Dr Sachin Solanki1, Shivkumar Pawar2, Aditya Kanojiya3, Shaily Chedge4
Department of Information Technology,
1,2,3,4Karmaveer Dadasaheb Kannamwar Engineering College, Nagpur
This paper relies on the knowledge and insights from structured and general data of the university, colleges, school to search out the Happiness Index with the assistance of the information Science. This research is essentially defining analysis of the information and defines the happiness index of the scholars using the constraint provided by them. Happiness index is prediction of the entire happiness of the group community, city or the realm within which that survey was been accomplished Happiness. To search out the rationale, on which constraint the happiness within the students and other people is lagging is that the main thing. So, by studying, analysing the information using the tools of the information science Happiness Index are going to be calculated.
Keywords – Happiness Index, Data Science, Data Analysis
Happiness Index is development philosophy as well as an index which is used to measure the collective happiness in a nation. The large data and survey report are needed to accomplish this process after that all the data needed to be process and by Big data analytic method the mood and happiness can be determined. It is the subject of debate on usage and meaning, and on possible differences in understanding by culture. This research is basically defining analysis of the data and defines the happiness index of the students of different colleges and the using the constraint provided by them. Happiness index is prediction of the total happiness of the group community, city or the area in which that survey was been accomplished
II. LITERATURE REVIEW AND HYPOTHESIS
The practice of establishing happiness index relies on all kinds of measuring tools and data required. Objective indexes such as education distribution and wage level should be considered as whether associate units could help obtaining comprehensive data.. The algorithms built to calibrate a financial portfolio to the goals and risk tolerance of the user. Users enter their goals (for example, retiring at age 65 with Machine learning algorithms can be trained on millions of examples of consumer data (age, job, marital status, etc…) and financial lending or insurance results (did this person default, pay back the loan on time, get in a car accident, etc…?). There are various constraints required to find happiness Index like whether they are happy by going college or schools. Whether they really like the infrastructure of college or school, they like to go school or not? [Jun Feng LIU] in paper says that the how happiness Index in the china farmers vary according to the indicators given such as Economic condition sub-system, Health status sub-system, family status sub-system, Employment status sub-system, Social status sub-system. This results in inconsistent estimates as historical data is not always an accurate standard to predict future behaviour. Machine learning allows analysis of real-time data of recent transactions, market conditions and even latest news to identify potential risks in offering credit . The World Happiness Index Report is a landmark survey of the state of global happiness that ranks 156 countries by how happy citizens perceive themselves to be. This years World happiness Report focuses on happiness Index and the community how happiness has evolved over the past dozen years, with a focus on the technologies, social norms, conflicts and government policies that have driven those chances. The robo-advisor then spreads investments across asset classes and financial instruments in order to reach the user’s goals. The system then calibrates to changes in the user’s goals and to real-time changes in the market, aiming always to find the best fit for the user’s original goals. The machine learning used in the Robo-advisor is very effective. Robo-advisors have gained significant traction with millennial consumers who don’t need a physical advisor to feel comfortable investing Similarly, AI-enabled personal finance intelligence applications are helping consumers manage their finances, analyse spending, automate tax form filing, and make financial recommendations with a business model not predicated to generating fees from investments. This research is basically defining analysis of the data and defines the happiness index of the students of different colleges and the using the constraint provided by them. Happiness index is prediction of the total happiness of the group community, city or the area in which that survey was been accomplished
III. METHODOLOGY USED
The technology used in this paper is related with the data science. As we know that Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.
Predictive causal analytics –If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
Prescriptive analytics:If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
Machine learning for making predictions:If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
Machine learning for pattern discovery: If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
There is some part of machine learning used in the data science such as some types of analysis. Machine learning (ML) is scientific study of algorithms and statistical model that computer system use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of Artificial Intelligence. Descriptive analytics is a description of the past is useful gain insights. The second is the Diagnostic Analytics can be said as once you know what happened you are usually interested on knowing why it happened. Machine learning is usually associated with predictive analytics. The last one is the Prescriptive Analytics. This is in practice the rarest, but also the most valuable, form of analytics, where actions are either suggested by evidence or initiated directly. Python can be the best option for the data science because it is easy to learn and the libraries present in the python for the data science are very useful to build something new. .Anyone want to build project that should have the strong knowledge of analytics, programming, and domain knowledge. Following are some of the Python libraries that are used in this paper. The common libraries that are used in the data science are NumPy, matplotlib, PyTorch, TensorFlow, SciPy, PANDAS, Plotly and etc.
Trends can be continuously analyzed to detect trends that might influence lending and insuring into the future (are more and more young people in a certain state getting in car accidents? Are there increasing rates of default among a specific demographic population over the last 15 years? Traditional systems relied on historical data like transaction history, credit history and income growth over years to understand the risk associated with every loan extended. This results in inconsistent estimates as historical data is not always an accurate standard to predict future behaviour. Machine learning allows analysis of real-time data of recent transactions, market conditions and even latest news to identify potential risks in offering credit. With the help of predictive analytics, an ML algorithm can analyse petabytes of data to understand micro activities and assess the behaviour of parties to identify a possible fraud. This is something impossible for human investors to perform manually.
IV. DESIGN AND IMPLEMENTATION;
The first step is to collect data from the colleges and the universities in the form of the raw data which having the proper constraints. The data should be analysed first and then the different operations should be performed using the different libraries. And the last step is that the happiness index is found in the scale of 1 to 10.
Python is a multi-paradigm programming language: a sort of Swiss Army knife for the coding world. It supports object-oriented programming, structured programming, and functional programming patterns, among others. Anaconda for data science is the basic IDE used to build any kind of data science project. Anaconda provides the tools needed to easily collect data from files, databases, and data lakes also manage environments with Conda, Share, collaborateon and reproduce projects.
The conclusion of a project needs to summarize the content and purpose of the project without seeming too wooden or dry. In future , the project can be extended by adding the online attendance system which will be helping the students to check their attendance ratio . This project can be extended from the college to the University level.
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