There are 1 repository under data-merging topic.
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.
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.
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
Combine Airbnb data from CSV, Excel, and TSV files to analyze prices, reviews, and room types in NYC’s rental market using pandas.
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.
Python Java. Research Proposal RP
This project provided practice with the pandas library and data analysis
Data Analysis: Merge, Impute, and Interpret
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.
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
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.
Analyzed athletic sales data using Pandas, employing techniques like concatenation, joins, groupby, and pivot tables to identify top-performing regions, retailers, and product categories. The project highlighted advanced data combination and reshaping skills to uncover key sales insights.
Developed a movie recommendation system by sourcing data from the New York Times Article Search and The Movie Database APIs. Extracted, merged, and cleaned data to create a comprehensive dataset, enabling users to find movie reviews and related titles based on their preferences.
In-depth Data analysis and visualization of Medicare inpatient hospital data.
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.
Data Analysis with the Pandas Library 📊
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 📊
Merge CSV/TXT/SQL files for harmonized ELN import; supports joins and preview with export
This repository contains experiments on data wrangling techniques, focusing on methods for handling missing values, filtering, aggregation, and more.
Interoperability enabler component for SEDIMARK
An end-to-end data science project analyzing internal and external drivers of rental prices across Victoria, Australia. The project includes web scraping, data preprocessing, geospatial integration, exploratory data analysis, and predictive modeling. Built using Python, Pandas, GeoPandas, Scikit-learn, and web scraping frameworks.