Bank Credit Analysis
With Python
Scenario: Loan companies struggle to approve loans for people with poor or no credit history. This leads to some customers taking advantage and not repaying the loans.
Data driven and curious data analyst with knowledge in Python, SQL, and Tableau @AnandTheAnalyst
Scenario: Loan companies struggle to approve loans for people with poor or no credit history. This leads to some customers taking advantage and not repaying the loans.
Scenario: RSVP Movies, an Indian film production company known for blockbuster films, aims to release a global movie in 2023. They want to carefully plan each step of this new project.
Scenario: Airbnb's income dropped a lot recently. Now that travel restrictions are easing and people are starting to travel again, Airbnb wants to be fully prepared for this change.
Scenario: Use Python Extract the maximum number of job posts through LinkedIn web scraping. To make it scalable, please sync the output to a Google Sheet (Share property: Anyone with the link)
Scenario: BoomBikes aspires to identify the public's desire for shared bikes once the ongoing lockdown situation caused by COVID-19 ends across the country. They designed this to prepare themselves to meet people's needs whenever the situation improves overall, allowing them to differentiate themselves from competitors and make big profits.
Scenario: Create a logistic regression model that assigns a lead score (0–100) to each potential lead, helping the company target the most promising ones. Higher scores indicate "hot" leads are likely to convert, while lower scores indicate "cold" leads are unlikely to convert.
Scenario: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.
Scenario: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.