In ancient analytics, processing was within the kind of batches. That is, processing that was solely historical in nature and not a period of time. This caused issues for numerous industries that needed a period of time knowledge so as to realize insights into these circumstances. However, with the advancements in technologies and the development of dynamic knowledge pipelines, it's currently attainable to access the info with minimum possible latency.
Real-time analytics is the analysis of immediate data which becomes available as soon as the data enters our system. It forms a crucial form of a business where daily insights are to be drawn for immediate actions to be taken in order to update dataset and change strategies according to the immediate driven insights.
We work on real-time analytics to give your business an edge over others by working on customer relationship management, fraud detection, individual customer choices, etc to take immediate actions with regards to business needs and current market trends.
The market is unpredictable therefore the investors who invest in stock markets are usually unaware of the stock market behavior. They face the problems in trading as they are unable to decide about the buying and selling of stock which can gain them more profits. Also, analysis of economic news and articles so as to extract helpful data exceeds human capabilities. Analysis of past price developments and the traditional time series analysis, where predictions are made solely based on the technical and fundamental data need some kind of assistance to make the business profitable.
Using Long Short Term Memory (LSTM) - Recurrent Neural Networks (RNN),Auto-Regressive Integrated Moving Average (ARIMA) and FB-Prophet, stock prediction modules have been developed that give accurate results and are capable of handling sudden market fluctuations.
|Area||-||Deep Learning, Reinforcement Learning, ML, AI, Analytics|
|Business Area||-||Stock Market, Finance|
|Libraries||-||matplotlib, numpy, pandas, seaborn, sci-kit-learn, statsmodels, Keras|
Spam calls are generally contacted recipients with the intention of getting some personal or financial information from them. As such, they use scams, attempting to swindle you out of your contact number, your financial information, your identity, or anything else of value through dishonest means. They also attract calls from their targets by placing legitimate-looking technical assistance advertisements in popular search engines and high-traffic websites.
Machine learning and Natural Processing Language enables the Implementation of Phone Call Scam System by using 2 approaches to cluster phone call scams. It Utilises well-developed algorithms like Kmeans and DBSCAN and the clusters were formed using these algorithms. In the second approach, a novel technique of self-declared clusters based on similarity metrics is being used and accordingly the scam is categorized into the desired types.
|Area||-||Machine Learning, NLP|
You can hire a single developer or any number of dedicated developers from our pool of 65+ expert coders. We have 3 unique hiring models designed according to your precise needs on the basis of time. These models are available part-time, full time, or on an hourly basis. Those who wish to hire our developers for their world-class business project can choose any of the three options according to their needs and preferences.