This dataset contains crime data in Chicago, including details such as the type of crime, location, arrest status, and year. It is suitable for predictive modeling, trend analysis, and visualization to understand patterns in crime occurrence.

Objective To forecast the incidence rates of tuberculosis in Shaanxi province from 2020 to 2025, in order to provide updated data for the completion of tuberculosis elimination objectives across different levels of healthcare organizations.Methods The trends in the incidence of tuberculosis in Shaanxi from 2004 to 2022 were analyzed using joinpoint regression, while a seasonal differential recursive autoregressive moving average model was used to predict actual tuberculosis incidence from 2020 to 2025.Results The incidence of tuberculosis in Shaanxi has decreased from 90.896/100,000 in 2004 to 35.364/100,000 in 2022, indicating a general downward trend with a mean annual change of -7.72% (P<0.001). During the period from 2015 to 2019, the rate of decline slowed with an annual percent change of -0.69% (P=0.814). The largest decline occurred during the period from 2020 to 2022, with an annual percent change of -13.26% (P=0.01). Through prediction, it was found that the incidence rates during the three years from 2020 to 2022 were higher than those reported, with an estimated incidence rate of 51.342/100,000 in 2022. By 2025, it is predicted that the incidence rate of tuberculosis in Shaanxi will be 48.354/100,000.Conclusion It is expected that Shaanxi will not achieve the target set by the World Health Organization by 2025, but it will meet the requirements of the "Healthy China 2030" planning document and approach the objectives of the "Shaanxi Anti-Tuberculosis Action Plan (2020-2022)".

NOTE FOR USERS: For local-level projections, such as at a township and municipal-level, please use the original “2018 Series”. This is the data CMAP recommends be used for planning, grant applications, and other official purposes. CMAP is confident in the updated regional-level population projections; however, the projections for township and municipal level populations appear less reflective of current trends in nearterm population growth. Further refinements of the local forecasts are likely needed.CONTENTS:Filename: ONTO2050OriginalForecastData2018.zipTitle: Socioeconomic Forecast Data, 2018 SeriesThis .zip file contains data associated with the original ON TO 2050 forecast, adopted in October 2018. Includes:Excel file of regional projections of population and employment to the year 2050:CMAP_RegionalReferenceForecast_2015adj.xlsx (94kb)Excel file of local (county, municipality, Chicago community area) projections of household population and employment to the year 2050: ONTO2050LAAresults20181010.xlsx (291kb)GIS shapefile of projected local area allocations to the year 2050 by Local Allocation Zone (LAZ): CMAP_ONTO2050_ForecastByLAZ_20181010.shp (19.7mb)Filename: ONTO2050OriginalForecastDocumentation2018.zipTitle: Socioeconomic Forecast Documentation, 2018 SeriesThis .zip file contains PDF documentation of the original ON TO 2050 forecast, adopted in October 2018. Includes:Louis Berger forecast technical report (2016): CMAPSocioeconomicForecastFinal-Report04Nov2016.pdf (2.3mb)Louis Berger addendum (2017): CMAPSocioeconomicForecastRevisionAddendum20Jun2017.pdf (0.6mb)ON TO 2050 Forecast appendix (2018): ONTO2050appendixSocioeconomicForecast10Oct2018.pdf (2.6mb)Filename: Socioeconomic-Forecast-Appendix-Final-October-2022.pdfTitle: Socioeconomic Forecast Appendix, 2022 SeriesDocumentation & results for the updated socioeconomic forecast accompanying the ON TO 2050 plan update, adopted October 2022. PDF, 2.7mbFilename: RegionalDemographicForecast_TechnicalReport_202210.pdfTitle: 2050 Regional Demographic Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional demographic forecast, developed in coordination with the Applied Population Lab at the University of Wisconsin, Madison. PDF, 1.7mbFilename: RegionalEmpForecast_TechnicalReport_202112.pdfTitle: 2050 Regional Employment Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional employment forecast, developed by EBP and Moody's Analytics. PDF, 0.8mbFilename: CMAPRegionalForecastONTO2050update202209.xlsxTitle: Regional Projections, 2022 SeriesProjections of population and employment to the year 2050, produced for the ON TO 2050 plan update adopted October 2022. 60kbFilename: CMAPLocalForecastONTO2050update202210.xlsxTitle: County and Municipal Projections, October 2022 (2022 Series)Projections of population and employment to the year 2050 at the county and municipal level, produced for the ON TO 2050 plan update adopted October 2022.

Data that allows researchers to reproduce the results in Designing Prediction Markets to Forecast Multi-Stage Elections: The 2022 French Presidential Election in PS: Political Science & Politics

PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

Forecast: Bananas Yield in India 2022 - 2026 Discover more data with ReportLinker!

