Abstract
There has been tremendous growth for the need of analytics and BI tools in every organization, in every sector such as finance, software, medicine and even astronomy in order to better overall performance. C-factor Computing has the same vision of empowering their existing products through data analysis and forecasting to better suit the need of customers and decision making of stakeholders. The project involves 5 key aspects in Analytics - Data Acquisition, Big data or data Storage, Data Transformation (Unstructured to Structured), Data Wrangling, Predictive Modeling / Visualization. Data Acquisition involves gathering existing transactional and search data of customers and travel aggregators who use the product. This data is used to create powerful dashboards capable of predictive analytics which help the company make informed choices. The key aspects mentioned can be achieved through various tools available but requires testing at every stage in order to realize the appropriate software for the data present in the company. Hence the project deals with studying and implementing selected tools in order to provide the right framework to achieve an interactive dashboard capable of predictive analytics which can also be integrated into the existing products of the company.
Publisher
Inventive Research Organization
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference17 articles.
1. [1] Ariyo, A. O. Adewumi and C. K. Ayo (2014) ‘Stock Price Prediction Using the ARIMA Model’, UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, Cambridge, pp. 106-112, doi: 10.1109/UKSim.2014.67.
2. [2] Singh, N. Thakur and A. Sharma (2016) ‘A review of supervised machine learning algorithms,’ 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 1310-1315.
3. [3] V. Patil and R. S. Bichkar, (2006) ‘A Hybrid Evolutionary Approach To Construct Optimal Decision Trees With Large Data Sets,’ IEEE International Conference on Industrial Technology, Mumbai, pp. 429-433, doi: 10.1109/ICIT.2006.372250.
4. [4] F. F. Lubis, Y. Rosmansyah and S. H. Supangkat, (2014) ‘Gradient descent and normal equations on cost function minimization for online predictive using linear regression with multiple variables,’ International Conference on ICT For Smart Society (ICISS), Bandung, pp. 202-205, doi: 10.1109/ICTSS.2014.7013173.
5. [5] F. Harrou, M. Nounou and H. Nounou, (2013) ‘A statistical fault detection strategy using PCA based EWMA control schemes,’ 9th Asian Control Conference (ASCC), Istanbul, pp. 1-4, doi: 10.1109/ASCC.2013.6606311.
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