300 Data Science Projects with Source Code

Chronos: Pretrained Models for Time Series Forecasting

Automatically visualize your pandas dataframe via a single print! 📊 💡

Probabilistic time series modeling in Python

Davinci is a DVsaaS (Data Visualization as a Service) Platform

Probabilistic reasoning and statistical analysis in TensorFlow

🙌 Welcome open-source Python mini-project contributions!

SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source.

Dashboards and notebooks in a single place. Create powerful and flexible dashboards using code, or build beautiful Notion-like notebooks and share them with your team.

Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.

Preswald is a WASM packager for Python-based interactive data apps: bundle full complex data workflows, particularly visualizations, into single files, runnable completely in-browser, using Pyodide, DuckDB, Pandas, and Plotly, Matplotlib, etc. Build dashboards, reports, and notebooks that run offline, load fast, and share like a document.

A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

A framework for collecting and analyzing prediction market data, including the largest publicly available dataset of Polymarket and Kalshi market and trade data.

Vizro is a low-code toolkit for building high-quality data visualization apps.

A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

The machine learning toolkit for time series analysis in Python

Visualize and compare datasets, target values and associations, with one line of code.

Beautiful charts for React Native. Line, area, bar, pie, donut, progress, and contribution heatmaps for dashboards, reports, and data-rich mobile apps.

An easy to use, self-service open BI reporting and BI dashboard platform.

Quantitative analysis, strategies and backtests

C++ DataFrame for statistical, financial, and ML analysis in modern C++

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

Survival analysis in Python

Single-cell analysis in Python. Scales to >100M cells.

[AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"

An open source user-empowering data visualization Vue 3 components library for eloquent data storytelling

Beautiful visualizations of how language differs among document types.

AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.

Datart is a next generation Data Visualization Open Platform

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight)

Library for animated data visualizations and data stories.

[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

An easy-to-use BI server built for SQL lovers. Power data analysis in SQL and gain faster business insights.

SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.

DATAGEN: AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing.

[PVLDB 2024 Best Paper Nomination] TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

Powerful Analytics Solution. Setup in 30 seconds. Display all your data on a Simple, AI-powered dashboard. Fully self-hostable and GDPR compliant. Alternative to Google Analytics, MixPanel, Plausible, Umami & Matomo.

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Unified Training of Universal Time Series Forecasting Transformers

Chat with your data - AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake, SQLite...

:bar_chart: :clipboard: Dashboards using YAML or JSON files

Stock Price Prediction using Machine Learning Techniques

Drone Log analyzer: A high-performance universal dashboard application for organizing and analyzing DJI/Litchi flight logs privately in one place. Supports plugin for custom flight log formats. Built with Tauri v2, DuckDB, and React.

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

Deep Learning Toolkit for Medical Image Analysis

Build and share data reports in 100% Python

Interactively explore unstructured datasets from your dataframe.

NFStream: a Flexible Network Data Analysis Framework.

Automatically find issues in image datasets and practice data-centric computer vision.

AI-powered Jupyter Notebook. Use AI to generate and edit code cells, automatically fix errors, and chat with your data

A Python tool that automatically cleans data sets and readies them for analysis.

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

Open Source Business Intelligence Tool

[ICLR 2025 Spotlight] Official implementation of "Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts"

Power BI AI skills and Power BI agents for Claude Code and GitHub Copilot: a plugin marketplace of Power BI skills, subagents, and hooks for semantic models, DAX, TMDL, reports, and AI dashboards. Includes Microsoft Fabric skills and Fabric agents. Weekly updates.

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

A Python package for modular causal inference analysis and model evaluations

scikit-mobility: mobility analysis in Python

TreeMap is a fully downloadable app that can run on mac, windows, and linux. That visualizeses all of your disk-space into charts and square board. It also lets you delete unwanted files and opens files for you aswell.

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

Materials for following along with Hands-On Data Analysis with Pandas – Second Edition

Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"

Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch

A stand alone, light-weight web server for building, sharing graphs created in ipython. Build for data science, data analysis guys. Aiming at building an interactive visualization, collaborated dashboard, and real-time streaming graph.

Abixen Platform is a microservices based software platform for building enterprise applications delivering functionalities through creating particular microservices and integrating by provided CMS.

The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)

Plotlars is a Rust library designed to facilitate the integration between the Polars data analysis library and Plotly library.

