Habr, hello. I translated a post that goes strictly (!) To bookmarks and is passed on to colleagues. It has a list of notebooks and ML and Data Science libraries for various industries. All codes are in Python, and are hosted on GitHub. They will be useful both for expanding horizons and for launching an interesting startup.







I’ll note that if there are any readers who want to help and add a suitable project to any of the sub-sectors, please contact me. I will add them to the list. So, let's start exploring the list.

1. Real estate and food

1.1. Nutrition

• RobotChef - improving food recipes based on user reviews;
• Food Amenities - forecasting the demand for food products using neural networks;
• Recipe Cuisine and Rating - predicting the name of the cuisine of any dish based on a list of its ingredients;
• Food Classification - food classification of food using the Keras library;
• Image to Recipe - translation of a food image into a recipe for its preparation;
• Calorie Estimation - calorie rating from food photos;
• Fine Food Reviews - Segmented food analysis based on reviews from Amazon Fine Food.

1.2. Restaurants

• Restaurant Violation - prediction of violation of food inspection rules;
• Restaurant Success - predicting whether a restaurant will be successful;
• Predict Michelin - prediction that the restaurant will be given a Michelin star;
• Restaurant Inspection - analysis of the level of restaurant loyalty and popularity based on Yelp ratings;
• Sales - sales forecasting for restaurants with LTSM;
• Visitor Forecasting - booking and forecasting the number of visits;
• Restaurant Profit - restaurant regression analysis;
• Competition - analysis of the competitiveness of the restaurant;
• Business Analysis - a project for the analysis of the restaurant business;
• Location Recommendation - a recommendation on the future location of the restaurant;
• Closure, Rating and Recommendation - three forecasting tasks using Yelp data;
• Anti-recommender - recommendation of restaurants that you (based on your preferences) do not want to visit;
• Menu Analysis - analysis of the restaurant based on its menu;
• Menu Recommendation - recommendation of new restaurants based on your favorite menu;
• Food Price - forecasting the cost of food;
• Automated Restaurant Report - Automated restaurant report based on machine learning.

1.3. The property

• Peer-to-Peer Housing - the impact of one-time rental housing on its condition;
• Roommate Recommendation - recommendation of a roommate;
• Room Allocation - distribution of premises;
• Dynamic Pricing - hotel dynamic pricing calculations;
• Hotel Similarity - comparison of competing hotels with each other;
• Hotel Reviews - hotel reviews;
• Predict Prices - prediction of hotel room prices;
• Hotels vs Airbnb - hotel comparison with Airbnb;
• Hotel Improvement - analysis of reviews for the hotel;
• Orders - forecast cancellation of orders for hotels;
• Fake Reviews - Recognize whether reviews are fake or spam;
• Reverse Image Lodging - finding the right housing for you in the image.

2. Accounting

2.1. Machine learning

• Chart of Account Prediction - use of tagged data for the account name of each transaction;
• Accounting Anomalies - identification of anomalies in accounting;
• Financial Statement Anomalies - detection of anomalies (using R) before submitting documents to the tax;
• Useful Life Prediction (FirmAI) - forecasting the useful life of assets using sensory observations and the development of functions;
• AI Applied to XBRL - A standardized representation of XBRL in AI and ML.

2.2. Analytics

• Forensic Accounting - a collection of case studies on forensic accounting using data analysis;
• General Ledger (FirmAI) - data processing in FirmAI;
• Bullet Graph (FirmAI) - visualization of Bullet Graph helps to track sales, commissions and other indicators;
• Aged Debtors (FirmAI) - an example of analysis to identify debtors;
• Automated FS XBRL is a collection of PDF files containing over 700 pages of XBRL information.

2.3. Text analysis

• Financial Sentiment Analysis - analysis of leverage of optimization in trade;
• Extensive NLP - comprehensive NLP methods for accounting.

2.4. Data, Parsing and API

• EDGAR - a step-by-step guide to obtaining EDGAR data;
• PyEDGAR - a library for downloading, caching and accessing EDGAR files;
• IRS - access and analysis of IRS files;
• Financial Corporate - Rutgers corporate financial data sets;
• Non-financial Corporate - non-financial corporate data set Rutgers;
• PDF Parsing - extract useful data from PDF documents;
• PDF Tabel to Excel - Create an Excel file from data in PDF.

