The "TRY - USD Exchange Rate in 2013 - 2023" dataset includes the exchange rates between the Turkish Lira (TRY) and the United States Dollar (USD) from the year 2013 to 2023. This dataset is typically utilized by researchers, economists, and data scientists who are interested in monitoring, analyzing, or predicting currency exchange rate fluctuations during a specific period.

Key components of the dataset may include:

Date: Specific dates corresponding to the exchange rate data. TRY-USD Exchange Rate: The exchange rate of Turkish Lira against the United States Dollar for each date.

Datasets like these are commonly employed for financial analysis, understanding economic trends, or predicting future currency exchange rate movements. Professionals and researchers in fields related to finance and economics often use such datasets. Effective utilization may involve visualizing the data, applying statistical analyses, and tracking changes over time.

The Daily Forex Exchange Rate Dataset provides historic and up-to-date exchange rates for over 160 currencies. This dataset is updated daily

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Interesting Task Ideas:

1. Analyze the historical exchange rates to identify trends and patterns.
2. Build a currency prediction model to forecast future exchange rates.
3. Compare exchange rates across different currencies to identify relative valuations.
4. Create visualizations to showcase the volatility of different currency pairs.
5. Calculate currency conversion rates for specific dates and currencies.
6. Explore the impact of economic events on currency exchange rates.
7. Use the dataset for financial analysis and investment strategies.

Photo by Markus Spiske on Unsplash

The EUR/USD exchange rate rose to 1.1619 on December 2, 2025, up 0.08% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 0.86%, and is up by 10.57% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on December of 2025.

View the live USD/MXN rate, historical performance, and forecasts for the Mexican Peso. Stay up to date with charts, data, and analysis from Trading Economics.

EUR/USD Historical Data (2000–2025)

This dataset provides historical exchange rate data for the EUR/USD (Euro to US Dollar) currency pair. It includes daily price movements, opening and closing prices, highs and lows, and percentage changes over time. The dataset can be useful for financial analysis, time series forecasting, and algorithmic trading.

Dataset Overview :

Time Period: Covers multiple years, up to February 2025. Frequency: Daily data. Source: Collected from historical market records. Format: CSV file with 11,284 rows and 7 columns.

Columns Description:

Date (string) – The date of the recorded exchange rate (Format: DD-MM-YYYY). Price (float64) – The closing price of the EUR/USD currency pair for that day. Open (float64) – The opening price of EUR/USD for that day. High (float64) – The highest price reached during that day. Low (float64) – The lowest price recorded during that day. Vol. (float64, but all values are NaN) – Trading volume (not available in this dataset). Change % (string) – Percentage change in price from the previous day.

Possible Uses of This Dataset

Time Series Forecasting: Train machine learning models to predict future EUR/USD exchange rates. Algorithmic Trading Strategies: Backtest trading algorithms based on historical price trends. Volatility Analysis: Identify periods of high volatility and economic impact on currency movements. Financial Research: Study how external factors affect exchange rate fluctuations.

Data Notes

The Volume (Vol.) column is empty, meaning trading volume data is unavailable. The Change % column is stored as a string with a percentage sign (e.g., "-0.22%"). This may require conversion to a numeric format for analysis.

The USD/CNY exchange rate fell to 7.0696 on December 2, 2025, down 0.05% from the previous session. Over the past month, the Chinese Yuan has strengthened 0.81%, and is up by 3.15% over the last 12 months. Chinese Yuan - values, historical data, forecasts and news - updated on December of 2025.

Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Conservative Scenario data was reported at 75.156 RUB/USD in 2036. This records an increase from the previous number of 74.739 RUB/USD for 2035. Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Conservative Scenario data is updated yearly, averaging 71.279 RUB/USD from Dec 2016 (Median) to 2036, with 21 observations. The data reached an all-time high of 75.156 RUB/USD in 2036 and a record low of 58.335 RUB/USD in 2017. Russia MED Forecast: Foreign Exchange Rate: Year Average: US Dollar: Conservative Scenario data remains active status in CEIC and is reported by Ministry of Economic Development of the Russian Federation. The data is categorized under Global Database’s Russian Federation – Table RU.ME002: Foreign Exchange Rate: Year Average: US Dollar: Forecast: Ministry of Economic Development.

Introduction: A Comprehensive Historical Tracking of PKR, INR, and USD Exchange Rates from 1947 to 2024. Delve into Pakistan and India's economic narratives in reference to the US Dollar, witnessing the fluctuations, trends, and pivotal moments that shaped their currencies over eight decades. Gain insights into geopolitical shifts, economic policies, and global events, understanding the intricate dynamics of these currencies in the ever-changing financial landscape.

Background: I'm teaching my students about the difference between the Pakistani Rupee and the Indian Rupee since 1947. We're using the US Dollar as a standard for comparison. I gathered this dataset from various internet sources.

About the data: The dataset is tabular and contains three columns: 1. Year: Ranging from 1947 to 2024. 2. INR (Rs): Indian Rupee value equivalent to 1 US Dollar. 3. PKR (Rs): Pakistani Rupee value equivalent to 1 US Dollar.

