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https://github.com/firmai/business-machine-learning
A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)
https://github.com/firmai/business-machine-learning
business-machine-learning datascience example jupyter jupyter-notebook machine-learning practical-machine-learning
Last synced: 13 days ago
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A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations (by @firmai)
- Host: GitHub
- URL: https://github.com/firmai/business-machine-learning
- Owner: firmai
- License: cc0-1.0
- Created: 2019-02-19T19:02:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-18T09:24:32.000Z (almost 3 years ago)
- Last Synced: 2024-04-18T11:26:09.949Z (7 months ago)
- Topics: business-machine-learning, datascience, example, jupyter, jupyter-notebook, machine-learning, practical-machine-learning
- Language: Jupyter Notebook
- Homepage: https://www.linkedin.com/company/firmai
- Size: 384 KB
- Stars: 756
- Watchers: 55
- Forks: 222
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-resources - **[List, Practice - machine-learning?style=social) (Table of Contents)
README
# Business Machine Learning and Data Science Applications
---------
## 🌟 We Are Growing!
We're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If you’re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study.
### 🚀 About Sov.ai
Sov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with **three of the top 10** quantitative hedge funds, and with many mid-sized and boutique firms.
Our platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions.
By joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.
### 🔍 Research and Project Opportunities
We offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:
- **Predictive Modeling with GitHub Logs:** Develop models to predict market trends and investment opportunities using GitHub activity and developer data.
- **Satallite Data Analysis:** Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.
- **Data Imputation Techniques:** Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.
Please visit [docs.sov.ai](https://docs.sov.ai) for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them.
### 🌐 Why Join Sov.ai?
- **Innovative Environment:** Engage with the latest technologies and methodologies in machine learning and finance.
- **Collaborative Team:** Work alongside a team of experts passionate about driving innovation in investment insights.
- **Flexible Projects:** Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.
- **Experienced Researchers:** Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.
- **Post Research:** Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.
### 🤝 How to Apply
If you’re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, we’d love to hear from you! Please reach out to us at [[email protected]](mailto:[email protected]) with your resume and a brief description of your research interests.
Join us in shaping the future of investment insights and making a meaningful impact in the world of finance!
## Table of Contents
### Department Applications
- [Accounting](#accounting)
- [Machine Learning](#accounting-ml)
- [Analytics](#accounting-analytics)
- [Textual Analysis](#accounting-text)
- [Data](#accounting-data)
- [Research and Articles](#accounting-ra)
- [Websites](#accounting-web)
- [Courses](#accounting-course)
- [Customer](#customer)
- [Lifetime Value](#customer-clv)
- [Segmentation](#customer-seg)
- [Behaviour](#customer-behave)
- [Recommender](#customer-rec)
- [Churn Prediction](#customer-cp)
- [Sentiment](#customer-sent)
- [Employee](#employee)
- [Management](#employee-man)
- [Performance](#employee-perf)
- [Turnover](#employee-general-turn)
- [Conversations](#employee-con)
- [Physical](#employee-ph)
- [Legal](#legal)
- [Tools](#legal-tools)
- [Policy and Regulatory](#legal-pr)
- [Judicial](#legal-judicial)
- [Management](#management)
- [Strategy](#management-strat)
- [Decision Optimisation](#management-do)
- [Causal Inference](#management-causal)
- [Statistics](#management-stat)
- [Quantitative](#management-quant)
- [Data](#management-data)
- [Operations](#operations)
- [Failures and Anomalies](#operations-fail)
- [Load and Capacity Management](#operations-load)
- [Prediction Management](#operations-predict)
#### Also see [Python Business Analytics](https://github.com/firmai/python-business-analytics)
#### Machine Learning
* [Chart of Account Prediction](https://github.com/agdgovsg/ml-coa-charging ) - Using labeled data to suggest the account name for every transaction.
* [Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb) - Using deep-learning frameworks to identify accounting anomalies.
* [Financial Statement Anomalies](https://github.com/rameshcalamur/fin-stmt-anom) - Detecting anomalies before filing, using R.
* [Useful Life Prediction (FirmAI)](http://www.firmai.org/documents/Aged%20Debtors/) - Predict the useful life of assets using sensor observations and feature engineering.
* [AI Applied to XBRL](https://github.com/Niels-Peter/XBRL-AI) - Standardized representation of XBRL into AI and Machine learning.
