exploratory-data-analysis-eda

A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.

An open-source Python library for Data Scientists & Data Analysts designed to simplify the exploratory data analysis process. Using Edvart, you can explore data sets and generate reports with minimal coding.

Exploratory data analysis of Airbnb bookings in New York City to gain insights into the travel industries and Uncovers trends, patterns, user preferences and behavior. Utilizes Python libraries for data exploration, data cleaning, manipulation, and visualization. Provides valuable insights for travelers, hosts, and the Airbnb business.

A pure-Rust workspace for classical ML: scikit-learn-style preprocessing & models (datarust) plus one-call data profiling & quality reports (datarust-profile). Zero dependencies by default.

This data project on Python uncovers and visualizes untapped patterns regarding all Nobel Prize laureates up to date.

Building And Deploying A Netflix Recommender System On Heruko

Built an interactive Tableau dashboard to analyze Airbnb data and developed a Streamlit application for trend analysis, pattern recognition, and data insights using EDA. Explored variations in price, location, property type, and seasons with interactive plots and charts, greatly aiding decision-making in the hospitality and real estate industries.

Abstractive summarization of Reddit datasets with Transformers.

# Women's E-Commerce Clothing Reviews Data Analysis

This repository contains a Jupyter Notebook demonstrating a practical example of data science and machine learning for heart disease classification.

Pandas practice notebooks for data analysis.

This project demonstrates an end-to-end approach to financial transaction reconciliation, focusing on how raw transactional data can be cleaned, standardized, and systematically matched to identify discrepancies. The notebook walks through the full workflow from business context and exploratory analysis to feature engineering & reconciliation

NYC health is one of the well-known centers in New York City to offer PCR tests for COVID-19 the center decided to establish ten mini examination centers in MTA stations. Thus NYC health is now in a mission to find the most crowded stations in New York City based on analyzing the MTA stations dataset which will give a better understanding of the…

Exploratory Data Analysis (EDA) of used car listings to uncover insights into car prices, brands, models, vehicle age, mileage, fuel type, transmission, engine, power, and seller type. The project includes data cleaning, statistical analysis, feature exploration, and data visualization to identify key factors influencing used car prices.

Performed feature engineering and data cleaning on text data using lemmatization techniques and stop word removals.

This project analyzes and preprocesses the Online Retail dataset to uncover insights into customer purchasing behaviors, sales trends, and product performance. It includes data cleaning, exploration, and visualization, with the goal of enhancing understanding of online retail dynamics.

Customer behavior analytics project using Python, Pandas, and SQL to analyze purchasing patterns, customer segmentation, and discount impact through real-world business queries.

This repo contains my Week 3 Data Analyst Internship project at Logic Stack. The project focuses on supply chain analytics using Python (Pandas) for data cleaning, exploratory data analysis (EDA) & visualizations, along with an interactive Power BI dashboard to analyze shipment performance, delivery trends, vendor efficiency & logistics insights.