Use Cases: here are some use cases for this dataset.

1. Historical Analysis: Researchers or economists could use the dataset to analyze the historical trends and fluctuations in exchange rates between PKR, INR, and USD over the years. This analysis could provide insights into the economic performance and stability of Pakistan and India in relation to the US.

2. Educational Purposes: As you're using it for teaching, the dataset can serve as a valuable educational resource for students to understand the economic differences between Pakistan and India since their independence, using the USD as a benchmark.

3. Investment Analysis: Investors and financial analysts could use the dataset to analyze the historical performance of the currencies and make informed decisions about investments or currency trading in the Pakistan and India markets.

4. Policy Making: Government policymakers could utilize the dataset to understand the impact of various economic policies on currency exchange rates and formulate effective strategies for maintaining stability and growth.

5. Cross-Border Transactions: Businesses engaged in cross-border trade between Pakistan, India, and the US could use the dataset to forecast currency exchange rates and mitigate risks associated with fluctuations in currency values.

6. Comparative Studies: Scholars or analysts interested in comparative studies between Pakistan and India could use the dataset to explore the differences and similarities in their economic development trajectories over time.

7. Forecasting: Economists or analysts could develop models to forecast future exchange rates based on historical trends and factors affecting the economies of Pakistan, India, and the US.

The DXY exchange rate rose to 99.4202 on December 2, 2025, up 0.01% from the previous session. Over the past month, the United States Dollar has weakened 0.45%, and is down by 6.53% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on December of 2025.

Mexico BDM Forecast: Exchange Rate against US$: Average: Plus 2 Years data was reported at 20.780 MXN/USD in Mar 2019. This records a decrease from the previous number of 20.890 MXN/USD for Feb 2019. Mexico BDM Forecast: Exchange Rate against US$: Average: Plus 2 Years data is updated monthly, averaging 18.475 MXN/USD from Nov 2001 (Median) to Mar 2019, with 52 observations. The data reached an all-time high of 21.050 MXN/USD in Dec 2016 and a record low of 10.360 MXN/USD in Dec 2001. Mexico BDM Forecast: Exchange Rate against US$: Average: Plus 2 Years data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.M006: Foreign Exchange Rates: Forecast.

The USD/KRW exchange rate fell to 1,468.4800 on December 2, 2025, down 0.22% from the previous session. Over the past month, the South Korean Won has weakened 2.69%, and is down by 3.75% over the last 12 months. South Korean Won - values, historical data, forecasts and news - updated on December of 2025.

This dataset contains the predicted prices of the asset The future of money over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset consists of seven columns and 2740 rows collected from thirteen different sources for digital currencies. The dataset includes information on the opening price, closing price, highest price, lowest price, and volume, as well as the percentage change and the currencies collected in March 2024.

Here's a description of the contents based on the available columns in the data:

Last Price: The most recent recorded price of Bitcoin. Open Price: The opening price of Bitcoin at the start of the specified time period. Max: The maximum price of Bitcoin during the specified time period. Min: The minimum price of Bitcoin during the specified time period. Size: This may refer to the trading volume of Bitcoin during the specified time period, but requires further clarification to confirm its meaning. Change Persent: The percentage change in the price of Bitcoin compared to the previous time period, it seems there's a typographical error and it might mean "Change Percent". Class: The classification of the currency, in this context, all the data is classified under "Bitcoin". This data could be useful in financial market analytics, especially for those interested in cryptocurrencies and the dynamics of Bitcoin prices. It can be used to study price changes, market fluctuations, or even to develop models for predicting cryptocurrency prices.

Applications in Machine Learning and Beyond This dataset, focusing on Bitcoin prices and their fluctuations, has a wide range of applications, especially within the realm of machine learning and financial analysis:

Price Prediction: Utilizing historical data to train models that can predict future Bitcoin prices. Techniques like time series analysis, regression models, and more sophisticated neural networks (e.g., LSTM) could be applied. Volatility Modeling: Analyzing the variability in Bitcoin prices over time. Machine learning models can help understand patterns in price fluctuations, potentially leading to insights for investors about risk and volatility. Trend Analysis: Identifying long-term trends in Bitcoin's market performance. Machine learning algorithms can detect underlying patterns and trends, helping investors make informed decisions. Anomaly Detection: Spotting unusual patterns or outliers in Bitcoin prices that could indicate market manipulation, fraud, or significant market events. Machine learning models, especially unsupervised algorithms, are adept at detecting anomalies. Sentiment Analysis: By integrating this dataset with social media and news sentiment data, models can assess how public sentiment impacts Bitcoin prices. This involves natural language processing (NLP) techniques to gauge sentiment and correlate it with price movements. Portfolio Management: In the broader scope of financial management, machine learning models can use such datasets to optimize cryptocurrency portfolios, balancing risk and return based on historical performance. Risk Assessment: Analyzing the data to evaluate the financial risk associated with Bitcoin investments. Machine learning can provide probabilistic estimates of future price drops or gains, aiding in risk management strategies. Overall, the detailed data on Bitcoin's pricing and trading volume offers a rich foundation for various analytical and predictive modeling efforts in both academic research and practical financial applications. ​

Collected and Preprocessing: Wisam Abdullah , Dr. Modhar , and Dr. Ahmed Alsardly are lecturers in Tikrit University.

