This project aims to develop a solution to the Predicting Molecular Properties Kaggle competition (https://www.kaggle.com/c/champs-scalar-coupling/overview)
The development code for this project is implemented on an iPython Notebook based in the Google Colaboratory environment.
Datasets for this project are provided by Kaggle and can be found at the following link: https://www.kaggle.com/c/champs-scalar-coupling/data
Alternatively, the data can also be downloaded using the Kaggle API:
!kaggle competitions download -c champs-scalar-coupling
The libraries used in this project are:
- NumPy: a fundamental package for scientific computing with Python. It contains a powerful N-dimensional array object, broadcasting functions, and useful linear algebra, Fourier transform and random number capabilities.
- Pandas: an open source library providing high-performance, user-friendly data structures and data analysis tools.
- Matplotlib: a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments. Plots, histograms, bar charts, scatterplots etc can be generated using Matplotlib.
- Seaborn: a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Scikit Learn: contains simple and efficient tools for data mining and data analysis, and is built on NumPy, SciPy and MatplotLib
- Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.