CONNECTIVITY:

Database:  A database (DB), in the most broad sense, is a composed accumulation of information. Briefly, a database is an electronic framework that enables information to be effectively accessed, controlled and refreshed.

A database is utilized by an organization as a strategy for putting away, overseeing and recovering data.

Unstructured Data: Unstructured data presents any data that does not have a noticeable structure and it is unorganized and can be non-textual or textual. Unstructured data impute to data that follows a form that is less ordered than items like spreadsheet pages, database tables or other linear or ordered data sets.

Flat file: is a file of data that does not contain links to other files or is a non-relational database. A flat database is much easier to understand and setup than a traditional database, but may be inadequate for any program that is frequently used or contains millions of entries. 

EXPLORATION & CLEANING:

Summary statistics:

Summary statistics (or summary metrics) characterize a complex set of data (or whole population) with some basic metrics. Summary statistics, in other words, summarize large amounts of data by describing key characteristics such as the average, distribution, potential correlation or dependence, etc.

Visualization: is the process of representing data graphically and interacting with these representations in order to gain insight into the data.

Outlier detection:

Outlier is an observation point that is distant from other observations. Inconsistencies, or anomalies, can be a difficult issue when preparing machine learning calculations or applying measurable systems. They are regularly the consequence of mistakes in estimations or extraordinary framework conditions and hence don’t define the basic working of the hidden framework. Without a doubt, the best practice is to implement an outlier removal techniques before continuing with further examination.

Missing Data Imputation:

Data imputation is the phase of replacing missing data with substituted values. Missing data can also create problems for analyzing data, imputation is seen as a solution to avoid complication involved with listwise deletion of cases that have missing values. Mean imputation is also the well known technique for that.

Dimensionality Reduction:

Dimensionality reduction aims to diminish the number of random variables in data. It includes feature selection and feature extraction. Analyzing data becomes much easier and faster through dimensionality reduction for machine learning algorithms without secondary variables to process, making machine learning algorithms faster and simpler in turn. LDA and PCA are often used approach.

TRANSFORMING:

Types of tranformations:

  • Scaling or normalizing features within a range, say between 0 to 1.
  • Principle Component Analysis and its variants.
  • Random Projection.
  • Neural Networks.
  • SVM (support vector machine)
  • Transforming categorical features to numerical.

Feature Engineering:

It transforms domain knowledge of the data to features that makes machine learning algorithms work. If feature engineering is implemented properly, it boosts the predictive power of machine learning algorithms that help facilitate the machine learning process. Features are particularly reliant on the fundamental issue.

Feature Selections:

There is no need to use every feature redundant for creating an algorithm. It should only be selected important features for feeding the system.

It is possible to automatically select those features in your data that are most useful or most relevant for the problem you are working on. This is a process called feature selection.

LEARNING:

Classification: Classification is the classification system method that use class labels. That is a two-level process comprised of a learning step and a classification step. A classification model is also built in the learning step and the classification step is used to foretell the class labels for the provided data. Classification is based on supervised learning.

Regression: Regression is by far the most commonly used forecasting method. Regression anticipates some parameter’s real value output (statistical value). Also the most popular example of a regression algorithm is far linear regression.

Clustering: Clustering is a method for coordinating a data cohort into classes and clusters in which the objects currently reside within a cluster are extremely similar and the objects of two clusters are totally different. Clustering is also recognised as learning without supervision.

Anomaly Detection: Detection of anomalies is a technique used to detect abnormal patterns. It is not consistent with normal behaviour, called outliers.

It has several enterprise applications, from intrusion detection systems to system remote monitoring, from fraud detection in credit card transactions to fault detection systems in operating areas.

Time Series: Analysis of time series is a statistical technique that handles data from time series or analysis of trends. Data from the time series means that the data are in a series of specific time periods or interval. Time series forecasts use historical values and related pattern information to predict future activity. This is most often related to trend analysis, analysis of cyclical fluctuations and seasonality issue

Hyperparameter Tuning: In machine learning, the problem of selecting a set of optimal hyperparameters for a learning algorithm is hyperparameter optimization or tuning. Hyperparameter tuning optimizes the specified target variable. The final destination variable is called the metric hyperparameter.

Model Ensemble: Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

One common example of ensemble modeling is a random forest model. 

VISUALIZATION:

Model Evaluation/Performance:

Measuring metrics identify a model’s performance. The ability to discriminate between model results is an important aspect of evaluation metrics. One of the well – known metrics for measuring the model is the confusion matrix.

DEPLOYMENT:

Gui:

A graphical user interface (GUI) is an interface that interacts with electronic devices such as computers, handheld devices and other equipment.

In contrast to text-based interfaces, where data and commands are in text, this interface uses icons, menus and other visual indicator (graphics) representations to display information and related user controls.

Api: An API (Application Programming Interface) is essentially a gateway that enables software to communicate with other software – and also defines how this conversation takes place.

This allows a programmer to write code to one software, which can then perform certain desired actions without scratching the programmer.

Code Deployment: Providing an application available to end users

OTHERS:

Visualization/Reporting: Data visualization is a general term that describes any effort by placing data in a visual context to help people understand the importance of data.

Data visualization software can easily expose and recognize patterns, trends and correlations that can go undetected in text-based data.

White Labeling: Also some businesses may give a specific service without technology or investments in infrastructure. The producers can increase revenue by enabling a white label edition of their goods or services to be purchased by another firm. The firm that needs to pay to disseminate a white label product gains by simply adding another good or service to its brand without the assets necessary to develop it.

OPTIWISDOM UNIQUE:

Classification with scoring: This is more than scoring. Our customers will have more precise scoring grace this “+” feature.

Streaming Data: Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). This data needs to be processed sequentially and incrementally on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics including correlations, aggregations, filtering, and sampling. 

Funnel Displaying: We use any noisy, dirty or missing values as input and process self-developed methods and shape it and visualize it as a funnel approach. Making to the customer operations more meaningful and significant for their business decisions.

Matching: Its features gives more power for your business, our solutions are not limited to scoring, more step further, we are matching the probable sides.

Augmented Dimensionality Reduction: This patent protected algorithm significantly reduce and transform your data to ultra-compact size without any loss and you can reverse back to your original data format whenever you need. This feature gives us computation speed for stream and also lower memory consumption and requirements.