Introduction To Data Mining:
Contents hide
Data Mining, or the process of extracting useful information from raw data, has been in use for many years now. By making use of complex algorithms, companies look for patterns within large repositories of data.
The whole process is broken down into a few basic steps- collecting, storing, organizing and presenting in an easy-to-share format. Today, it is used everywhere- from departmental stores that use it to determine when to put its products on sale, to presidential campaigns.
While data mining has come a long way, certain avenues left untouched. Hence we will discuss some of the data mining projects that can be a hit in 2020.
1. Climate Prediction System:
An extensive climate prediction system built that can have new data points added to it, as and when available. The historical weather data of a specific region such as max temperature, precipitation, wind speed used for starters. More data points added along the way- the mean increase in temperature over the last few years. The increases in pollution in that particular area, the percentage of recovery of the ozone layer, and more. What this will do is that as and when new factors found to be affecting the climate, to improve the predictive model.
2. Employee Performance Evaluator:
One cannot rely on algorithms alone when it comes to assessing the performance of an employee. While one can make ample use of data metrics such as the number of days taken off work, late in-times, complaints by colleagues (dare that obnoxious guy to crack a joke now), time taken to complete a project, and more. The assessment would also need to have that human touch as we cannot govern numbers. That human touch could be anything from an appreciative remark like “dependable” to acknowledging that someone is a charm to work with. Tiny details that a machine won’t pick up. This data used as input and a final computed score that sums up the ability of each employee used by a company to judge an employer. A team should be a unit of heterogeneous abilities working together to overcome others’ weaknesses and showcase individual talents. With the employee evaluator, you could build your perfect team. It could also be helpful during appraisals, a factor that would drive your employees to give their best throughout the year.
3. Scam Identification Through Data Mining:
Millions of people all over the globe fall prey to online scammers every year. What these scammers do is very simple. They create fake messages with a working link which when the user clicks on, used to steal his bank details. One way to tackle this problem would be identifying and collecting the numbers, emails, and links through which these scammers operate. After that, you would need to feed the data to a system that will locate the keywords, links, and patterns present in the data. Thus, when any user gets a scam message again, an application would flag it and also add it to the repository of spam messages. This way spam messages and even calls reduced by continuous learning of the system.
4. Data Mining Helps In Recipe Generation:
Globalization and the Internet have made people curious. So, one can find croissants in India, chicken curry in Japan and sushi in Australia. The world is transforming into a global village in the cultural sense of the phrase. But, one does not indulge in cooking. Lebanese is still limited to hummus, Italian is pasta- this is all we know. A wonderful way to enrich cultures and bring everybody together through food would be a recipe generator. You enter the ingredients available in your refrigerator and get recipes of the dishes that you could make using what you have. This could easily be by mining thousands of recipes available on the Internet. So, when a user enters the list of ingredients, it will scan through all recipes and bring the ones that have the most keyword matches. Bon appetit!
5. Fraud Detection On eCommerce Websites:
Many eCommerce websites suffer losses due to fraudulent sellers and in some cases, even customers. Going through customer reviews, or complaints can divulge fake products. A person or many persons from the same locality could be returning products claiming that they received an empty box. Such fraudulent behavior cannot track owing to the large number of dealings happening across websites every day. But, through data mining it is achievable. Mining customer reviews, sales and returns data would help in detecting suspicious activity. The sites can identify sellers that cheat their customers and vice versa.
6. Identifying Suspicious Behavior On Online Chatrooms:
Since their identities kept anonymous, online chatrooms often become a hub for seasoned criminals. Policing these chatrooms is a tough job. Incel, human trafficking, pedophilia, terrorism garner online communities with passionate followers. The need for the hour is creating a means of tracking down these criminals. At first, you would need to go through all previous chats across chatrooms. Then, you will have to mine the data to locate trigger words, words that induce violence or hate and find a pattern within the words, so that your mining algorithm can go through terabytes of data later and locate a similar pattern or sentence formation in a new environment. Thus, what you can build is a system that can go through millions of chats in real-time and identify possible criminal activity. For example, “gun”, “shoot”, “Friday”, “mall” located across a conversation in a chatroom picked up by this software.
7. Data Mining In Automatic Content Generation:
This one can be a savior for content creators. An automatic content generator can be built that would use data mining to produce results. You would need to feed in the topic, heading and subheadings, and keywords that needed to be in the text. Once done, the system can use these keywords and sentences to extract matching data from the web. The data would transform to produce a balanced article. Since a level of AI that would match a human has not in creation yet, there might be some need for manual intervention, before the generated content is usable.
8. Integrating Customer Behavior Detecting Into UI:
For any eCommerce website, offering user experience ensures its longevity. To do so, you would need to track customer behavior on your site. This behavioral data would then need feeding into algorithms to generate patterns and find solutions. Sites like Amazon noticed that customers would add products to their carts and then leave without buying. This is a problem that plagues most websites, the reason being the long process that precedes buying. This has made Amazon create its “Buy Now” option which has shortened the process and made use of impulse buying.
9. Travel Planner:
Using data mining, one can build a system that would use data from the internet to tell you the best way to travel from point A to point B while covering points C, D, E, and more. This can come in handy when you are going on a trip. But are not sure of the sequence in which you should cover specific locations or how to travel from one location to the other. The project will integrate both web scraping and data mining to give you final results. People buy travel dataset to decide on the hotel and place they want to stay in. It helps them position themselves financially before the choose the correct place to stay.
Conclusion:
Data extraction, mining, and analytics have all grown hand in hand due to the rise of the internet as well as devices connected to the internet and produce tons of data every minute. For testing your algorithms that you might want to use for data mining or machine learning on real-world data, you can use our service DataStock. DataStock has data from many industries such as hospitality and eCommerce at affordable prices and also comes with an option for you to view a sample of each dataset to decide which one would suit your purpose best.
0 Comments