Forecast: Cocoa, Chocolate and Sugar Confectionery Production Value in Italy 2022 - 2026 Discover more data with ReportLinker!

This dataset contains predictions of Earth orientation parameters (EOP) submitted during the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC). The 2nd EOP PCC has been carried out by Centrum Badań Kosmicznych Polskiej Akademii Nauk CBK PAN in Warsaw in cooperation with the GFZ German Research Centre for Geosciences in Potsdam (Germany) and under the auspices of the International Earth Rotation and Reference Systems Service (IERS) within the IERS Working Group on the 2nd EOP PCC. The purpose of the campaign was to re-assess the current capabilities of EOP forecasting and to find most reliable prediction approaches. The operational part of the campaign lasted between September 1, 2021 and December 28, 2022. Throughout the duration of the 2nd EOP PCC, registered campaign participants submitted forecasts for all EOP parameters, including dX, dY, dPsi, dEps (components of celestial pole offsets), polar motion, differences between universal time and coordinated universal time, and its time-derivative length-of-day change. These submissions were made to the EOP PCC Office every Wednesday before the 20:00 UTC deadline. The predictions were then evaluated once the geodetic final EOP observations from the forecasted period became available. Each participant could register more than one method, and each registered method was assigned an individual ID, which was used, e.g., for file naming. The dataset contains text files with predicted parameters as submitted by campaign participants and MATLAB file which is a database with all correct predictions from each participant loaded into a structure. Campaign overview and first results are described in the following articles: Śliwińska, J., Kur, T., Wińska, M., Nastula, J., Dobslaw, H., & Partyka, A. (2022). Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC): Overview. Artificial Satellites, 57(S1), 237–253. https://doi.org/10.2478/arsa-2022-0021 Kur, T., Dobslaw, H., Śliwińska, J., Nastula, J., & Wińska, M. (2022). Evaluation of selected short ‑ term predictions of UT1 ‑ UTC and LOD collected in the second earth orientation parameters prediction comparison campaign. Earth, Planets and Space, 74. https://doi.org/10.1186/s40623-022-01753-9

Heart Disease Prediction Dataset

This dataset contains 1,888 records merged from five publicly available heart disease datasets. It includes 14 features that are crucial for predicting heart attack and stroke risks, covering both medical and demographic factors. Below is a detailed description of each feature.

Feature Descriptions:

1. age: Age of the patient (Numeric).
2. sex: Gender of the patient. Values: 1 = male, 0 = female.
3. cp: Chest pain type. Values: 0 = Typical angina, 1 = Atypical angina, 2 = Non-anginal pain, 3 = Asymptomatic.
4. trestbps: Resting Blood Pressure (in mm Hg) (Numeric).
5. chol: Serum Cholesterol level (in mg/dl) (Numeric).
6. fbs: Fasting blood sugar > 120 mg/dl. Values: 1 = true, 0 = false.
7. restecg: Resting electrocardiographic results. Values: 0 = Normal, 1 = ST-T wave abnormality, 2 = Left ventricular hypertrophy.
8. thalach: Maximum heart rate achieved (Numeric).
9. exang: Exercise-induced angina. Values: 1 = yes, 0 = no.
10. oldpeak: ST depression induced by exercise relative to rest (Numeric).
11. slope: Slope of the peak exercise ST segment. Values: 0 = Upsloping, 1 = Flat, 2 = Downsloping.
12. ca: Number of major vessels (0-3) colored by fluoroscopy. Values: 0, 1, 2, 3.
13. thal: Thalassemia types. Values: 1 = Normal, 2 = Fixed defect, 3 = Reversible defect.
14. target: Outcome variable (heart attack risk). Values: 1 = more chance of heart attack, 0 = less chance of heart attack.

Dataset Details:

This dataset is a combination of five publicly available heart disease datasets, with a total of 1,888 records. Merging these datasets provides a more robust foundation for training machine learning models aimed at predicting heart attack risk.

Datasets Used:

1. Heart Attack Analysis & Prediction Dataset
   Number of Records: 304
   Reference: Rahman, 2021

2. Heart Disease Dataset
   Number of Records: 1,026
   Reference: Lapp, 2019

3. Heart Attack Prediction (Dataset 3)
   Number of Records: 295
   Reference: Damarla, 2020

4. Heart Attack Prediction (Dataset 4)
   Number of Records: 271
   Reference: Anand, 2018

5. Heart CSV Dataset
   Number of Records: 290
   Reference: Nandal, 2022

Description:

This dataset includes 14 features known to contribute to heart attack risk. It is ideal for training machine learning models aimed at early detection and prevention of heart disease. The records have been cleaned by removing missing data to ensure data integrity. This dataset can be applied to various machine learning algorithms, including classification models such as Decision Trees, Neural Networks, and others.