Track your Data Science. Skore's open-source Python library accelerates ML model development with automated evaluation reports, smart methodological guidance, and comprehensive cross-validation analysis.

Complete-Life-Cycle-of-a-Data-Science-Project

Kernel Density Estimation in Python

Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc

TimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.

DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow

DeepSeek Harness Desktop App: a local AI desktop workspace for DSH Sessions, projects, files, web research, plugins, and Office artifacts.

Unofficial implementation of iTransformer - SOTA Time Series Forecasting using Attention networks, out of Tsinghua / Ant group

Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.

skimpy is a light weight tool that provides summary statistics about variables in data frames within the console.

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

MDV — a Markdown superset for documents, dashboards, and slides with embedded data and visualizations. HTML + PDF export, live preview, VS Code extension.

Code review for data in dbt

Top paper collection for stock price prediction, quantitative trading. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP.

[ICLR 2024] Official Implementation of "Diffusion-TS: Interpretable Diffusion for General Time Series Generation"

Extração de dados da API do CartolaFC, análise exploratória dos dados e modelos preditivos em R e Python - 2014-22. [EN] Data munging, analysis and modeling of CartolaFC - the most popular fantasy football game in Brazil. Data cover years 2014-23.

Rust for data analysis encyclopedia (WIP).

Powerful & Easy way for big data discovery

Zero-shot Time Series Forecasting with TabPFN (work accepted at NeurIPS 2024 TRL and TSALM workshops)

Using data analytics alongside popular trading strategies and indicators, to identify best trading actions based solely on the price action.

Repository for the ActivitySchema spec and supporting materials

Stock price prediction with LSTMs in TensorFlow

Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

A general-purpose framework for solving problems with machine learning applied to predicting customer churn

Materials for following along with Hands-On Data Analysis with Pandas.

A framework for ad hoc JSON data analysis, shareable server-less reports and dashboards

A machine learning approach to classify songs by mood.

Datasets & Analyses for Formula 1 World Championship

DANCE: a deep learning library and benchmark platform for single-cell analysis

A library of Power BI dashboards, design files, datasets, and visualization resources. Learn dashboard design, explore real-world business scenarios, and accelerate your Power BI development.

Network Plugin for Kibana

Tellery lets you build metrics using SQL and bring them to your team. As easy as using a document. As powerful as a data modeling tool.

Fast DuckDB-powered TypeScript library for tabular, geospatial, vector, AI, Google Sheets, and data visualization workflows on Deno, Node.js, and Bun.

A project providing a Graphic Walker Pane for use with HoloViz Panel.

Book on basic to intermediate level Python projects

Repository for the paper: 'Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models'

[TKDD 2023] Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution

Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

PyTorch GPU implementation of the ES-RNN model for time series forecasting

Illuminate your data. Agent framework turning natural language into SQL, charts, dashboards and reports.

Python package to perform enrichment analysis from omics data.

[KDD 2025 Most Influential Paper] DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting

This program goes thru reddit, finds the most mentioned tickers and uses Vader SentimentIntensityAnalyzer to calculate the ticker compound value.

Official implement for "SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion"(NeurIPS'24) in PyTorch.

Interactive Dashboards and Data Apps with Plotly and Dash, published by Packt

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

An open source library for Fuzzy Time Series in Python

Coeus 🌐 is an OSINT ToolBox empowering users with tools for effective intelligence gathering from open sources. From social media monitoring 📱 to data analysis 📊, it offers a centralized platform for seamless OSINT investigations.

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

🚀 Modern Go-Native Business Intelligence (BI) Engine – API-first, lightweight BI for analytics, charting, and automated reporting with 3D visualization.

Official implementation for "AutoTimes: Autoregressive Time Series Forecasters via Large Language Models"

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

trackintel is a framework for spatio-temporal analysis of movement trajectory and mobility data.

Classification of Breast Cancer diagnosis Using Support Vector Machines

A fast, safe, and intuitive DataFrame library.

AI-powered key driver analysis tool that pinpoints root cause behind metrics fluctuation in one minute.

Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting"

Real-time sentiment analysis in Python using twitter's streaming api

📄 Smart Resume AI is a powerful tool designed to revolutionize your job application process. With features like 📚 professional template building, 🔍 ATS-friendly analysis, and 🎯 AI-driven optimization, it ensures your resume stands out to recruiters. Smart Resume AI provides everything you need to craft a tailored, eye-catching resume. 🚀

time series analysis models source code

A Power BI template that provides easy to understand, actionable flow metrics and predictive analytics for your agile teams using Azure DevOps, Azure DevOps Server and/or TFS.

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

Stack overflow is a professional community for developers. This repo analysis 3 years of developer Survey done by Stackoverflow and do visualization and predict the salary of Data Scientist in future.

Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

Time Series Analysis and Forecasting in Python

AlphaSuite is an open-source quantitative analysis platform that gives you the power to build, test, and deploy professional-grade trading strategies. It's designed for traders and analysts who want to move beyond simple backtests and develop a genuine, data-driven edge in the financial markets.

An open source book to learn data science, data analysis and machine learning, suitable for all ages!

A Python toolkit for flow cytometry analysis supporting GatingML and FlowJo workspaces

A highly customizable Gauge component for Angular apps and dashboards

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Build dashboards in Jupyter Notebook with numeric and chart boxes

A Python package for heart rate variability analysis

Edalitics is an open-source business intelligence platform that lets you build interactive dashboards, analyze data from multiple sources, and connect AI assistants to your business data in minutes.

A lightweight Sichuan digital twin operation dashboard with Canvas map rendering, realtime simulation and bilingual documentation.

Automatically generate viral-ready vertical short clips from long-form gameplay footage using AI-powered scene analysis, GPU-accelerated rendering, and optional AI voiceovers.

HandySpark - bringing pandas-like capabilities to Spark dataframes

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

Open Source Local Data Analysis Assistant.

Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Open AI data scientist agent that automates complex data analysis tasks using the ReAct framework. Execute Python code locally or in the cloud, upload datasets, and generate detailed analytical reports with minimal setup.

Py3plex - A multilayer complex network visualization and analysis library in python3

Python package to scrape flight data from Google Flights and analyzes prices. Can determine optimal flight from date, place, and price

A great companion for finding key support and resistance levels on financial charts, cryptocurrencies.

A Keras library for multi-step time-series forecasting.

ChartScanAI is an advanced app for detecting patterns in stock and cryptocurrency charts using deep learning and YOLOv8. It automates chart pattern recognition, providing traders with a powerful tool for making informed decisions. Key features include real-time analysis, high accuracy for Buy/Sell signals, and support for various charts.

Machine Learning and Data Analysis Coursera Specialization from Yandex and MIPT

LLM based AI Agent to automate Data Analysis for dbt projects with remote MCP server

Visualize your Laravel app data in simple dashboards.

Datasette plugin to create interactive dashboards

Forecast evaluation library

Build a data visualization dashboard with simple snippets of python code

Epidemiology analysis package

A Python library for easy data analysis, visualization, exploration and modeling

Predicting stock price using historical data of a company, using Neural networks (LSTM).

Single cell analysis in the browser

An advanced, unified platform for code quality, architectural metrics, and Agile analytics. Features Python AST parsing, Java static analysis (code smells), and interactive project health dashboards built with Streamlit.

Distance-based Analysis of DAta-manifolds in python

DocMind AI is a powerful, open-source Streamlit application leveraging LlamaIndex, LangGraph, and local Large Language Models (LLMs) via Ollama, LMStudio, llama.cpp, or vLLM for advanced document analysis. Analyze, summarize, and extract insights from a wide array of file formats, securely and privately, all offline.

Official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024)

Turn any file into a beautiful, interactive, shareable HTML — WhatsApp logs, PDFs, transcripts, code, anything.

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016

This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

Table analysis and plotting application written in PySide2/PyQt5

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on Data Science filed.

Pytorch implementation of NIPS'23 paper: Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective

A python project starter template for data-analytics and data-science.

🧪📈 🐍. The purpose of the panel-chemistry project is to make it really easy for you to do DATA ANALYSIS and build powerful DATA AND VIZ APPLICATIONS within the domain of Chemistry using using Python and HoloViz Panel.

The analytical and reporting solution for MongoDB

Competing Risks and Survival Analysis

A free, simple, and easy-to-use technology-style UI component, developed based on vue3

Impy is a Python3 library with features that help you in your computer vision tasks.

Interactively browse multimodal tabular data

Statistical Interactive Visualization with pandas+Jupyter integration on top of Echarts.

