Data prediction using Machine learning Methods

Choosing a UNIQUE pattern classification approach and implementing that approach using one of the machine learning Methods in Python/MATLAB/Weka.

Structure investigation using the following steps:

Data pre-processing

 Normalization, outlier detection, censoring of bad data, etc. 

Handling of missing data, records of varying length, etc. 

Feature extraction/selection 

To generate many features from the time-series data provided and ultimately only use a subset inclassifier. 

Partition data & establish experiment design 

Train/validation/test sets, balancing classes (optional), etc. 

Train classifier 

What approach used, what parameters required, how they were tuned, etc. 

Testing & expected accuracy 

What is predicted accuracy, how was it computed, provide a standard error / standard deviation on your estimate (e.g. "the minimum of my 3 accuracies will be 0.73 ± 0.04") 

Meta-learning approaches

 Implement at least one meta-learning strategy (e.g. CME-voting, bagging, boosting), and investigate its effect on accuracy.

Квалификация: Python, Machine Learning (ML), Hadoop, Matlab and Mathematica, Алгоритмы

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ID проекта: #22317077

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