This project is not just an Excel utility; it is a full-fledged "Analyst in a Box" powered by a Generative AI (DeepSeek LLM). It is engineered to transform raw spreadsheet data into comprehensive analytical reports and actionable insights. The platform empowers non-technical business users to perform deep qualitative and quantitative analysis that previously required hours of manual work by dedicated analysts.
- Instant Insight Generation: Go from raw data in Excel to a full analytical report in minutes, not days.
- Unstructured Data Analysis: Unlock the value of thousands of text-based cells (customer reviews, technical logs, comments) by automatically classifying them and summarizing key themes.
- Routine Reporting Automation: Use savable profiles and a library of business-centric prompts to completely automate weekly or monthly reporting tasks.
- Deep Contextual Analysis: The ability to upload supplementary files (instructions, past reports, company policies) allows the model to analyze data within a unique business context, significantly improving relevance and accuracy.
This platform was deployed in a leading hospitality group to process over 50,000 customer reviews from various sources.
Implementation Results:
- Reduced manual analysis time by 95%: A process that previously took a team of analysts a full week is now completed automatically in a few hours.
- Identified key drivers of dissatisfaction: The AI accurately identified the top 3 reasons for low ratings, allowing management to focus their efforts, which resulted in a 0.4-point increase in average satisfaction scores within one quarter.
This platform is a strategic asset for an airline and a direct demonstration of the Strong understanding of Large Language Models (LLMs) required by the job description. It is designed to solve critical tasks such as:
- Passenger Feedback Analysis: Automatically process thousands of comments from post-flight surveys, social media, and review sites. The AI can classify them by topic (in-flight service, boarding process, cabin condition), determine sentiment, and generate executive summaries.
- Flight Safety Report Analysis: Analyze technical logs and pilot reports to identify recurring anomalies, hidden risks, and trends that might be missed by manual review, enhancing proactive safety measures.
- Competitive Intelligence: Ingest spreadsheets of competitor pricing, routes, and promotions to automatically generate reports on the market landscape and identify strategic opportunities.
- HR Analytics: Analyze internal employee survey results to gauge engagement levels, identify areas of concern, and develop targeted initiatives to improve corporate culture.
This section provides all the necessary information to install and run the application locally.
- Python 3.10 or higher
- A valid DeepSeek API key
# Clone the repository
# git clone <repository_url>
# cd deepseek_excel
# Create and activate a virtual environment
python -m venv venv
# On Windows:
# venv\Scripts\activate
# On macOS/Linux:
# source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
3. Running the Application
Bash
streamlit run app.py
The application will be available at http://localhost:8501.
📖 User Guide
Step 1: API and Model Configuration
On the sidebar, enter your DeepSeek API Key and select the desired Analysis Mode. You can also configure LLM parameters like Temperature if needed.
Step 2: Data Loading
On the "Data Loading" tab:
Click to upload your .xlsx or .xls file. A preview and statistics will be displayed.
Optionally, upload supplementary text files (.txt, .csv, .md) to provide extra context for the analysis.
Step 3: Analysis Configuration
On the "Analysis Configuration" tab:
Choose a prompt from the built-in library or write your own.
Configure the analysis mode:
Row-by-Row Analysis: Select a target column and additional context columns.
Full Table Analysis: Optionally specify key columns to focus on.
Combined Analysis: Configure both modes and their execution order.
Step 4: Execution
Click the "Start Data Processing" button to begin the analysis.
Step 5: Viewing Results
The "Results" tab will open automatically upon completion, showing the generated analysis. You can download the results and view processing logs.
✨ Key Features & Capabilities
Three Analysis Modes: Choose between row-by-row, full-table, or a combined approach.
Business Prompt Library: Use pre-built templates for common business tasks (e.g., customer feedback analysis, financial reporting).
Context Enhancement: Upload additional files to provide richer context to the LLM.
Savable Profiles: Configure and save settings (API key, model parameters, prompts) for quick reuse.
Robust Retry System: A built-in mechanism to recover from intermittent API failures.
Detailed Logging: A complete history of API interactions for debugging and transparency.
Advanced Features
Profile Management: Save, load, and manage different analysis configurations via the sidebar.
Automatic Pre-processing: The app automatically handles data cleaning, normalization, and type detection.
LLM Parameter Tuning: Advanced users can adjust Temperature, Max Tokens, and Top P for fine-grained control over the AI's output.
🔧 Troubleshooting
API Errors: Check if your API key is correct and your internet connection is active. Try reducing Max Tokens.
Incomplete Results: Increase Max Tokens. Provide more context through additional columns or context files.
Slow Performance: Reduce the size of the input file or the number of rows being analyzed.
Roadmap
Q1 2025: Support for more file formats (PDF, DOCX); integration with other LLMs (OpenAI, Anthropic).
Q2-Q3 2025: Interactive data visualizations; template system for recurring tasks.
2025+: Collaborative team mode; advanced predictive AI features.