Predictive Analysis consists of a number of advanced statistical analysis technologies, which require intensive computer programming work to create variables (feature engineering) and develop models (train models) to achieve optimal fits. Predictive analysis, including predictive modeling, is complicated and consumes much more computer resources than data aggregation. Thanks to powerful statistical software such SAS, Python, and R, which make predictive analysis feasible. Statistical Models are used for revealing cause and effect relationships, predicting future events, detecting hidden patterns, developing strategies, and optimizing resources. In recent decades, predictive models have been used in customer loyalty management, demand forecasting, fraud detection, weather forecasting, and more. We provide services in developing algorithms for predictive analyses and models, and applying models to predict future events and detect hidden patterns in Python or SAS software. Furthermore, we have great interest in working with clients to apply models to develop optimal solutions for clients particular business needs.
* Predictive Modeling
* Machine Learning
* Event Forecasting
* Anomaly Detection
* Cause and Effect Analysis
* Linear Models
* Non-Linear Models
* Bayesian Network
* Clustering Analysis
* Principal Component Analysis
* Sentiment Analysis
* Pattern Recognition
* Image Detection
We offer statistical analysis services for business operations, marketing, manufacturing, and research & development. With large amounts of data, it may be impossible to spot out patterns, trends, and relationships without in-depth statistical analyses. Some commonly used analyses for such situations are Anova, Clustering Analysis, Factor Analysis, Principal Component Analysis, Bayesian Analysis, and Statistical Modeling, .
We provide the services of predictive modeling for linear or non-linear models. Predictive modeling involve a series of deep data analyses such data manipulation, variable creation, model selection, and model evaluation. Good models are greatly useful in predict future events, detect abnormalities, and uncover cause and effect relationship. Statistical modeling have been widely used in targeted marketing, fraud prevention, and demand forecasting, etc.
As analytical software and IT technologies evolve, advanced statistical analyses ( predictive modeling and classification) can be achieved by computer programs. Specially designed algorithms compute data through thousands of iterations to generate good models. Modeling and machine learning need great efforts, and experiences help balance cost and best. We provide services in developing machine learning algorithms and performing machine learning data analysis for business uses.
In real world, many things happening are due to existences of other things. Our SQL based algorithms can automatically compute Bayesian probabilities as well as correlations for large size of data. The processes can be used for any industry data and can run on clouds or on-premise servers. We also experimented the applications based on Bayesian probability results for developing business strategies and improving operation efficiency.
Many things behave or appear in their certain ways. Pattern recognition is to identify patterns and find causes by progressively computing based on large amounts of data. We develop specific algorithms to automatically recognize hidden patterns, predict possible events, and develop strategies to optimize resources and prevent unwanted losses.
Technologies in image recognition or detection are rapidly improving and getting more popular uses in many industries and commercial businesses. Our Neural Network based image detection modeling process can automatically train models and use the models to detect target objects in the images. We are pleased to work with companies for image detection modeling and services.
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