Helps Making Better Business Decisions with
Predictive Analytics
Many organizations realized that there is a tremendous amount of data customers leave behind while using their services online or offline or both. This data could be used to extract valuable information and customer patterns to help organizations to design better business strategies for the future.
Predictive analytics uses customer’s historical data and apply statistical techniques and machine learning modeling to transform data into meaningful insights and identify trends and behaviors. Health Care, Manufacturing, Retail, Finance, and almost all other industries are increasingly looking for predictive analytics to improve their business productivity and performances.
A few key use-cases Predictive Analytics help us
- Banks and other financial institutions use predictive analytics to ensure their customer’s superlative experience and most importantly protect them from fraudulent transactions, fake credit applications, identify thefts, and false insurance claims.
- Healthcare takes advantage of predictive analytics to detect early signs of patient deterioration, identify at-risk patients in their homes, and better patient care. According to a report by Zion Market Research, the global healthcare analytics market is expected to be $4.37 billion by 2026 from $1.6 billion in 2018.
- Predictive analytics help manufacturing industries to increase efficiency, reducing raw material waste, and streamline their processes right from the supply chain management to the distribution and by forecasting future demands by geographical locations.
- Many retail and media industries such as Amazon and Netflix use predictive analytics to personalize their users content and recommend the right products and services based on your previous history and search patterns.
How crossML Helped the Customer
Financial analysis and stock market trends have been the hardest problem to solve at all times. With our financial and technological expertise crossML solving this hardest problem for a long time and providing meaningful and reliable financial insights to our customers.
- Understanding historical data and desired outcomes helps us to generate valuable input features, technical indicators, and correlated assets from different data sources.
- Fundamental analysis is a key element to move the market in a certain direction, so we analyze the company performance using 10-K and 10-Q reports, analyze ROE, P/E, etc.
- Predict sentiment analysis from the company’s potential news and upcoming events using NLP (Natual Language Processing).
- Apply different types of transformations and statistical tests such as Fourier Transforms, Heteroskedasticity, Multicollinearity, etc to normalize and standardize the data.
- Implement several model architectures such as ARIMA, CNN, RNN, LSTM, GAN using all the latest advancements in machine learning and deep learning.
- Prevent model overfitting and assure correct bias-variance trade-off using several techniques such as Hyperparameter Tuning, Regularization, Dense-sparse-dense, Early Stopping, etc.
