Streamlit Forex App: Real-Time Dashboards for Financial Insights

Streamlit Forex App: Real-Time Dashboards for Financial InsightsStreamlit Forex App: Real-Time Dashboards for Financial Insights

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Aarsh Vyas

pro

• Apr 4

Problem: Olsen Financial Technologies manages vast volumes of high-frequency forex data, but clients lacked an intuitive platform to quickly interpret real-time and historical trends, making analysis time-consuming and limiting actionable insights. Solution: Developed a Streamlit-based Forex Currency App that transforms complex datasets into interactive dashboards with real-time updates, dynamic charts, and customizable time filters, enabling faster, data-driven decision-making and improved market trend visibility.

Data Analysis Data Engineering Desktop Apps Development Python Streamlit

Gabriely Lopes

• Apr 5

I conducted web research to collect publicly available company information and organized it into a structured spreadsheet.

The data includes company names, websites, email contacts, and locations.

This project demonstrates my ability to gather accurate information, verify sources, and present data in a clean and organized format.

Data Analysis Lead Generation Google Sheets Microsoft Excel Research

Geethasree Naguboina

pro

• Apr 8

Structuring web research into a clean spreadsheet with verified sources is more work than it looks — the accuracy check step is what separates useful data from noisy data. Company name, website, email and location is a solid base structure too. Do you usually work with a specific industry or take on any niche?

Ankita Banubakode

• Apr 5

Superstore Sales Dashboard & Forecasting🙌 • Developed interactive dashboard analyzing 10,000+ transactions to track revenue and profit metrics. • Performed trend analysis and 15-day forecasting, reducing manual reporting effort by 40%. • Provided insights to improve inventory planning and sales monitoring.

Data Analysis Microsoft Excel Microsoft Power BI

Geethasree Naguboina

pro

• Apr 8

Adding a 15-day forecast alongside the sales dashboard is a smart move — most dashboards only show what happened, not what's coming. The YoY monthly comparison view gives really clear context too. Did you build the forecasting model using Power BI's built-in analytics or a custom formula approach?

Ankita Banubakode

• Apr 5

🛍️ Mall Customer Segmentation Project

Built an end-to-end machine learning solution to segment customers using K-Means clustering based on demographics and spending behaviour.

🔍 Performed data cleaning, pre-processing, and exploratory data analysis (EDA) 📊 Applied K-Means clustering with the Elbow Method to identify optimal customer segments 🧩 Engineered features to improve clustering performance 📈 Developed an interactive dashboard using Plotly Dash for real-time segment exploration

💡 Identified high-value and low-value customer groups, enabling targeted marketing and improved customer engagement strategies

🛠️ Tools Used: Python, Pandas, Scikit-learn, Plotly Dash

🚀 Delivered a business-focused solution for customer segmentation and data-driven decision-making