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Understanding the business needs is important for any project, but it is easy to get blinded by technological possibilities.
The Data scientist and Data Analyst team, gets insight from the data through the proposed algorithm.
The Data Scientist and Data Analyst team, come up with algorithm which can solve the business problem.
The Data Engineering team along with the guidance of Data Analyst team and Data Scientist team will work on the data pre-processing.
The successful prediction of a stock's future price could yield significant profit.
The Data Analyst team will work on back testing of the results through performance metrics.
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Implementation of Artificial intelligence logic over an analysis.
Understanding the business needs is important for any project, but it is easy to get blinded by technological possibilities.
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Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series.
Churn in the broadest sense is a measure of the number of individuals or items moving out of a collective system over a specific period of time. It is one of two primary factors that determine the steady-state level of customers a business supports
Regressions subset selection is considered as all possible subsets of the pool of explanatory variables and finds the model that best fits the data according to some criteria (e.g. Adjusted R2, AIC and BIC).
Prediction of stock market price is done using trend and momentum indicators. Stock market prediction using data mining techniques is a common practice as data mining is a powerful tool for data analysis.
This case study shows how a strategy can be evaluated using backtesting over a historical data. We start with defining a strategy explaining the importance of planning and the use of backtesting.
Stock market prediction is an act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange.
GARCH model is a Generalised Auto regressive Conditional Hetero-skedasticity that is an extension of the ARCH model used to help predict the volatility of returns on financial assets.
The main objective of processing the consumer sales data is to explore the insights (Sales & Profit) from the consumer sales data using MongoDB Big-Data processing database.
This case study shows the implementation of a trading strategy and the stages involved to arrive at a final strategy.