#### Analytics
* [Forensic Accounting](https://github.com/mschermann/forensic_accounting) - Collection of case studies on forensic accounting using data analysis. On the lookout for more data to practise forensic accounting, *please get in [touch](https://github.com/mschermann/)*
* [General Ledger (FirmAI)](http://www.firmai.org/documents/General%20Ledger/) - Data processing over a general ledger as exported through an accounting system.
* [Bullet Graph (FirmAI)](http://www.firmai.org/documents/Bullet-Graph-Article/) - Bullet graph visualisation helpful for tracking sales, commission and other performance.
* [Aged Debtors (FirmAI)](http://www.firmai.org/documents/Aged%20Debtors/) - Example analysis to invetigate aged debtors.
* [Automated FS XBRL](https://github.com/CharlesHoffmanCPA/charleshoffmanCPA.github.io) - XML Language, however, possibly port analysis into Python.
* [Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis) - Sentiment, distance and proportion analysis for trading signals.
* [Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb) - Comprehensive NLP techniques for accounting research.
* [EDGAR](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb) - A walk-through in how to obtain EDGAR data.
* [IRS](http://social-metrics.org/sox/) - Acessing and parsing IRS filings.
* [Financial Corporate](http://raw.rutgers.edu/Corporate%20Financial%20Data.html) - Rutgers corporate financial datasets.
* [Non-financial Corporate](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html) - Rutgers non-financial corporate dataset.
* [PDF Parsing](https://github.com/danshorstein/python4cpas/blob/master/03_parsing_pdf_files/AR%20Aging%20-%20working.ipynb) - Extracting useful data from PDF documents.
* [PDF Tabel to Excel](https://github.com/danshorstein/ficpa_article) - How to output an excel file from a PDF.
* [Understanding Accounting Analytics](http://social-metrics.org/accountinganalytics/) - An article that tackles the importance of accounting analytics.
* [VLFeat](http://www.vlfeat.org/) - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.
* [Rutgers Raw](http://raw.rutgers.edu/) - Good digital accounting research from Rutgers.
* [Computer Augmented Accounting](https://www.youtube.com/playlist?list=PLauepKFT6DK8TaNaq_SqZW4LIDJhCkZe2) - A video series from Rutgers University looking at the use of computation to improve accounting.
* [Accounting in a Digital Era](https://www.youtube.com/playlist?list=PLauepKFT6DK8_Xun584UQPPsg1grYkWw0) - Another series by Rutgers investigating the effects the digital age will have on accounting.
#### Lifetime Value
* [Pareto/NBD Model](https://github.com/zabahana/Customer-LifeTime-Value-Analysis/blob/master/CLV%20Analysis.ipynb) - Calculate the CLV using a Pareto/NBD model.
* [Cohort Analysis](https://github.com/iris9112/Customer-Segmentation/blob/master/Chapter1-Cohort_Analysis.ipynb) - Cohort analysis to group customers into mutually exclusive cohorts measured over time.
* [E-commerce](https://github.com/jalajthanaki/Customer_segmentation/blob/master/Cust_segmentation_online_retail.ipynb ) - E-commerce customer segmentation.
* [Groceries](https://github.com/harry329/CustomerFinding/blob/master/customer_segments.ipynb ) - Segmentation for grocery customers.
* [Online Retailer](https://github.com/Vinayak02/CustomerCentricRetail/blob/master/CustomerSegmentation/Customer_Segmentation_Online_Retail.ipynb) - Online retailer segmentation.
* [Bank](https://github.com/Mogbo/Customer-Clustering-Segmentation-Challenge) - Bank customer segmentation.
* [Wholesale](https://github.com/SyedAdilAli93/Identifying-Customers/blob/master/customer_segments.ipynb) - Clustering of wholesale customers.
* [Various](https://github.com/abalaji-blr/CustomerSegments/tree/master/deliver ) - Multiple types of segmentation and clustering techniques.
* [RNN](https://github.com/DaniSanchezSantolaya/RNN-customer-behavior/tree/master/src) - Investigating customer behaviour over time with sequential analysis using an RNN model.
* [Neural Net](https://github.com/Vinayak02/CustomerCentricRetail/blob/master/DemandForecasting/NeuralNetworks.ipynb) - Demand forecasting using artificial neural networks.
* [Temporal Analytics](https://github.com/riccotti/CustomerTemporalRegularities) - Investigating customer temporal regularities.
