Project Requirement (Step-by-Step Implementation) DATASET - https://business.yelp.com/data/resources/open-dataset/ Requirements - (Fully clear implementation with explanation and workings) Step 1 — Business Question & Topic Framing The notebook should clearly define the business problem: Question: “In a given city, which business categories look most promising to start a new business, and what factors correlate with high customer ratings?” Expected outputs: Top 10 “high-opportunity” business categories in the selected city A short checklist of key success drivers derived from data A simple predictive model to classify businesses as High Rated vs Not Step 2 — Input: Import Data from Web Dataset Files Use the public Yelp Open Dataset Programmatically read: business.json optionally a sampled review.json Parse JSON line-by-line (streaming approach) to handle large files efficiently Computing concepts demonstrated: File I/O JSON parsing Functions and modular code Step 3 — Processing (Part A): Database Storage Automatically create an SQLite database Define database schema for business (and review if used) Create indexes for efficient querying Insert parsed data into tables programmatically Concepts demonstrated: Database creation Schema design Indexing Step 4 — Processing (Part B): Data Warehouse SQL Queries Run analytical SQL queries such as: Average star rating by category within a city Business count per category (competition level) Categories with high ratings but low competition Top benchmark businesses within promising categories Concepts demonstrated: GROUP BY HAVING ORDER BY Aggregations Analytics-style SQL queries Step 5 — Data Structures & Algorithms (Core Logic) Implement a category opportunity scoring algorithm: Use dictionaries / Counters to aggregate category metrics Use heapq (priority queue) to efficiently extract top-K categories Example logic: Demand proxy: average stars + average review count Competition proxy: number of businesses Combine these into a custom “Opportunity Score” Concepts demonstrated: dict, set, list heap / priority queue sorting custom algorithm design Step 6 — Output: Analytics Model & Visualization Implement a simple analytics model (low complexity): Logistic Regression to predict whether stars >= 4 Use features such as: review_count number of categories is_open status Outputs to display: Model accuracy and confusion matrix Feature importance (model coefficients) 2–3 clear plots showing opportunity ranking and insights Concepts demonstrated: Analytics model application Data visualization Step 7 — Final Deliverables for Class Jupyter Notebook that runs end-to-end using Run All No manual inputs, no API keys, no web scraping A short slide deck (5–7 slides) summarizing: Project topic and architecture OOP class design Database schema and SQL queries Data structures and algorithms used Final results and insights Technical Constraints Keep implementation simple and robust Execution time under 1 minute Focus on clarity and explainability over complexity Deliverables One well-commented Jupyter Notebook (.ipynb) SQLite database created programmatically Clean visual outputs and final summary section