Data Science projects on various problem statements and datasets using Data Analysis, Machine Learning Algorithms, Deep Learning Algorithms, Natural Language Processing, Business Intelligence concepts by Python

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

Real time scrapping of stock data in order to get the most recent available information. Cleaning, structuring and parsing of relevant data to provide an interactive and informative dashboard. Dashboard features include: Price history (chart) Basic information (name, sector, industry, market cap...) Insider and institutional ownership information Share return over the years (absolute growth and cagr) Business summary Earnings & growth analysis (revenue, gross profit. EBITDA, net income, free cash flow...) Profitability analysis (roa, roe, roic, gross profit margin, net income margin...) Financial health analysis (debt to equity, interest coverage, quick ratio, current ratio...) Valuation analysis (p/e, p/s, p/b, p/fcf, peg) + Discounted Cash Flow Model

CovsirPhy: Python library for COVID-19 analysis with phase-dependent SIR-derived ODE models.

DORA (Data Oriented Report Automator) automates Exploratory Data Analysis (EDA) to help you effortlessly explore datasets. Generate insightful statistics, visualizations, and reports with just a click! Streamline your data exploration workflow and uncover trends, patterns, and relationships in your data with ease.

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

scivision: a framework for scientific image analysis

Systematically learn and evaluate the latent geometry of high-dimensional data, with a focus on scRNAseq analysis

Code release for "Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction" https://arxiv.org/abs/2309.00073

Datacmd is the fastest, coolest way to turn raw data into stunning terminal dashboards. No setup, no fluff — just run a command and boom, your CSV or API becomes a live data experience. ⚡ CLI dashboards have never been this fun.

An open-source tool for quick Time Series Analysis and Forecasting

From geospatial to spatial -omics

Official implementation of "Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting" (https://arxiv.org/abs/2405.06419)

Berlin Time Series Analysis Repository

Valid and adaptive prediction intervals for probabilistic time series forecasting.

A Python library for moderation, mediation and conditional process analysis.

🔍 Table Extraction Tool: A powerful open-source solution combining OCR and computer vision for extracting structured tabular data from images. Ideal for LLM preprocessing, data analysis, and automation. 🚀

✨ Build dashboards with end-to-end version control. 🔋 CLI w/ batteries included, no infra required. Develop on your laptop for instant results, deploy changes safely (with automated checks), and keep every report trustworthy for stakeholders, analysts and agents 🤖

🔬 A Researcher&Agent-Friendly Framework for Time Series Analysis. Train Any Model on Any Dataset!

Earth Observation Data Analysis Library

This repository is a case study, analysis and visualization of COVID-19 Pandemic spread along with prediction models.

A cloud-native data pipeline and visualization project analyzing Formula 1 racing data using Azure, Databricks, Delta Lake, Tableau, and Python for insightful EDA and interactive dashboards.

Material for the exercise sessions of master course Machine Learning for Economic Analysis @UZH

Is there a relationship between popularity of a given technology on Stack Overflow (SO) and Hacker News (HN)? And a few words about causality

Code for IoTJ 2024 paper "SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting".

A Tensorflow implementation of the Adaptive Neuro-Based Fuzzy Inference System (ANFIS)

A project to visualize time range series data using the Sankey diagram.

X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

My Power BI World Championship 2026 Round I report, open source. Full pbix, TMDL model, 223 DAX measures organised into folders, 74 HTML components and the theme.

[TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".

A Deep Learning model that predict forecast the power generated by wind turbine in a Wind Energy Power Plant using LSTM (Long Short Term Memory) i.e modified recurrent neural network.

[SIGKDD'2025] Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.

An Exhaustive WhatsApp Chat Data Analysis.

Strategy dashboard for all F1 races since 2018

Time Series Analysis with Python Cookbook, Second Edition - Published by Packt

High-performance data visualization library for Flutter with native rendering and cross-platform support

An analysis of historical Hacker News data to determine the ranking algorithm

A cli tool to streamline financial markets data analysis :wrench:

This project provides an end-to-end machine learning pipeline for predicting customer churn. Using Python and the scikit-learn library, it implements a Random Forest Classifier to train a model on a synthetic dataset.

An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.

Bank customers churn dashboard with predictions from several machine learning models.

The projects I've done over the course of time for practicing my EDA and Data Science skills. You can check the code out and learn!

