Software Services

Data Mining

Data Mining can be described as a process where usable data is extracted from larger set of raw data. It involves analysing data patterns in large batches of data using one or more software. Data mining has applications in multiple fields like science, research, telecom, healthcare, environment etc. As an application of data mining, businesses can learn more about their customers and develop more effective strategies related to various business functions and in turn leverage resources in a more optimal and insightful manner. This helps businesses be closer to their objective and make better decisions. Data mining involves effective data collection and warehousing as well as computer processing.

Key features of data mining include:

• Automatic pattern predictions
• Prediction based on likely outcomes.
• Creation of decision-oriented information
• Focus on large data sets and databases for analysis.
• Focus on large data sets and databases for analysis.

Exploratory Data Analytics

Exploratory Data Analysis (EDA) is the first step in any data analysis process. The data which has been collected has to be understood and manipulated to get the answers.EDA helps us to tackle specific tasks such as:

• Spotting mistakes and missing data
• Mapping out the underlying structure of the data
• Identifying the most important variables
• Listing anomalies and outliers
• Testing a hypotheses / checking assumptions related to a specific model
• Establishing a parsimonious model (one that can be used to explain the data with minimal predictor variables)
• Estimating parameters and figuring out the associated confidence intervals or margins of error

Our Team has expertise in specific statistical functions and techniques with R and Python to perform Exploratory Data Analytics.

Predictive Analytics

Predictive Analytics uses past data to predict the future. Important trends are captured by building a mathematical model by utilizing the historical data. This model developed is known as the predictive model which uses the current data to predict what will happen next or suggests actions to be taken which will have the best optimal outcome. A predictive Analytics workflow is given below:

• Import data from varied sources, such as web archives, databases, and spreadsheets.
• Clean the data by removing outliers and combining data sources.
• Develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning.
• Integrate the model into a load forecasting system in a production environment.

Computer Vision (CV)

The easiest definition is when the computer has sight it is known as computer vision. Some facts about Computer Vision:

• Computer Vision or Machine Vision’s main role is to determine whether or not data in an image consists of some specific object or activity.
• While a lot work’s been done to apply computer vision techniques to extract info from video, automated computer vision is not yet reliable enough to detect anomalies or track objects. Hence, in such cases, humans are still in the analysis loop to assess the situation.
• Facial recognition techniques used by social networks and other companies for tagging people in photographs is based on CV. Facebook has taken this tech to such a level that it can, with its facial recognition software, identify a person even if his/her face was covered up.
• The applications of CV include augmented reality, biometrics, face recognition, gesture Analytics and robotics.
• CV is used extensively in remote-controlled drones.