Movie revenue prediction from IMDB dataset. The slides include how I clean up data, perform EDA analysis, and build up models. All of the codes are included in my Github (https://github.com/ChungHsuanKao/1stCapstoneProject_github)
This document analyzes movie data sets to determine production company ratings, movies by rating and top actors, profits from low budget movies by genre, and movie percentages from 1939 to 2015. Graphs and pivot tables are used to show which production companies have good ratings, movies by rating and starring actors, and that low budget comedy movies tend to earn more profit. The analysis also examines trends in movie genres over time.
This document summarizes a report analyzing factors that influence the worldwide sales of video games. The author built a hierarchical linear regression model to predict global video game sales based on platform, genre, critic and user ratings, and other variables. The model included random intercepts for publisher. The most significant predictors were found to be platform manufacturer, genre, critic and user review counts, and critic ratings. The model explained around half of the variance in global sales.
IRJET- Movie Success Prediction using Data Mining and Social MediaIRJET Journal
The document discusses predicting the success of movies using data mining and machine learning techniques. Specifically, it aims to use linear regression models to predict box office revenues and classify movies as hits or flops based on attributes like the cast, directors, genres, and other movie features. Sentiment analysis of social media data like tweets is also used to predict success. The proposed approach involves feature selection, normalization, training regression models on historical movie data, and using the models to predict revenues for new movies. Evaluation of the models shows they can accurately predict box office figures. The goal is to help movie studios and reduce uncertainty in decision making.
movie recommender system using vectorization and SVD techUddeshBhagat
This system used overall TMDB Vote Count and Vote Averages to build Top Movies Charts, in general and for a specific genre. The IMDB Weighted Rating System was used to calculate ratings on which the sorting was finally performed.
We built two content based engines; one that took movie overview and taglines as input and the other which took metadata such as cast, crew, genre and keywords to come up with predictions. We also devised a simple filter to give greater preference to movies with more votes and higher ratings.
The document discusses IBM's predictive analytics solution for demand forecasting in the media and entertainment industry. It describes how IBM built models to predict movie box office performance using online audience behavior data like Twitter volume, sentiment, YouTube views and other variables. The models achieved high accuracy, with errors of +/-25% up to 8 weeks before release. Certain genres, release periods and larger budget films were predicted most accurately. Improving the models by adding more data sources, like YouTube views, increased accuracy. The solution extracts sentiment, intent and audience segments from social data to help media companies better understand demand.
Bollywood has reached an amazing level in terms of movies produced, its reach in the whole world and providing employment to manpower. The returns obtained are uncertain in nature. Due to this it becomes a matter of interest to develop a model which can forecast the success of movies. In this paper, a model is proposed to forecast performance of Bollywood movies. The proposed work involves collecting data from various websites. Data mining algorithms like multi-linear regression and min-max normalization algorithm are used. The results been generated will help the movie industry as well as common people to take decisions regarding movies i.e. it will act as a decision support system.
Евгений Цымбалов, Webgames - Методы машинного обучения для задач игровой анал...AIST
ML is helping a large Russian game developer and publisher called WebGames analyze data from their free-to-play games. They collect over 80 million records daily from their 400k daily players across various platforms. They use ML for tasks like churn prediction, revenue prediction, user classification, A/B testing, balance, and recommendations. Specifically, they build 30 different models to predict LTV for users based on their behavior in the first 30 days. They also use kNN and cohort-based approaches for user classification and Bayesian A/B testing to dynamically adjust testing over time. Rule-based modeling and midgame support based on classification help balance games. Content recommendations are done through static and dynamic clustering.
This document compares models to predict whether a movie will break even financially in the US market. It finds that gradient boosting and neural network models accurately predict break even status over 75% of the time for sample data and over 70% for future data. The most influential factors are IMDB user votes, reviews, and scores, while Facebook likes for the top three actors and overall cast are also important. Further analysis could incorporate additional data like theater receipts and timestamps.
