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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

Data Science In Gaming Industry

S
Sharvari Last Updated : 05 May, 2021
7 min read
This article was published as a part of the Data Science Blogathon.

Introduction

With users spending more time than ever in sports, sports companies became an integral part of the global entertainment industry. These companies are masters of integrated entertainment as they excel in reviving social morality, art, and sharing. The global gaming market will reach $ 115.8 billion by 2018 with 45% mobile phone seizures, Newzoo said in its Global Games Market report.

It’s all about smartphones and social media now and this idea has brought down the market for video game consoles like PlayStation, Xbox, or Nintendo. Not only international countries like Electronic Arts (EA), Sony, or Microsoft but also developers and developers are entering the gaming business as a promising field.

Data Science In Gaming image
Photo by Florian Olivo on Unsplash

By playing to set foot on social media, there are a lot of details for sports companies to deal with. Gambling company, Zynga has launched games such as Zynga Poker, Farmville, Chess with Friends, Speed ​​Guess Something, and Words with Friends on social media. This has brought a lot of user connections and produces big data sets.

This is where the gaming industry needs data science to use this data collected from players across all social networks. Data analysis gives gamers a compelling, novel distraction to stay ahead of the race! One of the most interesting uses of data science is within the features and processes surrounding game development.

Game Collection and Data Collection

Photo by Igor Karimov on Unsplash

Games are used as a data collection method. The main idea is to track the data entered in the form of a periodic click and to record the user input of that image frame. This data is then used to draw the final result, for example – the final points.

From creating artificial intelligence systems to finding transferable algorithms, Data Scientists use data sources from in-depth learning games. This type of learning is appealing from a data scientist’s point of view as it helps in finding common and common ways to use not only the current sports project but also the games and future programs.

Game Data Scientist

Data Science In Gaming - game data scientist
Photo by Fredrick Tendong on Unsplash

A data scientist develops and investigates, designs and uses ideas, and organizes tests to test a variety of ideas. They are also responsible for creating mathematical models and automated analytics tools for identifying points and using the game. If you want to join the gaming industry as a data scientist and are interested in playing games, data mining, and modeling, then it is an added benefit for you.

To be part of the Advanced Mathematics Team, an in-depth data learning scientist needs to extract large amounts of data and perform extensive data analysis, and develop descriptive, predictable, and descriptive models using in-depth / machine learning algorithms. If you have a passion for problem-solving, creative solutions, and continuous learning, then this is the role you want to play.

 

Game Data Analytics

Data analytics is responsible for evaluating and visualizing service performance and user conversion data to find opportunities to improve user engagement and user retention. They use data analysis techniques to identify logical relationships, patterns, styles, and user behavior models from complex data sets to guide road maps and build automated detection systems and track their performance.

 

Identifying an object

Realistic graphics, the use of artificial ingenuity, and pressing the boundaries of graphic realities are now among the key functions of game developers and designers. Image recognition technology is predicted to transform the gaming industry. Along with object acquisition models, they are used by the engineer to create a natural transformation of scenes and movements in the real gaming space.

For example, these models are often used to distinguish players from different teams and to give instructions to a particular character within a team. The difference between forms, objects, obstacles, and figures becomes easier and faster for the player. In addition, object identification models and algorithms are used to identify body movements in order to transmit and display these actions on the interactive game screen.

Real-life Use Case: Activision uses Data Science to stop game hacker or cheaters

Activision, the main engineer of the first video game series for people Call of Duty (COD), is one company that uses big data to improve their games.
Game Science Division (GSD), which is in charge of collecting and analyzing Big Gaming Data at Activision, had to deal with the issue of empowerment among its players in COD.
In speaking of players, empowerment refers to the attempt to increase someone else’s sports scores by negative means such as meeting one player better than the other players and throwing the game on both sides where one side deliberately loses to allow the other side to win.
It sounds like cheating when you think about it and magnification has its consequences. Not only does a growing player gain unprecedented fame, but the strategy of promoting and can affect other players ’ratings and the balance of the prize systems.
That’s why augmentation is looked down upon by many players and needs to be addressed before it can be played by innocent players.
The GSD of Activision creates programs based on machine learning to detect power boosting, identifies key indicators for increasing COD, and tracking those indicators. The two references are players to a list of friends that end up in different teams equally after the game and birthplaces where death is found after a very short time and again and again.
After analyzing the verification indicator data, GSD then communicates with its users on Twitter about expanding and collecting data across all COD tweets to help make decisions about game and player base features.

Personalized Marketing And Ads

Personalized Marketing And Ads
Photo by Carlos Muza on Unsplash

Customized marketing is actively used in various industries to avoid useless, annoying, and ineffective advertising. Both marketers and game developers are interested in customer-focused interactions and lead to the creation of meaningful marketing messages and send them to the right people. In any case, video game providers are collecting data that will help attract more audiences.

Personalized marketing in games helps to increase user activity and at the same time attract new ones. This can be achieved due to the specific adjustment of the advertising message. To ensure that your ads are clearly visible you need to know which players are responding to the ad and which are not.

Data-driven ads in Games 

Suppose we are playing a collaborative mobile game called an Episode, in which we help the main character of the story choose a course of action or a sentence to say, which ultimately determines what happens later in the story. The story may contain a number of episodes and the World needs to include each episode.
In order for game developer Pocket Gems to earn money automatically, they play two 30-second ads every time We complete an episode. And since we get four Passes every few hours, we would complete the Passes by inserting four episodes in a row and we have to wait a few hours to renew the Passes.
Another way to wait is to buy Passes for real money, which not everyone wants to do. Pocket Gems may have found this as an opportunity to earn money through multiple ads by giving players the opportunity to play the ad to cut the waiting time by half an hour.
Ads are usually made by companies or personalities based on my country,  the game shows us ads based on our location or nationality.

Fraud Detection

Fraud Detection
Photo by Element5 Digital on Unsplash

All actions and decisions in the sports world are fast. The high speed of all these processes raises the issue of high interest in fraudsters. Therefore, companies face the need to prevent fraudulent activity, yet to maintain a high level of customer satisfaction. Safety issues are a challenge for all industries.

Various player verification solutions are widely used in gaming. The point is that those game developers are obliged to use player certification legally. Also, various verification strategies allow for the detection of questionable accounts and actions in advance. In addition, these strategies are used to prevent identity theft, which is widespread in the visual gaming world.

Payment fraud is also very common in sports. Fraudsters often create special bots to obtain the required payment details. Therefore, gaming companies need to ensure maximum security for personal information about the activity and transactions made.
Machine learning algorithms help game companies. Their application allows for quick identification of suspicious activity. They make fraud detection more automated and effective because of the amount of data they can process.

 

Game Design

Game Design - Data Science In Gaming
Photo by Igor Karimov on Unsplash

Game design has become an art with the rapid development of modern technology. In addition, game design has become an amazingly popular site for building successful developers’ work. It is a complex process that requires a variety of programming, visualization, and animation skills.

The use of good visual effects is no longer to keep players engaged. Gambling data details and engineering innovations help create a complex interactive game environment. Data from game analytics is used to obtain specific information about what a player wants, predicting game issues, consultations, and time. New game concepts, news stories, and machines are built using previously acquired data.

The gaming industry makes full use of data analytics. Technology, finance, game, marketing, strategy, just say the domain of statistics and you will find it works with the revenue in the gaming industry.

Thanks For Reading!

About Me:

Hey! I am Sharvari Raut and I love to write!

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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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