Phase-1 Submission Template
Student Name: Fateh Mohammed AR
Register Number: 510623104024
Institution: C ABDUL HAKEEM COLLEGE OF
ENGINEERING & TECHNOLOGY
Department: B.E. COMPUTER SCIENCE AND ENGINEERING
Date of Submission: 26-05-2025
1.Problem Statement
In today’s digital-first world, customer support is a crucial aspect of any business.
As customer bases grow and expectations for quick and effective service
increase, traditional customer support systems are struggling to keep up. Human
agents often face limitations in terms of availability, speed, and consistency,
leading to long wait times, repetitive questions, and overall dissatisfaction for
users.
Companies are spending significant resources hiring and training support staff, yet
many queries—like password resets, account details, or delivery tracking—are
repetitive and could be easily automated. Additionally, customers expect 24/7
service, which is hard to maintain with a human-only team due to time zones,
shift scheduling, and burnout.
This project aims to address these inefficiencies by developing an intelligent
chatbot capable of providing automated customer support. Unlike traditional
chatbots that rely on rigid rule-based responses, this solution will incorporate
Natural Language Processing (NLP) and Machine Learning (ML) to
understand customer intent, deliver accurate and helpful responses, and learn
from interactions over time.
The chatbot will act as the first line of support—resolving common queries instantly
and efficiently—while escalating complex issues to human agents when needed.
This hybrid approach not only reduces the load on support teams but also
ensures customers receive timely assistance, resulting in improved user
satisfaction, cost savings, and scalability for the business.
The problem is particularly relevant today, as businesses across industries—from
ecommerce and banking to healthcare and education—are looking for smarter
ways to engage with their customers and streamline support operations. A
welldesigned intelligent chatbot has the potential to revolutionize customer
service by making it faster, smarter, and always available.
2.Objectives of the Project
• Develop an intelligent chatbot capable of understanding and responding to
customer queries accurately.
• Automate routine customer support interactions to reduce the workload on
human agents.
• Enhance customer satisfaction through faster response times and 24/7
availability.
• Analyze customer interaction data to continuously improve chatbot
performance.
• Provide a scalable solution that can be integrated with websites or support
platforms.
3.Scope of the Project
Features to be built:
• Natural language understanding to interpret user queries.
• Intent recognition and entity extraction.
• Integration with FAQs or support databases for relevant responses.
• Feedback mechanism to train the chatbot.
• Optional escalation to human support when necessary.
Limitations/Constraints:
• Limited to English language for the initial version.
• Deployment scope limited to a web-based interface (no mobile apps).
• Initial dataset based on synthetic and public FAQs due to limited access to real
support logs.
4.Data Sources
• Synthetic Data: Created based on common customer service queries.
• Public Datasets: E.g., customer support datasets from Kaggle.
• Static Dataset: Primarily used in model training and validation. • Source:
Public and self-generated datasets.
5.High-Level Methodology
Data Collection:
• Download datasets from Kaggle and generate sample chat interactions. Data
Cleaning:
• Remove duplicates, handle null values, and normalize text (lowercase, remove
punctuation, etc.).
Exploratory Data Analysis (EDA):
• Visualize most common queries, intents, and keywords.
• Analyze response patterns.
Feature Engineering:
• Text vectorization using TF-IDF or word embeddings (e.g., Word2Vec or
BERT).
• Create features such as query length, sentiment, etc.
Model Building:
• Use NLP models like RNNs, Transformer-based models (e.g., BERT).
• Rule-based intent classifiers for fallback. Model Evaluation:
• Accuracy, F1 Score for intent classification.
• User satisfaction rating simulation for response quality.
Visualization & Interpretation:
• Dashboards showing chatbot performance (e.g., accuracy over time, top
intents).
• Charts for common issues, response times, etc. Deployment:
• Web-based chatbot using Streamlit or Flask. • Option for Gradio
interface for user testing.
6.Tools and Technologies
• Programming Language: Python
• Notebook/IDE: Google Colab / Jupyter Notebook
• Libraries:
o Data Processing: pandas, numpy o Visualization: seaborn, matplotlib,
plotly o NLP: nltk, spaCy, transformers, sklearn o Modeling:
TensorFlow, PyTorch
• Optional Tools for Deployment:
o Streamlit, Flask, Gradio, FastAPI
7.Team Members and Roles
Mohammed Musaddiq. M [510623104059]-Project Lead & Problem Definition
Responsible for defining the problem statement, coordinating tasks, and ensuring the
project follows the timeline. Oversees final documentation and submission.
Mohammed Ammar Saqib [510623104055] - Data Collection & Cleaning
Gathers relevant datasets from public sources or generates synthetic data. Handles
data preprocessing (cleaning, formatting, normalization).
Ghani Adnan Faiz [510623104005] - Exploratory Data Analysis (EDA)
Analyzes data to uncover patterns and insights. Creates visualizations using
matplotlib/seaborn/plotly.
Fateh Mohammed [510623104024] - Feature Engineering & Model Building
Designs features, selects and trains models (e.g., intent classifiers, response
generators). Chooses appropriate NLP techniques.
Abraar. A [510623104003] - Model Evaluation & Interpretation
Evaluates model performance using metrics (accuracy, F1 score, etc.). Prepares
interpretation reports and validation results.
Mohammed Ibrahim – [510623104058] - Deployment & Frontend Integration
Builds and deploys the chatbot using Streamlit/Gradio/Flask. Handles web interface
design and chatbot testing.
