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.

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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

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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.

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 movements inside the stations and the persona.

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

🚗 Built an interactive Automobile Sales Dashboard using Python & Plotly Dash. Features dynamic filters, multi-chart views, and KPIs to analyze sales by year, region, and vehicle type. Demonstrates data wrangling, visualization, and dashboarding skills for business insights.

Determine the breed of a dog in an image

A ML application focused on EDA and basketball analytics, showcasing data visualization and insights using Python and relevant libraries.

This project offers an Exploratory Data Analysis (EDA) on company stakeholders, including management, employees, shareholders, and others. Conducted in Python via Google Colab, it covers data transformation, clustering, statistical analysis, PCA, and predictive modeling. Visualizations provide insights into stakeholder roles and influence.

Successfully developed a resume classification model which can accurately classify the resume of any person into its corresponding job with a tremendously high accuracy of more than 99%.

End-to-end healthcare ML workflow featuring data ingestion, DQA procedures, feature engineering, RandomForest modeling, and accuracy/recall optimization. Designed to simulate a production-ready analytics pipeline supporting clinical decision-making.

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

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

Exploratory data analysis of a food delivery aggregator dataset to identify order trends, restaurant performance, and delivery efficiency. Provides actionable insights for operations and marketing.

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.

To predict the strength of the password

This project explores supervised machine learning algorithms for heart disease prediction using the UCI Heart Disease Dataset. Various classification models like KNN, SVM, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Gradient Boosting, and XGBoost are implemented and compared based on accuracy, precision, recall, and F1-score.

A diagnostic and exploratory analysis (EDA) of the Olist dataset in BigQuery. This project transforms raw data into strategic insights on customer satisfaction, product performance, and operational efficiency.

A Spotify data analysis project focusing on exploratory data analysis (EDA) of track features, trends, and popularity. Includes data cleaning, visualization, and insights using Python, Pandas, and Matplotlib/Seaborn.

📊 Analyze Spotify track data to uncover insights on popularity and audio features through comprehensive exploratory data analysis and visualizations.

Cafeteria Sales Data Analysis using Python & Pandas — Cleaned 10,000+ rows of raw sales data, performed EDA, created Matplotlib visualizations & correlation analysis with business insights and revenue strategies.