Customer_Complaint_Analyzer

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Customer_Complaint_Analyzer

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# Customer Complaint Analyzer – AI Agent

### IBM SkillsBuild Winter Certification Program – Applied Artificial Intelligence

---

## Project Overview

Customer Complaint Analyzer is an AI-powered system that automatically analyzes customer complaints to help organizations improve customer service efficiency and customer satisfaction.

The system uses Natural Language Processing (NLP) and Machine Learning techniques to classify complaints, detect sentiment, assign priority levels, and generate suitable response recommendations.

---

## Key Features

### Sentiment Analysis

Detects the emotional tone of customer complaints:

* Angry
* Frustrated
* Neutral
* Mild

### Automated Complaint Categorization

Classifies complaints into five business categories:

* Billing Issues
* Product Issues
* Service Issues
* Delivery Issues
* Technical Issues

### Priority Scoring

Automatically assigns urgency levels:

| Priority Level | Description                  |
| -------------- | ---------------------------- |
| High           | Immediate attention required |
| Medium         | Standard support handling    |
| Low            | Non-critical issue           |

### Resolution Recommendations

Generates personalized response suggestions and action plans based on complaint type and severity.

### Interactive Dashboard

Provides visual analytics including:

* Category distribution
* Sentiment breakdown
* Priority distribution
* Complaint trends
* Word cloud visualization

### Real-Time Analysis

Allows users to enter new complaints and receive instant analysis results.

---

## Business Value

This solution helps organizations:

* Improve customer response times
* Automatically route complaints to appropriate departments
* Prioritize critical issues efficiently
* Reduce manual processing effort
* Identify recurring customer problems
* Improve customer satisfaction through faster resolution

---

## Technology Stack

| Category             | Technologies          |
| -------------------- | --------------------- |
| Programming Language | Python                |
| NLP                  | NLTK, TextBlob, VADER |
| Machine Learning     | Scikit-learn          |
| Feature Engineering  | TF-IDF Vectorization  |
| Data Processing      | Pandas                |
| Visualization        | Matplotlib, Seaborn   |
| Word Analysis        | WordCloud             |
| User Interface       | IPyWidgets            |

---

## Machine Learning Techniques Used

### Natural Language Processing (NLP)

* Text Cleaning
* Tokenization
* Stopword Removal
* Lemmatization
* Sentiment Analysis

### Machine Learning

* Logistic Regression
* TF-IDF Feature Extraction
* Multi-Class Text Classification

### Rule-Based Intelligence

* Priority Scoring Algorithm
* Urgency Keyword Detection
* Response Template Generation

### Data Analytics

* Exploratory Data Analysis
* Statistical Summaries
* Interactive Visualizations

---

## Project Structure

```text
customer_complaint_analyzer/
│
├── customer_complaint_analyzer.ipynb
├── requirements.txt
├── README.md
└── complaint_analysis_dashboard.png
```

---

## Installation

### Option 1: Google Colab (Recommended)

1. Upload `customer_complaint_analyzer.ipynb` to Google Colab.
2. Run all notebook cells.
3. Required libraries will install automatically.
4. Use the interactive widgets to test complaints.

---

### Option 2: Local Jupyter Notebook

#### Install Dependencies

```bash
pip install -r requirements.txt
```

#### Launch Notebook

```bash
jupyter notebook customer_complaint_analyzer.ipynb
```

#### Run All Cells

Execute all notebook cells sequentially.

---

## Sample Analysis Output

### Customer Complaint

```text
I was charged twice for the same order. This is unacceptable.
```

### Analysis Results

```text
CUSTOMER COMPLAINT ANALYSIS REPORT

CATEGORY:
Billing (Confidence: 89.2%)

SENTIMENT:
Angry (Score: -0.847)

PRIORITY:
High (Score: 75/100)

RECOMMENDED ACTION:
Route to senior support immediately.

RESPONSE TIME:
2–4 Hours
```

---

## Dashboard Visualizations

The dashboard provides:

* Complaint Category Distribution
* Sentiment Distribution
* Priority Level Distribution
* Complaint Analytics
* Word Cloud Visualization

---

## Future Enhancements

* Email Support Integration
* Live Chat Integration
* CRM Platform Integration
* Multi-Language Complaint Analysis
* Historical Trend Analysis
* Predictive Analytics
* Real-Time Critical Issue Alerts
* Deep Learning-Based Classification Models

---

## About the Project

| Field          | Details                                      |
| -------------- | -------------------------------------------- |
| Project Type   | AI Agent for Business Automation             |
| Domain         | Customer Service and Customer Support        |
| Program        | IBM SkillsBuild Winter Certification Program |
| Specialization | Applied Artificial Intelligence              |
| Year           | 2025                                         |

---

## License

This project was developed for educational and learning purposes as part of the IBM SkillsBuild Applied Artificial Intelligence Certification Program.

---

## Acknowledgments

Special thanks to:

* IBM SkillsBuild
* Google Colab
* Scikit-learn Community
* NLTK Contributors
* Open-Source Python Community

for providing the tools and resources that made this project possible.

---

## Author

**Karthik**

IBM SkillsBuild Applied Artificial Intelligence Project

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