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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How Aviation Industry Uses Data Science?

S
Sharvari Last Updated : 14 Apr, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Aviation Industry does use AI or rather data science and machine learning to automate or speed up operations. So, in this article, we will see how Aviation Industry uses Data Science with the help of real-life use cases.

 

Revenue Management And Route Planning

Aviation Industry Uses Data Science - Revenue Management And Route Planning
Dmitry Demidko on Unsplash

Understanding traveler demand for specific city pairs and pricing flights are among the main problems airlines solve to survive. To do that carriers must consider thousands of factors when analyzing data. While analysts still can use traditional statistical approaches.

Data science allows for more sophisticated ways to accomplish demand analysis. IATA suggests that airlines can use traveler behavioral data, abandon searches on the online travel agents, and metasearch sites or social media chatter can help define leisure demand.

Data from professional networking sites recruitment and procurement activities may signal emerging business travel destinations. In a 2017 showcase for airlines, Skyscanner used machine learning-based clustering to the group about 50,000 origins and destinations by similarities. They considered about 30 parameters like the month of travel, the time a reservation is made, how long people stay at the destination, and many more.

Some events like festivals conferences or expos, drive short-term spikes in demand so revenue teams can rely on event data to raise fares for specific routes and dates to benefit from rising demand. Aviation ranked by Predict HQ uses ranking algorithms that match historical flight bookings with event data to reveal how much a given event may affect traveler demand.

In-flight Sales And Food Supply

Revenue Management And Route Planning - In-Flight Food Supply
Carolina Ortiz on Unsplash

Some people never order airplane meals so the airline supply management specialists must estimate how many snacks and drinks they onboard to deliver to food eaters without being wasteful. EasyJet CEO John Lundgren tossed the data science team to analyze the demand for food items.

Depending on a route the team learned that demand for items on a 6:00 a.m. flight to Edinburgh is very different from that of a Friday night flight to Ibiza. So, the jet was throwing three fresh food items in the trash after each flight or nearly 800,000 annually. John Lundgren noted that such a mistake cost the carrier millions of pounds. Eventually, data scientists created a new algorithm for demand prediction the insights helped the airline save a significant amount of money and do the right thing for the environment.

 

Fuel Consumption And Optimisation

Fuel Consumption And Optimisation Data Science in Aviation Industry
Jose Lebron on Unsplash

Carbon emissions increased by 32% over the past five years. That’s why aircraft manufacturers and airlines are looking for ways to improve their fuel efficiency. In 2018 airlines spent 23.5% of total expenses on jet fuel. to become more fuel-efficient an airline must accurately predict how much fuel it needs for every scheduled flight to supply a plane the best scenario is to have a single analytical tool.

Southwest Airlines worked on such a solution in its fuel consumption project. The team developed 8 predictive models that included time series algorithms and neural networks the system could produce 9600 fuel consumption forecasts for each month. It generates forecasts for a 12-month horizon and considers such influencing factors as fuel price, number of trips, and time to make predictions much more accurate.

 

Boarding And Checking Bags With Facial Recognition

Boarding And Checking Bags With Facial Recognition
Pascal Müller on Unsplash

Airlines use this biometric technology as a boarding option. The equipment scans travelers’ faces and matches them with photos stored in border control agency databases. These can be photos from passports visas or other travel documents.

Here’s how it works, travelers first gain themselves and then their passports at self-service then proceed to check-in their backs with a scanner. This technology allows for creating a seamless travel experience that’s faster and safer. Delta Airlines opened a biometric terminal at the Atlanta Airport.

In the summer of 2019, Delta introduced facial recognition in another Atlanta Airport terminal as well as in Minneapolis, Detroit and other airports that are 49 more gates equipped with facial recognition software travelers seem to like the new boarding option. Delta surveyed customers at Atlantic terminal and discovered for instance that 70% of them found the biometric boarding experience appealing and 72% favored it over the traditional one.

 

Preparing Plane For Next Flight

Preparing Plane For Next Flight
Markus Winkler on Unsplash

Sometimes we have to wait at the gate to get on a plane when an aircraft isn’t ready for boarding and departure maybe a catering truck is late or the cleaning team is busy with a different jet. In 2018 U.S. passenger airlines were losing on an average of $74.29 per minute, due to delays. The US Department of Transportation calculated that delays caused by plane servicing accounted for 5.8 percent of all delayed flights.

Assaia, a startup that provides software that processes video streams from airfields using image recognition algorithms. Neural networks that power the software recognize objects’ movements and interactions. Airline employees can monitor in real-time how the plane is being prepared for the next flight including fueling, cargo loading, or catering delivery so they can decide whether they need to take measures or not.

Lufthansa systems address the same problem with its deep turnaround solution. The IT service provider for the aviation industry opened its subsidiary zero-g to work on its AI initiatives. The solution also analyzes video data and updates users on what’s going on during airplane services in real-time airlines.

Use of data science and machine learning to evaluate passenger demand across different routes, use data insights to optimize aircraft ground handling and fuelling or redefine passengers airport experience with biometric boarding with new distribution technologies we may expect airlines to start personalizing offers for individual travelers based on their preferences and willingness to pay.

So, these were the real-time use cases in the Aviation industry. Hope you liked it!

Thank You for reading!

Connect with me on:

Twitter: https://twitter.com/aree_yarr_sharu

Linkedin: https://t.co/g0A8rcvcYo?amp=1

The media shown in this article on Data Science in Aviation Industry are not owned by Analytics Vidhya and is used at the Author’s discretion. 

aviation industryblogathondata science
S
Sharvari

I am Sharvari Raut. I love to write. I am a final year student in Computer Science and Engineering from NCER Pune. I have worked as a freelance technical writer for few startups and companies. Having 2 yrs of experience in Technical Writing I have written over 100+ technical articles which are published till now. Writing for Analytics Vidhya is one of my favourite things to do.

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