* [POS Analytics](https://github.com/IBM/customer_pos_analytics/blob/master/code/Customer%20Ranking%20POS%20wip.ipynb) - Analytics driven customer behaviour ranking for retail promotions using POS data.
* [Wholesale Customer](https://github.com/kralmachine/WholesaleCustomerAnalysis/blob/master/WhosaleCustomerAnalysis.ipynb) - Wholesale customer exploratory data analysis.
* [RFM](https://github.com/espin086/customer_growth/blob/master/rfm/rfm.ipynb) - Doing a RFM (recency, frequency, monetary) analysis.
* [Returns Behaviour](https://github.com/adarsh2111/Customer-Returns-Analysis-Customer-Fraud-Detection-/blob/master/Returns%20Analysis.ipynb) - Predicting total returns and fraudulent returns.
* [Visits](https://github.com/Ryanfras/Customer-Visits/blob/master/Customer%20Visits.ipynb) - Predicting which day of week a customer will visit.
* [Bank: Next Purchase](https://github.com/albertcdc/Project_CAJAMAR) - A project to predict bank customers' most probable next purchase.
* [Bank: Customer Prediction](https://github.com/rohangawade/Predicting-Target-customers-for-Bank-Policy-subscribtion-using-Logistic_Regression_Transparency) - Predicting Target customers who will subscribe the new policy of the bank.
* [Next Purchase](https://github.com/Featuretools/predict-next-purchase) - Predict a customers’ next purchase also using feature engineering.
* [Customer Purchase Repeats](https://github.com/kpei/Customer-Analytics/blob/master/customer_zakka.ipynb) - Using the lifetimes python library and real jewellery retailer data analyse customer repeat purchases.
* [AB Testing](https://github.com/sushant2811/customerAnalyticsWithA-BTesting/blob/master/customerAnalyticsWithA-BTesting.ipynb) - Find the best KPI and do A/B testing.
* [Customer Survey (FirmAI)](http://www.firmai.org/documents/Customer%20Survey/) - Example of parsing and analysing a customer survey.
* [Happiness](https://github.com/rohit6205/predictHappiness/blob/master/predictingHapiness.ipynb) - Analysing customer happiness from hotel stays using reviews.
* [Miscellaneous Customer Analytics](https://github.com/mapr-demos/customer360) - Various tools and techniques for customer analysis.
* [Recommendation](https://github.com/annalucia1/Customer-Behavior-Analysis-Recommendation/blob/master/recomendation_by_RatingScore.ipynb) - Recommend the songs that a customer on a music app would prefer listening to.
* [General Recommender](https://github.com/Vinayak02/CustomerCentricRetail/blob/master/RecommenderSystem/Recommender.ipynb) - Identifying which products to recommend to which customers.
* [Collaborative Filtering](https://github.com/codeBehindMe/CustomerIntelligence/blob/master/CollaborativeFiltering.ipynb ) - Customer recommendation using collaborative filtering.
* [Up-selling (FirmAI)](http://www.firmai.org/documents/Expected%20Value%20Business%20Model%20Performance/ ) - Analysis to identify up-selling opportunities.
* [Ride Sharing](https://github.com/MSopranoInTech/Churn-prediction/blob/master/Churn%20Prediction.ipynb) - Identify customer churn rates in order to target customers for retention campaigns.
* [KKDBox I](https://github.com/naomifridman/Deep-VAE-prediction-of-churn-customer) - Variational deep autoencoder to predict churn customer
* [KKDBox II](https://github.com/Featuretools/predict-customer-churn) - A three step customer churn prediction framework using feature engineering.
* [Personal Finance](https://github.com/smit5490/CustomerChurn) - Predict customer subscription churn for a personal finance business.
* [ANN](https://github.com/AgarwalGeeks/customer-churn-Analysis/blob/master/ANN.ipynb) - Churn analysis using artificial neural networks.
* [Bike](http://www.firmai.org/documents/Customer%20Segmentation/) - Customer bike churn analysis.
* [Cost Sensitive](https://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/10_CS_Churn.ipynb) - Cost sensitive churn analysis drivenby economic performance.
* [Topic Modelling](https://github.com/Chrisjw42/ZLSurveyAnalysis) - Topic modelling on a corpus of customer surveys from the VR industry.
