Dat a Mining Strategies and Methods to Develop Microfinance Market - Use Case Currency Exchange
Bibliographic record
Abstract
The intrinsic characteristics of data mining are being inculcated in the market of microfinance. The use case that is brought under our consideration is of Currency Exchange. The idea was conceived and perceived by the current financial crises in the world market in the year 2008-09.The financial recession in world wide compelled individuals to think and start micro businesses rather than macro businesses. In our paper we conceived and designed some algorithms by using data mining techniques to have general micro currency exchange businesses for a developing country. Our algorithm processes two years historical data of currency rates and applies data mining strategies. The Median Method and Rise & Fall Method with probabilistic approach are being presented. It can be applied to N year’s data with unless desired results are achieved. This is to give best choice to micro currency business men to take decision either to buy or to sell currency. Some previous currency rates (i.e. Ups & Down) are also recorded from a popular bank of Canada & currency open markets as a proof of concept using our algorithm. The statistical and graphical analysis are being made on the data .Our algorithm can be efficiently used by all those who wish to initialize a small business (Cottage Industries) with a profitable income with less investment. Our research will lead to a new dimension in the fields of Micro finance and Data mining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".