This dataset provides the historical air quality prediction coverages generated by the GRAL model in the day specified in the title, it contains one time instant for each of the following 48 hours This dataset has been created in the context of the TRAFAIR project - https://trafair.eu/

The compound annual growth rate (CAGR) of the polypropylene market volume worldwide is expected to vary by application from 2022 to 2027. The global market volume of polypropylene film and sheets is predicted to exhibit the largest CAGR, at **** percent between 2022 and 2027. This was followed by polypropylene injection molding, which is expected to have a CAGR of **** percent.

This dataset provides the historical air quality prediction coverages generated by the GRAL model in the day specified in the title, it contains one time instant for each of the following 48 hours This dataset has been created in the context of the TRAFAIR project - https://trafair.eu/

These data files have been used as input for the models described and evaluated in "Can meteorological data improve the short term prediction of individual milk yield in dairy cows?", accepted by the Journal of Dairy Science on February 23, 2023. Each file corresponds to one of the seven data subsets (for both breeds) used in the modeling part.

DOI: https://doi.org/10.3168/jds.2022-22980

The dataset contains the data used in the Genomes to Fields 2022 Maize Genotype by Environment (G x E) Prediction Competition, including phenotypic, genotypic, soil, weather, environmental covariate data, and metadata information. The Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize G x E project field trials, leveraging the datasets previously generated by this project (https://www.genomes2fields.org/resources/) and other publicly available data.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Can we predict stock market using machine learning? (WY Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Global Gene Prediction Tool Market Report 2022 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2022-2028. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

Background:Mortality following hospital discharge remains a significant threat to child health, particularly in resource-limited settings. In Uganda, the Smart Discharges risk-prediction models have demonstrated success in their ability to predict those at highest risk of death after discharge and use this to guide a risk-based approach to post-discharge care in children admitted with suspected sepsis. Respective prediction models for post-discharge mortality in ages 0-6 months and ages 6-60 months were developed in this cohort but have not yet been validated outside of Uganda. This study aimed to externally validate existing risk prediction models for pediatric post-discharge mortality within the Rwandan context. Methods: Prospective cohort of children 0d-60 mos admitted with suspected sepsis at two hospitals in Rwanda: Ruhengeri Referral Hospital in Musanze (rural) and University Hospital of Kigali in Kigali (urban) from May 2022 to February 2023. Vital status follow up was conducted at 2-, 4- and 6-months post-discharge. Five existing models from Smart Discharges Uganda were validated in this cohort: two models for children 0-6 months, and three for children 6-60 months. Models were applied to each participant in the Rwanda cohort to obtain a risk score which was then used to calculate predicted probability of post-discharge death. Model performance was evaluated by comparing to observed outcomes and to determine sensitivity, specificity, and AUROC. Threshold was set at 80% sensitivity. . Findings:In a cohort of 1218 children, 1123 children (96.7%) completed follow up. The overall rate of post-discharge mortality was 4.8% (n=58). The highest performing models had an AUROC of 0.75 (0-6 mos) and 0.74 (6-60mos), respectively. All five prediction models tested achieved an AUROC greater than 0.7 (range 0.706 - 0.738). Model degradation (determined by the percent reduction in AUC between the original model and the derived model) was relatively low, ranging from from 1.1% to 7.7%. Calibration plots showed good calibration for all models at predicted probabilities below 10%. There were too few outcomes to assess calibration among those at higher levels of predicted risk. Data Processing Methods: Ethics Declaration: Ethical approval was obtained from the University of Rwanda College of Medicine and Health Sciences (No 411/CMHS IRB/2021); University Teaching Hospital of Kigali (EC/CHUK/005/2022), University of California San Francisco (381688) and the University of British Columbia (H21-02795). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Forecast: Infant Mortality Rate in France 2022 - 2026 Discover more data with ReportLinker!

A collection of TFBS for mm9, mm10, hg19, and hg38.

This dataset contains data produced for the dissertation. "Real-time prediction of Wikipedia articles' quality". The project was conducted by student Pedro Miguel Moás (up201705208@edu.fe.up.pt) at FEUP, University of Porto, for the Master in Informatics and Computing Engineering. Our end goal is to provide Wikipedia users with a reliable and transparent tool for automatically assessing quality within Wikipedia. That way, readers will know beforehand if an article is worth reading, while editors may easily detect existing flaws in the articles they encounter. We thus propose creating an extension for the Google Chrome browser that uses machine learning to predict, in real-time, the quality of Wikipedia articles. The readme file provides the dataset structure.