This document analyzes movie data sets to determine production company ratings, movies by rating and top actors, profits from low budget movies by genre, and movie percentages from 1939 to 2015. Graphs and pivot tables are used to show which production companies have good ratings, movies by rating and starring actors, and that low budget comedy movies tend to earn more profit. The analysis also examines trends in movie genres over time.
This document summarizes a report analyzing factors that influence the worldwide sales of video games. The author built a hierarchical linear regression model to predict global video game sales based on platform, genre, critic and user ratings, and other variables. The model included random intercepts for publisher. The most significant predictors were found to be platform manufacturer, genre, critic and user review counts, and critic ratings. The model explained around half of the variance in global sales.
IRJET- Movie Success Prediction using Data Mining and Social MediaIRJET Journal
The document discusses predicting the success of movies using data mining and machine learning techniques. Specifically, it aims to use linear regression models to predict box office revenues and classify movies as hits or flops based on attributes like the cast, directors, genres, and other movie features. Sentiment analysis of social media data like tweets is also used to predict success. The proposed approach involves feature selection, normalization, training regression models on historical movie data, and using the models to predict revenues for new movies. Evaluation of the models shows they can accurately predict box office figures. The goal is to help movie studios and reduce uncertainty in decision making.
movie recommender system using vectorization and SVD techUddeshBhagat
This system used overall TMDB Vote Count and Vote Averages to build Top Movies Charts, in general and for a specific genre. The IMDB Weighted Rating System was used to calculate ratings on which the sorting was finally performed.
We built two content based engines; one that took movie overview and taglines as input and the other which took metadata such as cast, crew, genre and keywords to come up with predictions. We also devised a simple filter to give greater preference to movies with more votes and higher ratings.
The document discusses IBM's predictive analytics solution for demand forecasting in the media and entertainment industry. It describes how IBM built models to predict movie box office performance using online audience behavior data like Twitter volume, sentiment, YouTube views and other variables. The models achieved high accuracy, with errors of +/-25% up to 8 weeks before release. Certain genres, release periods and larger budget films were predicted most accurately. Improving the models by adding more data sources, like YouTube views, increased accuracy. The solution extracts sentiment, intent and audience segments from social data to help media companies better understand demand.
Bollywood has reached an amazing level in terms of movies produced, its reach in the whole world and providing employment to manpower. The returns obtained are uncertain in nature. Due to this it becomes a matter of interest to develop a model which can forecast the success of movies. In this paper, a model is proposed to forecast performance of Bollywood movies. The proposed work involves collecting data from various websites. Data mining algorithms like multi-linear regression and min-max normalization algorithm are used. The results been generated will help the movie industry as well as common people to take decisions regarding movies i.e. it will act as a decision support system.
Евгений Цымбалов, Webgames - Методы машинного обучения для задач игровой анал...AIST
ML is helping a large Russian game developer and publisher called WebGames analyze data from their free-to-play games. They collect over 80 million records daily from their 400k daily players across various platforms. They use ML for tasks like churn prediction, revenue prediction, user classification, A/B testing, balance, and recommendations. Specifically, they build 30 different models to predict LTV for users based on their behavior in the first 30 days. They also use kNN and cohort-based approaches for user classification and Bayesian A/B testing to dynamically adjust testing over time. Rule-based modeling and midgame support based on classification help balance games. Content recommendations are done through static and dynamic clustering.
This document compares models to predict whether a movie will break even financially in the US market. It finds that gradient boosting and neural network models accurately predict break even status over 75% of the time for sample data and over 70% for future data. The most influential factors are IMDB user votes, reviews, and scores, while Facebook likes for the top three actors and overall cast are also important. Further analysis could incorporate additional data like theater receipts and timestamps.
