GDP PREDICTION AND ANALYSIS USING DATA MINING TECHNIQUESBharat Nagalia
The document discusses predicting future GDP values for countries using a neural network algorithm. It defines GDP and explains that traditional econometric models cannot accurately predict nonlinear characteristics of GDP. The authors collected data on import, export, population growth, GNI, labor, and agriculture from the World Bank to train and test a neural network model. They implemented k-means clustering to categorize countries as developing, developed, or underdeveloped. The neural network was used to forecast 2012 GDP for countries and calculate error rates compared to actual values. The document concludes that nonlinear models like neural networks provide more precise predictions than linear models.
This document summarizes a project that uses machine learning to predict stock market performance based on news headlines. It discusses using natural language processing and logistic regression to analyze headlines and learn the impact of news on stock values. The methodology involves collecting training and test data, preprocessing the text, applying machine learning algorithms, and evaluating predictions against actual values. Advanced modeling with n-gram analysis improved the accuracy of predictions from 42% to a higher percentage.
This document discusses the use of Kirlian photography, also known as gas discharge visualization (GDV), to diagnose diseases by capturing images of the human aura or bioenergy field. It provides background on the development of Kirlian cameras and the GDV Bio-Well camera, which can take aura photographs digitally in daylight conditions. The document presents examples of healthy versus broken aura photographs and describes how breaks or disturbances in the aura can indicate future health issues based on their location. It proposes using Kirlian photography every six months to predict and prevent major diseases, especially for those under high stress. The goal is to integrate these alternative diagnostic methods with advances from the Human Genome Project.
The document discusses using machine learning algorithms and text mining of financial news headlines to predict stock market changes. It tests various algorithms, including Bayesian classifiers and support vector machines, on headline data from seven companies. The Bayesian classifier achieved the best results but prediction accuracy remained below 50%. While sophisticated models may eventually outperform by better understanding language, current methods cannot easily match human-level analysis of headlines. With continued improvement in algorithms and data mining, prediction accuracy may increase in the future.
This document presents a proposed methodology for earthquake detection using analysis of seismic signals. It begins with background on earthquakes and existing detection methods. The proposed method involves analyzing seismic signals using wavelet transforms to extract energy and frequency parameters. These parameters are then used to calculate magnitude, wavelength, and rupture area. The methodology is tested on real seismic datasets containing over 10,000 signals. Results show the method can accurately detect earthquakes with magnitudes above 4.0 based on analyzing the extracted energy and frequency characteristics.
Ridge and random forest regression techniques were used to develop a mathematical model to calculate the cross-validation score and predict stock price volatility of companies. The model aims to determine if a firm's stock prices remain fluctuating or stable and identify trends in real-time price changes over time. Researchers found directional stock price movements were over 90% predictable given past opening and closing prices, though the magnitude of price changes could not be determined with the same certainty.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
This document summarizes a seminar presentation on using data mining techniques for telecommunications. It discusses three main types of telecom data: call summary data, network data, and customer data. It then describes using a genetic algorithm approach to mine sequential patterns from telecom databases. The genetic algorithm uses country codes to represent chromosomes and applies genetic operators and fitness functions to iteratively find sequential patterns in the telecom data. The approach provides non-optimal solutions faster than traditional algorithms.
This document summarizes various techniques that have been used to predict stock market performance, including data mining, artificial neural networks, hidden Markov models, neuro-fuzzy systems, and rough set data modeling. It reviews several studies that have applied these techniques to predict movements in stock market indices. Specifically, it discusses research that used support vector machines and neural networks to predict changes in the Hang Seng Index, and that proposed a hybrid decision tree and neuro-fuzzy system to predict trends in four major international stock markets. The document concludes that while various techniques have been implemented, fusion models combining hidden Markov models, neural networks, and genetic algorithms may help control and monitor stock price behavior and fluctuations.
This document outlines an approach to using machine learning algorithms like hidden Markov models to predict stock prices. It discusses how technical analysis and increasing computational power allows algorithms to analyze large datasets. Human analysis is still important for interpretation. The document then provides an overview of using a hidden Markov model to account for different strategies in the stock market and modeling price data more accurately over time.
Stock Market Prediction using Hidden Markov Models and Investor sentimentPatrick Nicolas
This presentation describes hidden Markov Models to predict financial markets indices using the weekly sentiment survey from the American Association of Individual Investors.
The first section describes the hidden Markov model (HMM), followed by selection of features (investors' sentiment) and labeled data (S&P 500 index).
The second section dives into HMMs for continuous observations and detection of regime shifts/structural breaks using an auto-regressive Markov chain
The last section is devoted to alternative models to HMM.
This document discusses using hidden Markov models (HMM) for stock price prediction. HMMs can model time series data as a probabilistic finite state machine. The document explains that HMMs can handle new stock market data robustly and efficiently predict similar price patterns to past data. It provides an overview of HMM components like states, transition probabilities, and emission probabilities. The document also demonstrates building an HMM model on stock data using the RHMM package in R, including training the model with Baum-Welch and predicting state sequences with Viterbi.
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