2.5. Research and articles

• Understanding Accounting Analytics - An article about the importance of accounting analytics.
• VLFeat is an open and portable library of computer vision algorithms with the Matlab toolkit.

2.6. Web sites

• Rutgers Raw - Digital Accounting Research by Rutgers.

2.7. Courses

• Computer Augmented Accounting - A video from Rutgers University dedicated to using computing to improve accounting
• Accounting in a Digital Era is another series of articles by Rutgers University on the impact of the digital age on accounting.

3. Agriculture

3.1. Economy

• Prices - forecast of prices for agricultural products 1;
• Prices 2 - forecast of prices for agricultural products 2;
• Yield - agricultural yield analysis in Ukraine;
• Recovery - strategic use of land in agriculture, taking into account ecosystem restoration;
• MPR - agricultural price reporting data. US Department of Agriculture products.

3.2. Development

• Segmentation - segmentation of agricultural fields using satellite imagery;
• Water Table - predicting the depth of groundwater in agricultural areas;
• Assistant - laptops from the virtual Agriculture Assistant;
• Eco-evolutionary - eco-evolutionary dynamics;
• Diseases - identification of crop diseases and pests using the Deep Learning framework for images;
• Irrigation and Pest Prediction - analysis of irrigation and prediction of the likelihood of pests.

4. Banking and insurance

4.1. Consumer finance

• Loan Acceptance - making loan decisions based on classification and time series analysis;
• Predict Loan Repayment - prediction of loan repayment using the automated design function;
• Loan Eligibility Ranking - a system that helps banks verify whether a client can receive a given (specific) loan;
• Home Credit Default (FirmAI) - credit default forecasting;
• Mortgage Analytics - extensive mortgage analytics;
• Credit Approval - a system for approving a credit card;
• Loan Risk - a predictive model that helps reduce write-offs and loss of% of loans;
• Amortisation Schedule (FirmAI) - A simple depreciation chart in Python.

4.2. Management and operations

• Credit Card - CLV assessment of credit card customers;
• Survival Analysis - analysis of LTV clients;
• Next Transaction - a deep learning model for predicting the amount of the transaction and the days until the next transaction;
• Credit Card Churn - prediction of the outflow of customers with credit cards;
• Bank of England Minutes - the main ideas of the preliminary processing of the text using the minutes of the meetings of the Bank of England Monetary Policy Committee;
• CEO - An analysis of the correlation between the remuneration of a male CEO and a female CEO.

4.3. Rating

• Zillow Prediction - Zillow score forecast made by Kaggle;
• Real Estate - forecasting prices for urban real estate;
• Used Car - used car price forecast.

4.4. Fraud

• XGBoost - fraud detection by configuring XGBoost hyperparameters;
• Fraud Detection Loan in R - detection of fraud in bank loans;
• AML Finance Due Diligence - search for news articles to finance AML DD;
• Credit Card Fraud - detection of credit card fraud.

4.5. Insurance and Risks

• Car Damage Detective - assessment of vehicle damage using convolutional neural networks;
• Medical Insurance Claims - forecasting health insurance claims;
• Claim Denial - forecasting waiver of insurance compensation;
• Claim Fraud - predictive models to determine which car claims are fraudulent;
• Claims Anomalies - anomaly detection system for these insurance claims;
• Actuarial Sciences - A series of actuarial instruments in R;
• Bank Failure - prediction of bankruptcy;
• Risk Management - financing of risk management courses;
• VaR GaN - market risk management cost estimation using Keras and TensorFlow;
• Compliance - compliance management with bank requirements;
• Stress Testing - ECB stress testing;
• Stress Testing Techniques - notebook with various exercises for stress testing;
• BoE stress test - stress test results and scheduling;
• Recovery - reimbursement of money due;
• Quality Control - quality control of banks using LDA.

4.6. Useful

• Bank Note Fraud Detection - authentication using DNN Tensorflow Classifier and RandomForest;
• ATM Surveillance - monitoring of ATMs in banks.

5. Biotechnology and science

5.1. Are common

• Programming - programming for biologists in Python;
• Introduction DL - a textbook on the advanced study of genomics;
• Pose - animal pose assessment using DL;
• Privacy - the exchange of clinical data, while maintaining confidentiality;
• Population Genetics - population genetic conclusion;
• Bioinformatics Course - course materials in computational biology and bioinformatics;
• Applied Stats - Applied Statistics for High Performance Biology;
• Scripts - Python scripts for biologists;
• Molecular NN - a mini-framework for building and training neural networks for molecular biology;
• Systems Biology Simulations - practical system biology when writing simulations with F # and Z3;
• Cell Movement - LSTM for predicting the biological movement of cells;
• Deepchem - deep learning for the discovery of new drugs, quantum chemistry, materials science and biology.

