ML Model Training and Benchmarking

We choose python for ML and data analysis. Because:

• Simple syntax and easy to use
• ML Library and framework support

The python libraries and frameworks we choose for ML are:

1. TensorFlow

• High performance (GPU support/ highly parallel)
• Easy to debug
• visualization support

1. Numpy

• Easy matrix manipulation
• datatype with high compatibility

1. Pandas

• High efficiency when handling large data
• Dataset manipulation and customization

1. Matplotlib

• Simple and easy to use

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.