Lucy Brown
AI & Data Analytics Expert | Insights & Solutions
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Overview
Built an AI-powered prediction API that exposes a machine learning model through a fast, reliable REST API. The solution allows applications to send input data and receive real-time predictions in JSON format.
What I Did
Prepared and processed the dataset
Trained and evaluated a machine learning model
Built REST API endpoints with FastAPI
Added request validation using Pydantic
Implemented real-time prediction responses
Structured the API for easy integration with web and mobile applications
Technologies
Python, FastAPI, Scikit-learn, Pandas, Pydantic, REST API
Result
Created a reusable and scalable AI prediction service that connects machine learning models with real-world applications.
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Short Description
Built an AI-powered document analysis system that allows users to upload PDFs and ask natural-language questions about their content.
Description
I developed an AI-powered document analysis solution that enables users to upload PDF documents and interact with their content using natural language.
The system combines Generative AI, Large Language Models, Retrieval-Augmented Generation (RAG), and vector database technology to retrieve relevant information and generate context-aware answers.
Technologies
Python · Generative AI · LLM · RAG · OpenAI API · Vector Database
Key Deliverables
PDF document processing
Text extraction and preprocessing
Document chunking
Embedding generation
Vector database integration
Semantic search
RAG pipeline
LLM integration
Natural-language question answering
Context-aware responses
Results
The solution allows users to:
Upload business documents
Search documents using natural language
Ask questions about document contents
Retrieve relevant information quickly
Generate AI-powered answers based on document context
It can be applied to:
Business reports · Financial documents · Research papers · Technical documentation · Internal knowledge bases · Customer support documentation
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Short Description
Built a machine learning solution to identify customers at risk of leaving and provide insights that can support customer retention strategies.
Description
I developed a machine learning solution for predicting customer churn.
The project involved analyzing customer behavior, preparing and cleaning the dataset, engineering relevant features, training multiple machine learning models, and evaluating their predictive performance.
The objective was to identify customers who are more likely to leave so that businesses can take proactive retention actions.
Technologies
Python · Pandas · NumPy · Scikit-learn · Machine Learning · Matplotlib · Seaborn
Key Deliverables
Data cleaning and preprocessing
Exploratory Data Analysis
Feature engineering
Customer behavior analysis
Machine learning model development
Model evaluation
Feature importance analysis
Prediction pipeline
Business recommendations
Results
The final solution provides a practical framework for:
Identifying high-risk customers
Understanding major churn factors
Supporting targeted retention campaigns
Using predictive analytics for business decisions
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Description
I developed a data analysis solution to transform raw business data into clear and actionable insights.
The project included data cleaning, exploratory data analysis, statistical analysis, visualization, and identification of key business trends.
I used Python-based data analysis techniques to identify patterns in sales, customers, products, and business performance.
~Technologies:
Python · Pandas · NumPy · Matplotlib · Seaborn · SQL · Exploratory Data Analysis
Key Deliverables
Data cleaning and preprocessing
Missing-value and duplicate handling
Exploratory Data Analysis (EDA)
Customer and sales analysis
Trend and performance analysis
Data visualization
KPI analysis
Business recommendations
~Results:
Transformed raw datasets into structured analytical information
Identified important business trends and customer patterns
Created clear visualizations for decision-making
Delivered actionable insights based on the analyzed data
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