This document summarizes the use of machine learning to optimize advertising campaigns for a media company. It discusses collecting click and conversion data from various sources, cleaning the data, then using exploratory analysis and linear regression to see correlations between clicks, conversions and state aids by year, gender and location. Different machine learning models like linear regression and XGB regression are tested on partitioned training and test sets, with XGB regression showing best accuracy. The models help identify states most affected by state aids and allow limiting ads in those states to reduce state aids while optimizing click to aid ratios. This helps prolong the client contract by increasing pure sales.
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle AlphaQuantopian
From QuantCon 2017: At J.P. Morgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.
In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.
How ml can improve purchase conversionsSudeep Shukla
- What is Machine Learning and what problems can it solve?
- Basic Machine Learning models
- Data gathering and data cleaning
- Parameters for judging whether the model is performing well?
- Making it easy for sales & marketing teams to use the ML program
ML Drift - How to find issues before they become problemsAmy Hodler
Over time, our AI predictions degrade. Full Stop.
Whether it's concept drift where the relationships of our data to what we're trying to predict as changed or data drift where our production data no longer resembles the historical training data, identifying meaningful ML drift versus spurious or acceptable drift is tedious. Not to mention the difficulty of uncovering which ML features are the source of poorer accuracy.
This session looked at the key types of machine learning drift and how to catch them before they become a problem.
"Alpha from Alternative Data" by Emmett Kilduff, Founder and CEO of Eagle AlphaQuantopian
From QuantCon 2017: At J.P. Morgan's annual quantitative conference 93% of investors said alternative data will change the investment landscape.
In this presentation, Emmett will discuss the rapidly increasing adoption of alternative data, give a detailed overview of the 24 different types of alternative data, outline the applications of alternative data for quantitative funds, discuss interesting datasets that are available (including Asian datasets) and present case studies that evidence value in alternative datasets.
How ml can improve purchase conversionsSudeep Shukla
- What is Machine Learning and what problems can it solve?
- Basic Machine Learning models
- Data gathering and data cleaning
- Parameters for judging whether the model is performing well?
- Making it easy for sales & marketing teams to use the ML program
Turnover Prediction of Shares Using Data Mining Techniques : A Case Study csandit
Predicting the Total turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task at hand. Data mining is a
well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of
predicting future trends, their efficiency is questionable as their predictions suffer from a high
error rate. The objective of this paper is to investigate various existing classification algorithms
to predict the turnover of different companies based on the Stock price. The authorized datasetfor predicting the turnover was taken from www.bsc.com and included the stock market valuesof various companies over the past 10 years. The algorithms were investigated using the ‘R’
tool. The feature selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the Total Turnover of
the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious
stock markets trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of the stock market attributes was established as well.
Going Big : Why Companies Need to Focus on Operational Analytics Capgemini
Given that digitization had such a transformative effect on customer behavior and relationships, it is perhaps not surprising that many organizations focused their digital transformation efforts on the customer experience front-end. However, in the race to focus on the customer, it was all too easy to ignore operations. Our 2013 research with MIT Sloan Management Review found that while 40% of digital initiatives were focused on the customer experience, this dropped to 26% for operations.
Times are changing, however. Our latest survey of more than 600 executives from the US, Europe and China finds that over 70% of organizations now put more emphasis on operations than on consumer-focused processes for their analytics initiatives. Analytics in operations is increasingly seen as a strategic priority for organizations. Over 80% of respondents agreed that analytics in operations plays a pivotal role in driving profits or creating competitive advantage
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
The importance of benchmarking software projects - Van Heeringen and OgilvieHarold van Heeringen
Benchmarking is a crucial management activity that enables organizations to understand how competitive they are. Using functional size measurement methods and historical data enables organizations to improve their processes and become more succesful.
View Related videos:-
Truth about Supply Demand Planning:-
http://www.youtube.com/watch?v=K66q2o1ED3c
Demantra Vs Oracle Demand Planning
http://www.youtube.com/watch?v=QwAzP3T6ut4
Another slideshare PPT:-
http://www.slideshare.net/amitforu78/demantra-vs-oracle-demand-planning
Contact me at www.ezdia.com
<a>AsiaLinks</a>
Earned Value Management Meets Big DataGlen Alleman
The Earned Value Management System (EVMS) maintains period–by–period data in its underlying databases. The contents of the Earned Value repository can be considered BIG DATA, characterized by three attributes – 1) Volume: Large amounts of data; 2) Variety: data comes from different sources, including traditional data bases, documents, and complex records; 3) Velocity: the content is continually being updated by absorbing other data collections, through previously archived data, and through streamed data from external sources.