In this projet, we analyze a dataset about 10,000 movies which was orginally generated from the TMDb movie database APi and published by kaggle https://www.kaggle.com/tmdb/tmdb-movie-metadata. We've analyzed the dataset, in order the answer different research questions:
- Most popular movies by genre,
- relations between movie popularity and rating with the production budget and revenue
The document discusses a software estimation challenge hosted at the Nesma Conference 2020. It provides context about the conference and challenge, describes the inputs, tasks, and deliverables of the challenge. It then details Metri's approach to completing the tasks, which included estimating the functional size using various methods, estimating the impact of non-functional requirements, and using historical project data to estimate the effort required to develop the software.
This document provides an overview of four methods for project analysis and decision making: regression analysis, sensitivity analysis, Monte Carlo simulations, and decision trees. Regression analysis uses past data to forecast future trends through mathematical modeling. Sensitivity analysis evaluates how changes to variables impact outcomes like net present value. Monte Carlo simulations model projects probabilistically by assigning distributions to variables and running simulations. Decision trees visually represent decisions, consequences, probabilities, and opportunities to break down complex situations. Examples are provided for each method.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
The document provides a suggested process and template for creating a business presentation. It includes sections to describe the market problem and cost, target customer, product, technology, competition, business model, go-to-market strategy, financial forecast, risks, team, and a summary. The template guides the presenter to concisely communicate key information about the business opportunity in a clear and structured manner over the course of 30-40 slides.
This document describes a movie recommendation system project that will use collaborative filtering techniques to predict movie ratings and recommend movies to users. The project will use the MovieLens dataset to identify user demographics and movie genres and classify them using different algorithms. Conditional inference trees and random forests will be implemented and evaluated on the MovieLens data, with the highest accuracy achieved using age, gender, occupation, and genre features. Exploratory data analysis of the MovieLens data found that most users are students aged 20-30 and most movies are from the 1990s across many genres.
Custom event prospecting: Winning with Targeting in the Data Gold RushGabe Kwakyi
This document discusses using event prospecting to better target users. It begins with introducing the speaker and their background. It then discusses the goal of studying events to influence desired user behaviors. It provides examples of how Facebook has used event prospecting. The document outlines methods for event prospecting including defining user segments, gathering data, and correlation, inflection point, and decision tree analysis. It discusses applications like targeting quality users, activating power users, retaining users, making more money, and preventing undesirable behaviors. It also covers challenges and lessons learned from applying event prospecting via user acquisition.
A lecture in digital analytics at Aalto University. The lecture is a part of a module in Information Technology Program (ITP).
Summer 2015, Helsinki
--
Dr. Joni Salminen is a lecturer in digital marketing. Besides online marketing, his interests include startups and web platforms. Contact: joolsa@utu.fi
Abstract— The movie making is a multibillion-dollar industry. In 2018, the global movie business has generated nearly $41.5 billion in box office and more than that in merchandise revenues. But it is not a guaranteed business: every year we witness big buster and budget movies that become either a “hit” or a “flop”. The success of a movie is mainly judged by looking at ratio of its gross revenue over its budget, but some may also call a movie successful if it bagged critics praise and awards, both of which do not necessarily convert to financial revenue. In our project we look from an investor point of view, who largely favour financial return over any other attribute. But to predict the success of a movie, an investor can’t only rely on superficial attributes, a typical reason why Machine Learning (ML) prediction will prove to be very useful. We are going to implement this prediction using two ML methods that we have studied during the subject CMPE542, namely Random Forest and Neural Network. These are very adapted for discriminating classes, and can thus help us very effectively in pointing to successful or failed movies after being trained on a set of 5043 movies which data have been scraped from IMDB. At the end of the project, we should be able to know which method has the highest accuracy, what movies sell the best at the box office and most importantly for movies producers, what movie features are the most decisive in making a movie profitable.
Quality Control PowerPoint Presentation Slides SlideTeam
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
How to use data to improve software development teams and processes. Presented at the Prairie Dev Con Deliver conference October 2016. http://www.prdcdeliver.com
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Trymain Rivero AFCU Presentation (for OSDC)TrymainRivero
This was a presentation for a data analytics competition at the University of Central Florida. The task was to create a data-driven member-by-member method for determining whether a credit limit adjustment could be made and for what amount.
