Chatbot Statistical Analysis

Overview

This project presents a comprehensive statistical analysis of chatbot interactions. Utilizing a dataset of chatbot conversations, we aim to uncover patterns, user behaviors, and insights to enhance chatbot performance and user satisfaction.

Features

• Data Cleaning and Preprocessing: Handling missing values, removing duplicates, and preparing the data for analysis.
• Descriptive Statistics: Summarizing data to understand central tendencies and dispersions.
• Visualization: Employing various plots to illustrate data distributions and relationships.
• Sentiment Analysis: Assessing user sentiments to gauge satisfaction levels.
• Keyword Extraction: Identifying common topics and intents from user inputs.

Technologies Used

• Python: Core programming language for analysis.
• Jupyter Notebook: Interactive environment for combining code execution with rich text.
• Pandas: Data manipulation and analysis.
• Matplotlib & Seaborn: Data visualization libraries.
• NLTK: Natural Language Toolkit for text processing.

Getting Started

1. Clone the Repository:

   git clone https://github.com/Jackkkerrrr/Chatbot-Statistical-Analysis.git

2. Navigate to the Directory:

   cd Chatbot-Statistical-Analysis

3. Install Dependencies: Ensure you have Python installed. Then, install the required packages:

   cd Chatbot-Statistical-Analysis

4. Open the Jupyter Notebook: Launch Jupyter Notebook and open chatbot.ipynb to explore the analysis:

   jupyter notebook chatbot.ipynb
   
   

Results

The analysis provides insights into:

• User Engagement: Metrics on user interaction frequencies and durations.
• Common Issues: Frequently occurring problems or queries.
• Sentiment Trends: Overall user satisfaction over time.
• Performance Metrics: Chatbot response times and resolution rates.

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your enhancements or bug fixes.