With this time series information in the repository, analysis of trends, cost and schedule forecasts, and confidence levels of these performance estimates can be calculated using statistical analysis techniques enabled by the Autoregressive Integrated Moving Average (ARIMA) algorithm provided by the R programming system. ARIMA provides a statistically informed Estimate At Completion (EAC) and Estimate to Complete (ETC) to the program in ways not available using standard EVM calculations. Using ARIMA reveals underlying trends not available through standard EVM reporting calculations.
With ARIMA in place and additional data from risk, technical performance and the Work Breakdown Structure, Principal Component Analysis can be used to identify the drivers of unanticipated EAC.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
#ATAGTR2021 Presentation : "Unlocking the Power of Machine Learning in the Mo...Agile Testing Alliance
Interactive Session on "Unlocking the Power of Machine Learning in the Mobile NFT world" by Niruphan Rajendran,Senior Manager Qualitest, Karthikeyan Lakshminarayanan Non-Fucntional Test Consultant Qualitest at #ATAGTR2021.
#ATAGTR2021 was the 6th Edition of Global Testing Retreat.
The video recording of the session is now available on the following link: https://www.youtube.com/watch?v=DIDZjUEnfyw
To know more about #ATAGTR2021, please visit:https://gtr.agiletestingalliance.org/
6 levels of big data analytics applicationspanoratio
6 levels of big data analytics applications: what you can expect from descriptive, investigative, advanced, adaptive, predictive, prescriptive analytics applications.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
Turnover Prediction of Shares Using Data Mining Techniques : A Case Study csandit
Predicting the Total turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task at hand. Data mining is a
well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of
predicting future trends, their efficiency is questionable as their predictions suffer from a high
error rate. The objective of this paper is to investigate various existing classification algorithms
to predict the turnover of different companies based on the Stock price. The authorized datasetfor predicting the turnover was taken from www.bsc.com and included the stock market valuesof various companies over the past 10 years. The algorithms were investigated using the ‘R’
tool. The feature selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the Total Turnover of
the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious
stock markets trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of the stock market attributes was established as well.
Going Big : Why Companies Need to Focus on Operational Analytics Capgemini
Given that digitization had such a transformative effect on customer behavior and relationships, it is perhaps not surprising that many organizations focused their digital transformation efforts on the customer experience front-end. However, in the race to focus on the customer, it was all too easy to ignore operations. Our 2013 research with MIT Sloan Management Review found that while 40% of digital initiatives were focused on the customer experience, this dropped to 26% for operations.
Times are changing, however. Our latest survey of more than 600 executives from the US, Europe and China finds that over 70% of organizations now put more emphasis on operations than on consumer-focused processes for their analytics initiatives. Analytics in operations is increasingly seen as a strategic priority for organizations. Over 80% of respondents agreed that analytics in operations plays a pivotal role in driving profits or creating competitive advantage
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
The importance of benchmarking software projects - Van Heeringen and OgilvieHarold van Heeringen
Benchmarking is a crucial management activity that enables organizations to understand how competitive they are. Using functional size measurement methods and historical data enables organizations to improve their processes and become more succesful.
View Related videos:-
Truth about Supply Demand Planning:-
http://www.youtube.com/watch?v=K66q2o1ED3c
Demantra Vs Oracle Demand Planning
http://www.youtube.com/watch?v=QwAzP3T6ut4
Another slideshare PPT:-
http://www.slideshare.net/amitforu78/demantra-vs-oracle-demand-planning
Contact me at www.ezdia.com
<a>AsiaLinks</a>
Earned Value Management Meets Big DataGlen Alleman
The Earned Value Management System (EVMS) maintains period–by–period data in its underlying databases. The contents of the Earned Value repository can be considered BIG DATA, characterized by three attributes – 1) Volume: Large amounts of data; 2) Variety: data comes from different sources, including traditional data bases, documents, and complex records; 3) Velocity: the content is continually being updated by absorbing other data collections, through previously archived data, and through streamed data from external sources.
With this time series information in the repository, analysis of trends, cost and schedule forecasts, and confidence levels of these performance estimates can be calculated using statistical analysis techniques enabled by the Autoregressive Integrated Moving Average (ARIMA) algorithm provided by the R programming system. ARIMA provides a statistically informed Estimate At Completion (EAC) and Estimate to Complete (ETC) to the program in ways not available using standard EVM calculations. Using ARIMA reveals underlying trends not available through standard EVM reporting calculations.
With ARIMA in place and additional data from risk, technical performance and the Work Breakdown Structure, Principal Component Analysis can be used to identify the drivers of unanticipated EAC.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
#ATAGTR2021 Presentation : "Unlocking the Power of Machine Learning in the Mo...Agile Testing Alliance
Interactive Session on "Unlocking the Power of Machine Learning in the Mobile NFT world" by Niruphan Rajendran,Senior Manager Qualitest, Karthikeyan Lakshminarayanan Non-Fucntional Test Consultant Qualitest at #ATAGTR2021.
