data visualization techniques, and navigating the interface to export .csv files for submission!
Accomplishments that we're proud of
We are proud of successfully implementing our first machine learning algorithm!
What we learned
We learned lots about Python, machine learning, and data analytics.
What's next for Predictive Equipment Failure Logistic Regression Model
Built With
- kaggle
- keras
- machine-learning
- python
- sklearn
- tensorflow
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