The DXY exchange rate fell to 98.4660 on July 21, 2025, down 0.02% from the previous session. Over the past month, the United States Dollar has weakened 0.46%, and is down by 5.60% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on July of 2025.

USD index is expected to strengthen in the near term due to persistent safe-haven demand amid global economic uncertainties. The risk associated with this prediction is the potential for a correction if risk appetite improves or the Federal Reserve signals a dovish pivot.

The EUR/USD exchange rate rose to 1.1669 on July 21, 2025, up 0.41% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 0.79%, and is up by 7.16% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on July of 2025.

The market for predictive analytics software was valued at **** billion U.S. dollars in 2020 and is forecasted to grow to ***** billion U.S. dollars by 2028. Predictive analytics are often used to analyze consumer behavior, and manage supply chains and business operations.

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.

Dollar Index (Live)

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

Report Overview

As per the latest insights from Market.us, The Global Predictive Analytics in EdTech Market is projected to reach approximately USD 5,892 Million by 2034, rising sharply from USD 680.1 Million in 2024, at a robust compound annual growth rate (CAGR) of 24.10% during the forecast period from 2025 to 2034. This surge is being fueled by increasing demand for personalized learning, real-time academic performance tracking, and data-driven decision-making in educational institutions. The integration of AI and machine learning into education systems is accelerating adoption across both K–12 and higher education sectors.

The market for predictive analytics in EdTech is experiencing significant growth, driven by the increasing demand for data-driven decision-making in education. Several key factors are propelling the growth of predictive analytics in the EdTech sector. The increasing digitization of educational content and the proliferation of online learning platforms have resulted in vast amounts of data, which can be harnessed for predictive insights. Additionally, the shift towards personalized learning approaches necessitates tools that can adapt to individual student needs, making predictive analytics essential.

https://market.us/wp-content/uploads/2025/02/Predictive-analytics-in-EdTech-Market-size-1024x593.jpg" alt="Predictive analytics in EdTech Market size" class="wp-image-138868">

As of 2019, forecasts suggest that the predictive analytics market will reach over *********** U.S. dollars in total revenue. By 2022 the market is expected to reach nearly ** billion dollars in annual revenue as an increasingly large number of businesses make use of predictive analytics techniques for everything from fraud detection to medical diagnosis. Predictive analytics The field of predictive analytics involves the use of various statistical methods and models within businesses to make predictions about a wide range of future outcomes. Predictive analytical analysis is already one of the most widely adopted intelligent automation technologies in the world, with over ** percent of major enterprises deploying smart analytics that include predictive analytics. As business interactions around the world become increasingly digitalized, massive amounts of data are created which can be evaluated through predictive analytics tools in order to give users a better understanding of market dynamics and underlying trends. Considering this, it is no surprise that predictive models rank as the one of the top big data technology trends around the world.

Predictive Analytics Market is growing at a CAGR of 18.5% from 2023 to 2032 | Projected to reach US$ 51,443.63 Mn by 2032 | Valued at US$ 13,484.21 Mn in 2022

Tesla/Nasdaq USD Prediction with Artificial Intelligence RNN Neural Network - Emirhan BULUT

I developed Tesla/Nasdaq USD Prediction with RNN Neural Network software with Artificial Intelligence. I predicted the fall on April 8, 2022 with 97.04% accuracy in the TESLA/USD pair. '0.0325847058254806' MAE Score, '0.001749750789686177' MSE Score, 97.04% Accuracy Question software has been completed.

The TESLA/USD pair forecast for April 8, 2022 was correctly forecasted based on data from Nasdaq.

Software codes and information are shared with you as open source code free of charge on GitHub and My Personal Web Address.

Happy learning!

Emirhan BULUT

Senior Artificial Intelligence Engineer and Inventor

The coding language used:

Python 3.9.8

Libraries Used:

Tensorflow - Keras

NumPy

Matplotlib

Pandas

Scikit-learn (SKLEARN)

https://github.com/emirhanai/Tesla-Nasdaq-USD-Prediction-with-Artificial-Intelligence-RNN-Neural-Network/blob/main/Tesla-Nasdaq%20USD%20Prediction%20with%20Artificial%20Intelligence%20RNN%20Neural%20Network.png?raw=true" alt="Tesla/Nasdaq USD Prediction with Artificial Intelligence RNN Neural Network- Emirhan BULUT">

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : emirhan@isap.solutions

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

The USD/TWD exchange rate rose to 29.4640 on July 21, 2025, up 0.18% from the previous session. Over the past month, the Taiwanese Dollar has strengthened 0.54%, and is up by 10.24% over the last 12 months. Taiwanese Dollar - values, historical data, forecasts and news - updated on July of 2025.

BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data was reported at 1.100 USD/EUR in 2027. This stayed constant from the previous number of 1.100 USD/EUR for 2026. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data is updated yearly, averaging 1.100 USD/EUR from Dec 2016 (Median) to 2027, with 12 observations. The data reached an all-time high of 1.200 USD/EUR in 2022 and a record low of 1.100 USD/EUR in 2027. BMF Forecast: Foreign Exchange Rate: US Dollar: Annual Average data remains active status in CEIC and is reported by Federal Ministry of Finance. The data is categorized under Global Database’s Austria – Table AT.M007: Foreign Exchange Rate: US Dollar: Forecast.