RIVET is a tool for Topological Data Analysis, in particular two-parameter persistent homology.

Exploratory, Inferential and Predictive data analysis. Feel free to show your :heart: by giving a star :star:

DataFrame in Pharo - tabular data structures for data analysis

React components for real-time telemetry dashboards — robotics, aerospace, autonomous vehicles, industrial IoT. PFD, HSI, time-series, gauges, GPS maps, and ROS2/MQTT adapters at 60fps.

Implementation of DenseNet model on Standford's MURA dataset using PyTorch

A New Interactive Approach to Learning Data Analysis

Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs

breadroll 🥟 is a simple lightweight library for data processing operations written in Typescript and powered by Bun.

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

Classwork projects and home works done through Udacity data engineering nano degree

Malloy Composer is a simple application to build dashboards or run ad-hoc queries using an existing Malloy model

📈🔍 Lets Python do AB testing analysis.

A python package that performs exploratory data analysis for users. Additionally, it generates 3 types of output files (cleaned CSV, plots and a text report).

Python package for text mining of time-series data

A data-driven approach to predicting football match outcomes using advanced machine learning techniques. This project integrates various algorithms to forecast game results, providing insights for sports betting, team performance analysis, and sports enthusiasts.

A library for Time Series EDA (exploratory data analysis)

The binclass-tools package contains a set of Python wrappers and interactive plots that facilitate the analysis of binary classification problems.

Method-driven agent skills for knowledge work: strategy, research, critical thinking, data analysis, docs, slides. Works with Claude Code, Codex, Cursor and any agentskills.io-compatible agent.

Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018. Best Presentation Winner.

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Analyzing the HR Criteria of a Company and how they promote their Employees and keep Balance between them using Data Analytics, Data Visualizations, and Machine Learning Models for Classification Purposes.

This project aims to develop a stock prediction application using Machine Learning

This repository contains the code for the Project "Forex Stock Price Prediction using Transformers and Time Embeddings" written in Tensorflow 2.9.1

:seedling: Crop Yield Prediction using Machine Learning

Stock price prediction and automated trading using Deep Reinforcement Learning and Machine Learning

This project focuses on predicting customer churn for a bank using machine learning. By analyzing features like credit score, age, balance, and geography, the model identifies at-risk customers, uncovers key churn drivers, and provides insights to help the bank implement targeted retention strategies.

Jupyter notebooks and Python code for analyzing air quality (fine particle, PM2.5)

In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.

Demo DVC project training a classification model on tabular data

An End to End Customer Churn Prediction solution using AWS services.

Stock Price Prediction using Regressions with Fast Fourier Transform (FFT) - Machine Learning Nanodegree capstone project (2017)

Stock price prediction using Bidirectional LSTM and sentiment analysis

A web-based stock analysis application built with React and Flask, offering interactive visualizations and machine learning-based stock price predictions.

Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.

A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.

The air quality data of various states in India is analyzed.

This project leverages data analytics to gain insights into gender diversity and inclusion within the company. The Power BI dashboard and KPIs allow HR and management to track important metrics, identify issues, and make informed decisions to improve the representation and experience of all genders in the workforce.

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

MLOps End-to-End: Customer Churn Prediction System

End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API

Build a machine learning model to predict weekly sales with 97.4% accuracy. Integrated Exploratory Data Analysis tools to analyze trends, patterns, and actionable insights. The solution enables detailed sales comparisons, evaluates feature impacts and ranges, and identifies top performers, greatly enhancing decision-making in the retail industries.

The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022. We need to predict whether the customer will churn, stay or join the company based on the parameters of the dataset.

Black Friday Sales Analysis explores customer demographics, purchasing behaviors, and product trends to uncover insights and patterns driving sales during Black Friday events.

Infuse AI into your application. Create and deploy a customer churn prediction model with IBM Cloud Private for Data, Db2 Warehouse, Spark MLlib, and Jupyter notebooks.

A end-to-end MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.

An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

In this project, we will scrape the data from ipl website, and analyze the data and deploy the data visualization dashboard on Heroku.

This repository demonstrates how data science can help to identify the employee attrition which is part of Human Resource Management

Project examines factors affecting the change in car sales between 2019 and 2020 utilizing real world data, python, pandas, matplotlib and jupyter notebook.