5.2. Sequence

• DNA, RNA and Protein Sequencing - a new view for biological sequences;
• CNN Sequencing - a set of tools for studying the sequence of DNA / RNA using convolutional neural networks;
• NLP Sequencing is a language transfer learning model for genomics.

5.3. Chemoinformatics and drug discovery

• Novel Molecules - a convolutional network that can study functions;
• Automating Chemical Design - creating new molecules for effective research;
• GAN drug Discovery - a method that combines generative models with training and reinforcement;
• RL - generating compounds predicted as active;
• One-shot learning - the use of machine learning in the field of drug search in simple and convenient ways.

5.4. Genomic

• Jupyter Genomics - a collection of laptops in computational biology and bioinformatics;
• Variant calling - determination of deviations from the reference genome in human DNA;
• Gene Expression Graphs - the use of convolutions in images;
• Autoencoding Expression - extracting appropriate patterns from large gene expression data sets;
• Gene Expression Inference - predicting the expression of these target genes from a panel of approximately 1000 pre-selected “reference genes”;
• Plant Genomics - material for presentations and examples for plant genomes and pathogens.

5.5. The science

• Plants Disease - an application that identifies diseases in plants using a deep learning model;
• Leaf Identification - identification of plants through leaves based on their shape, color and texture;
• Crop Analysis - image library for detecting and tracking the future position of ears on corn plants;
• Seedlings - plant seedlings, classification from Kaggle;
• Plant Stress - an ontology containing plant stress;
• Animal Hierarchy - a package for calculating animal dominance hierarchies;
• Animal Identification - deep animal identification training;
• Species - big data analysis of various animal species;
• Animal Vocalisations - generative network for animal vocalization;
• Evolutionary - a tool for evolution strategies;
• Glaciers - educational material about glaciers.

6. Construction machinery

6.1. Building

• DL Architecture - an image generator for building architecture;
• Construction Materials - course on building materials;
• Bad Actor Risk Model - a risk model to increase the safety of construction;
• Inspectors - definition of assigned inspections;
• Corrupt Social Interactions - identifying potential corrupt interactions between industry employees and DOB employees;
• Risk Construction - definition of high-risk construction;
• Facade Risk - risk model for forecasting unsafe facades;
• Staff Levels - staff level forecasting for advanced workers;
• Injuries - modeling the number of injuries associated with construction;
• Productivity - analysis and performance testing.

6.2. Engineering

• Structural Analysis - 2D Structural Analysis in Python;
• Structural Engineering - structural engineering modules;
• Nusa - structural analysis using the finite element method;
• StructPy - a structural analysis library for Python based on the direct stiffness method;
• Aileron - structural analysis of Boeing 737 ailerons;
• Vibration - educational vibration programs;
• Civil - a collection of civil engineering tools in FreeCAD;
• GEstimator - preparation of cost estimates for construction and electrical work with a detailed analysis of tariffs;
• Fatpack - functions and classes for analyzing the fatigue of a series of data;
• Pysteel - computer-aided design of steel structures;
• Structural Uncertainty - a quantitative assessment of structural uncertainty based on deep learning;
• Pymech - Python module for mechanical engineers;
• Aerospace Engineering - astrodynamics and statistics;
• Interactive Quantum Chemistry - the combination of Psi4 and Numpy for education and development;
• Chemical and Process Engineering - various resources of chemical and technological engineering;
• PyTherm - applied thermodynamics;
• Aerogami - aerodynamics using airplanes;
• Electro geophysics - interactive applications for electromagnetism in geophysics;
• Graph Signal - a tutorial on processing signals on a graph;
• Mechanical Vibrations - mechanical vibrations at the University of Louisiana;
• Process Dynamics - the dynamics of the process and management;
• Battery Life Cycle - predicting battery life based on data;
• Wind Energy - Python for wind energy;
• Energy Use - standard methods for calculating normalized energy consumption;
• Nuclear Radiation - how people are exposed to radiation from nuclear power plants.