The metrics that matter using scalability metrics for project planning of a d...Mary Chan
Have you expanded your organization across multiple locations, or are you a client that utilizes external partners that provide outsourcing services? Both have their "cost savings" challenge where cost savings analysis is often a topic well scrutinized. However, in the grand scheme of your organization, is it a metric that really matters? See actual analytics on multiple game projects and why cost savings isn't as important a metric when making informed decisions about project planning for scalable and distributed development. It's all about the Metrics that Matter.
Pitch Deck For Pre Seed Funding Powerpoint Presentation SlidesSlideTeam
"You can download this product from SlideTeam.net"
This is an early stage investment which the owner requires to start the business. This is also known as pre seed capital or pre seed money. Business owners can raise this money from friends, family or investors and give stakes in the company in exchange. The presentation is helpful for start ups looking to raise funding for the initial development of the product, to set up a business, or to build a new team. This presentation will help the start ups to present their business or business idea and future growth plans in front of the potential investors. This presentation comprises the following sections Company Overview, Company introduction, unique business idea, business model, revenue streams, historical events, products, and services etc. Market Overview Target audience identification and segmentation, competitive landscape, market size and opportunities etc. Financials Overview Income statement, revenue, and cash flow projections, capitalization tables, valuation, break even point, and cost analysis etc. Investment and funding overview Funding requirements, use of raised funds, future plans, the exit strategy for the investors etc. This presentation will help the organizations to move from the situation, where they need funds for initial business development to set the future targets, use, and goals of raised funding. https://bit.ly/33qDPxI
An exploration of machine learning models to predict movie profitability - originally created as a term project for TECH-GB 2336 (Technical Data Science for Business) @ NYU Stern, and based on the Kaggle competition [TMDB Box Office Prediction](https://www.kaggle.com/c/tmdb-box-office-prediction).
Pitch Deck For Pre Seed Funding PowerPoint Presentation SlidesSlideTeam
This is an early stage investment which the owner requires to start the business. This is also known as pre seed capital or pre seed money. Business owners can raise this money from friends, family or investors and give stakes in the company in exchange. The presentation is helpful for start ups looking to raise funding for the initial development of the product, to set up a business, or to build a new team. This presentation will help the start ups to present their business or business idea and future growth plans in front of the potential investors. This presentation comprises the following sections Company Overview, Company introduction, unique business idea, business model, revenue streams, historical events, products, and services etc. Market Overview Target audience identification and segmentation, competitive landscape, market size and opportunities etc. Financials Overview Income statement, revenue, and cash flow projections, capitalization tables, valuation, break even point, and cost analysis etc. Investment and funding overview Funding requirements, use of raised funds, future plans, the exit strategy for the investors etc. This presentation will help the organizations to move from the situation, where they need funds for initial business development to set the future targets, use, and goals of raised funding. https://bit.ly/3btoJWg
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
In this projet, we analyze a dataset about 10,000 movies which was orginally generated from the TMDb movie database APi and published by kaggle https://www.kaggle.com/tmdb/tmdb-movie-metadata. We've analyzed the dataset, in order the answer different research questions:
- Most popular movies by genre,
- relations between movie popularity and rating with the production budget and revenue
The document discusses a software estimation challenge hosted at the Nesma Conference 2020. It provides context about the conference and challenge, describes the inputs, tasks, and deliverables of the challenge. It then details Metri's approach to completing the tasks, which included estimating the functional size using various methods, estimating the impact of non-functional requirements, and using historical project data to estimate the effort required to develop the software.