* [Customer Satisfaction](https://github.com/BoulderDataScience/kaggle-santander) - Predict customer satisfaction using Kaggle data.
#### Management
* [Personality Prediction ](https://github.com/jcl132/personality-prediction-from-text) - Predict Big 5 Personality from text.
* [Salary Prediction Resume](https://github.com/Artifelse/Prediction-salary-on-the-base-of-the-resume/blob/master/NLP.ipynb) - Textual analyses over resume to predict appropriate salary [Project Disappeared, still a cool idea]
* [Employee Review Analysis](https://github.com/jackyip1/Indeed-Reviews/blob/master/Python%20scripts/Indeed%20-%20Main.ipynb) - Review analytics for top 50 retail companies on Indeed.
* [Diversity Analysis](https://github.com/mtfaye/Employee-Diversity-in-Tech/blob/master/Data%20Viz%20Special%20Edition.ipynb) - A simple analysis of gender and race disparity in the tech industry.
* [Occupation Prediction](https://github.com/RashmiSingh24/OccuptionPrediction/blob/master/BurningGlass.ipynb) - Predict the likelihood that an occupation is analytical.
#### Performance
* [Training Hours Performance](https://github.com/niqueerdo/MLpredictemployeedevelopment/blob/master/Working%20Ntbk_MODELS_Clustering.ipynb) - The impact of training ours on employee performance.
* [Promotion Prediction](https://github.com/AbinSingh/Employee-Promotion-Prediction/blob/master/Employee_Promotion_Prediction.ipynb) - Analysing promotion patterns.
* [Employee Attendance prediction](https://github.com/lokesh1233/Employee_Attendance/tree/master/notebooks) - Various tools to predict employee attendance.
#### Turnover
* [Early Leaving Employees](https://github.com/anushuk/ML-Human-Resources-Analytics/blob/master/Human%20Resources%20Analytics.ipynb ) - Identifying why the best and most experienced employees leaving prematurely.
* [Employee Turnover](https://github.com/randylaosat/Predicting-Employee-Turnover-Complete-Guide-Analysis/blob/master/HR%20Analytics%20Employee%20Turnover/HR_Analytics_Report.ipynb) - Identifying factors associated with employee turnover.
#### Conversations
* [Slack Communication Analysis](https://github.com/stiebels/slack_nlp/blob/master/Slack%20Analytics.ipynb) - Producing meaningful visualisations from slack conversations.
* [Employee Relationships from Conversations ](https://github.com/yuwie10/cultivate) - Identifying employee relationships from emails for improved HR analytics.
* [Categorise Employee Requests](https://github.com/denizn/Request-classification-via-TFIDF) - Classifying employee requests via TFDIF Vectorizer and RandomForestClassifier.
#### Physical
* [Employee Face Recognition](https://github.com/ckarthic/Face-Recognition) - A face recognition implementation.
* [Attendance Management System](https://github.com/mrsaicharan1/face-rec-a) - An attendance management system using face recognition.
#### Tools
* [LexPredict](https://github.com/LexPredict/lexpredict-contraxsuite ) - Software package and library.
* [AI Para-legal](https://github.com/davidawad/lobe) - Lobe is the world's first AI paralegal.
* [Legal Entity Detection](https://github.com/hockeyjudson/Legal-Entity-Detection/blob/master/Dataset_conv.ipynb) - NER For Legal Documents.
* [Legal Case Summarisation](https://github.com/Law-AI/summarization) - Implementation of different summarisation algorithms applied to legal case judgements.
* [Legal Documents Google Scholar](https://github.com/GirrajMaheshwari/Web-scrapping-/blob/master/Google_scholar%2BExtract%2Bcase%2Bdocument.ipynb ) - Using Google scholar to extract cases programatically.
* [Chat Bot](https://github.com/akarazeev/LegalTech) - Chat-bot and email notifications.
#### Policy and Regulatory
* [GDPR scores](https://github.com/erickjtorres/AI_LegalDoc_Hackathon) - Predicting GDPR Scores for Legal Documents.
* [Driving Factors FINRA](https://github.com/siddhantmaharana/text-analysis-on-FINRA-docs) - Identify the driving factors that influence the FINRA arbitration decisions.
* [Securities Bias Correction](https://github.com/davidsontheath/bias_corrected_estimators/blob/master/bias_corrected_estimators.ipynb ) - Bias-Corrected Estimation of Price Impact in Securities Litigation.