Business Analysis project related to Realtime Product Sales Analysis

🌊 Deep Sales Analysis of Olist E-Commerce: EDA | Time Series| Viz | RFM | NLP | Geospatial | Segmentation & Actionable Business Recommendations.

HR Analytics Dashboard project built using Excel and Power BI to analyze employee data, attrition trends, and workforce insights. It features data cleaning, visualization, and interactive dashboards to support data-driven HR decision-making and improve employee retention.

E-commerce Sales Insights Dataset and Power BI Dashboard

The Air Quality Analysis in Lima project aims to collect, analyze and visualize air quality data in the city of Lima, Peru. We will use API to obtain real-time data from https://waqi.info/

This project aims to analyze and predict the air quality index of various cities and states in India using machine learning. The dataset used in this project contains historical data of air quality index readings from various monitoring stations across India.

Identifying employee relationships from emails for improved HR analytics: Cultivate consulting project, Insight DS.17C

Proyecto de análisis de datos en recursos humanos con fines pedagógicos para personas hispano parlantes

Repo for the Fall 2021 Senior Design Group.

Air Quality Analysis CLI Assessment Project for Fizyr

Now that this year's IPL is over, let's not curb our cricket love and start analyzing the whole of IPL with this latest and complete Indian Premier League dataset. It contains the match descriptions, results, winners, player of the matches, ball by ball dataset and much more. So, stop thinking and start analyzing . Content This dataset consists of three separate CSV files : matches and deliveries. These files contain the information of each match summary and ball by ball details, respectively.

Welcome to the Netflix Data Analysis project, a comprehensive exploration of Netflix's content catalog. In this project, we meticulously examine Netflix's raw data, employing a rigorous analytical process to ensure accuracy and reliability. From data extraction to visualization, every step is performed with precision and attention to detail.

Smart Environmental Intelligence System — Real-time Air Quality Analysis

An interactive Microsoft Excel dashboard analyzing the Sample Superstore dataset using Pivot Tables, Pivot Charts, KPIs, Slicers, and business insights to visualize sales, profit, customer, and regional performance.

📊 An interactive Power BI dashboard analyzing Super Store sales data (2011–2014) across 4 US regions — featuring KPIs, monthly trends, profit forecasting, category breakdowns, and geographic visualizations built with DAX & Power Query.

Interactive Tableau dashboard for analyzing Superstore sales, profit trends, top products, customer segments, and regional performance.

Cricket is one of the popular game in India. After the Start of IPL, Indian cricket standards reached an ultimate level and many talented players got a chance to prove themselves in a platform like IPL where many international cricketers play together. IPL is the one of the leading cricket tournament in the world. So, i am here to describe the IPL analysis using Python.

1. Take the win_by_wickets dataset and plot frequency distribution graph On x-axis - Win by Wickets on y-axis Win by Wickets margin 2. Relative frequency distribution graph using the same data set win_by_wickets Hint: use normalize = True argument for pandas.Series.value_counts Instead of showing frequency show percentage of values 3. Plot Cumulative relative frequency graph Use function pandas.Series.cumsum 4. Find out the probability of winning a match by 6 wickets or less? Find out the probability using the cumulative relative frequency graph Draw a vertical line from 6 on x-axis till it intercepts the curve and the draw the horizontal line till it intercepts y-axis Hint: Answer is 54% 5. Plot the normal distribution for win_by_wickets data. Calculate mean and standard deviation for win_by_wickets data Plot Histogram Plot Line Graph Plot Normal distribution between 1 and 10 using mean and standard deviation as calculated above 6. Calculate z-score if the team wins by 35 runs. Calculate mean and standard deviation for win_by_wickets data Use win_by_runs dataset 7. Calculate percentile using z-score. Use scipy.stats.norm.cdf function

Explore IPL data using Python libraries like Pandas, NumPy, and Matplotlib. Analyze team and player performance, match outcomes, toss impact, and trends through visualizations and insights. A great project for data analysis and EDA practice with real-world sports data.

A data analysis project based on the Netflix dataset using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. This project explores Netflix content trends, including movies vs TV shows, content distribution by country, ratings, genres, release years, and more through data cleaning, visualization, and exploratory data analysis (EDA).

updated version of Kalonji Bankole workbook - this one will work

Interactive Tableau dashboard for analyzing Superstore sales, profit trends, top products, customer segments, and regional performance.