This repository is dedicated to showcasing the academic projects completed during my Master in Data Science & AI. The main objective is to show a collection of projects in various data science fields, including: data cleaning & preprocessing, data analysis, data visualization, machine learning, clustering, among others.

Data Analysis with the Pandas Library 📊

Analyzed the World Economic Indicator Dataset to investigate the factors driving sustainable economic growth in countries and regions. Delivered insights on strategies for achieving long-term economic stability.

In a distributed survey conducted via Amazon Mechanical Turk between December 3rd and 5th, 2016, data was collected from 30 Fitbit users. These users consented to sharing their minute-level physical activity, heart rate, and sleep monitoring data.

AniSearchModel leverages Sentence-BERT (SBERT) models to generate embeddings for synopses, enabling the calculation of semantic similarities between descriptions. This allows users to find the most similar anime or manga based on a given description.

This project provided practice with the pandas library and data analysis

A comprehensive guide to mastering Pandas for data analysis, featuring practical examples, real-world case studies, and step-by-step tutorials. For general information, see

通过 python 脚本将两个相对不完整的文档合并为一个完整的文档 / merge two relatively incomplete documents into one complete document via python script

Data Analysis: Merge, Impute, and Interpret

A versioned, distributed key-value store designed with a focus on data integrity. Each value boasts a comprehensive history, ensuring eventual consistency across the system. It features seamless merging capabilities to harmonize divergent data states.

Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.

This comprehensive analysis delves into the crucial role of cash holdings in determining a firm's future performance and market dynamics.

Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.

Exploring Google Play Store apps dataset to identify key factors for app engagement and success, revealing correlations between reviews, installs, categories, ratings, and user preferences.