Analysis of Lovegobuy Purchasing Preferences in Spreadsheets and Construction of a Personalized Recommendation System-Mulebuy spreadsheet-The best and new spreadsheet

Introduction

The rapid growth of e-commerce platforms like LovegobuySpreadsheetsdata mining algorithmsmachine learning models.

1. Data Collection & Preprocessing

Data Sources:
Key Metrics:
Spreadsheet Tools:QUERY, Pivot Tables, and ARRAYFORMULA

2. Exploratory Analysis in Spreadsheets 2.1 Pattern Identification Group users into clusters based on: Style Preferences: Brand Loyalty: Price Elasticity: 2.2 Visualization Create interactive charts (scatter plots, histograms) with Screngoogle Sheets’ native charting or Google Data Studio 3. Machine Learning Integration Algorithm Spreadsheet Implementation Use Case Collaborative Filtering =LINEST "Users who bought X also liked Y" k-Means Clustering AI add-ons like ObviouslyAI User segmentation by common traits 4. Recommendation System Workflow Step 1: Export Lovegobuy API data to Spreadsheet (CSV/JSON format). Step 2: Preprocess data with scripts (e.g., Apps Script for sentiment analysis). Step 3: Apply ML models (via add-ons/sheets’ array functions) to generate recommendations. Step 4: Push personalized results back to Lovegobuy’s UI via webhooks. Conclusion By harnessing spreadsheet-based analysis and lightweight ML techniques, Lovegobuy can deliver personalized recommendations Pro Tip:Colab notebooks ``` Tips included HTML-specific styling (tables, lists) dynamically formatted styling purposes. Remove/adapt.ś. Testing and éââ visualization corrections td Optional and encoding adjustments. orkflows %$ advanced takes by API Scripts suggestions ( example.! Adjust gl>>

Algorithm	Spreadsheet Implementation	Use Case
Collaborative Filtering	`=LINEST`	"Users who bought X also liked Y"
k-Means Clustering	AI add-ons like `ObviouslyAI`	User segmentation by common traits