6.3. Materials Science

• Python Materials Genomics - material analysis code used in an established project;
• Materials Mining - scripts for modeling and analysis of materials;
• Emmet - creation of material properties databases;
• Megnet - graph networks as an ML framework for molecules and crystals;
• Atomate - workflows for computational materials science;
• Bylaws Compliance - prediction of property fines;
• Asphalt Binder - building materials, free energy and chemical composition of cementitious asphalt pavement;
• Awesome Materials Informatics is a curatorial list of well-known works in the field of materials science.

7. Economics

7.1. General

• Trading Economics API - Trading Economics API, information for 196 countries;
• Development Economics - microeconomics of development;
• Applied Econ & Fin - Applied Computing Economics and Finance;
• Macroeconomics - Macroeconomics topics with examples from notebook.

7.2. Machine learning

• EconML - automated training and analysis of cause and effect relationships;
• Auctions - the best auctions using deep learning.

7.3. Calculations

• Quant Econ - NYU quantitative economics course;
• Computational - computational methods in economics;
• Computational 2 - Small Course in Computational Economics;
• Econometric Theory - econometric theory notebooks for beginners.

8. Education and research

8.1. Students

• Student Performance - student performance calculated on the basis of machine learning;
• Student Performance 2 - student exam;
• Student Performance 3 - student performance in secondary schools;
• Student Performance 4 - assessment of student performance using Feature Engineering;
• Student Enrolment - registration of students and analysis of results;
• Academic Performance - the study of demographic and family characteristics that affect student performance;
• Grade Analysis - analysis of student achievement.

8.2. School

• School Choice - data analysis for choosing a school;
• School Performance - the practice of analyzing data using data from data.utah.gov about school performance;
• School Performance 2 - using Pandas to analyze school and student performance in the district;
• School Performance 3 - Philadelphia School Performance;
• School Performance 4 - NJ School Performance;
• School Closure - identification of schools at risk of closure by performance indicators and other characteristics;
• School Budgets - tools and methods for school budgeting;
• School Budgets - tools and methods for school budgeting part 2;
• PyCity - school analysis;
• PyCity 2 - a comparison of the school budget and school results;
• Budget NLP - classification of NLP for budget resources;
• Budget NLP 2 - further classification of exercises;
• Budget NLP 3 - Budget Classification;
• Survey Analysis - analysis of a survey in the field of education.

9. Emergencies

9.1. Prevention

• Emergency Mapping - detection of destroyed houses in California;
• Emergency Room - support for making urgent decisions;
• Emergency Readmission - adjusted risk of emergency readmission;
• Forest Fire - forest fire detection using UAV images using CNN;
• Emergency Response - emergency response analysis;
• Emergency Transportation - a transport hint for emergency services;
• Emergency Dispatch - reduced response time due to intelligent modeling, optimization and automation;
• Emergency Calls - emergency call analysis project;
• Calls Data Analysis - call data analysis 911;
• Emergency Response - RL Chemical Plant.

9.2. Crime

• Crime Classification - time analysis of serious attacks, incorrectly classified LAPD;
• Article Tagging - natural language processing in a news article in Chicago;
• Crime Analysis - finding association rules from spatial data for crime analysis;
• Chicago Crimes - Exploring Chicago's public crime data in Python;
• Graph Analytics - The Hague Crimes;
• Crime Prediction - classification, analysis and prediction of Crime in the city of Indore;
• Crime Prediction - developed prognostic models of crime rates;
• Crime Review - analysis of crime review data.
• Crime Trends - analysis of crime trends and problematic conditions that encourage this;
• Crime Analytics - analysis of crime data in Seattle and San Francisco.

9.3. Ambulance

• Ambulance Analysis - a study of changes in the time of ambulance arrival in Victoria;
• Site Location - ambulance location;
• Dispatching - application of game theory and simulation of discrete events to find the optimal solution for dispatching ambulances;
• Ambulance Allocation - time series analysis of ambulance departures in the city of San Diego;
• Response Time - analysis of the improvement in the response time of the ambulance;
• Optimal Routing - a project to find the optimal routing of ambulances;
• Crash Analysis - predicting the probability of accidents in this segment at a given time.

9.4. Disaster management

• Conflict Prediction - Notebooks on Conflict Prediction ;
• Burglary Prediction - spatio-temporal modeling for the prediction of hacks;
• Predicting Disease Outbreak - prediction of disease outbreaks;
• Road accident prediction - forecast for the type of victims of federal traffic accidents in Brazil;
• Text Mining - disaster management using Text mining;
• Twitter and disasters - prediction, disaster tweets;
• Flood Risk - the impact of catastrophic floods;
• Fire Prediction - 4 algorithms for calculating the probability of future fires.

