 नूलिज मैंज्मेंट की टेकनालोगीज की हम दिसक्छन कर रहे हैं और इस में कुछे इंपोटन तूलस हैं जे नहीं हम देटा माइनिंग, नूले दिसकवरी या अनलिटिस के लिए लिए use करते हैं. तो यह देखते हैं यह कोंसे तूलस हैं तूल की एक एक अजम्पल आपके सामने है, के स्टोरीकल डेटा हमारे पास है, इस डेटा को हमने डेटा माइनिंग के सिस्टमे डाला, और डेटा माइनिंग के अंडर इस तरा की आलगरिदम दीगाई है, जो स्टाटिस्टिक्ल परनषिपल्ट से बैस करती हैं जो भग़ा लगा खे, तो क्रनीषन्स लगाखे, तो फ़िर पह ज़फ में देखते हैं, इस फ़जादवान। लिए बगास्धर को विना मगाचों थै. तो अवगासं और उफनी बगास्धर जाथ, इस तरा से देटा को दिप्रन्ट फामुला की मददद से उस में से पैट्रन्ट को अच्टट कर के, तो प्रदिक्षन का काम किया जाता है. अप प्रदिक्ष्टिप मोडल जो हम इस में यूस करते है, उनकी ताइप्स क्या है. The types of models produced range from easy to almost impossible to understand. They are very easy and very difficult to understand. Easy to understand models are decision trees for example. The decision trees we make are very simple and easy. Regression analysis are moderately easy to understand. Neural networks are black boxes. Neural networks are not easy to understand. The major drawback of the black box models is that it becomes very difficult to hypothesize about causal relationships. There are so many variables in it that the interaction between the variables is so complex that it becomes difficult to make a hypothesis for us. This is a black box model in front of us. Here you see that some variables have been given such as age, education, eye color. Apart from this, there are many other variables. When we put all these variables in a model, then the model is complex. But the benefit of this is that even with this complex technique, there is an outcome in front of us. We can predict how well will the student perform on the exam or the entrance test. How will the students perform on the exam based on all these variables. This is such a predictive model but it is a complex model. After this applications of data mining or knowledge discovery have taken place. These are the examples of market segmentation in front of us. We should do segmentation of our customers. The customer is profiling. We should do profiling on their characteristics and group them. After that there is fraud detection. How to reject fraud in banks or other institutions? After this there is retail promotion evaluation. How to promote the work of retail? Then there is credit risk analysis. These financial firms are related to them. After that there is market basket analysis. The market basket means that what things people buy or what things are being sold by the shops. So there is an analysis of that which you can decide based on which things will be needed in the shop in the next generation. And whose demand is more or less. After this there is the concept of analytics. What is analytics? It is studying past historical data to research potential trends, to analyze the effects of certain decisions or events or to evaluate the performance of a given tool or scenario. When we have a lot of data of any work, then we will analyze it in such a way that this kind of evaluation will be done in front of us. And these kind of results will come in front of us. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes. What kind of improvements do we need? What kind of changes do we need? For that data helps us. And by using analytics software, by analyzing that data, we reach the conclusion that how our work was going before, its evaluation is there and based on that we improve our business. For analytics, the software used are the tools of statistical analysis. Like S.A.S., S.A.S. and S.P.S.S. Their work is to discover patterns in numerical or quantitative data to produce descriptive information such as what is the average or mean and what is the range, means minimum and maximum values of the data. Along with that, data mining software like S.A.S., like Enterprise Miner, their work is to discover relationships and patterns in data from different sources. Data is coming from different sources. Keep it in one place to analyze it and look at patterns and trends from it. When we talk about analytics in knowledge management, in its examples, text mining can be done in such a way that we have a lot of text in front of us and by using this software, we capture that text of analytics and then we do thematic analysis. After that, web mining is there. Web mining means that we have the text on the internet or on www. We have to look at its patterns. After that, skill mining and expertise profiling we have the data of skills like in emails or other places. Out of that, we want to make our expertise location system and for that, this tool is our work. After that, email mining means that based on emails, based on the contents of email, we can frequently ask questions and make a database and it can be easily used for our users.