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essay on data mining in decision making
essay on data mining in decision making
essay on data mining in decision making
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Every day, almost every moment, we are making decisions. The decision-making process is extremely important in our life. Since as long as you made a decision, we will contribute most of our capital, time, focus and energy on the direction you selected. We believe that better decision can make life better. Different decision results come from, sometimes, different knowledge set or preferences people have. Before doing this project, we have reached a consensus that knowledge is power. And data mining can give us better knowledge to make better decision. This project study will introduce the detail of our working, including data-preprocessing, exploratory data analysis, predictive model construction, result analysis. The report is designed to have 4 sections. Section 1 will be a brief project introduction. Section 2 is about data description and data preprocessing. The data mining methodologies we employed is detailed in Section 3. Section 4 shows the results of this data mining project. Problem Description and Objective Venture capital is financial capital provided to some young, high-potential, high risk companies. The venture capital fund makes money by owning equity in the companies it invests in, which usually have a novel technology or business model in high technology industries, such as biotechnology, IT and software. In addition, venture capital is attractive for new companies with limited operating history that are too small to raise capital in the public markets and are hardly qualified to secure a bank loan. The typical venture capital investment happens when venture capitalists show strong interests on the targeted start-ups and expect high returns at the time when they exit, which is usually with the company going IPO or being acquired. The initial idea we have seems irrational at first. We supposed that we are the investment manager of a venture capital fund. We have 10 million in hand. Generally, if we do nothing with this money, our money keeps watered due to the inflation. In effect, the existence of a huge amount of historical data shows that data mining can provide a competitive advantage over human inspection of these data. Even though, economics theory named the Efficient Market Hypothesis suggests that the markets adapt so rapidly in terms of price adjustments that there is no space to gain profits in a consistent way. However, this theory does not always align with the reality in the financial market, which leaves the investors some space for speculation. The general goal of venture capital is to maintain a portfolio of equities of some early-stage, and high-potential companies.
To support their growth and offset portfolio losses by their venture capital investors, management was ready to raise additional capital through a public equity offering.
In particular, startups conform to a set of formalized, ritualistic practices in order to obtain venture capital (VC) funding during the “seed” phase. Almost paradoxically, new companies are regarded as a kernel of innovation and invention in the economy and yet they seem to emulate each others’ routines in the pursuit of early investment, decoupled from the actual products or services they plan to sell to the
Organizations and businesses are constantly vying for new and existing customers’ attention and business. In order to achieve this, marketing of products and or services are needed to advertise these to customers. One of the major ways that organizations and businesses acquire their information is through data mining. They create customer profiles from all of the information that they collect to create these profiles. These
P/F/438. Role of venture capitalists in IPO marketThe paper examines the issues of venture capital investments discussing the role of venture capitalists in affecting IPO (initial public offering) pricing, and reviewing the hypothesis on the correlation between the presence of venture capitalists in the IPO market and a reduction of information asymmetry.
Venture Capitalists generally prefer to invest in larger businesses, due to transparency of information, and constant transaction costs, regardless of the size of the firms. So, this leads to an equity gap in the...
Society is increasingly subjected to predictions on subjects as diverse as economic development, finance, fashion and even relationships. For instance, Economists forecast the gross domestic product of countries; Financial Analysts model the likely increase in earnings per share of a company based on potential sales of future products; Fashion forecasters predict how the mood of consumers determine the styles for next season’s haute couture collections; and websites encourage a person to input data about them self and an algorithm tries to predict their most suitable partner.
Value; oppose to a growth company which is investing in younger/smaller companies with a high growth trajectory.
Since both consumers and businesses advantage from the use of data mining, each party has to honour the right of the other one in order to keep an ethical function of the data mining relationship between the two of them. Long ago, data mining was only about essential and voluntary information collected from customers who were aware that their information is being gathered. Nowadays, the ethical issues raised are whether the data collected will be used against customers’ rights, and whether it will become a part that is accessible in the future by others. The strategies proposed by Payne and Trumbach, with regard to Data mining(1) and consumers’ information, propose that in the right moral structure, data mining can be ethically effective and protective to consumers’ right. Six principals are needed for a productive ethical data mining strategy: anonymity, disclosure, choice, time limits, trust and accuracy of data (Payne & Trumbach, 2009).
Capital task assets might be utilized by government, nearby government and certain not-for-profit associations when they are attempted sure sorts of capital ventures. The reserve is utilized to represent the assets utilized in capital ventures including the development of new structures, increases to structures and certain buys of gear. The capital tasks store will be utilized amid the life of a capital undertaking and will be shut toward the finish of the venture. There are standards and confinements with regards to the utilization of assets raised, contingent upon the wellspring of those assets. According to Weikart, Chen, and Sermier (2013) Funding can come from conventional taxable borrowing through a bank or from the capital markets, or
The case study is about an interview, conducted to four venture capitalists from four of the most prominent VC Silicon Valley firms, Kleiner Perkins Caufield & Byers (KPCB), Menlo Ventures, Trinity Ventures and Alta Partners. These firms invest both in seed as well as in later-stage companies, which operate mostly in the information technology sector. However, each VC has developed different sector portfolio depending on the expertise of the venture capitalists, the partner network and other factors. Professor Mike Roberts and Lauren Barley a senior research associate, both from Harvard Business School, have made a series of seven questions to their interviewees to understand how they evaluate potential venture opportunities and what they look at in order to decide if they will fund them and in which way. The questions were dealing with how VC’s evaluate potential venture opportunities, how they conduct due diligence, what process id followed for the decision making, what financial analyses is performed, the role of risk in the evaluation and how they think of potential exit routes. These questions were asked individually and revealed several similarities as well as differences in the strategy and the criteria that are used for the evaluation.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
2. In contrast, it promotes risk sharing between provider of capital (investor) and the user of funds (entrepreneur).
The size of database has increased rapidly in recent years This has led to a growing interest in the development of tools capable in the automatic extraction of knowledge from large collection of data. Data mining or knowledge discovery in database has been adopted for a area of research .It dealing with the automatic discovery of implicit information or knowledge within the databases. The implicit information within databases, mainly the interesting association relationships among sets of objects that lead to association rules may disclose useful patterns for marketing policies, decision support, financial forecast, even medical diagnosis and many other applications. In this paper, study includes depth analysis of algorithms and discusses some problems of generating frequent itemsets from the algorithm.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.