10. Finance

10.1. Trade and investment

• Deep Portfolio - prediction of bond volume based on deep learning;
• AI Trading - modern AI trading methods;
• Corporate Bonds - forecasting the volume of purchase and sale of corporate bonds;
• Simulation - the study of simulation, as part of computational finance;
• Industry Clustering - a project of clustering industries on financial grounds;
• Financial Modeling - HFT-trading and modeling of volatility;
• Trend Following - futures trend after portfolio investment strategy;
• Financial Statement Sentiment - extracting forecasts from financial statements using neural networks;
• Applied Corporate Finance - study of empirical behavior in the stock market;
• Market Crash Prediction - market forecasting using the LPPL model;
• NLP Finance Papers - compilation of quantitative financial documents using machine learning;
• ARIMA-LTSM Hybrid - a hybrid model for predicting future price correlation coefficients of two assets;
• Basic Investments - basic investment tools in Python;
• Basic Derivatives - basic forward contracts and hedging;
• Basic Finance - source codes for laptops of basic financial applications;
• Advanced Pricing ML - additional implementation of achievements in the field of financial machine learning;
• Options and Regression - a financial engineering project for options valuation methods;
• Quant Notebooks - Educational notebooks on quantum finance, algorithmic trading and investment strategy;
• Forecasting Challenge - the task of financial forecasting from G-Research;
• XGboost - a trading algorithm using XgBoost;
• Research Paper Trading - implementing a paper-based strategy using Alpaca Markets;
• Various - options, distribution, modeling;
• ML & RL NYU - Machine Learning and Finance Learning.

10.2. Data

• Datastream - Datastrem from Thomson Reuters, available through Python;
• AlphaVantage - an API wrapper to simplify the process of obtaining free financial data;
• FSA - A project to translate SEC Edgar Filings financial data into custom financial reporting analysis models;
• TradeConnector - relations with market data providers;
• Employee Count SEC Filings - exact values ​​of the number of employees for companies from SEC applications;
• SEC Parsing - NLP for searching and extracting specific information from long unstructured documents;
• Open Edgar - OpenEDGAR;
• Rating Industries - stories from several agencies, converted to CSV format.

11. Health

11.1. General

• zEpid - epidemiological analysis package;
• Python For Epidemiologists - A tutorial on the introduction of epidemiological analysis in Python;
• Prescription Compliance - analysis of prescription and medical compliance;
• Respiratory Disease - tracking respiratory diseases in Olympic athletes;
• Bubonic Plague - Bubonic plague and SIR model.

12. Justice, law and regulation

12.1. Instruments

• LexPredict - control over your contracts;
• AI Para-legal - the world's first AI assistant attorney;
• Legal Entity Detection - detection of a legal entity;
• Legal Case Summarisation - the implementation of various summation algorithms in relation to court decisions;
• Legal Documents Google Scholar - Using Google scholar for programmatic case extraction;
• Chat Bot - chat bot and email notifications;
• Data Generator GDPR is a dummy data generator for GDPR compliance.

12.2. Policy and Regulation

• GDPR scores - forecasting GDPR indicators for legal documents;
• Driving Factors FINRA - definition of factors influencing FINRA arbitration decisions;
• Securities Bias Correction - assessment of the effect of prices on securities litigation;
• Public Firm to Legal Decision - a public firm for legal decisions;
• Night Life Regulation - Australia's nightlife, its regulation and police activities;
• Comments - public comments on government regulation;
• Clustering - clustering Canadian rules;
• Environment - regulation of energy and the environment;
• Risk - the risk of various financial situations;
• FINRA Compliance - the best simulation for compliance.

12.3. Arbitrage practice

• Supreme Court Prediction - prediction of the ideological direction of decisions of the Supreme Court;
• Supreme Court Topic Modeling - thematic modeling in the Supreme Court;
• Judge Opinion - the use of text analysis and machine learning to analyze the opinions of judges on specific issues;
• ML Law Matching - a manufacturer of machine learning matches;
• Bert Multi-label Classification - Bert's multiglare classification.

13. Production

13.1. General

• Green Manufacturing - Mercedes-Benz Greener competition at Kaggle;
• Semiconductor Manufacturing - data analysis of the Semiondutor production line;
• Smart Manufacturing - collaboration on modeling methodology;
• Bosch Manufacturing is a production project of Bosch, Kaggle.