* [Public Firm to Legal Decision](https://github.com/anshu3769/FirmEmbeddings) - Embed public firms based on their reaction to legal decisions.
#### Judicial Applied
* [Supreme Court Prediction](https://github.com/davidmasse/US-supreme-court-prediction) - Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model.
* [Supreme Court Topic Modeling](https://github.com/AccelAI/AI-Law-Minicourse/tree/master/Supreme_Court_Topic_Modeling) - Multiple steps necessary to implement topic modeling on supreme court decisions.
* [Judge Opinion](https://github.com/GirrajMaheshwari/Legal-Analytics-project---Court-misclassification) - Using text mining and machine learning to analyze judges’ opinions for a particular concern.
* [ML Law Matching](https://github.com/whs2k/GPO-AI) - A machine learning law match maker.
* [Bert Multi-label Classification](https://github.com/brightmart/sentiment_analysis_fine_grain) - Fine Grained Sentiment Analysis from AI.
* [Some Computational AI Course](https://www.youtube.com/channel/UC5UHm2J9pbEZmWl97z_0hZw) - Video series Law MIT.
#### Strategy
* [Topic Model Reviews](https://github.com/chrisjcc/DataInsight/blob/master/Topic_Analysis/Topic_modeling_Amazon_Reviews.ipynb) - Amazon reviews for product development.
* [Patents](https://github.com/agdal1125/patent_analysis) - Forecasting strategy using patents.
* [Networks](https://github.com/JohnAnthonyBowllan/BusinessAI/blob/master/DataAnalysis_FeatureEngineering/businessCommunitiesMethod.ipynb) - Business categories from Yelp reviews using networks can help to identify pockets of demand.
* [Company Clustering](https://github.com/DistrictDataLabs/company-clustering) - Hierarchical clusters and topics from companies by extracting information from their descriptions on their websites
* [Marketing Management](https://github.com/Jiseong-Michael-Yang/Marketing-Management) - Programmatic marketing management.
#### Decision Optimisation
* [Constraint Learning](https://github.com/abrahami/Constraint-Learning) - Machine learning that takes into account constraints.
* [Fairlearn](https://github.com/Microsoft/fairlearn) - I think it is called cost-sensitive machine learning.
* [Multi-label Classification](https://github.com/ej0cl6/csmlc) - Cost-Sensitive Multi-Label Classification
* [Multi-class Classification](https://github.com/david-cortes/costsensitive) - Cost-sensitive multi-class classification (Weighted-All-Pairs, Filter-Tree & others)
* [CostCla](http://albahnsen.github.io/CostSensitiveClassification/) - Costcla is a Python module for cost-sensitive machine learning (classification) built on top of Scikit-Learn
* [DEA Software](https://araith.github.io/pyDEA/) - pyDEA is a software package developed in Python for conducting data envelopment analysis (DEA).
* [Covering Set (FirmAI)](http://www.firmai.org/documents/Covering%20Set/) - Constraint programming analysis.
* [Insurance (FirmAI)](http://www.firmai.org/documents/Insurance/) - CP Insurance analysis.
* [Machine Learning + CP (FirmAI)](http://www.firmai.org/documents/MachineLearningand%20Optimisation/) - Machine Learning + Optimisation.
* [Post Office (FirmAI)](http://www.firmai.org/documents/Post%20Office/) - Post Office optimisation.
* [Soda - CP (FirmAI)](http://www.firmai.org/documents/soda_promotion-adapted-cp/) - Constraint Programming + ML.
* [Soda - Knapsack (FirmAI)](http://www.firmai.org/documents/soda_promotion-adapted-knapsack/) - Knapsack algorithm + ML.
* [Soda - MLP (FirmAI)](http://www.firmai.org/documents/soda_promotion-adapted-mip/) - MLP analysis + ML.
#### Casual Inference
* [Marketing AB Testing](https://github.com/chrisjcc/DataInsight/tree/master/ABtesting) - A/B Testing Experiment.
* [Legal Studies](https://github.com/Akesari12/Intro_Causal_Inference) - Instrumental and discontinuity causal approach.
* [A-B Test Result (FirmAI)](http://www.firmai.org/documents/Analyze_ab_test_results/) - Initial A-B Results.
* [Causal Regression (FirmAI) ](http://www.firmai.org/documents/causal_regression/) - Regression technique for causal estimate.
