Ai-driven embeddings for enhanced retail customer insights

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Reference no: EM133976573

Artificial Intelligence Programming in Business Analytics

Assessment - AI-Driven Embeddings for Enhanced Retail Customer Insights

Tasks

Implement document storage and retrieval using ChromaDB.
Perform cosine similarity analysis to compare customer reviews and product descriptions.
Utilise an LLM (like LLAMA or GPT-Neo) to enhance search results using RAG (Retrieval- Augmented Generation)

Learning outcome 1: Develop exploratory software to ethically source, store, prepare, and analyse datafor AI applications.

Learning outcome 2: Create an AI application within a business context by applying fundamental software programming principles.

Learning outcome 3: Utilise AI to analyse and evaluate business decisions and processes. Enjoy trusted, budget-friendly assignment help from today onward!

Learning outcome 4: Create advanced business insights through the ethical utilisation of an AIapplication.

In this assignment, you will utilise AI-driven techniques for document retrieval and similarity analysis to derive business insights. You will work with vector databases, LLMs (Large Language Models), and cosine similarity to analyse and recommend improvements for an industry scenario.
You will:
Complete the provided Python code (fill in missing parts).
Answer a multiple-choice questionnaire.
Analyse the results and provide a business recommendation.

Industry Scenario: AI-Powered Market Intelligence for E-commerce
You are working for a data-driven e-commerce company that sells consumer electronics. The company wants to improve customer experience and product recommendations using AI-driven search and analytics.

The company has:
Product descriptions, customer reviews, and support documents in multiple formats (PDF, DOCX, and XLSX).
A search system where users want contextually relevant product recommendations.
A similarity system that helps analyse customer reviews and product descriptions to improve product categorisation.

Part 1: Complete the Python Code
Complete the Python notebook by filling in the gap.
Create a markdown, write down your name and student number

Part 2: Business Analytics Questionnaire
Based on the results of your AI-powered document retrieval, cosine similarity analysis, and AI- generated recommendations, answer 10 multiple-choice questions.

These questions are designed to test your business analytics thinking and ability to interpret data- driven insights.

The questionnaire can only be attempted once.

Backtracking of questions is not allowed. You must complete the question before moving on to the next one. You will not be able to go back to the previous question.

Part 3: Business Analytics Report Task - 1200 words

Your report should be structured as a professional business analytics document intended for senior management and key stakeholders. Ensure a data-driven approach, use tables where necessary, and include actionable insights.

Executive Summary (on a single page) - 100 words

Business Context and Problem Statement - 100 words

Data Analytics Process - 300 words (divided into 3.1 & 3.2)

Document Retrieval and Vector Search Analysis

Cosine Similarity Analysis for Customer Sentiment

AI-Generated Business Insights (RAG) - 150 words

Business Recommendations for Stakeholders - 350 words, 8 marks Use diagrams/visual charts to explain your recommendations
Using your findings, propose three to five actionable recommendations for different business functions:

Product Development Team
Marketing Strategy
Customer Experience & Support

Reference no: EM133976573

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