13.2. Maintenance

• Predictive Maintenance 1 - forecasting the remaining life of aircraft engines;
• Predictive Maintenance 2 - time to failure (TTF) or remaining useful life of the engine (RUL);
• Manufacturing Maintenance - simulation of maintenance in production systems.

13.3. Mistakes

• Predictive Analytics - a method for predicting hardware failures;
• Detecting Defects - anomaly detection for defective semiconductors;
• Defect Detection - intelligent defect detection for tablet manufacturing;
• Manufacturing Failures - reduction of production failures;
• Manufacturing Anomalies - Intelligent anomaly detection for the production line.

13.4. Quality

• Quality Control - detection of failures in quality control;
• Manufacturing Quality - an intelligent forecast of production quality;
• Auto Manufacturing - auction car sales data.

14. Media and publishing

14.1. Marketing

• Video Popularity - HIP model for predicting video popularity;
• YouTube transcriber - automatically transcribes YouTube videos;
• Marketing Analytics - marketing analytics, case studies;
• Algorithmic Marketing - models from introduction to the book "Algorithmic Marketing";
• Marketing Scripts - data marketing applications;
• Social Mining - mining of social networks.

15. Physics

15.1. General

• Gamma-hadron Reconstruction - instruments used in ground-based astronomy of gamma radiation;
• Curriculum - Newtonian notebooks;
• Interaction Networks - interaction networks for the study of objects, relationships and physics;
• Particle Physics - generation and analytical code for studying particle physics;
• Computational Physics - repository of computational physics;
• Medical Physics — Python ;
• Medical Physics 2 — , Python ;
• Flow Physics — Python.

15.2.

• Physics ML and Stats — ;
• High Energy — ;
• High Energy GAN — ;
• Neural Networks — .

16.

16.1. Social politics

• Triage — ;
• World Bank Poverty I — , ;
• World Bank Poverty II — , II;
• Overseas Company Land Ownership — ;
• CFPB — ;
• Cannabis Legalisation Effect — ;
• Public Credit Card — ;
• Recidivism Prediction — ;
• Household Poverty — -;
• NLP Public Policy — ;
• World Food Production — ;
• Tax Inequality — ;
• Sheriff Compliance — ICE;
• Apps Detection — ;
• Social Assistance — ;
• Computational Social Science — ;
• Liquor and Crime — ;
• Animal Placement Kennels - optimizing the placement of animals in shelters;
• Staffing Wall is an independent research project on the US Mexican border;
• Worker Fatalities - OSHA Occupational Accident Card.

16.2. Charity

• Census Data API - extracting variables from a 5-year survey of the American community;
• Donor Identification - a machine learning project in which you need to find donors for charity;
• Charity Effectiveness - Collect online data about charities to understand their effectiveness.

16.3. Election analysis

• Election Analysis - analysis of choices and forecasting models;
• American Election Causal - use of ANES data with causal relationship models;
• Campaign Finance and Election Results - study of the relationship between campaign finance and the results of subsequent elections;
• Voting System - voting methods of proportional representation;
• President Vote - vote on the analysis of income.

16.4. Politics

• Congressional politics - House of Representatives of the US Congress;
• Politico - a platform for profiling public figures in Brazilian politics;
• Bots - tools and algorithms for analyzing Paraguayan tweets during the elections;
• Gerrymander tests - many metrics to quantify Gerrymandering;
• Sentiment - analysis of newspapers for their political conviction using subjective sentiments of party representatives;
• DL Politics - A comparison of a socialist party versus a popular party in Brazil;
• PAC Money - the influence of PAC money on US policy;
• Power Networks - creating a watchdog for Indian corporate and political networks;
• Elite - the political elite in the USA;
• Debate Analysis - a program for analyzing political debates;
• Political Affiliation - forecast political affiliation using Twitter metadata;
• Political Ads - an investigation into Facebook of political ads and targeting;
• Political Identity - a multiaxial political model of political identity;
• YT Politics - display policies on YouTube;
• Political Ideology - An uncontrolled study of political ideology using verbal vector projections.

