Predictive Analysis

Credit analysis significantly benefits from advanced predictive analytics. Logistic regression, a linear classification algorithm, is commonly used for its efficiency and strong performance in evaluating the likelihood of default based on borrower features like credit scores and income. This method is valued for its fast computation and clarity in linear relationships.

Decision Trees (DT) offer an easily interpretable analysis, capturing nonlinear relationships between borrower characteristics and credit risk. They provide clear insights, making it easy to base predictions on key influencing factors and handling both categorical and continuous features effectively.

Random Forests enhance these predictions by combining multiple decision trees to improve accuracy and reduce overfitting. This ensemble method ensures robustness in predictions while maintaining computational efficiency, making it ideal for comprehensive credit risk evaluation.

Sentiment analysis holds a pivotal role in the hospitality industry, where customer satisfaction is the cornerstone of success. It provides deep insights into guest experiences, transcending numerical ratings to reveal the nuances of what customers value or criticize. Utilizing a dataset from Booking.com, this analysis leverages attributes like 'Reviewer_Nationality', 'Negative_Review',and 'Positive_Review' to capture sentiments from a global audience and the impact of word usage on guest perceptions.
‍
Ultimately,this sentiment analysis transcends its technical roots to become a catalyst forbusiness growth and guest experience innovation. It forges a link between datascience and customer service, translating complex datasets into valuableoperational strategies, and setting a new standard for hospitality excellence.

For any bank, retaining an existing customer typically demands less effort and cost than attracting a new one. Banks need to understand why customers churn to develop a sustaining retention strategy andmaintaining competitiveness.
‍
We employ statistical and regression analysis to help companies to understand customer attrition and predict customer churn probability using model that fits the best. Our analysis would be very significant for banks as a reference to sustain customer loyalty and help their margin increase.

This project delivers a pivotal analysis of customer preferences for MovieNow, an online movie rental service, by harnessing advanced SQL techniques for deep data exploration.

We utilize complex queries and OLAP extensions to perform comprehensive data aggregation and analysis. This meticulous approach allows for an intricate understanding of consumer behaviors, enabling MovieNow to categorize key segments effectively.

The insights derived from this analysis furnish MovieNow with actionable intelligence, markedly improving its market positioning. By enabling targeted marketing strategies, this project significantly contributes to enhanced customer engagement and retention, underpinning the company's competitive advantage.