A powerful web-based tool designed to help you easily upload, analyze, visualize, and understand your data, with a special focus on leveraging artificial intelligence to uncover insights.
This application, DataLens AI, is a powerful web-based tool designed to help you easily upload, analyze, visualize, and understand your data, with a special focus on leveraging artificial intelligence to uncover insights. Here's a breakdown of its key features: **Versatile File Upload:** - You can upload data from common file formats: - CSV (.csv) - Excel (.xls, .xlsx) - JSON (array of objects) - The app includes a user-friendly form that validates file types. **Interactive Data Table:** - Once uploaded, your data is displayed in a clear, sortable, and searchable table. - Column names and their automatically inferred data types (like number, string, date, boolean) are shown. **Comprehensive Data Summary:** - The app automatically generates a detailed statistical summary for each column in your dataset. - For numerical columns, you'll see metrics like mean, median, min, max, standard deviation, and sum. - For string columns, it shows unique value counts, most common values, and length statistics. - For boolean columns, it provides counts of true/false values. - For date columns, it shows the earliest and latest dates. - It also highlights the number of missing values for each column. - You can search and sort these column summaries for easier navigation. **Rich Data Visualization (Charts):** - Automatic Chart Generation: DataLens AI intelligently creates a variety of charts based on your data's characteristics. This can include: - Bar charts - Line charts (especially for time-series data) - Pie and Doughnut charts (for proportions) - Histograms (for distributions of numerical data) - Scatter plots (to show relationships between two numerical variables) - Stacked Bar charts - Radar charts - Heatmaps - Custom Bar Charts: You have the control to create your own bar charts by selecting which categorical column to use for the X-axis and which numerical column for the Y-axis. - Enhanced Readability: Charts are displayed one below another (single-column layout) to ensure they are large and easy to interpret. - You can search through the auto-generated charts and sort them by title or type. **AI-Powered Insights & Q&A (AI Studio):** - AI Data Summary: Get a concise, natural language summary of your entire dataset generated by AI. - AI Data Explanation: Select a specific column (and optionally a value within it) to receive an AI-generated explanation of its significance and characteristics within the broader dataset. - AI Suggested Insights: The AI analyzes your data to suggest potential trends, patterns, anomalies, and actionable insights you might not have spotted. - Interactive AI Q&A: This is a powerful feature where you can ask direct questions about your data in plain English (e.g., "What is the average profit for product X?", "Are there any correlations between column A and column B?"). The AI uses the raw data, the statistical summary, and information about the types of charts that can be generated from your data to provide comprehensive answers. **Modern User Interface:** - The app features a clean, professional, and responsive design built with Next.js, React, ShadCN UI components, and Tailwind CSS. - It uses a tabbed interface to easily switch between the Data Table, Summary, Charts, and AI Insights views. - It's styled with a dark theme for comfortable viewing. In essence, DataLens AI aims to be your go-to assistant for quickly turning raw data files into understandable visualizations and actionable, AI-driven insights.
Non-technical users, such as business analysts or marketers, often have valuable data in CSV or Excel files but lack the coding skills to analyze it effectively. They need a tool to quickly upload data, get statistical summaries, create visualizations, and ask questions in plain English without writing any code.
Solo Project. I designed and built the entire application, including the file parsing logic, the data summary generation, the dynamic chart rendering, and the "AI Studio." The most complex part was architecting the Genkit flow for the interactive Q&A feature.
Building DataLens AI taught me the importance of a well-designed data processing pipeline. A significant challenge was ensuring the AI could robustly handle various data formats and user questions. Crafting the Genkit prompts to provide accurate answers based on the dataset, its summary, and chart capabilities was a lesson in prompt engineering. It also reinforced the value of a clean, tab-based UI for managing different stages of data analysis.