17. Real estate, rental and leasing

17.1. The property

• Finding Donuts - neighborhood forecasting;
• Neighborhood - forecasting real estate prices in a city;
• Real Estate Classification - classification of the type of property taking into account real estate, satellite communications and street view;
• Recommender - a recommender system of the top 5 real estate objects that match the user's search;
• House Price - predicting the price of a house using linear regression and GBR;
• House Price Portland - prediction of housing prices in Portland;
• Zillow Prediction - Zillow score forecast made by Kaggle.

17.2. Rent and leasing

• Analyzing Rentals - analysis and visualization of rental list data;
• Interest Prediction - prediction of people's interest in renting specific apartments in New York;
• Predict Household Poverty - Forecasting Household Poverty in Costa Rica;
• Airbnb public analytics competition - Airbnb public analytics competition .

18. Utilities

18.1. Electric power

• Electricity Price - Comparison of electricity prices in Singapore;
• Electricity-Coal Correlation - determination of the correlation between state tariffs for electricity and coal production over the past decade;
• Electricity Capacity - An analysis of the Los Angeles Times analysis of costly electricity in California;
• Electricity Systems - the optimal system of electricity for European countries;
• Load Disaggregation - intelligent load marking on hidden Markov models;
• Price Forecasting - forecasting electricity prices for the day ahead in the German trading zone with deep neural networks;
• Carbon Index - calculation of CO₂ and electricity intensity in the regions of the country, NERC since 2001;
• Demand Forecasting - forecasting electricity demand in Austin;
• Electricity Consumption - an estimate of electricity consumption from household surveys;
• Electricity French Distribution - analysis of electricity data provided by the French distribution network (RTE);
• Renewable Power Plants - time series of the total installed capacity;
• Wind Farm Flow - a repository of wind farm flow models connected to FUSED-Wind;
• Power Plant - the data set contains 9568 data points collected by the combined cycle power plant for 6 years (2006-2011).

18.2. Coal, Oil and Gas

• Coal Prediction - forecasting coal production;
• Oil & Gas - forecast oil and natural gas prices using ARIMA and neural networks;
• Gas Formula - calculation of the potential economic effect of the price indexation formula;
• Demand Prediction - forecast of demand for natural gas;
• Consumption Forecasting - forecasting the consumption of natural gas;
• Gas Trade is a global natural gas trading model.

18.3. Water pollution

• Safe Water - prediction of drinking water quality disorders based on human health in the United States;
• Hydrology Data - a set of convenient functions for studying water data in Python;
• Water Observatory - monitoring the water level in lakes and reservoirs using satellite images;
• Water Pipelines - the use of machine learning to find water pipes in aerial photographs;
• Water Modelling - Australian community modeling system for assessing water resources;
• Drought Restrictions - analysis of water use in Los Angeles;
• Flood Prediction - applying LSTM to river water level data;
• Sewage Overflow - Sanitary Overflow Analysis (SSO);
• Air Quality Prediction is the air quality forecast (aq) in Beijing and London over the next 48 hours.

18.4. Logistics

• Transdim - creating accurate and effective solutions for the tasks of calculating and predicting the spatio-temporal data of traffic flows of machines;
• Transport Recommendation - context-sensitive recommendation for multimodal transportation;
• Transport Data - data and laptops for Toronto transport;
• Transport Demand - forecasting the demand for public transport in Nairobi;
• Demand Estimation - the introduction of a dynamic assessment of the demand of the sender-receiver;
• Congestion Analysis - analysis of transport systems;
• TS Analysis - analysis of time series according to transport data;
• Network Graph Subway - vulnerability analysis for transport networks;
• Transportation Inefficiencies - quantitative assessment of the inefficiency of transport networks;
• Train Optimization - optimization of train schedules;
• Traffic Prediction - forecasting urban traffic;
• Predict Crashes - fault prediction using multiple data sources;
• AI Supply chain - supply chain optimization system;
• Transfer Learning Flight Delay - the use of variational encoders in Keras to predict flight delay;
• Replenishment is a code for supply chain management.

19. Wholesale and retail trade

19.1. Wholesale

• Customer Analysis - analysis of wholesale customers;
• Distribution - JB Wholesale Distribution Analysis;
• Clustering - clustering of data on product costs collected for customers;
• Market Basket Analysis is a public Instacart dataset with information about which products are often bought together.

19.2. Retail

• Retail Analysis - an online retail data set;
• Online Insights - analysis of online transactions in the UK;
• Retail Cohort - cohort analysis.

On this, our post on the application of ML and DS in industry came to an end. I hope you learned something new for yourself. If you have something that you can share yourself - write in the comments.



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