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). These criteria assign scores to each model and allow us to choose the model with the best score.
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. Then emonstrate how to run a backtest and evaluate by applying our strategy in a Stock Market data.
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. The successful prediction of a stock's future price could yield significant profit.
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.
Understanding the business needs is important for any project,
but it is easy to get blinded by technological possibilities.
The Data Engineering team along with the guidance of Data Analyst team and Data Scientist team will work on the data pre-processing part.
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 Analyst team will work on back testing of the results through performance metrics.
The Machine Learning solution will be builded in such a way that it is flexible to reevaluate.
FinTech companies are optimizing all areas of their business from risk analysis and portfolio optimization to marketing, in order to make data-driven decisions that lead to increased profitability.
Machine learning has already become a significant asset to large e-commerce for all sizes of online retailers and finally able to deliver the right products by providing intelligence powered shopping experiences.
Machine learning algorithms can accurately incorporate analysis results of customer feedback in social media. This helps in building vehicle and sub-systems performance for guiding future product design.
Machine learning is found in the manufacturing world where machines can send large amounts of data to optimize production. This has been problematic in the past as the amount of data that is sent has typically been too large to interpret.
“ Financial Data Scientist, Technical Architect and Independent Consultant having 20 years of experience in Big Data Analytics.”
“Has more than 10 years of experience in Data Mining and holds a Masters Degree in Software Engineering. ”
“Technical Manager with 8 years of experience in Data Science .”