This document provides an overview of four methods for project analysis and decision making: regression analysis, sensitivity analysis, Monte Carlo simulations, and decision trees. Regression analysis uses past data to forecast future trends through mathematical modeling. Sensitivity analysis evaluates how changes to variables impact outcomes like net present value. Monte Carlo simulations model projects probabilistically by assigning distributions to variables and running simulations. Decision trees visually represent decisions, consequences, probabilities, and opportunities to break down complex situations. Examples are provided for each method.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
The document provides a suggested process and template for creating a business presentation. It includes sections to describe the market problem and cost, target customer, product, technology, competition, business model, go-to-market strategy, financial forecast, risks, team, and a summary. The template guides the presenter to concisely communicate key information about the business opportunity in a clear and structured manner over the course of 30-40 slides.
This document describes a movie recommendation system project that will use collaborative filtering techniques to predict movie ratings and recommend movies to users. The project will use the MovieLens dataset to identify user demographics and movie genres and classify them using different algorithms. Conditional inference trees and random forests will be implemented and evaluated on the MovieLens data, with the highest accuracy achieved using age, gender, occupation, and genre features. Exploratory data analysis of the MovieLens data found that most users are students aged 20-30 and most movies are from the 1990s across many genres.
Custom event prospecting: Winning with Targeting in the Data Gold RushGabe Kwakyi
This document discusses using event prospecting to better target users. It begins with introducing the speaker and their background. It then discusses the goal of studying events to influence desired user behaviors. It provides examples of how Facebook has used event prospecting. The document outlines methods for event prospecting including defining user segments, gathering data, and correlation, inflection point, and decision tree analysis. It discusses applications like targeting quality users, activating power users, retaining users, making more money, and preventing undesirable behaviors. It also covers challenges and lessons learned from applying event prospecting via user acquisition.
A lecture in digital analytics at Aalto University. The lecture is a part of a module in Information Technology Program (ITP).
Summer 2015, Helsinki
--
Dr. Joni Salminen is a lecturer in digital marketing. Besides online marketing, his interests include startups and web platforms. Contact: joolsa@utu.fi
Abstract— The movie making is a multibillion-dollar industry. In 2018, the global movie business has generated nearly $41.5 billion in box office and more than that in merchandise revenues. But it is not a guaranteed business: every year we witness big buster and budget movies that become either a “hit” or a “flop”. The success of a movie is mainly judged by looking at ratio of its gross revenue over its budget, but some may also call a movie successful if it bagged critics praise and awards, both of which do not necessarily convert to financial revenue. In our project we look from an investor point of view, who largely favour financial return over any other attribute. But to predict the success of a movie, an investor can’t only rely on superficial attributes, a typical reason why Machine Learning (ML) prediction will prove to be very useful. We are going to implement this prediction using two ML methods that we have studied during the subject CMPE542, namely Random Forest and Neural Network. These are very adapted for discriminating classes, and can thus help us very effectively in pointing to successful or failed movies after being trained on a set of 5043 movies which data have been scraped from IMDB. At the end of the project, we should be able to know which method has the highest accuracy, what movies sell the best at the box office and most importantly for movies producers, what movie features are the most decisive in making a movie profitable.
Quality Control PowerPoint Presentation Slides SlideTeam
Every organization needs to adapt to the ever-changing business environment. Sensing this need, we have come up with these content-ready change management PowerPoint presentation slides. These change management PPT templates will help you deal with any kind of an organizational change. Be it with people, goals or processes. The business solutions incorporated here will help you identify the organizational structure, create vision for change, implement strategies, identify resistance and risk, manage cost of change, get feedback and evaluation, and much more. With the help of various change management tools and techniques illustrated in this presentation design, you can achieve the desired business outcomes. This business transition PowerPoint design also covers certain related topics such as change model, transformation strategy, change readiness, change control, project management and business process. By implementing the change control methods mentioned in the presentation, you will be able to have a smooth transition in an organization. So, without waiting much, download our extensively researched change management framework presentation. With our Change Management Presentation slides, understand the need for change and plan to go through it without any hassles.