* [Frequentist vs Bayesian A-B Test (FirmAI)](http://www.firmai.org/documents/frequentist-bayesian-ab-testing/) - Comparison between frequentist and bayesian A-B testing.
* [A-B Test Power Analysis (FirmAI)](http://www.firmai.org/documents/Power%20analysis%20for%20AB%20tests/) - Sample size estimation to match testing power.
* [Variance Reduction A-B test (FirmAI)](http://www.firmai.org/documents/variance-reduction/) - Techniques to reduce variance in A-B tests.
#### Statistics
* [Various](https://github.com/khanhnamle1994/applied-machine-learning/tree/master/Statistics) - Various applies statistical solutions
#### Quantitative
* [Applied RL](https://github.com/mimoralea/applied-reinforcement-learning) - Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks
* [Process Mining](https://github.com/yesanton/Process-Sequence-Prediction-with-A-priori-knowledge) - Leveraging A-priori Knowledge in Predictive Business Process Monitoring
* [TS Forecasting](https://github.com/khanhnamle1994/applied-machine-learning/tree/master/Time-Series-Forecasting) - Time series forecasting for important business applications.
####
#### Data
* [Web Scraping (FirmAI)](www.firmai.org/data/) - Web scraping solutions for Facebook, Glassdoor, Instagram, Morningstar, Similarweb, Yelp, Spyfu, Linkedin, Angellist.
#### Failure and Anomalies
* [Anomalies](https://github.com/yzhao062/anomaly-detection-resources) - Anomaly detection resources.
* [Intrusion Detection](https://nbviewer.jupyter.org/github/albahnsen/ML_SecurityInformatics/blob/master/exercises/05-IntrusionDetection.ipynb) - Detecting network intrusions.
* [APS Failure](https://github.com/Nisarg9795/Anomaly-Detection-APS-failures-in-Scania-trucks/blob/master/1_LR_Final_Code.py ), [Data](https://archive.ics.uci.edu/ml/datasets/APS+Failure+at+Scania+Trucks) - Investigating APS failures in Scania trucks.
* [Hardware Failure](https://github.com/AbertayMachineLearningGroup/machine-learning-SIEM-water-infrastructure) - Using different machine learning techniques in detecting anomalies.
* [Anomaly KIs](https://github.com/haowen-xu/donut),[Paper](https://arxiv.org/abs/1802.03903) - Anomaly detection algorithm for seasonal KPIs.
#### Load and Capacity Management
* [House Load Energy](https://github.com/giorgosfatouros/Appliances-Energy-Load-Prediction) - Linear, SVR and Random Forest models to predict house's appliances energy Load.
* [Uber Load Management](https://github.com/brianallen131/Uber-Predictive-Load-Management) - Uber predictive load management.
* [Capacity Management](https://github.com/nerdiejack/capacity_management/blob/master/notebooks/MyWebshopAssignmentWithSolution.ipynb) - Investigating IT stability issues are caused by capacity constraints.
* [Bike Sharing](https://github.com/chrisjcc/DataInsight/blob/master/DataChallenge/BikeShare_Challenge.ipynb) - XGBRegressor, RandomForestRegressor, GradientBoostingRegressor combined with feature selection.
* [Airline Fleet Segmentation](http://htmlpreview.github.io/?https://github.com/atul-shukla-INSEAD/GroupProjectBDA/blob/master/GroupProject.html) - Analysis of Delta airlines.
* [Airbnb](http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/AirbnbReport2016Jan.html) - Airbnb Booking Analysis.
#### Prediction Management
* [Dispute Prediction](https://github.com/zhanghaizhen/Financial-Service-Complaint-Management/tree/master/ipynb) - Financial service complaint management.
* [Fight Delay Prediction](https://github.com/cavaunpeu/flight-delays/blob/master/notebooks/flight-prediction.ipynb) - Transfer learning for flight-delay prediction via variational autoencoders in Keras.
* [Electric Fault Prediction](https://github.com/susano0/Electric-Fault-Prediction/blob/master/Fault_pred.ipynb) - Predict tripping at grid stations by applying simple machine learning algorithms.
* [Popularity Prediction in R](https://github.com/s-mishra/featuredriven-hawkes/blob/master/code/marked_hawkes_point_process.ipynb) - Marked Hawkes Point Process .