EdoChiari

Automatidata-Project

This project uses taxi trip data to identify key factors that influence tipping, providing insights to help drivers maximize tips through optimized service.

Bike_sharing-Data_analysis

This project analyzes a bike-sharing dataset to uncover trends in rental counts across seasons, weather, and days. Key insights include peak rental periods and seasonal patterns, aimed at optimizing bike-sharing operations and decision-making.

Coffee_Sales-Data_analysis

This project involves creating a dynamic Coffee Sales Performance Dashboard in Excel, offering actionable insights into sales across various dimensions. Users can filter and explore data interactively, focusing on total sales, sales by country, and top customers, helping stakeholders identify trends and make informed decisions.

Credit_risk-Data_analysis

This project analyzes credit scoring to predict the likelihood of profit or loss for lenders based on applicants’ financial behaviors. Key analyses cover customer demographics, income, credit-to-debt ratios, and work experience to address business objectives in marketing, performance metrics, and bad debt management.

EdoChiari

HR-Project

Predictive modeling project to analyze employee turnover for HR insights. Includes a one-page summary, code notebook, model evaluation, visualizations, and ethical considerations.

International_debt-Data_analysis

This project focuses on analyzing a dataset that contains information about international debt. The data includes debt figures for various countries, along with indicators specifying the type of debt. The goal of this analysis is to derive insights such as total debt, country rankings based on debt, and debt distribution across indicators.

Kickstarter-Data_analysis

This study analyzes Kickstarter project data to uncover insights into crowdfunding trends and performance. Using SQL queries, it examines project outcomes, backer participation by country, success rates by category, and financial goals, providing a detailed view of key characteristics within the Kickstarter ecosystem.

Layoffs-Data_cleaning

SQL script for cleaning a dataset related to work layoffs. It removes duplicates, standardizes data, handles null values, and eliminates irrelevant columns and rows, ensuring data integrity

SP500-Data_analysis

The Stock Analysis Dashboard provides a detailed view of stock performance over time, comparing one-year industry returns and displaying annualized returns across different periods, from one month to ten years.

Customer_Clustering-Project

This project applies K-Means clustering to segment customers based on RFM metrics, helping identify key customer groups for targeted marketing and loyalty strategies.

Superstore-Data_analysis

This project analyzes a Superstore dataset to uncover business insights aimed at boosting profitability. Key analyses include trends in sales, profit, and discounts, top-performing locations, consumer preferences by product category, shipping mode efficiency, and customer segment composition.

TikTok-Project

This project builds a predictive model to help TikTok classify user-reported content claims, improving moderation efficiency by identifying and prioritizing content that may need review. Insights from this model enable TikTok to manage reports more effectively, ensuring a safer and more engaging platform.

Waze-Project

This project develops a churn prediction model to identify Waze users at high risk of stopping app usage. By analyzing user data, the model aims to reveal factors contributing to churn, helping Waze retain more users through targeted engagement strategies.