#ATAGTR2021 was the 6th Edition of Global Testing Retreat.
The video recording of the session is now available on the following link: https://www.youtube.com/watch?v=DIDZjUEnfyw
To know more about #ATAGTR2021, please visit:https://gtr.agiletestingalliance.org/
6 levels of big data analytics applicationspanoratio
6 levels of big data analytics applications: what you can expect from descriptive, investigative, advanced, adaptive, predictive, prescriptive analytics applications.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Performed predictive Data analytics for “Black Friday Sales Dataset” wherein the company wants to predict the purchase amount against the products using Rapid Miner Tool.
Similar to Practical Machine Learning at Work (20)
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Practical Machine Learning at Work
1. QUALITY • ANALYTICS • PERFORMANCE
Machine Learning At Work
QUALITY • ANALYTICS • PERFORMANCE
December 6, 2017
Prepared for Data Science Event
2. 2
Introduction
The Stealth Media – Media Advertisement Startup Agency on
Facebook
Clients – 1800Dentist, FIJI Water, FabFitFun, Wonderful Company,
and etc
Role at the company – Data Analyst & Jack-of-all-trades
Banking & Quantitative Solutions LLC – Founder/Data Scientist of a
Data Analytics Startup
Main Project – Building AI machines and Recommendation
systems
Current Company:
Previous Company:
3. 3
Definitions & The Objective
How to reduce state aids when maximizing clicks that lead to
conversions?
Is there a correlation between clicks and state aids?
If there is a correlation between the two, what can we do to
optimize the situation?
Clicks – a number of times that a user clicks on a specific facebook
advertisement.
State Aids – a number that shows a given conversion received an
aid from the State where the conversion occurred.
Conversion – a number of purchase
The Objective for this client:
Definitions:
4. 4
Collecting & Compiling Data
Each element of data contains year, month, and day information
besides media information so data can easily be organized,
compiled, or downloaded by year, month, or day.
For the purpose of this presentation, a portion of data was
extracted from the database in csv form.
Data is collected from multiple sources: Facebook and 3rd party
pixel recording softwares.
Once Data iscollected from multiple sources, it is uploaded in our
database (MySql).
Collection:
Compilation:
5. 5
Tidying Data
In a simple phrase, data preprocessing means data cleansing and
normalizing so that it can produce an accurate analysis.
Preprocessing:
Example Coding:
8. 8
Exploratory Data Analysis (Continued)
There is a high correlation between the two by gender.
Visiual Analysis by Gender:
9. 9
Exploratory Data Analysis (Continued)
There is a high correlation between the two by location.
Visiual Analysis by Location:
10. 10
Exploratory Data Analysis (Continued)
Linear Regression – As we saw from the visual analyses, variables
such as gender and year did not affect the graphs too much. Now,
we need to find which states are affected by state aids the most.
Linear Regression
Clicks ~ Location
11. 11
Exploratory Data Analysis (Continued)
California, 5-state states, and Standard states seem to be affected by state
aids the most.
State Aids ~ Location
12. 12
Data Partition
Training set is used to train the selected model: LM & XGB.
Normally, 70% of the data are chosen to be a training set and 30%
become a test set. A training set can be used over and over but a
test set can only be used once to avoid over-fitting.
Use the createDataPartition function to partition the data into 70%
training and 30% test sets.
Caret Package:
Training vs. Test Sets
13. 13
Definitions
Regression – Output variable takes continuous values
Classification – Output variable takes class labels
Supervised Learning – All data is labeled and algorithms are used
to predict the output from the input data.
Unsupervised Learning – All data is not labeled and algorithms are
used to learn inherent structure from the input data.
Supervised vs. Unsupervised Learnings
Regression vs. Classification
15. 15
Machine Learning (Part 1 – Speed)
You delete more features as you train the model. The accuracy should
increase when the test set is fed into the trained model.
The last column shows the
predicted values.
16. 16
Machine Learning (Part 1 – Speed Continued)
The linear regression is very quick to calculate however it seems that its
accuracy is not that great.
17. 17
Machine Learning (Part 2 – Accuracy)
One-hot encoding – A method of converting categorical variables
into columns of binary variables so that XGBoost model can
process them.
Extreme Gradient Boosting for Regression (XGB)
20. 20
Outcome & Conclusion
We shut down some of the high performing ads in each of those 3
regions as soon as we got an alert from our AI machine and
focused on other regions. It greatly limited the state aid reception
by the client and optimized the state aid and click ratio.
What the machine learning did:
This did not necessarily increase our profit but it definitely
prolonged our contract with the company that we worked with as
their pure sales went up.