Analysis of NSS data using Machine Learning.

Data collection: Collection of data from various sources to meet the optimization requirements of the datasets.

Dataset cleaning and optimization: Removal of unnecessary data and filling the blanks in the data etc. Selection of required columns from the data to select the parameters for training the data.

Seperation of training, cross validation and testing datasets: When we apply some algorithm on the training data, the data may be unfit, fit just right or overfit with some high variance. This is exactly why we need to test machine learning models on unseen data. Otherwise, we have no way of knowing whether the algorithm has learned a generalizable pattern or has simply memorized the training data. Implementation of different Machine Learning Algorithms: Supervised learning algorithms. Unsupervised learning algorithms. Reinforcement Learning Algorithms

Selection of Algorithms based on accuracy.

Setting up accuracy.

Practical Implementation.