Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource (HR) attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform [1]. Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees' satisfaction level as compared to other attributes.

We report decision tree (DT) modeling of randomly textured tandem silicon solar cells characteristics. The photovoltaic modules of silicon-based solar cells are extremely popular due to their high efficiency and longer lifetime. Decision tree model is one of the most common data mining models can be used for predictive analytics. The reported investigation depicts optimum decision tree architecture achieved by tuning parameters such as Min split, Min bucket, Max depth and Complexity. DT model, thus derived is easy to understand and entails recursive partitioning approach implemented in the "rpart" package. Moreover the performance of the model is evaluated with reference Mean Square Error (MSE) estimate of error rate. The modeling of the random textured silicon solar cells reveals strong correlation of efficiency with "Fill factor" and "thickness of a-Si layer".

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The differences between countries go far beyond the physical and territorial aspects. Hence, for analytical purposes, it is essential to classify countries in groups based on some of their attributes. Investment in Research and Development (R&D) influences innovations which in turn stimulates growth of a country. In this context the productivity of the R&D expenditure is analysed pragmatically. Present study aims to discover impact of R&D expenditure on its productivity in terms of number of journal articles published, patent applications filed and trademark applications registered. A more significant analysis by means of designing prominent clusters of countries by applying unsupervised learning has been presented. In this division, percentage of Gross Domestic Product (GDP) spending on R&D and its productivity are considered.

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