Anubhav Gupta, Gaurav Singh Thakur, Ankur Bhardwaj and Biju R Mohan, National Institute of Technology - Karnataka, India

This paper uses a case based study – “product sales estimation” on real-time data to understand the applicability of linear and non-linear models. We use a systematic approach to address the given problem statement of sales estimation for a given product by applying both linear and non-linear techniques on a data set of selected features from the original data set. Feature selection is a process that reduces the dimensionality of the data set by eliminating those features which contribute minimal to the prediction of the dependent variable. The next step is training the model which is done using two techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Re-modeling has then been done to extract new features from the data set by changing the structure of the dataset & the performance of the models is checked again. Data Remodeling often plays a crucial role in boosting classifier accuracies by changing the properties of the dataset. We then try to analyze the reasons due to which one model proves to be better than the other & hence try and develop an understanding about the applicability of linear & non-linear models. The target mentioned above being our primary goal, we also aim to find the classifier with the best possible accuracy for product sales estimation in the given scenario.

Machine Learning, Prediction, Linear and Non-linear models, Linear Regression, Random Forest, Dimensionality Reduction, Feature Selection, Homoscedasticity