The Global Currency Historical Prices Dataset provides a comprehensive collection of historical data for multiple currencies from around the world. This dataset includes daily open, high, low, and closing prices for each currency. The dataset is updated regularly to include the latest available data.

The dataset includes historical data for a variety of currencies, such as the US dollar, the Euro, the Japanese yen, the British pound, the Canadian dollar, the Swiss franc, the Australian dollar, and many others. This dataset covers a broad range of currencies and includes data from various countries, making it a valuable resource for anyone interested in analyzing global currency trends and patterns.

The data is provided in a user-friendly format, making it easy to download and use. The dataset includes data on currency prices for each day, as well as additional data such as currency exchange rates and volume data. The data is presented in a CSV format, making it compatible with most data analysis and machine learning tools.

This dataset is ideal for researchers, financial analysts, traders, and anyone interested in studying the historical trends and patterns of global currency prices. It can be used for a variety of purposes, such as developing trading strategies, backtesting models, and creating machine learning models for predicting future currency prices.

Overall, the Global Currencies Historical Prices Dataset is a valuable resource for anyone looking to analyze the historical trends and patterns of global currency prices. It provides comprehensive data on multiple currencies from various countries, making it an excellent tool for financial analysis and research.

The USD/TWD exchange rate fell to 31.4070 on December 2, 2025, down 0.01% from the previous session. Over the past month, the Taiwanese Dollar has weakened 1.54%, but it's up by 3.52% over the last 12 months. Taiwanese Dollar - values, historical data, forecasts and news - updated on December of 2025.

The USD/UAH exchange rate fell to 42.4344 on December 2, 2025, down 0.04% from the previous session. Over the past month, the Ukrainian Hryvnia has weakened 0.91%, and is down by 1.64% over the last 12 months. Ukrainian Hryvnia - values, historical data, forecasts and news - updated on December of 2025.

The USD/BRL exchange rate fell to 5.3270 on December 2, 2025, down 0.55% from the previous session. Over the past month, the Brazilian Real has strengthened 0.57%, and is up by 11.87% over the last 12 months. Brazilian Real - values, historical data, forecasts and news - updated on December of 2025.

The USD/VND exchange rate rose to 26,375.0000 on December 2, 2025, up 0.02% from the previous session. Over the past month, the Vietnamese Dong has weakened 0.24%, and is down by 3.86% over the last 12 months. Vietnamese Dong - values, historical data, forecasts and news - updated on December of 2025.

Dataset Description: Predict Cryptocurrency Prices

This dataset contains various attributes that can be used to predict cryptocurrency prices. The data includes a range of features related to market and technical indicators. Each row represents a specific time period with the following columns:

• hp: Health Points at a given time.
• hpperlevel: Increase in health points per level.
• mp: Mana Points at a given time.
• mpperlevel: Increase in mana points per level.
• movespeed: Movement speed.
• armor: Armor value.
• armorperlevel: Increase in armor per level.
• spellblock: Spell blocking capability.
• spellblockperlevel: Increase in spell blocking per level.
• attackrange: Attack range.
• hpregen: Health points regeneration rate.
• hpregenperlevel: Increase in health points regeneration per level.
• mpregen: Mana points regeneration rate.
• mpregenperlevel: Increase in mana points regeneration per level.
• crit: Critical hit rate.
• critperlevel: Increase in critical hit rate per level.
• attackdamage: Attack damage value.
• attackdamageperlevel: Increase in attack damage per level.
• attackspeedperlevel: Increase in attack speed per level.
• attackspeed: Attack speed.
• name: Name of the cryptocurrency.

Potential Uses

This dataset can be used for various predictive modeling tasks, including but not limited to: - Predicting future cryptocurrency prices based on historical data. - Analyzing the impact of different attributes on price changes. - Building machine learning models to forecast market trends.

Acknowledgements

Please provide proper attribution if you use this dataset in your work or research.

This dataset is curated for those who are interested in predicting Bitcoin prices using historical data. It contains comprehensive information on Bitcoin's market behavior over time, including daily prices, trading volumes, and other relevant financial indicators. This dataset can be used to develop and test predictive models, analyze trends, and gain insights into the cryptocurrency market.

Features: Date: The date corresponding to each entry. Open: The opening price of Bitcoin for the given date. High: The highest price reached by Bitcoin on the given date. Low: The lowest price reached by Bitcoin on the given date. Close: The closing price of Bitcoin for the given date. Volume: The total volume of Bitcoin traded on the given date. Market Cap: The total market capitalization of Bitcoin on the given date. Adjusted Close: The closing price adjusted for any dividends or stock splits. Usage: This dataset can be used for various purposes, including:

Time Series Analysis: Understanding how Bitcoin prices fluctuate over time. Predictive Modeling: Building models to predict future prices based on historical data. Market Research: Analyzing trends and patterns in the cryptocurrency market.