How to use data to improve software development teams and processes. Presented at the Prairie Dev Con Deliver conference October 2016. http://www.prdcdeliver.com
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Trymain Rivero AFCU Presentation (for OSDC)TrymainRivero
This was a presentation for a data analytics competition at the University of Central Florida. The task was to create a data-driven member-by-member method for determining whether a credit limit adjustment could be made and for what amount.
The metrics that matter using scalability metrics for project planning of a d...Mary Chan
Have you expanded your organization across multiple locations, or are you a client that utilizes external partners that provide outsourcing services? Both have their "cost savings" challenge where cost savings analysis is often a topic well scrutinized. However, in the grand scheme of your organization, is it a metric that really matters? See actual analytics on multiple game projects and why cost savings isn't as important a metric when making informed decisions about project planning for scalable and distributed development. It's all about the Metrics that Matter.
Pitch Deck For Pre Seed Funding Powerpoint Presentation SlidesSlideTeam
"You can download this product from SlideTeam.net"
This is an early stage investment which the owner requires to start the business. This is also known as pre seed capital or pre seed money. Business owners can raise this money from friends, family or investors and give stakes in the company in exchange. The presentation is helpful for start ups looking to raise funding for the initial development of the product, to set up a business, or to build a new team. This presentation will help the start ups to present their business or business idea and future growth plans in front of the potential investors. This presentation comprises the following sections Company Overview, Company introduction, unique business idea, business model, revenue streams, historical events, products, and services etc. Market Overview Target audience identification and segmentation, competitive landscape, market size and opportunities etc. Financials Overview Income statement, revenue, and cash flow projections, capitalization tables, valuation, break even point, and cost analysis etc. Investment and funding overview Funding requirements, use of raised funds, future plans, the exit strategy for the investors etc. This presentation will help the organizations to move from the situation, where they need funds for initial business development to set the future targets, use, and goals of raised funding. https://bit.ly/33qDPxI
An exploration of machine learning models to predict movie profitability - originally created as a term project for TECH-GB 2336 (Technical Data Science for Business) @ NYU Stern, and based on the Kaggle competition [TMDB Box Office Prediction](https://www.kaggle.com/c/tmdb-box-office-prediction).
Pitch Deck For Pre Seed Funding PowerPoint Presentation SlidesSlideTeam
This is an early stage investment which the owner requires to start the business. This is also known as pre seed capital or pre seed money. Business owners can raise this money from friends, family or investors and give stakes in the company in exchange. The presentation is helpful for start ups looking to raise funding for the initial development of the product, to set up a business, or to build a new team. This presentation will help the start ups to present their business or business idea and future growth plans in front of the potential investors. This presentation comprises the following sections Company Overview, Company introduction, unique business idea, business model, revenue streams, historical events, products, and services etc. Market Overview Target audience identification and segmentation, competitive landscape, market size and opportunities etc. Financials Overview Income statement, revenue, and cash flow projections, capitalization tables, valuation, break even point, and cost analysis etc. Investment and funding overview Funding requirements, use of raised funds, future plans, the exit strategy for the investors etc. This presentation will help the organizations to move from the situation, where they need funds for initial business development to set the future targets, use, and goals of raised funding. https://bit.ly/3btoJWg
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
2. Movie business ≠ Guaranteed profit
Is the probability of making a profitable movie
similar to flipping a coin???
