Prediction Analysis/Deep Learning
Budget: $30 – $250 USD
Hello! I urgently require a competent deep learning expert skilled specifically in time series prediction, focusing on the LSTM and LightGBM etc frameworks.
Key Requirements:
Task! You have to train the LSTM algorithm (prediction) and make comparison graph plots with LigtGBM,Random Forest,Decision,Gradient Boosting,xgboost which one performs better. Display the plot comparison : time series data (data will be provided).
Also MSE, MAE, and R2 (Random Forest,Decision Tree,Linear Regression,Support Vector Regressor,Gradient Boosting, XGBoost) evulation. Dataset regarding proton exchange membrane fuel cell analysis
1-state of health (SOH)
2: Remaining useful life (RUL) (estimation): comparative analysis of PEMFC
Machine learning prediction (prognostic)
Note: You have to explain and share all the raw data with me.
Caution! The CHATGPT code is not acceptable.
Proficiency in LSTM (Long Short-Term Memory) networks
Experience with LightGBM
ability to work with time series data for prediction purposes
capable of rapid execution and quick turnover
Ideal Candidate:
- Has a solid background in machine learning and deep learning
- Has successfully implemented time-sensitive predictive models
- Shows vast knowledge in time series analysis
- Can provide fast results without compromising on accuracy
If you have the required skills and can work efficiently under a tight schedule, I invite you to submit your bid with a relevant portfolio showcasing your expertise in deep learning for time series prediction.
Thank you
Key Requirements:
Task! You have to train the LSTM algorithm (prediction) and make comparison graph plots with LigtGBM,Random Forest,Decision,Gradient Boosting,xgboost which one performs better. Display the plot comparison : time series data (data will be provided).
Also MSE, MAE, and R2 (Random Forest,Decision Tree,Linear Regression,Support Vector Regressor,Gradient Boosting, XGBoost) evulation. Dataset regarding proton exchange membrane fuel cell analysis
1-state of health (SOH)
2: Remaining useful life (RUL) (estimation): comparative analysis of PEMFC
Machine learning prediction (prognostic)
Note: You have to explain and share all the raw data with me.
Caution! The CHATGPT code is not acceptable.
Proficiency in LSTM (Long Short-Term Memory) networks
Experience with LightGBM
ability to work with time series data for prediction purposes
capable of rapid execution and quick turnover
Ideal Candidate:
- Has a solid background in machine learning and deep learning
- Has successfully implemented time-sensitive predictive models
- Shows vast knowledge in time series analysis
- Can provide fast results without compromising on accuracy
If you have the required skills and can work efficiently under a tight schedule, I invite you to submit your bid with a relevant portfolio showcasing your expertise in deep learning for time series prediction.
Thank you