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.

Dollars and Sense: The Correlation Between US Total Reserves and the Dollar Index

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

Title: Historical INR to USD Currency Exchange Rate Dataset (March 2020 - March 2023)

Description:

This comprehensive dataset, titled "Historical INR to USD Currency Exchange Rate Dataset," presents a meticulous collection of currency exchange rates between the Indian Rupee (INR) and the United States Dollar (USD) over a span from March 4, 2020, to March 2, 2023. With granular data points capturing each month's date, year, and INR price against the USD, this dataset offers a valuable resource for researchers, analysts, and data enthusiasts seeking to explore, analyze, and derive insights from currency market trends.

Key Features:

Temporal Coverage: The dataset spans a period of three years, enabling a thorough examination of currency exchange rate fluctuations and trends over a diverse range of economic conditions. High-Quality Data: The exchange rates are meticulously recorded, ensuring accuracy and reliability for various research and analytical applications. Month-wise Granularity: Each entry includes the month's date and year, allowing users to discern intra-month fluctuations and patterns. Analytical Flexibility: Researchers can harness the dataset to develop predictive models, backtesting strategies, conducting econometric analyses, and identifying factors that influence currency movements. Multidisciplinary Applicability: This dataset is valuable to professionals across finance, economics, data science, and other fields, serving as a foundation for a plethora of research endeavors. Potential Use Cases:

Currency Forecasting: Researchers can leverage this dataset to build and evaluate models for predicting INR to USD exchange rates, contributing to the development of more accurate forecasting methods. Economic Analysis: Analysts can examine historical exchange rate trends to understand the impact of geopolitical events, economic policies, and market dynamics on currency valuation. Investment Strategies: Traders and investors can backtest trading strategies and assess risk exposure based on historical exchange rate data. Academic Research: Economists and scholars can utilize this dataset for academic studies, contributing to the broader understanding of currency markets and their implications. By making this dataset available to the Kaggle community, we aim to foster collaborative research and knowledge sharing among data enthusiasts, empowering them to uncover new insights, develop innovative models, and make informed decisions in the realm of currency exchange rate analysis. Whether you're a seasoned data scientist or a curious learner, this dataset invites you to embark on a journey of exploration and discovery in the world of currency markets.

We encourage users to explore the dataset, engage in discussions, and contribute their findings and methodologies to advance our collective understanding of currency exchange rate dynamics. Your insights and contributions could pave the way for more accurate forecasting, better risk management, and enhanced economic decision-making.

Note: The dataset is provided in CSV format and is for research and educational purposes only. Users are encouraged to cite the dataset appropriately when using it in their work.

[Dataset Link]

Keywords: currency exchange rates, Indian Rupee, United States Dollar, historical data, financial markets, forecasting, data analysis, economic trends, Kaggle dataset.

Introduction

The Global Financial Predictive Analytics Market is poised for remarkable growth, expected to expand from USD 4.7 billion in 2024 to USD 56.9 billion by 2034, at a CAGR of 28.3%. North America led the market with a 34.1% share in 2024, generating USD 1.6 billion in revenue.

The surge in AI adoption, big data, and real-time decision-making across financial sectors is fueling the market expansion. The U.S., a major contributor with USD 1.4 billion in 2024, is projected to grow steadily. The Solutions segment dominates with 82.7% market share, while the BFSI sector remains the leading end-user, emphasizing risk forecasting and fraud detection.

https://sp-ao.shortpixel.ai/client/to_auto,q_lossy,ret_img,w_1216/https://market.us/wp-content/uploads/2025/04/Financial-Predictive-Analytics-Market-Size.png" alt="Financial Predictive Analytics Market">

Predictive Analytics Market is growing at a CAGR of 18.5% from 2023 to 2030 | Projected to reach US$ 51,443.63 Mn by 2030 | Valued at US$ 13,484.21 Mn in 2022

Explore the growth potential of Market Research Intellect's Data Science And Predictive Analytics Market Report, valued at USD 50 billion in 2024, with a forecasted market size of USD 140 billion by 2033, growing at a CAGR of 15% from 2026 to 2033.

The USD/CAD exchange rate fell to 1.3684 on July 21, 2025, down 0.22% from the previous session. Over the past month, the Canadian Dollar has strengthened 0.37%, and is up by 0.57% over the last 12 months. Canadian Dollar - values, historical data, forecasts and news - updated on July of 2025.

Global predictive analytics market was valued at USD 14.2 billion in 2024 and is projected to reach USD 46.8 billion by 2031, growing at a CAGR of 18.7% during the forecast period.

In 2023, the Predictive Maintenance Market reached a value of USD 5.93 billion, and it is projected to surge to USD 32.30 billion by 2030.

Prices for USDLKR US Dollar Sri Lankan Rupee including live quotes, historical charts and news. USDLKR US Dollar Sri Lankan Rupee was last updated by Trading Economics this July 21 of 2025.