https://sciencelens.co.nz/2012/06/01/flip-a-coin-day/
3. How to tackle this problem?
Step 1
Data wrangling
Preprocess
raw data
Storytelling
&
Inference
Modeling
& results
Classification
models
Explore
insights
Step 2 Step 3
4. Data source & feature classification
IMDB information:
IMDB score
Critic for reviews
User for reviews
Voted users
Social media information:
Director
Major actors
Cast
Movie
Descriptive information:
color
duration
movie_title
facenumber_in_poster
plot_keywords
aspect_ratio
content_rating
title_year
language
country
genres
movie_imdb_link
People information:
director
actor $$$:
gross
budget
revenue
Facebook likes
name
Predictor variables
Response variables
IMDB movie dataset
Observation: 5043 movies
Features: 28 variables
number
5. Data wrangling
[Cleaning step]
• Checking the percentage of missing values in each variable (column) and observation (row)
• It tells me how to prioritize the recovery steps
• Duplicates removal
[Categorical variables]
• Proofread ‘’movie title” column
• Remove unnecessary words and spaces
• Manually fix “color”, “country”, “language” columns
• Fill up NaN values
• Use one hot encoding
• “content_rating” column
Remove TV series
Fill in NaN by web scraping (However, scraped data shows most of them are TV-series or
Not rated. I would just skip the fill-in)
Group them into 4 and dummify them
• Dummify “genres” column
• Replace “Actor_name” and “director_name” columns into frequency
6. [Numeric variables]
• Fill in "title_year" column by web-scraped data and subgroup it
• Fill in "budget" column with web-scraped data
• Fill in "gross" column” with web-scraped data
• Add “month” column by web-scraped data
• Impute "num_critic_for_reviews", "director_facebook_likes", "actor_3_facebook_likes",
"actor_1_facebook_likes", "facenumber_in_poster", "actor_2_facebook_likes",
"aspect_ratio", "duration", "num_user_for_reviews" columns with median
[Final steps]
• Remove “movie_imdb_link” column
• Remove all rows with NaN
• Save it to ‘final_wrangle.csv’
Data wrangling
7. [Prepare target variable: revenue]
• Create a new column called ‘revenue’ by ‘gross’ - ‘budget’
• Change its unit to 1 million
[Outliers]
• Use seaborn.pairplot to get histograms of all predictive variables
• Check target variable
[High correlation between each predictor]
• Create a correlation heatmap
• Identify high positive and low negative correlation between variables
• Remove the variable which is highly related to the other variable positively or negatively
[Save the preprocessed data]
• final_pre.csv
Data preprocessing
8. Data storytelling & Data inference
[Strategy for numerical features]
Here I use simulated null hypothesis to test the significance (p-value) between each
predictor and the revenue.
module.py includes functions:
• ‘pearson_permuttion_plot’ for null hypothesis simulation, p-value calculation,
and plotting
[Strategy for categorical features]
Here I calculate the mean difference between the categories and test it with simulated
null hypothesis.
module.py includes functions:
• ‘mean_diff_testing’ for plotting
• ‘mean_diff_p’ for calculating p-value between different means
9. Is IMDB score a good indicator of the revenue?
The correlation is 24% and significant.
10. How reviews affect the revenue?
[Correlation & significance]
• Voted users: 49%, significant
• Users for reviews: 38%, significant
• Critics for reviews: 24%, significant
11. 1. No correlation between the total budget and revenue.
2. Positive and Negative revenue has correlation with budget, perspectively
Is the budget correlated to the revenue? Invest
more, earn more back?
Recommendation:
There is a trend that most of failed
movies (negative revenue) are
supported by a big budget
Positive
revenue
Negative
revenue
Total budget
12. How seasonality and title year affect the revenue?
[Month]
The mean difference is significant.
(1: June and December; 0: the rest of month)
[Title year]
The mean difference is not significant.
(1: after 1966; 0: before 1966)
13. How genres affect the revenue?
[Significant genre & p-value]
PG-13 0.0067
R 0.0
Adventure 0.0
Animation 0.0001
Comedy 0.0018
Crime 0.0003
Drama 0.0
Family 0.0
Fantasy 0.0002
History 0.0
Sci-Fi 0.0453
Thriller 0.0
War 0.0002
14. Feature importance
[Top10 features]
• Voted users
• Users for reviews
• Critics for reviews
• IMDB score
• Social network-related
features
• Primary actor’s name
frequency
16. Future plans
• Test this dataset in the neural network
• Merge more features from different movie
dataset
– NLP: voters reviews (text)
– other score system (Rotten tomatos)