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README.md

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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+Call Center Data Analysis
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+A powerful data analysis tool for call center logs, built on Hugging Face Spaces (free tier). This demo showcases after-the-fact analysis of call center data, including data cleaning, statistical visualization, and export options for downstream AI modeling in SageMaker or Azure AI. It reflects over 5 years of AI expertise, focusing on real-world challenges in junk data mitigation for enterprise CX workflows.
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+Features
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+Data Parsing and Cleaning: Processes large call center CSVs, removing nulls, duplicates, short entries, malformed queries, and invalid timestamps, ensuring data integrity.
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+Statistical Visualization: Generates plots for call duration distribution, satisfaction scores by agent, and query frequency by language using Matplotlib and Seaborn.
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+Export Options: Provides downloadable cleaned CSV for SageMaker/Azure AI modeling and a PDF report summarizing data quality and statistics.
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+Gradio-Powered Interface: A responsive, dark-themed UI for viewing raw data, cleanup stats, and visualizations, optimized for enterprise workflows.
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+Setup
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+Clone this repository to a Hugging Face Space (free tier, public visibility).
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+Upload your call_center_logs.csv to the Space.
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+Populate requirements.txt with the specified dependencies, ensuring compatibility with Python 3.9+ and CPU-only execution.
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+Deploy app.py and launch the Space with Gradio SDK.
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+Usage
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+Click the "Analyze Data" button to process the call center logs.
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+View the raw data (first 50 rows), cleanup statistics, and statistical plots.
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+Download the cleaned CSV (cleaned_call_center_logs.csv) for SageMaker/Azure AI modeling.
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+Download the PDF report (data_analysis_report.pdf) summarizing the analysis.
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+Technical Architecture
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+Core Stack:
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+Python 3.9+: Foundation for data processing and analysis.
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+Pandas: High-performance CSV parsing and data cleaning.
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+Matplotlib/Seaborn: Statistical visualization of call center metrics.
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+Gradio: Interactive UI for data analysis and export.
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+ReportLab/Pillow: PDF report generation with embedded plots.
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+Free Tier Optimization: Designed for CPU-only execution, minimizing memory footprint.
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+Extensibility: Cleaned CSV is structured for SageMaker (e.g., BERT-based intent classification) and Azure AI (e.g., custom ML models).
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+Purpose
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+This Space demonstrates proficiency in after-the-fact data analysis for call center environments, addressing junk data challenges and preparing data for AI modeling, aligning with enterprise CX needs.