Develop 3 Machine LEarning models to predict temperature in Python. URGENT!!
Budget: $10 – $30 USD
Data Science Challenge
Processing
An unknown mining equipment is giving problems with its operation, as it heats up quickly. A series of tests are performed, and you are asked to develop a model to predict the equipment engine temperature.
The dataset given has the following columns:
- Vq,Vd,Iq and Id: are the active and reactive power components for the three phase balanced system in d-q coordinates.
- refrigerant_liquid: Temperature of the refrigerant liquid used in the engine
- Temperature 1, 2, 3, 4: Temperature of the engine measured in different places
- Ambient_Temperature: Ambient temperature during the test
- RPM: Speed of the engine in RPM
- Torque: Torque of the engine
- Test_id: Identifier of the test
- Temperature_Engine: Temperature of the engine at the interior. This is the variable that is required to predict.
The indexes are to be understood as time, with a 1Hz acquisition frequency.
The task
Develop a predictive model for the Temperature of the engine of the equipment, based on the given dataset. We expect you to build at least three models with different strategies and return the proper metrics for comparison. For all the models, explain the results as if it were to a stakeholder. How do you interpret the model? Which tools would you use to explain the models?
Fitting a model
Feel free to use wherever toolkit you like.
Submitting
A jupyter notebook containing all code and summary of the analysis done (markdown is sufficient).
How We Evaluate
• Methodology
• Correctness
• Clean code and Idiomatic Python
Processing
An unknown mining equipment is giving problems with its operation, as it heats up quickly. A series of tests are performed, and you are asked to develop a model to predict the equipment engine temperature.
The dataset given has the following columns:
- Vq,Vd,Iq and Id: are the active and reactive power components for the three phase balanced system in d-q coordinates.
- refrigerant_liquid: Temperature of the refrigerant liquid used in the engine
- Temperature 1, 2, 3, 4: Temperature of the engine measured in different places
- Ambient_Temperature: Ambient temperature during the test
- RPM: Speed of the engine in RPM
- Torque: Torque of the engine
- Test_id: Identifier of the test
- Temperature_Engine: Temperature of the engine at the interior. This is the variable that is required to predict.
The indexes are to be understood as time, with a 1Hz acquisition frequency.
The task
Develop a predictive model for the Temperature of the engine of the equipment, based on the given dataset. We expect you to build at least three models with different strategies and return the proper metrics for comparison. For all the models, explain the results as if it were to a stakeholder. How do you interpret the model? Which tools would you use to explain the models?
Fitting a model
Feel free to use wherever toolkit you like.
Submitting
A jupyter notebook containing all code and summary of the analysis done (markdown is sufficient).
How We Evaluate
• Methodology
• Correctness
• Clean code and Idiomatic Python